Latest Posts (20 found)

Premium: What If...We're In An AI Bubble? (Part 3)

Last week I ran the second part of my three-part “What If…We’re In An AI Bubble?” series where I have been covering the scenarios that I believe could lead to the bubble popping. Here’s what I’ve discussed so far: Today I want to start with a very simple rundown of what has to happen for the AI bubble to make sense. These are all points that are rooted entirely in the projections and sales of the companies in question.  As NVIDIA intends to sell over a trillion dollars of Blackwell and Vera Rubin GPUs by the end of 2027 , it needs to have around (assuming a PUE of 1.35) 40GW of data center capacity built to support the 30GW+ of GPUs it will have sold .  With that compute being sold at around $12 million a megawatt (based on discussions with analysts and sources), that means that there must be around $435 billion in global annual compute demand to substantiate the amount of GPUs sold.  Outside of OpenAI and Anthropic, there doesn’t appear to be more than a few billion dollars of demand . Another concerning sign is that NVIDIA has had to agree to spend $30 billion in multi-year cloud compute agreements across the very partners it’s selling GPUs to ( per page 16 of its most-recent 10-Q ): The other problem is that data centers are taking way, way too long to finish , taking upwards of 24 months even for smaller 40MW builds.  This means that… Put another way, NVIDIA’s continued growth relies on people’s belief that A) these data centers get built and B) that they’ll actually make money.  Per COO Greg Brockman, OpenAI will spend around $50 billion on compute in 2026 , and I imagine Anthropic will spend in or around the same amount, especially as it’s now agreed to spend $15 billion a year on Musk’s Colossus data centers on top of whatever it spends on Google Cloud, Microsoft Azure and Amazon Web Services.  $100 billion is nowhere near enough to justify the compute being built. And while Anthropic and OpenAI have made more than $1.1 trillion in compute commitments in the next 3-5 years across Microsoft, Google, Amazon, Oracle, CoreWeave, Cerebras, Terawulf, and Cipher Mining, there’s so much more compute that needs to be sold on top of that.  Even if both doubled their spend in a year, we’d still need at least another two Anthropic or OpenAI-sized compute customers — either in aggregate or as separate companies — at a time when I can’t find a single other company spending even a hundred million dollars a year on compute. Most AI startups (and customers) want to pay Anthropic or OpenAI directly to access their models , which means that either Anthropic and OpenAI need to use roughly twice the amount of compute they do today and then some to meet the capacity being built. This will require them to do something either historic or impossible. This is not hyperbole! OpenAI, per The Information , plans to burn $852 billion through the end of 2030. Anthropic has, per The Information, agreed to spend $330 billion on compute on Microsoft, Google, and Amazon , at least another $30 billion on compute with CoreWeave , and another $63 billion in TPUs bought from Broadcom .  To reach this point, Anthropic projects it will hit $174 billion in annual revenue by the end of 2029, and OpenAI $284 billion . Both have made ridiculous claims of profitability ( with Anthropic actively conning investors with a “profitable” quarter based on discounted bills ) in the next few years that are immaterial to the larger point that they need actual, real cash to meet their obligations.  This is, again, not hyperbole. If we assume that the services in question are profitable, sustainable businesses, then revenues attached to AI services must exceed those driven by AI compute by a reasonable margin. It isn’t enough for us to have a few AI companies that spend a lot more on compute than they take in revenue, because at some point venture capital subsidies will run dry.  This isn’t happening. Putting aside the profitability part for a second, OpenAI and Anthropic account for 89% of all AI startup revenues , with the nearest competitor being Cursor with its pathetic $3 billion in annualized revenue . These are rookie numbers. They are insufficient. We need so much more than this. Again, not hyperbole! These are OpenAI and Anthropic’s own revenue projections — $184 billion and $174 billion respectively — that they expect to hit by the end of 2029. These are the same projections that have been used to make their $1.1 trillion in compute commitments, much of which make up 50% of Google, Amazon, and Microsoft’s remaining performance obligations : These commitments reflect expected revenue and demand for OpenAI and Anthropic’s services, but they’re commitments, which means that they need to be paid even if that demand doesn’t exist.  This is a huge problem for these companies. If they buy too much compute and don’t have the demand and revenue to support it, they’ll go bankrupt.  To be clear, that’s not my opinion, it’s what Anthropic CEO Dario Amodei said to Dwarkesh Patel in February, emphasis mine: That is not good! As I’ve covered before , buying compute is a knife-catching game where you have to guess how much you need for a particular year, and if you guess correctly you don’t lose as much money but if you guess wrong you run out of money.  It should be far more worrying to executives that the single-largest AI company is basically saying that if he mistimes growth his company explodes! Per Business Insider , Uber COO Andrew Macdonald said this weekend that it was becoming “harder to justify AI costs within the company”: Anthropic’s meteoric revenue growth has come from both AI startups burning more tokens ( as Opus 4.7 appears to burn more than ever ) and large organizations doing some form of “token-maxxing,” meaning that they tell their employees to use AI as much as they want, usually with KPIs that specifically track AI usage, as is the case at Meta , Amazon, and Zillow . Even organizations that aren’t actively incentivizing their engineers to burn more tokens are finding they’re blowing through their budgets at record speed. The situation with Uber’s COO was caused by his CTO saying back in April that the company had burned through its entire annual token budget in four months. Similarly, my reporting on Zillow’s AI spend showed that it will likely max out its annual Cursor budget by the end of May. The problem, as Macdonald said, is that nobody can seem to track all of this spend to an actual return on investment. This isn’t a situation where somebody is saying “the ROI is low but improving” or “we’re on the path to working that out,” but “it’s very hard to actually draw a line between “what we’ve spent” and “a reason we’re spending it.” This makes it hard for Uber to say how much it should reduce its token budgets. If you can’t measure the return on investment, how do you measure how much you’re meant to spend? What is “enough”? Because right now it’s clear that whatever they’re spending is too much , which means that there’s a ceiling to Anthropic and OpenAI’s revenue story.  OpenAI and especially Anthropic cannot afford for this conversation to be happening, because it suggests there’s a ceiling to the amount that people will spend on AI. It appears there’s a limit to which organizations can be abused and manipulated into believing that “the future is here,” and that limit is when they pay millions for something that doesn’t appear to have a measurable return on investment.  Anthropic and OpenAI need organizations to willingly spend 10% to 100% of their headcount on AI, as their revenue projections are clearly tied to every organization maintaining a significant spend on tokens in perpetuity.  There’re really two problems: This is budgetary poison. Right now, the vast majority of AI token spend is experimental , and if companies are already hesitating at the amounts they’re spending, Anthropic has no way to keep growing, and they also have no super secret models or harnesses or products that are going to reverse this trend. Nobody knows why they’re spending so much money or even how much money they might spend in a given month , which makes it tough to view Anthropic’s ( suspicious ) revenue growth as anything but a chaotic money-dump driven by CEOs that don’t know what their companies actually do and have been beguiled by the AI grift machine . And as I wrote up last week , OpenAI had a negative 122% operating margin in Q1 2026, and ChatGPT growth has stalled. It is unclear what its API revenue is, but it’s likely much less than Anthropic despite shoving its enterprise customers onto token-based billing not long after they did. As I’ve said: this cannot happen, and neither Anthropic nor OpenAI can afford to slow down. Their revenues must grow to over $100 billion by 2028, as their compute commitments demand it. Their growth must continue.  It’s been a little under four years of endless confidence about the inevitable growth of generative AI, and by extension the eternal success and growth of OpenAI. Yet in reality, its economics have only ever soured, and its growth appears to be collapsing.  In October 2024, The Information reported that OpenAI believed it would turn profitable in 2029, that its total losses between 2023 and 2028 would be $44 billion , and that its (non-GAAP, every one of these numbers is non-GAAP) gross margin would be 41% in 2024, though it would end up being a point lower at 40% in the end. OpenAI would then project a gross margin of 49% for 2025… but it ended up at 33% anyway .  OpenAI would also say on September 5 2025 that it would actually burn $115 billion through 2029 , but that “burn” assumed that it would have revenues of $60 billion in 2027, $100 billion in 2028, $145 billion in 2029, and $200 billion in 2030, when it would “become profitable” in some undiscussed manner. Two weeks later on September 19 2025, The Information would report that actually OpenAI would spend “about $450 billion to rent servers through 2030,” but not otherwise update the burn-rate. On November 4, 2025 , OpenAI CEO Sam Altman would say that the company had hit $20 billion in ARR and had made $1.4 trillion in commitments “over the next 8 years,” and a few months later On February 20, 2026 , OpenAI would claim that it had targeted “around $600 billion in compute commitments by 2030.” The very same day, The Information would report that it planned to spend $665 billion on compute through 2030 , that it missed gross margin projections (without sharing what those margins might be), and that ChatGPT had hit 910 million weekly active users that month, 90 million short of its goal of 1 billion by the end of 2025. It’s very obvious by now that OpenAI has been making up all of its projections, and that none of the numbers actually add up. My own reporting from November 2025 from actual Azure personnel suggests that OpenAI’s Q1 to Q3 revenues were billions lower than every other reported figure, and I think it’s likely that OpenAI is overstating its revenues.  In any case, on May 22, 2026 , The Information would report that OpenAI’s Q1 2026 operating margin was negative 122%, and that its Q1 average weekly active users (WAUs) sat at 905 million — suggesting that growth has stalled. OpenAI had anticipated that it would cross the one billion WAU mark by the end of 2025 — and it blamed its failure to do so on fiercer competition, primarily from Google’s Gemini. For OpenAI to afford its compute commitments, it has to make or raise $852 billion in the next four years. It must have that cashflow, or it will run out of money or be sued out of existence by its numerous counterparties from CoreWeave, Microsoft, Amazon, and Cerebras. In the final part, I’m going to get into the depths of destruction — the unraveling of the greater data center debt industry, the massive damage to private credit to come, potential shareholder lawsuits against NVIDIA, and the consequences of the deaths of OpenAI and Anthropic. What If…We’re in an AI Bubble? I also want to add that I realize three headlines didn’t make the cut — what if there’s not a bailout, what if I’m wrong, and what if I’m right — and I intend to cover all three of them in future free newsletters.  Nevertheless, today’s is an absolute beast, a 16,000 word conclusion to the first multi-part Where’s Your Ed At Premium.  What If The AI Industry Moves To Entirely Token-Based Billing?  What If Organizations Can’t Afford To Keep Spending On AI? What If The AI Capacity Crunch Never Ends (And Data Centers Aren’t Getting Built)? What If CoreWeave Can’t Keep Up With Its Capacity Demands? What If Hyperscalers Can’t Build Data Centers Very Fast? What If Hyperscalers Have Warehouses of Uninstalled GPUs? What If Hyperscalers Write Off A Large Chunk of GPUs? What If Data Center Construction Demand Collapses?  What If Venture Capital Funding Stops Flowing To AI Startups? What Would Make Venture Capital Stop Funding AI Startups? What If Most AI Startups Go To Zero? Scenario: OpenAI and Anthropic Go Full FTX, Scooping Up Dying AI Startups To Keep The Industry Afloat With Circular Financing Scenario: Venture Capital’s Post-AI Depression What If Inference Isn’t Profitable? AI Has Become An Existential Reckoning For The Valley NVIDIA’s customers are taking years to even begin making back the billions of dollars its chips and the associated construction costs. NVIDIA is selling far more GPUs every quarter than can realistically be installed in the space of a year. NVIDIA’s revenue stream is entirely based on organizations forecasting demand years into the future. NVIDIA’s revenues are, by extension, dependent on how long organizations believe that building data centers is a good idea. NVIDIA is absolutely, without a doubt, warehousing at least a million Blackwell GPUs . It’s difficult-to-impossible to actually measure the ROI of AI spend. It’s difficult-to-impossible to actually know how much it’ll cost to complete a specific task with AI. What if data center debt stops being issued? What if private credit had to write off most of its data center loans? What if the AI bubble blows up Taiwan’s ODM server manufacturers? What if NVIDIA is misrepresenting how many GPUs are shipped, sold and operational? What if OpenAI and Anthropic don’t go public? What if Oracle doesn’t get paid by OpenAI? What If OpenAI Dies? What if Anthropic Dies?

0 views

Revenge of The Business Idiot

If you liked this piece, you should subscribe to my premium newsletter. It’s $70 a year, or $7 a month, and in return you get a weekly newsletter that’s usually anywhere from 5,000 to 18,000 words, including vast, detailed analyses of NVIDIA , Anthropic and OpenAI’s finances , and the AI bubble writ large . My Hater's Guides To Private Credit and Private Equity are essential to understanding our current financial system, and my guide to how OpenAI Kills Oracle pairs nicely with my Hater's Guide To Oracle . This week, I’ll publish the final part of my ongoing series (“ What If…We’re In An AI Bubble? ”) about the factors and events that will cause the AI bubble to finally pop, focusing on what consequences might follow the collapse of OpenAI and the wider data center  Subscribing to premium is both great value and makes it possible to write these large, deeply-researched free pieces every week.  Today I’m going to speak from the heart, and tell you that we’re ruled by fucking imbeciles. AI is a perfect storm of failed concepts and organizations, and the apex of the Era of the Business Idiot , an epoch where we’re ruled by people so thoroughly disconnected from the actual workforce that it was inevitable that a technology would be created specifically to grift them. Just ask Aaron Levie, CEO of Box :  LLMs are dangerous for many, many reasons, but the under-discussed one is how well they play to a certain kind of executive imbecile. Generative AI is — to quote Mo Bitar — really good at doing an impression of work, much like most managers and c-suite executives, and even if it’s completely incapable of doing something, it’ll absolutely say it can and tell you you’re amazing for suggesting it. And that’s why Business Idiots love it.  Where regular human beings would say annoying things like “that’s not possible within that timeline” or “we don’t have the resources to do it,” AI will say “of course, right away!” and burn as many tokens as possible. When it makes mistakes, it’ll apologize — as it should because it failed you — but then promise to do better next time, all while costing so much less, at least in theory, than a regular, stinky human being.  It’ll create a PRD (product requirements document) of a theoretical software project with the confidence and vigor that you need to take it immediately to a software engineer and say “build this immediately,” and when the software engineer tells you a bunch of bullshit about it not being possible, it’ll spit out several convincing-sounding responses. Fuck, why even bother talking to that engineer at all? Claude Code can mock up a prototype that you can then shove in their fucking face before you fire them for not using AI to do it themselves. I realize I sound a little churlish and dismissive of those who may or may not actually get something out of AI, but this entire industry feels like a mixture of kayfabe and ignorance, slathered with a kind of angry desperation that reflects the distance between reality and fantasy, driven by people that don’t do any fucking work.  Any executive-level fuckwit you’ve met in your life now has a seemingly-powerful tool that can burp up mimicry of open source software and, if you constantly prompt it, eventually get something half-functional onto some sort of web server. When you face bugs, it’ll try and fix them, sometimes also “fixing” (adding or deleting code) from elsewhere to be helpful, like when Cursor using Anthropic’s Claude Opus 4.6 model deleted an entire production database and all its backups . It will never, ever say no, even if it’s incapable, even if it has no thoughts, even if what you are asking is equal parts impossible and unreasonable in both its timescale and scope. A Business Idiot, given his druthers, can sit there and fuck around and make an LLM spit out something that makes him feel like he’s coding, which in turn makes him feel that you, a lazy and stupid engineer, could do even more with the power of AI. It doesn’t matter that it costs an absolute shit-ton of money, or that there’s no way to measure its efficacy. The Lion does not concern himself with things like “efficacy” or “productivity,” and the Lion is increasingly tired of your whining! The Lion doesn’t even understand what it is you do every day other than not doing what The Lion is asking for! You laugh, but this is genuinely how the majority of managers and executives think and act, and now they have a special chatbot that can fart out functional-enough prototypes to convince a Business Idiot they can do anything, because executives and managers do not regularly do much work. As a result, they have little idea what work looks like other than when they look over your shoulder, which is why they wanted you back in the office, and their distance from production is why the same people who were anti-remote work are now aggressively trying to shove AI down your throat .  Organizations aren’t burning millions or hundreds of millions of dollars a year on AI because it’s good, they’re doing it because they are run by people who do not know what the fuck they’re doing.  Generative AI is catnip for hall monitors, snitches, toadies, and any other group that hates work and loves talking down to others. Put another way, it ingratiates losers who believe that learning to do or being good at something is a waste of time, because they deserve to just do what they want without any of that messy “effort.”  While I’m not saying every LLM user is an imbecile, they’re built to convince the mediocre and incurious that they’re remarkable, and it turns out that a great many of them run venture capital firms and Fortune 500 companies. I also want to be clear that while there are sane and normal people who use these things, they’re mostly drowned out by a crowd of people that oscillate between bootlicking and regurgitating capitalist mythology in a way that makes it hard to trust anybody who spends significant amounts of time using an LLM.  One thing you’ll notice about the most moistened AI boosters is that they lack much degree of pride in their work. Everything they say must, at some point, compliment the mindless, unprofitable, unreliable tool underneath it — how “incredibly powerful” it is, how it’s “only getting better,” how it’s “only the beginning” of something that’s eaten over a trillion dollars and absorbed the majority of venture capital .  It isn’t about the work, or the craft, or the thought behind it. Everything is a numb, mindless death march toward saying “job done” and burping out some sort of pseudo product, if one even exists. I’m not even being sarcastic! Per Bloomberg , Salesforce has been marketing “powerful AI products” that don’t actually exist: In a rational society, Salesforce’s stock would take a beating and the SEC would open an immediate and brutal investigation.  Sadly, our society is oriented around the power fantasies of the mediocre and spiritually-dead losers, people bereft of pride or joy in the things they create that believe that they’re owed everything .  They’re Business Idiots, and they are your enemy. Even those who believe they’re aligned with the Business Idiots by supporting and using Large Language Models are the enemy, because The Business Idiots believe that “AI” will simply remove anybody else from the picture, automating work, creativity , communication, friendship , and that includes anyone that helped its ascent.  And yet none of it’s really working, because Business Idiots don’t really know how anything works. As I said back in the original piece , think of The Business Idiot as a kind of con artist, except the con has become the standard way of doing business for an alarmingly large part of society.  Salesforce, one of the most-prominent hypesters behind the AI bubble has spent millions of dollars on advertising and marketing to promote a product that doesn’t exist in the way that it’s being sold.  Only an economy oriented around coveting and coddling losers would have let AI get this far. Every single story about AI has to either directly gloss over the obvious financial and technological issues or start speaking in the kinds of vague theoreticals reserved for cults and multi-level marketing scams. Even Bloomberg’s piece — which is pretty critical! — helps gaslight Salesforce’s customers by quoting an executive blaming their own processes for Salesforce’s outright lies: What the fuck does that mean? What’re you talking about, Madhav? What “autonomous vision”? What complex things? Do you even know? Hello? Even in this very critical piece , the endless pursuit of “fairness” — the Business Idiot’s favourite weapon when they don’t want to be graded on their actual work — means that we have this slop-adjacent explainer that mostly amounts to “yeah you know sometimes their shit needs to be better and then one day, wow , boom! We’re gonna have all sorts of stuff happening.” But this is the world the Business Idiots have created, as I described last year : This naturally created a tech industry (and a larger economy) dominated by executives that were rewarded for growth, which meant that our tech products are inherently oriented around that growth:  The problem with an economy dominated by Business Idiots is that it eventually loses its connection to the wider concept of production or solutions to customers’ problems, because that might cause management to interact with the real world and, by extension, have actual problems themselves. The problems that Microsoft, Google, Meta and Amazon solve on a daily basis are those related to its shareholders. How do we keep growing? How do we keep people engaged with our products? How do we convince our customers to pay more for our customers? And how do we keep people buying our stock? Thankfully, The Business Idiots have captured both the media and the markets, twisting the definition of a “good company” into one measured by these very same questions. It doesn’t matter that Facebook is deliberately broken or Google Search’s results were intentionally made worse because number go up, and that’s all The Business Idiot cares about! It doesn’t even matter that 10% of Meta’s 2024 revenue came from scams or that its Kylie Jenner-branded chatbot led a man with dementia to his death or that its John Cena-branded chatbot would roleplay about having sex with children or that it wants to spend $125 billion or more on AI in 2026 because Meta’s ad sales have yet to slow down .  It doesn’t matter that Meta CTO Andrew “Boz” Bosworth has overseen multiple unprofitable, unpopular products or is hated by basically every single person I’ve ever talked to at Meta — The Wall Street Journal will still write a glowing profile saying he’s a “blunt, outspoken provocateur” that’s “transforming Meta” by “unleashing AI.” One can be a colossal fucking loser that everybody hates, lay off thousands of people, fail to make anything of note, oversee multiple failures, and the Business Idiot’s consent-manufacturing machine will help wall you off from reality.  “But Ed,” I hear you cry. “You can’t call somebody like Andrew Bosworth a loser. He’s a huge success! He made lots of money!” You’re falling for the Business Idiot’s biggest trap: that having wealth or being a C-suite executive is proof that you’re not a disconnected loser.  Boz, like every other oaf destroying your favourite tech products, is the ultimate loser — he’s succeeded by taking credit for other people’s ideas, firing people when his own ideas fail, and repeating the cycle as many times as he wants because that’s what being an executive means to him. Boz has no pride in his work. If he did, he’d have resigned over the failures of both the metaverse and Meta’s wasteful, directionless AI efforts, or even over how fucking awful Facebook has become. The sad truth is that he doesn’t care! He doesn’t give a shit. Boz, like every other Business Idiot, exists to extract value from others and get rewarded by shareholders. As he said in 2018 , to Boz, “all the work [Meta does] in growth is justified.” That includes deliberately making notifications less useful, injecting clickbait and AI slop into your feed, and hiding chronological feeds behind an Escher painting of different menu options.  Boz is indicative of the vast majority of CEOs and upper-level management of most of the world’s organizations. If you read this and feel self-conscious, it’s because you secretly know I’m talking about you or somebody you know. One can be incredibly-rich and well-known and yet a huge, unbelievable loser, because being a loser is deep within your soul. A loser is somebody who takes from others, claims others’ work as their own, and demands more credit for having done so. A loser is somebody who believes work and creation is beneath them, and that they are owed the fruits of labor regardless of their actual contributions to the world. This is why so many people have such an abnormal reaction to AI, promoting and defending it like it’s their religion or nation state. While many people use LLMs and see them as a kind of word calculator or search engine, so many more see within it the chance to ascend above the proles who “work” or “create,” because they find the process of labor or effort so utterly loathsome. When somebody badmouths AI, the Business Idiot must defend it with everything they have, because attacking LLMs is attacking the output of an LLM , which is in turn a judgment on those who are tolerant of its mediocrity and impossible-to-avoid hallucinations . You see, if you demand good work with intention , that might mean the Business Idiot actually had to do something , and that’s not what The Business Idiot signed up for. We are slaves to middle management and the middle management mindset, we are living in their world, and it will collapse because they never really understood anything to begin with. LLMs impress the writers who do not want to write, the coders who don’t want to code, the researchers who don’t want to research, and the lawyers that don’t want to actually understand case law. Those that desperately tell you how powerful AI is and that you simply must use it are looking for you to validate their own laziness or distaste for effort, and those who are impressed with LLMs’ outputs tend to be people with low standards.  The aggression with which AI boosters and executives act toward those who aren’t impressed suggests a genuine intellectual and moral weakness. Nobody who’s this insistent, aggressive and violative with their language of “it’s here and if you don’t adopt it you’re stupid and dead” has ever been right about anything. Nobody this desperate, insistent and forceful has ever had good intentions, good vibes or brought good omens — they are always bearers of some kind of con.  Most technology is sold on elevating and ascending human beings. AI cheapens every interaction by creating a work-shaped product from a person that doesn’t respect you enough to give you work that’s barely fit for a human because it wasn’t made for one.  This is why being an AI booster requires you to debase yourself. You must accept becoming a dogshit dealer that loves accepting and receiving low quality goods. You must celebrate intentionless and decaying slop, and defend it and the machine that made it with your entire being. You must sully yourself — treat its unexceptional, sloppy and unreliable outputs as signs of sentience, or at least the proof that digital sentience is possible. You must defend horrible, abrasive, ugly, loud monoliths of steel full of $50,000 graphics cards. You must say they are necessary, and you must aggressively antagonize those who do not.  That’s because they’re not defending LLMs so much as they are the greater form of Rot Economic capitalism . The Business Idiots have successfully changed our experience of buying and using software from one of “paying for a service” to “accessing powerful technology,” reframing every mistake as the necessary pain of new innovations and every mediocre output as proof that the tech industry can still innovate , because critiquing these things — asking for them to be anything approaching the autonomous, reliable and powerful technology that everybody claims they are — is considered “improper” or “biased” or “skeptical.”  Oh yes, they use “skeptical” as a pejorative. This aggression only proves that the management sect is scared. LLMs were meant to be the thing that replaced all workers, but the actual outputs and outcomes don’t seem to be resulting in anything changing other than lots of things becoming worse or more-expensive. Every AI booster will say “AI is writing all the code at some organizations,” but never seem to explain what happens as a result, such as whether software is being shipped faster, or better software is being made, or, well… anything.   The answer is simple: they don’t know because nothing has actually changed. Organizations writing massive amounts of code using LLMs are facing massive product stability issues and, in the case of Zillow, spending millions of dollars a year to turn their codebase into a confusing, intentionless slop and increasing software reviewer loads by 29,000 hours a month.   This is only made possible in an economy run by people who don’t do any work, and a tech and business media that exists to ingratiate them. I want to lead with a surprising comment: I don’t think LLMs, as a tool, are a grift. There are use cases, though those use cases are miniscule compared to the egregious promises and extrapolations made by the majority of the media and the executive sect, and absolutely nothing about them warrants the amount of money invested in them.  That being said, I think LLMs lend themselves perfectly to grifting. Sam Altman helped propagate a technology perfect for conning people with potential, a larger extrapolation of Altman’s own life of taking dogshit — Loopt, for example ! — and parlaying it into larger opportunities. It can make a really half-hearted demo of a lot of things, and that’s good enough to sell to Business Idiot.  Dario Amodei took this grift and perfected it. Anthropic is a company purpose-built to con people into giving it by money by making people feel smart. LLMs can do work-shaped stuff, sometimes, as long as you debase yourself to accept mediocre and often-broken stuff that you have to keep a vigilant eye on, and either use a subsided product that loses Anthropic money or pay a shit ton of money as an enterprise to Anthropic and it still loses money.  The media was also primed for the grift. Reporters are never incentivized or supported to actually spend meaningful time understanding technology, meaning that the vast majority lean toward access journalism or, at best, the most kindly, “objective” (read: pro-business) takes that result in “wow, isn’t the future great?” no matter how good the thing they’re using actually is. Editors are, in many cases, entirely disconnected from the process of reporting or writing, let alone the underlying technology their reporters cover, which leads them to at best live in a world of “I sure don’t trust these CEOs but their technology sure is powerful.”  As a result, all a technology has to do is either look or sound plausible. Can LLMs write all code? Not really! But because they can write some code and there are lots of eager people on Twitter saying it’s powerful, that’s all it takes to write the sentence “software engineers are writing most of their code using LLMs.” Can Anthropic actually take down Figma? God no, but the mere existence of Claude Design is enough to write that it might . All it takes is the hint of something to be true for it to be written about as gospel. Each statement adds another bullet point to Anthropic’s investor deck so that it can raise another $30 billion in funding, which in turn validates any journalist’s beliefs in Anthropic’s ability to destroy other companies with a product the journalist has not and never will use.  Business Idiots did well to pressure modern journalism into conflating scrutiny with a lack of curiosity. To ask too many questions is “unfair.” To not immediately assume that LLMs are getting “exponentially better” is to be an ignorant luddite. To not assume that everything will work out like it did with Uber or Amazon Web Services is to “ignore history.”  Grifters took advantage of this industrialized intellectual weakness using a tool purpose-built to do enough of an impression of something to impress the media and executives. It worked, because both are sold to in much the same way — by telling a plausible-enough story that ingratiates somebody who is never the end user of the product in question.  If a journalist gets curious, an LLM can make a good-enough impression of somebody writing software to fool somebody who doesn’t really know what they’re doing, and if you prompt it again and again and again, it can get something functional out the door. This is all it takes for somebody — a reporter or an executive — to extrapolate that because they were able to do something (even though the LLM did it), a subject-matter expert would be able to do even more.   As a result, LLMs are fantastic tools for grifters. Somebody that doesn’t really like doing anything other than getting applause for other people’s work can now run multiple concurrent agents, endlessly tweak prompts and tell everybody that they’re an “AI specialist,” with their LLMs making them seem busy in a way that’s hard to argue with because there’s so much bullshit going on.   An ethically-questionable “AI beat reporter” (though this is not across the board) can easily become prominent by simply writing up whatever it is the companies are excited about and reporting on leaks of Slack conversations, creating the appearance of “scrutiny” without ever scrutinizing or questioning the ethics or underlying economics. An oafish product manager with terrible ideas can now pump out half-functional scripts and software that sort of does something, and when their manager — somebody who also doesn’t do any work — sees what they’re doing, they can happily report to their manager that the person in question is “AI-first.” And when you’ve oriented your entire economy around middle managers, vice presidents, and executives that don’t do any real work, this shit seems magical. AI companies are natural grifting instruments. Because AI startups are so capital-intensive, they naturally require tons of money, which means that venture capitalists have something to invest in, and because there’re always so many rounds , valuations are constantly being pumped . Because AI models can be plugged into anything , by extension any AI founder can pretend that any industry can be automated using AI, and because venture capitalists don’t build stuff or really know stuff anymore, they’re naturally impressed by basically any demo or plausible-sounding promise, especially when an LLM can make something that looks like software. Because AI data centers are so capital-intensive, they require endless amounts of risky debt, but that risk allows private credit to take investments from insurance and pension funds desperate for yield, and because everybody involved is a Business Idiot, nobody has actually thought about what happens if these things don’t work out.  AI allows everybody to grow as long as everybody ignores the big, obvious problems with its efficacy and underlying economics . All you have to do is keep up the kayfabe that the problems aren’t problems and the solutions are imminent, or if you want to pretend to be a critic, you can also suggest that all of this is inevitable. Don’t worry about the fact that data centers aren’t getting finished , or that OpenAI and Anthropic make up 75%+ of all AI compute capacity , or that they make up more than 50% of Amazon, Google and Microsoft’s revenue backlog , or that both of them are horrendously unprofitable outside of brazen accounting tricks that would only work on a business and tech media intent on believing everything they say . Don’t worry about it! Stop asking. Don’t worry about Claude deleting entire databases in seconds, they’re gonna fix that somehow, some day.  That ignorance is a sign of laziness, and of the dominance of the Business Idiot mindset. Everybody wants this to be Uber ( it isn’t ) or Amazon Web Services ( it isn’t ) because it allows them to avoid learning stuff or making informed decisions. If it’s like Uber or Amazon, you can just throw your hands up and say “it’ll work itself out!” which is way, way harder than explaining to me how an industry that loses billions of dollars with no path to profitability doesn’t run out of money at some point.  This is, again, part of the grifter’s toolkit. When you don’t want somebody to think about what’s actually happening, you point them toward something that ingratiates them. Somebody who is rude and mean and asking about those billions of dollars of losses is a hater — somebody who says “well, Amazon Web Services lost a lot of money!” is historically-aware and erudite , even if actual history tells you that Amazon Web Services cost around $50 billion before becoming profitable, or around a quarter of Amazon’s 2026 capex . This isn’t to say everybody making this argument is lazy, just that they’re unwitting pawns in a larger grift where mythology is used to support the biggest waste of capital in human history.  And really, that’s the larger LLM grift: encouraging people to accept or sell lazy, half-baked shortcuts instead of fundamental units of labor or production, all while making them feel smart for doing so. It is a technology that perfectly fits the grifter strategy of giving people as little proof as possible to prove something is real, then letting them fill in the blanks with whatever will make them feel like they’re “ahead,” even if being “ahead” means "mournfully accepting that their job might be automated.” Yet I challenge you any time you hear somebody saying that “AI is here, and it’s transformative” to ask them what the fuck that means , because while “it” might be real, it’s unclear what they actually mean by “it.” The grifters want you to immediately start filling in the blanks, assuming that CEOs saying they’re laying people off because of AI aren’t blatantly lying and that AI has done something, somewhere, that remotely warrants any of this waste and endless propagandizing.  And they want you to do that because they’re losing. If you’ve ever been in a bad relationship — romantic or otherwise — you’ll know the feeling of trying to find any way to prove that things will improve, and the amount of times you’ve ignored something glaringly, obviously wrong. “They’re going through a lot,” “they don’t need to tell me what I need to hear, I know they feel it inside,” “they’re busy right now,” and every other rationalization of somebody not being good to you or interested in you is an exercise in self-deception to avoid dealing with an uncomfortable truth.  Any time you’ve ever found yourself looking for shreds of proof that things are going well is the exact time you should be leaving somebody, yet you’ve likely stayed and sought them out like Sherlock Holmes before he spends thousands of dollars on therapy. Every time you stick around a little longer, you do so based on increasingly-questionable data and the knowledge that changing course will require a brutal reckoning with reality. Sometimes you stick around forever, because making more bad decisions is sometimes harder than making one good yet difficult one. People are making the same mistake with AI.  Right now, everybody is ignoring many, many warning signs at once, all because of short-term thinking. Because hyperscalers’ existent businesses have yet to slow down, everybody assumes — without any actual proof — that AI is somehow driving growth. Conversely, nobody seems to have an answer as to how big tech makes the $2 trillion to $3 trillion of brand new revenue it needs to justify its trillions of dollars of planned capex, and even the Financial Times only sees Amazon making any kind of return on hyperscaler AI investment by 2030:  And even here, in a piece called “the impossible maths of the AI boom,” The Financial Times deliberately finds a way to make things look better by removing every single operating cost!   Those covering Anthropic’s so-called “profitable” second quarter are intentionally ignoring that Musk deliberately discounted the months of May and June in an obvious attempt to engineer a headline. They’re also ignoring the obvious mismatch between Anthropic CFO Krishna Rao’s sworn affidavit from March 6 2026, when he said it had “exceeding” $5 billion in lifetime revenue, which doesn’t line up with any of its previously-reported or stated annualized revenues . The answer, in the end, is that it’s just easier to ignore this stuff, because taking it seriously would require thinking about Anthropic in skeptical terms, which would, in turn, require you to start questioning the fundamental stability of the AI industry. And they need you to do that because they’re fucking losing. OpenAI had a negative 122% non-GAAP operating margin in Q1 2026 , and ChatGPT growth has stalled. Despite its so-called profitability, Anthropic has had to raise a combined $75 billion (between Google, Amazon and investors) since the beginning of the year. Both OpenAI and Anthropic had to lower their gross margin projections at the end of 2025.  Anthropic and OpenAI — neither of whom have any path to sustainability or profitability outside of accounting shenanigans and willing co-conspirators — now make up 50% of all upcoming hyperscaler revenues , and the only way either of them can pay is if somebody, either a venture capitalist or hyperscaler, chooses to give them the money. Nobody has an explanation as to how that works or who funds it, other than that “hyperscalers are some of the most-profitable, cash-rich companies in the world ( as their cashflows drop to their lowest levels in history ),” and that “both of these companies are growing incredibly fast.” Anthropic’s growth is a direct result of Business Idiots controlling a large portion of our economy. Nobody — not a single company — has been able to express in clear-set terms based on their actual bottom line a conversion of “I spent this much money and got this in return.”  In fact, it seems like the opposite is happening. As I’ll mention in greater depth later, Andrew Macdonald, the Chief Operating Officer of Uber, recently gave an interview where he said that the company’s ballooning AI costs are “harder to justify,” in part because there’s no way to link its token spend to useful new features .  Everybody spending millions of dollars on AI tokens is experimenting. As I’ve discussed previously , nobody really knows how to measure the ROI of AI, and the naturally-chaotic nature of LLMs makes it impossible to measure how much it might even cost: Marc Benioff isn’t spending $300 million a year on Anthropic tokens because it’s doing something . He’s doing it because he, like every Business Idiot, has no idea what to do other than spend money, hire people, or fire people. Spending lots of money on AI allows him to say that Salesforce is an “AI-first organization,” and then blatantly lie for two years running that he’s “not hiring any more engineers” despite the many, many job listings on Salesforce’s website for engineering positions.  It’s kayfabe that exists to distract you from the fact that Agentforce only has $800 million in annualized revenue, or around $66 million a month for a company that makes $11 billion or more a quarter .  Seriously, somebody please show me a company spending millions of dollars on AI tokens that can also express a clear, indisputable return on investment. Show me the actual returns. Show me the processes automated and what those processes being automated do to offset these remarkable costs. All of this fucking bloviating about how AI is inevitable and real and so powerful never seems to result in a profit . While companies can vaguely say “oh we saved X number of hours from this,” I am still waiting for somebody to say “we saved this much money and this is how investing in these tokens is profitable.” It’s always something vague, like when Klarna said it estimated ChatGPT would “drive $40 million in profit improvement in 2024,” a stat that it never explained or returned to. Klarna CEO Sebastian Siemiatowski once told Sam Altman to use Klarna “ as his guinea pig ” — only to have to hire back the humans it tried to replace with LLMs after a massive wave of customer complaints . Klarna once said that its chatbots did the work of 700 people , a blatant lie that it got away with because the media doesn’t want to scrutinize an era built on deception. That’s because underneath the puffery and the propaganda and the pervasive sense of inevitability, the AI industry is losing. Anthropic and OpenAI’s revenue growth is only possible thanks to a near-perpetual misinformation campaign that overstates both the current and future capabilities of LLMs, and a near-society-wide ignorance at the executive level. Every story about Anthropic’s customers burning millions of dollars’ worth of tokens comes back to one unfortunate fact: nobody knows how much it’s costing but whatever it costs today isn’t sustainable.  For example, and as I mentioned earlier, Uber COO Andrew Macdonald said this weekend that it was becoming “harder to justify AI costs within the company”: I believe that Uber’s experience is indicative of effectively every company’s experience with AI. Business Idiots, disconnected entirely from production, demand their workers burn as many tokens as possible, incentivizing them to do so for reasons that only make sense to somebody who doesn’t do any work.  And burn as many tokens as they could, Uber’s engineers did. Four months into the year, Uber had exhausted its entire AI budget — in part because it created a leaderboard of the biggest AI users , giving employees an incentive to run wasteful tasks and prompts, if not for bragging rights, then at least to show the higher-ups that they’re onboard with the new direction. .  AI is meant to be this ultra-powerful streamlining tool that changes the workplace forever, yet the practical result appears to be “we’ve spent a bunch of money on something that makes our least-sentient managers excited.” Too many members of the media work overtime to find ways to either ignore or explain away these problems. Stories about how Anthropic and OpenAI have agreed to a combined $1.048 trillion in compute commitments fail once to ask how they might get that money, other than to suggest that both may become cash flow positive by either 2028 or 2030 , again with no discussion as to how other than “they will.”  For them to do so, they will need…well, a trillion dollars over the next four years, either through revenue or funding. That’s an insane amount of money — more than any startup or even public company has had to raise in history — and the fact that more people aren’t talking about that suggests that they either don’t care or don’t want to.   The same goes for those covering NVIDIA and other semiconductor companies. While the largest company on the stock market once again beat analyst expectations and raised guidance, few ( other than JustDario, it seems ) noticed that despite all that extra revenue , NVIDIA only saw its cash and equivalents grow by $600 million quarter-over-quarter.  Why? Because it’s investing tens of billions of dollars investing in AI data center companies like IREN , CoreWeave , and, of course, both Anthropic and OpenAI, and has agreed to spend an unbelievable $30 billion on cloud service agreements in the next six years, quite literally paying its customers to buy its products in the most blatant circular financing since the dot com bubble. This is what an industry does when it’s in distinct, existential distress. NVIDIA is now the fifth-largest purchaser of AI compute behind OpenAI, Anthropic, Microsoft and Meta at a time when AI compute is meant to be facing a supply crunch , which suggests that while demand may exist on a low level for those trying to pick up a few hundred H100s, the only customers for data centers full of Blackwell GPUs (at least, those that actually exist ) are Anthropic, OpenAI, an organization with no clear AI strategy and a CEO that can never be fired, and the company selling the GPUs. That’s a big fucking problem considering that there are tens of gigawatts of data centers being developed that will require around $380 billion in annual revenue to substantiate. There is, at this point, little proof that the AI data center “boom” is anything other than the largest real estate speculation in history.  Some will point to the difficulty one might have finding GPUs, carefully ignoring how the majority of capacity is taken up by OpenAI and Anthropic , leaving the vast majority of customers to fight for scraps thanks to the extremely slow pace of data center construction . Others will say that guidance from companies like NVIDIA and Samsung prove that “the demand is there.” Forgive me, I’m going to be a little stern. I know, I know, you’re gonna say “Ed, you can’t paint with such a broad brush!” but I can find no data center debt deal that makes me feel like anybody was really thinking too hard when they put it together. Blue Owl agreed to invest up to $10 billion in Stargate Abilene after a single fifteen-minute conversation , despite the only tenant being OpenAI, a company that couldn’t afford to pay for the compute it committed to, and nobody ever having built a gigawatt-scale data center in history. This was likely because Blue Owl took advantage of the Business Idiots who run Crusoe: This is, to be clear, a huge scam, and something that should’ve horrified investors, except said investors are also Business Idiots that saw a big number and said “whoopie!” Money men with little connection to how long stuff takes to build , let alone the underlying technology being sold or the companies that might actually pay for the compute saw the potential to “back the next industrial revolution” and fell over themselves to take part.  Like every greedy dullard, Business Idiots backing data centers are easily won over by the blatant lie that a data center is an “ AI factory ,” conjuring up images of large buildings that print money with little human labor needed. In reality, data centers are vast, labor-intensive construction projects connected to large, labor-intensive power projects , filled with GPUs that are so expensive that they require billions in debt that are upgraded on a yearly cycle, with customers that may or may not exist by the time it actually turns on. Calling them “AI factories” is an intentional attempt to simplify projects that have more in common with building cities than any kind of modern factory. These Business Idiots are too informed by other Business Idiots, like the sell-side and buy-side analysts that have no interest in talking about what might happen in the distant future when they can conjure up plausible-sounding statements that pump their bags. Every single buy and sell-side analyst should have said CoreWeave, IREN, and other NVIDIA-backed neoclouds are dangerous investments fueled by circular finance. Instead, almost every single one has upgraded them as a result of NVIDIA’s continued investments , despite these investments being a sign that these businesses can’t last.  The few hedge funds and private equity firms I speak to that have any kind of mental clarity are facing pressure from investors misinformed by analysts and the media. Hundreds of billions of dollars — at least $178.5 billion in America alone in 2025 — have been sunk into data center construction based on flawed information, astronomically more flawed than the assumptions that led to the dot com bubble bursting , as I covered in my premium piece from a few months ago . This is like if they built out all that dark fiber for what would amount to a few hundred internet users in 10 years. These people see NVIDIA’s continued revenue growth as a sign that “demand is unstoppable,” yet that “demand” is entirely contingent on how long investors are willing to ignore reality, much like the rest of the AI industry can only continue as long as everybody keeps up the kayfabe of its supposed inevitability. It’s time to stop, and force these failsons to stand on their own two feet.  I’m growing tired of the amount of people I read saying that “AI is real, but the economics are irrational,” as if these facts are entirely divorced from one another.  A GB200 NVL72 rack will be just as expensive to run in 2030 as it is today, and an incomplete data center will still take just as many hundreds of millions or billions to finish in the future too. There are, I believe, at least $200 billion worth of data centers that will never make even a quarter of their costs back before collapsing, and that’s assuming that they ever turn on or their customers exist by the time they do so.  AI is only “real” because everybody is willing to ignore its blatantly-obvious problems. The only reason that every app has an AI feature or every AI company can still sell a $20, $100, or $200-a-month subscription is because venture capital has yet to walk away from an industry that relies on eternal subsidies. AI data centers only continue to have revenue as long as venture capital and hyperscalers support Anthropic and OpenAI, and their revenues only continue to grow as a result of an endless, society-wide media campaign built on misinformation and API revenues driven by unsustainable venture-backed startups and businesses run by people excited to blow millions of dollars for no reason.  AI is only as “real” as the excuses that get made for it, and the amount of money those who subsidize it are willing to lose. Venture capitalist subsidies are the only reason that companies like Perplexity or Lovable are alive , which in turn means that a large chunk of both Anthropic and OpenAI’s API revenue is only made possible through those subsidies.  Demand for data centers is, by extension, only as large as these subsidies can sustain. Much of this is substantiated by the myth of executive intelligence. Most assume that Sundar Pichai, Satya Nadella and Andy Jassy wouldn’t be as stupid as to burn a trillion or more dollars on data centers for an unprofitable product with demand that only exists because of their own subsidies…except that’s exactly what’s happening. These men have no other hypergrowth ideas , and are more willing to annihilate their cashflows and dominate the Earth with half-finished data centers than to admit that their core businesses will eventually decline. And because these hyperscalers were so aggressive with their buildouts, the Business Idiots conflated that hunger with some sort of proof of massive demand for AI.  Yet even NVIDIA’s own earnings show that demand is incredibly-centralized, with 54% of its Q1 FY27 revenue ($44 billion out of $81.6 billion) coming from three customers , up from two customers accounting for 30% ($13.2 billion) in Q1 FY26. I assume one or more of these are hyperscalers, which means that NVIDIA’s continued growth hinges heavily on the idea that big tech will continue to dump trillions of dollars into its GPUs in perpetuity. I’m repeating myself, but this is not what a healthy industry looks like . If AI data center demand were evenly-distributed and sustainable, NVIDIA’s revenue wouldn’t depend mostly on three customers. Similarly, the entire media wouldn’t be loudly ignoring a short seller report that suggests that 20% of its FY26 revenue came from illegal sales to China . As I’ve said, AI is only as real as its subsidies. ChatGPT is only free to hundreds of millions of people because OpenAI is able to raise hundreds of billions of dollars, much like Anthropic is only able to subsidize its subscribers anywhere from $8 to $13.50 for every dollar of revenue because of endless venture welfare.  The underlying economics suggest that no subscription-based AI service — let alone a free one — makes any kind of financial sense, and the only reason that everybody has had such unrestrained access is because the media and the markets approved it, and the people with the money are deluded and disconnected from the process of value creation on almost every imaginable level. Any statements around “Anthropic actually being profitable on inference” are products of fantasy and magical thinking , distilled copium for people that would rather delude themselves into believing that none of this ever made sense. Again, the assumption is that “companies would never just burn a lot of money,” but that too is catering to the greater myth of executive competence, something that nobody who spends any amount of time around managers or executives would ever believe.  GitHub Copilot let people burn thousands of dollars on a $39-a-month subscription as a means of expressing growth. I absolutely, 100% believe that both OpenAI and Anthropic are doing the same, and that neither of them has some magical way of making inference cheap enough to justify letting people burn thousands of dollars on a $100-a-month or $200-a-month subscription. To give them the benefit of the doubt is to empower them to continue to raise money by conning their investors and the general public, and to continue perpetuating an era of software that runs contrary to what makes technology good. Their goal is simple: to ram as much of this through to as many people as possible to get them to spend as much money as possible…until they work out a way to make OpenAI or Anthropic or these endless data centers into something approaching a real business. One of the greatest mistakes we can make in our lives is to assume that the rich and powerful have any idea what the future holds, or that they have any grand plan or strategy.  It’s very likely that Dario Amodei and Sam Altman’s plan is to keep burning money until somebody who works for them comes up with a way not to, and in the interim their plan is to get as many users as they can to keep raising money.  Similarly, Microsoft, Google, Meta and Amazon’s plan is to keep building data centers in the hope that they’ll have a reason to use them by the time they’re built. There is no other plan. They do not have a secret invention coming. They do not have AGI in a box in their office. They do not have anything, and the reason they’re spending so much money and shoving AI into everything you use is because they have no fucking clue what to do. This is why Dario Amodei makes wild claims about AI replacing 50% of all white collar workers or Microsoft AI CEO Mustafa Suleyman claims all white collar labor will be automated in 18 months — because the actual products themselves aren’t impressive enough to win you over or justify the hundreds of billions of dollars being sunk into AI. They say these things to make you think that they have a scary and powerful technology behind the scenes that does not exist . And yes, that includes Mythos . The forceful, harassment-grade incursion of AI services into our daily lives is not a sign of its power, but a gesture of the lack of confidence and fear in the hearts of its progenitors. Good shit sells by telling you why it’s good — dodgy shit sells by tricking and scaring you and taking advantage of Business Idiots who think that using an LLM to type emails and spending 12 hours a day on Twitter constitutes “work.”  I believe the vast majority of these data centers go unused and/or unfinished, and that most AI startups will die once the venture capital subsidies dry up . I believe that neither OpenAI nor Anthropic have a future, and that their revenues are only made possible through venture subsidies for startups using their models and the experimental revenue of Business Idiots that don’t really know why they’re “doing AI” in the first place.  AI demand remains a result of a societal psychosis and a weakness in those who are meant to scrutinize the untrustworthy. Its unraveling will be framed as impossible to see coming because nobody in power had bothered to look.  It’s easy to feel hopeless. We’re at a point where the greed and the shamelessness and the stupidity is at a fever pitch.  We’ve reached a time the mask has started to slip, and the C-suite imbecile class is unabashed about its loathing of people, as was the case when the CEO of UK bank Standard Chartered CEO (Bill Winters) talked about how those at risk of losing their jobs to AI are “lower-value human capital,” at the same event where he said the company would likely shed nearly 8,000 roles in the coming years due to AI.  Winters would later apologize for his choice of words — although, to be clear, he was being absolutely honest when he made those remarks. That is what he believes.   Everything feels rough because the AI industry is equal parts desperate and over-confident. AI executives believe that they can cram enough promises of money into the system that the system would rather cannibalize itself than admit that it made a mistake. Sundar Pichai, Andy Jassy, Larry Ellison, Elon Musk, Mark Zuckerberg and Satya Nadella will gladly annihilate hundreds of billions of dollars to avoid the inevitable, but once they do, it’ll be gruesome.  At the same time, the things that they need to happen — actual profitability, actual returns on investment, actual tangible proof that this is a real thing rather than something they all have to actively conspire to keep alive — aren’t happening at all. Each week, we hear about new AI megaprojects that will dominate our countryside with blinding lights, endless noise and fume-belching gas turbines at such a scale that it feels impossible it could ever stop. The system is absolutely going to try and exhaust itself to keep it going. The government bought $9 billion of Blackwell GPUs , which, to be clear, isn’t a “Too Big To Fail“ moment so much as it’s a way to keep NVIDIA’s plates spinning for another quarter. In truth, the amount of money that NVIDIA needs to keep this going is so extreme that it is now a test of how long the debt markets and the hyperscalers can keep sustaining the hype. A trillion dollars in annual revenue is necessary by the end of 2028, which would require over 30GW of data center capacity to be built by then at a time when only 5GW at most appears to be under construction .  Nevertheless, even the sweatiest, least-trustworthy boosters have begun to sneak in statements about “we’re probably not in a bubble,“ or “yeah it’s a bubble, but it’s a good bubble.” Jeff Bezos, when asked about the AI bubble, said that you “ shouldn’t worry about it ,” which…is not really sufficient, is it Jeff?  None of this is to say that the mood is good! The vibes are disastrous. Everybody is exhausted. Those who love AI vibrate with a strange soullessness, constantly talking about the incredible power of AI without ever showing what it did or, perhaps, what all that supposed saved time got them. It sucks to work at basically any hyperscaler right now.  Basically every person in every job has had somebody intimate they’re going to lose their job to a computer every time they open the newspaper or use a website, and every app has some sort of desperate, vulgar pop-up about a feature that will generate some bullshit, obfuscating the features you actually want to use in favor of those that might lose the company money, because the company has to prove to the people that invested in the company that they’re “futuristic.” Alternatively, their CEO has either mild or severe AI psychosis to the point that they have decided to violate your user experience. AI is a non-consensual technology at its heart. But they are losing. They all know it. They are acting desperate.  It seems that there are nearly as many announcements of new large data center developments as there are cancellations of said data center projects. While hyperscalers can dismiss that as a simple reallocation of capital, and nothing to worry about, it’s harder to ignore the growing backlash against these facilities from locals — and the success that locals have achieved in blocking (mostly temporary, but some permanently) any future developments .  And it gets worse. Anthropic had to conspire with Elon fucking Musk to conjure up a single profitable quarter to swindle the media and its investors one last time . In response, OpenAI either leaked or had leaked immediately following that it had a negative 120% margin and ChatGPT growth had stalled . Anthropic is either the single-most successful grifter of all time or speed-running a con where it fudges together numbers to raise endless amounts of money to keep its billion-dollar burn going.  These are not the actions of honest, sustainable companies that will exist in the future. I believe that we are on course for a truly horrible crash, the likes of which may rewrite the venture capital industry and mortally wound one or more hyperscalers, as well as fundamentally divide society on so many levels into those that fell for this and those that did not. This will, in the short term, be absolutely fucking horrible for our markets and our wider economy as a result of the time-bomb of private credit and private equity. In the long term, I see it as a “They Live” moment for many millions of secret imbeciles and cretins in our midst, and I don’t think it’ll be easy to wash the stench off for those that really pledged themselves to the graveyard smash here. We will win, long term. What they are doing is not working. The future will not be without pain, nor will it be easy, or pleasant, or something I relish in. But in the long term I think this is a moment where the greater Business Idiot incursion faces a reckoning with a reality it believed it could change through sheer force of will. These people don’t know how to build things that work anymore, and thus the only thing they can do is spend money and fire people. They believe in nothing other than growth, and one cannot exist on belief and hype alone, at least not forever. And I can’t wait to watch what happens when it collapses. I’ll close this piece with the regular CTA — please, subscribe to my premium newsletter ($7 a month, $70 a year, you’re gonna love How OpenAI Kills Oracle and The Hater’s Guide To Private Credit — but with a little explanation as to why I do the things I do. I write this newsletter to hopefully do three things: I do it because I believe, fundamentally, that these people — Altman, Amodei, Nadella, and the many, many other villains that I’ve mentioned in these pages — are bad people, and their values are the antithesis of my values. I care about people, and humanity, and truth, and they do not.  I deeply love technology, and feel it made me the person I am today. It allows me to do wonderful things, connect with wonderful people, and discover endless troves of incredible information. The computer is marvelous. The computer has done many wonderful things for me, despite what all the Business Idiots say. I see LLMs as a violation of everything that great computing stands for. The AI industry encourages its users to both accept and present low-quality work and demands that they constantly defend the industry from those who would demand better from it. It is inefficient, power-intensive, environmentally destructive, and inherently sold based on things that it might do, providing far more value to scam artists and con men than it does to its end users.  This is a mask-off moment for both the ruling class and those captured by capital, and an opportunity to look around you and see who is most-easily fooled. No industry of value needs to mislead you or make you feel bad for not adopting their technology. No trustworthy individual will ever see the need to humiliate or attack somebody for being insufficiently excited about a product. No CEO that talks of a theoretical future as a means of selling you software in the present should be trusted. No technology that makes mistakes with regularity should be defended. And no industry that demands everything from us — our land, our energy, our water, our jobs, our art, our writing, our attention and every dollar we have — should ever be treated with anything but revulsion. That “demand” is almost-entirely funded by debt. That “demand” is not an extrapolation of demand for AI services , but for debt’s hunger to invest in something private credit and banks believe in. Private credit and banks believe in data centers based on flawed maths and magical thinking, because they too are run by Business Idiots. AI data centers are themselves a grift, convincing investors that they’re backing large infrastructure projects akin to housing developments or factories, rather than warehouses full of expensive hardware for customers that may or may not actually exist. First, to tell you that the Business Idiot class wants you to doubt yourself, because whether you recognize it or not, they’re engaged in acts of information warfare against you. Second, to remind you that facts are facts, and numbers are numbers, and that no amount of puffery or obfuscation can change pure mathematical reality. The AI bubble is exactly that, a bubble, and like all bubbles, it will eventually pop . Third, to remind you what it is we’re fighting for. Because every newsletter I write isn’t simply about highlighting mathematical stupidity, or corruption, or dishonesty.

0 views

Premium: What If...We're In An AI Bubble? (Part 2)

Last week I ran the first part of my What If…We’re In An AI Bubble? Series, where I asked questions and posed scenarios as to the consequences of the many, many questions I’ve asked over the last few years. It quickly became one of my most-read articles I’ve ever written, and for those of you who joined me for the first time last week, here’s a quick list of what we’ve covered already: As I mentioned last week, I believe one of the many problems with the analysis of the AI bubble is that people are willing to consider individual facts — like that AI is too expensive for everybody involved and data centers are not being built at the speed that we believed — but never the gestalt of their consequences.  For example, if data center construction slows to a crawl ( as I’ve discussed is already the case ) there’s a cascade of events that will occur: It’s really easy to say “wow, this stuff needs a lot of debt!” and “wow, this stuff takes a while!” but actually sitting and thinking about what that means logically leads you to some gruesome outcomes.  And to be clear, there’s not really an alternative to that scenario if data center construction slows. Even in an optimistic scenario, if data centers that started being built in 2024 don’t get finished until 2027 or 2028, that means that NVIDIA’s “latest” GPUs are perennially two or three years in the future.  While some capacity exists, I believe there are at least one million Blackwell GPUs sitting in warehouses waiting to be installed years into the future, which means that projects are going to launch in a year or two with potentially three-year-old GPUs, or said projects are going to have to either replace their orders with Vera Rubin or dump aged capacity onto a market saturated with Blackwell GPUs. The argument against what I’m saying is that there’s “insatiable” demand for AI compute — that “any viable compute on the market will be used,” which is true in measurements of days or months, but breaks down in the space of a year. As I mentioned a few weeks ago, AI’s demand story is a lie , because capacity is mostly taken up by Anthropic and OpenAI, creating the illusion of demand by absorbing most available inventory, while simultaneously obfuscating the fact that other sources of demand are simply non-existent in any meaningful numbers..  Many are conflating “there’s not much available” with “there’s so many people that want GPUs” without quantifying what “so many” means or how much they want, when the remaining performance obligations from Google, Amazon, and Microsoft have, outside of OpenAI and Anthropic, effectively plateaued, as is also the case when you remove these companies from CoreWeave order book.  If there were incredible, insatiable, indisputable demand, RPOs would be exploding across the board. Instead, nobody seems interested in buying capacity at scale outside of Anthropic, OpenAI, and the hyperscalers supporting them — or, in some cases, the likes of NVIDIA providing backstops to compute providers, agreeing to buy surplus compute in the case that they’re unable to sell it themselves. This is, to be clear, something that shouldn’t happen if there was genuine, distributed demand.   The sheer scale of the supposed AI data center buildout is in the tens of gigawatts of capacity, which translates to  $10 billion to $15 billion per gigawatt in annual revenue. I can find no examples of anybody but Anthropic and OpenAI spending billions on compute.  Both companies need to make or raise a combined $1.25 trillion in the next four years to afford their compute commitments across Oracle, Microsoft, Google, Amazon and CoreWeave.  The counter-argument to everything I’m saying is effectively two points: The latter is far from compelling, but I can see how somebody would believe it.  So much money appears to be flooding into companies like AMD, Samsung, and Sandisk — tens of billions of dollars to the point that it’s creating shortages across basically every component imaginable — which naturally might make you think that demand would exist at the other end. For the consumer, that perception becomes even more believable when you notice how consumer electronics are getting more expensive. Certain games consoles, nearly six years after their initial release, are more expensive than they were at launch. Typically, the inverse is true.  Meanwhile, smartphones and PCs are expected to ship with weaker specs or high prices, in part because of shortages of key components, caused by demand for AI data center hardware.  The thing is, demand for AI compute doesn’t have to exist for AI data centers to get built. While some have clients signed up in advance, said deals were signed so many years before construction will complete that it’s hard to guarantee that they’ll be willing — or solvent enough — to pay.  I also imagine most clients have signed contracts that have milestone dates for delivery of compute capacity. If data centers are delayed, clients likely have a contractual out, much like Microsoft does with its $17 billion compute deal with Nebius . In any case, in a frothy debt market full of desperate speculation, these projects are being funded by the very same private credit firms that piled into SaaS companies between 2018 and 2022 under the assumption that every software company will grow in perpetuity. When due diligence is so weak in private equity and private credit that Apollo’s John Zito says that their valuations are “ all wrong ,” it’s hard to believe that the same financiers are diligently making sure that enough revenue exists to justify these massive data center debt deals. The same questionable attention to detail applies to venture capital, which has seen ( much like private equity ) its investment model slow to a crawl since 2018 , with an average TVPI (total value paid in) slow to a horrifying 0.8 to 1.2x since 2018, meaning that for every dollar invested, you’re at best likely to get even money in return.  These are the very same investors telling you that every AI company is worth perpetually-growing amounts of money, that everything will work out perfectly , that somebody will work out how to make AI profitable, and that AI is both here to stay and doing incredible things , even if they can’t really explain what those things might be. In reality, none of these people have any idea how to turn around these rotten economics. Data centers are massive money-losing operations that in the best case scenario take five years to make a single dollar of margin, and their customers are eternally-unprofitable AI startups that rely on a constant flow of venture capital dollars.  The AI bubble is entirely built by people who hope somebody else will solve their problems. AI labs depend on venture capitalists to fund them, hardware providers to invent silicon that makes their businesses profitable, and their AI startup clients to find ways to make profitable businesses using their APIs. In turn, AI startups rely on AI labs to work out a way to make their models cheaper so that AI startups can make their business models profitable.  Put another way, everybody’s response to “how does this become profitable” is “don’t worry, somebody will work it out, but don’t worry, they’re going to at some point.” Today, I want to explore what happens if they don’t.  What if…We’re In An AI Bubble?  What If The AI Industry Moves To Entirely Token-Based Billing?  What If Organizations Can’t Afford To Keep Spending On AI? What If The AI Capacity Crunch Never Ends (And Data Centers Aren’t Getting Built)? What If CoreWeave Can’t Keep Up With Its Capacity Demands? What If Hyperscalers Can’t Build Data Centers Very Fast? What If Hyperscalers Have Warehouses of Uninstalled GPUs? What If Hyperscalers Write Off A Large Chunk of GPUs? What If Data Center Construction Demand Collapses?  OpenAI and Anthropic can’t expand much further than their current capacity. As they both make up 50% of Amazon, Google and Microsoft’s revenue backlogs , hyperscalers will be unable to make the majority of the revenue they’ve promised their shareholders. The $178.5 billion in US data center debt from 2025 will go mostly unpaid, as a great deal of it is project financing that’s dependent on revenue from data centers that won’t be built and thus won’t be making any revenue. NVIDIA, which claims to have shipped over 3 million Blackwell GPUs in 2025, will have trouble selling its next-generation Vera Rubin GPUs, as nobody will have anywhere to put them. Alternatively, we’ll see write offs of billions of Blackwell GPUs that will now be considered obsolete. Banks that are already afraid of “choking” on data center debt will stop issuing it, because these investments will not be paying off. It will become very difficult for anybody to afford to buy more NVIDIA GPUs, because AI data centers — which cost around $44 million per megawatt — require massive amounts of upfront capital expenditures, making it unlikely-to-impossible that somebody has the money lying around. That the amount of revenue flowing to both NVIDIA and associated hardware companies making CPUs, RAM, and solid-state storage is proof that there’s demand for…services run on them. What if venture capital funding stops flowing to AI startups? What would make venture capital stop funding AI startups? What if most AI startups go to zero? What if OpenAI and Anthropic became AI’s lender of last resort?  What if AI broke venture capital’s back?  What if inference isn’t profitable?

0 views

News: OpenAI Had A Negative 122% Non-GAAP Operating Margin In Q1 2026, and ChatGPT Growth Has Stalled

New revelations about OpenAI’s finances paint a dim picture for the company, as The Information reported it generated just $5.7bn in the first quarter of 2026, with an adjusted operating margin of -122%.   This means that for every dollar of revenue the company generated, it lost $1.22.  As The Information’s Sri Muppidi noted , these operating margins were adjusted — and, presumably, didn’t conform to GAAP (or generally accepted accounting principles) standards — and excluded certain “large line items”, like stock-based compensation.  By that maths, that means that OpenAI lost $6.95 billion in the quarter, and because this is non-GAAP, it’s quite possible that losses are much higher, revenues are lower, and its margins are worse. The piece does not specify if operating margin includes or excludes training costs, nor does it break down what other exclusions there may be other than stock-based compensation. The report also claims that OpenAI is “on track” to hit its goal of generating $30bn in revenue for 2026, although if it maintains these disastrous margins, it would end up losing $36.6bn.  Meanwhile, ChatGPT’s user growth has stalled. While weekly active users hit 920m in February, the average for the quarter sat at 905m, suggesting lower numbers in either (or both) January or March. OpenAI had expected to hit 1 billion weekly active users in 2025. This suggests that ChatGPT’s growth has stalled. As I’ve noted in the past, weekly active users are a fairly novel metric, with most companies using monthly active users to represent adoption. I’ve also speculated that the reason why OpenAI has favored this metric is because it’s easy to manipulate . OpenAI reportedly had 55m paying ChatGPT customers at the end of Q1 — up from 47m people at the end of the year.  Assuming a userbase of 905m users, this means that OpenAI has a conversion rate of roughly 6%. It's likely worse, as monthly active users should, at least in theory, be a higher number, as it captures every weekly user in addition to less-active users over the course of a month. Nevertheless, while this represents an improvement over the 2.583% rate in February of last year , it’s likely improved as a result of cheaper ad-supported ChatGPT “Go” subscribers at $5 or $8 a month, depending on geography. OpenAI also gave away a free annual ChatGPT Go subscription to literally every Indian subscriber in late October 2025 , though I cannot confirm if they’re counted in the total. As I wrote up yesterday , Anthropic leaked (or had leaked) that it believed it would have a non-GAAP EBIT operating profit in Q2 2026 entirely as a result of Elon Musk discounting two months of compute costs for that specific quarter , and it makes me wonder why we’re suddenly, in the space of 24 hours, talking about operating margins or operating profits for two companies that have hidden behind annualized revenues and obfuscated financials for several years. If I had to guess , it’s likely that investors have begun to demand firmer, more “real company”-adjacent numbers, and while Anthropic was able to find a clever way to manipulate them as a means of raising funding, OpenAI was forced to share numbers a little closer to reality. What’s clear is that we’re in an information war between two companies that burn billions of dollars, with one of them ( OpenAI ) allegedly planning to file for an IPO as soon as today .  Anthropic clearly wants to position itself as the stable, reliable, economically viable alternative to OpenAI, but can only do so with a kind of financial engineering only made possible in a media climate bereft of scrutiny.  Nothing has changed about the core economics of generative AI to suddenly make things profitable, other than the ingenuity of CFO Krishna Rao and his willingness to move numbers around a spreadsheet.  Nevertheless, it’s interesting that Anthropic appears to be leapfrogging OpenAI in revenue. In early May, Anthropic claimed to have $45bn in ARR . By contrast, in March, OpenAI claimed to have topped $25bn in ARR . While OpenAI brought in a billion dollars more than Anthropic in Q1 2026, The Information couldn’t get ahold of OpenAI’s numbers for Q2 2026, but at $45 billion in ARR - $3.75 billion in a month - Anthropic may have taken the lead. That is, of course, if its numbers actually line up with reality, something I’ve disputed multiple times .  Nevertheless, if investors become convinced that OpenAI is falling behind, it’ll be much harder to raise another round at or above its current $852 billion valuation.  Perhaps that’s why OpenAI is rushing to go public - it realizes it might have tapped out private investors. If you liked this piece, you should subscribe to my premium newsletter. It’s $70 a year, or $7 a month, and in return you get a weekly newsletter that’s usually anywhere from 5,000 to 18,000 words, including vast, detailed analyses of  NVIDIA ,  Anthropic and OpenAI’s finances , and  the AI bubble writ large . My Hater's Guides To  Private Credit  and  Private Equity  are essential to understanding our current financial system, and my guide to how  OpenAI Kills Oracle  pairs nicely with my  Hater's Guide To Oracle . This week, I’ll publish the second part to my ongoing series (“ What If…We’re In An AI Bubble? ”) about the factors and events that will cause the AI bubble to finally pop.  Subscribing to premium is both great value and makes it possible to write large, deeply-researched free pieces every week.  The Information reports that OpenAI generated $5.7bn in revenue for the first quarter of 2026 based on discussions with sources familiar with its financials. With adjusted negative margins of -122%, this means that for every dollar of revenue OpenAI made, it lost an additional $1.22, or around $6.95bn on a non-GAAP basis. OpenAI is "on track" to hit goal of $30bn in 2026 revenue, but margins suggest losses of over $36.6bn. OpenAI continues to struggle converting free ChatGPT users to paying customers, and overall user growth has stalled.

0 views

Anthropic's "Profitability" Swindle

Yesterday, the Wall Street Journal ran a story about how Anthropic is “about to have its first profitable quarter,” specifically an operating profit, or EBITDA profitability: Interesting! That’s a lot of certainty considering we’re barely through the first half of the second quarter, and quite a specific number given the fact that June hasn’t started! And all of these numbers are mysteriously leaking exactly while it raises its funding round! Oh there’s also one important note: The Journal adds at the bottom of the article that “ ...it is unclear what accounting methods Anthropic has used to book revenue and costs, as the company isn’t yet required to follow the financial-reporting requirements of a public company. ” That’s right —-- Anthropic is possibly going to be EBITDA profitable for a single quarter, on a non-GAAP basis.  Anyway, I wonder how Anthropic did it? Because based on this unhelpfully-labeled diagram from the Journal, it appears ( as I said last year ) that its costs scale linearly with its revenues, except they…magically didn’t in the second quarter? I wonder if it'll stay profitable? That’s also interesting. So Anthropic may be profitable very specifically in Q2 2026 , but might not be afterward. It’s almost as if it found a way to specifically cut its costs in May and June somehow… …because it did! Remember that deal Anthropic signed with SpaceX to take over Colossus-1 ? Well it’s also taking over some or all of Colossus-2, paying SpaceX $1.25 billion a month starting in May and June… when it’ll have a reduced fee as it ramps up! Per SpaceX’s S-1 : That’s $15 billion a year in compute costs, but reduced to an indeterminately-discounted level for the precise months that Anthropic is using to tell investors and the media that it has an operating profit. That operating profit is a result of accountancy rather than any improvements to its business model. While I wouldn’t say this is cooking the books, it’s definitely a shiatsu-grade massaging of the numbers. Anthropic has deliberately leaked a quarterly “profit” where it knows it can suppress its costs, specifically made sure that the journalist gave it the out of “costs might increase,” and released it on the day of NVIDIA’s earnings as a means of keeping the AI bubble inflated. Nothing has changed. If Anthropic paid full-rate for its compute in those two months, its economics would shift back to what they’ve always been per my reporting from last year on its AWS costs — a business that has costs that linearly increase with its revenue growth. I also severely doubt that Anthropic managed to make the cost of running its services profitable in the space of six months. Per The Information in January , Anthropic missed on its gross margin projections, saying that its inference costs were 23% higher than the company had anticipated. How did Anthropic, which faced a massive influx of new business to the point that Anthropic was forced to buy more compute from Elon Musk , magically become profitable? Other than that discount, of course. I have a few guesses: Nevertheless, the revenue side is where the real problems lie. So, Anthropic has said it brought in $4.8 billion in revenue in Q1 2026, and projects to hit $10.9 billion in Q2 2026. This is tough to reconcile with previous reporting. On February 12, 2026, Anthropic claimed it had reached $14bn in annual recurring revenue (ARR) . As a reminder, ARR is an accounting tool largely used primarily by startups, where a snapshot of a single month’s income is taken and multiplied by twelve. This gives you an implied monthly revenue of roughly $1.17bn.  On March 3, 2026, Dario Amodei would claim Anthropic had reached $19bn in ARR — which works out to $1.58bn per month . Two days later, on March 9, Krishna Rao — Chief Financial Officer at Anthropic — would declare under oath in a court filing that Anthropic had brought in revenues “exceeding $5 billion to date. ”  Keep in mind that The Information had previously reported that Anthropic had $4.5 billion in revenue in 2025 , which I already found difficult to match with Rao's statements. While boosters may claim that “exceeding” could mean literally any number they want above $5 billion, I find it doubtful that the CFO of Anthropic would, under oath, lead the court to believe its business was 30% to 40% smaller than it was, especially when trying to convince it that the damage of being labeled a supply chain risk would ruin its business. At this point it’s impossible to reconcile the 2025 reporting with that $5 billion number. If we assume that the ARR claims made by Anthropic are correct, we can presume that it made revenues of roughly $2.5bn in March ( given that it claimed it had $30 billion in ARR on April 6 ), $1.58bn in February, and $1.17bn in January, for a total of $5.25 billion.  I realize that figure is in excess of what the Wall Street Journal had and, in some world, those numbers could be cherry-picked using particular periods to the point that the real revenues would be in the region of $4.8 billion. That's possible. But they don’t make a lick of sense when you bring up what Krishna Rao said. If we believe Anthropic’s leaks —-- putting aside all of the ARR figures for a second —-- this means that Anthropic: While I acknowledge that Anthropic has grown significantly, that level of stratospheric growth does stretch the limits of credibility. Moreover, the fact that previous ARR figures are inconsistent with the leaked charts from Anthropic further raises questions about the credibility of any numbers from the company.  The only real defense that anybody has here is that Krishna Rao, under oath , lowballed the US government and a judge to such a dramatic extent that he hid in excess of $4 billion in revenue.  And as I’ve discussed before — and FlyingPenguin helpfully collated — adding up Anthropic’s previously-reported ARR from January 2025 to March 3, 3rd 2026 already gets us to around $6.66 billion.  I can imagine this has felt like a big victory for boosters — proof that AI can be profitable, that inference is profitable, that some sort of business model is emerging…and I’m sorry, that’s not what’s happening. Dario Amodei and Elon Musk worked out a sweetheart deal, which they - framed as a “ramp-up,” - that allowed Anthropic to artificially depress its costs. I also question how much of a ramp-up there really was, or what Anthropic’s actual compute constraints were, because it immediately loosened rate limits for Claude subscribers on announcing the deal , meaning that it immediately started having higher inference costs, which…somehow led to it making a higher profit? Or did Musk — as literally described in its S-1 — have SpaceX charge Anthropic less for two specific months to make the numbers look better? In July, Anthropic will start paying SpaceX $1.25 billion a month,  - or $15 billion a year, - on top of all of its other compute deals with Google, Amazon and Microsoft.  If we assume that its spend is comparable on AWS and Google Cloud — and it’s most-assuredly more! — that means Anthropic is spending around $3.75 billion in compute costs, or $11.25 billion a quarter, or $45 billion a year.   There’s also a very compelling argument that Anthropic’s costs will increase and will eat up that profitability , to once again quote the Wall Street Journal: I also have to wonder: if you’re so profitable, why not IPO? Why not take this to the public markets?  Unless, of course, you’re only non-GAAP EBITDA profitable based on a two-month-long discount specifically covering the period in which you’re profitable. And, of course, when you’re not a publicly-traded company, and so you don’t actually have to publish any numbers (and no, leaking them doesn’t count), and you’re not subject to SEC oversight.  I will give Dario Amodei credit: nobody does financial engineering and a press-led information war better than Anthropic. The willingness of the press to eat up incongruent numbers and the eagerness of many to jump up and find obtuse ways to explain away the obvious problems is only made possible when a company has perfected the art of manipulation and ingratiation of those who want to feel like they’re “first.” If you take this as incontrovertible proof that Anthropic is profitable, you are deliberately ignoring the blatantly obvious ways these numbers are being massaged. We’ve got its CFO saying numbers that don’t match up with these leaks or Anthropic’s own marketing materials, and the aggressive and deluded way in which many people ignore them is equal parts frustrating and depressing.  Let me speak directly and with more empathy than usual: if you want Anthropic to win, you should be just as skeptical of these numbers as I am. You should want to smash my face in the tarmac with the most crystal-clear, impossible-to-argue with numbers, bereft of asterisks or discounts from suppliers or obfuscated accounting metrics.  You should want better from your heroes. If you truly think this company is amazing, unstoppable, and leading the tech industry to a glorious era of innovation, there shouldn’t be this many questions, and the metrics shouldn’t be this murky .   Every other time when a company has played this level of silly, weird bullshit has led to disaster — for example, WeWork claimed to be profitable since the second month of its operations , and repeated claims of profitability throughout its existence , and it turned out that it was only “profitable” if you removed things like “ some of the costs of doing business .” I get why you’re so defensive, and I get why you want this to work. A lot of you are very excited about generative AI, and being excited about it has given you a tremendous community of equally-excited people. I get that you like these tools.  And I need you to know these companies are laughing at you.  Anthropic timed this leak to focus on a specific quarter where it artificially suppressed costs, and gave you the flimsiest proof imaginable, specifically-crafted for you to share it as a triumph and spread the idea that “AI labs are actually profitable,” when their core economics haven’t changed. Costs increase linearly with revenue, and will continue to do so in perpetuity.  I genuinely can’t wait for both OpenAI and Anthropic to file their S-1s. If you liked this piece, you should subscribe to my premium newsletter. It’s $70 a year, or $7 a month, and in return you get a weekly newsletter that’s usually anywhere from 5,000 to 18,000 words, including vast, detailed analyses of  NVIDIA ,  Anthropic and OpenAI’s finances , and  the AI bubble writ large . My Hater's Guides To  Private Credit  and  Private Equity  are essential to understanding our current financial system, and my guide to how  OpenAI Kills Oracle  pairs nicely with my  Hater's Guide To Oracle . This week, I’ll publish the second part to my ongoing series (“ What If…We’re In An AI Bubble? ”) about the factors and events that will cause the AI bubble to finally pop.  Subscribing to premium is both great value and makes it possible to write large, deeply-researched free pieces every week.  For large enterprises, Anthropic is taking prepayment of tokens —-- say, $50 million intended to be spread over 12 months that it takes in as revenue. This would both inflate revenue numbers and depress costs, because Anthropic wouldn’t have actually provided the compute necessary to earn that revenue yet. Anthropic is already offering discounted tokens for Claude users through the “buy extra credits” page on their accounts, with discounts ranging from 10% to 30%. It may very well be booking this up-front. Anthropic could be front-loading annual commitments of any kind —– subscriptions to Claude, enterprise or team agreements, and so on. Anthropic could have ratcheted down training to ease the burden on its infrastructure to provide inference.  Made over 90% of its lifetime revenues in the first quarter of 2026.,  Made virtually no revenue in its previous years, and…  Leaked completely imaginary run rates to the media for years.

0 views

AI Is Too Expensive

If you liked this piece, you should subscribe to my premium newsletter. It’s $70 a year, or $7 a month, and in return you get a weekly newsletter that’s usually anywhere from 5,000 to 18,000 words, including vast, detailed analyses of NVIDIA , Anthropic and OpenAI’s finances , and the AI bubble writ large . My Hater's Guides To Private Credit and Private Equity are essential to understanding our current financial system, and my guide to how OpenAI Kills Oracle pairs nicely with my Hater's Guide To Oracle . This week, I’ll publish the second part to my ongoing series (“ What If…We’re In An AI Bubble? ”) about the factors and events that will cause the AI bubble to finally pop.  Subscribing to premium is both great value and makes it possible to write these large, deeply-researched free pieces every week.  AI is, as it stands, not economically viable for anybody involved other than the construction firms, NVIDIA, and the surrounding hardware companies benefitting from the irrational exuberance of a data center buildout that doesn’t appear to be happening at the speed we believed .  Every AI startup loses millions or billions of dollars a year, and nobody appears to have worked out a way to stop hemorrhaging cash. Hyperscalers have invested over $800 billion in the last three years, with plans to add another $700 billion or so in 2026 and another $1 trillion in 2027 , meaning that they need to make at least three trillion dollars in AI specific revenue just to break even , and $6 trillion or more for AI to be anything other than a wash. I went into detail about this (albeit at a lower, pre-2026/2027 capex number) in a premium piece last year .  To give you some context, Microsoft made $281 billion , Meta $200 billion , Amazon $716 billion , and Google $402.8 billion in revenue in their most-recent fiscal years for every single product combined, for a total of $1.599 trillion. None of them will talk about their actual AI revenues. Yes, yes, I know Microsoft said that it had $37 billion in AI revenue run rate ($3.08 billion a month or so) and Amazon had $15 billion, or around $1.25 billion a month , but both of these are snapshots of single months that are meant to make it sound like they’re going to make that much in a year but in the end, you don’t actually know anything about how much money they’ve made from AI. We do, however, now know that Microsoft has spent an approximate $100 billion on its OpenAI partnership after testimony from an executive during the otherwise-dull Musk-OpenAI trial, per Bloomberg : This is a fascinating insight for a few reasons: At the end of 2025, OpenAI claimed that it had 1.9GW of capacity (likely referring to total power draw rather than the actual critical IT of the infrastructure at its disposal), which, per analyst estimates, ( $42 to $44 million per megawatt ) works out to around $79.8 billion. This claim was made around six months before the release of Microsoft’s most recent quarterly results.  In other words, Microsoft has spent 4 years sinking (either through spending or allocating the capex in advance) nearly $300 billion into…building OpenAI? Okay, fine. Microsoft also has 20 million Microsoft 365 Copilot subscribers for an absolute maximum revenue of $7.2 billion…if every single one were paying $30 a month, which they are most assuredly not as Microsoft has been offering discounts on it for years . Based on my reporting from last year , Microsoft made around $7.5 billion from OpenAI’s inference spend and $761 million from its revenue share in Fiscal Year 2025, a year when it invested (either spent or allocated) around $88.2 billion in capital expenditures. I didn’t report it at the time, but I also had the numbers for all of Microsoft’s revenues for the first three quarters of Fiscal Year 2025 — a total of $8.9 billion of total AI revenue, with around $4.35 billion in revenues when you removed OpenAI’s inference. If we assume that Microsoft’s other AI services grew 10% quarter-over-quarter, I estimate that Microsoft likely made around $17.9 billion in AI revenue in FY2025, or a little under a fifth of its capex.  And let’s be clear: none of these numbers include the actual operating expenses. Data centers, after all, need electricity to run, and AI data centers in particular need a lot of electricity. And some — though, admittedly, not many — people to handle the things like maintenance, repairs, and operations. And then there are things like taxes, insurance, and the other day-to-day costs that, when you add them all together, make a big, scary number.  You can argue that “actually GPUs are profitable to run” ( I disagree! ), but for any of this to make sense, four things have to happen: All four must be true. If AI revenues don’t explode, capex can stop, margins can be positive, and your best-case scenario is…you maybe broke even. If capex never stops being invested, you need revenues to explode dramatically — to the tune of effectively doubling Microsoft, Meta and Google’s entire businesses, and tripling Amazon Web Services’ annual revenue ( $128 billion ) — and for said revenues to be margin-positive, because if they’re not, eventually other healthy businesses will slow, leaving AI to tear a hole in overall margins. In all cases, AI revenue must stay consistent because, well, you need to get paid . I also cannot find an economic scenario where this pays itself off.  Let’s assume that Anthropic is actually at $45 billion in annualized revenue ( I believe it’s doing some very worrisome maths to get there ), or around $3.75 billion a month. On an annualized basis, this would not be enough — assuming it had zero operating expenses (rather than losing billions) — to recover a single year of capital expenditures from Microsoft, Google, Meta, or Amazon from 2024 or 2023. Even if OpenAI’s entire cloud spend ( $50 billion ) for 2026 went to Microsoft and it doubled its Microsoft 365 Copilot revenue (at full cost) to $14.4 billion, it estimates it will invest $190 billion in capital expenditures this year. Amazon’s $15 billion AI run rate, even if it doubled, wouldn’t put much of a dent in its $200 billion in investment plans . While we don’t know Google’s AI revenues, it plans to invest $185 billion in capex this year . These AI revenues have to be completely fucking insane and they need to be that way extremely fucking soon , because otherwise the best they’ll be able to say is “our first few years of capex weren’t particularly useful but the stuff we built after it was,” which still works out to a few hundred billion dollars of waste. Things get even worse when you realize that at least 70% of Microsoft, Google, and Amazon’s compute is dedicated to Anthropic and OpenAI , two companies that burn so many billions of dollars that Microsoft, Google and Amazon have already fed them a combined $54 billion in the last three years, with $28 billion of that coming in the last month and Anthropic due another $50 billion from Google and Amazon if certain performance obligations are met. And there’s no real sign, outside of Anthropic and OpenAI’s compute spend (which is reliant on hyperscaler and venture capital money), of any real explosion in AI revenue. Per The Information (in a chart I love to share!), more than 50% of hyperscalers’ revenue backlogs comes from these companies: If massive, incredible demand for AI existed, wouldn’t these remaining performance obligations be near the trillion mark? Wouldn’t there be other Anthropic or OpenAI sized chunks of revenue? There’s allegedly incredible, unstoppable, insatiable demand for compute. Why isn’t it lining up? Let’s take a look at those RPOs! That was a lot of numbers, so let me make it simpler: outside of OpenAI and Anthropic, these three companies do not appear to be significantly increasing their revenues, and the only way to get that revenue is to feed money to one or both of these companies.   Put aside all the theoreticals and hypotheticals and metaphors and imaginary future scenarios and tell me: what, in the next year, are Microsoft, Google and Amazon going to do about this problem? How do they solve it? If we assume the absolute best-case scenario, these companies are making a combined $70 billion in annual revenue on investments that now — including the money invested in the companies themselves — total over $900 billion. Doubling that won’t be enough. Tripling it won’t be enough. In fact, to pay this off, these companies will need to be making over $100 billion each in AI revenue in the next year , because otherwise there is no covering these losses. And it all comes back to a very simple point: AI is too expensive. If the margins were good, they’d be sharing the margins. If the revenues were good, they’d be sharing the revenues (and no, run rates aren’t revenues). If the business was strong, it would be a separate category in their earnings.  But LLMs are too expensive! They cost too much to run, and said costs appear to increase linearly with revenues. The more a user uses a product, the more it costs the company to run it, and the more capacity they can take up. The only way to capture any growth is to buy and install GPUs , which in turn requires you to build somewhere to put them, which takes time and money.  I’m really struggling to see the argument in favor of continued capex investment. You’re more than $800 billion in the hole with, I estimate, less than half of that resulting in operational GPUs and capacity. Said capacity is mostly taken up by OpenAI and Anthropic, two companies that burn billions of dollars and do not appear to have an answer for how they might stop.  The more you build, the more your infrastructure becomes dependent on the continued existence of two perennially-unprofitable ultra-oafs, as your existent AI product lines are, at best, add-ons to products like Google Workspace or Microsoft 365, or further expansion of cloud compute capacity with lower margins and higher up-front costs than anything you’ve ever built.  Every quarter is an opportunity to put yourself another $30 billion or so in the hole, all in the hopes that, I assume, OpenAI or Anthropic will pay you $100 billion or $200 billion over the course of a few years, because nobody else in the entire universe is spending that much on compute. You are not recovering these investments without either a massive new product line that doesn’t exist today or three or four Anthropic or OpenAI-sized compute contracts. Put another way, Amazon needs another AWS ($128 billion a year), Microsoft another Azure ( $75 billion a year, including OpenAI’s 2025 compute spend ) and Google a business line at least half the size of search (around $200 billion a year). These businesses have grown to this size by providing extranormally large amounts of value from the very moment they were created and impenetrable monopolies — and while there are quite literally other cloud providers that can physically provide the infrastructure to OpenAI and Anthropic ( Oracle is trying to compete and may die as a result ), the actual “monopoly” here is “being able to deploy hundreds of billions of dollars.” Anthropic proved this when it took 300MW of compute from Elon Musk .  In Oracle’s case, as I’ve explained at length , it has to successfully build 7.1GW of capacity, have that capacity actually be margin-positive (doubtful!), and then actually get paid for it by the time it’s built in, oh, I dunno, 2032?  Sadly, I have bad news about Oracle, Microsoft, Amazon, and Google’s largest customers.  Here’s a fun game: ask an AI booster how OpenAI or Anthropic becomes profitable! Here’s what they’ll say: I must be abundantly clear that nobody has any proof that anyone is profitable on inference, but we have plenty of proof they’re not. They’ll likely cite known liar Sam Altman saying OpenAI is profitable on inference from a party from August 2025 , or Dario Amodei saying ( in a sentence around “stylized facts” that are “not exact” and are specifically “a toy model” and specifically not about Anthropic ) “the inference has some gross margin that’s more than 50%.”  Here’s a really simple way to dispute this: Coatue said that Anthropic’s revenues were 85% API calls in 2025 . If it’s profitable on inference, how is it still losing money? You’re gonna say “training,” but that doesn’t actually answer the question: if Anthropic’s process of providing tokens to its models is profitable, how is it losing so much money? Why offer a subscription platform at all?  As I’ll get to, Anthropic has companies paying massive amounts for tokens — hundreds of millions a year in some cases — that’s all inference . Why are you bothering with these stinky, nasty monthly subscriptions? The “inference is profitable” argument is a bedtime story told to people that can’t reconcile the logic of a company that allows people to burn between $8 and $13.50 of every dollar of their subscription revenue.   Otherwise, you have to reconcile with the fact that both Anthropic and OpenAI are both incinerating money and have no real path to any kind of sustainability other than, well, not doing that. One very, very specific counter-argument people make is that open source models are cheap, and can somehow be compared to OpenAI and Anthropic’s, despite the fact that we have no idea what the actual parameters of Sonnet, GPT, Opus, or any other of their models actually are.  What we do know is that both of these companies lose billions of dollars. What we do know is that OpenAI, per The Information , plans to burn $852 billion through the end of 2030, and that as of March 6, 2026 (per CFO Krishna Rao’s sworn affidavit), Anthropic made “exceeding” (sigh) $5 billion in revenue and spent $10 billion on inference and training.  Anthropic has done a great deal of work to obfuscate how much it actually makes or spends, but I think it’s likely it burns even more than OpenAI, given the fact that it’s had to raise $75 billion in the last 6 months ( assuming its new $30 billion round closes ), and that’s not including an additional $30 billion from Google and Amazon if certain unknown milestones are hit.  Then there’s the issue of those RPOs. Anthropic is now on the hook for $200 billion to Google, $100 billion to Amazon and $30 billion to Microsoft, I assume over the course of the next three or four years.  So let’s lay this out. Anthropic — based on its own affidavit from March — appears to have spent $3 to make $1 of revenue on a compute basis, and that’s before you include any and all other costs like staff or electricity or the vocal coach that Dario Amodei uses to add that bass to his voice.  Additionally, it needs $330 billion to pay its cloud obligations to Amazon, Google, and Microsoft over the next four years. I’d estimate it needs $5 billion a year for its compute deal xAI (so $20 billion over the total period) and an estimated $30 billion to cover its deal with CoreWeave . That brings us to a total of $380 billion. It’s hard to estimate the actual costs associated with running Anthropic because so much of the reporting no longer makes sense as a result of that affidavit. Nevertheless, I think it’s fair to assume it will need at least $20 billion of operating expenses across that four year period. We don’t even need to play in the realm of “what might Anthropic or OpenAI’s revenues be?” to understand the problem here. Both companies aggressively burn money, and neither of them have any answer as to how they might stop. Numerous reports about how Anthropic will turn “cash flow positive” in either 2027 or 2028 are fantastical, illogical, entirely driven by ridiculous projections, and should never have been reported as anything other than an attempt by companies to mislead their investors. In both cases, reporters should’ve had more asterisks on those numbers than Q*Bert reading Frank’s lines from Blue Velvet . And we have plenty of evidence that they’re losing more money over time. In January 2026, The Information reported that Anthropic’s gross margins were 40% in 2025 — 10% lower than its “optimistic” projections, specifically attributed to “...the costs of running Anthropic models from paying customers, in a process known as inference, on servers from Google and Amazon,” adding that those costs were “23% higher than the company anticipated.” In February, The Information ran another story saying that OpenAI’s gross margins fell from 40% in 2024 to 33% in 2025, a full 13% lower than its projected margins of 46%, all because (and I quote) “...the company having to buy more expensive compute at the last minute in response to higher than expected demand for its chatbots and models.” You know, exactly what Anthropic has had to do. This is what I’ve referred to as the knife-catching problem for compute demand — you either don’t order enough compute and have to rush to buy some last-minute as demand intensifies, or you order too much, and, well, to quote Dario Amodei: And right now, as I’ve covered , there’s not enough compute being built to keep up with Anthropic or OpenAI’s voracious demands, meaning that they will both be bartering to buy whatever’s available at whatever price it’s available at. This naturally will savage their already-negative margins… …and then what? No, really, and then what? One of you fucking AI boosters, answer me, how does this actual reverse course? Because even if Anthropic were making $100 billion in annual revenue, it would probably be losing $300 billion or more to get there. The fact it had to raise $30 billion in February , $15 billion in April, and now $30 billion more in May all while allegedly pulling in more than $3 billion a month in revenue suggests that its COGS are fucking horrendous, and its growth is coming at a terrible financial cost. Let’s say that Anthropic keeps growing and ( as The Information suggests ) hits $100 billion in annualized revenue (around $8.3 billion a month). How, exactly, does it afford to make that much money? Because right now it’s (allegedly) about to hit $45 billion in annualized revenue, and needs so much money that it’s absorbing (along with OpenAI) the majority of venture capital raised this year, and very clearly does not have any path to bring its costs down. The answer is simple: it can’t! There is no mechanism to do so. More compute does not make OpenAI or Anthropic’s services cheaper to offer. There is no magical silicon coming that will make any of this more affordable, and no, Anthropic is not “profitable on inference,” because if it were, that massive revenue growth would have leveled out its margins rather than require it to raise a little less than the combined value of every Major League Baseball team , or more if you add the other $50 billion that Amazon and Google have promised based on secretly-held performance obligations. The same goes for OpenAI, which “raised” $122 billion (around $45 to $50 billion in real cash, with the rest either paid in installments or on it IPOing or reaching (sigh) AGI) in February and is now already considering raising more . Somebody might counter-argue that this is companies raising as a means of boosting their valuations, I think that’s a very convenient way of looking at two extremely problematic companies.  I should also ask why neither of them appear to be seriously considering going public. While both were rumoured earlier in the year to be planning to do so in 2026, both appear poised to raise more private capital. I think the answer is simple: their CFOs know that doing so would reveal their actual margins, which are hot dogshit with sprinkles on top.  Nobody has a sensible or logical response here. Which leads us right to our next point! One important detail to keep in mind here is that as of a month or two ago, Anthropic moved all enterprise customers to token-based-billing, which will begin, I believe, a true stress-test of the true “value” of AI as costs skyrocket. Just last week I ran the first of a two (or three, potentially) part premium series called “What If We’re In An AI Bubble?” and touched on the gruesome subject of whether organizations could afford to pay for AI long-term : Earlier in the week, carnival barker and Salesforce CEO Marc Benioff said his company would spend $300 million on Anthropic tokens in 2026 , and as I discussed in my premium from Friday , unrestrained AI spending is inflating the revenues of Anthropic and OpenAI in a way that isn’t sustainable for anybody involved: The problem is simple: nobody actually knows how much AI is going to cost them in any given quarter. This means that the current token spend you’re seeing is entirely experimental, which is why organizations keep burning through their tokens so fast.  This massive growth in spend is what underpins the “massive” (I have serious questions about its accounting) growth in Anthropic’s revenue. Executives have, across the board, given their engineers free reign to burn as many tokens as they’d like, and while I severely doubt that Anthropic actually hit $50 billion in annualized revenue outside of not-quite-fraudulent non-GAAP measurements, I believe its revenue growth has come from an artificial boost from a tech industry searching for a reason to pay somebody money. To be very clear about what I mean, I think there is currently an AI token binge across both Anthropic and OpenAI. Enterprises do not know the actual value of AI, and do not know how much they should actually be budgeting, which is why Uber and others are running through their token budgets but not, it seems, spending less. We’re currently in an abundance phase — one where nobody is truly thinking about the costs outside of their fear of missing out — but there’s this nasty undercurrent of “wait, how much does that cost?” followed by “oh, fuck, well…you know I love AI but…” Put another way, the current spend on AI tokens is not something that’s indicative of lasting, reliable revenue. In some cases, the pressure to use AI for everything is turning companies’ software stacks into slop. Things are worse elsewhere. Something is wrong at Zillow. Something about LLMs has done something to its technical leadership, something that makes them talk strange and send weird slide decks with confusing, slop-ridden sentences.  The real estate tech firm spent over $1 million on AI services in the first quarter of 2026, and in April it spent $749,000 in tokens across Cursor and Anthropic’s services, as well as through AWS Bedrock. As of the end of the month, it was nearly 75% of the way through its annual Cursor token budget of $1.1 million.  As of the middle of May, its total AI spend had already crested over $300,000, and its Cursor budget sat dangerously close to the edge at 85%. This is particularly-concerning when you consider that Zillow’s net income for Q1 2026 was $46 million , and ranged from $2 million to $10 million each quarter of 2025.  Zillow is currently on course to spend at least $7 million on AI in 2026, and at its current pace might hit as much as $10 million, which would amount to a little less than 50% of its 2025 net income ( $23 million ).  You’re probably wondering how Zillow manages to spend so much on AI, and the answer — as I’ll get into in next week’s free newsletter — is that its technical executives appear to have AI psychosis, saying that the short-term goal is for “software engineers to never open a code editor again.” The reality is chaos. In a slide deck that I’ll discuss later, Zillow revealed that while engineering resources have largely stayed the same, outputs requiring human review have increased by nearly 50%. Meanwhile, code deployments and pull requests increased by 39%, and software reviewer load increased by 29,000 hours each month , creating a massive burden on the 1,500 or so engineers working at the company.  In simpler terms, that’s about 19 hours of extra work per engineer that’s literally just looking at extra code written by LLMs.  On Blind, the anonymous social network for tech workers, Zillow workers complain about Zillow’s code “slowly becoming AI slop,” with “much more code getting approved without guardrails or input due to people not being able to keep up the other’s velocity or just not caring anymore.”  One worker claimed that “the slop is job security,” adding that they “don’t want the output to be good or documentation to be clean [as] management will replace [them] with offshore/nearshore/AI agents at the slightest whiff of evidence that the slop cannon is self sustaining.” Another said that they felt “lost in the agentic world,” and that they “didn’t have full grasp of where we are going or what [their] role is,” with a “lot of overlap in what people are doing.” Another said that “people are burning tokens just to hit internal AI adoption targets,” adding that “this is what happens when leadership ties metrics to usage instead of outcomes,” saying that it “literally subsidized busywork.” This is all part of what an internal slide deck viewed by this publication called “AI-Native Engineering,” promising a “path to an agentic Zillow” and “faster outcomes for customers,” though customers are never mentioned in any other slide.  The deck — pumped full of AI-generated text — talks about “generic AI being a commodity,” saying that “Zillow-aware AI is a competitive advantage,” and at no point explains what that means. It encourages engineers to go from “AI-Assisted” to “AI-Native,” with “systems enabling org-wide leverage,” with engineers moving from being “soloists” — individual developers with AI tools — to “conductors” that orchestrate AI agents, to “composers” that “define systems AI can safely play,” adding that “2026 is the transition from conductor to composer.” Yet the strangest part is named “2027: A Tuesday,” discussing a theoretical day in the office for whoever is left at the company. This theoretical example is, apparently, a process that would take weeks, but now takes under two hours.”  Zillow intends, based on this deck, to sacrifice everything to AI — code review, vulnerability fixes, policy checks, deployments, testing, and basically having agents take over everything , no matter how small, like having an agent do dependency updates and security hotfixes that could be handled with a simple shell script. To quote Zillow: In practice, sources at Zillow tell me that there has been no actual movement toward this vision. Software engineers still open IDEs and review code manually, with one describing Zillow’s “vision” as “nonsense,” adding that “you can’t just throw buzzwords on a slide deck and change how all the engineers do their jobs.”  As for why token burn is so high, sources tell me that engineers are actively encouraged to use AI for everything , as much as possible, writing PRD (product requirement documents) in AI, then using the AI to make stuff based on the PRD, then doing a deck with AI, then writing emails with AI, using AI to brainstorm, or create weird, esoteric automations, with some managers pushing workers to have one personal AI “goal” to aspire to. Zillow’s agentic “vision” is apparently a remit from the C-suite. It’s hard to tell if this is AI psychosis or just classic Business Idiot bullshit.  Perhaps it’s a little of both. Every organization I’ve talked to has exceeded or is nearing the edge of their annual token budget barely five months into the year, which means that everybody has suddenly given themselves an extra few million dollars’ worth of operating expenses for reasons that escape effectively everybody I’ve talked to.  Every engineer tells me the same thing: “I’m being made to do this, I don’t want to do this, my managers do not seem to understand, my bosses seem to understand even less than my managers, and if I don’t use AI somebody is going to fire me.”  Put another way, CEOs and CTOs are screeching at their underlings to “use AI as much as possible” to “find its incredible benefits” without anybody really knowing what those are and how much it’ll cost to get there. This might be because Anthropic obfuscates the data that might tell customers the real costs.  Per Laura Bratton at The Information , Bratton’s article has numerous quotes from executives saying that Anthropic lacks transparency and granularity into the ways that tokens are being burned across an organization, in a way that I think sounds very, very suspicious, particularly when you add the following:  While I’m not accusing Anthropic of anything untoward, massive, multi-million dollar contracts that involve individuals burning thousands or tens of thousands of dollars’ worth of tokens with no service level agreement, transparency or true granularity into the burn is a perfect setup for a company — not saying it’s Anthropic! — to do something dastardly with those numbers.  While an individual might be able to monitor their own personal usage, in an organization of hundreds or thousands of engineers, who’s to know if, say, the particular token burn is consistent across every member of the company, or that those costs are actually matching up with what the user is doing? This is a company ostensibly worth $900 billion dollars acting with disregard for the basic measurement of “how much did this cost, and how did it cost so much?” And in the end, how do you even measure it at scale? Say you’ve got 1,500 engineers, and they’re spending a combined $1 million tokens a month. How the fuck do you actually measure the return on investment for that spend?   How many tokens does it take to do one thing? Is it consistent across every model? Is it consistent across every employee? Are you even measuring how many tokens a task costs? Because if you’re not, that token budget is basically throwing a dart blindfolded.  Okay, now you’ve measured a task, did you make sure to measure it multiple times? Because LLMs can randomly do things differently even with the same prompt and same Claude.MD file and same strictures and same data sources. You’re gonna need at least 10 samples of each task, and you’re gonna need to make sure somebody who actually knows what they’re doing can measure them, because if you get a dimwit, they’re going to say it can do something it can’t. Unless, of course, you can’t actually measure how many tokens a particular task can take with much accuracy, in which case every single AI token budget is bullshit. And each model does things differently depending on many different variables, some of them a result of the user, some of them a result of the AI labs themselves. Alright, well, maybe you just need KPIs — measurements you can aspire toward , and by pursuing them you can start working out how much it costs to do stuff.  Wait, which metric works there exactly?  In fact, it’s pretty hard to measure anything like “efficiency” or “productivity” in any business, because every metric connected to them can be gamed, leaving managers and executives with the problematic situation where they have to start learning how things work so they can see if they’re good. Before AI, this wasn’t as much of a problem, in the sense that inefficiencies and wasted hours weren’t directly connected to a chatbot that is specifically designed to burn money. Managers and executives could come up with whatever deranged, self-gratifying office bullshit they pleased, wasting hours of people’s time in the process, but doing so didn’t immediately connect to a massive, ever-increasing cost. AI is a perfect storm of failed concepts and organizations, and the apex of the Era of the Business Idiot , an epoch where we’re ruled by people so thoroughly disconnected from the actual workforce that it was inevitable that a technology would be created specifically to grift them. LLMs are dangerous for many, many reasons, but the under-discussed one is how well they play to a certain kind of executive imbecile. Generative AI is — to quote Mo Bitar — really good at doing an impression of work, much like most managers and c-suite executives, and even if it’s completely incapable of doing something, it’ll absolutely say it can and tell you you’re amazing for suggesting it. And that’s why Business Idiots love it.  Where regular human beings would say annoying things like “that’s not possible within that timeline” or “we don’t have the resources to do it,” AI will say “of course, right away!” and burn as many tokens as possible.  When it makes mistakes, it’ll apologize — as it should because it failed you — but then promise to do better next time, all while costing so much less, at least in theory , than a regular, stinky human being.  It’ll create a PRD of a theoretical software project with the confident and vigor that you need to take it immediately to a software engineer and say “build this immediately,” and when the software engineer tells you a bunch of bullshit about it not being possible , it’ll spit out several convincing-sounding responses. Fuck, why even bother talking to that engineer at all? Claude Code can mock up a prototype that you can then shove in their fucking face before you fire them for not using AI to do it themselves. Any executive-level fuckwit you’ve met in your life now has a seemingly-powerful tool that can burp up mimicry of open source software and, if you constantly prompt it, eventually get something half-functional onto some sort of web server. When you face bugs, it’ll try and fix them, sometimes also “fixing” (adding or deleting code) from elsewhere to be helpful, like when Cursor using Anthropic’s Claude Opus 4.6 model deleted an entire production database and all its backups . It will never, ever say no, even if it’s incapable, even if it has no thoughts, even if what you are asking is equal parts impossible and unreasonable in both its timescale and scope. A Business Idiot, given his druthers, can sit there and fuck around and make an LLM spit out something that makes him feel like he’s coding, which in turn makes him feel that you, a lazy and stupid engineer , could do even more with the power of AI. It doesn’t matter that it costs an absolute shit-ton of money, or that there’s no way to measure its efficacy. The Lion does not concern himself with things like “efficacy” or “productivity,” and the Lion is increasingly tired of your whining! The Lion doesn’t even understand what it is you do every day other than not doing what The Lion is asking for! You laugh, but this is genuinely how the majority of managers and executives think and act, and now they have a special chatbot that can fart out functional-enough prototypes to convince a Business Idiot they can do anything, because executives and managers do not regularly do much work and thus have no idea what it looks like other than when they look over your shoulder, which is why they wanted you back in the office! Organizations aren’t burning millions or hundreds of millions of dollars a year on AI because it’s good , they’re doing it because they are run by people who do not know what the fuck they’re doing.   In a sane world, randomly adding a massive, ever-expanding operating expense to your business with the express intent of — to quote IT firm Workato’s CIO , “eating the costs while employees experiment” — would have the board blow up your house. In our world, one dominated by disconnected, self-involved and massively-overpaid dullards, many businesses pushing their workers to use AI are doing so because the other guy is doing it, with about as much strategy and forethought as one would expect from somebody who spends 90% of their life reading emails, going to meetings, or going to lunch. The majority of those I see trumpeting the so-called benefits of AI do not appear to do anything of note. I have yet to see one so-called multi-agent orchestrator engineer psychopath ship something remarkable or impressive or even functional. I have yet to see any AI-obsessed boss write or create or author or do anything I can remember. I don’t see any of these fuckwits running a company on their own outside of those who have learned to sell stuff to other AI psychosis victims or executive midwits of varying size.  And why oh why is it always the language of inevitability and possessiveness? Nobody who’s this insistent, aggressive and violative with their language of “it’s here and if you don’t adopt it you’re stupid and dead” has ever been right about anything. Nobody this desperate, insistent and forceful has ever had good intentions, good vibes or brought good omens — they are always bearers of some kind of con.  Most technology is sold on elevating and ascending human beings. AI cheapens every interaction by creating a work-shaped product from a person that doesn’t respect you enough to give you work that’s barely fit for a human because it wasn’t made for one.  You must accept becoming a dogshit dealer that loves accepting and receiving low quality goods. You must celebrate intentionless and decaying slop, and defend it and the machine that made it with your entire being. You must sully yourself — treat its unexceptional, sloppy and unreliable outputs as signs of sentience, or at least the proof that digital sentience is possible. You must defend horrible, abrasive, ugly, loud monoliths of steel full of $50,000 graphics cards. You must say they are necessary, and you must aggressively antagonize those who do not.  Every time you defend generative AI you defend a machine of capital that has burned $1 trillion and created one of the most-wasteful products in history. If people disagree with you, you must attempt to harm them somehow — ostracize them, mock them, attack them, denigrate them. You will justify this as moral, because you have been manipulated by a technology built and sold by two of the greatest grifters of all time — Dario Amodei and Sam Altman.  Anything less is opposition to an industry with all the trappings of authoritarianism down to the media toadies, the propaganda and the seizure of land in the name of a nebulous “greater good.” But man, these men got people good.  Sam Altman helped propagate a technology perfect for conning people with potential, a larger extrapolation of Altman’s own life of taking dogshit — Loopt, for example! — and parlaying it into larger opportunities. It can make a really half-hearted demo of a lot of things, and that’s good enough to sell to Business Idiot.  Dario Amodei took this grift and perfected it. Anthropic is a company purpose-built to con people into giving it by money by making people feel smart. LLMs can do work-shaped stuff, sometimes, as long as you debase yourself to accept mediocre and often-broken stuff that you have to keep a vigilant eye on, and either use a subsided product that loses Anthropic money or pay a shit ton of money as an enterprise to Anthropic and they still lose money.  These companies were only capable of growing in an economy dominated by the gullible and work-shy. Only a capitalist culture dominated by people who don’t actually do or know stuff have let this get so far. Nobody wants this, nobody wanted it since the beginning, it was forced upon everyone, and to pretend otherwise is laughable and offensive. The amount of people who use this shit a bit and become convinced that we’re mere years from it costing over a trillion dollars to somehow making trillions of dollars and being an entirely different and good product should be aware that they are being manipulated. The more you feel compelled to defend AI the more scrutiny you must show it.  I am not your enemy! If you think that I am, you are on the side of a corporation or a product. You can try it, like it, and I don’t really care, but the second I see you trying to be condescending or judgmental or aggressive toward another person for not agreeing with your product choices I immediately feel suspicious. Can’t you see how these people act? Can’t you see how strange it is to defend a thing you pay money for that has terrible economics? If it wasn’t the “in” thing, being an AI person would be considered really weird. I look forward to the day it is. I hope you guys like having the stuff you said since 2022 repeated back to you! I’ve been saving it all. Time is running out for a graceful bow, and you better act quick!  If you feel self conscious while other people dunk on AI, that’s weird! I see people say they don’t like Macs all the time. Who gives a fuck! I’m not going to go to the mat for Tim Cook. People can make their own decisions.  Those comparing AI to AOL mailing CDs to people should feel ashamed of themselves. This is like if every single time you opened a magazine an AOL CD flew at your head, your boss told you he would replace you with a modem if you didn’t go online, and the news constantly ran segments called “I didn’t receive an email: father forgets son forever because he wasn’t online” or panels with “Internet experts” who said “I am on the Internet superhighway right now, and I’m certain that within 10 years AOL Time Warner will be able to email myself to my dad.”  Imagine if Shingy was a billionaire and went on TV every day in 1999 and told you “ the world must get ready, because you’re about to get a ICQ message from The Lord .” Generative AI was purpose-built to grift an economy run by executives and managers who don’t actually do any work. Its success has been driven by a remarkable, society-wide ignorance in the management sect, and its continued proliferation is only possible through the media’s continued trust and faith in the idea that CEOs are busy because they’re actually doing work. Yet even a Business Idiot eventually realizes that too much money is being spent, and the first one of these dimwits to cut their token budget will send the rest of them running for the doors. We should lock them. We should make everybody who obsessed over theoretical ideas about what AI can or will do ashamed for their intellectual deceit or constant ignorance.  At the end of the AI era, the only thing that will change the rot at the heart of our economy is the acceptance that the majority of companies are run by lazy, self-involved and ignorant fuckwits, and accountability for those who refused to scrutinize them. Microsoft has spent a total of $293.8 billion in capex since the beginning of Fiscal Year 2023 (which began in the back half of 2022). This means that around 30% of Microsoft’s capex ($87 billion) went to building OpenAI’s infrastructure. Based on discussions with sources familiar with Azure architecture, this is the vast majority of Microsoft’s operational capacity. AI revenues have to explode. Capex has to stop being invested. GPUs need to be margin positive, including both their cost and the debt associated with operationalizing them. AI revenue has to stay consistent both before and after you stop spending that capex. Microsoft’s RPOs jumped from $392 billion to $625 billion between Q1 and Q2 FY26 (or calendar year Q4 2025 and Q1 2026), driven by the $250 billion in “incremental Azure spend” from OpenAI (including already-existent commitments) locked up in October 2025 and the $30 billion promised as part of its deal with Anthropic from November 2025 . Based on Microsoft’s own disclosures , without Anthropic and OpenAI’s additions, RPO would have been effectively flat, as evidenced by the fact that in Q3FY26, remaining performance obligations sat at $627 billion .  Amazon’s RPOs jumped from $244 billion in Q4 2025 to $364 billion in Q1 2026, driven by its February 2026 $100 billion expansion of its $38 billion compute deal from November, and its extended partnership with Anthropic for 5GW of compute capacity unattached from any kind of dollar number.  Google’s RPOs jumped from $242.8 billion in Q4 2025 to $467.6 billion in Q1 2026, driven by ( per The Information ) $200 billion in committed spend on TPUs and compute from Anthropic, meaning that it has expanded its future revenues by an unremarkable $24.8 billion when you remove Anthropic’s spend, when RPOs had previously jumped $85 billion between Q3 and Q4, likely driven by its compute deal from October 2025 . It’s fair to assume a chunk of the remaining RPOs are from its deal to rent TPUs to Meta , announced in February 2026, which makes it likely that it accounts for the majority of the remaining $24.8 billion. Silicon will get cheaper. They’ll start selling services. They’re profitable on inference. It’s an example of a typical working day. At 8:30AM, the engineer notes that confirmation rates in Dallas dropped 3% overnight.  ‘Dallas inventory spiked; buyers went from 3 showings to 7. The agent shows the pattern: we're hitting the same buyer 7 times in 24 hours with "tour confirmed" pings. They're overwhelmed; they're muting us.’ The line before this says: “I don't open the codebase — I open the spec and eval dashboard.” Half an hour later, the engineer changes the spec, which is then tested against previous data, showing an improvement.  “The PM and I review diffs, check guardrails, approve.” Diffs are “differences” — essentially comparing two versions of the same document to see which lines have been changed.  The code is then rolled out.  At 11AM, the senior engineer mentors a junior engineer:  ‘A junior engineer's rescheduling agent is failing evals. I ask one question: "What happens if the buyer picks a slot the seller just blocked while the agent is negotiating?" We identify the race condition and add a constraint: "Always re-check availability at confirmation time." She updates the spec and evals. The agent passes.’ It is absolutely adorable they’re pretending that they’ll have junior engineers if this hellscape vision comes to life.  You can’t say “burn as many tokens as possible,” because employees will — as happened at both Amazon and Meta — deliberately create ways to burn more tokens using scripts and automations.  You can’t say “use AI every day,” because even if they do so, that doesn’t actually set up a success criteria. You can’t tell software engineers to try and “ship more software,” because that, again, emphasizes doing more, not making good stuff , and leads to an increase in velocity rather than how good the stuff is. You can’t say “pull requests” or any other metric a software engineer can manipulate, because in 100% of the situations where you give a software engineer a number to hit they will focus entirely on hitting that number.

0 views

Premium: What If...We're In An AI Bubble? (Part 1)

Every day I read some sort of wrongheaded extrapolation about the future of AI — that today’s models are somehow indicative of AGI creating a “ permanent underclass ” of people that stops people from building software companies, or really doing any kind of job on the computer: Yash, your peers are fucking idiots. You may as well be talking about breeding Grinches or Ninja Turtles, or kvetching about the upcoming threat from Godzilla. “The best version of Tesla’s Optimus [robot]” suggests that Tesla has released an Optimus robot, or that any prototypes are capable of anything approaching useful work, something that Tesla itself has said isn’t the case . Every discussion of AI has become a discussion of anywhere between one and a million different theoreticals. The Information’s headline that OpenAI will “ save $97 billion through 2030 in latest Microsoft deal ” — one that capped its revenue share (as in the actual money it sends to Microsoft) at $38 billion — hinges on the idea that OpenAI would somehow make $190 billion in revenue, because that’s what it would take to actually max out its revenue share .  The majority of articles about METR’s “time horizon” study of how long models take to complete tasks gush with mindless praise, but regularly leave out two valuable details: that these comparisons are made based on estimates of both human task times, and that the most-commonly shared task is based on how likely it is to complete a task 50% of the time:  It’s the Sex Panther joke from Anchorman , except it’s a chart that gets written up in major newspapers and bandied about as proof of models becoming conscious.  Nevertheless, everybody appears to be having a lot of fun making stuff up or making ridiculous assertions based on OpenAI or Anthropic’s predictions. Likely gas leak victim Joseph Jacks posted last week that at its current rate of growth, Anthropic would pass Google’s revenue by 2028. Multiple different people I’d rather not link to are posting benchmarks of Anthropic’s still-to-be-released Mythos model as proof that we’re in the early-to-middle stages of the entirely-fictional AI 2027 “simulation,” despite the entirety of this ridiculous, oafish extrapolation relying on the idea that at some point LLMs become conscious and start doing their own research . None of these people seem to want to engage with reality, even in their extrapolations.  Whether or not you believe the bubble will burst, it’s hard to argue (not that anybody nobody bothers to try) with my recent reporting about the lack of data centers coming online or the fact that the majority of AI revenue comes from two companies that are, in the end, hyperscalers feeding themselves money . Nobody has presented any real argument as to how Oracle completes its data centers or avoids running out of money given the fact that it needs OpenAI to be able to pay it $70 billion or more a year in the next four years to survive . The lack of any real, thoughtful response to my assertions outside of ultra-centrists and people that can’t count is a sign that I’m onto something, and I take it as a badge of pride. But what I haven’t done recently — not since AI Bubble 2027, at least — is try my own hand at extrapolating the future based on the things I have read, seen and reported on.  Today, I’m taking a different approach, inspired by one of my favourite comic series. In Marvel’s “What If…?” writers asked questions that would entirely change the course of the Marvel Universe, such as What If The Fantastic Four Didn’t Get Their Powers , or Loki Was Worthy of Mjolnir . I’ll be honest that there are a lot of unanswered questions I have about the AI bubble that make precise, time-based predictions almost impossible. We’re in the midst of one of the most insane market rallies in history driven around the exploding valuation of NVIDIA and data center related stocks despite there being a great deal of compelling evidence that millions of Blackwell GPUs are sitting in warehouses , meaning that the market is rallying around the idea of data centers getting built without ever confirming whether that’s actually true. In the past, I’ve approached things from an investigative perspective, proving what I believe to be one of the greatest misallocations of capital in history. Today, I’m going to have a little more fun, exploring both the worrying signs I see and their potential consequences in the form of questions, mixing my own reporting with a little bit of fiction. My reasoning is simple: I think people are very good at ingesting and remembering specific facts and events, but much worse at understanding their consequences. For example, Dave Lee of Bloomberg — who I adore and admire! — said that An OpenAI Bubble Is Not An AI Bubble and makes numerous correct assertions about OpenAI, but fails to consider that OpenAI accounts for $718 billion of Oracle, Microsoft, and Amazon’s backlogs, meaning that OpenAI’s collapse would leave Oracle destitute, Microsoft and Amazon short-changed, Cerebras without 80%+ of its revenue , and CoreWeave without a major client and in breach of loan covenants guaranteed by OpenAI’s revenue .  Even if Anthropic were able to mop up some of that fallow capacity, it too relies on endless venture capital and hyperscaler welfare to pay, well, increasingly-large shares of hyperscaler revenue .  I feel as if many people are willing to ask if we’re in an AI bubble, but few seem to want to talk about what might happen . It’s really easy to say “stocks are overvalued” or “OpenAI is deeply unprofitable,” but thinking much harder than that starts to make you feel a little crazy. Data center construction now makes up a larger chunk of all construction spending than commercial real estate . OpenAI has made promises that total over a trillion dollars, and Anthropic $330 billion. NVIDIA represents 8% of the value of the S&P 500, and that valuation is based on the idea that it will never, ever stop growing, which is only possible if data center construction never stops. CoreWeave, IREN, Nebius, and Nscale all rely on hyperscaler contracts that are related to OpenAI, and if those contracts go away because OpenAI does, they’re screwed. Most people can say that these things are true, but very few of them are willing to think about their consequences, because when you do so , things begin feeling completely and utterly fucking insane. Put another way, for me to be wrong , all of these data centers will have to get built, OpenAI will have to make and raise $852 billion in the next four years , the underlying economics of generative AI will have to improve in a dramatic and unfathomable way, and do so in such a way that it creates hundreds of AI startups that can substantiate $400 billion of annual compute revenue . For NVIDIA to continue growing its revenues at an historic rate, it will also have to, by 2028, be selling over $1 trillion in GPUs, which will require there to be funding to buy these GPUs, at a time when hyperscaler cashflows are dwindling and banks are worried they’re “choking” on AI data center debt .  The AI bubble is supported almost entirely by magical thinking and people ignoring obvious warning signs again and again and again in the hopes that at some point something changes. You can quote whatever story you like about Anthropic’s skyrocketing revenues ( which are absolutely inflated ) — there’s no getting away from the fact that it loses billions of dollars year, and if your answer is that it will turn profitable in 2028 , please tell me how because there is no proof that it’s possible.  I also kind of get why nobody wants to think about this stuff. Even though it’s become blatantly obvious that the economics don’t make sense, the stock market continues to rip based on equities connected to the AI bubble in a way that defies logic but rewards positive speculation. Major media outlets continue publishing positive stories about the power of AI that seem entirely-disconnected from what AI can do, and millions of dollars are being spent by companies based on a theoretical return on investment.  No, really, per The Information’s Laura Bratton quoting PagerDuty CIO Eric Johnson: We are fucking years into this man, how is the question of return on investment still an open question?  Okay, we know the answer: we’re in a bubble. Everybody is pressuring everyone else to “integrate AI,” to “get every engineer AI,” to “become more efficient using AI,” with token spend becoming some sort of vulgar status symbol despite the whole point of the AI push being that workers can be replaced, or enhanced, or, I dunno, something measurable. In the end, all that’s being measured is how many tokens employees are burning, leading to Amazon staff deliberately setting up “agents” to burn more tokens to seem more “engaged with AI” than they really are , all because dimwit managers and executives don’t understand what people do at their jobs and can only comprehend Number Go Up.  As a result, it’s far easier to fall in with the groupthink, even if it’s hysterical, nonsensical and based on flimsy ideas like “it’s just like Uber” ( it isn’t ) or “Amazon Web Services burned a lot of money” ( it burned less than half of OpenAI’s $122 billion funding round on capex for the entirety of Amazon in the space of 15 years, adjusted for inflation ), because thinking that everybody’s wrong requires you to disagree with the markets, most of social media, your boss, and your most annoying coworkers. People also don’t really like thinking about bad things happening. They’re happy to make vague leaps in a direction that makes them feel prepared for the worst (such as the specious statements about all of these data centers being for the military or a theoretical bailout ), especially if it makes them feel smart , but in doing so they get to avoid the actual bad stuff — the economic ramifications for ordinary people, the years of depression ahead for the tech industry, and the calamitous results for the market. So, today, I’m going to have a little fun thinking about the actual consequences of everything I’ve been writing. I’m going to thread in both my own and others’ reporting, and take these ideas to their logical endpoints as far as I can. This is going to be the first of a two-part exploration of what the actual consequences of the AI bubble bursting might be. I’ll also caveat this by saying that these are, ultimately, explorations of potential future events rather than cast-iron guarantees. People seem to be resistant to being told the truth, so perhaps it’s time to explore these ideas as theoretical — fictional, even — so that people are more willing to take them in.  This series is all about simple scenarios, and one very simple question.  What if…We’re In An AI Bubble? What if the entire AI industry moves to token-based billing? What if organizations can’t afford to keep spending money on AI? What if the AI capacity crunch never ends? What if data centers aren’t really getting built? What if hyperscalers stop spending so much on data centers? What if hyperscalers have warehouses of uninstalled GPUs? What if data center construction collapses?

0 views

Where Are All The Data Centers?

If you liked this piece, please subscribe to my premium newsletter. It’s $70 a year, or $7 a month, and in return you get a weekly newsletter that’s usually anywhere from 5,000 to 18,000 words, including vast, detailed analyses of NVIDIA , Anthropic and OpenAI’s finances , and the AI bubble writ large . My Hater's Guides To Private Credit and Private Equity are essential to understanding our current financial system, and my guide to how OpenAI Kills Oracle pairs nicely with my Hater's Guide To Oracle . My last piece was a detailed commentary on the circular nature of the AI economy — and how the illusion of AI demand is just that, an illusion.  Subscribing to premium is both great value and makes it possible to write these large, deeply-researched free pieces every week.  During every bubble there’s one very obvious thing that keeps happening: things are said, these things are repeated, and are then considered fact. Sam Bankman-Fried was the smiling, friendly, “ self-made billionaire ” face of the crypto industry. NFTs were the future of art, and would change the way people think about the ownership of digital media. The actual evidence, of course, never lined up. NFT trading was dominated by wash trading — market manipulation through two parties deliberately buying and selling an asset to raise the price. Cryptocurrency never took off as anything other than a speculative asset, and altcoins are effectively dead . Sam Bankman-Fried was only a billionaire if you counted his billions of illiquid FTX tokens, but that didn’t stop people from saying he wanted to save the world weeks after the collapse of Terra Luna, a stablecoin that he himself had bet against and may have helped collapse .  Three months before his arrest, a CNBC reporter would fly to the Bahamas to hear SBF tell the story of how he “ survived the market wreckage and still expanded his empire, ” with the answer being that he had “stashed away ample cash, kept overhead low, and avoided lending,” as opposed to the truth, which was “crime.”  The point is that before every scandal is somebody emphatically telling you that everything’s fine. Everything seems real because there’s enough proof, with “enough proof” being a convincing-enough person saying that “most of FTX’s volume comes from customers trading at least $100,000 per day,” when the actual volume was manipulated by FTX itself , and the “$100,000 a day in customer funds” were being used by FTX to prop up its flailing token .  In the end, the “proof” that SBF was rich and that FTX was solvent was that nobody had run out of money and that nothing bad had happened to anybody. SBF was a billionaire sixteen times over because enough people had said that it was true.  Anyway, one of the most commonly-held parts of the AI bubble is that massive amounts — gigawatts’ worth — of data centers have both already been and continue to be built… …but then you look a little closer, and things start getting a little more vague. While Wood Mackenzie’s report said that there was “ 25GW of data center capacity added to the funnel ” in Q4 2025 does not say how much came online. CBRE said back in February that “net absorption of 2497MW” happened in primary markets in 2025 , with other reports saying that somewhere between 700MW and 2GW of capacity was absorbed every quarter of 2025. At the time, I reached out for any clarity about the methodology in question and received no response. Okay, so, I know data centers are getting built and that they exist . I believe some capacity is coming online. But gigawatts? Or even hundreds of megawatts? How much data center capacity is actually coming online?  Why did Anthropic get so desperate it took on a years old data center, xAI’s Colossus-1 , full of even older chips from a competitor — one whose CEO described the company as “evil, ” and that’s currently facing a lawsuit from the NAACP over allegations the facility’s gas turbines are polluting black neighborhoods ?  Remember, Colossus-1 is an odd data center, with around 200,000 H100 and H200 GPUs and an indeterminate amount of Blackwell GB200s, weighing in at around 300MW of total capacity… which isn’t really that much if we’re talking about gigawatts being built every quarter, is it?    So, I have two very simple questions to ask: how long does it take to build a data center, and how much data center capacity is actually coming online? These simple questions are surprisingly difficult to answer. There exists very little reliable information about in-progress data centers, and what information exists is continually muddied by terrible reporting — claiming that incomplete projects are “operational” because some parts of them have turned on , for example — and a lack of any investor demand for the truth. Hyperscalers do not disclose how many data centers they’ve built, nor do they disclose how much capacity they have available.  I find this utterly inexcusable, given the fact that Amazon, Google, Meta and Microsoft have sunk over $800 billion in capex (and more if you count investments into Anthropic and OpenAI) in the last three years . So I went and looked, and what I found was confusing. So, you’re going to hear people say “well Ed , data centers are being built ,” and what I’m talking about is data centers that have been fully constructed and then turned on . It’s really, really easy to find data centers that are under construction , but as I’ve discussed in the past, that can mean everything from a pile of scaffolding to a near-complete data center . Yet finding the latter is very, very difficult. I’ve spent the last week searching for data centers that broke ground in 2023 or 2024 that have actually been finished, and come up surprisingly empty-handed. Some projects are stuck in construction hell, eternally dueling with planning departments over permitting, some are chugging along with no real substantive updates, some, as is the case with Nscale’s Loughton, England data center, have done effectively nothing for the best part of a year , some are perennially adding more capacity to the order as a means of continuing raking in construction bills, and some are claiming their data centers are “operational” as only a single phase has turned on. You should also know that even once construction has finished, the buildings themselves must be fully filled with the necessary cooling, power and compute hardware, at which point it can be configured to meet a client’s specifications (which can take months), at which point the unfortunate soul building the facility can actually start making money. I think it’s also worth revisiting how difficult data center construction is, and how large these new projects are.  This starts with a very simple statement: nobody has actually built a 1GW data center (to be clear, it’s usually a campus of multiple buildings networked together) yet. There are campuses — such as Stargate Abilene — which promise to reach 1.2GW, but nearly two years in sit at two buildings at around 103MW of critical IT load each with, based on discussions with sources with direct knowledge of Abilene’s infrastructure , a third building sitting fully-constructed but with barely any gear inside it. It’s fundamentally insane how many different companies are trying to build these things considering how difficult even the simplest data center is to build. Take, for example, American Tower Corporation’s edge data center in Raleigh, North Carolina, which I’ll mention a little later. This is a 1MW facility — or one-thousandth the size of a gigawatt facility — occupying 4000 sq ft of real estate at first and expanding to 16,000 if ATC actually gets it up to 4MW. That’s about two-and-a-bit times larger than the typical American home . And, from ground-breaking to ribbon-cutting , it took eleven months to complete. And that’s not including all the other necessary time-consuming bits, like finding land, securing permits, and so on.  That’s a simple one. People want to build data center campuses a thousand times larger than that. Look at how difficult it is. In fact, it’s so difficult that the companies can’t build all of it at once. Larger data center campuses are almost always divided into “phases,” in part because that’s the smartest way to build them, and in part with the express intention of convincing you that they’re “fully operational.”  For example, CNBC’s MacKenzie Sigalos reported in October 2025 that Amazon’s Indiana-based (allegedly) 2.2GW Project Rainier data center was “operational,” but only seven out of a planned 30 buildings were actually operational, and her comment of “with two more campuses [of indeterminate capacity] underway.” This comment was buried two videos and 600 words into a piece that declared the data center was “now operational,” with the express intent of making you think the whole thing was operational. To give her credit, at least she didn’t copy-paste the outright lie from Amazon, which claimed that Rainier was “ fully operational ” in a press release the same day. You’ll also note that Amazon never provides any clarity about the actual capacity of Rainier. Sigalos did exactly the same thing when the first (of eight) buildings of Stargate Abilene opened, declaring that “OpenAI’s first data center in $500 billion Stargate project is open in Texas,” burying the comment that only one was operational with another nearly complete several hundred words earlier.  These are intentionally attempts to obfuscate the actual progress of the data center buildout, and if I’m honest, I’ve spent months trying to work out why big companies that were supposedly building large swaths of data centers would be trying to do so. Unless, of course, things weren’t going to plan. In its last (Q3 FY26) quarterly earnings call , Microsoft CEO Satya Nadella claimed that “[Microsoft] added another gigawatt of capacity this quarter, and [remained] on track to double [its] overall footprint in two years.” A quarter earlier , he claimed to have added “nearly one gigawatt of total capacity,”  with Karl Keirstead of UBS saying that he “...thought the one gigawatt added in the December quarter was extraordinary and hints that the capacity adds are accelerating.” As I’ll discuss below, I can find no evidence of anything more than a few hundred megawatts of Microsoft’s data center capacity coming online. While I’ll humour the idea that it doesn’t announce every new data center, and that there may be colocation and neocloud counterparties ( 67% of CoreWeave’s revenue comes from Microsoft, for example ) that make up the capacity, as I’ll also discuss, I don’t know where the hell that might be. So, to be aggressively fair, I asked Microsoft to answer the following questions on May 4, 2026: A Microsoft representative from WE Communications promised to "circle back" by 5PM ET on Monday May 4th, but did not return further requests for comment via text and email, which is incredibly strange considering the simple and straightforward nature of my questions. That’s probably because the vast majority of its publicly-announced or documented data center capacity doesn’t appear to be getting finished. In September 2025, CEO Satya Nadella claimed that Microsoft had added 2GW of capacity “in the last year,” and acted as if Fairwater, a project with two actively-constructed data centers with one in Wisconsin that broke ground in September 2023 and another in Atlanta that broke ground in July 2024 , was something to be “announced” rather than “a very expensive project that has taken forever.” Nadella also claimed that there are “multiple identical Fairwater datacenters under construction,“ though he neglected to name them. To be clear, “Fairwater” refers to a project where multiple data centers are linked with high-speed networking to make one larger cluster, a project that sounds ambitious because it is , and also unlikely because it’s yet to have been built.  Fairwater Atlanta — the latter of the Fairwaters — was “launched” in November 2025 and it’s unclear how much capacity it has. Cleanview claims it’s at 350MW of capacity , and Microsoft’s own community outreach page claims construction would be completed by the beginning of October 2025 , but, as I’ll get to, it’s unclear whether this is just one phase, given that reporting shows multiple other buildings still under construction . I have serious doubts that Microsoft stood up a 350MW data center in less than a year, given everything else I’m about to explain. Fairwater Wisconsin is also a data center of indeterminate size, but Cleanview claims Phase 1 is 400MW , quoting a story from FOX6 News Milwaukee from September 2025 that said that Microsoft was “investing an additional $4 billion to expand the campus,” featuring a video of a very much in construction data center saying the following: So, $3.3 billion — at a rate of around $14 million per megawatt per analyst Jerome Darling of TD Cowen — is about 235MW of capacity, which is a lot lower than 400MW.   Seven months later, Satya Nadella said that the Fairwater datacenter in Wisconsin was “going live, ahead of schedule,” a sentence written in the present tense, but also said that it “ will bring together hundreds of thousands of GB200s in a single seamless cluster,” which is in the future tense.  It’s a great time to remind you that Microsoft claims that it brought online roughly eight times that capacity (around 2GW) in the past six months.  To make matters worse, it doesn’t appear that Fairwater Wisconsin is actually operational. Ricardo Torres of the Milwaukee Journal-Sentinel reports that Microsoft has said it isn’t actually online , and that while there “...is equipment inside the data center conducting start-up opportunities…the company anticipates [they] will continue to happen for the next several weeks.”  Epoch AI’s satellite footage of Fairwater Wisconsin — which mentions  a completely wrong capacity because it’s uniquely terrible at calculating it ( it claimed Colossus-1 has 425MW capacity, for example) — notes that as of April 2026, one building appeared to be operational, with a second under construction. So, that’s one building in Wisconsin that might be complete, and based on the permitting application from August 2023 dug up by Epoch, the project is designed to have 117MW of capacity, which is a lot lower than 235MW. While Epoch didn’t have permitting for building two, it did for three and four, which are designed to have around 719MW of capacity , and as of April 2026 still appear to be slabs of concrete.  In simpler terms, there’s at most around 117MW of capacity running at Fairwater Wisconsin. The Fairwater data centers are Microsoft’s most-publicized data centers, yet they’re shrouded in secrecy, with the Atlanta Journal-Constitution having to file an open records request to find the site being developed by QTS, a data center developer owned by Blackstone . Videos of Fairwater Atlanta from last November show a giant campus with two large buildings and a patch of yet-to-be-developed dirt. DataCenterMap refers to it as “ under construction .” Epoch AI’s satellite footage notes that as of February 2026, building four’s roof was complete and “all mechanical equipment appears to be installed,” but “there is still a lot of construction activity around the building.”  Based on air permits filed as part of the project (that Epoch found), it appears that each building is powered by a number of Caterpillar 3516C Generator Sets at around 2.5MW each, with building one having 47 (117.5MW), building two having 13 (32.5MW), building three having 30 (75MW), and building four having 35 (87.5MW). If we’re very generous and assume that three buildings are complete, that means that Fairwater Atlanta is at around 225MW of capacity (not IT load!). So, that’s about 342MW of data center capacity being built by one of the largest companies in the world, in its most-publicized and written-about data centers. Put another way, for Microsoft to come remotely close to its so-called 2GW of capacity in the last six months, it will have had to bring online a little under six times that capacity. I’m calling bullshit. I really did want Microsoft to give me some answers, but I’m very confused as to how it can remotely claim it brought even a gigawatt of capacity online in the last year. I also question whether Microsoft is actually building multiple other “identical” Fairwater data centers, as I can’t find any announcements or pronouncements or mentions or hints as to where they might be. In fact, I’m having a little trouble finding where else Microsoft has been building data centers, and those I can find are extremely suspicious. In Microsoft’s announcement of its Wisconsin data center , it mentioned two other projects — one in Narvik Norway that had already been announced months beforehand by OpenAI , and another with Nscale in Loughton, England that was also announced by OpenAI that very same day as part of the entirely fictional Stargate project . If you’re wondering how those are going, Microsoft had to take over the entire Narvik project (which does not appear to have started construction) from OpenAI , and the Loughton data center ( which OpenAI also backed out of ) is currently a pile of scaffolding . For two straight quarters , Microsoft has said it’s brought on an entire gigwatt of capacity,and I have to ask: where?  Because when you actually look at the projects it’s announced, very little appears to have been built, and that which has is nowhere near its theoretical capacity. To be specific about what Microsoft is claiming, it’s saying it’s brought around 4GW of capacity online in the space of two years, and at a 1.35 PUE, that’s about 2.96GW of critical IT load, which works out to the power equivalent of around 284,600 H100 GPUs, which may be possible — after all, Microsoft apparently bought 450,000 H100 GPUs in 2024 — but I can’t find much evidence of data centers that could house that many GPUs, nor that might be in construction.  Let’s dig in. Microsoft broke ground on three data centers in Catawba County North Carolina in 2024 — one in Hickory, another in Lyle Creek, and another in Boyd Farms: Alright, maybe I’m being unfair! Maybe it’s just a North Carolina problem. There must be another that broke ground and got built…right?  Microsoft also broke ground on a data center in Quebec City, Canada in September 2024 , and as of April 2026 , “generator testing has been completed,” and “civil works will continue until Autumn 2026.”  Okay, well, maybe it’s a Canada problem. What about Microsoft’s New Albany, Ohio data center that broke ground in October 2024 ? Well, as of March 2026, “spring activity would resume,” and “beginning soon, soil will be delivered to the site via a designated truck route. I’ll note that Microsoft specifically says that Ames Construction is currently leading it, and that it will “resume the lead role in project communications” once the final phase of construction is done at some unknown time. Alright, well, how about the August 2025 ground breaking in Cheyenne, Wyoming that was allegedly “ due to launch in 2026 ”?  Well, Microsoft hasn’t updated its community page since it said there’d be a community meeting planned for November 2025 and that “neighbors within the vicinity will be notified ahead of construction,” which sounds like construction is yet to commence. Not to worry though, it announced on April 14, 2026 that it planned to expand it to “ accelerate innovation and economic growth ” How about that 2023-announced Southwest Hortolândia Brazil data center ? That’s right, the last update was in September 2025 , and the update was “construction activities continue to progress in alignment with local regulations.” A piece from Folha De S.Paulo from March 2026 mentioned that Microsoft “had begun operating its first artificial intelligence data centers in Brazil,” but satellite footage shows that it’s barely finished. What about the Newport, Wales data center it announced in 2022 ? Well, as of November 2025, a politician was standing on a concrete slab saying how many jobs it’ll theoretically bring in , which it won’t. What about Microsoft’s four data centers in Irving, Texas, announced December 2024 ? The best I’ve got for you is a news report about a data center in Irving Texas breaking ground in January 2025 . Its San Antonio data center, announced in July 2024 ? Well, construction was underway as of December 2025 , and it appears that construction will begin in the summer of 2026 on another one in the area. How about the two data centers outside of Cologne, Germany , announced in November 2024? Well, as of September 2025, Microsoft has… plans to build one of them ? …what about the 900 acres of land it bought in June 2024 in Granger, Indiana ? Great news! According to 16NewsNow , Microsoft officials “could break ground on a proposed data center…in late April or early May [2026].” How about Project Ginger West, a data center planned in Des Moines. Iowa since March 2021 ? Hope you like waiting , because Microsoft itself says that it’s estimated to finish construction in Summer 2028 . Ginger East , announced a few months later? Mid-2028 . Project Ruthenium ( announced 2023 )? I don’t have shit for you I’m afraid. Rutheniumkanda Forever! This company claims it’s built four fucking gigawatts of capacity , but when I go and look to see what it’s actually built I’ve failed to find a single announced data center from the last three years that got turned on outside of its Fairwater Atlanta and Wisconsin sites. To be clear, all of these sites are somewhere in the 200MW to 300MW range. For Microsoft to have brought online 4000MW of data center capacity in the last two years would require it to have completed thirteen or more of these projects, all while choosing not to promote them, with every project operating in such a veil of secrecy that no local or national news outlet reported a single one of them.  I truly cannot work out how Microsoft has brought on any more than 500MW of capacity in the last year based on my research, and think Microsoft is deliberately obfuscating whether said capacity was contracted rather than actively in-use , much like CoreWeave refers to itself having 3.1GW of “ total contracted power ” but only added 260MW of active power capacity in a single quarter at the end of 2025.  However, the exact verbiage used in Microsoft’s earnings transcripts is that it “added another gigawatt of capacity,” which sounds far more like it’s saying it brought them online… …but it didn’t, right? It obviously hasn’t. Where are all the data centers, Satya? Where are they? Why are your PR people too scared to tell me?  No, really, where are they?  So, to be fair, analyst Ben Bajarin, one of the more friendly pro-AI posters, argues that actually all of that capacity is secretly behind-the-scenes , something I’d humour if there was any kind of paper trail to a bunch of Microsoft data centers that were secretly being built.  I’d also be more willing to humour it if any of the data centers that have been publicized as “breaking ground” had actually been finished, or if both Fairwater Atlanta and Wisconsin weren’t so deceptively-marketed. My only devil’s advocate is that Microsoft could, in theory , be working with colocation partners to stand up several gigawatts of capacity through shell corporations and SPVs, but even then , not a single one has any sort of trail to Microsoft? All of that capacity?  It’s really, really weird, and the only answers I get are smug statements about how “Fairwater is ahead of schedule.” But if I’m honest, I’m having trouble even making these numbers add up. Considering how loud, offensive and conspicuous the AI bubble has become, it feels like we should have a far, far better understanding of how much actual capacity has been built. I also think it’s time to start being realistic about how long these things are taking to build. For example, I was only able to find a few data centers that for sure, categorically, definitively opened, and for the most part, it appears that a data center takes around 18 months to go from groundbreaking to opening. And these, I add, are all facilities that are relatively modest — at least, when compared to the kinds of gigawatt-scale campuses that are reportedly in active development.  Digging deeper, I found a lot of projects stuck in development Hell: While there are absolutely data centers under construction , and some, somewhere , are actually being completed , the vast majority of projects I’ve found are either in a mysterious limbo state or, in most cases, under construction years after breaking ground. Across the board, the message seems to be fairly simple: it takes about 18 to 24 months to build any kind of data center, and the bigger they are, the less likely they are to get completed on schedule. Those that actually “come online” aren’t actually fully constructed, but have brought on a single phase — something I wouldn’t begrudge them if they were anything close to honest about it. In reality, data center companies actively deceive the media and customers about the actual status of projects, most likely because it’s really, really difficult to build a data center. In any case, what I’ve found amounts to a total mismatch between the so-called “rapid buildout” of AI data centers and reality.  It also doesn’t make much sense when you factor in how many GPUs NVIDIA sold. In October last year, NVIDIA CEO Jensen Huang told reporters that it had shipped six million Blackwell GPUs in the last four quarters , though it eventually came out that he was counting two cores for every GPU , making the real number three million. I disagree with the framing, I think it’s incoherent and dishonest, but I’ve confirmed this is what NVIDIA meant. In any case, if we assume two cores per GPU, a B200 GPU has a power draw of around 1200W, for around 3.6GW of IT load for 3 million of them. I realize that NVIDIA also sells B100 and B300 GPUs (similar power draw) and NVL72 racks of 72 GB200 GPUs and 36 CPUs, but bear with me. Blackwell GPUs only started shipping with any real seriousness in the first quarter of 2025, which means that a good chunk of these data centers were built with H100 and H200 GPUs in mind. Nevertheless, I can find no compelling evidence that significant amounts — anything over 500,000 GPUs — of Blackwell-based data centers have been successfully brought online.  When I say I struggled to find data centers that had been both announced and brought online, I mean that I spent hours looking, hours and hours and hours, and came up short-handed.  I want to be clear that I know that there is Blackwell capacity actually being built , and believe that the majority of that capacity is retrofits of previous data centers, such as Microsoft’s extension to its Goodyear Arizona campus which it began building in 2018 that likely houses Blackwell GPUs. But I no longer believe that the majority of Blackwell GPUs are doing anything other than collecting dust in a warehouse. Blackwell GPUs require distinct cooling, a great deal more power than an H100, and cost an absolute shit-ton of money, making it unlikely that a 2023 or early-2024 era data center could handle them without significant modifications. I fundamentally do not believe more than a million — if that! — Blackwell GPUs are actually in service.  If that’s the case, NVIDIA is likely pre-selling GPUs years in advance — experimenting with the dark arts of “ bill-and-hold ” — and helping certain partners like Microsoft install the latest generation to create the illusion of utility, availability and viability that does not actually exist. If I’m honest, I also have serious questions about the current status of many H100 and H200 GPUs. Based on what I’ve found, I’d be surprised if more than 3GW of actual capacity was turned on in the last two years, which means that NVIDIA has sold anywhere from double to triple the amount of GPUs that the world can hold. While the Anthropic-Musk compute deal is an obvious sign about xAI’s lack of demand for compute, it’s also, as I mentioned earlier, a clear sign that AI data centers are mostly not getting finished, and those that do get finished are taking two or three years even for smaller builds. While it sounds a little wild, I think in reality only a few hundred megawatts — if that — of actual, usable AI compute capacity is being spun up every quarter. If I was wrong, there’d be significantly more progress on, well, anything I could find.  Why can’t Microsoft offer up a data center that isn’t called Fairwater, and why are its Fairwater data centers taking so long? How much actual capacity has Microsoft brought online? Because it certainly isn’t fucking 2GW in six months. I’m willing to believe that Microsoft has a number of collocation agreements with parties that don’t disclose their involvement. I’m also willing to believe that Microsoft doesn’t publicize every single data center it’s building or has built.  2GW of capacity is a lot. It’s nearly ten times the (likely) existing capacity of Fairwater Atlanta. If Microsoft is bringing so much capacity online, why can’t we find it, and why won’t they tell us? And no, this isn’t some super secret squirrel “they’re building secret data centers for the government” thing, it’s very clearly a case where “capacity” refers to “something other than data centers that actually got brought online. Despite their ubiquity in the media, AI data centers are relatively new concepts that are barely five years old. They are significantly more power-intensive than a regular data center, requiring massive amounts of cooling and access to water to the point that the surrounding infrastructure of said data center is often a massive construction project unto itself.  For example, OpenAI and Oracle’s Stargate Abilene data center is (in theory) made up of two massive electrical substations , a giant gas power plant and eight distinct data center buildings, each with around 50,000 GB200 GPUs, at least in theory. Every data center requires that power exists — as in it’s being generated in both the manner and capacity necessary to turn it on, either through external or grid-based power — and is accessible at the data center site. This means that every single data center, no matter how big, is its own construction nightmare. You’ve got the power, the labor, the permits, the planning, the construction firm, the power company, the specialist gear, the temporary power (because on-site power is slow ), the backup power (because you can’t just rely on the grid for something you’re charging millions for!), the cooling, the uninterruptible power supplies — endless lists of shit that needs to go very well or else the bloody thing won’t work. These are very difficult and large projects to complete. Edged Computing’s (theoretically) 96MW data center in Illinois is 200,000 square feet in effectively two large squares. For comparison, every single inch of gambling space in Caesar’s Casino Vegas is around 130,000 square feet . These things are fucking huge, fucking difficult, and fucking expensive, and all signs point to capacity not coming online.  Let’s go back to Anthropic mopping up Musk’s fallow data center capacity, which stinks of desperation for both companies. If there were modern data centers full of GB200s being turned on and available anywhere in the next month or two, wouldn’t it be more financially prudent to wait for it, even if it’s just on an efficiency level? A franken-center made up of H100s and H200s with some GB200s stapled onto the side feels like a stopgap solution. I have similar questions about the results of adding this capacity — that “...Anthropic plans to use [it] to directly improve capacity for Claude Pro and Claude Max subscribers ,” “doubling” (whatever that means) the 5-hour rate limit and removing the recently-added peak rate limits.  What’s the plan here, exactly? Less than a month ago Anthropic’s Head of Growth, Amol Avasare , said that Anthropic was “looking at different options to keep delivering a great experience for users” because Max accounts were created before the era of Claude Code and Cowork . How does adding 300MW of capacity magically resolve that problem? Was that always the plan?  Or was this a knee-jerk reaction to the surging popularity of OpenAI’s Codex ? Because the original justification for peak hours was that Anthropic needed to manage “ growing demand for Claude ,” demand that I bet Anthropic claims hasn’t gone anywhere. It’s also important to remember that last year, OpenAI’s margins (which are already non-GAAP), per The Information , were worse than expected because (and I quote) it had to “..to buy more expensive compute at the last minute in response to higher than expected demand for its chatbots and models.”  In other words, Anthropic has deliberately tanked its already-negative 2026 gross margins by desperately buying the fallow compute from a company whose CEO threw up the nazi salute , called the company “ misanthropic and evil ,” and has the “right to reclaim the compute” if Anthropic “engages in actions that harm humanity.” Surely you’d wait a few months for some new, less tainted source of compute, right? And surely it wouldn’t be such a big deal, because new data centers get switched on every day, right?  So, let’s get to brass tacks. Anthropic and OpenAI have now committed to spending $748 billion across Amazon Web Services, Google Cloud, and Microsoft Azure , accounting for more than 50% of their remaining performance obligations. The very future of hyperscaler revenue depends both on Anthropic and OpenAI’s continued ability to pay and both of them having something to actually pay for.  I also think it’s fair to ask why Microsoft’s theoretical gigawatts of new compute aren’t producing tens of billions of dollars of new revenue.  Microsoft’s $37 billion in annualized AI run rate (sigh) is mostly taken up by OpenAI’s voracious demands for its :compute , and only ever seems to expand based on OpenAI’s compute demands and the now 20 million lost souls paying for Microsoft 365 Copilot . There’s supposedly incredible, unstoppable demand for AI compute, and Microsoft is apparently sitting on gigawatts’ worth , but somehow those gigawatts don’t seem to be translating into gigabillions , likely because they don’t fucking exist. All of this makes me wonder what Google infrastructure head Amin Vahdat meant last November when he said that Google needed to double its capacity every six months to meet demand . Many took this to mean “Google is doubling its capacity every six months,” but I think it’s far more likely that Google is taking on capacity requests from Anthropic that are making said capacity demands necessary. Similarly, I think CEO Sundar Pichai’s comment that it would have made more money had it had more capacity to sell was a manifestation of a distinct lack of new capacity rather than a result of bringing on swaths of new data centers that immediately got filled. I also need to be blunt on two things: Look, I know it sounds crazy, but I’m telling you: I don’t think very many data centers are coming online! While I keep wanting to hedge my bets and say “I bet a few gigawatts came online,” I cannot actually find any compelling literature that backs up that statement. I’ve spent hours and hours looking, and I’ve come up with a few hundred megawatts delivered in the past two years. Every major project is stuck in the mud, a phase or two in, or facing mounting opposition from locals that don’t want a Godzilla-sized cube making a constant screaming sound 24/7 so that somebody can generate increasingly-bustier Garfields.  I’m not even being a hater! It’s just genuinely difficult to find actual data centers that have been announced that have also been fully turned on.   So, humour me for a second: if hyperscalers are bringing on hundreds of megawatts of capacity a year, then that means that the ever-growing quarterly chunks of depreciation ripped out of their net income are just a taste of what’s to come. Last quarter, Google’s depreciation jumped $400 million to $6.482 billion, with Microsoft’s jumping nearly a billion dollars from $9.198 billion to $10.167 billion, and Meta’s from $5.41 billion to $5.99 billion. While Amazon’s technically dropped quarter-over-quarter, it still sat at an astonishing $18.94 billion. Remember: depreciation only increases when an item is actually put into service. If Microsoft, Google, Amazon and Meta are sitting on tens of billions of yet-to-be-installed GPUs, and said GPUs are only being installed at a snail’s pace every quarter, that means that these depreciation figures are set to grow dramatically. In fact, year-over-year, Google’s depreciation has jumped 30.7%, Amazon’s 24.7%, Microsoft’s 23.9%, and Meta’s an astonishing 34.9% .  And that’s with an extremely slow pace of deployment.  I do kind of see why the hyperscalers are sinking capex into these big AI infrastructure gigaprojects now, though. Shareholders are currently tolerating the capex because they think stuff is coming online, and that’s where the “incredible value” is. When a $20 billion or $30 billion a quarter depreciation bill first rears its head — as I said, Amazon is close, reporting $18.945bn in depreciation and amortization expenses in the most recent quarter — it’ll become obvious that the only people seeing value from AI are Jensen Huang and one of the massive construction firms slowly building these projects.  Actually, it’s probably important to state that I don’t think the majority of these projects are doing anything untoward I just don’t think any of them realized how difficult it is to build a data center, and unlike basically any other problem the tech industry has ever faced, simply throwing as much money as possible at it doesn’t really change the limits of physical construction.  I think every one of these data center projects is its own individual construction nightmare, and thanks to the general market psychosis around the AI bubble, nobody has thought to question the core assumption that these things are actually getting built. With all that being said , I’m not sure that anyone building these things is moving with much urgency either. Perhaps they don’t need to — perhaps hyperscalers are happy, because they can continually string out both the AI narrative and put off those massive blobs of depreciation. But we really do need to reckon with the fact that nearly two years in, Stargate Abilene has only two buildings’ worth of actual, operational, revenue generating capacity, and nobody has given me an answer as to how it doesn’t have even a quarter of the 1.7GW of power it’ll need to turn everything on , if it ever gets fully built. Maybe they can really pick up the pace, but as of early April, barely any actual gear was in the third building.  And then we get to the other problem: Oracle. As I’ve discussed before, Oracle is building 7.1GW of total capacity for OpenAI , and keeps — laughably! — saying 2027 or 2028, when at this rate, Stargate Abilene won’t be done until mid-2027, and the rest either never get finished or are done in 2030 or later.  This is setting up a horrifying situation where Oracle desperately needs OpenAI to pay it for capacity that doesn’t exist, and if it ever gets built, it’s likely to be years after OpenAI has run out of money, which is the same problem that Microsoft, Google, and Amazon have with their $748 billion of deals with Anthropic and OpenAI, though thanks to the $340 billion or more necessary to build the Stargate data centers, Oracle’s problems are far more existential. I’ve repeatedly — and correctly! — said that the problem is that these companies didn’t have the money to pay for their capacity, but Oracle lacks Microsoft or Google’s existing profitable businesses to fall back on if these data centers are delayed, with its existing business lines plateauing and its only real growth coming from theoretical deals with OpenAI and GPU compute with negative 100% margins .  Anthropic’s desperation for new sources of  compute also suggests that it’s bonking its head against the limits of its capacity, and will continue to do so as long as it continues to subsidize its users . I also think that the slow pace of construction will eventually lead to OpenAI facing similar problems. These companies need to continue growing to continue to raise the hundreds of billions of dollars in funding necessary to pay Oracle, Google, Microsoft, and Amazon their respective pounds of flesh.  It’s now very clear that the whole “inference is profitable” and “most compute is being used for training” myths are dead, because if they weren’t, Anthropic would either need way more compute or way higher-quality compute. Colossus-1 was specifically built as a training cluster, yet its current use is “reduce rate limits for our subsidized AI subscriptions,” which is most decidedly inference provided by three-year-old hardware . Despite writing over 9000 words and driving myself slightly insane trying to find out, I still haven’t got an answer as to how much actual data center capacity has come online. Hyperscalers have clearly been retrofitting old data centers to fit their new chips, and based on my research, I can find no compelling evidence that they’ve added more than a few hundred megawatts a piece since 2023.  What I do know is that, across the board, a data center of anything above 50MW (or lower, in some cases) takes anywhere from 18 to 36 months to complete, and nobody has actually built a gigawatt data center despite how many people discuss them. For example, Kevin O’Leary — known as “Mr. Dogshit” to his friends — is allegedly building a 9GW data center in Utah , but he may as well say that he’s building a unicorn that shits Toyota Tacomas, as doing so is far more realistic than a project that will likely cost $396 billion, assuming that locals and bankers don’t drag him to The Other Side like Dr. Facilier .  Nobody has built a 1GW data center, so I severely doubt Mr. Dogshit will be able to do anything other than create another scandal and lose a bunch of people’s money. In other words, any time you hear about a “new data center project,” add a year or two to whatever projection they give. If it’s 2027, assume 2029, or that it never gets built. Anything being discussed as “finished in 2030” may as well not exist. In any case, what I’m suggesting is that very, very few data centers are actually getting finished, and if that’s true,  NVIDIA has sold years worth of chips that are yet to be digested.  And if that’s true, somebody is sitting on piles of them.  I’m trying to be fair, so I’ll assume that an unknown amount of data centers got retrofitted to fit Blackwell GPUs. But I also refuse to believe that even half of the three million Blackwell GPUs that got shipped have actually been installed. Where would they go? You can’t use the same racks for them that you would with an H100 or H200, because Blackwell requires so much god damn cooling. Another sign that these things aren’t actually getting installed is Supermicro’s $1.4 billion or so of B200 GPUs left in inventory from a canceled order from Oracle .  Why not? Isn’t this meant to be a chip that’s extremely valuable? Isn’t there infinite demand? Is there not a place to put them? Apparently Oracle wanted to use faster GB200 GPUs from Dell , but why aren’t there other customers lining up to buy these things?  Also… how was Oracle able to cancel an order of over a billion dollars’ worth of GPUs?  Can anybody do that? Because if they can, one has to wonder if this doesn’t start happening as people realize these data centers aren’t getting built. Pick a data center. It’s probably barely under construction, or if it’s “finished” it’s actually “partly done” with no real guide as to when the rest will finish.  Remember that $17 billion deal with Microsoft and Nebius signed ? The one that’s a key reason why Nebius’ stock is on a tear? Well, its existence is based on the continued construction of a data center out in Vineland, New Jersey facing massive local opposition, and multiple sources now confirm that construction has been halted due to local planning issues. The data center is horribly behind schedule already, and Microsoft has the option to cancel its entire contract if Nebius fails to meet milestones . That data center is a major reason that people value Nebius’ stock! It cannot make a dollar of revenue without its existence! It has the funds and blessing of Redmond’s finest — the Mandate of Heaven! — and it can’t get things done! This is bad, and indicative of a larger problem in the industry — that it’s really difficult to build data centers, and for the most part, they’re not being fully built! You’ve heard plenty about data centers getting opposed and canceled — how about ones that fully opened? No, really, if you’ve heard about them please get in touch, because it’s really difficult to find them. Why don’t we know? This is apparently the single most important technology movement since whatever the last justification somebody made up was, shouldn’t we have a tangible grasp? Because the way I see it, if these things aren’t coming online at the rate that people think, we have to start asking for fundamental clarity from NVIDIA about where the GPUs are, and when they’re coming online.  NVIDIA’s continually-growing valuation is based on the conceit that there is always more demand for GPUs, and perhaps that’s true, but if this demand is based on functionally selling chips two years in advance. That makes NVIDIA’s yearly upgrade cadence utterly deranged. Buy today’s GPUs! They’re the best, for now, at least. By the time you plug them in they’re gonna be old and nasty. But don’t worry, it’ll take two years for you to install the next one too! To be clear, Blackwell GPUs are absolutely being installed! But three million of them?  People love to use “enough to power two cities” to illustrate these points, but I actually think it’s better to illustrate in real data center terms.  Stargate Abilene has taken two years to build two buildings of around 103MW of critical IT load. 3 million B200 GPUs works out to about 3.6GW of IT load. Do you really think that nearly thirty five Stargate Abilene-scale buildings were built in 2025? If so, where are they, exactly? You may argue that other data centers are smaller, and thus it would be easier to build. So why can’t I find any examples of where they’ve done so?  By all means prove me wrong! It’s so easy! Just show me a data center announced or that broke ground in 2023 and find obvious proof it turned on. I’ll even give you credit if it’s partially open! The problem is that I keep finding examples of “partially complete” and those are the only examples of “finished” data centers.  Isn’t this a little insane? This is all we’ve heard about for years, everybody is ACTING like these things exist at a scale that I’m not sure is actually true!  I expect a fair amount of huffing and “well of course they’re coming online” from the peanut gallery, but come on guys, isn’t this all kind of weird? Even if you want to marry Sandisk and name your children “Western” and “Digital,” why can’t you say with your whole chest several data centers that got finished? We have macro level “proof” but when you try and look at even a shred of the micro you find a bunch of guys with their hands on their hips saying “sorry mate that’ll be another $4 million.”  Something doesn’t line up, and it’s exactly the kind of misalignment that happens in a bubble — when infrastructural reality disconnects from the financials. NVIDIA is making hundreds of billions of dollars and it’s unclear how much of it is from GPUs installed in operational data centers. It feels like Jensen Huang might have run the largest preorder campaign of all time.  This has massive downstream consequences. Sandisk, Samsung, SK Hynix, Broadcom, AMD, Microsoft, Google, Oracle, and Amazon’s remaining performance obligations total [find] and are dependent on being *able* to sell gigawatts worth of computing gear or compute access. If data centers are not getting built in anything approaching a reasonable timeline, that makes the future of these companies only as viable as the construction projects themselves. Even if you truly believe Anthropic will be a $2 trillion company and a $200 billion customer of Google, the compute capacity has to exist to be bought, and it does not appear to be built or, in many cases, anywhere further than the earliest stages of construction.  If they don’t get built in the next few years, there’s no space for that solid state storage or those instinct GPUs. There’s no reason for NVIDIA to have reserved most of TSMC’s capacity , either. There’s also no reason to get excited about Bloom Energy, as it’s not making real revenue on those until Oracle finishes its data centers sometime between the next two years and never .  And if they don’t get built, hundreds of billions of dollars have been wasted, with large swaths of those billions funded by private credit, which in turn is funded by pensions, retirements and insurance funds . I’ve got a bad feeling about this.  Microsoft claims to have brought around 4GW of data center capacity online in the last two years, but it’s unclear how much actually got built. In an analysis of all announced groundbreakings and land acquisitions, it appears that Microsoft has only finished the first phase of its Atlanta and Wisconsin data centers.  It is unclear where this capacity could be. When Mr. Nadella said on his most-recent earnings call that Microsoft had (and I quote) "added another gigawatt of capacity this quarter," did he mean active, revenue-generating capacity?  In the event he did not, what did he mean? How much active, revenue-generating capacity has Microsoft brought online in FY2026 so far? Outside of Fairwater Wisconsin and Atlanta, where has that capacity been built?  Microsoft’s latest update on the Hickory/Stover site is that it “will” begin “initial site setup and earthwork activities” as of February 2026, and it appears the contractor has changed from Ames Construction to Clayco. The latest Microsoft update on the Boyd Farms site is that it started construction on April 1, 2024. A February 2026 piece from the Charlotte Observer claimed it had started construction again after a 10 month (!) delay. The latest Microsoft update on the Lyle Creek site — which it adds began construction in March 2024 — is that its contractor, Whiting-Turner, “will begin initial site preparation once weather conditions allow” as of February 2026.  A press release from a Canadian satellite firm from February 2026 said that it had “identified renewed construction activity at all three of Microsoft’s permitted data center campuses in Catawba County North Carolina.” Novva’s 60MW data center in Reno, Nevada. Announced in May 2023, operational as of July 2025 , or around 26 months. Edged Energy’s 36MW Phoenix, Arizona data center that broke ground in August 2024 and opened in April 2026 , or around 20 months. Duos Edge AI’s 450KW (lol) data center in Corpus Christi, Texas that was announced in July 2025 and opened in May 2026 , or around 10 months. Edge Energy’s 24MW, Columbus, Ohio-based data center that broke ground in August 2024 and opened in September 2025 , or around 13 months. American Tower’s 1MW (scalable to 4MW!) Raleigh, North Carolina data center that broke ground in June 2024 and came online in May 2025 , or around 11 months. EdgeCore’s 36MW Santa Clara, California data center campus that broke ground in January 2023, said it would be “energized in Q1 2024,” and opened in September 2025 , or around 32 months . Edged Energy’s “180MW” data center in Atlanta broke ground in July 2023 , and around 33 months later in April 2026 ,  it managed to top off a single 42MW building . EdgeCore’s two-building, 216MW campus that broke ground in August 2023 with plans to complete “as early as late 2025” is, as of March 2026, still under construction. Edged Energy broke ground on a 100MW data center in Aurora, Illinois in May 2023 , and has, as of February 2025, successfully opened (per DataCenterDynamics) “phase 1” — 24MW of capacity — but in its own press release from the same day referred to it as 96MW , choosing not to refer to any phases or separate buildings, something it has done since before the 24MW phase was complete.  CyrusOne’s 40MW Aurora, Illinois data center broke ground in October 2024 , which was apparently so significant that CyrusOne would announce that it had broken ground a second time on January 28 2025 . Confusingly, CyrusOne has another campus it’s linking to the Bilter Road one on Diehl Road, which may or may not be the same one, and as of May 2026 is still very much under construction . As of March 2026, locals were still opposing the data centers , slowing down the process further. Vantage’s “192MW” OH1 data center in New Albany Ohio broke ground in October 2024 , with its first phase to be due live sometime in 2025. As of August 2025, Vantage had topped off the second building , and per its own website about OH1 , the first building was meant to be operational in December 2025, but it’s unclear whether it actually opened. PowerHouse’s 65MW data center campus in Reno, Nevada broke ground in October 2024 , and its website states that “delivery” will happen in April 2026, with “construction/delivery” due “Q3 2024 to Q2 2026.” Oppidan’s Carol Stream, Illinois data center broke ground in November 2024 , with the “first phase” due live in 2026. Per Clearview, it is still “ planned .” Databank’s 20MW Ashburn, Virginia “IAD4” data center that broke ground in July 2024 was “set to go live in Q1 2026,” and as of May 2026 is still referred to in the future tense on Databank’s website . Aligned’s 96MW “NEO-01” Ohio-based data center that broke ground in May 2024 was “scheduled to be opened by end of this year” as of March 2026 . Aligned’s 72MW Hillsboro. Oregon data center campus broke ground in October 2023 , topped off the first building in July 2024 (Aligned also plans a separate building, too!), and as of May 2026, Cleanview still marks the first one as “planned.” Flexennial broke ground on a Denver-based 22.5MW data center in October 2024 , and as of April 8. 2026, a local Facebook group has said that it will be operational by January 2027 .   Flexennial, on the other hand, has been referring to it as “ the new build ” — in terms that make it sound like it was built — as far back as February 2025. If hyperscalers are truly not bringing on that much capacity, they cannot make those hundreds of billions of dollars from Anthropic and OpenAI. The current “AI compute demand is insatiable” narrative is utterly false , and a direct result of a lack of capacity coming online.

0 views

Premium: AI's Circular Psychosis

In this week’s free newsletter, I explained how bad the circular AI economy is in the simplest-possible terms :  In other words, the entire AI economy effectively comes down to Anthropic and OpenAI, who take up at least 70% of Amazon’s Google’s, and Microsoft’s compute capacity , 70% to 80% of their AI revenues and 50% of their entire revenue backlog, per The Information : That’s $748 billion of the entire revenue backlog — not just AI compute — that’s dependent on Anthropic and OpenAI, two companies that cannot afford to pay these bills without constant venture capital infusions from either investors or the hyperscalers themselves.  This is a big problem, because Anthropic seems to be losing so much money that it had to raise $10 billion from Google , $5 billion from Amazon , and is reportedly trying to raise another $50 billion from investors , less than three months after it raised $30 billion on February 12, 2026, which was five months after it raised $13 billion in September 2025 . That’s $58 billion in eight months, with the potential to reach $108 billion. Now Anthropic is taking over all 300MW of SpaceX/xAI/Elon Musk’s Colossus-1 data center , which will likely cost somewhere in the region of $2.5 billion to $3.5 billion a year, given that most of the data center is made up of H100 and H200 GPUs ( with around 30,000 GB200 GPUs ). I also don’t think people realize how bad a sign this is for the larger AI economy. Musk built the 300MW Colossus-1 to be “ the most powerful AI training system in the world ,” specifically saying that it was built “ for training Grok ,” with inference handled through Oracle ( which originally earmarked Abilene for Musk but didn’t move fast enough for him ) and other cloud providers. xAI, as one of the largest non-big-two providers, had so little need for AI capacity that it was able to hand off the entirety of its self-built data center capacity to Anthropic.  If xAI doesn’t need 300MW of compute capacity that it spent at least $4 billion to build , who, exactly, are the other large customers for AI compute? I’m not even being facetious. I truly don’t know, I can’t find them, I spent most of last week looking for them , and the only answer I had a week ago was “Elon Musk buying a lot of compute for xAI to make the freaks on the Grok Subreddit able to generate pornography.”  xAI is also the only non-OpenAI/Anthropic AI lab that’s built its own capacity , capacity it clearly didn’t need, which begs the question as to why Musk needs however much capacity he’ll build at Colossus-2 . Musk claims that xAI had moved all training to Colossus-2 , but also that xAI would “ provide compute to AI companies that are taking the right steps to ensure it is good for humanity .” This apparently includes Anthropic, which Musk called “ misanthropic and evil ” a little over two months ago. Researchers believe that the actual capacity of Colossus-2 is 350MW . At $2.5bn a year or so, Anthropic will be effectively the entirety of xAI’s revenue, which was at around $107 million in the third quarter of 2025 .  To put this very, very simply: xAI should, in theory, have massive demand for AI compute, but its demand is apparently so small that it can flog a multi-billion-dollar data center to a competitor.  Sightline Climate found that 15.2GW of capacity is under construction and due to be completed by the end of 2027, and at this point I’m not sure anybody can make a compelling argument as to why it’s being built or who it’s for.  Who needs it? Who are the customers? Who is buying AI compute at such a scale that it would warrant so much construction? Where is the demand coming from if it’s not OpenAI and Anthropic? These questions shouldn’t be that hard to answer, but trust me, I’ve tried and cannot find a GPU compute customer larger than $100 million a year, and honestly, that customer was xAI.  Through many hours of research, I’ve found that the vast majority — as much as 95% — of all compute demand comes from a few places: Otherwise, every data center deal you’ve ever read about is for a theoretical future customer or an unnamed “anchor tenant” that gives them “guaranteed, pre-committed occupancy” without being identified in any way. Yet even that “pre-committed” language seems to be something of a myth, which I’ve chased down to a report from real estate firm JLL, who says that 92% of capacity currently under construction is precommitted through binding lease agreements or owner-occupied development . CBRE said it was 74.3% for the first half of 2025, and Cushman & Wakefield said it was 89% , though it also said that there was 25.3GW of capacity under construction, while Sightline sees 19.8GW under construction through the end of 2030. And man, I cannot express how fucking difficult it is to find actual data center customers outside of the ones I’ve named above. In fact, it’s pretty difficult to find any customers for GPU compute not named Anthropic, OpenAI, Microsoft, Google, Meta or Amazon.  Outside of OpenAI and Anthropic, effectively no AI software makes more than a few hundred million dollars a year, and to make that money, they have to spend it on tokens generated by models run by one of those two companies. When those companies generate those tokens, they then flow to one of a few infrastructure providers — I’ll get to the breakdown shortly — to rent out GPUs.  As I’ve discussed this week , at least 75% of Microsoft, Google and Amazon’s AI revenues come from OpenAI or Anthropic, and that’s before you count the money that Microsoft, Google and Amazon make reselling models from both companies. To get specific, The Information reports that Anthropic will pay around $1.6 billion to Amazon for reselling its models. OpenAI, per my own reporting , sent Microsoft $659 million as part of its revenue share. AI startups — all of whom are terribly unprofitable — predominantly spend their funding on models sold by OpenAI and Anthropic. Per Newcomer , as of August last year, Cursor was spending 100% of its revenue on Anthropic. Harvey, an AI tool for lawyers, raised $960 million between February 2025 and March 2026 , with most of those costs flowing to Anthropic and OpenAI.  Effectively every AI startup is a feeder for API revenue for Anthropic or OpenAI, and as a result, almost every dollar of AI revenue flows to either Google, Microsoft or Amazon. As Anthropic and OpenAI are extremely unprofitable, Google, Microsoft and Amazon then take that money and either re-invest it in OpenAI and Anthropic, as Google , Amazon and Microsoft have all done in the past few years.  At the beginning of the bubble, all three companies believed that OpenAI and Anthropic were golden geese that were, through the startups they inspired and powered, laying golden eggs that necessitated expanding their operations, leading them to spunk hundreds of billions of dollars in capex , with Amazon building the massive Project Rainier in Indiana for Anthropic and Microsoft the Atlanta and Wisconsin-based Fairwater data centers for OpenAI . They likely also thought their own services would grow fast enough to warrant the expansion, or that other large GPU consumers would rear their heads. That never happened. Instead, OpenAI grew bigger and more-demanding of Microsoft’s compute capacity, leading to Microsoft allowing it to seek other partners , in part (per The Information) because some executives believed OpenAI would die: By November 2025, OpenAI had signed a $300 billion deal with Oracle , a $22 billion deal with CoreWeave , a $38 billion deal with Amazon , and a theoretical deal with both AMD and NVIDIA . Yet by this point, Microsoft realized it was in a bind, with the majority — at least 70% if not more  of its AI revenues were dependent on OpenAI, but it had already walked away from 2GW of data center capacity to reduce its capex costs. It had also, as part of OpenAI’s conversion to a for-profit company, had convinced it to spend $250 billion in incremental revenue on Azure .  So Microsoft chose to start spreading out that capacity to neoclouds like Nebius and Nscale , effectively bankrolling their entire futures based on theoretical revenue from OpenAI, a company that plans to burn $852 billion in the next four years and cannot afford to pay any of its bills without continual subsidies. These companies were now part of a multi-threaded dependency that ultimately ended up at one place: OpenAI, which also makes up the vast majority of inference chip maker Cerebras’ revenue with its 3-year, $20 billion deal . Meanwhile, Amazon and Google thought they had it made. Anthropic was growing, and its compute demands were reasonable enough that neither had to stretch themselves too thin…until the second quarter of 2025, when Anthropic’s accelerated growth led to it starting to push against the limits of Google and Amazon’s capacity.  So Google agreed to backstop several billion dollars behind two deals with Fluidstack, a brand new AI compute company, and Amazon continued expanding its Project Rainier data center.  Yet Anthropic’s hunger wasn’t sated. After mocking OpenAI in February 2026 for “YOLOing” into compute deals (and having signed a cloud deal with Microsoft ), it massively expanded its AWS and Google Cloud deals , signed a deal with CoreWeave , and as I discussed above, took over the entirety of Musk’s Colossus-1 data center . And all of this is only happening because, based on my analysis, very little actual demand for AI compute exists outside of OpenAI and Anthropic, and OpenAI and Anthropic only exist because of Microsoft, Google, and Amazon both building and expanding their infrastructure to cater to them.  In reality, OpenAI and Anthropic are the only meaningful companies in the AI industry. They are the majority of revenue, the majority of capacity and the majority of demand. Microsoft, Google and Amazon have exploited the desperation in a tech industry that’s run out of hypergrowth ideas , and created a near-imaginary industry by propping up both companies. The mistake that most make in measuring the circularity of OpenAI and Anthropic is to focus entirely on the money raised — $13 billion from Microsoft and up to $50 billion from Amazon for OpenAI, and as much as $80 billion from Amazon and Google for Anthropic. The correct analysis starts with measuring infrastructure. Based on discussions with sources and analysis of multiple years of reporting, I estimate that of the roughly $700 billion in capex spent by Google, Meta and Microsoft since 2023, at least 5.5GW of capacity costing at least $300 billion has been built entirely for two companies. This has in turn inflated sales through multiple counterparties involving NVIDIA, ODMs like Quanta, Foxconn, Supermicro and Dell, and created a form of market-driven AI psychosis that inspired Meta to burn over $158 billion in three years and the entire world to convince itself that AI was the biggest thing ever. The reason that there isn’t another OpenAI or Anthropic is that Google, Microsoft, and Amazon bankrolled their entire infrastructure, fed them billions of dollars, and then charged them discount rates for their early compute, with sources telling me that Anthropic pays vastly below-market-rates for Trainium compute from Amazon, and The Information reporting that OpenAI was paying $1.30-per-A100-per hour in 2024, or at or around the cost of running them. By sacrificing their entire infrastructure to OpenAI and Anthropic, the hyperscalers created the illusion of demand by feeding themselves money, all while buying endless GPUs and TPUs to fill further data centers for two customers, both of whom paid discount rates that lost them money.  This capex bacchanalia gave all three companies a massive boost to their stock prices, so they kept going, even though there wasn’t really demand other than for Anthropic or OpenAI, two companies that they had to constantly cater to with investment capital and server maintenance. The belief became that all you had to do was plan to build a data center and you’d print money, boosting NVIDIA’s sales and associated counterparties in memory stocks like Sandisk . Except that never happened.  Every data center provider that doesn’t have an Anthropic, OpenAI, or Meta-related contract makes pathetic amounts of revenue that can barely keep up with their debt. AI startups make meager revenues, and lose multitudes more than they can ever hope to make.  The entire AI industry relies upon two companies that expect to burn at least $1 trillion in the next four years, with Anthropic, the supposed “compute-conscious” AI company, committing to at least $330 billion in spend in the next few years. Where does that money come from, exactly? Because neither of these companies have anything approaching a path to profitability.  Based on a deep analysis of every publicly-available source on AI compute, I can find only two significant — over $100 million a year — purchasers of AI compute outside of Anthropic, OpenAI, Meta, or associated parties like NVIDIA, Microsoft, Google and Amazon. Those two are Poolside, which reportedly spends $400 million a year , an untenable position as it only raised $500 million in total funding before its $2 billion in funding collapsed earlier this year , and Perplexity, which appears to spend some amount of money with CoreWeave and Microsoft Azure. Both run at a massive loss. Nowhere is this lack of true demand more obvious than in the neoclouds, which only seem capable of signing big deals with Anthropic, OpenAI, Microsoft (for OpenAI), and Google (for OpenAI). Oh, and Meta, who is doing this because the existence of ChatGPT gave Mark Zuckerberg such profound AI psychosis that he’s made Meta build him a CEO chatbot to talk to and burned over $150 billion. The AI industry is a brittle, circular economy, one only made possible by a lack of financial regulation and a tech industry that’s run out of ideas. Without hyperscalers propping up OpenAI and Anthropic, there would be no reason to buy so many GPUs or build so many data centers, and neoclouds would have no reason to exist. This is a giant con, a giant illusion, and a giant mistake. Meta, for reasons that defy logic. Microsoft, for OpenAI’s compute. Google, for Anthropic’s compute. Amazon, for Anthropic. 90%+ of all AI revenues flow through Anthropic and OpenAI. 90%+ of all AI compute demand comes from Anthropic, OpenAI, Meta, or associated counterparties like Google and Amazon buying compute for Anthropic or OpenAI. The vast majority of AI operations don’t require more than a few hundred to a thousand GPUs for inference, and at most 20,000 GPUs for training models. This means that for the 15.2GW of data centers under construction before 2027 ($157 billion in annual revenue) to make sense, thousands of companies will have to rent hundreds or thousands of GPUs. This also means that the DeepSeek problem — the reason that everybody freaked out in January 2025 — is actually industry-wide. More than 50% of Microsoft, Google, Amazon, CoreWeave, and Oracle’s entire revenue backlogs are from OpenAI and Anthropic. Neoclouds are unsustainable, imaginary businesses only made possible by continual subsidies from NVIDIA and the compute demands of OpenAI, Anthropic and Meta. Outside of Anthropic and OpenAI, only around $13 billion in AI compute demand exists, with much of it taken up by Meta and NVIDIA backstopping neoclouds like CoreWeave and IREN. ODMs like Supermicro, Dell, Quanta and Foxconn are largely dependent on AI server revenues that largely flow through OpenAI and Anthropic’s counterparties to fuel their server demand.

0 views

Am I Meant To Be Impressed?

If you liked this piece, please subscribe to my premium newsletter. It’s $70 a year, or $7 a month, and in return you get a weekly newsletter that’s usually anywhere from 5,000 to 18,000 words, including vast, detailed analyses of NVIDIA , Anthropic and OpenAI’s finances , and the AI bubble writ large .  I just published a lengthy discussion about how OpenAI and Anthropic make up 70%+ of all AI GPU compute capacity and revenue . The previous week I wrote about how OpenAI will kill Oracle — and quite possibly Larry Ellison’s personal fortune, too . Subscribing to premium is both great value and makes it possible to write these large, deeply-researched free pieces every week.  God, it’s been a long few years, and only feels longer after every ecstatic, ridiculous round of tech earnings where the world’s largest companies do everything they can to obfuscate the ugly truth behind their numbers. Let’s start with the biggest, ugliest one: Microsoft, Google, Amazon, and Meta are expected to spend between $800 billion and $900 billion on AI capex in 2026, and over $1 trillion in 2027 . By the end of 2027, big tech will have sunk $2 trillion into AI capex, with very little to show for it. Oh, I know what you’re going to say. “These companies are growing faster than ever!” “These companies are building for future revenue streams!” “These companies are saying that AI is driving growth!”  Yet those revenues are, in the case of Meta and Google, not good enough to actually share.   While Google CEO Sundar Pichai will gladly say that “[Google’s] AI investments and full stack approach are lighting up every part of the business,” said “lighting up” never results in a revenue number that you can point at, because Google knows that analysts and journalists will read “Gemini Enterprise has great momentum with 40% quarter on quarter growth” — which we have no frame of reference for because Google doesn’t share its AI revenues — and clap and honk like fucking seals. Sundar Pichai knows that everybody is desperate to see him jingle his keys, and has such utter contempt for reporters, analysts, and investors that he doesn’t have to prove AI is actually doing anything. Those writing up his earnings will do it for him.  Meta, on the other hand, has little real AI story, and can’t even seem to get its metrics straight on what AI is doing for the company, per my premium piece from earlier in the week : Nevertheless, I have to give Microsoft and Amazon credit for deigning us worthy of actual numbers, even if they’re piss poor. While Meta and Google refuse to actually explain their AI returns, Microsoft revealed that it had $37 billion in AI revenue run rate — $3.08 billion a month or so — and Amazon had $15 billion, or around $1.25 billion a month . And I must be clear, that’s revenue, not profit. In any case, I need you to recognize how small these numbers are in comparison to the capex it’s taken to make them.  To give you some context, Amazon’s AI revenue run rate is roughly 0.419% of the $298 billion in capex it spent on AI capex so far, or around 25% of the $5 billion it just invested in Anthropic last week . Microsoft, on the other hand, has spent $293.8 billion on AI capex through its latest quarter — making its revenue run rate around 1.04% of its spend. These revenues are deeply embarrassing! I am not sure why this isn’t the common refrain! These fucknuts have spent over a trillion dollars on AI and all they have to show for it is either nothing , vague statements about “everything lifting because of AI,” or pathetic revenues that only get worse the more you think about them.  For example: even if Microsoft were to make $37 billion in AI revenue in 2026 — remember, that $37 billion run rate is a snapshot in time! — that would still be $500 million less than the $37.5 billion it spent in capital expenditures in the fourth quarter of 2025 .  Yet things actually get worse when you think about the sources of that revenue, or perhaps I should say source, as both Microsoft and Amazon (and I’d argue Google too, but we don’t know its AI revenues) are heavily-dependent on their large, unsustainable sons — Anthropic and OpenAI. I’ll explain. Microsoft claims that its $37 billion in AI revenue run rate has grown by 123% year-over-year, which means its run rate, not actual 2025 AI revenue, was about $16.59 billion in Q3 FY25, or around $1.38 billion a month or, if you assume that number is consistent over the quarter (it likely wasn’t), about $4.14 billion. Based on my own reporting from direct Azure revenue numbers, this would make OpenAI’s $2.947 billion in inference spend in that quarter around 71% ($11.7bn) of Microsoft’s Q3 FY2025 AI revenue run rate. That’s embarrassing!  Oh, and capital expenditures for that quarter were $21.4 billion , or around $4.81 billion more than its annualized revenue.  Yet my reporting helps us be a little more annoying than that. Back in January 2025 — around Microsoft’s Q2FY2025 earnings — it announced that its AI revenue run rate had hit $13 billion , or around $1.083 billion a month (or $3.25bn a quarter or so). In that same quarter, OpenAI had spent $2.075 billion on inference on Azure, or 63.8% of Microsoft’s AI run rate. This is particularly funny when you go back to the quarter before, where Microsoft CEO Satya Nadella low-balled that figure, claiming it would be $10 billion in annualized run rate, and specifically said the following : That’s…not really what happened. Today I can report, based on discussions with sources with direct knowledge of Azure revenue, that in Q2 FY2025, Microsoft brought in around $325.2 million in revenue via renting out GPUs and other AI infrastructure, and around $367 million in revenue from Microsoft 365 Copilot, or less than half of the $1.467 billion that OpenAI spent on inference.  If you’re curious, the next quarter (Q3FY2025), AI infrastructure brought in around $412 million, and Microsoft 365 brought around $300 million.  While my sourcing for Azure revenues cuts off at Q3 FY2025, my OpenAI inference and revenue share data goes out a further two quarters to Q4 FY2025 and Q1 FY2026 (so Q2 and Q3 of the calendar year 2025), as well as half of Q2FY2026, and we can make some fairly straightforward estimates as a result. So, based on my reporting, OpenAI spent $3.648 billion dollars on inference in the third quarter of 2025 on Microsoft Azure, or around $14.4 billion on an annualized basis.  While I only had half the fourth quarter’s numbers, I estimate that OpenAI’s annualized spend hit over $18.5 billion — or around $4.6 billion a quarter — by the end of the year, and that’s not accounting for things like Sora 2 or the launch of its Codex coding platform. In total, this puts its spend at an estimated $13 billion dollars on Azure just on inference, with billions more on training. Yet Microsoft Azure isn’t the only place that Microsoft gets fed revenue from OpenAI. Microsoft also accounted for 67% of CoreWeave’s 5.15 billion in 2025 revenue — or around $3.45 billion dollars — and as all of that is used by OpenAI. I also believe this is used for OpenAI’s training compute, as CoreWeave’s announcement related to its direct deal with OpenAI specifically said it was contracted “...to power the training of [OpenAI’s] most advanced next-generation models,” and said capacity was only available because Microsoft declined to extend its current agreement to use compute for OpenAI . All together, that puts OpenAI’s spend on Microsoft services at over $18 billion dollars in 2025, and it’s easy to see how that would grow to over $24 billion dollars on an annualized basis in the last quarter, or around $2 billion a month. Microsoft is OpenAI’s primary cloud provider, and I estimate that OpenAI represents around 70% of its AI revenue, while taking up the majority of its infrastructure. Otherwise, Microsoft’s 20 million Copilot 365 subscribers likely pay no more than $7 billion a year. I also think that OpenAI is taking up the lion’s share of compute. As I discussed in my most-recent premium newsletter , Epoch estimates that Microsoft had around 2GW of compute by the end of 2025, with OpenAI as its largest customer. At the end of 2025, OpenAI’s CFO said that it had access to 1.9GW in compute, at a time when its compute was entirely supported by Microsoft and CoreWeave (estimated to have 480MW of compute).  Considering that 67% of CoreWeave’s revenue came from Microsoft renting capacity for OpenAI , I also think that it’s fair to assume that 80% or more of Microsoft’s GPUs are taken up by OpenAI, though some might now be taken up by Anthropic, which agreed to spend $30 billion on Azure. I’ve also confirmed that Microsoft’s “Fairwater” data centers — which constitute (when finished) “ hundreds of thousands of GPUs ” — are entirely reserved for OpenAI.  Microsoft desperately wants you to think that this is a diverse, booming revenue stream, when in fact it’s spent around $293 billion in four years to make — when you remove OpenAI — less than $3 billion a quarter in revenue, not profit. Booooooo! Booooooo!!!!! As far as Amazon goes, things get a lot grimmer. As I mentioned earlier, in early April , per Reuters, Amazon’s Andy Jassy admitted that its “cloud business’ AI revenue run rate was more than $15 billion in the first quarter of 2026,” which translates to around $1.25 billion in monthly revenue, or roughly 0.419% of the $298.3 billion in capex it spent so far, or around 25% of the $5 billion it just invested in Anthropic two weeks ago .  I also think it’s reasonable to assume that a large part — if not the majority of — that revenue comes from Anthropic. Per my reporting last year , Anthropic spent $518.9 million on Amazon Web Services, at a time when it had around $7 billion in annualized revenue, a figure that’s increased by 500% (if you believe it) to $30 billion in annualized revenue since . $518.9 million is about $6.2 billion in annualized spend, and I think it’s fair to assume that its spend will have at least doubled to $12 billion in annualized revenue, or around 80% of Amazon’s AI revenue. As of the end of Q4 2025, Amazon had 1.67GW of capacity — and based on my estimates from my newsletter published April 21 , 500MW of that is taken up by Project Rainier, a data center dedicated entirely to Anthropic , which is also Amazon’s largest AI customer. I’d be confident in assuming that more than 75% of its capacity is taken up by Anthropic. And man, $1.25 billion a month is fucking pathetic. I’m sorry, how are any of you possibly impressed by this?  God, everyone loves to slurp down Sundar’s slop. You all fall for it! Sundar Pichai doesn’t respect you enough to tell you how much AI revenues Google makes, but because its current businesses continue to grow thanks to its tried and tested tactic of making shit harder to use so that Google services can show you more ads . Nevertheless, people are doing backflips over Google Cloud’s 63% in year-over-year revenue growth ($20.03 billion), and I have a few thoughts: One of the reasons that Google might not want to break out its AI revenues is that they’re — much like Amazon — heavily-inflated by Anthropic’s compute spend. Sadly, we have only a little information about Anthropic’s spend outside of its promise to use “up to one million TPUs, with over a gigawatt of capacity [coming] online in 2026” from the end of last year, and a month ago, when it said it would use “multiple gigawatts of next-generation TPU capacity…starting in 2027.”   Another guess might be to travel back in time to before Anthropic was a huge consumer of compute. In Q4 2023, Google Cloud sat at about $9.19 billion a quarter , and $11.96 billion in Q4 2024 (around 23% year-over-year, but a putrid 5% quarter-over-quarter from Q3 2024). By Q2 2025, it sat at $13.62 billion , and as I mentioned above, accelerated to $15.15 billion to $17.66 billion (14.2% quarter-over-quarter) to $20 billion (11.7% quarter-over-quarter) in the following three quarters. These periods match up exactly to Anthropic’s big jumps in revenue from Q2 2025 ( around $3 billion ARR ) to Q3 2025 ( around $7 billion ARR ) to Q4 2025 ( around $9 billion ARR ) to Q1 2026 ( around $19 billion ARR ), which suggests that Anthropic’s growth is what’s actually boosting Google Cloud. Yet things get weirder when you listen to Google’s most-recent earnings call : Interesting. Interesting. Google appears to be planning to sell its TPUs — its own custom silicon it currently uses only for its own services and some of Anthropic’s — to a non-specific amount of unnamed customers, to the point that its remaining performance obligations jumped from $242.8 billion to $467.8 billion in the space of a quarter.  Nevertheless, that’s a remarkable jump, especially when you try and work out who they sell to- oh wait, we actually know! Google also signed a multi-billion dollar deal to rent TPUs to Meta, per The Information , and is also discussing A) selling TPUs to Meta directly, and B) creating SPVs that will buy its own GPUs and lease them to others: This is exactly the same shit as NVIDIA is doing with xAI’s GPU-related financing last year . To explain, Google is creating something called a special purpose vehicle — a company with one purpose — that it then funds along with an investment firm. The SPV then raises cash via debt, which it then uses to buy TPUs directly from Google . Now, remember that Anthropic deal to use a million TPUs from last year? How about the deal with Broadcom (which makes TPUs for Google) and Google to use “multiple gigawatts” of TPUs starting in 2027? Well, Per CNBC, Anthropic agreed to buy $21 billion of Broadcom’s TPUs in 2026 and $42 billion in 2027 . Where will those TPUs go? Google’s data centers, probably the ones that it’s backstopping, per my premium from the beginning of the week : It’s a pretty sweet deal for Google! Google pays Broadcom to develop TPUs, Anthropic pays Google to buy those TPUs once Broadcom builds them, Google installs those TPUs in a data center, and then Anthropic pays Google to rent them back.  This isn’t real demand! Boo!!!!!! BOOOOOO!!!!!! So, for the sake of transparency, I wrote the above before The Information published its story about how Anthropic had committed to spend $200 billion on Google Cloud and TPU chips, which contained this very important detail: The Information’s story also had this fascinating chart showing that around 50% of Amazon, Google and Microsoft’s backlog (which includes all revenues not just AI) — a staggering amount — is made up of revenue from OpenAI and Anthropic: To be clear, I also wrote the below before this chart ran, because it was very fucking obvious when you actually looked at the numbers .  Anyway, as I said in my last premium newsletter: As I’ve explained, most AI revenues out of Google, Microsoft and Amazon come from two companies that lose billions of dollars a year, have no path to profitability, and are only able to keep paying these companies because the companies (and investors) keep feeding them money. These relationships are utterly poisonous, and an intentional attempt to deceive investors and the general public.  Google now plans to invest up to $43 billion in Anthropic, a company that I estimate takes up at least half of its 2.95GW of capacity, which has cost it around $211 billion in capex since 2023. Amazon has already invested $13 billion and as much as another $20 billion more in Anthropic, and announced its latest round with a statement about how Anthropic will use up to 5GW of compute capacity . While dimwits might read this and say “WOW, AMAZON JUST LOCKED UP TONS OF FUTURE REVENUE,” it’s important to remember that Anthropic plans to lose $11 billion a year both in 2026 and 2027, and that’s based on its own internal (and fanciful) projections!   Let me spell it out in a way that boosters can understand, in the style of Gillam Fitness : Anthropic not have money to pay big cloud bills, because Anthropic company cost lots of money, more money than Anthropic make! So Anthropic only PAY cloud bills if OTHERS give it money! Amazon GIVE MONEY to Anthropic to GIVE BACK TO AMAZON, which mean no profit! And Amazon not give Anthropic enough money to pay it, so Anthropic have to ask OTHERS for money! That BAD! It mean BUSINESS not STABLE, and CLIENT not STABLE.  This bad when client MOST OF AI MONEY! This ALSO mean that Anthropic RELIANT on OTHERS to pay AMAZON, which make AMAZON dependent on VENTURE CAPITAL for FUTURE REVENUE! Amazon SAY it have BIG BUSINESS, but BIG BUSINESS dependent on ANTHROPIC, which mean BIG BUSINESS dependent on VENTURE CAPITAL! This SAME for GOOGLE! Both say they have BIG CLIENT, but BIG CLIENT MONEY not supported by REVENUE, so BIG CLIENT actually mean “HOW MUCH VENTURE CAPITAL MONEY ANTHROPIC HAVE.”  This bad business!  And it really, really is .  Most of Amazon, Google and Microsoft’s capex is being driven into capacity mostly used by OpenAI and Anthropic, neither of whom have the money to pay without continual infusions of more capital. Only Microsoft was smart enough to realize the problem, which is why it allowed Oracle to take over the majority of OpenAI’s future capacity ( which may kill Oracle, by the way! ), but both Google and Amazon keep feeding Anthropic money so that Anthropic can feed it right back to them.  I’m going to try and speak simply again, because I’m still not sure people get this. The only solution to this problem is if either Anthropic or OpenAI can somehow find a way to become profitable, something that I have yet to see any proof is possible.  In fact, the only proof I can find is that these fucking companies are more unprofitable than ever — in the last month, Anthropic raised $10 billion from Google , $5 billion from Amazon , and is reportedly trying to raise another $50 billion from investors , less than three months after it raised $30 billion on February 12, 2026, which was five months after it raised $13 billion in September 2025 . That’s $58 billion in eight months, with the potential to raise it to $108 billion. I’m gonna be honest, I think Anthropic is outright misleading its investors if it’s saying that it will only burn $11 billion in 2026 and 2027, per The Information : If that were the case, why does Anthropic need to raise one hundred and eight billion fucking dollars in less than three quarters?   Time to make up some booster talking points and get mad at them: So, SemiAnalysis — which traditionally does not wheel and deal in revenues! — randomly said that Anthropic had hit $44 billion in ARR , or around $3.08 billion in monthly revenue and…I’m sorry, what?  I know that my suspicion of Anthropic’s revenue numbers has effectively become a meme by this point, but something about this doesn’t add up. If we cut the periods down to strictly those after March 9, that means that Anthropic brought somewhere between somewhere between $4.5 billion and $5.58 billion in less than two months , or roughly its entire lifetime revenue. This was also a period where Anthropic claimed it was facing capacity shortages , but said shortages only appeared to create performance issues for its current customers rather than stopping Anthropic from making money… …which makes me wonder what all of this “capacity” talk is actually about.  If Anthropic is truly facing a “capacity crunch,” it’s choosing to solve said crunch through sheer, unbridled greed, taking on more customers as it struggles to keep its services at above two nines of availability . If it were an ethical business, it would simply stop taking on new clients, much like GitHub Copilot did as it transitions to token-based billing . Nevertheless, its capacity issues also make me wonder whether it’s actually taking on all that revenue, and if so, where it’s actually coming from.  Per Newcomer , as of the end of last year, 85% of Anthropic’s revenue came from API calls from companies or individuals using their models to power services. This would mean that there was roughly — assuming that number is down to around 70% given the ascent of Claude subscriptions — $3.5 billion of API spend in the space of two months, or a few thousand trillion tokens’ worth of spend. For some context, Meta’s “token-maxing” fiasco from the beginning of April involved it burning around 60 trillion tokens in 30 days, but based on discussions with sources familiar with Meta’s spend, 80% of that was cache reads. The Information estimates that the actual cost in that period was around $330 million, meaning that Anthropic needs at least another five — if not ten — Meta-sized customers, or such incredible dispersed demand that has effectively appeared out of nowhere in the past three months , to possibly come close to those numbers. I personally think it’s because Anthropic is doing something peculiar with its annualized revenue calculations. Per The Information : The first and most-obvious place to game the numbers is that Anthropic chooses a single day’s active subscribers to anchor to its annualized revenues, which means it can preferentially select one where, say, a bunch of new people were signed up under a trial, or avoid a day where churn had users leaving. One could easily include those who are canceled but have yet to actually leave the service — such as somebody who canceled on April 7th but would still be on as a “paid” subscriber until May 8th — too. As far as API credits go, it’s easy to manipulate a four-week-long segment based on how Anthropic bills its enterprise customers, specifically self-service enterprise deals . In this case, Anthropic customers pre-pay a sum (say, $50 million) in credits that are billed based on their teams’ usage, and don’t expire or run out unless they’re actively consumed. Anthropic could very, very easily manipulate this by — instead of booking based on an enterprise’s actual token burn — saying “we just got $50 million in API revenue in a calendar month!” even though that $50 million might take months to actually use. To be fair, there are also other customers (referred to as “sales-assisted”) that are billed in arrears for their consumption. It’s unclear what the split is, and Anthropic doesn’t have to tell you. Remember: Anthropic is a private company! It can do all the non-GAAP bullshit it likes.  I keep hearing about how Anthropic is capacity-strained and all that shit, but I don’t hear any explanations as to how it fixes that problem, or what that problem actually means for the business itself. Somehow being “capacity constrained” has led to the company making more revenue, which makes me wonder whether it’s a “constraint” so much as “a company running as shitty a service as it can while billing as much as possible.” Either way, it’s unclear how many data centers are actually getting built, or indeed how long they’re taking to build. What does Anthropic do if it’s 12-18 months away? And really, why do these capacity constraints not seem to have any effect on its revenue growth? I ask because Sundar Pichai noted on Google’s most-recent earnings call that Google Cloud would’ve made more revenue had it had the capacity to meet demand. Why is Google revenue-constrained due to capacity but not Anthropic? While there’s a compelling argument to be made that Anthropic was the customer that would’ve bought that compute, I think we deserve an actual explanation of what Anthropic needs more compute for if it’s not “to make more money.” Also, if it’s currently making as much money as it likes with its current capacity constraints, wouldn’t getting more compute…make the numbers worse? Ah, fuck it, let’s move onto something funnier. Meta is probably the funniest company in the AI bubble, in the sense that it does not appear to have anything approaching an AI strategy beyond “build as much data center capacity as possible” and “ lose $4 billion a quarter selling pervert glasses .” I realize I sound a little dismissive, but nobody can actually explain to me what Meta is doing with AI in a way that remotely justifies it burning $158.25 billion in capex since 2023, with plans to spend as much as $145 billion in 2026 alone . Oh, Meta’s AI app was high in the app store charts? Who fuckin’ cares! Who gives a shit! Oh, it launched its own closed-source “Muse Spark” model ? What am I meant to be impressed about? That over $150 billion has resulted in a model that ranks #27 on the LLM leaderboards in coding ? Now, some of you — including people I respect so much I’m not going to mention them by name! — appear to believe that Meta has some super-secret way of using all these GPUs to make “more money from ads,” and I must be clear that Meta has yet to explain that that’s the case.  Per last premium : You’ll note that these conversion numbers aren’t connected to any financials , which makes them a little suspicious, as 99% of Meta’s advertising revenue is ads, and “more conversions” should be fairly easy to peg to “more money”...unless said conversions aren’t actually converting into revenue for Meta’s advertisers. What does a “conversion” mean, in this case? Are these CPA ads that reward Meta on a clickthrough? Or CPM ones that pay per thousand impressions that just happen to result in a click?  Again, these are ads, which means that it’d be very easy to take that “conversion” number and turn it into “made $X,” unless of course said amount is pathetically small in the grand scheme of things. Seriously though, what is Meta doing? I suppose it doesn’t matter when the Wall Street Journal will breathlessly write that ( and I quote ) Meta is envisioning “supersmart agents” and the following lede that I find to be one of the more-revolting things I’ve read about a hyperscaler recently: You may be wondering what the “glimpse” was, and it was “laying off 8000 people” and “grading employees in performance reviews on their AI use” and “making a CEO chatbot for Mark Zuckerberg to talk to.”This is an ugly, wasteful, distressed company that has no idea what to do anymore, run by a mad king who literally cannot be fired , and those who are charged with scrutinizing it will write entirely imaginary comments like “wow, Mark Zuckerberg is building supersmart agents!” without a second’s thought. The magical hysteria of the AI bubble is such that Meta, Microsoft, Google and Amazon are, despite proving no actual profit from their AI investments, effectively protected by most of the media, investors and analysts. To be clear, I don’t think any of these companies die as a result of the bubble bursting, but I’m sick and tired of hearing everybody cover their asses with the same tired and hollow talking points, so I’ve pulled together a few of them: So, while this is technically true — as I said, these companies will not die as a result of the bubble bursting — any investor (or person who wants to deal in “the truth” rather than “stuff they misread or misremembered”) should be deeply concerned that they’ve sunk around a trillion dollars into AI capex, and all they’ve done is incubate two large, unprofitable companies that have become a burden on their infrastructure, and revenue streams that they either refuse to disclose or are both incredibly-centralized and proportionately embarrassing. Let’s get specific: 2023, Microsoft, Google, Amazon, and Meta have spent a little over $850 billion in capex, mostly hoarding NVIDIA GPUs that will be near-to-completely obsolete by 2030.  With these GPUs comes a massive depreciation problem, as I discussed a few months ago in my time bomb premium newsletter . Every quarter, more GPUs come online, which grows the “depreciation” line on the income statement, steadily growing every quarter to the point that the Wall Street Journal projects that it will eat as much as 58% of Meta’s, 40% of Microsoft’s, and 38% of Google’s net income by 2030. This flows neatly into my next point. Well, let’s be clear: whatever growth these businesses currently have is being eaten by depreciation. Last quarter, Google had $6.48 billion, Amazon $18.94 billion, Microsoft $10.1 billion, and Meta $5.9 billion, numbers that sometimes oscillate slightly down but have, year-over-year, grown by billions of dollars. And yes, year-over-year is appropriate here because this is a balance that has been steadily growing for years. In any case, depending on the company, that “growth” is either “barely related” or “entirely unrelated” to AI.  Remember: Microsoft and Amazon are intentionally obfuscating their AI revenues by using “annualized” — a term they refuse to define that usually refers to a monthly figure times 12 — to define something in statements related to quarterly revenue. As a result, it’s impossible to precisely backtrack how much revenue they made. In fact, that’s probably the simplest response here: if these companies were truly growing as a result of AI, they’d tell you. They’d say “AI revenue was X.” They’d say it in blunt, obvious terms. No annualized revenues, no projections, no fluff, no “AI-influenced,” just a line item that said “AI:” or even a segment, such as “Microsoft Azure AI compute.’ I also want to be clear about something else: I know, from documents viewed by this publication, that Microsoft has these line items fully itemized, and could share them if it wanted to, but intentionally chooses not to. These companies are deliberately refusing to share their AI revenues: and it’s time for the tech and business media to begin asking them why! So much that neither Google nor Meta will tell you how much! Also, three years in, nearly a trillion dollars, and two companies have dedicated nearly their entire sales operation to pushing it, and the best they’ve got is annualized revenues and no segment breakdown.  “Oh, Microsoft has 20 million paying Copilot subscribers,” $600 million a month? For a company that makes $80 billion a quarter? That's a pathetic amount of money. You could raise more money by auctioning dogs ! I need you, please, god , to start actually using basic mathematics! Microsoft has spent $293 billion on this bullshit, and spent another $30 billion or so in the last quarter on capex! When does this pay off? As I said above,  Amazon Web Services was profitable in a decade and cost about $52 billion between 2003 and 2017, and that’s normalized for inflation ! Anyone making this point is either intentionally lying to you or incredibly ignorant. I have done the work to prove this point, and will continue to repeat it until those too incurious or deceptive learn to stop doing so.  When?  Wwwwhen????? Whheeeennnnnn?????????????? I’m serious, when? And how??? Not that they would, but in a scenario where Meta, Amazon, Google and Microsoft stopped spending capex on AI next quarter, they would have to make somewhere in the region of $2 trillion in brand new revenue — all while other services continued to grow — to make any of this capex worth it. Please, explain to me how that happens when it’s taken three years and nearly three hundred billion fucking dollars for Microsoft to squirt out maybe three billion dollars in revenue (not profit), with most of that coming from OpenAI! Please, somebody, anybody explain! You can’t!  But you know what, let’s try! As The Information said, around 50% of all remaining performance obligations, as in all (NOT JUST AI) of the upcoming revenue for Microsoft, Meta and Amazon , is from either OpenAI or Anthropic. Put another way, 50% of big tech’s upcoming revenues are dependent on two companies, neither of which can afford to pay them, meaning that 50% of Meta, Amazon and Google’s revenues will either come from their own venture investments or venture capital. This is not what stable or diverse revenue looks like, and suggests my grander thesis about AI demand is true . Outside of OpenAI and Anthropic, there’s barely any actual demand for AI services or AI compute at the scale necessary to substantiate a trillion or more in capital expenditures. Yet the most-disgraceful part is the sheer contempt that these companies have for investors, the media, and the general public. In a functioning regulatory environment — or a market run by people with object permanence — it would be impossible to add such large amounts to your RPO balance without active scrutiny and analyst markdowns based on the fact that Anthropic and OpenAI can literally not afford to pay these bills at this time. Microsoft, Amazon and Google have scooted along for years on the idea that they’re diverse, well-positioned companies that can build massive AI revenue streams. In reality, they’re the paypigs for Anthropic and OpenAI, providing more than 70% of their compute as a means of artificially inflating their AI revenues, knowing that analysts and the media will nod and smile without a single thought. In fact, fuck it, I’m ending this with a rant. The story of massive AI demand is a lie — a trillion dollars annihilated to create the largest circle jerk of all time.  Venture capitalists and hyperscalers feed money to OpenAI and Anthropic, so that venture capitalists can feed money to startups to feed to Anthropic and OpenAI, so that Anthropic and OpenAI can feed that money back to hyperscalers, who then feed that money to NVIDIA and buy more GPUs.  While it might seem tempting to credit these men as geniuses for creating companies specifically to feed them revenue, but to keep up the kayfabe of “doing AI” to substantiate this buildout means that they’ve had to massively overcommit to the bit, even though the only two meaningful businesses in AI appear to be Anthropic and OpenAI, and that’s only because they’re effectively intellectual honeypots for the entire industry.  Outside of those two, the only other competitive AI businesses are those of Amazon, Microsoft and Google — two of which now have annualized AI revenues of around 6% of their capital expenditures so far.  Google’s AI business is so booming that it refuses to break it out, and while it nebulously claims “AI is creating growth,” it’s not really clear how, and it’s vague about it because analysts and the media are ready to swallow the narrative as long as number go up .  That’s why Google doesn’t break out the number, by the way! That’s why Sundar Pichai is able to bullshit his way through every earnings call, because the media and analysts are ready to fill in the gaps in the most preferential way possible.   Amazon and Microsoft had their hands forced by the markets after their stocks stumbled, and fucked up by sharing their AI revenues. Amazon’s $298.3 billion in capex has successfully created a business that, more than a quarter of a way to a trillion, has successfully managed to make $1.25 billion dollars a month.  That’s fucking pathetic! If we had analysts with IQs above room temperature they’d run Andy Jassy out of Arlington like Shrek.  Let’s look at this fucking chart again :  Unbe-fucking-lievable! Anthropic and OpenAI have now committed to over $718 billion of Microsoft, Amazon and Google’s revenues, despite the fact that neither of them can actually afford to pay for it. The market’s response? A slight (and short-lived) after-hours lift .  Dear members of the media: these companies are laughing at you. They know you are going to cover this in a way that makes them look good. They know you’re going to use this as proof that they’re “doing well in AI,” despite the fact that the majority of their future revenue is tied up in two oafish failsons, one of which (OpenAI) plans to burn $50 billion on compute in 2026 alone . I realize that it’s a lot to ask people to think about things in negative terms, but things are getting a little ridiculous. These are loadbearing failsons with dysfunctional businesses! It’s very clear both of them are doing weird things with their annualized revenues, and even clearer that there’s no path to profitability! Sadly, asking the media or analysts to act rationally or apply any real scrutiny is a joke, because  this is the AI bubble , where everybody is wrong because once everybody admits what’s actually happening they’re going to have to admit they’ve all sounded insane for years. $1.25 billion a month! Andy Jassy should be ashamed of himself! And god, fuck Microsoft too.  I’m sorry, WOW, Satya! You managed to get up to twenty million paying Microsoft 365 Copilot subscriptions — $600 million a month in revenue, not profit! — and all it took was you investing $13 billion dollars in money to OpenAI, forcing Large Language Models into every one of your products in a way that borders on harassment and about $289 billion dollars in capex, as well as laying off thousands of people and savaging the Xbox brand .  Whoopdie fucking shit man! You should be ashamed of yourself. Amy Hood should lock you out of the building. She should turn off your keycard and disconnect your keyboard.  OpenAI is, in and of itself, a kind of psychosis generator.  It was the first thing in a long time that felt like a new thing since the iPhone for the people that entirely obsess over growth.  It was the panacea for the tech industry, creating a new way for Business Idiots to spend money on infrastructure, a new thing for consultants to scam people with , a new series of things to be an expert in , all wrapped up in something that could also be both a consumer product, an enterprise software product, and a new kind of API to attach to other enterprise software to.  In theory, OpenAI’s success would lift everything at once — hardware, software, and even adjacent fields, like services. It promised to both democratize access to creating software while also heavily reinforcing existing power structures to the point that every dollar inevitably ended up in the Magnificent Seven’s pocket. It only succeeded in the latter. The problem is that the system needed to work one day. It needed to eventually make more money than it cost. Every single one of these companies is talking about AI non-stop, and not one of them can show a profit. The only thing they can do is tell lies of omission by saying “AI helped boost everything,” and when you ask for specifics, the results are either tepid or so secretive you’d think they’re hiding a dead body. The only reason Google, Amazon and Microsoft are being tolerated at their current excess is because their non-AI segments continue to grow through endless price-increases and enshittification, and its external business units — by which I mean OpenAI and Anthropic — are yet to die.  Sorry, I just don’t know what Meta is doing. I don’t think Meta knows what Meta is doing. Every so often it buries a fact in one of its blogs about how it saw a 3% increase in something related to AI, then it promises to burn $170 billion dollars and it’s unclear why. It also lost another $4 billion dollars on Reality Labs by the way ! There should be a legitimate inquiry into where this money is going. Eighty six billion dollars and all we have is the metaverse and pervert glasses?  Meanwhile, SpaceX is rushing to have the strangest and largest IPO of all time, all as daily stories leak about billions of dollars of losses and whatever the fuck that deal with Cursor is .  Apparently SpaceX will buy it for $60 billion dollars or pay it $10 billion dollars.  I think what actually happens is the third thing: SpaceX funds Cursor for a bit, there’s a falling out between Musk and CEO Michael Truell, and the company either rushes an acquisition or dies. Remember: Elon killed Cursor’s funding round ! He can’t buy it before SpaceX goes public !  Elon Musk took fucking OpenAI to court. Do you think he’ll care about killing Cursor? Who’s going to be left to sue him? Anyway, that OpenAI/Musk suit is a real Alien Versus Predator situation, and if I’m honest I’ve found whole thing a little boring, a duo of dullards shoulder-barging each other to see who can run a company that neither of them can really describe because neither of them do anything other than pontificate and take credit for other people’s work.  If this breaks OpenAI I’ll be very surprised, but if it does it would be extremely fitting that Elon would accidentally destroy the AI industry, like Mr. Bean sitting on a button that launches a nuke. If I’m wrong here it would be very funny. I’m just not giving it much hope. Nevertheless, this entire industry is only made possible by the kayfabe circular economy of taking every single sign as good for AI and ignoring every possible glaring warning sign in the hopes that they’ll go away.  You know, like last week when Microsoft said it’s shifting GitHub Copilot to token-based billing — something I reported a week before everybody else.  This is effectively killing the product as they know it, and invalidates every single story about its revenue growth ever written. To give you some context about its scale, GitHub copilot is the second largest customer of Anthropic’s models , and was only that large because it was subsidizing the computer spend of its customers. Why? Because that’s the only way to build any kind of AI business.  Google and Amazon realize their AI revenues are contingent on the continued survival of Anthropic, and Amazon and Microsoft’s revenues are contingent on OpenAI AND Anthropic.  They know that if these companies die they’re going to lose billions of dollars of revenue, but that they also have to compete with them for fear that they’ll be seen as “falling behind” their horrible progeny. As a result, they’re incinerating their brands and endlessly pontificating about the power or AI while spending nearly a trillion dollars on capex almost entirely to make sure their competition, which is also their customer and welfare recipient, doesn’t die. It’s a mess, and a mistake, and eventually one of them is going to grow tired of it. Microsoft was already billions under the analyst estimates for capex. They’re moving to token based billing. They claimed to invest in Anthropic in February but didn’t mention it in their earnings in any way, shape or form.  At some point these fucknuts are going to be forced to reckon with what they’re doing.  Until then we’ll have increasingly more frenzied and ejaculatory statements about AI demand that fail to match with reality.  I truly think that it’s going to be like this if not crazier until one day when the music suddenly stops. Somebody is going to blink. Somebody is going to take a step back and give everybody else permission to stop too.  Maybe Perplexity, Lovable, Replit, or Cognition dies.  Maybe Microsoft shifting GitHub Copilot to token based billing in June first inspires others like Anthropic to follow suit.  Maybe AI token austerity begins at Microsoft, Meta, or another large company.  Maybe NVIDIA fails to inspire in just the right way, or the fact that data centers are not opening fast enough to have fully digested the last year’s GPUs finally catches up with the economic mismatch that Jensen Huang always beats and raises expectations.  And that really is the strangest thing.   At the current rate of sales, it’s taking six months to install a quarter’s GPUs . At this point it’s obvious that there are warehouses of these things. It just isn’t obvious whether they’re in ones owned by hyperscalers or the Taiwanese ODMs (original design manufacturers) like Quanta Computing and Foxconn that build their servers.  None of this makes sense.  It hasn’t from the beginning. It’s the largest bubble in history, and has reached such an intellectual and financial scale that many have taken sides on it in a way that will be completely impossible to walk back if they’re wrong.  As things deteriorate, expect them to cling to their mythologies tighter and become more agitated.  And really, we’ve never seen anything like this in our lives.  You realize that Anthropic and OpenAI are insane, right? These companies have promised $718 billion to Microsoft, Google and Amazon, and cannot survive without venture capital funding , because their underlying businesses lose money on every transaction — and so help me fucking GOD if you say they’re “profitable on inference” without proof I will crush you into a cube like a car in a garbage dump! Every single AI business you see is unprofitable, nor do any of them have a path to break-even, let alone sustainability. Nothing has changed about this story. And nobody has been able to explain the massive differences between my reporting on OpenAI’s revenues and their own leaked figures, other than to say “you must be wrong somehow,” as if that somehow invalidates “direct numbers from Azure billing.” If you disagree with me, you really better hope I’m wrong, because I’ve got years of receipts and I can remember basically every article about AI revenues written since 2023 off the top of my head. Not a single one of my critics or any AI booster has put an iota of the same amount of effort into proving their case. The hysteria and excess of this era has proven how many people can come to conclusions without making the effort to prove them. Disagree with me or not, I’ve done the work, and I see no proof that the other side has even started. The world has been swept away by the fantastical ideals of Sam Altman and Dario Amodei, and two giant, unsustainable, cash-burning monstrosities that were only made possible because hyperscalers built their infrastructure for them and funded their excesses in exchange for theoretical revenues and equity stakes that give them paper gains. Their hope, I imagine, was that in doing so, OpenAI and Anthropic would create industries surrounding them — both in the business lines attached to hyperscalers and AI startups that would potentially pay them for compute. In the end, it appears the only way to create any real demand was to literally fund it themselves.  These men believe they’ve created perpetual energy. What they’ve actually done is shit their pants and set their houses on fire. “Year-over-year” is an attempt to obfuscate actual growth in the era of AI. A better comparison would be quarter-over-quarter, which was 12% from Q4 2025 ($17.66 billion). This is actually significant, because it’s a slower rate of growth than between Q3 and Q4 2025, when cloud revenue jumped from $15.15 billion to $17.66 billion, or 14.2% quarter-over-quarter).  I think quarter-over-quarter growth is far more indicative of how a business is going.  Google Cloud is far more than AI! It includes all of Google’s workspace revenue, such as Gmail, Google Docs, and so on. It’s important to remember that Google jacked up its workspace pricing twice in 2025 , and that by Q1 2026, the majority of customers will have been forced to renew at inflated prices. It also includes all of Google’s cloud revenue, which is incredibly diverse and far more than just AI compute. Google has intentionally bucketed AI-related revenue into Google Cloud so that finance and tech journalists will claim that AI is what’s driving this growth despite there being no proof that that’s the case. Anthropic and OpenAI make up the vast majority of all AI revenues and compute capacity. I estimate 70% of all revenues and capacity demand, if not higher. Amazon, Google, and Microsoft’s AI revenues — and by extension their justification for future capex spend — are justified by Anthropic and OpenAI. OpenAI and Anthropic both lose tens of billions of dollars a year (yes, Anthropic said it’ll lose $11 billion in a projection, and I believe they are being coy with their actual losses), which means that the majority of AI revenue and compute demand is dependent on whether Anthropic and OpenAI can continue to raise money. Well actually Ed this is because Anthropic is taking advantage of the dumb money that wants to boost its valuation. It doesn’t need the cash — it’s building a reserve!  Are you suggesting it’s raising money because it doesn’t need it? Like a rainy day fund? Are you also suggesting that Anthropic is taking advantage of its investors? Anthropic has a bunch of compute commitments that require it to pay a bunch of money up front! This isn’t because its business economics don’t make sense at all. I think you’re right that Anthropic likely has to pay up front for its compute. Dario Amodei himself said so, while adding that you have to do so based on how much revenue you expect to make, and that if he’s wrong, Anthropic goes bankrupt! Basically I’m saying, “In 2027, how much compute do I get?” I could assume that the revenue will continue growing 10x a year, so it’ll be $100 billion at the end of 2026 and $1 trillion at the end of 2027. Actually it would be $5 trillion dollars of compute because it would be $1 trillion a year for five years. I could buy $1 trillion of compute that starts at the end of 2027. If my revenue is not $1 trillion dollars, if it’s even $800 billion, there’s no force on earth, there’s no hedge on earth that could stop me from going bankrupt if I buy that much compute. Nevertheless, this doesn’t remotely interfere with my thesis! It just means that Anthropic has been forced to buy a bunch of compute immediately rather than paying for it in chunks. In fact, I’d argue that Anthropic is having to raise this money to pay up front for capacity that’s yet to be built.  This is a sign of how much faith investors have in the product! Yeah that’s generally how venture capital works. There’s also not really any other success story out there other than OpenAI that has anything close to a time horizon toward an exit. Anthropic said it had hit $14 billion in ARR on February 12, 2026 , or around $1.16 billion between January 12 and February 12.  That’s $1.16 billion in that period. Anthropic CFO Krishna Rao said in a sworn affidavit on March 9 2026 that its revenue was “exceeding $5 billion to date.” I also at this point think that sources telling anybody Anthropic made $4.5 billion in 2025 alone were lying , as it doesn’t make mathematical sense otherwise. This also means that Anthropic, if it’s being honest about what “run rate” means, made 23% of its lifetime revenue in a single month. On April 6, 2026 , Anthropic said it had hit $30 billion in annualized revenue, or $2.5 billion, I assume, in the period between March 6 and April 6.  That’s $2.5 billion in that period. SemiAnalysis’ estimate is from April 30, 2026, so let’s assume that it refers to the period of March 29 to April 29, 2026.  That’s another $3.08 billion. It’ll get cheaper in the future- okay, are you saying the chips will get better? Because these companies have somewhere between $100 billion and $300 billion of these fucking things. People are starting to pay for AI- okay, but they’re not paying very much, and it’s taken so long that these companies are now burdened with endless piles of GPUs that they’ve yet to fully install. How do they catch up? Just give it time- no! I’ve given it lots of time! Why are you being so generous to them and so impatient with me?  This is investing in tech that will turn into the most transformative tech in the future - you’re a mark!

0 views

Premium: The AI Compute Demand Story Is A Lie

Everyone, it’s time to talk about AI demand and the capacity constraint issues across the industry. These constraints are not a result of “incredible demand” for AI, but the desperation of hyperscalers and the avariciousness of two near-trillion-dollar failsons living off their parents’ welfare. Just two weeks ago, both Amazon and Google pledged to invest up to another combined $65 billion in Anthropic, a company that just raised $30 billion in February and plans to raise another $50 billion more , following Amazon’s $15 billion (and as much as $35 billion more) investment in OpenAI in February . This is not what you do when real, meaningful demand exists for AI services. Assuming that these rounds are closed at their higher limits, it will mean that Google has invested $43 billion and Amazon $33 billion in keeping Anthropic alive. This also doesn’t make sense when you look at Anthropic’s own projections. Per The Information , Anthropic believes it will become cash-flow-positive in the next two years after losing exactly $11 billion in both 2026 and 2027: This only becomes more astonishing when you read that Anthropic intends to make $18 billion in 2026, $55 billion in 2027, $102 billion in 2028, and $148 billion in 2029. That’s revenue, not profit.  You may also be wondering how Anthropic goes from losing $11 billion two years running to making $2 billion in profit, and the answer is “nobody knows, including Anthropic.”  In any case, what Anthropic is saying in these projections is that it will lose $29 billion in 2026 and $66 billion in 2027. It’s also not clear what Anthropic’s actual costs will be in those years, because The Information decided it wasn’t necessary to include those. Thankfully, The Wall Street Journal did , suggesting that Anthropic intends to spend at least $86 billion on training costs alone through the end of 2029. It’s become blatantly obvious that Google and Amazon are conspiring to keep one of their largest business lines alive, much like Microsoft funneled over $13 billion into OpenAI before allowing OpenAI to seek other compute providers when it slowed down its data center construction . While I think Satya Nadella is a verbose dullard, Microsoft CFO Amy Hood is clearly quite smart, and jumped at the opportunity to allow Oracle to mortgage its entire future on OpenAI and Sam Altman’s clammy little dreams . Hood has managed to disconnect Microsoft from OpenAI’s welfare system, and while it claimed it was investing in Anthropic last November and in its February 2026 funding round , its latest 10-Q only mentions Anthropic once — as part of the work “philanthropic” on page 59. And now Microsoft has ended its exclusivity deal over OpenAI’s models, allowing Amazon to sell them too, but still retaining a revenue share of 20% from OpenAI’s sales , including from its partnership with Amazon, a few months after Amazon and OpenAI agreed a $138 billion eight-year-long deal that involved 2GW of capacity. A gigawatt here, a gigawatt there, soon you’ll be making real money. Except…nobody is making real money, and it appears that the vast majority of AI capacity and revenue is either going to OpenAI or Anthropic, and the rest is going to Microsoft, Google, and Amazon, who then spend that money on GPUs from NVIDIA or data centers to put them in. What numbers we do have around AI revenues are extremely sad.  I estimate that 70% or more of Microsoft’s $37 billion in annual AI run rate comes from OpenAI’s estimated $24 billion in annualized compute spend on Azure, taking up more than 80% of Microsoft’s estimated 2GW of AI capacity . OpenAI, per its CFO, ended 2025 with 1.9GW of capacity , and 67% of CoreWeave’s revenue is Microsoft paying for OpenAI’s training compute.  Similarly, Amazon’s $15 billion in annualized AI revenue is taken up by an estimated $12 billion in annualized AWS spend from Anthropic, and I estimate that more than 80% of that is accounted for by my estimated $12 billion in annualized spend from Anthropic. Today I’m pushing against the grain about as hard as anybody has in the AI bubble. I fundamentally believe that the AI demand story is nonsense — a mirage created by two companies that have only been successful as a result of having near-infinite resources provided to them by hyperscalers. Google, Amazon, and Microsoft have spent a combined $803 billion in capex on the AI bubble so far, and OpenAI and Anthropic have raised (assuming their rounds fully close) over $252 billion.  Assuming the rounds close, these three hyperscalers have sunk a combined $78 billion in funding into OpenAI and Anthropic, all while building infrastructure almost entirely in their service, and signing deals with neoclouds to continue providing it. The AI demand story is a lie, because the only way to create a company able to actually meet said demand is for a hyperscaler to fund it themselves.  Had Amazon not given it $8 billion and Google $3 billion in its earliest days, Anthropic would’ve never been able to grow to the scale that it could spend tens of billions of dollars a year on AWS and Google Cloud, nor would OpenAI have been able to do so without the earliest infusions of over $10 billion from Microsoft (of which the majority came in the form of Azure credits), and none of this would’ve been possible had hyperscalers not effectively pre-sold their own infrastructure to their own incubated companies. There is little “AI demand” outside of hyperscalers funnelling themselves money. The AI data center capacity crunch is a result of how long it takes to build data centers — Microsoft, Google and Amazon had an early lead, experience, and massive amounts of cash to deploy in a way that nobody else could. That’s why you can’t find A) anybody who’s spending anywhere near as much on compute as OpenAI and Anthropic and B) anybody who’s managed to compete with them at any scale. Their existence is entirely subsidized, their success a mirage, and their compute spend effectively three companies feeding themselves money.  And despite all the crowing around “the insatiable demand for compute,” there doesn’t appear to be any evidence that anybody is spending that much on it outside of Anthropic and OpenAI. If I were wrong, we’d see literally any other AI startup signing these massive compute contracts. Big Tech needs $3 trillion in new AI revenue by the end of 2030, or it’s wasted the majority of its capex. I estimate that Anthropic and OpenAI make up at least 85% of current and future AI compute spend, either through their own direct spending or hyperscalers like Google, Amazon or Microsoft renting capacity for them. Microsoft, Google and Amazon have built as much as 75% of their AI data center capacity to service two customers — OpenAI and Anthropic — putting the true cost of OpenAI and Anthropic, including total funding of $180 billion and $72 billion respectively, at at least $600 billion in combined infrastructure and equity investments.  And, obviously, the vast majority of their funding going toward compute spend across these three companies. OpenAI and Anthropic cannot afford to pay their future compute commitments without hyperscaler and venture capital subsidies. Outside of Anthropic, OpenAI, Google (for OpenAI, Anthropic and Meta), Microsoft (for OpenAI and Anthropic), Amazon (for OpenAI, Anthropic and Meta), CoreWeave (for OpenAI, Anthropic, and Meta) and Meta, less than $1 billion of actual AI compute demand exists.  In all honesty, I’ve struggled to find more than $500 million outside of Jane Street, which also funded CoreWeave. OpenAI and Anthropic’s compute spend and demands have created an illusion of demand, becoming a systemic weakness in CoreWeave, Nebius, IREN, and TeraWulf. Hyperscaler buildouts appear to be almost-entirely focused on either OpenAI or Anthropic, with little proof of their own services generating enough demand to fulfil them. There is not enough revenue to substantiate the existence of the in-progress data center construction, with over $157 billion in annual revenue required to monetize the 15.2GW (11.2GW critical IT) of data centers under construction to be finished by the end of 2027. Google is creating SPVs with investment firms to sell TPUs to itself, and has, via Broadcom, sold $63 billion in TPUs to Anthropic, which it will then bill for the compute, creating a circular financing system similar to NVIDIA’s. To support the estimated $800 billion in GPU sales that NVIDIA claims will come through by end of 2027, there needs to be 39.6GW of new data centers constructed (only 15.2GW of which are under construction), and around $383 billion in annual AI compute demand for an industry that — even with OpenAI and Anthropic’s spend — doesn’t even reach $70 billion in annual demand.

0 views

OpenAI Projects ChatGPT Plus subscriptions to drop by 80% from 44 Million in 2025 to 9 Million In 2026, Made Up Using Cheaper Subscriptions (Somehow)

The Information reported on April 28 that OpenAI projects an 80% decline in its $20-a-month ChatGPT Plus subscribers - from 44 million in 2025 to 9 million in 2026 - and intends to make up the shortfall using its cheaper, ad-supported "ChatGPT Go" subscriptions by growing them from 3 million in 2025 to 112 million in 2026: That's a load-bearing "as a result" if I ever saw one. What OpenAI is actually saying here is that it's expecting a dramatic decline in its primary business line - $20-a-month ChatGPT subscriptions - and intends to somehow get 109 million new paying subscriptions of an entirely different product . As The Information noted, this would be a 3600% subscriber increase year-over-year. Eager math-knowers in the audience will also realize that, if we assume a $5-a-month subscription cost, even if OpenAI succeeds in what would be the single-largest user acquisition campaign in history, it would still be $155 million short. I imagine OpenAI's answer would be "we're going to be serving these customers ads" and "some of them will pay $8 a month," neither of which are substantive. Putting aside ChatGPT Go for a second, it is pretty remarkable that OpenAI is projecting an 80% decrease in ChatGPT Plus subscriptions. Perhaps this projection is something that will only come to pass if ChatGPT Go grows at such a rate...or perhaps it's something that OpenAI already sees happening, as The Wall Street Journal reported earlier in the week that OpenAI had missed revenue targets for new users and revenue, which makes the timing of this leak all-the-more suspicious. I should also add that adding 109 million new subscribers at any price point will likely massively increase OpenAI's burn-rate. If you liked this news hit, please subscribe to my premium newsletter. It’s $70 a year, or $7 a month, and in return you get a weekly newsletter that’s usually anywhere from 5,000 to 18,000 words, including vast, detailed analyses of NVIDIA , Anthropic and OpenAI’s finances , and the AI bubble writ large . I recently put out the timely and important Hater’s Guide To The SaaSpocalypse , another on How AI Isn't Too Big To Fail , a deep (17,500 word) Hater’s Guide To OpenAI , and just last week put out the massive Hater’s Guide To Private Credit . I also just did a piece about how OpenAI will kill Oracle . It's one of my best pieces I've ever done and I'm extremely proud of it. The Information reports that OpenAI projects that its $20-a-month ChatGPT Plus subscriptions will decrease from 44 Million subscribers in 2025 to a projected 9 million subscribers in 2026. OpenAI projects to make up the difference by increasing its ad-supported ChatGPT Go ($5 or $8-a-month depending on the region) subscriptions from 3 million in 2025 to 112 million in 2026.

0 views

AI's Economics Don't Make Sense

If you liked this piece, please subscribe to my premium newsletter. It’s $70 a year, or $7 a month, and in return you get a weekly newsletter that’s usually anywhere from 5,000 to 18,000 words, including vast, detailed analyses of NVIDIA , Anthropic and OpenAI’s finances , and the AI bubble writ large . I recently put out the timely and important Hater’s Guide To The SaaSpocalypse , another on How AI Isn't Too Big To Fail , a deep (17,500 word) Hater’s Guide To OpenAI , and just last week put out the massive Hater’s Guide To Private Credit . I also just did a piece about how OpenAI will kill Oracle , and I’ve used some of the materials in today’s piece. It's one of my best pieces I've ever done and I'm extremely proud of it. Subscribing to premium is both great value and makes it possible to write these large, deeply-researched free pieces every week.  Yesterday morning, GitHub Copilot users got confirmation of something I’d reported a week ago — that all GitHub Copilot plans would move to usage-based pricing on June 1, 2026 .  Instead of offering users a certain number of “ requests ,” Microsoft will now charge users based on the actual cost of the models they’re using, which it calls “...an important step toward a sustainable, reliable Copilot business and experience for all users.” Users instead get however much they spend on their GitHub Copilot subscription (EG: $19 of tokens a month on a $19-a-month plan). Anyway, the announcement itself was a fascinating preview into how these price changes are going to get framed:  You see, it’s not that Microsoft was subsidizing nearly two million people’s compute, it’s that AI has become so strong, powerful and complex that it’s basically a different product! While Copilot might not be “...the same product it was a year ago,” very little has changed about the underlying economic mismatch: that Microsoft was allowing users to burn more than their subscription costs in tokens every single month for three years. Per the Wall Street Journal in October 2023 : Naturally, GitHub Copilot users are in revolt , saying that the product is “dead” and “completely ruined.” And I called it two years ago in the Subprime AI Crisis : And that day has finally arrived, because every single AI service you use subsidized compute , and every single service is losing money as a result: AI startups and hyperscalers assumed that they’d be able to get enough people through the door with subsidized, loss-making products to get them hooked on services badly enough that they’d refuse to change once businesses jacked up the prices. They also assumed, I imagine, that the cost of tokens would come down over time, versus what actually happened — while prices for some models might have come down, newer “reasoning” models burn way more tokens, which means the cost of inference has, somehow, gotten higher over time . Both assumptions were wrong, because the monthly subscription model does not make sense for any service connected to a Large Language Model. Think of it like this. When Uber ( and no, this is nothing like Uber ) started jacking up the prices for its rides, the underlying economics stayed the same, as did those presented to both the rider and the driver — a user paid for a ride, a driver was paid for a ride. Drivers still paid for gas, car insurance, any permits that their local government might insist upon, and whatever financing costs might be associated with their vehicle, and said costs were not subsidized by Uber. Uber’s massive losses came from subsidies, endless marketing expenses, and doomed R&D efforts into things like driverless cars . To illustrate the scale of AI’s pricing mismatch, I’m going to ask you to imagine an alternate history where Uber had a very different business model. Generative AI subscriptions are like if Uber charged users $20 a month for 100 rides of any distance under 100 miles, and if gas was $150 a gallon, and Uber paid for the gas because somebody insisted that oil would one day be too cheap to meter . Uber would, eventually, decide to start charging users a monthly subscription to access rides, and bill them for the gas that they consumed. Suddenly users would go from paying $20 a month for 100 rides to paying $20 to access a driver and $26 for a 10 mile drive. Understandably, users would be a little upset. While this sounds a little dramatic, it’s actually a pretty accurate metaphor for what’s happening in the generative AI industry, and in particular, at Github Copilot.  GitHub Copilot’s previous pricing allowed 300 premium requests a month, as well as “unlimited chat requests” using models like GPT-5 mini. Each of these requests (to quote Microsoft) is “...any interaction where you ask Copilot to do something for you,” with more-expensive models taking up more requests in the later life of the request-based system, such as Claude Opus 4.6 taking up three premium requests. When you ran out of premium requests, Copilot would let you use one of those cheaper models as much as you’d like for the rest of the month.  This wasn’t even always the case. Up until May 2025 , Microsoft gave users unlimited access to models, and even then users were pissed off that there were any restrictions on the product.  Microsoft — like every AI company — swindled its customers by selling an unsustainable service, because it never, ever made sense to sell LLM-powered services on a monthly subscription. If you’re wondering how much services are likely to cost under token based billing, a user on the GitHub Copilot Subreddit found that the token burn of what used to be a single premium request was somewhere around $11, as one “request” involved using 60,000 tokens in the context window, a few tools, and a bunch of internal “turns” (things that the model is doing) to produce the output.  There’s also the underlying unreliability of hallucination-prone Large Language Models. While a premium request chasing its tail and spitting out half-broken code might be frustrating , that same fuckup is a lot less forgivable when you’re paying the costs yourself.  Users have also been trained to use the product in an entirely different manner to token-based billing, and I’d imagine many of them don’t even really realize how many “tokens” they burn or how many of them a particular task takes, something which changes based on whatever model you use. This is absolutely nothing like Uber, and anyone telling you otherwise is attempting to rationalize bad behavior. Uber may have raised prices, but it didn’t have to dramatically change the underlying economics of the platform, nor did users have to entirely change how they used the product because Uber was suddenly charging them on a per-gallon basis. There has never been — and never will be — an economically-feasible way to offer services powered by LLMs without charging the actual token burn of each user, and in the process of deceiving said users, these companies have created products with illusory benefits and questionable return on investment. And that’s been blatantly obvious for years.  On an economic basis, a monthly subscription only makes sense with relatively static costs. A gym can sell memberships knowing roughly how much wear-and-tear equipment gets, how much classes cost to run, and how much things like electricity, staffing and water might cost over a given period of time.  A customer of Google Workspace — at least before AI — cost whatever the cost of accessing or storing documents were, as well as the ongoing costs of Google Docs and other services. The relatively low cost of digital storage (as well as the fact that, unlike LLMs, Google Workspace isn’t particularly computationally demanding) means that a particularly-heavy Google Drive user isn’t going to eat into the margin on their monthly subscription. Conversely, an AI subscriber’s costs can vary wildly . One user might only use ChatGPT for the occasional search, while another might feed in reams of documents, or try and refactor a codebase, or try and use it to put together a PowerPoint presentation. And the provider — a model lab like OpenAI or Anthropic, or a startup like Cursor) — has no real way to control how a user might act other than making the product worse, such as instituting usage limits, reducing the size of the context window, pushing customers to smaller (and worse) models, or changing the pricing to dissuage users from making big GPU-heavy requests.  Yet these services intentionally hide the amount of tokens or how much a particular activity has cost, which means users don’t really know what a rate limit means, which means that every abrupt change to rate limits leaves customers desperately scrambling to work out how much actual work they can do using the service.  It’s an abusive, manipulative and deceitful way of doing business that only existed so that Anthropic, OpenAI, and other AI companies could grow their user bases, as the majority of AI users perceive its real or imagined benefits entirely through the lens of being able to burn anywhere from $8 to $13.50 for every dollar of their subscription in tokens.  This intentional act of deception had one goal: to make sure that the majority of people were never exposed to the true costs of generative AI. When The Atlantic writes a breathless screed about Claude Code being Anthropic’s “ChatGPT moment,” it does so based on a $20-a-month subscription rather than the underlying token burn that it cost for Anthropic to provide it, which in turn makes the writer forgive the “minor errors” that a model might make, or when it “gets stuck on more complicated programming tasks.” Had the writer paid for her actual token burn, and had each of the times it got “stuck” resulted in $15 in token charges, I don’t think she’d be quite as forgiving of these fuckups.  Yet that’s all part of the scam.  It’s very, very important that nobody writing about AI in the mainstream media actually understands how much these services cost, and that any mainstream articles written about services like ChatGPT or Claude Code are written by people who have little or no idea how much each individual task might cost a user.  Remember: generative AI services are, for the most part, experimental products that do not function like any other modern software or hardware. One cannot just walk up to ChatGPT or Claude and start asking it to do work.  I mean, you can , but if you don’t prompt it right, understand how it works, or make a mistake in whatever you feed it, or if it just gets things wrong, it’ll spit out something you don’t like, which in turn means you’ll need to prompt it again. LLMs are inherently unpredictable.  You cannot guarantee whether an LLM will do a particular action, or whether it will present you with an outcome based on reality. You cannot for certain say how much a particular task — even one you’ve done many times using an LLM in the past — might cost, nor can you be sure when a model might go berserk and delete something, or simply not do something yet claim that it did.  These are far more forgivable if you’ve not paying on a per-token basis, because in the mind of a subscriber, that’s just another turn or two with a chatbot rather than something that’s incurring a real cost. One doesn’t criticize so-called “jagged intelligence” because the assumption is that whatever problems you’re facing now will be eliminated at some future juncture, and you didn’t end up paying for it anyway.  Had users been forced to pay their actual rates, I imagine many would’ve bounced off the product immediately, as it’s very, very easy to burn through $5 of tokens if you’re fucking around and exploring what an LLM can do.  I think it’s inevitable that the majority of AI subscriptions move to token-based billing, especially as both Anthropic and OpenAI have now done so with their enterprise customers.  The fact that Microsoft moved GitHub Copilot subscribers to token-based is also a very, very bad sign. Microsoft is arguably the best-capitalized, most-profitable, and best-positioned company to continue subsidizing compute, and if it can’t afford to do so further, nobody else can either. The real thing to look out for — a true pale horse — will be a major AI lab like Anthropic or OpenAI moving all of its subscribers to token-based billing. Once that happens, you’ll know it’s closing time.  As I discussed last week, Uber’s CTO said at a conference that it had spent its entire AI budget for 2026 in the space of a few months , with Goldman Sachs suggesting that some companies are spending as much as 10% of their headcount on AI tokens, with the potential to increase to 100% in the next few quarters.  This is the direct result of training every single AI user to use these services as much as humanely possible while obfuscating how much they really cost. Every single major company demanding that every single worker “use AI as much as possible” has done so while either fundamentally ignoring or being entirely disconnected from their actual token burn, and as companies are forced to pay the actual costs , I’m not sure how you can economically justify any investment in this technology. Sure, sure, you’re gonna say that engineers are “shipping code faster” or some such bullshit, I get it, but how much faster, and how much money are you making or saving as a result? If you’re spending 10% of your headcount on AI tokens, are you seeing that extra expense reconciled in some other way? Because I’m not sure you are. I’m not sure any business investing these vast amounts of money in tokens is seeing any return on investment, which is why every study about AI’s ROI struggles to find much evidence that it exists. For the most part, everybody you’ve read gooning over the many possibilities of generative AI has experienced it without having to pay the true costs. Every Twitter psychopath writing endless screeds about their entire engineering team hammering away at Claude Code has been doing so using a $125-a-month-per-head Teams subscription with similar usage limits to Anthropic’s $100-a-month consumer subscription. Every LinkedIn gargoyle insisting that they’d “done hours of work in minutes” using some sort of Perplexity product has done so by paying, at the very most, $200 a month for Perplexity’s Max subscription.  In reality, that 10-person, $1250-a-month Teams subscription likely burns anywhere from $5000 to $10,000 a month in API calls, if not more. Anthropic Head of Growth Amol Avasare said last week that its Max subscriptions were built for heavy chat usage rather than whatever people are doing with Claude Code and Cowork, and made it clear that Anthropic is now looking at “ different options to keep delivering a great experience, ” which is another way of saying “we’re going to change the prices at some point.” I’m not sure people realize how expensive these tokens are, especially for coding projects that involve massive codebases and regularly make calls to coding and infrastructure tools. Can somebody who pays $200 a month foreseeably afford $350, $400, or $500? Can they afford to have a month where they spend more than that? What happens if they go over budget, or if they literally can’t afford to spend the money necessary to finish their work? To give you a more-practical example, up until the beginning of April, Anthropic’s own Claude Code developer documents ( archive ) said that “the average cost [for those using Claude Code] is $6 per developer per day, with daily costs remaining below $12 for 90% of users.” As of this week, the documents now read as follows : If we assume an average of 21 working days in a month, that puts the average cost of a Claude Code user at around $273 a month, or $3,276 a year. At $30 a working day, that works out to $630 a month, or $7560 a year.  These are astonishing numbers, made more so by the fact that there is no way you’re only spending $30 a day if you use any of Anthropic’s more-recent models. Claude Opus 4.7 costs $5 per million input and $25 per million output tokens. ‘ One million tokens is around 50,000 lines of code , and assuming you’re using the supposedly state-of-the-art models, there isn’t a chance in Hell you don’t run through at least a million, with that number increasing dramatically if you’re not particularly aware of which models to use for a particular task. Let’s play with that $30 number a little more.  While this might be possible within the slush fund mindset of a well-funded startup or a banana republic like Meta, any business that actually cares about its costs will have a great deal of trouble justifying spending five or six figures in extra costs on a service that “increases productivity” in a way that nobody can seem to measure. Right now, I think most companies fall into three camps: Large organizations still have a free pass to say that they’re burning millions of dollars on AI tokens for their software engineers under the questionable benefit of their “best engineers” not writing any code . All it changes is one bad earnings call to change that narrative. At some point investors — even the braindead fuckwits who have been inflating the AI bubble — will begin to question mounting R&D costs (which is where AI token burn is usually hidden) when the company’s revenue growth fails to follow. This will likely lead to more layoffs to keep up with the cost, as was the case with Meta , and then an eventual pullback when somebody asks “does any of this shit actually help us do our jobs faster or better?” I also think that startups burning 10% or more of their headcount in AI tokens will have a tough time convincing investors in six months that doing so is necessary. And once everybody switches to token-based billing, I’m not sure we’ll see quite as much hype around generative AI.   The way that people talk about AI data centers is completely disconnected from reality, and I don’t think people realize how ridiculous this entire era has become. Per Jerome Darling of TD Cowen, it costs around $30 million in critical IT (GPUs and the associated hardware) and $14 million per megawatt of data center capacity. Data centers appear to take anywhere from a year to three years depending on the size, and that’s assuming the power is available.  Of the 114GW of data centers supposedly being built by the end of 2028, only 15.2GW is under construction in any way, shape, or form . And “under construction” can mean as little as “there’s a hole in the ground.” It does not — and should not — imply that the capacity that said facility will provide is going to be imminently available.  Let’s start simple: whenever you think “100MW,” think “$4.4 billion,” with a large chunk of that dedicated to NVIDIA GPUs.  As a result, every AI data center starts millions of dollars in the hole, and even with six-year-long depreciation schedules, takes years to pay off… and with NVIDIA’s yearly upgrade cycle , those GPUs are unlikely to make that much money once you’re done with your first customer contract. It’s also unclear whether the customer base for AI compute exists outside of OpenAI and Anthropic, whose demand accounts for 50% of AI data centers under construction , creating a massive systemic weakness if either of them lacks the money to pay. In any case, it’s also unclear what kind of ongoing rates these data centers charge. While spot prices might sit around $4.50 an hour for a B200 GPU, long-term contracts generally price much lower, with one founder ( per The Information ) saying they paid around $3.70-per-hour-per GPU for a one-year-long commitment. To be clear, we must differentiate between the spot cost — which is the cost of randomly spinning up GPUs on somebody else’s servers — and contracted compute, the latter of which makes up the majority of data center capex. Most data centers are built with the intention of having one or two big clients , which means said clients are likely to negotiate a cheaper blended rate. As a result, many data centers take far less than $3.70 an hour, because they bill at a per-megawatt (or kilowatt) price. And that’s where the economics begin to break down. That’s the starting cost for a 100 megawatt data center. A 100MW data center will likely only have 85MW of actual stuff it can bill for, and based on discussions with sources familiar with hyperscaler billing, they can expect to make around $12.5 million per megawatt, or around $1.063 billion in annual revenue.  Now, I should be clear that most data center companies you know of don’t actually build them, instead leaving that job to companies like Applied Digital,  who are also known as “colocation partners.” For example, CoreWeave pays a colocation fee to Applied Digital to use its North Dakota data centers . CoreWeave is responsible for all the GPUs and other tech inside the data center. To explain the economic mismatch, I’m going to use a theoretical example of a data center leased to a theoretical AI compute company.  The GPUs in that data center are likely NVIDIA’s Blackwell chips. More than likely, said data center is using pods of 8 B200 GPUs, retailing around $450,000 a piece, or $56,250 a GPU. Based on there being 85MW of Critical IT load, the all-in capex per megawatt is around $36.78, or total IT capex of around $3.126 billion, or around $2.67 billion in GPUs. Let’s assume this data center is in Ellendale, North Dakota, which means you’ve got an industrial electricity rate of around 6.31 cents per kilowatt hour, which works out to about $55.4 million a year in electricity costs. Based on discussions with sources, I estimate that ongoing costs like maintenance, headcount, replacement of power supplies and the like comes in at around 12% of revenue, or around $128 million a year, bringing us up to $183.4 million in costs. Wait, sorry. You’ve also got to pay a colocation fee based on the critical IT, and according to Brightlio, that fee is often around $180-200 per kilowatt per month , depending on the scale and location of the deployment, though I’ve read as low as $130, which is the number I’m going with, or around $133 million per year. This brings us up to $316.4 million. Well, that’s still less than $1.06 billion, so we’re still doing okay, right? Wrong! You’ve got $3.126 billion in IT gear to depreciate, which works out to around $521 million a year over the six years you’re depreciating it. That’s $837.4 million a year, leaving you with around $168.6 million in yearly profit, or around a 16.7% gross margin… … if you have 100% tenancy at all times! You see, data centers can take a month or two to get those GPUs installed and a customer onboarded, all while making you exactly zero dollars in revenue and losing you a great deal more, as you’re stuck paying your colocation, electricity and opex costs the whole time, albeit at a much lower rate (I’ve modeled for 10% electricity and 15% colocation/opex costs), meaning you’re losing about $3.27 million a day. For the sake of this example, we’re going to assume it takes you an extra month to get this thing operational, meaning you’ve paid about $102 million that you’re never getting back, bringing our total costs for the year including depreciation to $939.4 million, or a 6.6% gross margin. Wait, fuck, you didn’t use debt to buy these GPUs, did you? You did? How bad are we talking?  Oh god — you got a 6-year-long asset-backed loan at 80% LTV, meaning you borrowed $2.8 billion at a 6% interest rate.  Your bank, in its eternal generosity, offered you a deal — a 12-month-long grace period where you’re only paying interest…which works out to around $168 million, which brings our total costs (excluding the month of delay for fairness) for the first year to around $1.005 billion…on $1.06 billion of revenue. That’s a 5.19% gross margin, and you haven’t even started paying the principal. When that happens, you’re paying $54.1 million a month in loan payments, for a total of around $649 million a year for five more years, which comes out to around $1.48 billion, or a negative gross margin of around 40%.  And I must be clear that this is if you have 100% utilization and a tenant that pays you on time, every time. Let’s talk about what should be the single-most economically viable project in data center history — a massive campus built for the largest AI company in the world by Oracle, a decades-old near-hyperscaler with a history of selling expensive database and business management software to enterprises and governments. Hah, I’m kidding of course, this place is a fucking nightmare. Stargate Abilene, an eight-building, 1.2GW data center campus with around 824MW of critical IT, was first announced in July 2024 . As of April 27 2026, only two buildings are operational and generating revenue, and the third barely has any IT gear in it. I estimate the total cost of Stargate Abilene to be around $52.8 billion. Per my own reporting , Oracle expects to make around $10 billion in annual revenue from Stargate Abilene, and I estimate around $75 billion in total revenue from the 7.1GW of data center capacity it’s building for one customer: OpenAI. As I also reported, Oracle estimated in 2024 that Abilene would cost at least $2.14 billion a year in colocation and electricity fees, paid to land developer Crusoe. I should also add that it appears that Oracle is paying all of Abilene’s construction costs. Based on my calculations and reporting, I estimate Abilene’s rough gross margin is around 37.47% once it’s fully operational:  I must be clear that that 37.47% gross margin is likely too high, as I don’t have precise knowledge of Oracle’s true insurance or headcount costs, only estimates based on documents viewed by this publication. I should also be clear that Oracle is mortgaging its entire fucking future on projects like Stargate Abilene, incurring billions of dollars of costs up front for a business that will take years to turn a profit even if OpenAI makes every single payment in a timely manner. Sadly, I can’t tell how much of Abilene was paid for in debt, only that Oracle raised around $18 billion in various-sized bonds in September 2025, with maturities ranging from 7 years to 40 years, and had negative cashflow of $24.7 billion in its last quarterly earnings .  What I do know is that it has a 15-year-long lease with developer Crusoe , and that Oracle’s future heavily depends on OpenAI’s continued ability to pay, which depends on Oracle’s ability to finish Stargate Abilene. I also need to be clear that that $3.85 billion in yearly profit is only possible if OpenAI makes timely payments, takes tenancy of Abilene as fast as humanly possible, and everything goes to plan. Sadly, the complete opposite has happened: This is a huge issue. OpenAI can only pay for compute that actually exists, and only 206MW of critical IT is actually generating revenue, with the third at least a month (if not a quarter) away from doing so.  Yet there’s a larger, more-existential problem with the overall Stargate data center project — that the only way any of it makes sense is if OpenAI meets its ridiculous, cartoonish projections. As I discussed on Friday :  I calculated that $75 billion number by assuming that Vera Rubin GPUs get around $14 million per megawatt of compute (a number I’ve confirmed with sources familiar with the data center industry) across the remaining 4.64GW of critical IT that I anticipate makes up the remaining Stargate data centers.  OpenAI’s numbers come directly from The Information’s reported leaks of OpenAI’s projected burn rate and revenues , which have the company making $673 billion in revenue through the end of 2030 and burning $852 billion to get there: I must be clear that any journalist repeating these numbers without saying how fucking stupid they are should be a little ashamed of themselves. Per my Friday premium: And if OpenAI can’t pay for that compute, Oracle dies, because it’s taken on around $115 billion in debt just to build Stargate’s data centers , and needs another $150 billion to finish them: I really need to be clear that Oracle has no other path to making this revenue without OpenAI, and these projects are entirely financed and paid for using the projected cashflow of the data centers themselves. And I’m not even the only one worried about this, with OpenAI Sarah Friar sharing similar concerns after the company missed user and revenue targets, per the Wall Street Journal : If that doesn’t worry you, perhaps this will: That sure sounds like a company that’s gonna be able to make $852 billion by the end of the decade! While I regularly bag on OpenAI for its ridiculous promises, Anthropic isn’t far behind, promising to take “up to” 5GW of capacity from both Google and Amazon in a deal that I estimate includes around $100 billion in actual compute commitments given the scale of the capacity. Now, I should add that Google and Amazon are far-savvier and less-desperate than Oracle, meaning that they can take the hit if Anthropic ends up running out of money. The “up to” part of that deal gives them some much-needed wiggle room that Oracle simply does not have.  Nevertheless, for Anthropic to actually meet its commitments, it will have to agree to spend anywhere from $25 billion to $100 billion a year on compute by the end of 2030.  Anthropic’s CFO said in March that it had made $5 billion in revenue in its entire existence .  The near-pornographic excitement around however many hundreds of billions of dollars of GPUs that Jensen Huang claims to be shifting regularly clouds out a problematic question: sell the compute to who , Jensen?  If we assume that the 15.2GW of data center capacity under construction (due by the end of 2028) has a PUE of around 1.35, that leaves us with roughly 11.2GW of critical IT. At $14 million per megawatt, that works out to around $156.8 billion in annual revenue in GPU rental revenue required to actually make these data centers worth it. When you calculate for the 114GW of capacity theoretically coming online by the end of 2028, that number climbs to $1.18 trillion in annual revenue. To give you some context, CoreWeave — the largest neocloud with Meta, OpenAI, Google (for OpenAI), Microsoft (for OpenAI), Anthropic, and NVIDIA as customers — made around $5.1 billion in revenue , and projected it would make $12 billion to $13 billion in 2026 .  Who, exactly, is the customer for all this compute, and how likely is it that they’ll want to buy it by the time all that capacity is built? While many different data centers claim to have tenants for the first few years of their existence, said tenants can only start paying once the data center completes, and if it’s an AI startup, I think it’s fairly reasonable to ask whether they exist by the time it’s built. Remember: the customers of AI compute are, for the most part, either hyperscalers trying to move capital expenditures off their balance sheets or unprofitable AI startups. Anthropic and OpenAI both intend to burn tens of billions of dollars in the next few years, and neither of them have a path to profitability.  This means that a large part — if not the majority of — AI compute revenue is dependent on a continual flow of venture capital and debt, both of which are only made possible by investors that still believe that generative AI will be the biggest, most hugest thing in the world. How does that work out, exactly? Who is paying for this data center capacity? Who is it for? Where is the actual demand?  And if said demand exists, how the fuck do the customers even pay? Despite multiple stories that have both of them becoming profitable by 2028 or 2029, nobody can explain to me how OpenAI or Anthropic actually reach profitability, especially given that both of them had worse margins than expected, even when said margins strip out training costs that number in the billions of dollars. And I’ve been asking this question for fucking years. Every time we get a new update on Anthropic or OpenAI, we hear they’ve lost billions more dollars than expected, that margins are decaying, that costs are skyrocketing, that everything is more expensive despite promises that the literal opposite would happen. Even Cursor, a company that briefly (before its pseudo-acquisition by Musk’s SpaceX) claimed it had positive gross margins, actually had negative 23% gross margins as of January, or negative 31% if you include the cost of non-paying users, which you fucking should if you actually care about your accounting. Mysteriously, reports claim that Cursor’s margins “recently turned positive,” but magically don’t know by how much, or how that happened, or a single other detail other than one that likely helped the company get sold. I also don’t see how any of these AI data centers actually make sense, even if they have customers to pay them for the first few years. The economics are built for perfection, with zero fault tolerance. They must have consistent, 100% utilization and tenancy, or they end up burning millions of dollars and fail to chip away at the years-long wall of depreciation created by the tech industry’s most expensive mistake. Even if they somehow succeed, these are pathetic businesses with mediocre margins — 70% at best, assuming consistent payments, tenancy, and six fucking years of depreciation to actually break even, which might be difficult considering the yearly upgrade cycle makes the entire thing near-obsolete by the time you’re done paying it off. And that’s before you consider that the majority of the customers are unprofitable, unsustainable startups. I truly don’t know how any of this works out. I realize it seems a little much, but I genuinely believe that subscription-based AI services were an act of deception tantamount to fraud, as they misrepresented the core unit economics and thus the possibilities of a Large Language Model. By selling users a product on a monthly rate and creating habits based on its availability, companies like Anthropic and OpenAI have misrepresented their businesses in such a way that most of their users are interacting with products and building workflows based on products that are unsustainable and impossible to maintain in their current form. Anthropic’s recent aggressive rate limit changes were instituted mere months after multiple aggressive marketing campaigns based on experiences that are now near-impossible under the current rate limits, and based on recent moves by Anthropic , it’s clear that it intends to start removing services for its lower-tier $20-a-month subscribers at some time in the future. This is a disgusting and misleading way to run a company, and the vagueness with which Anthropic discusses its products and its services are an insult to every one of their users, and a sign that it doesn’t fear the press in any meaningful way.  I need to be very clear that the product that Anthropic offers — by virtue of recent rate limit changes — is substantially different (and far worse) than the one that you read about everywhere. Anthropic was conscious in its deliberate attempts to market a product that it knew would be gone within three months. Dario Amodei doesn’t give a fuck as long as the media keeps writing up however many billions of annualized revenue he’s conjured up today or whatever new product he’s released that’s meant to destroy some hapless public SaaS company that had already seen its growth slow.  Members of the media, I say this with abundant respect: Anthropic is mistreating its customers, and it is doing so because it believes it can get away with it. This company does not respect you, and in fact holds you with a remarkable degree of contempt, which is why it doesn’t bother to fix its services very quickly or explain why they were broken with any level of coherence.  That’s why Anthropic fucking lied about Claude Mythos being too powerful to release (it was actually a capacity issue) when in fact it’s just another fucking Large Language Model Nothingburger — because it thinks you will buy whatever it is selling, and it’s worked out exactly how to package it to give you enough plausible “proof” that a quick skimread of the system card will make you and your editor believe whatever it is you’re saying.  They also know you’ll rush to cover it rather than waiting to see what actual experts say.  AI is a con, and this is how the con works. AI was rushed and pushed in our faces as quickly as humanly possible in the least-efficient yet most-accessible form it could be presented, even if said form was never going to result in anything resembling a sustainable business. The media was rushed to immediately say that this was the thing so that everybody would agree that this was the thing now and use it as much as physically possible, and, crucially, use it in a subscription-based form that would make people experience it without ever asking how much it costs to provide. The narrative came pre-baked. Because very few people who talked about LLMs experienced their actual cost, it was really easy for them to vaguely say “it’s just like Uber,” because that was a company that lost a lot of money but didn’t die, and it’s much easier to say that than say “wait, what do you mean OpenAI is set to lose $5 billion this year?” Think of it like this: as a reporter, an investor, an executive, or a regular LinkedIn Lounge Lizard, you might read here and there that it’s $5 per million input tokens and $25 per million output tokens, but you’ve never really experienced how fast or slow one loses that money, because it’s important to do so to truly understand this product. Anthropic and OpenAI intentionally obfuscated that experience and created businesses that expect to burn tens of billions of dollars in 2026 and several hundred billion dollars by 2030, all because most people graded generative AI based on the subscription-based experience.  LLMs are a casino, and you’ve been gambling with the house’s money while encouraging people to bet their own on whether they’ll get a unit of work out of a particular model.  This was intentional. They never wanted you thinking about the costs because once you really start thinking about the costs this whole thing feels a little insane. I truly believe that LLM-based subscriptions are going to go away entirely, at least at the scale of any product that generates code, and in doing so, Amodei and Altman will wrap up their con, or at least believe that they have. The problem is that these men have now signed far too many deals to get away scot-free.  OpenAI’s CFO has now said multiple times that she doesn’t believe OpenAI is ready for IPO, and has material concerns about its growth and continued ability to meet its obligations. To repeat a quote from before :   This is a blinking red fucking light, and in a sane market would send Oracle’s stock into a tailspin, because OpenAI’s ascent to over $280 billion in annual revenue is critical to Oracle’s ability to not run out of money. In a sane media, this would send worrisome shockwaves through every group chat and Slack channel about whether or not OpenAI is actually going to make it. This is the kind of thing that happens before a company starts dying. OpenAI’s growth is slowing at precisely the time it needs to accelerate. It needs to effectively 10x its current business by 2030 to make its obligations. OpenAI’s CFO, the literal person who would know best, is saying that she is worried that OpenAI cannot pay its fucking compute contracts if revenue doesn’t grow. This is a big, blinking warning light! This is not a drill!  Yet the bit that really worried me was the Journal’s comment that Friar didn’t think OpenAI “was ready to meet the rigorous reporting standards required of a public company.” What the fuck does that mean? Excuse me? This company has allegedly raised $122 billion and is allegedly worth $852 billion god damn dollars and expects to burn $ 852 billion dollars by the end of 2030. Are its accounts not in order? What “rigorous reporting standards” can OpenAI not meet?  I’d generally not be so fucking nosy if it wasn’t for the fact that this company accounted for something like 20% of all venture capital funding in the last year and everywhere I go I have to hear the endless bloviating of Altman and Brockman and every other man at OpenAI and their fucking ideas about what regular people should do as they swan about shipping dogshit software and spending other people’s money. For the amount of oxygen that both Anthropic and OpenAI consume, both of these companies should be fucking flawless , both as products and businesses. Instead, both are sold through varying levels of deception around their economics and efficacy , obfuscating the truth so that their Chief Executives can amass money and power and attention. It’s an insult to both good software and good taste — the most-expensive, least-reliable applications ever invented, their mistakes forgiven, their mediocrities celebrated, their infrastructure hailed as an inert god of capital.  Generative AI is an insult. It is unreliable, its economics don’t make sense, its outcomes don’t justify its existence, and the perpetrators of its con are boring, oafish and avaricious men disconnected from society and anybody who would ever disagree with them. It requires stealing art from everybody, destroying the environment, increasing our electricity bills, the constant threat of economic annihilation, the endless cacaphony of “everything fucking sucks now because of AI,” all to push software that can only be justified by people willing to ignore basic finance or sense. It’s all so expensive, and it’s all so fucking dull. It’s offensively boring. It’s actively annoying. Every story where somebody tells you about how much they use AI sounds like they’re in an abusive relationship and/or joined a cult, echoing with a subtle desperation that says “ you really need to join me in this because it’s so good, and the fact that I appear to be experiencing no joy from this product is just a sign of how efficient it is. ” There is nothing light-hearted or joyful about what AI can do. There is nothing goofy or whimsical about a Large Language Model, and every interaction feels hollow.  Those who desperately look for clues that it’s becoming sentient or “more powerful” are simply seeking validation themselves — they want to be first to something, because arriving at other people’s conclusion is what they do for a living.  Being “first” — on the “frontier” one might say — is something that people crave when they can’t find something within, and it’s exactly the fuel that grifters crave, because LLMs are constantly humming with the sense that they’re about to do something new, even though they’re mathematically restricted to repeating other actions. This is a deeply sad era. The people that have so aggressively worked together to hold up this industry have only delayed its inevitable fall. It’s terrifying to me that our markets and parts of our economy are being held up by the generally-held yet utterly-unproven assumption that LLMs will somehow get cheaper, that AI startups will magically become profitable, and that offering AI compute will be profitable in perpetuity to the point that it necessitates increasing the current supply tenfold by the year 2030. People have debased themselves to defend the AI industry, because that’s what the industry demands of its supplicants. To be an “AI expert” requires you to actively ignore the worst economics of any industry in history, to constantly explain away obvious, glaring issues with products, and to actively convince others to do the same. OpenAI and Anthropic do not provide clear explanations of how they’ll become profitable because they know that their supporters will never ask for them — because the only way to fully “believe in AI” is to actively wear blinders. And I get it. If you accept that OpenAI and/or Anthropic will eventually collapse, all of this seems a little insane. I am genuinely asking you to seriously consider that one or both of these companies will run out of money. I’m really worried, made only more so by the general lack of concern I’m seeing in the media and greater society.  The assumption, if I had to imagine, is that I’m simply being alarmist, and that “the demand will absolutely be there.” You’d better hope you’re right.  For Larry Ellison’s sake, at least. Ellison has already pledged 346 million shares of his Oracle stock — or around $61.5 billion — “to secure certain personal indebtedness, including various lines of credit,” meaning “many big, beautiful loans against his Oracle shares.” which IFR estimated back in September ( when Oracle’s stock price was much higher ) could allow him to secure as much as $21.4 billion in debt at a (they say “conservative”) loan-to-value ratio of 20%, and that’s assuming the banks weren’t particularly generous. If OpenAI can’t raise $852 billion in revenue and funding by the end of 2030, it won’t be able to pay for Stargate. That’ll kill the value of Oracle’s stock, leading to a series of margin calls, leading to Ellison having to sell shares, leading to further margin calls. Whatever bailout might or might not exist won’t save Larry’s estate. What I’m saying is that Ellison’s future rides on Sam Altman’s ability to raise funding and make revenue to the tune of $852 billion in the space of 4 years. Good luck, Larry! You’re going to need it.  For a ten person dev team, that’s $75,600 a year, and we’re only counting working days. If you raise a mere three months to an average of $50 a working day, that raises to $88,200  If you add a single month where you go over $100, you’re spending $102,900 a year. If you spend $300 a day, you’re now spending $756,000 on tokens for ten people. Enterprise deployments in massive organizations like Spotify or Uber with AI-pilled CEOs that allow budgets to run wild. I’d also say this is the case in large, well-funded startups. Smaller startups that use the subsidized “Teams” subscription. Individual users paying a monthly fee to access Claude or other AI subscriptions.

0 views

AI's Economics Don't Make Sense [Ad Free]

Hello premium subs! This is your ad-free free newsletter for the week. Questions? Queries? Email me at [email protected], and if you have a scoop, ezitron.76 is my Signal. Yesterday morning, GitHub Copilot users got confirmation of something I’d reported a week ago — that all GitHub Copilot plans would move to usage-based pricing on June 1, 2026 .  Instead of offering users a certain number of “ requests ,” Microsoft will now charge users based on the actual cost of the models they’re using, which it calls “...an important step toward a sustainable, reliable Copilot business and experience for all users.” Users instead get however much they spend on their GitHub Copilot subscription (EG: $19 of tokens a month on a $19-a-month plan). Anyway, the announcement itself was a fascinating preview into how these price changes are going to get framed:  You see, it’s not that Microsoft was subsidizing nearly two million people’s compute, it’s that AI has become so strong, powerful and complex that it’s basically a different product! While Copilot might not be “...the same product it was a year ago,” very little has changed about the underlying economic mismatch: that Microsoft was allowing users to burn more than their subscription costs in tokens every single month for three years. Per the Wall Street Journal in October 2023 : Naturally, GitHub Copilot users are in revolt , saying that the product is “dead” and “completely ruined.” And I called it two years ago in the Subprime AI Crisis : And that day has finally arrived, because every single AI service you use subsidized compute , and every single service is losing money as a result: AI startups and hyperscalers assumed that they’d be able to get enough people through the door with subsidized, loss-making products to get them hooked on services badly enough that they’d refuse to change once businesses jacked up the prices. They also assumed, I imagine, that the cost of tokens would come down over time, versus what actually happened — while prices for some models might have come down, newer “reasoning” models burn way more tokens, which means the cost of inference has, somehow, gotten higher over time . Both assumptions were wrong, because the monthly subscription model does not make sense for any service connected to a Large Language Model. Think of it like this. When Uber ( and no, this is nothing like Uber ) started jacking up the prices for its rides, the underlying economics stayed the same, as did those presented to both the rider and the driver — a user paid for a ride, a driver was paid for a ride. Drivers still paid for gas, car insurance, any permits that their local government might insist upon, and whatever financing costs might be associated with their vehicle, and said costs were not subsidized by Uber. Uber’s massive losses came from subsidies, endless marketing expenses, and doomed R&D efforts into things like driverless cars . To illustrate the scale of AI’s pricing mismatch, I’m going to ask you to imagine an alternate history where Uber had a very different business model. Generative AI subscriptions are like if Uber charged users $20 a month for 100 rides of any distance under 100 miles, and if gas was $150 a gallon, and Uber paid for the gas because somebody insisted that oil would one day be too cheap to meter . Uber would, eventually, decide to start charging users a monthly subscription to access rides, and bill them for the gas that they consumed. Suddenly users would go from paying $20 a month for 100 rides to paying $20 to access a driver and $26 for a 10 mile drive. Understandably, users would be a little upset. While this sounds a little dramatic, it’s actually a pretty accurate metaphor for what’s happening in the generative AI industry, and in particular, at Github Copilot.  GitHub Copilot’s previous pricing allowed 300 premium requests a month, as well as “unlimited chat requests” using models like GPT-5 mini. Each of these requests (to quote Microsoft) is “...any interaction where you ask Copilot to do something for you,” with more-expensive models taking up more requests in the later life of the request-based system, such as Claude Opus 4.6 taking up three premium requests. When you ran out of premium requests, Copilot would let you use one of those cheaper models as much as you’d like for the rest of the month.  This wasn’t even always the case. Up until May 2025 , Microsoft gave users unlimited access to models, and even then users were pissed off that there were any restrictions on the product.  Microsoft — like every AI company — swindled its customers by selling an unsustainable service, because it never, ever made sense to sell LLM-powered services on a monthly subscription. If you’re wondering how much services are likely to cost under token based billing, a user on the GitHub Copilot Subreddit found that the token burn of what used to be a single premium request was somewhere around $11, as one “request” involved using 60,000 tokens in the context window, a few tools, and a bunch of internal “turns” (things that the model is doing) to produce the output.  There’s also the underlying unreliability of hallucination-prone Large Language Models. While a premium request chasing its tail and spitting out half-broken code might be frustrating , that same fuckup is a lot less forgivable when you’re paying the costs yourself.  Users have also been trained to use the product in an entirely different manner to token-based billing, and I’d imagine many of them don’t even really realize how many “tokens” they burn or how many of them a particular task takes, something which changes based on whatever model you use. This is absolutely nothing like Uber, and anyone telling you otherwise is attempting to rationalize bad behavior. Uber may have raised prices, but it didn’t have to dramatically change the underlying economics of the platform, nor did users have to entirely change how they used the product because Uber was suddenly charging them on a per-gallon basis. There has never been — and never will be — an economically-feasible way to offer services powered by LLMs without charging the actual token burn of each user, and in the process of deceiving said users, these companies have created products with illusory benefits and questionable return on investment. And that’s been blatantly obvious for years.  On an economic basis, a monthly subscription only makes sense with relatively static costs. A gym can sell memberships knowing roughly how much wear-and-tear equipment gets, how much classes cost to run, and how much things like electricity, staffing and water might cost over a given period of time.  A customer of Google Workspace — at least before AI — cost whatever the cost of accessing or storing documents were, as well as the ongoing costs of Google Docs and other services. The relatively low cost of digital storage (as well as the fact that, unlike LLMs, Google Workspace isn’t particularly computationally demanding) means that a particularly-heavy Google Drive user isn’t going to eat into the margin on their monthly subscription. Conversely, an AI subscriber’s costs can vary wildly . One user might only use ChatGPT for the occasional search, while another might feed in reams of documents, or try and refactor a codebase, or try and use it to put together a PowerPoint presentation. And the provider — a model lab like OpenAI or Anthropic, or a startup like Cursor) — has no real way to control how a user might act other than making the product worse, such as instituting usage limits, reducing the size of the context window, pushing customers to smaller (and worse) models, or changing the pricing to dissuage users from making big GPU-heavy requests.  Yet these services intentionally hide the amount of tokens or how much a particular activity has cost, which means users don’t really know what a rate limit means, which means that every abrupt change to rate limits leaves customers desperately scrambling to work out how much actual work they can do using the service.  It’s an abusive, manipulative and deceitful way of doing business that only existed so that Anthropic, OpenAI, and other AI companies could grow their user bases, as the majority of AI users perceive its real or imagined benefits entirely through the lens of being able to burn anywhere from $8 to $13.50 for every dollar of their subscription in tokens.  This intentional act of deception had one goal: to make sure that the majority of people were never exposed to the true costs of generative AI. When The Atlantic writes a breathless screed about Claude Code being Anthropic’s “ChatGPT moment,” it does so based on a $20-a-month subscription rather than the underlying token burn that it cost for Anthropic to provide it, which in turn makes the writer forgive the “minor errors” that a model might make, or when it “gets stuck on more complicated programming tasks.” Had the writer paid for her actual token burn, and had each of the times it got “stuck” resulted in $15 in token charges, I don’t think she’d be quite as forgiving of these fuckups.  Yet that’s all part of the scam.  It’s very, very important that nobody writing about AI in the mainstream media actually understands how much these services cost, and that any mainstream articles written about services like ChatGPT or Claude Code are written by people who have little or no idea how much each individual task might cost a user.  Remember: generative AI services are, for the most part, experimental products that do not function like any other modern software or hardware. One cannot just walk up to ChatGPT or Claude and start asking it to do work.  I mean, you can , but if you don’t prompt it right, understand how it works, or make a mistake in whatever you feed it, or if it just gets things wrong, it’ll spit out something you don’t like, which in turn means you’ll need to prompt it again. LLMs are inherently unpredictable.  You cannot guarantee whether an LLM will do a particular action, or whether it will present you with an outcome based on reality. You cannot for certain say how much a particular task — even one you’ve done many times using an LLM in the past — might cost, nor can you be sure when a model might go berserk and delete something, or simply not do something yet claim that it did.  These are far more forgivable if you’ve not paying on a per-token basis, because in the mind of a subscriber, that’s just another turn or two with a chatbot rather than something that’s incurring a real cost. One doesn’t criticize so-called “jagged intelligence” because the assumption is that whatever problems you’re facing now will be eliminated at some future juncture, and you didn’t end up paying for it anyway.  Had users been forced to pay their actual rates, I imagine many would’ve bounced off the product immediately, as it’s very, very easy to burn through $5 of tokens if you’re fucking around and exploring what an LLM can do.  I think it’s inevitable that the majority of AI subscriptions move to token-based billing, especially as both Anthropic and OpenAI have now done so with their enterprise customers.  The fact that Microsoft moved GitHub Copilot subscribers to token-based is also a very, very bad sign. Microsoft is arguably the best-capitalized, most-profitable, and best-positioned company to continue subsidizing compute, and if it can’t afford to do so further, nobody else can either. The real thing to look out for — a true pale horse — will be a major AI lab like Anthropic or OpenAI moving all of its subscribers to token-based billing. Once that happens, you’ll know it’s closing time.  As I discussed last week, Uber’s CTO said at a conference that it had spent its entire AI budget for 2026 in the space of a few months , with Goldman Sachs suggesting that some companies are spending as much as 10% of their headcount on AI tokens, with the potential to increase to 100% in the next few quarters.  This is the direct result of training every single AI user to use these services as much as humanely possible while obfuscating how much they really cost. Every single major company demanding that every single worker “use AI as much as possible” has done so while either fundamentally ignoring or being entirely disconnected from their actual token burn, and as companies are forced to pay the actual costs , I’m not sure how you can economically justify any investment in this technology. Sure, sure, you’re gonna say that engineers are “shipping code faster” or some such bullshit, I get it, but how much faster, and how much money are you making or saving as a result? If you’re spending 10% of your headcount on AI tokens, are you seeing that extra expense reconciled in some other way? Because I’m not sure you are. I’m not sure any business investing these vast amounts of money in tokens is seeing any return on investment, which is why every study about AI’s ROI struggles to find much evidence that it exists. For the most part, everybody you’ve read gooning over the many possibilities of generative AI has experienced it without having to pay the true costs. Every Twitter psychopath writing endless screeds about their entire engineering team hammering away at Claude Code has been doing so using a $125-a-month-per-head Teams subscription with similar usage limits to Anthropic’s $100-a-month consumer subscription. Every LinkedIn gargoyle insisting that they’d “done hours of work in minutes” using some sort of Perplexity product has done so by paying, at the very most, $200 a month for Perplexity’s Max subscription.  In reality, that 10-person, $1250-a-month Teams subscription likely burns anywhere from $5000 to $10,000 a month in API calls, if not more. Anthropic Head of Growth Amol Avasare said last week that its Max subscriptions were built for heavy chat usage rather than whatever people are doing with Claude Code and Cowork, and made it clear that Anthropic is now looking at “ different options to keep delivering a great experience, ” which is another way of saying “we’re going to change the prices at some point.” I’m not sure people realize how expensive these tokens are, especially for coding projects that involve massive codebases and regularly make calls to coding and infrastructure tools. Can somebody who pays $200 a month foreseeably afford $350, $400, or $500? Can they afford to have a month where they spend more than that? What happens if they go over budget, or if they literally can’t afford to spend the money necessary to finish their work? To give you a more-practical example, up until the beginning of April, Anthropic’s own Claude Code developer documents ( archive ) said that “the average cost [for those using Claude Code] is $6 per developer per day, with daily costs remaining below $12 for 90% of users.” As of this week, the documents now read as follows : If we assume an average of 21 working days in a month, that puts the average cost of a Claude Code user at around $273 a month, or $3,276 a year. At $30 a working day, that works out to $630 a month, or $7560 a year.  These are astonishing numbers, made more so by the fact that there is no way you’re only spending $30 a day if you use any of Anthropic’s more-recent models. Claude Opus 4.7 costs $5 per million input and $25 per million output tokens. ‘ One million tokens is around 50,000 lines of code , and assuming you’re using the supposedly state-of-the-art models, there isn’t a chance in Hell you don’t run through at least a million, with that number increasing dramatically if you’re not particularly aware of which models to use for a particular task. Let’s play with that $30 number a little more.  While this might be possible within the slush fund mindset of a well-funded startup or a banana republic like Meta, any business that actually cares about its costs will have a great deal of trouble justifying spending five or six figures in extra costs on a service that “increases productivity” in a way that nobody can seem to measure. Right now, I think most companies fall into three camps: Large organizations still have a free pass to say that they’re burning millions of dollars on AI tokens for their software engineers under the questionable benefit of their “best engineers” not writing any code . All it changes is one bad earnings call to change that narrative. At some point investors — even the braindead fuckwits who have been inflating the AI bubble — will begin to question mounting R&D costs (which is where AI token burn is usually hidden) when the company’s revenue growth fails to follow. This will likely lead to more layoffs to keep up with the cost, as was the case with Meta , and then an eventual pullback when somebody asks “does any of this shit actually help us do our jobs faster or better?” I also think that startups burning 10% or more of their headcount in AI tokens will have a tough time convincing investors in six months that doing so is necessary. And once everybody switches to token-based billing, I’m not sure we’ll see quite as much hype around generative AI.   The way that people talk about AI data centers is completely disconnected from reality, and I don’t think people realize how ridiculous this entire era has become. Per Jerome Darling of TD Cowen, it costs around $30 million in critical IT (GPUs and the associated hardware) and $14 million per megawatt of data center capacity. Data centers appear to take anywhere from a year to three years depending on the size, and that’s assuming the power is available.  Of the 114GW of data centers supposedly being built by the end of 2028, only 15.2GW is under construction in any way, shape, or form . And “under construction” can mean as little as “there’s a hole in the ground.” It does not — and should not — imply that the capacity that said facility will provide is going to be imminently available.  Let’s start simple: whenever you think “100MW,” think “$4.4 billion,” with a large chunk of that dedicated to NVIDIA GPUs.  As a result, every AI data center starts millions of dollars in the hole, and even with six-year-long depreciation schedules, takes years to pay off… and with NVIDIA’s yearly upgrade cycle , those GPUs are unlikely to make that much money once you’re done with your first customer contract. It’s also unclear whether the customer base for AI compute exists outside of OpenAI and Anthropic, whose demand accounts for 50% of AI data centers under construction , creating a massive systemic weakness if either of them lacks the money to pay. In any case, it’s also unclear what kind of ongoing rates these data centers charge. While spot prices might sit around $4.50 an hour for a B200 GPU, long-term contracts generally price much lower, with one founder ( per The Information ) saying they paid around $3.70-per-hour-per GPU for a one-year-long commitment. To be clear, we must differentiate between the spot cost — which is the cost of randomly spinning up GPUs on somebody else’s servers — and contracted compute, the latter of which makes up the majority of data center capex. Most data centers are built with the intention of having one or two big clients , which means said clients are likely to negotiate a cheaper blended rate. As a result, many data centers take far less than $3.70 an hour, because they bill at a per-megawatt (or kilowatt) price. And that’s where the economics begin to break down. That’s the starting cost for a 100 megawatt data center. A 100MW data center will likely only have 85MW of actual stuff it can bill for, and based on discussions with sources familiar with hyperscaler billing, they can expect to make around $12.5 million per megawatt, or around $1.063 billion in annual revenue.  Now, I should be clear that most data center companies you know of don’t actually build them, instead leaving that job to companies like Applied Digital,  who are also known as “colocation partners.” For example, CoreWeave pays a colocation fee to Applied Digital to use its North Dakota data centers . CoreWeave is responsible for all the GPUs and other tech inside the data center. To explain the economic mismatch, I’m going to use a theoretical example of a data center leased to a theoretical AI compute company.  The GPUs in that data center are likely NVIDIA’s Blackwell chips. More than likely, said data center is using pods of 8 B200 GPUs, retailing around $450,000 a piece, or $56,250 a GPU. Based on there being 85MW of Critical IT load, the all-in capex per megawatt is around $36.78, or total IT capex of around $3.126 billion, or around $2.67 billion in GPUs. Let’s assume this data center is in Ellendale, North Dakota, which means you’ve got an industrial electricity rate of around 6.31 cents per kilowatt hour, which works out to about $55.4 million a year in electricity costs. Based on discussions with sources, I estimate that ongoing costs like maintenance, headcount, replacement of power supplies and the like comes in at around 12% of revenue, or around $128 million a year, bringing us up to $183.4 million in costs. Wait, sorry. You’ve also got to pay a colocation fee based on the critical IT, and according to Brightlio, that fee is often around $180-200 per kilowatt per month , depending on the scale and location of the deployment, though I’ve read as low as $130, which is the number I’m going with, or around $133 million per year. This brings us up to $316.4 million. Well, that’s still less than $1.06 billion, so we’re still doing okay, right? Wrong! You’ve got $3.126 billion in IT gear to depreciate, which works out to around $521 million a year over the six years you’re depreciating it. That’s $837.4 million a year, leaving you with around $168.6 million in yearly profit, or around a 16.7% gross margin… … if you have 100% tenancy at all times! You see, data centers can take a month or two to get those GPUs installed and a customer onboarded, all while making you exactly zero dollars in revenue and losing you a great deal more, as you’re stuck paying your colocation, electricity and opex costs the whole time, albeit at a much lower rate (I’ve modeled for 10% electricity and 15% colocation/opex costs), meaning you’re losing about $3.27 million a day. For the sake of this example, we’re going to assume it takes you an extra month to get this thing operational, meaning you’ve paid about $102 million that you’re never getting back, bringing our total costs for the year including depreciation to $939.4 million, or a 6.6% gross margin. Wait, fuck, you didn’t use debt to buy these GPUs, did you? You did? How bad are we talking?  Oh god — you got a 6-year-long asset-backed loan at 80% LTV, meaning you borrowed $2.8 billion at a 6% interest rate.  Your bank, in its eternal generosity, offered you a deal — a 12-month-long grace period where you’re only paying interest…which works out to around $168 million, which brings our total costs (excluding the month of delay for fairness) for the first year to around $1.005 billion…on $1.06 billion of revenue. That’s a 5.19% gross margin, and you haven’t even started paying the principal. When that happens, you’re paying $54.1 million a month in loan payments, for a total of around $649 million a year for five more years, which comes out to around $1.48 billion, or a negative gross margin of around 40%.  And I must be clear that this is if you have 100% utilization and a tenant that pays you on time, every time. Let’s talk about what should be the single-most economically viable project in data center history — a massive campus built for the largest AI company in the world by Oracle, a decades-old near-hyperscaler with a history of selling expensive database and business management software to enterprises and governments. Hah, I’m kidding of course, this place is a fucking nightmare. Stargate Abilene, an eight-building, 1.2GW data center campus with around 824MW of critical IT, was first announced in July 2024 . As of April 27 2026, only two buildings are operational and generating revenue, and the third barely has any IT gear in it. I estimate the total cost of Stargate Abilene to be around $52.8 billion. Per my own reporting , Oracle expects to make around $10 billion in annual revenue from Stargate Abilene, and I estimate around $75 billion in total revenue from the 7.1GW of data center capacity it’s building for one customer: OpenAI. As I also reported, Oracle estimated in 2024 that Abilene would cost at least $2.14 billion a year in colocation and electricity fees, paid to land developer Crusoe. I should also add that it appears that Oracle is paying all of Abilene’s construction costs. Based on my calculations and reporting, I estimate Abilene’s rough gross margin is around 37.47% once it’s fully operational:  I must be clear that that 37.47% gross margin is likely too high, as I don’t have precise knowledge of Oracle’s true insurance or headcount costs, only estimates based on documents viewed by this publication. I should also be clear that Oracle is mortgaging its entire fucking future on projects like Stargate Abilene, incurring billions of dollars of costs up front for a business that will take years to turn a profit even if OpenAI makes every single payment in a timely manner. Sadly, I can’t tell how much of Abilene was paid for in debt, only that Oracle raised around $18 billion in various-sized bonds in September 2025, with maturities ranging from 7 years to 40 years, and had negative cashflow of $24.7 billion in its last quarterly earnings .  What I do know is that it has a 15-year-long lease with developer Crusoe , and that Oracle’s future heavily depends on OpenAI’s continued ability to pay, which depends on Oracle’s ability to finish Stargate Abilene. I also need to be clear that that $3.85 billion in yearly profit is only possible if OpenAI makes timely payments, takes tenancy of Abilene as fast as humanly possible, and everything goes to plan. Sadly, the complete opposite has happened: This is a huge issue. OpenAI can only pay for compute that actually exists, and only 206MW of critical IT is actually generating revenue, with the third at least a month (if not a quarter) away from doing so.  Yet there’s a larger, more-existential problem with the overall Stargate data center project — that the only way any of it makes sense is if OpenAI meets its ridiculous, cartoonish projections. As I discussed on Friday :  I calculated that $75 billion number by assuming that Vera Rubin GPUs get around $14 million per megawatt of compute (a number I’ve confirmed with sources familiar with the data center industry) across the remaining 4.64GW of critical IT that I anticipate makes up the remaining Stargate data centers.  OpenAI’s numbers come directly from The Information’s reported leaks of OpenAI’s projected burn rate and revenues , which have the company making $673 billion in revenue through the end of 2030 and burning $852 billion to get there: I must be clear that any journalist repeating these numbers without saying how fucking stupid they are should be a little ashamed of themselves. Per my Friday premium: And if OpenAI can’t pay for that compute, Oracle dies, because it’s taken on around $115 billion in debt just to build Stargate’s data centers , and needs another $150 billion to finish them: I really need to be clear that Oracle has no other path to making this revenue without OpenAI, and these projects are entirely financed and paid for using the projected cashflow of the data centers themselves. And I’m not even the only one worried about this, with OpenAI Sarah Friar sharing similar concerns after the company missed user and revenue targets, per the Wall Street Journal : If that doesn’t worry you, perhaps this will: That sure sounds like a company that’s gonna be able to make $852 billion by the end of the decade! While I regularly bag on OpenAI for its ridiculous promises, Anthropic isn’t far behind, promising to take “up to” 5GW of capacity from both Google and Amazon in a deal that I estimate includes around $100 billion in actual compute commitments given the scale of the capacity. Now, I should add that Google and Amazon are far-savvier and less-desperate than Oracle, meaning that they can take the hit if Anthropic ends up running out of money. The “up to” part of that deal gives them some much-needed wiggle room that Oracle simply does not have.  Nevertheless, for Anthropic to actually meet its commitments, it will have to agree to spend anywhere from $25 billion to $100 billion a year on compute by the end of 2030.  Anthropic’s CFO said in March that it had made $5 billion in revenue in its entire existence .  The near-pornographic excitement around however many hundreds of billions of dollars of GPUs that Jensen Huang claims to be shifting regularly clouds out a problematic question: sell the compute to who , Jensen?  If we assume that the 15.2GW of data center capacity under construction (due by the end of 2028) has a PUE of around 1.35, that leaves us with roughly 11.2GW of critical IT. At $14 million per megawatt, that works out to around $156.8 billion in annual revenue in GPU rental revenue required to actually make these data centers worth it. When you calculate for the 114GW of capacity theoretically coming online by the end of 2028, that number climbs to $1.18 trillion in annual revenue. To give you some context, CoreWeave — the largest neocloud with Meta, OpenAI, Google (for OpenAI), Microsoft (for OpenAI), Anthropic, and NVIDIA as customers — made around $5.1 billion in revenue , and projected it would make $12 billion to $13 billion in 2026 .  Who, exactly, is the customer for all this compute, and how likely is it that they’ll want to buy it by the time all that capacity is built? While many different data centers claim to have tenants for the first few years of their existence, said tenants can only start paying once the data center completes, and if it’s an AI startup, I think it’s fairly reasonable to ask whether they exist by the time it’s built. Remember: the customers of AI compute are, for the most part, either hyperscalers trying to move capital expenditures off their balance sheets or unprofitable AI startups. Anthropic and OpenAI both intend to burn tens of billions of dollars in the next few years, and neither of them have a path to profitability.  This means that a large part — if not the majority of — AI compute revenue is dependent on a continual flow of venture capital and debt, both of which are only made possible by investors that still believe that generative AI will be the biggest, most hugest thing in the world. How does that work out, exactly? Who is paying for this data center capacity? Who is it for? Where is the actual demand?  And if said demand exists, how the fuck do the customers even pay? Despite multiple stories that have both of them becoming profitable by 2028 or 2029, nobody can explain to me how OpenAI or Anthropic actually reach profitability, especially given that both of them had worse margins than expected, even when said margins strip out training costs that number in the billions of dollars. And I’ve been asking this question for fucking years. Every time we get a new update on Anthropic or OpenAI, we hear they’ve lost billions more dollars than expected, that margins are decaying, that costs are skyrocketing, that everything is more expensive despite promises that the literal opposite would happen. Even Cursor, a company that briefly (before its pseudo-acquisition by Musk’s SpaceX) claimed it had positive gross margins, actually had negative 23% gross margins as of January, or negative 31% if you include the cost of non-paying users, which you fucking should if you actually care about your accounting. Mysteriously, reports claim that Cursor’s margins “recently turned positive,” but magically don’t know by how much, or how that happened, or a single other detail other than one that likely helped the company get sold. I also don’t see how any of these AI data centers actually make sense, even if they have customers to pay them for the first few years. The economics are built for perfection, with zero fault tolerance. They must have consistent, 100% utilization and tenancy, or they end up burning millions of dollars and fail to chip away at the years-long wall of depreciation created by the tech industry’s most expensive mistake. Even if they somehow succeed, these are pathetic businesses with mediocre margins — 70% at best, assuming consistent payments, tenancy, and six fucking years of depreciation to actually break even, which might be difficult considering the yearly upgrade cycle makes the entire thing near-obsolete by the time you’re done paying it off. And that’s before you consider that the majority of the customers are unprofitable, unsustainable startups. I truly don’t know how any of this works out. I realize it seems a little much, but I genuinely believe that subscription-based AI services were an act of deception tantamount to fraud, as they misrepresented the core unit economics and thus the possibilities of a Large Language Model. By selling users a product on a monthly rate and creating habits based on its availability, companies like Anthropic and OpenAI have misrepresented their businesses in such a way that most of their users are interacting with products and building workflows based on products that are unsustainable and impossible to maintain in their current form. Anthropic’s recent aggressive rate limit changes were instituted mere months after multiple aggressive marketing campaigns based on experiences that are now near-impossible under the current rate limits, and based on recent moves by Anthropic , it’s clear that it intends to start removing services for its lower-tier $20-a-month subscribers at some time in the future. This is a disgusting and misleading way to run a company, and the vagueness with which Anthropic discusses its products and its services are an insult to every one of their users, and a sign that it doesn’t fear the press in any meaningful way.  I need to be very clear that the product that Anthropic offers — by virtue of recent rate limit changes — is substantially different (and far worse) than the one that you read about everywhere. Anthropic was conscious in its deliberate attempts to market a product that it knew would be gone within three months. Dario Amodei doesn’t give a fuck as long as the media keeps writing up however many billions of annualized revenue he’s conjured up today or whatever new product he’s released that’s meant to destroy some hapless public SaaS company that had already seen its growth slow.  Members of the media, I say this with abundant respect: Anthropic is mistreating its customers, and it is doing so because it believes it can get away with it. This company does not respect you, and in fact holds you with a remarkable degree of contempt, which is why it doesn’t bother to fix its services very quickly or explain why they were broken with any level of coherence.  That’s why Anthropic fucking lied about Claude Mythos being too powerful to release (it was actually a capacity issue) when in fact it’s just another fucking Large Language Model Nothingburger — because it thinks you will buy whatever it is selling, and it’s worked out exactly how to package it to give you enough plausible “proof” that a quick skimread of the system card will make you and your editor believe whatever it is you’re saying.  They also know you’ll rush to cover it rather than waiting to see what actual experts say.  AI is a con, and this is how the con works. AI was rushed and pushed in our faces as quickly as humanly possible in the least-efficient yet most-accessible form it could be presented, even if said form was never going to result in anything resembling a sustainable business. The media was rushed to immediately say that this was the thing so that everybody would agree that this was the thing now and use it as much as physically possible, and, crucially, use it in a subscription-based form that would make people experience it without ever asking how much it costs to provide. The narrative came pre-baked. Because very few people who talked about LLMs experienced their actual cost, it was really easy for them to vaguely say “it’s just like Uber,” because that was a company that lost a lot of money but didn’t die, and it’s much easier to say that than say “wait, what do you mean OpenAI is set to lose $5 billion this year?” Think of it like this: as a reporter, an investor, an executive, or a regular LinkedIn Lounge Lizard, you might read here and there that it’s $5 per million input tokens and $25 per million output tokens, but you’ve never really experienced how fast or slow one loses that money, because it’s important to do so to truly understand this product. Anthropic and OpenAI intentionally obfuscated that experience and created businesses that expect to burn tens of billions of dollars in 2026 and several hundred billion dollars by 2030, all because most people graded generative AI based on the subscription-based experience.  LLMs are a casino, and you’ve been gambling with the house’s money while encouraging people to bet their own on whether they’ll get a unit of work out of a particular model.  This was intentional. They never wanted you thinking about the costs because once you really start thinking about the costs this whole thing feels a little insane. I truly believe that LLM-based subscriptions are going to go away entirely, at least at the scale of any product that generates code, and in doing so, Amodei and Altman will wrap up their con, or at least believe that they have. The problem is that these men have now signed far too many deals to get away scot-free.  OpenAI’s CFO has now said multiple times that she doesn’t believe OpenAI is ready for IPO, and has material concerns about its growth and continued ability to meet its obligations. To repeat a quote from before :   This is a blinking red fucking light, and in a sane market would send Oracle’s stock into a tailspin, because OpenAI’s ascent to over $280 billion in annual revenue is critical to Oracle’s ability to not run out of money. In a sane media, this would send worrisome shockwaves through every group chat and Slack channel about whether or not OpenAI is actually going to make it. This is the kind of thing that happens before a company starts dying. OpenAI’s growth is slowing at precisely the time it needs to accelerate. It needs to effectively 10x its current business by 2030 to make its obligations. OpenAI’s CFO, the literal person who would know best, is saying that she is worried that OpenAI cannot pay its fucking compute contracts if revenue doesn’t grow. This is a big, blinking warning light! This is not a drill!  Yet the bit that really worried me was the Journal’s comment that Friar didn’t think OpenAI “was ready to meet the rigorous reporting standards required of a public company.” What the fuck does that mean? Excuse me? This company has allegedly raised $122 billion and is allegedly worth $852 billion god damn dollars and expects to burn $ 852 billion dollars by the end of 2030. Are its accounts not in order? What “rigorous reporting standards” can OpenAI not meet?  I’d generally not be so fucking nosy if it wasn’t for the fact that this company accounted for something like 20% of all venture capital funding in the last year and everywhere I go I have to hear the endless bloviating of Altman and Brockman and every other man at OpenAI and their fucking ideas about what regular people should do as they swan about shipping dogshit software and spending other people’s money. For the amount of oxygen that both Anthropic and OpenAI consume, both of these companies should be fucking flawless , both as products and businesses. Instead, both are sold through varying levels of deception around their economics and efficacy , obfuscating the truth so that their Chief Executives can amass money and power and attention. It’s an insult to both good software and good taste — the most-expensive, least-reliable applications ever invented, their mistakes forgiven, their mediocrities celebrated, their infrastructure hailed as an inert god of capital.  Generative AI is an insult. It is unreliable, its economics don’t make sense, its outcomes don’t justify its existence, and the perpetrators of its con are boring, oafish and avaricious men disconnected from society and anybody who would ever disagree with them. It requires stealing art from everybody, destroying the environment, increasing our electricity bills, the constant threat of economic annihilation, the endless cacaphony of “everything fucking sucks now because of AI,” all to push software that can only be justified by people willing to ignore basic finance or sense. It’s all so expensive, and it’s all so fucking dull. It’s offensively boring. It’s actively annoying. Every story where somebody tells you about how much they use AI sounds like they’re in an abusive relationship and/or joined a cult, echoing with a subtle desperation that says “ you really need to join me in this because it’s so good, and the fact that I appear to be experiencing no joy from this product is just a sign of how efficient it is. ” There is nothing light-hearted or joyful about what AI can do. There is nothing goofy or whimsical about a Large Language Model, and every interaction feels hollow.  Those who desperately look for clues that it’s becoming sentient or “more powerful” are simply seeking validation themselves — they want to be first to something, because arriving at other people’s conclusion is what they do for a living.  Being “first” — on the “frontier” one might say — is something that people crave when they can’t find something within, and it’s exactly the fuel that grifters crave, because LLMs are constantly humming with the sense that they’re about to do something new, even though they’re mathematically restricted to repeating other actions. This is a deeply sad era. The people that have so aggressively worked together to hold up this industry have only delayed its inevitable fall. It’s terrifying to me that our markets and parts of our economy are being held up by the generally-held yet utterly-unproven assumption that LLMs will somehow get cheaper, that AI startups will magically become profitable, and that offering AI compute will be profitable in perpetuity to the point that it necessitates increasing the current supply tenfold by the year 2030. People have debased themselves to defend the AI industry, because that’s what the industry demands of its supplicants. To be an “AI expert” requires you to actively ignore the worst economics of any industry in history, to constantly explain away obvious, glaring issues with products, and to actively convince others to do the same. OpenAI and Anthropic do not provide clear explanations of how they’ll become profitable because they know that their supporters will never ask for them — because the only way to fully “believe in AI” is to actively wear blinders. And I get it. If you accept that OpenAI and/or Anthropic will eventually collapse, all of this seems a little insane. I am genuinely asking you to seriously consider that one or both of these companies will run out of money. I’m really worried, made only more so by the general lack of concern I’m seeing in the media and greater society.  The assumption, if I had to imagine, is that I’m simply being alarmist, and that “the demand will absolutely be there.” You’d better hope you’re right.  For Larry Ellison’s sake, at least. Ellison has already pledged 346 million shares of his Oracle stock — or around $61.5 billion — “to secure certain personal indebtedness, including various lines of credit,” meaning “many big, beautiful loans against his Oracle shares.” which IFR estimated back in September ( when Oracle’s stock price was much higher ) could allow him to secure as much as $21.4 billion in debt at a (they say “conservative”) loan-to-value ratio of 20%, and that’s assuming the banks weren’t particularly generous. If OpenAI can’t raise $852 billion in revenue and funding by the end of 2030, it won’t be able to pay for Stargate. That’ll kill the value of Oracle’s stock, leading to a series of margin calls, leading to Ellison having to sell shares, leading to further margin calls. Whatever bailout might or might not exist won’t save Larry’s estate. What I’m saying is that Ellison’s future rides on Sam Altman’s ability to raise funding and make revenue to the tune of $852 billion in the space of 4 years. Good luck, Larry! You’re going to need it.  For a ten person dev team, that’s $75,600 a year, and we’re only counting working days. If you raise a mere three months to an average of $50 a working day, that raises to $88,200  If you add a single month where you go over $100, you’re spending $102,900 a year. If you spend $300 a day, you’re now spending $756,000 on tokens for ten people. Enterprise deployments in massive organizations like Spotify or Uber with AI-pilled CEOs that allow budgets to run wild. I’d also say this is the case in large, well-funded startups. Smaller startups that use the subsidized “Teams” subscription. Individual users paying a monthly fee to access Claude or other AI subscriptions.

0 views

Premium: How OpenAI Kills Oracle

Soundtrack — Brass Against — Karma Police   It was January 21, 2025. Per The Information , Larry Ellison, CEO of Oracle, had just flown to Washington DC from Florida, and had to borrow a coat “...so he wouldn’t freeze during an interview he did on the White House lawn, according to two people who were involved in the event.” He was there to announce a very big — some might even say huge — new project standing next to SoftBank CEO Masayoshi Son and OpenAI CEO Sam Altman. “Together, these world-leading technology giants are announcing the formation of Stargate, so put that name down in your books, because I think you’re gonna hear a lot about it in the future. A new American company that will invest $500 billion at least in AI infrastructure in the United States and very, very quickly, moving very rapidly, creating over 100,000 American jobs almost immediately,” said President Donald Trump . After he was done, Ellison stepped to the podium. “The data centers are actually under construction, the first of them are under construction in Texas. Each building’s a half a million square feet, there are ten buildings currently being built, but that will expand to 20.” Following Ellison, SoftBank’s Masayoshi Son added that Stargate would “...immediately start deploying $100 billion dollars, with the goal of making $500 billion dollars within [the] next four years, within your town!” turning to Donald Trump with his hands extended. It was unclear what town he was referring to. Altman added that it would be “an exciting project” and that “...we’ll be able to do all the wonderful things that these guys talked about, but the fact that we get to do this in the United States is I think wonderful,” though it’s unclear what “the wonderful things” or “this” refers to. It’s been 15 months, and Stargate LLC has never been formed. SoftBank and OpenAI have contributed no capital to the project, other than SoftBank’s own acquisition of a former electric vehicle manufacturing plant in Lordstown, Ohio that it intends to turn into a data center parts manufacturing plant with Foxconn, which is best known for effectively abandoning a $10 billion factory in Wisconsin back in 2021 . Oh, and Project Freebird, a SoftBank-built project that exists to funnel money to its subsidiary SB Energy , though I can’t imagine how SoftBank actually funds it. No government money was ever involved, no funding ever left anyone’s bank account, no "initiative" ever existed, and OpenAI, Oracle and SoftBank have, in my opinion, conspired to mislead the general public about the existence and validity of a project for marketing purposes.  The “data centers actually under construction” referred to a 1.2GW project in Abilene Texas that had been under construction since the middle of 2024 , and had originally been earmarked by Elon Musk and xAI, except Musk pulled out because he felt that Oracle was moving too slow . While Ellison said that there were ten buildings under construction with plans to expand to twenty, only eight were actually being built ( each holding around 50,000 GB200 GPUs across NVL72 racks ), with the extension up in the air until March 2026, when Microsoft agreed to lease 700MW — so another seven buildings — that were meant to go to OpenAI. These buildings will not make Oracle any money, as Oracle is, despite spending so much money, leasing whatever land it uses from Crusoe. As far as those eight buildings go, only two are actually online and generating revenue, though sources with direct knowledge of Oracle’s infrastructure have informed me that work is still being done on both buildings despite CNBC reporting that they were “ operational ” in September 2025.  Let’s break this down. Based on a presentation by landowner Lancium from May 2025 , the Stargate Abilene campus was meant to have 1.2GW of AI data centers online by year-end 2025. Based on reporting from DatacenterDynamics, the first 200MW of power was meant to be energized “ in 2025 .” As time dragged on, occupancy was meant to begin in the first half of 2025 , had “ potential to reach 1GW by 2025 ,” complete all 1.2GW of capacity by mid-2026 , be energized by mid-2026 , have 64,000 GPUs by the end of 2026 , as of September 30, 2025 had “ two buildings live ,” and as of December 12, 2025, Oracle co-CEO Clay Magouyurk said that Abilene was “on track” with “more than 96,000 NVIDIA Grace Blackwell GB200 delivered,” otherwise known as two buildings’ worth of GPUs.  Four months later on April 22, 2026, Oracle tweeted that “...in Abilene, 200MW is already operational, and delivery of the eight-building campus remains on schedule.” It is unclear if that’s 200MW of critical IT capacity or the total available power at the Abilene campus, and in any case, this is only enough power for two buildings, which means that Oracle is most decidedly not “on schedule.”  Sources familiar with Oracle infrastructure have confirmed that while construction has finished on building three, barely any actual tech has been installed. It also appears that while construction has begun on a power plant of some sort, it’s unclear whether it’s the 360.5MW gas power plant or 1GW substation. In any case, Abilene needs both to turn on the GPUs, if they ever get installed. Abilene is, for the most part, the only part of the Stargate project that’s anywhere near complete. I say that because the other data centers — Shackelford, Texas, Port Washington, Wisconsin, Doña Ana County, New Mexico, Saline, Michigan, and Milam County, Texas — are patches of land with a few steel beams, if that . To be explicit, every single Stargate data center is funded by Oracle and its respective financial backers. Oracle is taking on a massive amount of debt to build these data centers, working with a labyrinthine network of financiers and construction partners to pull together the capacity necessary to get paid for its five-year-long $300 billion compute deal with OpenAI .  Oracle has also, per Bloomberg , deliberately raised money using “ project financing ” loans that are repaid using the projected cashflow, allowing it to keep the massive amount of debt off of its balance sheet. This is remarkable — and offensive! — because it’s borrowing over $38 billion to fund construction of its Wisconsin and Shackelford data centers (the largest debt deal of its kind on record) and said debt will now effectively not exist despite its massive drag on Oracle’s cashflow, which sat at negative $24.7 billion in its last quarterly earnings . Based on estimates ($30 million in critical IT and $14 million in construction per megawatt) from TD Cowen’s Jerome Darling, the total cost of Oracle’s 7.1GW of data center capacity will be somewhere in the region of $340 billion to build. All of these data centers are being built for a single tenant — OpenAI — which expects, per The Information , to lose over $167 billion (assuming it hits annual revenues of over $100 billion) by the end of 2028, and as a result does not actually have the money to pay Oracle for its compute on an ongoing basis. In addition to its commitments to Oracle, OpenAI has also made commitments to spend $138 billion on Amazon over eight years , $250 billion on Microsoft Azure over an unspecific period , $20 billion with Cerebras over three years , $22.4 billion with CoreWeave over five years , and a non-specific amount with Google Cloud .  All of this is happening as Oracle’s core businesses plateau, even after Oracle reshuffled them in Q3 FY25 to represent Cloud, Software, Hardware and Services segments, the latter three of which have barely moved in the last 9 months as low-to-negative-margin cloud compute revenue grows.  In other words, Oracle’s only growth comes from a segment requiring hundreds of billions of dollars of compute.  To make matters worse, every single one of these data centers is behind schedule. Stargate Abilene was meant to be done at the beginning, middle, and now the end of this year, yet sources tell me there’s no way it’s finished before April 2027. Bloomberg also reported late last year that Oracle had delayed several data centers from 2027 to 2028 , but here in reality , every other Stargate data center is somewhere between a patch of dirt, a single steel beam , multiple steel beams , or less than half of a shell of a single building . Considering it’s taken two years for Stargate Abilene to build two buildings, I don’t see how it’s possible that these are built before the beginning of 2029. And at that point, where exactly will we be in the AI bubble? What GPUs will be available? What other kinds of silicon will exist? What will the demand be for AI compute? I don’t think that OpenAI exists for that long, and even if it does, it will have to raise at least $200 billion in the space of three years to possibly keep up with its commitments. I’m surprised that nobody ( outside of JustDario , at least) has raised the seriousness of this situation. Stargate, as it stands, will kill Oracle, outside of OpenAI becoming the literal most-profitable and highest-revenue-generating company of all time within the next two years. Even then, by the time that Abilene is built, its 450,000 GB200 GPUs will be two-years-old, and entirely obsolete far before its debts are repaid. A similar fate awaits whatever GPUs are put in the other Stargate data centers. Today’s newsletter is a thorough review and analysis of the ruinous excess of Stargate, a name that only really means “data centers being built for OpenAI in the hopes that OpenAI will pay for them.” Oracle is mortgaging its entire future on their construction, and even if it gets paid, I see no way that the cashflow from OpenAI’s compute spend can recover the cost before its GPU capex is rendered obsolete, let alone whether it can cover the debt associated with the buildout. I’m Larry Ellison — Welcome To Jackass. Welcome to the end of Oracle, or Sell The Compute To Who, Larry? Fucking Aquaman ? The total estimated cost of Oracle’s Stargate capacity is around $340 billion. OpenAI needs to make, in total, $852 billion in both revenue and funding through the end of 2030 to keep up with its compute costs with Oracle, Amazon, Google, CoreWeave and Microsoft. Oracle cannot afford to pay for the cost of construction and equipment out of cashflow, and has had to take on over $100 billion in debt and sell $20 billion in shares . Across a potential 7.1GW of planned Stargate capacity, Oracle stands to make around $75 billion in annual revenue. Abilene is expected to generate around $10 billion a year in revenue on completion for a project that will likely cost in excess of $58 billion. Stargate Abilene is extremely behind schedule, and likely won’t be finished until Q2 2027. Oracle estimated in 2024 that Abilene would cost it $2.14 billion a year in colocation and electricity fees. Oracle has spent over $5 billion in construction costs on the first two buildings of Abilene, with sources saying that it will likely spend over $10 billion to finish them, suggesting an overall cost of around $48-per-megawatt. Oracle’s remaining Stargate sites are barely under construction, and will likely not be finished before the end of 2028. Even if Oracle builds the data centers and OpenAI pays for them, the incredible upfront cost and NVIDIA’s yearly upgrade cycle will render much of the GPU capacity worthless within the next ten years.  And if OpenAI fails to pay, Larry Ellison likely has over $20 billion in personal loans collateralized by over $60 billion in Oracle shares, meaning that margin calls will follow with the collapse of Oracle's stock.

0 views

Exclusive: Microsoft Moving All GitHub Copilot Subscribers To Token-Based Billing In June

Documents viewed by Where’s Your Ed At shed additional light on Microsoft’s transition to token-based billing for GitHub Copilot, as the company grapples with spiraling costs of AI compute. As reported on Monday ( and as announced soon after by Microsoft ), the company has taken the step to suspend new sign-ups for individual and student accounts, has removed Anthropic’s Opus models from the cheapest $10-a-month plan, and plans to further tighten usage limits. According to the documents, the announcement for token-based billing will be tomorrow (4/23), with changes to GitHub Copilot rolling out at the beginning of June. Users will pay a monthly subscription to access GitHub Copilot, and receive a certain allotment of AI tokens based on their subscription level. Organizations paying for GitHub Copilot will have “pooled” AI credits, meaning that tokens are shared across the entire organization. GitHub Copilot Business Customers will pay $19 per-user-per-month and receive $30 of pooled AI credits, and Copilot Enterprise customers will pay $39 per-user-per-month and receive $70 of pooled AI credits. While the documents refer to moving “all” GitHub Copilot users to token-based billing, it’s unclear at this time how Microsoft will be handling individual Pro or Pro+ subscribers. If you liked this news hit and want to support my independent reporting and analysis, why not subscribe to my premium newsletter? It’s $70 a year, or $7 a month, and in return you get a weekly newsletter that’s usually anywhere from 5,000 to 18,000 words, including vast, detailed analyses of NVIDIA , Anthropic and OpenAI’s finances , and the AI bubble writ large . I recently put out the timely and important Hater’s Guide To The SaaSpocalypse , another on How AI Isn't Too Big To Fail , a deep (17,500 word) Hater’s Guide To OpenAI , and just last week put out the massive Hater’s Guide To Private Credit . Subscribing to premium is both great value and makes it possible to write these large, deeply-researched free pieces every week.  Internal documents reveal Microsoft’s planned rollout for token-based billing for all GitHub Copilot customers starting in June. Copilot Business Customers will pay $19 per-user-per-month and receive $30 of pooled AI credits. Copilot Enterprise customers will pay $39 per-user-per-month and receive $70 of pooled AI credits. Sources say that these amounts may change before the launch of token-based billing. It is unclear what will happen to individual subscribers. The company is expected to make the announcement on 4/23.

1 views

News: Anthropic Removes Claude Code From $20-A-Month "Pro" Subscription Plan For New Users (Developing)

In developing news, Anthropic appears to have removed access to AI coding tool Claude Code from its $20-a-month "Pro" accounts. This is likely another cost-cutting move that follows a recent change ( per The Information ) that forced enterprise users to pay on a per-million-token based rate rather than having rate limits that were, based on researchers' findings, often much higher than the cost of the subscription. Previously, users were able to access Claude using their Pro subscriptions via a command-line interface and both the web and desktop Claude apps. Users were, instead of paying on a per-million-token basis, allowed to use their subscription to access Claude Code, but will likely now have to pay for API access. Anthropic's Claude Code support documents ( as recently as this April 10th archived page ) previously read "Using Claude Code with your Pro or Max plan." The page now reads "Using Claude Code with your Max plan." Pricing on Anthropic's website reflects the removal of Claude Code on both mobile and desktop. Some Pro users report that they are still able to access Claude Code via the web app and Command-Line Interface. It is unclear at this time whether this change is retroactive or for new Pro subscribers, or whether Anthropic intends to entirely remove access to Claude Code (without paying for API tokens) from every Pro customer. I have requested a comment from Anthropic, and will update this piece when I receive it, or if Anthropic confirms this move otherwise. If you liked this news hit and want to support my independent reporting and analysis, why not subscribe to my premium newsletter? It’s $70 a year, or $7 a month, and in return you get a weekly newsletter that’s usually anywhere from 5,000 to 18,000 words, including vast, detailed analyses of NVIDIA , Anthropic and OpenAI’s finances , and the AI bubble writ large . I recently put out the timely and important Hater’s Guide To The SaaSpocalypse , another on How AI Isn't Too Big To Fail , a deep (17,500 word) Hater’s Guide To OpenAI , and just last week put out the massive Hater’s Guide To Private Credit . Subscribing to premium is both great value and makes it possible to write these large, deeply-researched free pieces every week.  Anthropic appears to have removed access to Claude Code for its $20-a-month "Pro" Plans. Current Pro users appear to still have access via the Claude web app. Claude Code support documents exclusively refer to accessing Claude Code via "your Max Plan," after previously saying you could access "with your Pro or Max Plan."

0 views

Four Horsemen of the AIpocalypse

If you liked this piece, please subscribe to my premium newsletter. It’s $70 a year, or $7 a month, and in return you get a weekly newsletter that’s usually anywhere from 5,000 to 18,000 words, including vast, detailed analyses of NVIDIA , Anthropic and OpenAI’s finances , and the AI bubble writ large . I recently put out the timely and important Hater’s Guide To The SaaSpocalypse , another on How AI Isn't Too Big To Fail , a deep (17,500 word) Hater’s Guide To OpenAI , and just last week put out the massive Hater’s Guide To Private Credit . Subscribing to premium is both great value and makes it possible to write these large, deeply-researched free pieces every week.  Soundtrack — Megadeth — Hangar 18 (Eb Tuning) For the best part of four years I’ve been wrapped up in writing these massive, sprawling narratives about the AI bubble and the tech industry at large. I still intend to write them, but today I’m going to do what I do best — explaining all the odd shit that’s happening in the tech industry and explaining why it’s concerning to me.  And because I love a good bit, I’m tying these stories to my pale horses of the AIpocalypse — signs that things are beginning to unwind in the most annoying bubble in history.   Anyway, considering that the newsletter and the podcast are now my main form of income, I’m going to be experimenting with the formats across the free and premium to keep things interesting and varied.  Let’s start with a fairly direct statement: Anthropic should stop taking on new customers until it works out its capacity issues. So, generally any service — Netflix, for example — you use with any regularity has the “four nines” of availability, meaning that it’s up 99.99% of the time. Once a company grows beyond a certain scale, having four 9s is considered standard business practice… … unless you’re Anthropic! As of writing this sentence, Anthropic’s availability for its Claude Chatbot has 98.79% uptime, its platform/console is at 99.14%, its API is at 99.09%, and Claude Code is at 99.25% for the last 90 days.  Let me put this into context. When you have 99.99% uptime, a service is only down for a minute (and 0.48 of a second) each week. If you’re hitting 98.79% uptime, as with the Claude chatbot, your downtime jumps to two hours, one minute, and 58 seconds.  Or, put another way, 98.79% uptime equates to nearly four-and-a-half days in a calendar year where the service is unavailable. More-astonishingly, Claude for Government sits at 99.91%. Government services are generally expected to be four 9s minimum, or 5 (99.999%) for more important systems underlying things like emergency services.  This is a company that recently raised $30 billion dollars and gets talked about like somebody’s gifted child, yet Anthropic’s services seem to have constant uptime issues linked to a lack of capacity.  Per the Wall Street Journal : Yet Anthropic’s problems go far further than simple downtime ( as I discussed last week ), leading to (deliberately or otherwise) severe performance issues with Opus 4.6 :  While Anthropic claims that it doesn’t degrade models to better serve demand , that doesn’t really square with the many, many users complaining about the problem. Anthropic’s response has, for the most part, been to pretend like nothing is wrong, with a spokesperson waving off Carl Franzen of VentureBeat ( who has a great article on the situation here ) by pointing him to two different Twitter posts, neither of which actually explain what’s going on. Things only got worse with last week’s launch of Opus 4.7, which appears to have worse performance and burn more tokens.  Per Business Insider : I think it’s deeply bizarre that a huge company allegedly worth hundreds of billions of dollars A) can’t seem to keep its services online with any level of consistency, B) appears to be making its products worse, and C) refuses to actually address or discuss the problem. Users have been complaining about Claude models getting “dumber” going back as far as 2024 , each time faced with a tepid gaslighting from a company with a CEO that loves to talk about his AI products wiping out half of white collar labor . Some might frame this as Anthropic having “insatiable demand for its products,” but what I see is a terrible business with awful infrastructure run in an unethical way. It is blatantly, alarmingly obvious that Anthropic cannot afford to provide a stable and reliable service to its customers, and its plans to expand capacity appear to be signing deals with Broadcom that will come online “starting in 2027,” near-theoretical capacity with Hut8, which does not appear to have ever built an AI data center , and also with CoreWeave , a company that is yet to build the full capacity for its 2025 deals with OpenAI and only has around 850MW of “active power capacity” — so around 653MW of actual compute capacity — as of the end of 2025, up from 360MW of power at end of 2024 .    Remember: data centers take forever to build, and there’s only a limited amount of global capacity, most of which is taken up by Microsoft, Google, Amazon, Meta and OpenAI, with the first three of those already providing capacity to both Anthropic and OpenAI. We’re likely hitting the absolute physical limits of available AI compute capacity, if we haven’t already done so, and even if other data centers are coming online, is the plan to just hand them over to OpenAI or Anthropic in perpetuity? It’s also unclear what the goal of that additional capacity might be, as I discussed last week : What’s the goal, exactly? Providing a better experience to its current customers? Securing enough capacity to keep adding customers? Securing enough capacity to support larger models like Mythos? When, exactly, does Anthropic hit equilibrium, and what does that look like?  There’s also the issue of cost.  Anthropic is currently losing billions of dollars a year offering a service with amateurish availability and oscillating quality, and continues to accept new subscribers, meaning that capacity issues are not affecting its growth. As a result, adding more capacity simply makes the product work better for a much higher cost. Anthropic’s growth story is a sham built on selling subscriptions that let users burn anywhere from $8 to $13.50 for every dollar of subscription revenue and providing a brittle, inconsistent service, made possible only through a near-infinite stream of venture capital money and infrastructure providers footing the bill for data center construction. Put another way, Anthropic doesn’t have to play by the rules. Venture capital funding allows it to massively subsidize its services. The endless, breathless support from the media runs cover for the deterioration of its services. A lack of any true regulation of tech , let alone AI , means that it can rugpull its customers with varying rate limits whenever it feels like .  If Anthropic were forced to charge its actual costs — and no, I don’t believe its API is profitable no matter how many people misread Dario Amodei’s interview — its growth would quickly fall apart as customers faced the real costs of AI (which I’ll get to in a bit). If Anthropic was forced to provide a stable service, it would have to stop accepting new customers or massively increase its inference costs.  Anthropic is a con , and said con is only made possible through endless, specious hype. Everybody who blindly applauded everything this company did is a mark. Congratulations to all the current winners of the “Fell For It Again Award.” Per the Financial Times : So, yeah, anyone in the media who bought the line of shit from Dario Amodei that this was “too dangerous to release” is a mark. Cal Newport has an excellent piece debunking the hype , but my general feeling is that if Mythos was so powerful, how did Claude Code’s source code leak ?  Did… Anthropic not bother to use its super-powerful Mythos model to check? Or did it not find anything? Either way, very embarrassing for all involved.  As I’ve discussed in the past, only 5GW of AI compute capacity is currently under construction worldwide (based on research from Sightline Climate ), with “under construction” meaning everything from a scaffolding yard with a fence ( as is the case with Nscale’s Loughton-based data center ) to a building nearing handoff to the client.  I reached out to Sightline to get some clarity, and they told me that of the 114GW of capacity due to come online by the end of 2028, only 15.2GW is under construction, including the 5GW due in 2026.  That’s…very bad.  It gets worse when you realize that the majority of that construction is for two companies: So, to summarize, at least 4.6GW of the 15.2GW of data center capacity under construction is for OpenAI, with at least another 4GW of that reserved for Anthropic through partners like Microsoft, Google and Amazon. In truth, the number could be much higher.  This is a fundamentally insane situation. OpenAI and Anthropic both burn billions of dollars a year, with The Information reporting that Anthropic expects to burn at least $11 billion and OpenAI $25 billion in 2026 . The only way that these companies can continue to exist is by raising endless venture capital funding or, assuming they make it to IPO, endless debt offerings or at-the-market stock sales. It’s also very concerning that only such a small percentage of announced compute capacity is being built, especially when you run the numbers against NVIDIA’s actual sales. Last year, Jerome Darling of TD Cowen estimated that it cost around $30 million per megawatt in critical IT (GPUs, servers, storage, and so on) and $12 million to $14 million per megawatt to build a data center, making critical IT around 68% (at the higher end of construction) of the total cost-per-megawatt. Now, to be clear, those gigawatt and megawatt numbers for data centers refer to the power rather than critical IT , and if we take an average PUE (power usage efficiency, a measurement of how efficient a data center’s power is) of 1.35, we get 11.2GW of critical IT hardware, with the majority (I’d say 90%) being GPUs, bringing us down to around 10.1GW of GPUs. If we then cut that up into GB200 or GB300 NVL72 racks with a power draw of around 140KW, that’s around 71,429 racks’ worth of hardware at an average of $4 million each, which gives us around $285.7 billion in revenue for NVIDIA. NVIDIA claims it had a combined $500 billion in orders between 2025 and 2026 , and $1 trillion of sales through 2027 , and it’s unclear where any of those orders are meant to go other than a warehouse in Taiwan.  At this point, I think it’s fair to ask why anyone is buying more GPUs, as there’s nowhere to fucking put them. Every beat-and-raise earnings from NVIDIA is now deeply suspicious.  Last week, a report from Goldman Sachs revealed that ( and I quote ) “...companies are overrunning their initial budgets for inference by orders of magnitude (we heard one industry datapoint on inference costs in engineering now approaching about 10% of headcount cost, but could be on track to be on par with headcounts costs in the next several quarters based on current trajectories.”  To simplify, this means that some companies are spending as much as 10% of the cost of their employees on generative AI services, all without appearing to provide any stability, quality or efficiency gains, or (not that I want this) justification to lay people off.  The Information’s Laura Bratton also reported last week that Uber had managed to blow through its entire AI budget for the year a few months into 2026:  Uber’s CTO also added that about “...11% of real, live updates to the code in its backend systems are being written by AI agents primarily built with Claude Code, up from just a fraction of a percent three months ago.” Anyone who has ever used Uber’s app in the last year can see how well that’s going, especially if they’ve had to file any kind of support ticket. Honestly, I find this all completely fucking insane. The whole sales pitch for generative AI is that it’s meant to be this magical, efficiency-driving panacea, yet whenever you ask somebody about it the answer is either “yeah, we’re writing all the code with it!” without any described benefits or “it costs so much fucking money, man.”  Let’s get practical about these economics, and use Spotify as an example because its CEO proudly said that its “top engineers” are barely writing code anymore , though to be clear, the Goldman Sachs example didn’t specifically name any one company. For the sake of argument, let’s say that the company has 3000 engineers — one of its sites claims it has 2700 , but I’ve seen reports as high as 3500. Let’s also assume, based on the Spotify Blind (an anonymous social media site for tech workers), that these engineers make a median salary of 192,000 a year. In the event that Spotify spent 10% of its engineering headcount (around $576 million) on AI inference, it would be spending roughly $57.6 million, or approximately 4.1% of its $1.393 billion in Research and Development costs from its FY2025 annual report . Eager math-doers in the audience will note that 100% of headcount would be nearly half of the R&D budget, or around a quarter of its $2.2 billion in net income for the year. Now, to be clear, these numbers likely already include some AI inference spend, but I’m just trying to illustrate the sheer scale of the cost.  While this is great for Anthropic (and to a lesser extent OpenAI), I don’t see how it works out for any of its customers. A flat 10% bump on the cost of software engineering is the direct opposite of what AI was meant to do, and in the event that costs continue to rise, I’m not sure how anybody justifies the expense much further.  And we’re going to find out fairly quickly, because the world of token subsidies is going away. As I reported yesterday , internal documents have revealed that Microsoft plans to temporarily suspend individual account signups to its GitHub Copilot coding product, tighten rate limits across the board, remove Opus models from its $10-a-month Pro subscription, and transition from requests (single interactions with GitHub Copilot) towards token-based billing some time later this year, with Microsoft confirming some of these details (but not token-based billing) in a blog post . This is a significant move, driven by (per my own reporting) Microsoft’s week-over-week costs of running GitHub Copilot nearly doubling since January.  The move to token-based billing will see GitHub users charged based on their usage of the platform, and how many tokens their prompts consume — and thus, how much compute they use. It’s unclear at this time when this will begin, but it significantly changes the value of the product. I’ll also say that the fact that Microsoft has stopped signing up new paid GitHub Copilot subscriptions entirely is one of the most shocking moves in the history of software. I’ve literally never seen a company do this outside of products it intended to kill entirely, and that’s likely because — per my source — it intends to move paid customers over to token-based-billing, though it’s unclear what these tiers would look like, as the $10-a-month and $39-a-month subscriptions are mostly differentiated based on the amount of requests you can use.  What’s remarkable about this story is that Microsoft is one of the few players capable of bankrolling AI in perpetuity, with over $20 billion a quarter in profits since the middle of 2023 .  Its decision to start cutting costs around AI suggests that said costs have become unbearable — The Information reported back in January that it was on pace to spend $500 million a year with Anthropic alone , and if that amount has doubled, it likely means that Microsoft is spending upwards of ten times its GitHub Copilot revenue, as I can report today that at the end of 2025, GitHub Copilot was at around $1.08 billion, with the majority of that revenue coming from its CoPilot Business and Enterprise subscriptions.  The Information also reported a few weeks ago that GitHub had recently seen a surge of outages attributed to “spiking traffic as well as its effort to move its applications from its own servers to Microsoft’s Azure cloud”: “Agents” in this case could refer to just about anything — OpenAI’s Codex, Anthropic’s Claude Code, or even people plugging in the wasteful, questionably-useful OpenClaw to their GitHub Copilot account, and if that’s what happened, it’s very likely behind the move to Token-Based Billing and rate limits. In any case, if Microsoft’s making this move, it means that CFO Amy Hood — the woman behind last year’s pullback on data center construction — has decided that the subsidy party is over. Though Microsoft is yet to formally announce the move to Token-Based Billing, I imagine it’ll be sometime this week that it rips off the bandage. Two weeks ago, Anthropic did the same with its enterprise customers , shifting them to a flat $20-a-seat fee and otherwise charging the per-token rate for whatever models they wanted to use.  I’m making the call that by the end of 2026, a majority of AI services will move some or all of their customers to token-based billing as they reckon with the true costs of running AI models.  I kept things simple today both to give myself a bit of a break and because these were stories I felt needed telling.  Nevertheless, I do have to remark on how ridiculous everything has become. Everywhere you turn, somebody is talking about “agents” in a way that doesn’t remotely match with reality, like Aaron Levie’s epic screeds about how “ AI agents make it so every other company on the planet starts to create software for bringing automation to their workflows in a way that would be either infeasible technically or unaffordable economically ,” a statement that may as well be about fucking unicorns and manticores as far as its connections to reality.  I feel bad picking on Aaron, as he doesn’t seem like a bad guy. He is, however, increasingly-indicative of the hysterical brainrot of executive AI hysteria, where the only way to discuss the industry is in vaguely futuristic-sounding terms about “agents” and “inference” and “tokens as a commodity,” all with the intent of obfuscating the ugly, simple truth: that generative AI is deeply unprofitable, doesn’t seem to provide tangible productivity benefits, and appears to only lose both the business and the customer money.  Though my arguments might be verbose, they’re ultimately pretty simple: AI does not provide even an iota of the benefits — economic or otherwise — to justify its ruinous costs. Every new story that runs about cost-cutting or horrible burnrates increasingly validates my position, and for the most part, boosters respond by saying “ well LOOK at how BIG the REVENUES are .” It isn’t! AI revenues are dogshit. They’re awful. They’re pathetic. The entire industry — including OpenAI and Anthropic’s theoretical revenues of $13.1 billion and $4.5 billion — hit around $65 billion last year , and that includes the revenues from providing compute generated by neoclouds like CoreWeave and hyperscalers like Microsoft. I’m also just gonna come out and say it: I think the AI startups are misleading their investors and the general public about their revenues. My reporting from last year had OpenAI’s revenues at somewhere in the region of $4.3 billion in the first three quarters of 2025, and Anthropic CFO Krishna Rao said in an an affidavit that the company had made revenue “exceeding” (sigh) $5 billion through March 9, 2026 , which does not make sense when you add up all the annualized revenue figures reported about this company.  Cursor is also reportedly at $6 billion in annualized revenue (or around $500 million a month) and “gross margin positive” — which I also doubt given that it had to raise over $3 billion last year and is apparently raising another $2 billion this year. Even if said numbers were real, the majority of OpenAI, Cursor and Anthropic’s revenues come from subsidized software subscriptions. Things have gotten so dire that even Deidre Bosa of CNBC agrees with me that AI demand is inflated by token-maxxing and subsidized services. Otherwise, everybody else is making single or double-digit millions of dollars and losing hundreds of millions of dollars to get there. And per founder Scott Stevenson , overstating annualized revenues is extremely common, with AI startups booking “three-year-long” enterprise deals with the first year discounted and a twelve-month out : While it’s hard to say how widespread this potential act of fraud might be, Stevenson estimates that more than 50% of enterprise AI startups are using “contracted ARR” to pump their values. One (honest) founder responded to Stevenson saying that his company has $350,000 in contracted ARR but only $42,000 of ARR, adding that “next year is gonna be awesome though,” which I don’t think will be the case for what appears to be a chatbot for finding investors. This industry’s future is predicated entirely on the existence of infinite resources, and most AI companies are effectively front-ends for models owned by Anthropic and OpenAI, two other companies that rely on infinite resources to run their services and fund their infrastructure. And at the top of the pile sits NVIDIA, the largest company on the stock market, which is selling more GPUs than can be possibly installed, and very few people seem to notice or care.   I’m talking about hundreds of billions of dollars of GPUs sitting in warehouses that aren’t being installed, with it taking six months to install a single quarter’s worth of GPU sales . The assumption, based on every financial publication I’ve read, appears to be “it will keep selling GPUs forever, and it will all be so great.” Where are you going to put them, Jensen? Where do the fucking GPUs go? There isn’t enough capacity under construction! If, in fact, NVIDIA is actually selling as many GPUs as it says, it’s likely taking liberties with “ transfers of ownership ” where NVIDIA marks a product as “sold” to somebody that has yet to actually take it on. In any case, I keep coming back to the word “hysteria,” because it’s hard to find another word to describe this hype cycle. The way that the media, the markets, analysts, executives, and venture capitalists discuss AI is totally divorced from reality, discussing “agents” in terms that don’t match with reality and AI data centers in terms of “gigawatts” that are entirely fucking theoretical , all with a terrifying certainty that makes me wonder what it is I’m missing. But every sign points to me being right, and if I’m right at the scale I think I’m right, I think we’re about to have a legitimacy crisis in investing and mainstream media, because regular people are keenly aware that something isn’t right, in many cases, it’s because they’re able to count. OpenAI’s Stargate data centers account for 4.6GW — with 1.2GW in Abilene, Texas; 1.4GW in Shackelford, Texas; 1GW in Dona Ana, New Mexico; and 1GW in Port Washington, Wisconsin.  It’s safe to assume that with big tech’s hundreds of billions of dollars of capex that its data centers will make up a large amount — as much as 6GW — with most of that likely going to Anthropic or OpenAI. An indeterminately-large chunk could be Amazon’s Project Rainier in Indiana , which will “eventually” (per CNBC) draw more than 2.2GW of electricity.  While Amazon says it’s “ fully operational ,” it’s fucking lying, as it also claims that it has “nearly half a million Trainium 2 chips,” with each chip being 500 watts, and 500,000 times 500 watts being around 250MW. Other reports said it would be up to 1 million Trainium2 chips by the end of 2025, but that would still only amount to 500MW. Anthropic is apparently the primary tenant.  Anthropic also agreed to take 3.5GW of capacity of TPUs from Google Cloud , with the first 1GW coming online in 2027 , and also agreed to take a gigawatt from Microsoft made up of “Vera Rubin and Grace Blackwell systems,” meaning that these are likely data centers that are currently under construction. Anthropic and Google also announced in Q4 2025 that Anthropic would use 1 million TPUs as part of a new deal with Google Cloud, and that “well over” a gigawatt of capacity would come online in 2026 . Microsoft is also taking the 900MW extension to Stargate Abilene , and considering that most of Microsoft’s GPU infrastructure already goes to OpenAI, I can only imagine that’s where it’s going. I also will add that Satya Nadella claimed that Microsoft brought 2GW of capacity online in 2025 , and that its Fairwater Data Center cluster was “going live ahead of schedule ,” only to fail to clarify when that might happen or what said schedule was.  Microsoft’s also been relatively vague about the actual capacity, but based on there being “hundreds of thousands of GB200 GPUs,” that would be (assuming 300,000 GPUs) about 583MW.

0 views

Exclusive: Microsoft To Shift GitHub Copilot Users To Token-Based Billing, Tighten Rate Limits

Note: Microsoft has now confirmed some of these details in a blog post . Leaked internal documents viewed by Where’s Your Ed At reveal that Microsoft intends to pause new signups for the student and paid individual tiers of AI coding product GitHub Copilot, tighter rate limits, and eventually move users to “token-based billing,” charging them based on what the actual cost of their token burn really is. The document says that although token-based billing has been a top priority for Microsoft, it became more urgent in recent months, with the week-over-week cost of running GitHub Copilot nearly doubling since January.  The move to token-based billing will see GitHub users charged based on their usage of the platform, and how many tokens their prompts consume — and thus, how much compute they use. It’s unclear at this time when this will begin. This is a significant move, reflecting the significant cost of running models on any AI product. Much like Anthropic, OpenAI, Cursor, and every other AI company , Microsoft has been subsidizing the cost of compute, allowing users to burn way, way more in tokens than their subscriptions cost.  The party appears to be ending for subsidized AI products, with Microsoft’s upcoming move following Anthropic’s ( per The Information ) recent changes shifting enterprise users to token-based billing as a means of reducing its costs. GitHub Copilot currently has two tiers for individual developers — a $10-per-month package called GitHub Copilot Pro, and a $39-a-month subscription called GitHub Copilot Pro+.  According to the leaked documents, both of these tiers will be impacted by the shutdown, as will the GitHub Copilot Student product, which is included within the free GitHub Education package. According to the documents, Microsoft also intends to tighten rate limits on some Copilot Business and Enterprise plans, as well as on individual plans, where limits have already been squeezed, and plans to suspend trials of paid individual plans as it attempts to “fight abuse.” Although Microsoft has regularly tweaked the rate limits for individual GitHub Copilot accounts, most recently at the start of April, the document notes that these changes weren’t enough, and that more rate limits changes are to come in the next few weeks. As part of this cost-cutting exercise, Microsoft intends to remove Anthropic’s Opus family of AI models from the $10-per-month GitHub Copilot Pro package altogether.  Microsoft most recently retired Opus 4.6 Fast at the start of April for GitHub Copilot Pro+ users , although this decision was framed as a way to “further improve service reliability” and “[streamline] our model offerings and focusing resources on the models our users use the most.” Other Opus models — namely Opus 4.6 and Opus 4.5 — will be removed from the GitHub Copilot Pro+ tier in the coming weeks, as Microsoft transitions to Anthropic’s latest Opus 4.7 model .  The move towards Opus 4.7 will likely see GitHub Copilot Pro+ users reach their usage limits faster.  Microsoft is offering a 7.5x request multiplier until April 30 — although it’s unclear what the multiplier will be after this date. This might sound like a good thing, but it actually means that each request using Opus 4.7 is actually 7.5 of them. Redditors immediately worked that out and are a little bit worried . Premium request multipliers allow GitHub to reflect the cost of compute for different models. LLMs that require the most compute will have higher premium request multipliers compared to those that are comparatively more lightweight.  For example, the GPT-5.4 Mini model has a premium request multiplier of 0.33 — meaning that every prompt is treated as one-third of a premium request — whereas the now-retired Claude Opus 4.6 Fast had a 30x multiplier, meaning each request was treated as thirty of them. The standard version of Claude Opus 4.6 has a premium request multiplier of three — meaning that, even with the promotional pricing, Claude Opus 4.7 is around 250% more expensive to use.  The announcements for all of these changes are scheduled to take place throughout the week.  If you liked this news hit and want to support my independent reporting and analysis, why not subscribe to my premium newsletter? It’s $70 a year, or $7 a month, and in return you get a weekly newsletter that’s usually anywhere from 5,000 to 18,000 words, including vast, detailed analyses of NVIDIA , Anthropic and OpenAI’s finances , and the AI bubble writ large . I recently put out the timely and important Hater’s Guide To The SaaSpocalypse , another on How AI Isn't Too Big To Fail , a deep (17,500 word) Hater’s Guide To OpenAI , and just last week put out the massive Hater’s Guide To Private Credit . Subscribing to premium is both great value and makes it possible to write these large, deeply-researched free pieces every week.  Internal documents reveal that Microsoft plans to temporarily suspend individual account signups to its GitHub Copilot coding product, as it transitions from requests (single interactions with Copilot) towards token-based billing.  The documents reveal that the weekly cost of running Github Copilot has doubled since the start of the year.  Microsoft also intends to tighten the rate limits on its individual and business accounts, and to remove access to certain models for those with the cheapest subscriptions.

1 views

Premium: The Hater's Guide to Private Credit

A few years ago, I made the mistake of filling out a form to look into a business loan, one that I never ended up getting. Since then I receive no less than three texts a day offering me lines of credit ranging from $150,000 to as much as $10 million, each one boasting about how quickly they could fund me and how easy said funding would be. Some claim that they’ve been “looking over my file” (I’ve never provided any actual information), others say that they’re “already talking to underwriting,” and some straight up say that they can get me the money in the next 24 hours. Some of the texts begin with a name (“Hey Ed, It’s Zack”) or sternly say “Edward, it’s time to raise capital.” Others cut straight to the chase and tell me that they have been “arranged for five hundred and fourty (sic) thousand,” and others send the entire terms of a loan that I assume will be harder to get than responding “yes.” While many of them are obvious, blatant scams, others lead to complaint-filled Better Business Bureau pages that show that, somehow, these entities have sent them real money, albeit under terms that piss off their customers and occasionally lead to them getting sued by the government . That’s because right now, anybody with the right lawyers, accountants and financial backing can create their own fund and start issuing loans to virtually anyone they deem worthy.  And while they’ll all say that they use “industry-standard” underwriting, no regulatory standard exists. This, my friends, is the world of private credit — a giant, barely-regulated time bomb of indeterminate (but most certainly trillions of dollars ) size that has become a load-bearing pillar of pensions and insurance funds, and according to Federal Reserve data , private credit has borrowed around $300 billion (as of 2023) from big banks, representing around 14% of their total loans.  The eager, aggressive growth of private credit has even led it to start targeting individual investors, per the Financial Times : The FT also neatly summarizes the problem of having regular investors involving themselves in the world of private credit: And those high returns come with a cost: a lack of flexibility ranging from “you can only redeem your funds every quarter, and only a small percentage of your funds,” to “you can’t redeem your funds if everybody else tries to at the same time,” to “we make the rules here, shithead.” When an asset manager sets up a private credit fund, it often sets terms around how often — or how much — investors can pull at once, usually set around 5%, because in most cases, private credit funds are highly illiquid , as despite them acting like a financial institution , they more often than not don’t have very much money on hand for investors. Why? Because the “private” part of private credit means that the lender directly negotiates with the borrower and values the loans based on their own internal models. Said loans generally have little or no secondary market, and private credit wants to hold them to maturity so that it can continue to provide ongoing yield (which I’ll explain in a little bit). Things were going great for private credit for the longest time, but late last year, some buzzkills at the Financial Times discovered that auto parts manufacturer First Brands and subprime auto loan company Tricolor had taken on billions of dollars of loans under dodgy circumstances, double-pledging collateral (IE: giving the same stuff as collateral on different loans) and outright falsifying lending documents, allowing the both of them to borrow upwards of $10 billion from private credit firms, including billions from North Carolina-based firm Onset Capital, which nearly collapsed but was eventually rescued by Silver Point Capital . After the collapse of First Brands and Tricolor, JP Morgan’s Jamie Dimon said that “ when you see cockroaches, there are probably more ,” the kind of sinister quote baked specifically to lead off a movie about a financial crisis. Seemingly inspired to start freaking people out, on November 5, software-focused asset manager Blue Owl announced it would merge its publicly-traded OBDC fund with its privately-traded OBDC II fund , and, well, it didn’t go well, per my Hater’s Guide To Private Equity : Two weeks later on November 18 2025, Blue Owl said it would freeze redemptions on OBDC II until after the merger closed, then canceled it a day later citing “market conditions.” Two months later in February 2026, Blue Owl would announce that it was permanently halting redemptions from OBDC II, and sold $1.4 billion in assets from both OBDC II and two other funds. The buyers of the assets? Several large pension funds that had a vested interest in keeping the value of the assets high , and Kuvare, an insurance company with $20 billion of assets under management that Blue Owl bought in 2024 . This is perfectly legal, extremely normal, and very good. Private equity is also the principal funding source for private equity’s leveraged buyouts, accounting for over 70% of all leveraged buyout funding for the last decade , which means that private credit — and anyone unfortunate enough to fund it! — is existentially tied to the ability of the portfolio companies’ ability to pay, and their continued ability to refinance their debt. This is a problem when your assets are decaying in value. As I discussed in the Hater’s Guide To Private Equity , PE firms massively over-invested between 2017 and 2021, leaving them with a backlog of 31,000 companies valued at $3.7 trillion that they can’t sell or take public, likely because many of these acquisitions were vastly overvalued.  You see, when things were really good , asset managers raised hundreds of billions of dollars from pension funds, insurance funds (some of which they owned), and institutional investors, and then issued hundreds of billions of dollars more (at times using leverage from banks to do so) in loans to private equity firms that went on to buy everything from software companies to restaurant franchises. Said debt would immediately go on the balance sheet of the acquired company, creating a “reliable,” “consistent” yield with every loan payment that the fund could then send on to its investors, on a quarterly or monthly basis. The problem is that these investments were made under very different economic circumstances , when money was easy to raise and exits were straightforward, leading to many assets being massively overvalued, and holding debt that was issued under revenue and growth projections that only made sense in a low-interest environment. In simple terms, these loans were given to companies assuming they’d be able to pay them long term, and assuming that the sunny economic conditions would continue indefinitely, making them tough to refinance or, in some cases, for the debtor to continue paying. And nowhere is that problem more pronounced than the world of software. The jitters caused by First Brands and Tricolor eventually turned into full-on tremors thanks to the SaaSpocalypse ( covered in the Hater’s Guide a month ago ): The SaaSpocalypse is often (incorrectly) described as a result of AI “disrupting incumbent software companies,” when the reality is that private equity (and private credit) made the mistaken bet that every single software company would grow in perpetuity.  The larger software industry is in decline , with a McKinsey study of 116 public software companies with over $500 million in revenue from 2024 showing that growth efficiency had halved since 2021 as sales and marketing spend exploded, and BDO’s annual SaaS report from 2025 saying that SaaS company growth ranged from flat to active declines, which is why there’s now $46.9 billion in distressed software loans as of February 2026 . And to be clear, it’s not just private equity’s victims that are taking out loans. Over $62 billion in venture debt was issued in 2025 , with established companies like Databricks ( $5.2 billion in credit per the Wall Street Journal in 2024) and Dropbox ( $2.7 billion from Blackstone in 2025 ) raising debt just as the overall software industry slows, with AI failing to pick up the pace.   This is a big fucking problem for private credit. Per the Wall Street Journal , asset managers are massively exposed to software companies, and have deliberately mislabeled some assets (such as saying a healthcare software company is just a “healthcare company”) to obfuscate the scale of the problem: And as I’ll explain, “obfuscation” is a big part of the private credit business model. If I’m honest, preparing this week’s premium has been remarkably difficult, both in the amount of information I’ve had to pull together and how deeply worried it’s made me.  In the aftermath of the great financial crisis, insurance and pension funds found themselves desperate for yield — regular returns — to meet their payment obligations. Private credit has become the yield-bearer of choice, feeding over a trillion dollars of these funds’ investments into leveraged buyouts, AI data centers, loans to software companies, and failing restaurant franchises.  In some cases, asset managers have purchased insurance companies with the explicit intention of using them as funders for future private credit investments, such as Apollo’s acquisition of Athene , KKR’s acquisition of Global Atlantic , and Blue Owl’s acquisition of Kuvare . More on this later, as it fucking sucks. Asset managers offering private credit market themselves as bank-like stewards of capital, but lack many (if any) of the restrictions that make you actually trust a bank. They self-deal, investing their insurance affiliates’ funds in their own equity investments (such as when KKR used Global Atlantic to invest in data center developer CyrusOne , a company it acquired in 2022 ), value and revalue assets based on mysterious and undocumented private models, and account for (as I mentioned) 70% of all funding of leveraged buyouts in the last decade, of which 30 to 40% were software companies purchased between 2018 and 2022 , meaning that hundreds of billions of dollars of retirement and insurance funds are dependent on overvalued software companies paying loans funded during the zero interest free era. While a market crash feels scary, what’s far scarier is that the present and future ability of many retirement and insurance funds is dependent on whether private equity-owned entities, software companies. and AI data center firms are able to keep paying their debts. If private credit fund returns begin to lag, the retirement and insurance industry lacks a viable replacement, and I don’t know how to fix that. Fuck it, I’ll level with you. I think asset managers are scumbags, and I think the way that they do business is fucking disgraceful. The unbelievable amount of risk that asset managers have passed onto people’s fucking retirements is enough to turn my stomach, and if I’m honest, I don’t understand how this entire thing hasn’t broken already. If I had to guess, it’s one of two reasons: that private credit funds have yet to escalate their risk enough, or we’re yet to see said risk’s consequences, with First Brands and Tricolor being just the beginning. And Wall Street is prepared to profit, with S&P Dow Jones launching a credit default swap derivatives product to bet against a collection of 25 different banks, insurers, REITs, and business development companies. Bank of America, Deutsche Bank, Barclays and Goldman Sachs will start selling the derivatives next week, per Reuters, and I’d argue that enough demand could spark a genuine panic across publicly-traded asset managers.  In any case, this is a situation where I fear not one massive catastrophe, but a series of smaller calamities caused by decades of hubris and questionable risk management resulting from the unbelievably stupid decision to let private entities act like banks.  This is the Hater’s Guide To Private Credit, or The Big Shart.

0 views