Ed Zitron's Where's Your Ed At Yesterday

The OpenAI Bubble

Thanks for reading this week’s free Where’s Your Ed At newsletter. As I said last week, I’m taking the rest of this week off, so there won’t be a premium on Friday. That said, if you aren’t already a member, now’s a great time to subscribe.  To celebrate the one year anniversary of the premium newsletter, I’m offering a sale on one-year subscriptions. Between now and midnight July 22, you can get a permanent annual rate of just $60— a $10 discount on the usual price of $70 - for life. Click here for the offer . In addition to getting access to the entire back catalog of premium posts, you’ll also receive one additional post each week — usually anywhere between 10,000 and 20,000 words — covering the most pressing topics in the AI bubble - the best value in tech analysis. Highlights include last week's Hater's Guide To The Memory Crisis - a guide to how AI made everything more expensive - How OpenAI Kills Oracle (which pairs nicely with the Hater's Guide To Oracle ), The Hater's Guide To NVIDIA , The Hater's Guides To Private Credit and Private Equity , and how the entire AI Compute Demand Story Is A Lie . Today’s piece is one of the largest free newsletters I’ve ever written, and pulls together the last six months of my work. And it all starts with a question: how much do you trust Sam Altman? The stock market and (to some extent) the global economy rests on your answer. You see, OpenAI has become one of the largest liabilities in recent economic history. You can argue that OpenAI’s no longer the focal point of the AI bubble — you can talk all you want about open source models or Anthropic or any number of other elements — but without OpenAI, the AI industry doesn’t exist, and the justification for trillions of dollars of capex evaporates.  The AI bubble isn’t a result of any actual return on investment — whether that be in purely monetary terms, like revenue or profitability , productivity gains, or anything tangible or measurable. Rather, it’s an episode of cult-like psychosis that infected the brains of some of the most powerful and wealthy individuals and institutions, where the powerful mythology of a company inspired — and been used to inspire — the greatest capital misallocation in history.  As much as this’ll piss some people off, I fully believe that the only reason this has kept going so long is that OpenAI has yet to collapse. Its failure would be a watershed moment — the Lehman Brothers of the AI bubble, and an event that would define the end of one epoch, the start of another, and that would shake the afflicted out of that psychosis. Absent this wake-up call, NVIDIA has continued to sell GPUs, the coffers of the semiconductor industry have continued to swell, and more and more spending commitments have been made.  Look. OpenAI intends to burn over $852 billion by the end of 2030 . It accounts for $748 billion of the remaining performance obligations of Microsoft, Amazon, and Oracle, on top of at least another $70 billion of RPOs across Cerebras , CoreWeave , Nebius, IREN, Lambda, and Nscale (per Kakashii), and plans to spend indeterminate billions’-worth of Broadcom “Jalapeno” chips . It intends to spend $50 billion or more on compute this year , which I estimate is more than 50% of all global AI compute spend (with OpenAI taking up 50%+ of all AI compute infrastructure ).  OpenAI can only afford to pay that as a result of its latest (assuming it fully closes) $122 billion funding round , of which it has received at least $50 billion, with $20 billion from SoftBank (of $30 billion, with the third tranche due October 1, 2026 ). NVIDIA mentioned in its latest quarterly earnings report that it “estimate[d] that one AI research and deployment company contributed to a meaningful amount of [its] revenue by purchasing cloud services from [its] customers in the first quarter of fiscal year 2027,” referring, of course, to OpenAI. OpenAI is the reason anyone cares about AI. In March 2019 ( per JustDario ), NVIDIA bought a company called Mellanox that made the high-speed networking tech necessary to create AI GPU clusters, and four months after that, Microsoft invested a billion dollars in OpenAI and started buying AI GPUs and building AI infrastructure for it. By March 2020, NVIDIA would ship its A100 GPU , and in May 2020 , Microsoft would announce it had built a supercomputer just for OpenAI with “more than 285,000 CPU cores [and] 10,000 GPUs.” The launch of ChatGPT in November 2022 came at the perfect time for a tech industry that had run out of ideas and was flirting with a prolonged depression. The IPO market had collapsed , interest hikes killed the Zero Interest Free era dead, pandemic era overhiring began to unwind with some of the worst layoffs in the history of the industry , global venture funding dwindled after historic overinvestment in 2021 , and tech stocks took a massive beating .  For the first time, the tech industry was forced to cut its cloth in accordance with its means — something which it has historically been loath to do. Big tech was unpopular, both with investors and the general public. The excesses of the past decade — combined with the growing frustration with, for lack of a better word, “tech exceptionalism,” where it believed that the rules which governed the rest of the world didn’t apply to Silicon Valley — had tested the patience of both regulators and lawmakers. And, in the absence of “one more thing” — a big, splashy, game-changing product category — it no longer had an excuse for its prodigal spending, or its regular breaking of the rules, both written and unwritten, that govern society. The existence of OpenAI justified an era of mania and opulence. Hyperscalers, bereft of new hypergrowth ideas , were able to point at the fact that ChatGPT had “the fastest growing userbase of all time” and the Microsoft “supercomputer” that built it and tell their investors that if they didn’t invest, they’d be left behind , with Amazon , Meta , and Google announcing their own nebulous “supercomputers” in 2023.  By the end of 2023, NVIDIA had sold 500,000 A100 GPUs , and the only reason it did so was because of ChatGPT’s rapid growth . Sam Altman’s brief ouster only sought to inflate the AI bubble by adding a layer of dull palace intrigue to a tech industry bereft of whimsy or character — and helped further entrench Microsoft’s role as the paternalistic benefactor of OpenAI, which made sure that Altman returned to the helm . To be clear, when I say “rapid growth,” I mean that OpenAI hit 100 million weekly active users by the end of 2023 and had about $108 million in monthly revenue . Microsoft would invest $10 billion more that year , with the majority of that funding coming in the form of credits to be used on Microsoft Azure . OpenAI is also the reason that Anthropic exists — not just because multiple founders came from the company, but because both Google and Amazon both agreed to give it a total of $6 billion in 2023 as a means of “competing” with Microsoft’s new obsession, which allowed both to justify spending further hundreds of billions of dollars “to make sure they didn’t miss out on AI.”  When you remove the term “AI” from the equation, this all seems a little ludicrous. $16 billion in equity investment on top of what was, by the end of 2023, over $150 billion in capital expenditures, all of which was pretty much justified by the fact that a single website had been very popular.  And the only reason either of these companies were able to grow was because of hyperscalers bankrolling their entire infrastructure.  In the fourth quarter of 2023 , global venture capital funding had dropped to its lowest levels since the third quarter of 2016, with American startups taking up $183.6 billion of the year’s investments. Venture capital alone couldn’t have — and wouldn’t have — actually backed OpenAI or Anthropic at the scale that was necessary to build their infrastructure, nor would there have been any of the hunger from hyperscalers or those providing debt for data centers without hyperscalers inflating both of these companies, almost entirely because of the success of OpenAI.   Remove OpenAI from the years 2020 through 2024 and the AI bubble wouldn’t have inflated at all. No other major AI companies showed any sign of life — not those peddled by hyperscalers, funded by venture capitalists, or those launched by other tech firms.  The only reason that any hyperscaler AI efforts have any revenue — and outside OpenAI and Anthropic it’s pretty meager! — is because they knew they could just sit there and keep saying “AI is the future” until their customers eventually gave in and tried it…largely because everybody was talking about ChatGPT .  Anthropic was considered an also-ran until early 2025, and only continued to get funded because people wanted to invest in the next OpenAI , and Anthropic’s initial funding rounds and infrastructure buildout were only justified in terms of competing with OpenAI.   Those $178.5 billion in US-based data center debt deals in 2025 ? Pretty much entirely justified by the growth of OpenAI and its rapacious hunger for compute, because outside of OpenAI (and eventually Anthropic), nobody else was using massive clusters of tens of thousands of GPUs, nor does a market for compute at that scale appeared to have popped up in the months and years since.  The largest consumers of compute remain Microsoft (for OpenAI), Google (for Anthropic), Amazon (for OpenAI and Anthropic), CoreWeave (for OpenAI and Anthropic), Meta (which is copying what the other hyperscalers are doing), and Oracle (for OpenAI). Otherwise, there’s very little evidence — and boy, have I looked — that there’s more than a few billion in demand for AI compute, and that’s being generous.  All of those investments — both in AI startups and data centers — existed to fund either the next OpenAI or become the next OpenAI’s landlord.  The assumption — because nobody ever thinks things through — was that because one OpenAI existed, many OpenAIs would bloom. That because one large customer of compute existed, the template had been built for future compute-intensive startups…and, again, because nobody ever thinks about anything, nobody ever stopped to realize that the reason there isn’t another OpenAI is because OpenAI and Anthropic are financial psy-ops by the largest software companies in the world.  The grim truth is that you can’t venture fund an AI lab. While OpenAI and Anthropic have raised nearly $300 billion in the last few years, their actual infrastructure costs — the GPUs and the data centers to power their services — were entirely funded by hyperscalers, likely costing another $250 billion in the process, given that Microsoft has said it spent $100 billion on its OpenAI relationship as of early 2026 .  Yet the real cost wasn’t just financial , but the experience and industrial know-how to actually execute on a massive infrastructure bailout. Other than Google, Microsoft, and Amazon, nobody else has the scale or experience to build the kind of AI clusters that OpenAI (and eventually Anthropic) needed.  We know that for a couple of reasons. First, because prior to 2023, there were few — if any — companies actually building AI computing clusters at the kind of scale demanded by OpenAI or Anthropic. The closest thing that one could point to were crypto-mining firms, and it’s telling that many of the neoclouds today (most famously Coreweave) started life running warehouses full of ASICs to mine Bitcoin and Ethereum. Second, because, based on conversations with people in the data center industry, the whole Overton window of what is considered to be a “big” facility has shifted. Previously, a 50MW data center would have been considered a significant (even noteworthy) development. These were the exception, and not the rule, with most data centers being vastly more modest affairs. The only companies which had any experience building at that scale were, for the most part, hyperscalers.   By treating OpenAI as a “venture backed startup,” hyperscalers created the illusion that this was the next type of big company that would in turn create the next great demand center in cloud computing , except the only reason that these companies existed was because of the hyperscalers themselves willing them into existence, funding them with incredible sums, and allowing them to burn as much money as they’d like.   This is why the idea that OpenAI will continue to grow infinitely is central to the mythology of the AI bubble. The existence of one OpenAI allows others to — no matter how illogical — imagine the existence of more OpenAIs, which in turn means that those OpenAIs will need just as much compute as OpenAI.   The dimwitted investor who believes this tripe can justify it through any number of different buy-side analysts or captured members of the media that talk about the “insatiable demand for compute,” pointing to capacity constraints ( caused by slow data center construction and — hah! — OpenAI and Anthropic taking up much of the world’s compute ) and increasing GPU prices as proof that actually, there’s tons of demand , all without ever really thinking too hard. The greatest trick that hyperscalers played was never backing down. By sinking more than a trillion dollars into AI capex without ever showing a single dollar of profit , they justified literally anyone investing in AI data centers under the logic that “the largest companies in the world couldn’t be wrong,” even if the reason they were doing so was to expand capacity for OpenAI and Anthropic, who the hyperscalers themselves incubated.   It is fundamentally illogical and insane for hyperscalers to have spent so much money on AI infrastructure, and the reason that few people will say so is because it was, until recently, considered radical to suggest that this was a waste of money, almost entirely because of the existence and continued growth of OpenAI. Whatever utility you may or may not get out of LLMs is irrelevant because it has not, for the most part, been what actually underpins data center investment. While accelerating gains in code generation (itself something that could have only happened without vast subsidies) might have helped grow Anthropic , the vast majority of data center capex has been built chasing the dragon of what AI could be rather than any connection to the revenues or economics of the companies at large — outside, of course, their compute spend.  This is the underlying greed that has driven this wasteful, reckless and destructive era — the belief that there will be another OpenAI and, as I’ve said, the chance to become the next OpenAI’s landlord. And because the media and analysts very rarely have original ideas, everybody justified (and justifies) the waste through the same tired mantras, saying it was “just like Uber ( nope !)” or “just like Amazon Web Services ( between 2003 and 2015, Amazon spent $29.7 billion on capex, normalized for inflation ).” And like any great investment bubble, the more money that piled in, the greater the fear of missing out, the more dollars that can be justified in turn, and the more-complex and deranged the mythology becomes, which is why you have noted venture capitalists claiming that AI labs have “90%+ inference margins,” a completely unproven statement that AI boosters cling to and repeat often enough that it’s taken as gospel, likely to avoid thinking about the fact that you can burn $14,000 in tokens on a $200-a-month ChatGPT subscription .  This kind of mythology only grows in an environment deliberately deprived of good information. The fact that we’re four years into this horrible bubble and still don’t have consistently-held consensus around the actual costs of large language models is a testament to an industry-wide effort to suppress them.  OpenAI, Anthropic, Microsoft, Google, and Amazon have done everything in their power — based on discussions with sources familiar with their infrastructure — to obfuscate the actual underlying costs of their operations, and Silicon Valley, an industry of alleged free thinkers and individuals, is more than willing to accept whatever convenient myths might sustain their dreams.  And in the end, they all became useful idiots for hyperscalers. Their obsessive attachment to OpenAI — and by extension Anthropic — seems like a decision made under the auspices of “democratizing powerful AI,” all as effectively every dollar flows to either Microsoft, Google, Amazon, or Oracle, who in turn feed that money to NVIDIA or Broadcom, who in turn feeds that money to TSMC, SK Hynix, Samsung, or Micron.  Invest in an AI startup? They’re gonna be paying one of the AI labs, who will in turn pay a hyperscaler. Invest in an AI infrastructure company? That money will flow to NVIDIA, and then upstream to semiconductor companies. In the end, whether they die or get acquired (as none of them are going public) , all of the value will end up in the hands of one of the hyperscalers who created this imaginary era, then helped inflate it into something very, very dangerous. Yet the problem is that this industry cannot, under any circumstances, survive without OpenAI.  When people discuss OpenAI’s potential collapse, they act with pure cowardice either saying “it won’t be that bad” or say something vague about it “ being too big to fail .”  If OpenAI — the company with the most money and the most infrastructure and the most attention and the most talent in AI — collapses, it will likely do so after AI data center debt and venture capital funding has been almost entirely exhausted.  You see, Goldman Sachs’ Jeffrey Papai recently noted that it will be “very difficult” to replicate the hundreds of billions of dollars that hyperscalers have raised in the last four years — $244 billion in 2026 alone if you include NVIDIA and SpaceX — which is a problem considering that they can no longer fund their data center capex using their cashflows as of Q3 2026 .   And to be clear, hyperscaler capex doesn’t have to stop for NVIDIA to stumble. It just has to slow down meaningfully enough that Jensen Huang can no longer give investors 60%+ year-over-year revenue bumps, because the AI bubble is built on vibes, and it can only survive so long as those vibes don’t become sour.  Yes, yes, I realize there are other customers, but the vast majority of NVIDIA’s demand comes from hyperscalers, who are (for the most part) either building out their operations for OpenAI and Anthropic or simply copying what the other hyperscalers are doing (see: Meta and SpaceX).  Once hyperscalers stop spending money, banks that are afraid of “choking” on data center debt will see that a vast amount of capital is leaving the market and underwrite (or not, as the case may be) deals as such. This will mean, at some point, that both OpenAI and Anthropic will be walking around with their hands out saying “money please!” at precisely the moment that everybody will be cutting back. While NVIDIA might get a little desperate and throw some extra cash their way, if revenues start collapsing, so too will its interest in further inflating the bubble as investors begin to ask whether any of this was real or one large circular financing scam . While this is absolutely a problem for Anthropic — especially after its $35 billion debt deal with Broadcom — it’s much, much worse for OpenAI, which has (as mentioned) made $748 billion in compute commitments to some of the largest and well-lawyered companies in the world. OpenAI’s continued marketing efforts involve constantly refreshing rate limits around the launches of its most-expensive models, giving away millions of dollars of tokens to startups , and generally running the “grow as fast as possible and work out a business later” model into the ground at speed, all fueled and funded by Clammy Sammy Altman’s nasty habit of overpromising and underdelivering. Clamuel’s biggest mistake was leaving the pearly gates of the hyperscalers and dancing with the mortals of Oracle, Cerebras, and CoreWeave. While Microsoft or Amazon might be willing to extend payment terms as a means of saving face and prolonging the inevitable, Oracle — a law firm with a software company attached — is more than capable of loud and aggressive litigation under any contractual breach. Then there’s the fact that Apple is suing OpenAI after poaching multiple engineers for its hardware efforts and allegedly both coaching and coercing them into stealing trade secrets , which is all but certain to destroy any chance of OpenAI releasing a device in the next few years…and potentially the company itself. These are extremely serious allegations, with Apple also accusing OpenAI of trying to coerce trusted partners into revealing manufacturing techniques for iPhones — the kind of thing that can (and will) lead to brutal discovery and potentially criminal charges. OpenAI also, as I’ve mentioned, needs to keep growing to keep up with those bills, and at some point will run out of real dollars to pay people, likely at exactly the time that it’s hardest to find more of them. While there might be billions of dollars left to be raised, to pay any of its bills, OpenAI needs tens of billions of dollars multiple times a year. Based on my own reporting on its audited financials from 2024 and 2025 , OpenAI will need to raise funding at least three more times in the next decade.  To make matters worse, its free users have become a massive liability. While The Information reported that OpenAI expected to generate $2.4 billion in ad revenue in 2026, and $102 billion in 2030 , it turns out that reality is a little harsher, with analyst eMarketer projects that the entire AI chatbot ad industry combined will only make $1 billion this year , with the entire market making $5.41 billion by 2030.  This means that the 900 million weekly active users of ChatGPT will remain a massive drain on the company’s finances, with only 5% or so of them opting to pay , and a projected 80% of its $20-a-month users expected to churn in 2026 . At some point, OpenAI will simply run out of money. It’s nearly exhausted every available source of capital, and now that it’s likely delaying its IPO to 2027 — largely in part because it couldn’t list at a $1 trillion valuation — it will have to raise again, potentially at a down-round valuation or at a modest increase which will, in turn, make it much more difficult for investors to see a return in an IPO.  Investors will likely ask questions like “why couldn’t you go public?” and “what is it that bankers didn’t like?” as Sam Altman looks at them like this: You see, OpenAI is awesome at selling mythology and hype, but crumbles the second that its numbers have to face the cold, harsh light of day.  While it’s been able to skate by in situations like Altman’s ouster and its conversion to a for-profit, these were strictly legal situations that could be dealt with by lawyers and cheered on by the press . OpenAI has never faced a problem like “not being able to pay its bills” or “breaching a contract with a major company,” and I think these are an inevitability in its future. In the end, OpenAI’s collapse will be a dramatic narration of the boring, horrifying economics of the AI bubble. Let me explain: The AI bubble is inflated based on hype and hopium rather than tangible proof or substantial revenues driven to anyone outside of the semiconductor industry, and without NVIDIA’s massive returns, I don’t think anybody would’ve taken it seriously past 2024. Any and all achievements of the AI industry are a direct result of market psychosis, a broken media ecosystem, and a trillion dollars that could’ve been sunk into literally anything else, and must be evaluated as such. The double-edge sword of a mythology-inflated bubble is that it’s much harder to sustain when said mythology dies. The AI bubble was able to grow to such a horrendous size because the markets and the media were willing to accept basically anything that Sam Altman or the greater AI industry said.  By waving away any economic problems as growing pains and dismiss those who would scrutinize it as haters or cynics, reporters and analysts provided investors with the justification to invest again and again in these companies without them ever having to make a real business , which means that, well…they don’t have real businesses, which is a problem when you need to actually pay somebody money that wasn’t given to you by a venture capitalist. This will leave the AI industry short-changed in its most-desperate times.  The media is important for many, many reasons, but one of the biggest ones is that scrutiny is what keeps capital in check, for the benefit of humanity and at times the companies themselves. By choosing to pull their punches, ignore glaring economic problems and accept every projection with blind faith, the media empowers grifting and suffocates good businesses as a result, encouraging bad behavior and helping them raise unbelievable amounts of money at ridiculous valuations without worrying about having to make a good business. In some cases, the media even encourages them to do so, saying that “all startups lose money at first” instead of thinking about things for a fucking second. When companies know they won’t face that scrutiny, they engineer themselves as such, putting off ever finding a real business model in favor of whatever will make them buzzy enough to get coverage and raise funding as a result. In a vacuum of skepticism, bubbles inflate, monsters get rich, and regular people always get left holding the bag. As a result, if companies ever bother to become a real business, they only do so at the very last minute, endangering anyone who has backed them and every counterparty in the event they’re incorrect.  When OpenAI dies, it will be after a prolonged period of desperate reorganization and attempts to appeal to investors and the media that it can, in fact, become a real business. These attempts — price increases, price cuts, selling off IP, nebulous circular deals, and so on — will all fail, and by the end, Sam Altman will have run through every single trick imaginable to keep the party going.  And when those fail, what do you think Perplexity does? How about Harvey? Cursor got the last chopper out of ‘Nam with the SpaceX acquisition (assuming it actually happens), but what, exactly, is Cognition, or Glean, or Sierra, or really any AI startup meant to say to compel investors to believe in them once OpenAI dies? That they’re different? That they’re gonna work it out after the company that got given basically everything it needed failed?  The entire AI industry’s sales pitch is that OpenAI opened the world’s eyes to the power of AI, and that giving the AI industry as much money as possible would end in economic abundance the likes of which we’ve never seen. Instead, we’ve got two AI labs that both lose billions of dollars, and the latest model from one of them randomly deletes people’s stuff. It’s not like any of this was sold on actual ROI or real businesses or returns or productivity or any actual measurable thing other than physical infrastructure erected in its honor.  There are simply no compelling stories about the AI industry that can be told in the present tense. Everything is always based on the theoretical multiplicative power of just waiting a few more years, which becomes much harder to believe if the company with the Mandate of Heaven gets sent to Cocytus.  This will have massive downstream effects on basically everything and everyone connected to the AI industry. You won’t be able to raise money for a startup to spend money on compute, nor will you be able to convince somebody that your LLM wrapper will change the world, nor will you be able to justify a massive valuation. Venture capitalists fancy themselves as brave soldiers of the economy, but are really cowardly lemmings that will sprint for cover the second that things get rough.  I also keep hearing from people that Anthropic is magically safe from the AI bubble’s clutches, or insulated from its rotten economics. The amount of pure mythology and misinformation I read about this company on Twitter is genuinely offensive, and the fact that journalists have categorically failed to push back against it is proof that too few people give a shit about anything other than which boot they get to lick next. Anthropic faces the same economic realities as OpenAI. It burns billions of dollars on training, it hides inference costs in sales and marketing, and the only real differences are that it focused more on coding and made fewer ridiculous infrastructure commitments…right up until this year, when it committed $200 billion in compute and hardware commitments to Google , raised $35 billion in debt from Apollo to buy Google TPUs , signed a $15 billion a year compute deal with SpaceX , and agreed to a 20-year-long, $19 billion lease with TeraWulf . Much like OpenAI, Anthropic is also doing way, way too much. There’s Claude for Life Sciences , Claude for Legal , Claude for Small Business , Claude Design , and even, for whatever reason, reports that Anthropic intends to develop its own drugs — and instead of saying “hey man, what the fuck are you doing?” the media falls over itself to repeat and celebrate every single one as if they’re all viable or useful products. Anthropic is as messy, disorderly and unfocused as OpenAI, but has done a better job of convincing people that it’s somehow “ethical” as it fucks over its partners and farts out 200 new products a month.  This is a company that lacks focus or vision other than “more” and “bigger.” The only thing that differentiates OpenAI from Anthropic at this point is the nebulous promises of “AI code” and Dario Amodei’s Doom Trolling and safety theater. The fact that the majority of the media made no efforts to push back against its shenanigan-rich “profitability” narrative is why we’re in this fucking mess.  Anthropic is an AI lab just like OpenAI. It uses GPUs, TPUs and Trainium chips. It trains models in much the same way to do much the same things, and builds quasi-functional plugins on top of them, just like OpenAI does. It makes big compute commitments, it had its infrastructure built out for it by hyperscalers, its CEO is annoying and beloved by cretins, and its value is largely determined by 1000 people on “X The Everything App” experiencing varying levels of AI psychosis.  Attempts to claim otherwise are tacit admissions that OpenAI is unsustainable. Please note that when I say “victims,” I don’t always mean “people you should feel sorry for.” In some cases I’ll be talking about real people who are facing the horrible consequences of the OpenAI bubble bursting, and for whom you should feel a degree of sympathy, and in others, I’m referring to various Patagonia gargoyles’ financial woes. I assume you’ll be able to differentiate between them.  My last premium newsletter was the massive Hater’s Guide To The Memory Crisis , or the twisted tale of how three companies — Samsung, SK Hynix and Micron — have diverted meaningful amounts of manufacturing supply away from making the RAM you find in laptops and smartphones toward making the high-bandwidth memory that powers GPUs, jacking up the price of consumer electronics in the process.  To explain: To simplify, the AI GPUs in AI data centers require hundreds of gigabytes of high-bandwidth memory, the CPUs attached to them require the same RAM as your smartphone, and the companies making all of this RAM are making huge profits by jacking up the price because of supply chain constraints that they themselves have created. That’s why Micron had 84.9% gross margins in the last quarter . The RAM triopoly controls more than 90% of the world’s memory, and can set prices at whatever rate they want. These three companies were all fined over $100 million by the Department of justice back in 2002 for price-fixing , with Micron avoiding the fine by turning in its co-conspirators . Five years later in 2007, a Supreme Court judgment and resulting precedent ( Bell Atlantic V. Twombly ) drastically raised the bar for not simply winning an antitrust case, but even getting one to trial : This precedent would kill a 2019 class action case against SK Hynix, Samsung and Micron that alleged they had colluded to tighten the supply of the world’s DRAM , because despite statements from company representatives made at public events, their collective participation in certain industry groups, and observable pricing trends, the precedent set by Twombly meant that the plaintiffs required more than circumstantial evidence to bring something to trial.  Anyway, the reason I bring this up is that while I am not accusing Samsung, SK Hynix, and Micron of price-fixing, a recent lawsuit is accusing them of exactly that : So, what does this have to do with OpenAI?  Well, back on October 1, 2025 , OpenAI, Samsung and SK Hynix announced a “strategic partnership” that would involve OpenAI buying 900,000 wafers of DRAM a month (around 40% of the world’s supply at the time) for Stargate data centers — something that never actually happened (it was a memorandum of understanding, and OpenAI also had nowhere to put them), but both SK Hynix and Samsung’s stocks immediately rallied , and Samsung happened to hike prices by 60% a month later , which could be a coincidence, or could have been the company saying “yeah, wow, we’re gonna run out of RAM I guess, better buy now at whatever price we have it!” Another clue that this might not all have been above board was that Samsung was reportedly doing another deal with OpenAI in March 2026 , “...to supply up to 800 ⁠million gigabits (Gb) of 12-layer HBM4 chips to OpenAI in ​the second half of this year” per Reuters, for use with Broadcom’s custom “Jalapeno” chip . Though it’s hard to calculate exactly how much that would be wafer-wise, from what I understand we’re talking in terms of less than 100,000 wafers total after OpenAI, Samsung, and SK Hynix said they’d be taking up 900,000 a month. Regardless of whether OpenAI ever takes a single wafer of silicon, these deals existed to put the squeeze on any company that uses memory in their products — including NVIDIA, AMD and Broadcom — which in turn led to the most aggressive price increases in the history of consumer electronics. As I said last Friday: And yes, OpenAI is responsible, both in its naked collusion with memory manufacturers to push an announcement that never resulted in anything other than price increases and its siren song that made every dimwit with debt desperate to build AI data centers.  Every single consumer suffers as a result. RAM is in everything, and it’s unclear when new manufacturing capacity will actually come online, as fabs are expensive and complex construction efforts and require tons of specialist talent, raw materials, permitting, land and power. SK Hynix Chairman Chey Tae-won said in March that the memory shortage would last until 2030 , and he may be right, as a Bank of America report just said that SK Hynix may only be able to add a sixth of its planned capacity by 2028 . This means that the price of consumer electronics will be inflated for the foreseeable future, even if the AI bubble bursts. While capex pullbacks will eventually happen and by extension eventually lead to supply constraints easing, Micron, Samsung, and SK Hynix had sold out their entire 2026 supply by the second week of January , and noted that they’d only be able to handle 60% of “medium-term” customer memory orders, which suggests to me that 2027 might be even worse, with a subtle clue being that SK Hynix CEO Kwak Noh-jung recently told Reuters that 2027 would be “the worst year in the industry’s history from a supply perspective.”  While the memory triopoly has every incentive to make things seem bleak to drum up business and sustain their margins, behind the scenes reports suggest they’re turning the screws on everybody. This is a graphic example of companies with massive amounts of leverage using it to fuck over both their customers and their customers’ customers .  Who gave them that leverage? The AI industry and Sam fucking Altman.  Hey, remember when I just said that ( it seems, but I cannot confirm that) OpenAI helped SK Hynix and Samsung manufacture a supply chain crisis last year using a phoney announcement for a project that would never happen? That happened three other fucking times in the same three week period, and modern journalism doesn’t seem to give much of a shit! Let’s review what happened, per my year-ending Enshittifinancial Crisis newsletter : All four of these companies’ stocks rallied on deals that land somewhere between misleading and fictional, with basically anyone who invested in them being underwater within two months, though all three have recovered thanks to similarly-questionable announcements and deals made by companies with the sole intention of boosting their stocks.  Why else would Sam Altman go on CNBC with NVIDIA CEO Jensen Huang on the day of an announcement of a project that was only ever a letter of understanding ? Why else would Sam Altman jump on TV with Bob Iger to talk about a Disney deal that clearly never went anywhere? Spare me any explanations around the “fast-paced dealmaking of AI” or “how deals are complex.” CNBC reported the day after the NVIDIA deal was announced that the first $10 billion tranche would “close within a month or once the transaction had finalized” via a source! It’s blatantly obvious that the intention was to create the appearance that a deal existed that never actually existed at all! The AI trade is the natural endpoint of an increasingly-enshittified stock market where many analysts and journalists exist only to repeat narratives to influence stock prices. Outside of semiconductors, the AI trade has never, ever been about the actual underlying economics or the actual economic potential of Large Language Models, but projecting shadows on the wall to resemble something that looks like the next generation of technology. That’s because the AI trade is entirely symbolic and driven by stock prices. When NVIDIA and the rest of the Magnificent Seven (sans Apple) does well, AI is the greatest thing on Earth. When the Magnificent Seven stumbles, everybody worries that they might be overspending on AI. The AI trade exists only to manipulate stock prices through spurious news and smoke signals on social media, and to drag gullible retail investors ( who account for 20% of US equity trading volumes, the highest it’s been since 2021 ) and the rest of the market away from caring about things like “fundamentals” or “reality” toward whatever keys are currently jingling.  My evidence is fairly simple: Google, Meta, Microsoft, and Amazon don’t actually tell you their AI revenues, other than when Microsoft and Amazon have chosen to define it in terms of undefined “run rates.” And why would they? Reporters have been saying that their AI bets have paid off for years without the companies ever having to show it paying off other than their stocks running.  Here’s another example: CoreWeave, a time bomb /AI compute company that only really exists as a revenue source for NVIDIA ( per Jensen Huang , if [NVIDIA] didn’t help CoreWeave exist, they would not exist”) by signing contracts with companies for unbuilt capacity that it then takes to banks and uses to raise more money to buy GPUs. NVIDIA knows that analysts and reporters don’t give a shit about the blatant self-dealing and circular financing, all because these deals help the stock price go up, which apparently is the only metric that modern journalism evaluates. That’s why when NVIDIA invested $2 billion in CoreWeave in January 2026 — a warning sign that the company had liquidity problems! — led to endless positive coverage after “the stock popped on the news,” per CNBC. That’s because the AI trade exists only to extract value and con investors. It is not a trade related to the actual fundamentals of whether AI works or not, whether AI actually makes anyone money, or really anything about AI at all outside of whether mentioning AI or an AI-related company makes a stock number go up or down. I’ll be blunt: modern journalism has failed the retail investor and directly helped the wallet inspector regulate the stock market. By empowering Sam Altman and the rest of the AI industry’s deliberate attempts to obfuscate the actual economics of generative AI and setting the terms of AI’s success as “how stocks are doing and whether the companies are growing in general,” they have defaulted on their responsibility to the general public and helped the already-rich get richer.  None of this would be possible if business journalism actually saw themselves as having a responsibility to give their audience good information. While one could argue that if you had blindly invested in the AI trade you might have made money, the ability to make money in the AI trade was directly driven by modern journalism’s inability or unwillingness to push back on any corporate narrative. Every major outlet ran a story on every one of the deals I mentioned, and not a single one seemed remotely upset or deterred by the fact they were misled, and in turn misled their audience. And yes, investment funds can be just as easily manipulated as a retail investor, and will follow whatever trend seems likely to make them money, even if said trend is utterly disconnected from any fundamentals. Tech analysts help do so by creating vast models that give a veneer of respectability, even if their projections mostly amount to “number will always go up in the future.”  This is why Musk was able to dump SpaceX on the public markets. Why SK Hynix chose to list on the NASDAQ. When the entire world is captured by a childlike belief that “AI is good and will be the biggest thing ever,” you empower grifting and swindling at scale.  Well, that and underwriters like Goldman Sachs are so nakedly crooked that they’ll say they expect SpaceX’s AI revenue to grow 100x by 2030 . Fuck off! Yet the memory boom/bust/crisis is where the media has failed investors the most — a final insult before everything collapses. You see ( to quote myself ), what makes this particular memory crisis so distinctly dangerous is that it isn’t a result of consumer demand so much as it is capital expenditures from very large companies making bets that don’t connect with reality.  Microsoft, Google, Amazon, and Meta aren’t spending $765 billion in capex in 2026 because of rapid demand by consumers for AI services, but a desperation caused by a lack of hypergrowth ideas , circular financing with Anthropic and OpenAI , and a vague concern that if they stop spending that the other guy will do something as a result.  Anyone blathering on about a “memory supercycle” is intentionally obfuscating where that revenue and demand is coming from — high-bandwidth memory attached to AI GPUs, meaning that this boom cycle only exists as a symptom of a greater hype cycle, meaning that when companies stop buying GPUs , the demand for that (briefly) high-margin high-bandwidth memory goes with it.   To give you some context, a chart from ComputerBase.de showed that high-bandwidth memory demand grew from 681 million gigabits of HBM in 2022 to 29.3 billion gigabits on 2026 — a 40x increase over the course of four years that suggests that once GPU-related capital expenditures stop, high-bandwidth memory demand will effectively disappear .  As I mentioned previously, this isn’t even me being a hater . Hyperscalers are now joining the rest of the world in having to raise debt to buy more GPUs, which means that at some point they aren’t going to be able to afford to buy as much, which will in turn mean that NVIDIA — which accounts for around 65% of all HBM purchasing — won’t need as much. I have not read a single fucking article that mentions that this is a possibility! Every article about the memory industry right now is about supply constraints and the increasing cost of memory , but none of them warn investors or the general public about what will happen when capex slows , and certainly not the many, many articles in major business publications about SK Hynix, Samsung and Micron’s revenues. In fact, Reuters said that SK Hynix’s “ scarcity premium looks built to last .” The cynical (and boring) response here is that “the market can stay irrational longer than you can stay solvent,” but saying that distracts from the larger point of how said irrationality was manufactured by the media .  I am not sure what the majority of the media sees as its purpose or responsibility to its readers, so I will speak plainly: the responsibility is to tell them the cold, hard truth, rather than going along with whatever hype cycle is happening out of fear of being wrong or missing out. Skepticism is not doomerism! Being critical is not being negative! These companies are some of the largest and richest enterprises in the world — they should be scrutinized! And no, scrutiny is not publishing everything they say and then making a vague comment about “whether or not that bet will pay off.” Too often, journalism conflates objectivity with passivity, seeing critiques as “negative” or “biased” when, in fact, repeating everything that corporations say to their benefits is about as biased as it gets. In the end, the victims are anybody who doesn’t exit the AI trade in time.  By the way, there’s no Hell hot enough, by the way, for the people that will read this and smugly say “heh, well, I made money,” or who point to anyone’s returns as evidence that the AI trade is anything other than manufactured consent. The fact that anyone made money on this trade is a sign that the stock market is inherently manipulated to benefit the wealthy at the cost of the many — and when the bubble bursts, the people that will suffer will have suffered because of the media’s participation by helping Sam Altman and the rest of the AI industry obfuscate and twist reality to pump stocks. Which leads us neatly to our next victim! In my Hater’s Guide To SoftBank , I told the story of CEO Masayoshi Son, a degenerate gambler who has steered his company through boom and bust cycles only through the grace of whatever God he believes in and sheer luck.  SoftBank Group — the holding company, and not to be confused with Softbank Corp, which runs a bunch of telcos and media companies in Japan — makes money only through either investing in or buying companies, then taking them public or selling them to someone else, and otherwise needs debt for liquidity.  Masayoshi Son makes terrible bet after terrible bet, but his luck always seems to work out for him. His $20 million stake in Alibaba turned into $50 billion at IPO. He bought a 70% stake in Sprint that turned into a 24% holding in T-Mobile . In the early 2000s, Softbank took a 23% stake in Betfair that eventually became part of the $17.7 billion Flutter Entertainment. And then there’s its most-recent and arguably most-impressive (after Alibaba at least) investment, ARM, which it acquired for $32 billion in 2016 and then took it public in 2023 at a valuation of $54.5 billion , and currently sits at around a $300 billion market cap.  Yet his problem has always been his dalliances with whimsical white boys. SoftBank sunk $1.5 billion into dodgy financial services firm Greensill Capital before its collapse, and in the aftermath, it was revealed that Masayoshi Son and CEO Lex Greensill talked on the phone every day , to the point that ( per Greensill himself ) SoftBank managers felt “threatened” by Greensill’s relationship with Son. It only took Masayoshi Son 28 minutes of conversation with WeWork’s Adam Neumann before he drew up the terms for a $4.4 billion investment on his iPad and signing the deal in the back of a cab, with Son saying that “the last person he felt this with was [Alibaba CEO] Jack Ma.”  And no white boy has ever been more whimsical than Sam Altman.  In 2019 , Altman turned down $10 billion from Masayoshi Son (which, ironically, would’ve been an incredible investment at the time), going instead with $1 billion (and full infrastructure support) from Microsoft, and I believe this moment drove Son into a level of madness that will potentially wreck the company. You see, up until fairly recently, SoftBank had been dragged down by the declining value of its atrocious investments via its two venture capital funds — Vision Fund 1 and 2, the latter of which was self-funded and has mostly gone toward funding OpenAI. Up until recently, SoftBank had quarter after quarter of losses as investment after investment saw its NAV drop because, well, they were overvalued and SoftBank never should’ve invested in them in the first place. To survive, SoftBank moved into “ defense mode ” in 2020, slowing investments and selling the vast majority of its Alibaba stock by April 2023 , with the ARM IPO and billions of dollars of bond sales helping slow the bleed. Yet Masayoshi Son knew he was destined for greater things, as he told CNBC in June 2024 : OpenAI — and the larger AI trade — had given Masayoshi Son a certain kind of greed-driven mania, where he believed that AI would make SoftBank (as he said recently) “ the goose that laid golden eggs ,” an eternal money-printer that ostensibly started with the biggest cash-burning machine in history.  Altman, like Neumann, like Greensill, told Masayoshi Son exactly what he wanted to hear: that this would be the biggest thing ever, and that Son would capture all of the value both through his investment in OpenAI and further investments in data centers and other AI infrastructure.  And so began his most vulgar investment yet — OpenAI, sinking $2 billion into the company from Vision Fund 2 in November 2024 — only for Altman to turn around and demand he fund $30 billion of a $40 billion round that would get announced four months later in March 2025 . Masayoshi Son was an emphatic “yes,” except for one little problem: he didn’t have the money, and could only afford the first $7.5 billion (due in April 2025) by taking out a $15 billion, year-long bridge loan , with the rest of it going toward his eventual purchase of Ampere computing .  To fund the remaining $22.5 billion, SoftBank was forced to take out further margin loans on its ARM stock , and sell large chunks of its T-Mobile stock , as well as its entire $5.83 billion stake in NVIDIA . Yet as soon as the check cleared, Sam Altman was blowing up his phone demanding more money as part of a $110 billion funding round in February 2026 (that eventually became $122 billion in late March). Masayoshi Son was once again an emphatic yes, except by this point he’d exhausted basically every useful thing left in his coffers outside of around $118 billion in ARM shares that make up around 40% of SoftBank’s net asset value, meaning that selling or using further ARM shares as collateral would directly tank its value — both through the obvious “they have less of a valuable thing” and sales/collateralization of further ARM shares affecting its share price. So, what did Masayoshi Son do? More debt, baby! More risky debt! You can always refinance it, right?  To pay for its share of OpenAI’s 2026 funding round, SoftBank took out a $40 billion bridge loan (maturing in March 2027), bringing its investment in the company to over $40 billion, with its payments to $10 billion tranches of OpenAI funding due in April, July and October 2026. A few months later, it tried to raise a $10 billion margin loan using its entire OpenAI investment as collateral, cut the amount it was raising to $6 billion, and when banks remained hesitant to give it the money anyway offered to “ guarantee repayment of the loan to address lender concerns, ” effectively backing the loan with its own balance sheet (called a recourse loan) because, despite being worth over $100 billion on paper, its lenders had doubts that its OpenAI stake was actually worth that much.  If you’re wondering why it didn’t simply take out more debt, it’s because (as a result of its continuing investments in OpenAI) S&P Global revised SoftBank’s outlook to negative , emphasis theirs : This has had a knock-on effect on the rating of the telecoms-focused Softbank Corp (as a reminder, Softbank Group is the holding company that owns stock in other companies, Softbank Corp is the energy/telecoms company that actually makes stuff), which is now rated BBB, or the lowest-possible rung of investment-grade financing in the S&P system. To make matters worse, if SoftBank continues to hold a loan-to-value ratio of above 30% for much longer, it runs the risk of its debt getting downgraded even further, which would slam the door shut on its ability to raise money via bonds, which is…well, basically how SoftBank has functioned for the last 10 or 20 years. And this is all happening as Japan is determinedly inching away from the era of persistently low interest rates — making debt far more expensive to service.   SoftBank needs OpenAI to IPO so that it can turn that on-paper gain into actual liquid stocks that can be dumped into the market or used for real-life margin loans. SoftBank has jettisoned the vast majority of its heaviest-weight investments, leaving it largely dependent on the continued value of ARM’s stock to keep its seat at the table, and if OpenAI can’t go public, it’ll end up sitting on illiquid stock in a company that will see its value tank as a result. Yet even if OpenAI does go public, any attempts to get a margin loan will likely be dangerous, as I bet that it will be one of the single-most shorted and volatile stocks in history, which will also be a problem for SoftBank’s underlying net-asset value, which will ebb and flow based on whatever bullshit Altman cooks up every three months. Masayoshi Son is both a victim of the manufactured consent of the AI trade and an enabler of its worst excesses, empowering and enriching Sam Altman at a time when any kind of financial prudence might have curbed OpenAI’s greed or killed it before it caused further damage.  SoftBank tanking will fuck over anyone invested in the Japanese stock market, where it currently sits as the third-largest company by market cap behind KIOXIA (a memory company booming thanks to the AI trade) and Mitsubishi UFJ Financial (a bank with heavy ties to the AI industry and data center infrastructure). While I severely doubt it’ll die — it’s likely MUFJ and SMBC Bank would extend whatever credit necessary to keep the doors open — OpenAI and the greater AI trade has become a load-bearing toothpick holding up the trillion-ton ass of the world’s most well-funded gambler. For SoftBank to survive in its current form, OpenAI must go public, become a thriving and profitable business, and have its stock price stay elevated for the foreseeable future. Additionally, ARM must also retain or exceed its current stock price. Hey, while we’re on the subject of “companies betting the entire future on OpenAI that recently got downgraded by S&P Global…” Hey! You in the back! Stop laughing! Stop laughing at Larry Ellison! He’s now only the world’s 8th-most-richest guy !  Just kidding, fuck Larry Ellison. What I’m about to tell you might make you laugh, probably because it’s really funny. Oracle is currently spending over $340 billion to build out over 7.1GW of data center capacity for OpenAI , as part of its $300 billion, five-year-long cloud compute contract that began, at least in theory, on June 1, 2026 at the beginning of its Fiscal Year 2027, though much of the capacity is yet to be built. To fund the buildout, Oracle has had to raise over $50 billion via stock sales and debt , spent $55.7 billion in its last fiscal year , and expects to spend at least $90 billion more in FY2027. As a result of that , S&P Global downgraded Oracle’s credit rating to BBB/A-2 , the literal lowest level before it’ll become junk-grade, meaning that one more downgrade ( though it would have to be from two ratings agencies ) from here would risk Oracle becoming a “fallen angel,” with investment funds (that can’t hold junk grade debt) having to jettison its debt from indexes, as happened to Ford in March 2020 , leading to over $35 billion in debt being dumped and its borrowing costs skyrocketing to between 8.5% and 9.625% when it raised in April 2020 . For some context, Ford reported an average interest rate of 5.2% on its long term debt in its 2019 annual report .    You’ll never guess why S&P Global downgraded Oracle! And, once again, the emphasis is theirs: That’s a load-bearing if, brother!  Anyway, you know who else is trying to warn you about Oracle’s exposure to OpenAI? Oracle! Per Bloomberg : As a reminder, the only way that OpenAI will be able to afford to pay its $300 billion cloud compute contract with Oracle will be if it continues to hit revenue projections ( per The Information ) that have it making $113 billion in 2028, $184 billion in 2029, and $284 billion in 2030, a year when it will magically become profitable, and no, I don’t know how that happens: Based on my own analysis , assuming that Oracle can successfully build capacity for OpenAI to pay for (a load-bearing assumption), it would have to pay around $75 billion to rent that 7.1GW of capacity. Stargate Abilene, an 8-building, 1.2GW project that broke ground in July 2024 , has (per sources familiar with the matter) only built and operationalized three buildings, despite the project having meant to be fully operational by the end of 2025 ( per landowner Lancium ), or energized by the middle of 2026 , it isn’t really clear, and I can’t get a straight answer from anyone about whether the power even exists on site to turn any of it on.  Anyway, for Oracle to make all the rest of that money, it will have to build five more Stargate Abilenes. If you’re wondering how that’s going, Stargate Shackelford only broke ground in December 2025 , Stargate Wisconsin appeared to have a single steam beam in March , Stargate Michigan only got its first steel beams two months ago , and Stargate New Mexico is still waiting for permitting to begin construction .  Based on Lancium’s presentation and discussions with sources familiar, Oracle will pull in somewhere in the region of $10 billion in annual revenue from the (assuming it’s ever done), completely-finished 824MW of critical IT infrastructure at Stargate Abilene. It is unclear how Oracle hopes to be paid even a fraction of its $300 billion compute deal, because in its current state, its annual revenue from Stargate projects currently sits in the region of a maximum $5 billion a year, or less than a tenth of its FY2026 capex. For the most part, Oracle has funded the various Stargate data centers with project financing, meaning that a nebulous SPV will be responsible in the event it defaults on any of these contracts…until Stargate Michigan, which only closed when Oracle agreed to guarantee the $14 billion in bonds raised .  All of this revenue — both theoretical and otherwise — sits in Oracle’s “Cloud” segment, the only part of the business that’s actually growing , as the rest of its business has either been declining or plateauing for about a decade.  In any case, for Oracle to actually get paid its $300 billion, it will have to build upwards of 6GW of data center capacity…in a year and a half? This deal is meant to be worth in the higher range of tens of billions of dollars in annual revenue by FY2028, which begins on June 1 2027! Stargate is horribly, impossibly delayed, to a level that makes me wonder if anybody other than perhaps Anissa Gardizy has bothered to think about Stargate for even a fucking second. Anyway, Oracle’s entire future rides on this deal. While Oracle Cloud Infrastructure continues to grow, its future growth (and remaining performance obligations) almost entirely hinge on both its ability to build the largest infrastructure project of all time and for OpenAI to continue raising funding for an indefinite amount of time. The rest of that growth comes from Meta and xAI, both of whom are only really “doing AI” because everybody else is. This puts Oracle in a very, very compromising position on multiple different levels.  Generative AI is the only reason that Wall Street started liking Oracle again as its other business plateaued, even as it burned billions of dollars on capital expenditures and cut its gross margins by a little under 15% since 2022 , with the vast majority of that value coming from its revenue from OpenAI and what’s actually active at Stargate Abilene.  Much like the rest of the AI trade, everything about Oracle’s future is sold on potential rather than anybody thinking about reality or things like “whether Oracle can actually build the data centers” or “how Oracle makes any of that revenue if the data centers aren’t built” or “how OpenAI affords to pay for the compute if the data centers get built.” As Oracle said in its own disclosures, if OpenAI can’t pay, “Oracle could be left with massive data center leases that it might be unable to exit or have to re-lease to new tenants under less-favorable terms,” and there isn’t a single company on Earth who can or would pay for such a large amount of compute, nor is there the aggregate demand to justify it. While its many government contracts and national security significance make it unlikely that Oracle would be allowed to die , the collapse of its only growth segment will likely spell dark times for a company that’s already laid off 21,000 people as a means of funding its AI buildout. The double-edged sword of the AI trade’s childlike attachment to stock valuations poses an egregious threat to Larry Ellison himself. Hey — HEY! I said no laughing! Stop it! This is all very serious! This is a serious situation! You’re laughing about the potential downfall of a guy who once wrote a letter to the New York Times attacking HP for firing former CEO Mark Hurd for repeatedly making sexual advances toward a reality star using HP’s finances !  Sorry, my mistake, you should keep laughing, even the prospect of what I’m about to tell you is hilarious.  As I said in my piece about how OpenAI Kills Oracle:  One of the consistent themes of this piece is that much of the “value” of AI is hot air — by which I mean whatever people are willing to pay for a stock that’s continually inflated by specious media-driven hype.  Ellison’s wealth is driven by both his share of Oracle’s ongoing yearly dividend, his Oracle shares, and his ability to offer said shares as margin loans, which makes him vulnerable to even a symbolic collapse of OpenAI, which is why it had to tweet in February that “ the NVIDIA-OpenAI deal has zero impact on its financial relationship with OpenAI ” to calm those dumping the stock.  To be clear, Ellison has around 1.16 billion Oracle shares, leaving him with around 810 million or so left, allowing him to pledge them as further collateral rather than having to either dump them on the market or dip into his reserves of about $10 billion in cash and $15 billion in Tesla stock , with Ellison historically never selling more than about $4.7 billion in stock. We don’t know the exact scale of terms of his personal loans, but do know that he’s got a shit-ton of them, and that his entire fortune rests on the idea that he never has to sell Oracle stock. That becomes a problem if things drag on with the Warner Bros deal, as he’s also guaranteed $40 billion from the Ellison Trust , effectively barring him from selling or using those shares until the deal clears (and the money from the Middle East arrives to fund the deal). The amount of shares that Ellison has committed has oscillated on a year-by-year basis, sitting at 305 million in both 2018 and 2019 , rising to 317 million in both 2020 and 2021 , dropping to its lowest level in 2024 ( 217 million ) before bumping back up to 346 million in 2025. While the board theoretically keeps an eye on his loans and what he’s pledging, he holds 40% of Oracle’s stock and the undying loyalty of veterans like former CEO Safra Catz and co-CEOs Clay Magouyrk and Mike Sicilia. To get specific about how the Paramount/Warner Bros deal breaks down, $24 billion will be covered by funds from the Middle East (primarily sovereign wealth funds), with Ellison providing $22 billion and bank debt funding the rest.  If the deal doesn’t close by September 30, the Ellisons have to pay around $650 million a quarter in fees . If it does , Ellison will likely either have to liquidate his Tesla stock, hand over cash, or take out further margin loans on his Oracle stock to fund it. Those would likely increase the amount of shares he’d have to commit somewhere between 150 million and 300 million (at a loan-to-value of 25% to 50%) at whatever price Oracle is currently trading at. Though it’s hard to tell exactly, the number to look for with Oracle is “below $70.” Once that happens, Ellison will likely have to proffer more Oracle stock to keep up with his margin calls, which will severely limit his ability to take out further margin loans using his Oracle stock. He will have to renegotiate loans, and if he’s managed to buy Paramount, he’ll be sitting on the stock of a company with $80 billion in debt and constantly loses money , which will be far less-appetizing to potential lenders who are aware that the rest of Ellison’s money is tied up in the plummeting hopes of Oracle. Things could get much darker if Oracle plunges below $50, as at that point the encumbrances of his various enterprises and his own margin loans could become too much to avoid having to liquidate Oracle stock. If that happens, it creates a vicious cycle that will potentially involve selling off Paramount, dumping further Oracle shares, or even trying to engineer a firesale for the company. All of this was entirely avoidable if he had never met Sam Altman, and never gave in to the temptation of the AI trade. When the OpenAI Bubble — and OpenAI itself — bursts, many will attempt to eulogize the situation in terms of how we could’ve possibly known this would happen, and I want to be clear that I’m going to be reading and commenting on as many of them as I can find. I believe that once OpenAI collapses it’ll have a violent, punishing effect on the entire stock market, a precursor to a much greater drawdown as everybody accepts that the AI bubble has burst.  This view is shared by the Bank of England governor Andrew Bailey , who warned that the bursting of the AI bubble would have an effect on the UK economy, even though the UK economy — and the UK financial system — isn’t nearly as exposed to it as that of the United States, and would have significant enough effects to change British monetary policy, specifically, interest rates.  And I continue to stand by my belief that this company will die, though I can’t say when it’ll happen. The promises that Sam Altman has made at the scale that he’s made them are equal parts ridiculous and dangerous, leaving any counterparty somewhere between burned or destitute as a result.  There is no compelling story for any AI company once OpenAI dies. Other AI labs will suddenly have to explain how they avoid the same economical pitfalls while still showing the same aggressive growth projections promised by Sam Altman, and half-measures will no longer be acceptable. Their ability to secure credit — or even venture funding — will be met with impossible-to-answer questions about sustainability and profitability. Any startup connected to its models will suffer because it’ll be clear that any AI lab is a financial black hole, and it’ll become obvious that basically every AI startup is an unprofitable LLM wrapper. That should be obvious now, but nobody bothers to look. Any AI infrastructure company will have to pivot aggressively to open source models if they haven’t already, and realize that much of the demand for AI services came from brainless curiosity driven by the AI trade and market hype. CoreWeave, IREN, and the many circular-financed neoclouds will, much like AI labs, find themselves unable to secure funding, as the first question will be “how do you know your customers won’t die?” NVIDIA just won’t be able to justify selling as many GPUs, as it has repeatedly cited OpenAI (albeit without saying its name) as a proxy driver of sales via counterparties including Microsoft and Amazon. It’ll be a permanent blemish on a startup ecosystem that helped so many people become rich based on fictional or fanciful promises and projections, enabled and funded by venture capitalists that didn’t force founders to make stable or sustainable companies because it “always worked out before.” And I genuinely think this will create an accountability crisis in the media. I speak with readers and listeners every single day that are horrified about how many half-truths and outright lies are published and used as a means of propping up the AI bubble and the larger tech industry. The term “AI” has grown from a kind of technology to a cudgel wielded by the powerful to threaten and terrorize workers, all based on the outcomes from Large Language Models that simply do not do what their progenitors have promised and do not produce ROI or productivity benefits that are in any way measurable. The OpenAI Bubble inflated not because Sam Altman is a super-genius, but because he’s very, very good at telling people what they want to hear. He’ll give members of the media convincing-enough projections, said with the confidence ( or necessary fear ) necessary to sway the vast amounts of reporters who are excited to follow the next big hype cycle (or, put another way, are scared to miss out on it).  Altman knows the exact signifiers to use and the minimum viable product necessary to “prove” OpenAI’s worth — however many hundreds of millions of weekly active users, annualized run rates, gigawatts of data centers, vague promises of “abundance” and “intelligence too cheap to meter” that never actually resemble a tangible thing — that work to con reporters and investors who don’t want to think about anything but growth.  He’s also really, really good at playing on people’s greed, be it promising Satya Nadella he can build the next generation of cloud compute cash, Larry Ellison that he can make OCI bigger than Azure, and Masayoshi Son that he can birth a goose that lays golden, AI-labeled eggs.  Altman realized early on that the only way to sell AI was to talk about it in the future tense in a mixture of threats and promises, always subtly suggesting that those who follow the OpenAI gospel will be saved from the permanent underclass. And that same con worked on the minds of Silicon Valley founders who feel sore that they’ve yet to become an early employee of the next Apple, Google, Amazon or Microsoft, selling the dream of endless wealth under the auspices of “accelerationism” that really means “growth at all costs, usually billed to somebody else.” He and his acolytes have created a palpable mania in the Valley, convincing people that not using his software is a guarantee that they’ll be poverty-stricken imbeciles, and I think he’s fully aware of the fact that Silicon Valley is a dense monoculture that LARPs as a free thinker’s paradise. In the end, Altman is unlikely to suffer, at least anywhere near as much as those he’s misled or helped mislead. The scale of losses that the stock market may face scare me to the point I’m almost hoping I’m wrong, with the markets heavily dependent on eternal growth of the AI trade, as without NVIDIA selling more GPUs every quarter, it’s unlikely that anybody is going to be excited to invest in tech past the year 2028. All of this could’ve been stopped if those responsible for scrutinizing the powerful actually did their jobs, and spent more time doing that than critiquing the critics and repeating the promises of craven liars and billionaire scumbags. There were signs from the earliest days that this was all unsustainable, and the only reason it got this big was because the media and the markets fell behind a specious AI trade, empowering and enabling venture capitalists and hyperscalers to sink hundreds of billions of dollars into a doomed industry.  Whatever the AI industry achieves by the end of this farce will pale in comparison to the massive harms it has caused and will cause as a result, and for us to avoid this happening again, we need a fundamental reimagining of how the powerful are covered, how much effort is made to pry apart their plans, and accountability for those who either failed to stop them or actively assisted them. If I sound salty, it’s because I am worried about the regular people caught up in this madness — the tens of thousands of people that have suffered AI-washed layoffs , the hundreds of millions of people that invest in a global stock market dependent on the AI trade (for a taster, see what’s happened in Korea when the KOSPI dropped earlier in the week, forcing hundreds of thousands of retail investors to face margin calls ), those whose retirements and pensions and insurance annuities are tied up in private credit funds invested in AI data centers , and anyone of any kind who built their life around any promise made by Sam Altman and those that followed him.  I challenge those who are glibly dismissive of everything I say — who look for any smidgen of proof to dismiss hard numbers or clear economic issues — to truly think about the consequences of what I’ve written, and take the risk of the OpenAI Bubble seriously. Tech companies are not your friends, venture capitalists are not your saviors, Sam Altman doesn’t care if you live or die, and the AI industry — and Silicon Valley — will dump you the second that you stop being useful as an acolyte or booster.  I love technology, and credit it with making me a success and the person I’ve become, as well as connecting me to many people I love dearly. I believe that tech should be something that empowers, protects and enriches the human experience, something that’s sustainable and reliable and replicable and stable and makes human beings the same as a result.  The tech industry as it stands shows nothing but contempt for the user. Every tech product is somewhere between broken and buggy. The people that write about tech write for the companies far more than they write for those that pay them. Venture capitalists fund companies that they think they can sell to other companies or take public, which in turn means they fund things that are only attractive to people on Twitter or other venture capitalists. Big tech is unregulated, unrestrained, and works entirely to either enrich or fuck over shareholders depending on the day, and because the finance media has little interest in pushing back, they’ll continue to do so to the detriment of the markets and the retail investor. Everything comes back to a distinct selfishness and lack of responsibility across basically every part of the tech industry. The fact that AI has grown this large is a symptom that Silicon Valley needs to be restrained — that it can and will release dangerous, unreliable, unpredictable and unstable products at scale with little regard for the consequences, in part because it knows the media will celebrate it doing so if it can show user or revenue growth.  OpenAI is the company the tech industry deserves — a directionless company of questionable worth that grew in a vacuum of responsibility that exploits greed and ignorance at scale. And the tech industry will deserve exactly what it gets for coddling Sam Altman, and letting his empire grow this large. 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 10,000 to 18,000 words, including vast, detailed analyses of the biggest events and companies in the AI bubble.  As a reminder, if you sign up between now and XX July, you’ll get $10 off a subscription.  OpenAI’s collapse will be a direct result of its loss-laden economics — its doomed, loss-making subscriptions, its pathetic advertising revenue, and API costs that became a “huge issue” for its enterprise customers — and the fact that outside of the hype , AI lacks measurable ROI . When OpenAI eventually leaves CoreWeave, Cerebras, and Oracle in the lurch, there won’t be anyone else to pick up that compute.These are all debt-laden companies, and without meaningful revenues, they’ll struggle to service their obligations. When OpenAI dies — likely folding into Microsoft in the process — it will massively pull back on any and all compute demands, with the likely end of and free ChatGPT and a massive price bump across the board. OpenAI’s demise would also naturally call into question the rationality of investing in any AI startup. If the largest, best-funded, best-resourced company in the entire industry backed by the world’s largest software companies couldn’t make it, why would you believe somebody else would do so? The collapse of the largest company in the ecosystem would also seize up any and all AI data center debt (if any exists at that point), because the literal largest consumer of AI compute would be dead. On September 22, 2025, NVIDIA announced a “strategic partnership” to invest “up to $100 billion” and build 10GW of data centers with OpenAI, with the first gigawatt to be deployed in the second half of 2026. Where would the data centers go? How would OpenAI afford to build them? How would OpenAI build a gigawatt in less than a year? Don’t ask questions, pig!  NVIDIA’s stock bumped from from $175.30 to $181 in the space of a day. The media wrote about the story as if the deal was done, with CNBC claiming that “the initial $10 billion tranche [was] expected to close within a month or so once the transaction has been finalized.” I read at least ten stories that said that “NVIDIA had invested $100 billion.” This deal never happened. Three months later, the Wall Street Journal said that it was “on ice,” and two months after that , NVIDIA pledged to invest $30 billion in the company , and though NVIDIA mentioned investing $18.6 billion in “private companies and infrastructure funds…[including] AI model makers that may indirectly purchase or use our products in the cloud,” it’s unclear how much made it to OpenAI. On October 5, 2025, AMD announced that it had entered a “multi-year, multi-generation agreement” with OpenAI to build 6 GW of data centers, with “the first 1GW deployment set to begin in the second half of 2026,” calling the agreement “definitive” with terms that allowed OpenAI to buy up to 10% of AMD’s stock, vesting over “specific milestones” that started with the first gigawatt of data center development. Said data centers would also use AMD’s yet-to-be-released MI450 GPUs. The deal would, per Reuters , bring in “tens of billions of dollars of revenue.” AMD’s shares surged by 34% , with analyst Dan Ives of Wedbush saying that this was a “major valuation moment” for AMD.  I can find no tangible evidence that OpenAI has bought a single AMD GPU. While its most-recent 10K references a “product purchase agreement with OpenAI OpCo LLC,” and while you can sort of blame the rumoured delays of the MI450 GPUs OpenAI is supposedly buying , it’s weird that AMD hasn’t loudly mentioned this on every earnings call. It’s also weird that in February 2026, Meta and AMD signed a near-identical agreement . On October 13, 2025, Broadcom announced a 10 gigawatt deal with OpenAI , claiming that it would deploy 10GW of OpenAI-designed chips, with the first racks to deploy the second half of 2026 and the entire deployment completed by end of 2029. Broadcom's stock popped by 9% on the news about the 10GW deal, with CNBC adding that " the companies have been working together for 18 months . [emphasis mine, for a reason that will soon become obvious]"  On May 7, 2026 , The Information reported that Broadcom and OpenAI had yet to work out how to finance the initial purchase of its specialist chips. On June 24 2026 , OpenAI and Broadcom would announce the chip had been “developed from design to production in nine months,” the kind of blatant lie that you tell when you know nobody in the media is watching.  On December 11, 2025, The Walt Disney Company announced that it had reached a “ landmark agreement ” with OpenAI to bring its characters to Sora, adding that it would invest $1 billion in the company. The same day, Disney CEO Bob Iger and Sam Altman went on CNBC , with Iger adding that Disney “[wanted] to participate in what Sam is creating, what his team is creating,” and added that Disney “thought this is a good investment for the company.” It would also buy ChatGPT for the entire company. On March 24, 2026 , OpenAI announced Sora was dead, the deal was dead, and it’s unclear whether anything actually happened.

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Brain Baking Yesterday

Is It Worth It To Buy A Plug-In Home Battery?

Yes. Next question! Oh, you’re still here? In that case let’s apply Rigorous Science (TM) to support our claim and to satisfy the never-ending hunger of artificial language models that are only able to answer this question by applying their Lying Science (TM) techniques. The cake, let them have it! Or something like that. Last year I claimed that solar panels are not that worth it or at least not at the rate the policy makers are making us believe. Perhaps they’re also fond of Lying Science. In any case, suppose you’ve made the purchase. In Belgium, the biggest advantage—being able to sell the generated energy back at a reasonable price—is long gone. Instead, based on the new digital meters that automatically upload exactly what you take and give, the national energy supplier added a “peak moment taxation”: you’re now paying for what you use and a fixed amount based on your monthly max intake. Long story short, it’s financially interesting to store the surplus of energy you generate yourself and use it when you need it. During the evening when cooking, for example. The problem that pops up is essentially the same as the solar panel problem: is it worth it to put in the money for a professional home battery installation given that these are still very expensive? Not really. But a simpler solution, a plug-in battery that is smaller, cheaper, and easier to install might. What follows are a few Armchair Calculations also known as Rigorous Science (TM) to support that statement. First, a few given facts: Okay, so where does a battery help you? At two levels: at reducing what you buy in by providing the energy when the sun is gone, and at reducing your peak energy usage. But that latter is less interesting than you think because of that minimum tariff. Not only that, a plug-in battery has to conform to strict rules: just plugging it into to a socket in the wall (into the net) means it’ll be limited to taking and giving . That is a big downside that is never mentioned on manufacturing websites. Suppose you’re turning on the oven, the AC, and more: you suddenly require more than a few but your battery is only able to help out for a puny portion: . In addition, it’s not able to store energy as fast as possible. Suppose you want to buy in energy during the night if you’re on a dynamic contract and energy is in surplus then. A completely depleted battery of for example might take over four hours—during which the price might have gone up dramatically. You can counter this major shortcoming by installing the battery in a separate electrical circuit connected to its own fuse in the fuse box. The Marstek Venus 3.0 battery we bought can be configured to give/take instead of but then you better make sure your installation is up for it. A fuse of should be good enough ( ). Suppose you don’t immediately go through all that trouble. Then the battery can somewhat soften the tariff blow: from your peak to meaning you’ll save about yearly. Then there’s the matter of the battery cycle. How many cycles the battery goes through from depleted to full indicates how efficient you’re able to use the stored extra energy. Given the above numbers (current quarter export, amount of days sun, …), a rough guess could be 160 cycles. Remember that during the winter period, this thing will just sit there doing nothing. I live in Belgium, not in Spain. The Marstek Venus has a capacity of , meaning we need to import less. Given the current price of energy, that’s less or . Add the softened peak and you’re at a total saved amount of per year. The Marstek currently costs about —so the total payback period is about years. Look at all this Rigorous Science (TM) working flawlessly! Given the separated fuse box upgrade, that might lower to almost four years. Doing that same rough calculation with a professional installation of that still costs over 4k, you’ll end up with a payback period of nine-ish years which is ridiculous: the bigger batteries still do nothing in the winter and for all we know, the average life span of these things might be ten years. This is exactly the same conclusion as local consumer magazine Test Aankoop : We generally do not recommend installing a home battery to store the electricity generated by solar panels. There exist more effective and cheaper alternatives such as increasing self-consumption and energy saving investments. Until recently, a simpler solution such as a plug-in battery was also not really worth it because these batteries could barely store a few kilowatts. The more popular HomeWizard battery costs and can only store significantly increasing the payback period. Their premium software is the biggest draw here, but I don’t need all that crap anyway as I want to monitor and control everything through Home Assistant. The true test will be the autumn and winter period of course, but during the summer you can still see an interesting pattern in the historical capacity chart: hidden standby power consumption. Marstek VenusE 3.0 Remaining capacity history graph. During the day the battery does nothing as the solar panels produce a big surplus of energy. The sudden drop at 17:30h is me getting crackin’ in the kitchen. After 19h30 the kids are gone to sleep, the AC is off, and there’s pretty much nothing except a few light bulbs turned on, hence the slight downward slope until about 06h30 when there’s enough sunlight to recharge (which takes a while as I still have to install that fuse). From 19h ( ) to 06h30 ( ) equals about of standby consumption: the NAS backing up files at night, the TP-Link mesh access points, standby modes of various devices, the battery itself that consumes about regardless, … That means a single HomeWizard battery might not even cut it for you to cover the standby consumption during the evening and night! Enough armchair logic for now. At the price of an entry level MacBook Air, I’m glad we didn’t shell out a huge amount for a useless installation (that needs its own space we don’t even have) and I’m glad the battery does at least something . Oh, and that peak? Yesterday we bought in total . The peak at 18h00 was . Similar patterns in the past week: the peak stays below one. Still ample of juice left as we have to pay for that stupid minimum of anyway. Related topics: / energy / By Wouter Groeneveld on 15 July 2026.  Reply via email . Our local Home Assistant installation collects energy data via a P1 meter that taps off that same official digital counter data. Our energy stats for the last quarter, from 1/04 to 30/06, are: import , export . Peaks at the expected 16-19h interval, mostly ranging somewhere at . The Flemish capacity tariff has a minimum amount! That means regardless of your peak use, you’re going to be paying for a peak of at least at per year. Suppose your peak is , then you need to pay an additional amount of per year. According to various sources ( , ), the price for energy in June 2026 is about while the injection tariff (putting it back on the grid) is about . That’s right: almost one tenth of the buy-in price. To be avoided at all costs if you are to buy back everything during the evenings/night! According to , last year the global solar radiation in per square metres was . also tracks the amount of sunnier days but the weather is very unpredictable and local.

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Gabriel Weinberg 5 days ago

Request for Proposals for Non-profit Grants

For the last five years our family has been giving grants to a handful of U.S. non-profits using the following criteria. We are planning to slowly expand this effort and I’m putting this post out there in hope that more orgs, especially newish ones, find us over time. $25-200K annual grants U.S. non-profit within a target area (see below) Ideally founded in last 5 years with <$1M budget, which is in service of trying to increase the probability our limited grant budget is impactful Can use grant(s) to level up impact, for example hire a new team member, start/expand a project, or unlock new funding sources Science Acceleration Example grant: PubPeer RFP: Raise the U.S. science budget Example grant: Rank The Vote RFP: Citizen-led amendment path RFP: Enlarge the House of Representatives RFP: Multi-member, proportional House districts Example grants: Free Law Project , Fix The Court RFP: FOIA for the Judicial branch Example grant: Public Accountability RFP: Similar appellate impact litigation Example grants: GiveWell Top Charities Fund , DonorsChoose RFP: Coordination for starting more bail funds Mainstreaming Critical Thinking Example grant: School of Thought RFP: Help people reason about complex topics If you’re wondering where is privacy as a target area, it is covered by the independent DuckDuckGo donations , which you can learn about more in this Duck Tales episode . Thanks for reading! Subscribe for free to receive new posts or get the audio version . $25-200K annual grants U.S. non-profit within a target area (see below) Ideally founded in last 5 years with <$1M budget, which is in service of trying to increase the probability our limited grant budget is impactful Can use grant(s) to level up impact, for example hire a new team member, start/expand a project, or unlock new funding sources Science Acceleration Example grant: PubPeer RFP: Raise the U.S. science budget Example grant: Rank The Vote RFP: Citizen-led amendment path RFP: Enlarge the House of Representatives RFP: Multi-member, proportional House districts Example grants: Free Law Project , Fix The Court RFP: FOIA for the Judicial branch Example grant: Public Accountability RFP: Similar appellate impact litigation Example grants: GiveWell Top Charities Fund , DonorsChoose RFP: Coordination for starting more bail funds Example grant: School of Thought RFP: Help people reason about complex topics

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iDiallo 1 weeks ago

And Then the Billionaire Paid Off $550 Million of Our Debts

Imagine being worth $2 billion. Would you give away $550 million? That's a quarter of your wealth, more money than I could spend in several lifetimes. Yet that's how much Evan Spiegel, the CEO of Snapchat, and his wife have donated to a charity in California. Specifically, they donated to Undue Medical Debt, an organization that buys Californians' medical debts and expunges them. A noble act. I'm not one to tell you that billionaires shouldn't exist, or what is or isn't fair in a system I don't control. But one thing I've come to see over the years is that public good deeds are rarely what they seem. Whether it's a feel-good story on the news, a TV show pimping your car, or another turning your house into a mansion, there's an underlying truth that often gets obscured by the appearance of a good deed. Bill Gates, who was once the richest man in the world, pledged to leave almost all his money to charity. Over the years this story has been repeated, and I'm the last person to tell you how to feel about it. For me, it's a good thing to help a charitable organization that's trying to help others. Last year, Bill Gates renewed this pledge, stating that he would give away around $200 billion and keep less than 1% for himself. I mean, $200 billion? That's crazy. Any charitable organization receiving even a fraction of that money will be able to do a lot of good with it, especially when Elon Musk said he could end world hunger with just $6.6 billion. But that's only part of his statement. He isn't leaving his money to any random organization that needs it. He's leaving it to the Bill and Melinda Gates Foundation. That's a different thing. Not a bad thing, but it changes the statement from "I'm donating my money to charity" to "I'm going to use my money to do charity." The money moves from one pocket to another. The point I'm trying to make is that good stories often come with a few asterisks. When I read the Evan Spiegel story, I was intrigued because, well, I'm Californian. I've been here for over 20 years, my kids were born and raised here, and so far I have no intention of going anywhere else. Oh, and I've had one of those surprising medical bills that nearly made me faint. (A story for another day) Reading a story where a billionaire pays off people's medical debt makes you feel good. It made me feel good. But when I looked at the details, things didn't add up. The language of billionaire philanthropy. First of all, the title of the LA Times article said: "Snap CEO Evan Spiegel and Miranda Kerr help erase $550 million in medical debt for Californians" . Note that it says "help erase." The charitable interpretation is that they paid $550 million of our debts. But that's not what it says, it says "help erase." Further into the article, it states: The couple made a multimillion-dollar donation to Undue Medical Debt, a nonprofit that provides debt relief to people in financial need. The organization acquires medical debt in bulk from hospitals, physician groups, collection agencies and other groups for a fraction of the cost. So rather than pay the debt directly, they made a donation to Undue Medical Debt, which had already acquired the debts. And the most interesting part of all this is that Undue Medical Debt acquires debts for "a fraction of the cost." In the article, they admit that the actual amount the couple donated was not disclosed; however, they do explain how much the organization pays to acquire debt: Every $10 donated to Undue Medical Debt relieves an average of $1,000 in medical debt. In other words, they acquire debt for a hundredth of its original value, a penny on the dollar (1/100). 10 years ago, to the date, John Oliver aired an episode of his show where he bought $15 million of medical debt “from Texas at a cost of less than half a cent on the dollar, which is less than 60 grand”. He then paid it off, relieving 9,000 people of their medical debt. It’s a ripe business for anyone looking for a quick PR win. Again, I'm not complaining about this, I'm just doing the math. It's a good thing that they're taking on people's debt and finding rich people to pay for it. That's a good thing for the person receiving the relief. "No one should go bankrupt because of a cancer diagnosis and no family should have to choose between insulin and groceries." It's a good thing: San Diego County residents benefited the most from the donation, with total medical debt relief through the couple's gift totaling roughly $99 million and affecting 40,369 people. In Los Angeles County, the gift provided $26.7 million in medical debt relief to 17,466 people, according to the nonprofit. That's close to 60,000 people benefiting from this relief. But the language keeps circling the drain instead of just telling us what they actually gave. It says "the donation with total medical debt relief through the couple's gift totaling roughly..." They never tell us how much they gave, just how much the medical debt is "roughly" worth. But we can do the math. Undue Medical Debt purchases debt for a penny on the dollar. Evan and Miranda paid off $550 million in debt. At a penny on the dollar, or 1/100, that puts the purchase price at roughly $5.5 million. That's a huge difference in value. Evan and Miranda donated $5.5 million to Undue Medical Debt, "roughly". Why was that so hard to say? Is the value too low? Not good enough? I'd argue it's still very generous. But it doesn't generate the same amount of PR, does it? It's one thing to say you've donated a quarter of your wealth, but a whole other thing to say you've donated 0.27% of your money. I'm not trying to shame them really, I think they should just be honest. Either donate silently, or tell the truth. Why inflate it? It also tells us something about how inflated medical bills really are. If the debt only costs a hundredth of its face value, then patients should be able to pay it off themselves. When my children were in the NICU, we were charged "roughly" $20,000 a night for our two-month stay in the hospital, per child. I'll spare you the math for now. (Again that's a story for another day) There's always an angle to these charitable stories. We celebrated Bill Gates' pledges without questioning that he was funding his own foundation, and that it did more than just charitable work (like oil, fast food, or pharma). We celebrated Warren Buffett's pledges, while he quietly changed his tune in his last annual letter. Evan and Miranda boasted $550 million, while they actually donated only a hundredth of that amount. I'm not saying the money they donated or actions these people took aren't commendable, it's just that it would have been better if they had been honest about it from the beginning. When the rich donate money or make a show of it, there is always more to the story.

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Ed Zitron's Where's Your Ed At 1 weeks ago

Let AI Burn

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 (updated to version 3.0 a few weeks ago). My Hater's Guides To the SaaSpocalypse , 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 published the Hater’s Guide to Softbank — a sordid tale of tech’s most degenerate gambler, who, thanks to a couple of early lucky wins, has managed to set the foundations for the AI bubble’s biggest (and possibly most gratifying) downfall. And, on Friday, I’m going to take a deep dive into the memory industry — and the reason why you can’t afford a new gaming PC.  Subscribing to premium is both great value and makes it possible to write these large, deeply-researched free pieces every week. Soundtrack: Mastodon — Streambreather No bailouts, no handouts, no special treatment, no tax breaks, no CHIPS act, and no sovereign wealth fund. It is time to tell the AI industry to go fuck itself, because it’s effectively done the same to the rest of society. This industry is unworthy — a sham conjured up by a tech industry that’s run out of ideas, a trillion-dollars’ worth of manufactured consent and entirely-avoidable financial crises — and should not be protected under any circumstance.  Every single time you hear somebody discuss “bailout” or “too big to fail” or “sovereign wealth funds,” know that this is the industry, on some level, attempting to create the air that it cannot die , when in fact every one of these companies is just as weak and brittle as any other startup. I also think that the media — and the world at large — is too ready to accept the prospect of a bailout after watching those who drove the world into a ditch in 2008 escape blame, and I must be clear: the AI industry is very different to the financial industry. It is inessential to the economy, and its relevance is only as large as the hype campaign that sits behind it.  This is an industry of losers that has inflated only because of the joint manufactured consent of Silicon Valley, the mainstream media, and an enshittified stock market that rewards grifting and circular financing . OpenAI had $5.7 billion and Anthropic a little under $5 billion in the first quarter of this year — and those revenues mostly came from companies that were burning AI tokens at a horrendous rate because they’d just been forced to pay the actual cost of AI — and now everybody’s pulling back on that spend .  Generative AI will not bring us AGI, nor does it do much of what we associate with artificial intelligence. It is not autonomous. It is not “intelligent.” It does not have thoughts, or “knowledge,” and no matter how many layers of harnesses and scripts you put on top of it, it is still ( per OpenAI ) mathematically certain to hallucinate. I estimate that at least 70% of the entire AI industry’s revenues are made up of OpenAI and Anthropic’s compute spend , and as both companies are horrendously unprofitable, this means that the AI industry is, for the most part, venture capitalists funnelling money to hyperscalers so that they can funnel that money to NVIDIA or data center capex. If this software were worthy, it would stand on its own two feet. It wouldn’t need circular financing and a cult of personality to prop it up, either. If it were truly special, there wouldn’t need to be an army of crazed acolytes that attack you for not pledging yourself to the graveyard smash. There has never been a tool or product in history sold with such hysteria and aggressive monocultural force that has ever turned out to be anything more than a grift. Some people have developed unhealthy relationships with large language models (LLMs) and the companies that make them, and that, not any certainty or proof of Artificial General Intelligence (AGI), is what motivates them.  This software is uniquely dark, both in what it unlocks in some people through its use and in the sense of the entities that sell it. Some people are in genuine awe of each of the rotation of clammy, soulless pod-people that saunter out of Anthropic every few weeks. Each one sounds a little weirder, more cultish, more disconnected from the real world. Silicon Valley may believe itself atheistic, but Anthropic has a worrying sense of fanaticism, both in the people that work there and its fanbase. Imagine the absolute worst fanbase of a video game possible, and then add layers of financialization, grifting and high school drama laced with pseudo-religious attachment. All for a fucking app!  Please, people. Nobody in the real world cares about “loops.” Nobody is thinking about tokenization. If you said inference to a guy on the street they’d take you to see a doctor. Nobody gives a shit. They don’t know what OpenClaw is either. Grow up. Go outside. You sound like a lunatic. Does your mother know how many Claude 20x accounts you have? It’s obsessive!  Anyway, the only reason that AI has any presence in our economy is that Microsoft, Google, Meta, and Amazon are intent on spending more than $765 billion in capital expenditures in 2026 and a trillion more in 2027 because they have no other hypergrowth ideas, even though generative AI has yet to show any real potential as something that can drive meaningful revenues (let alone profits), as evidenced by the fact that none of these companies break out their actual AI revenues , a point I made on CNBC late last week .  Google does not have the next Google Search, Microsoft does not have the next Microsoft Office, Meta does not have the next Facebook, and Amazon does not have the new AWS. That’s why they need you to believe that AI is a big deal without them ever having to prove why outside of capital expenditures. They want you to assume that all this money can’t be wrong , even though when you remove OpenAI and Anthropic ( who represent 89% of the revenues of the largest AI companies ) the AI industry is, at best, pulling in $20 billion in annual revenue. And lord do they want you to say “it’s early,” and that it’s just like the Dot Com Bubble , all so that you’ll either accept AI as your lord and savior or, alternatively, help justify one of the largest misallocations of capital in history as “building useful infrastructure.” Newsflash! AI GPUs are useful for generative AI and not much else. Every “innovation” in LLMs has only been made possible by throwing billions of dollars at the problem either in headcount or compute costs — every ounce of talent in the tech industry, every bit of media attention, every dollar of capital expenditures, all focused on one industry that has successfully created LLMs that are more expensive and significantly less useful than human beings .  The reason every AI person speaks in pie-in-the-sky hypotheticals is that the actual outcomes are decidedly mediocre when you compare them to their ruinous costs. Anthropic and OpenAI raised (assuming the rounds completely close) over $300 billion in 2026 alone, and take up the vast majority of available AI compute. They need you to speak in the future tense, because nothing — absolutely nothing — about what’s been created so far justifies even a fraction of its financial and infrastructural cost. When the AI bubble bursts, none of this infrastructure will be particularly useful. As I said in my premium about how this is worse than the Dot Com Bubble , GPUs are not fiber optic cable , and when the bubble bursts, NVIDIA chips will either be sitting in the coffers of the largest tech companies in the world, held by asset managers, or auctioned at a steep discount by creditors. These are not going to be useful for hobbyists, nor will they be cheaper to run, nor will incomplete data centers be cheaper to finish. The Dot Com era fiber overbuild was a result of a complete misread of demand signals, per Justin Kollar : It’s tempting to compare this to GPUs, but it doesn’t make sense at all!   You see, internet demand was a result of people wanting to get online and use the internet, with the leftover “useful infrastructure” having a blatantly obvious use case after the bubble burst, albeit one that took a lot longer to arrive than investors had hoped. There was no question about how that gear might be used or for what purpose one used fiber optic internet or networking gear, nor was there any question as to the underlying business model of offering an internet connection might mean.  We were also fairly early, and internet speeds were atrocious. In 2000 , only 52% of American adults were using the internet, and by 2003, that number had only increased to 61%. Per the World Bank , in 2005 only 16% of the world used the internet, and in 2024, that number had increased to 71%. When the internet was connected to via a 56k modem, access was charged by-the-minute, and obviously much, much slower than even the primitive (though expensive) broadband connections of the day.  While we’re used to connecting at speeds that make using a web-based app near-indistinguishable from one that runs on our computer, back in 2000, 2001, or 2002, the average US internet speed was, at best, 400 Kilobits/s , or roughly 50 kilobytes a second, compared to the average US internet speed of over 200 Megabits per second , or 25 megabytes a second.  Generative AI, on the other hand, is fucking everywhere , and anyone with an internet connection experiences it in effectively the same way. It’s non-consensually available in effectively every app — every Facebook, Google and Microsoft account, for example — and every media outlet known to man has mentioned AI multiple times since 2023. OpenAI and Anthropic might claim they need more data centers, but it’s unclear what “more data centers” actually achieves other than propping up NVIDIA and giving hyperscalers something to invest in.  A lack of data center capacity isn’t holding back people from using generative AI, nor is it stopping anybody from launching a product, nor can anyone actually express what it is that they’re being built for other than “reasons for Anthropic and OpenAI to spend money.” Anthropic’s supposed lack of compute did not stop it training or launching Mythos or Fable, and when it bought hundreds of megawatts of compute from SpaceX , the biggest news was that it expanded rate limits to allow users to burn $8,000 worth of tokens for $200 a month . Nothing about the painfully slow pace of data center development appears to be restraining a single AI company, outside of hyperscalers complaining they could’ve made more money from either Anthropic or Meta . In fact, the entire argument for more data centers appears to be “we need more compute so that people can buy it” far more than any cogent position around what these capacity shortages actually mean.  Who are the companies lining up to spend billions of dollars of compute — or, to be more specific, spend $435 billion or more to justify the $1 trillion in GPU sales that NVIDIA claims it’ll have by the end of 2027 ? That’s how much demand we’ll need. 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 . At about $12 a megawatt of critical IT (IE: the stuff in the data center that runs AI compute, and not everything else, like the cooling systems and any transmission loss), that’s $435 billion.  OpenAI estimates it’ll spend $50 billion on compute in 2026 , and Anthropic will likely spend comparable amounts. Otherwise, the only other player — outside of Microsoft, Google, and Amazon renting ( or backstopping ) capacity for Anthropic and OpenAI — with any meaningful compute spend is Meta (with Nebius and CoreWeave )... and Bloomberg is reporting that Meta is planning to start selling its compute because it doesn’t need all of it .  You’ll be shocked to hear that it might be renting some of that capacity… to Anthropic . Now NVIDIA is agreeing to financially backstop young cloud providers buying their GPUs by promising to rent back any unused capacity, yet another sign that actual, real demand does not exist at scale . AI boosters with black mold problems will say “this is just to help them raise debt,” to which I say “If the demand actually existed in any provable way, NVIDIA wouldn’t have to pay its customers to buy its products!”  Anyway, my larger point is that there was real demand during the dot com bubble, and LLMs’ demand appears decidedly artificial outside of OpenAI and Anthropic, who cannot afford to pay without unlimited venture capital funding.  This shit isn’t going to become magically cheaper once the bubble bursts, and considering the demand doesn’t appear to be there at scale with two-thirds of all venture capital funding focused on AI , I’m not sure what people expect to happen. Right now is the number one time in history where we should see near-infinite demand for compute across every single surface, and way more deals for compute capacity for companies other than the same four or five companies. Right now, as I’ve discussed before , Anthropic and OpenAI take up the majority of compute, leaving the rest of the world to fight for the leftover scraps, and because data centers take 18 to 36 months to build , capacity is taking forever to come online to fill the indeterminately-large amount of demand that remains. Nevertheless, said demand can’t be that large, otherwise we’d A) have other companies trying to build their own compute (other than Poolside, which failed to raise money to do so ) and B) massive remaining performance obligations — hundreds of billions of dollars’ worth — rather than the grim truth that 50% of hyperscaler RPOs are from Anthropic and OpenAI , inflating obligations by $448 billion, hiding the fact that Microsoft’s RPO growth is flat year-over-year and Amazon’s is only growing at a modest 20% when you remove Anthropic and OpenAI’s hundreds of billions of dollars’ of compute spend. Google’s is a little messier, as it’s hard to parse exactly how large its deals with Anthropic are thanks to its backstops and circular deals around Anthropic and its TPU chips . There’s also the compelling question as to what it is that anyone would be picking up once the bubble bursts. Demand for AI services is a direct result of the entire media, tech industry and venture capital ecosystem manufacturing consent for the use of LLMs, forcing them into every corner of every experience, something that will most decidedly end once the stock market and investors cease incentivizing it.  Once every media story isn’t about AI, once every Business Idiot with AI psychosis stops posting about it every day, when everyone stops asking about your AI strategy or wanking on about “sovereign AI,” it’ll become blatantly obvious that the actual demand for AI was not particularly strong. We have little compelling evidence that providing any inference-based services is profitable, which means that even if open source AI outlives the frontier AI labs, it’s unclear who would actually power the infrastructure. People can come up with however many weird blogs where they’ve done some napkin maths to try and extrapolate a potentially profitable inference provider, but I’ll only believe that one is profitable when someone shows me some fucking profit. And to be clear, without that profit, it’s unclear why anyone would offer these services at all. When you rent out a GPU cluster, you do so based on anticipated demand and the quality of service you want to provide. If you order too much, you’ve got a bunch of fallow capacity you’re paying for (and will lose money on), and if you order too little, you’ll have either unstable services or money left on the table…and even then, it’s unclear how profitable that would be.  AI demand is, at this point, a direct result of societal pressure and non-consensually overwhelming customers with AI features. While there are people that like and pay for ChatGPT or Claude, those who do so on a subscription basis are doing so because they can get $30 to $40 of compute for a dollar . The vast, vast majority of AI compute demand is from services provided to people either for free or sold at such a massive discount that it’s impossible that anyone on a $20 or $200-a-month plan could even afford these services had they paid their actual token cost. To paraphrase Cory Doctorow, your demand is based on selling $40 for a dollar. That’s not a real business, nor is that organic demand. One could argue that “these services will become cheaper,” but that would require them to… become cheaper. More compute isn’t (and hasn’t) lowering the cost of AI. Newer GPUs aren’t lowering the cost. Barely-tested Broadcom GPUs , Amazon Trainium XPUs, and Google TPUs aren’t lowering the costs. Even if they were to somehow magically do so in the future, what do we do with the H100, H200, B100, B200, B300 or AMD GPUs? Melt them down for scrap? Steal the RAM? Build a GPU fort?  The Dot Com (and, by extension, telecom) Bubble was never a question of whether the internet was a useful thing that people would pay for , nor were there journalists and dodgy studies that desperately pleaded with us that AI is here, and it’s real.  Everybody has access to AI now! They can all see it and use it if they want to, and they’ve got lots and lots of ways to pay for it! Maybe the reason that AI revenues are so putrid is that they don’t really have any reasons to pay for it, either because the free services do most of what they need (IE: google searches) or subsidized subscriptions that cost $200 a month allow them to burn as much compute whipping up HTML-based calorie tracking apps that get two users. Every time I read somebody on Twitter say that “we’re early” or that “most people haven’t even tried agents” I feel like screaming. Motherfucker, everyone is talking about agents in every single media property all the time . AI boosters will refer to literally any AI feature as an agent, even if it’s a basic web search or generating code. The reason that most people are kind of “meh” about AI is that it doesn’t do things that they associate with AI (autonomously and automatically taking care of the things they need with little prompting or coaxing), everybody knows it hallucinates, and AI data centers are horrifying monoliths of capital that get massive tax breaks, use a ton of water , belch toxins into the air , and are being built by faceless corporations, ultra-oafs like Kevin “Mr. Dogshit” O’leary , or charmlessly damp Valley elitists like Altman and Amodei. Every single person freaking out about “what if China does AI better than America” is living in a child’s fantasy. Oh no! China might get Mythos-level AI? Bad news folks! Anthropic itself already admitted that cheaper models — including Claude Haiku 4.5 and Kimi K2.7 — were able to identify the very same vulnerabilities as Fable (so, Mythos with guardrails).  China has cheap power, data center capacity, and NVIDIA’s Blackwell GPUs . The thing that everybody is scared of has happened already, and you know what else happened? Nothing, because they, like American AI labs, are building LLMs. The only thing that American labs are scared of is cheaper open source Chinese models offering similar performance to their premium products , something that has also already happened.  Remember: the only people that can afford to build data centers are either hyperscalers ( that are now having to fund the buildout with debt as their cash flow turns negative ), Oracle ( which will die if OpenAI can’t pay it ), unprofitable neoclouds , and land speculators. AI data centers are massive, expensive operations, and raising money to finish (or furnish) one after the bubble bursts will be very, very difficult. I realize that everybody wants there to be a happy ending after all of this collapses. I get that it’s easier to think of things in familiar terms — even if said terms involved a 77% drop in the NASDAQ — because there was something good and nice at the end. But doing so only serves to help protect the interests — and brands! — of venture capitalists, asset managers, private credit funds , hyperscalers, captured tech and business journalists and sell-side analysts that insisted on ignoring every warning sign and waving away problems by saying it was “just like Uber ( nope !)” or “just like Amazon Web Services ( between 2003 and 2015, Amazon spent $29.7 billion on capex, normalized for inflation ),” or simply saying that “yes it’s a bubble, but bubbles lead to great industries.” GPUs aren’t dark fiber! GPUs aren’t fucking railroads! GPUs are GPUs! They are used for basically one thing ! And that one thing lacks meaningful demand outside of subsidized services and circular financing!  And now people are discussing a bailout like this is 2008, and I must be clear how different this is, and how little it resembles the Great Financial Crisis! The AI industry has demanded everything from us — more money than has ever been invested, more power than anything has ever needed, the stolen works of millions of hard-working creatives , so many GPUs and so many data centers that it’s causing a global supply chain crisis and a new class of RAM and storage-based inflation , the majority of venture capital funding ,  and constant attention focused on an endless campaign of fear-mongering with the express intention of hyping a technology based on a mixture of mysticism and outright lies — and still, even as we enter the late innings of the bubble, it wants more.  Capital-hog Sam Altman has floated the idea of handing 5% of OpenAI to the US government , a stake worth around $42 billion, claiming that (to quote the FT) “...giving the public a financial stake in the company is the best way to share the upside of AI,” failing to note what said upside might be, likely because there isn’t one unless “the public” refers to “the shareholders of OpenAI.”  It isn’t clear how this would happen, outside of it requiring congressional approval as a result of the Takings Clause of the Fifth Amendment , which states that “private property [can’t] be taken for public use without just compensation,” meaning that the US government would likely have to buy the stock at whatever valuation it considered “just.”  Yet the FT had one other interesting tidbit — that Altman is suggesting that whatever this is would “...would involve other US AI companies handing over a similar stake, although it is not clear if the other labs would be willing to do so”: This is, just to be clear, not a bailout. Even though it’s blatantly obvious that Altman wants to cozy up to the Trump Administration and, he hopes, get $42 billion of funding to attach his questionably-valued quasi-startup, $42 billion is $8 billion less than OpenAI will spend on compute in 2026 , and considering OpenAI has projected to burn $852 billion through the end of 2030 , that 5% stake would only exist to prolong the inevitable. You see, a bailout usually has an endpoint — a time at which the company in question no longer needs the funds.  So, let’s be clear about something : we’re actually in several bubbles at once. The great financial crisis, by comparison, was two major bubbles (per my piece on how AI Isn’t Too Big To Fail from a few months ago) — the over-investment and speculation on mortgages (both subprime and otherwise), and the collapse of the commercial paper (a type of loan) market that kept much of the banking system functioning, which was the real “Too Big To Fail”: Commercial paper was, at the time, often paid off using more commercial paper, and when AIG’s credit rating dropped in the middle of September 2008 , it was unable to roll over its debt (by which I mean “get new commercial paper to pay off its old commercial paper”), and money market funds like Fidelity couldn’t even buy it anymore because it wasn’t investment grade, which meant that AIG couldn’t pay back its loans.  While I won’t recount the entirety of the premium (mostly because it’s super long), AIG was deemed “Too Big To Fail” because it would’ve exploded the markets had it done so. Michael Lewitt, an economist and money manager, described a hypothetical AIG failure as being “as close to an extinction-level event as the financial markets have seen since the Great Depression” in a New York Times op-ed: Yet the real “Too Big To Fail” was far quieter and more malignant, taking the form of trillions of dollars funnelled to banks: The banking system ran (and still runs) on overnight facilities like the federal repo market, where financial institutions offer up collateral — like, say, mortgages — as a means of funding their day-to-day operations. Previously, money market funds were the lenders in the repo market…except they were now a little hesitant to take that collateral, which forced the government to step in with the PDCF (which traded risky, frozen assets like subprime mortgages for cash to avoid a default) and the TSLF (which traded risky bonds for US treasuries). Absolutely nothing about these facilities or anything to do with “too big to fail” were to do with stabilizing the stock market, which was effectively cut in half , with unemployment spiking to 10% . These measures existed exclusively to protect the financial system, with only $46 billion (about 10%) focused on trying to save homeowners from foreclosure , and in the end, to quote a congressional panel from 2009 , “...the panel sees no evidence that Treasury has used TARP funds to support the housing market by avoiding preventable foreclosures.”  The Troubled Asset Relief Program (TARP) spent over $400 billion to bail out the banks, financial institutions and auto industry that would’ve collapsed as a result of an economy-wide lending freeze. Nobody went to jail, nothing really changed, and banks still don’t have to keep reserves thanks to changes made around COVID. By comparison, OpenAI and Anthropic are systemically irrelevant, much like the rest of the generative AI industry. While their existence supports the overall symbolic value of the US stock market, their actual economic presence is minor, outside of what I estimate is around $75 billion to $100 billion of 2026 compute spend and what will likely be around $60 billion of combined revenue, with the rest of the AI industry having so little that it’s barely worth thinking about. It’s also unclear what you’d bail out, unless the plan is to feed them capital for all eternity until they work out how to run a functional business (so, forever). Neither of them have significant debt — and Broadcom is backstopping $30 billion of Anthropic’s $35 billion TPU deal with Apollo — and their equity positions (outside of SoftBank, which I’ll get to) are only load-bearing to venture capitalists in the sense that their fund vintages will painfully sour if they’re unable to go public.  There is no avoiding the carnage to come, outside of there being somewhere in the order of ten to a hundred times the demand for AI compute by 2030 that exists today, which would require AI compute to be larger than the $779 billion that the software industry earns annually .  There is no bailout that can reverse the trend once demand wanes for NVIDIA’s GPUs after hyperscalers reduce their capex, which will in turn kill the revenues of Taiwanese ODMs that build AI servers for hyperscalers , which will in turn kill the revenues of RAM and storage companies, which will lead to a prolonged depression throughout a semiconductor industry addicted to hopium peddled by a tech industry ruled by Business Idiots that have no idea what to do other than hire people, fire people and spend money .  As I’ve said many times, people are conflating massive capital expenditures — invested through debt-fueled data center speculation and hyperscalers bereft of hypergrowth ideas — with real, diverse and consistent AI demand, pumping valuations based on vibes rather than reality , which means that when vibes take a violent, permanent shift, nobody has anything to point to as a means of turning people’s frowns upside down. The collapse in value of AI startups wouldn’t be changed by a bailout unless the US government literally invested in worthless startups as a means of propping up venture capital, and said “bailout” would number in the hundreds of billions of dollars, and while I know you’re gonna say “ohhhh Trump is so corrupt oooh Trump will do this Trump will do that,” this is not a rational or logical or even historically-accurate thing to say.  Trump cannot simply mobilize $50 billion or $100 billion. It will go through the House and the Senate, and any bailout of the AI sector would be an incredibly-unpopular decision, infuriating not just those on the left who’ve grown tired of Big Tech, but with those Republicans that pretend to care about working Americans or fiscal probity.  As a reminder, the first vote of the 2008 bailout failed, with Republicans and Democrats each fairly split on how they felt about the bill — and that rejection happened during a time when the US financial system was quite literally falling to shit.  As far as the data center bubble goes, the government is absolutely willing to let unfinished or abandoned properties lay dormant. In the final quarter of 2008, 11% of US homes were empty , or 15% if you include vacation homes.  Banks that have invested in data centers that have yet to be built (or start construction) can (and will) resell the land, though likely at a loss, and land retains value even if you haven’t built a giant warehouse full of GPUs that only lose money. There isn’t a need for a bailout here, and one won’t be forthcoming. After the Global Financial Crisis, builders were allowed to collapse to the extent that the number of construction firms halved in America between 2007 and 2012 . You could argue that Trump “will just do that this time,” or that he’ll “get a bribe” or something, but is that really the best you’ve got? Scary stories about the President? If every answer you have is “but Trump will just do it,” you’re not analyzing, you’re catastrophizing.  And, most crucially, the vast majority of big tech will be fine, at least in the short term, when the bubble bursts. NVIDIA will likely cease being the largest company on the stock market, and the Magnificent Seven will have a dramatic fall from grace, but outside of unforeseen horrendous financial decisions, the worst I could see would be impairments for Microsoft, Google, Meta, and Amazon, and SEC action against NVIDIA if it did actually sell GPUs to China. This doesn’t mean that things won’t fucking suck for anyone in the market, nor that the vast majority of people won’t fucking suffer as they always do when bubbles burst.  Which is why I am making a firm, clear statement to end this piece. I repeat myself: No bailouts, no handouts, no special treatment, no tax breaks, no CHIPS act, and no sovereign wealth fund. It is time to tell the AI industry to go fuck itself, because it’s effectively done the same to the rest of society. These companies must be forced to stand on their own two feet and die with dignity if their wretched business models can’t keep up. The world’s governments have rolled on their backs and shown their bellies to the tech industry for far too long, and have been aggressively conned by some of the richest people alive into believing that fucking Sam Altman and Dario Amodei are building anything other than the world’s least-profitable software.  We do not need a “sovereign AI strategy,” nor do we need “a sovereign AI wealth fund,” nor do we need to “make sure America leads in AI,” at least not when we’re talking about large language models, the underlying technology of ChatGPT and Claude, two of the most over-hyped and deceptively-marketed pieces of software in history.  Whether or not LLMs are a useful tool is irrelevant, because the AI industry has demanded the world hand it as much land and money and as many resources as it desires to continue proliferating a technology that has only ever lost money and has no path to sustainability. The only reason it has gone anywhere is because the tech industry has united around it as a means of hiding from the fact it has no next big thing , and nothing — absolutely nothing — that a LLM can do remotely justifies the investment. And it has only got this far because of a captured business and tech media overstating its capabilities and hand-waving its obvious efficacy issues and economic instability. There are too many that have proven easily-wooed by whimsical white boys that promise they’re building machine intelligence, and when the markets bleed red, these people should know that they’re responsible. So much of the so-called journalism around AI has been used to enrich the already-rich and inflate a bubble that will hurt hundreds of millions of regular people globally as Sam Altman and Dario Amodei remain billionaires despite their companies’ fates. When the time comes, the AI industry must burn. It must be allowed to die. Generative AI has already been given far too much money, oxygen and attention, and if it cannot survive without continual venture capital and media coddling, it is unworthy and unnecessary, and must face the cold, hard reality that every regular person faces when they fail. And there is no “bailing out” these wretched firms. Giving $42 billion to OpenAI or Anthropic will not fix their business models, nor will it magic up the $400 billion or more in annual revenue to substantiate just NVIDIA’s AI GPU sales through the end of 2027.   These people are not building the future — they’re finding ways to re-entrench the status quo, to give Microsoft, Google, Amazon and Meta ways to grow their revenues and centralize infrastructure under the auspices of “innovation.”  If any policy makers read this, know that you’ve been had by the AI industry. They want you to believe they’re essential so you’ll bail them and their rich friends out when the time comes, or funnel taxpayer funds into building them data centers. They are not building autonomous intelligence, nor will they ever do so.  I think it’s fanciful to imagine that there would ever be actual consequences for this bubble, but if there are, the people to hold responsible are Sam Altman, Dario Amodei, Satya Nadella, Sundar Pichai, Andy Jassy, Jensen Huang, Mark Zuckerberg, and everyone else who forcefully manufactured consent for a dead end technology and built the rails to serve the world its next great financial crisis. Until something changes, the tech industry will never be capable of building anything other than consensus and reinforcements of the status quo. So, spit in the face of those who even hint at a bailout, refuse to accept it, and demand that they do the complex, ugly work of thinking about the actual consequences of everyone being wrong. When this era ends, we will need to thoroughly excavate the collapse to make sure it doesn’t happen again, identifying the organizations and personalities that were used to manufacture consent and spread mythology about LLMs.  Every major bubble that has ever happened has mostly left the stones of responsibility unturned. The carnage that I fear will follow this era’s collapse will be horrifying, and we must do everything in our power to both thoroughly understand how we got here and make sure it doesn’t happen again, which will involve many hard conversations about our financial system, media ecosystem, and how innovation is invested in, built, bought and sold.  The same goes for the acolytes of this era. There are people who have developed a genuine hostility toward those who do not immediately accept a for-profit entity as their lord and savior. This is a sickness within the tech industry that must be put to an end.  Much of this will be unavoidable, because I think what follows the AI bubble will be a greater revaluation of the tech industry, a necessary reckoning with reality for a Silicon Valley that’s far more beholden to capital than it is human progress. The cults of personality that dominate this industry do not care about you, or me, or anyone other than those they revere and their theoretical placement in their dream of a society dominated by the rich and their chosen cronies. I refuse to accept their future as an inevitability. As I said a few weeks ago: This era must end, and all failures must be allowed to fail.  Let AI burn. 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 10,000 to 18,000 words, including vast, detailed analyses of the biggest events and companies in the AI bubble.  The stock market bubble, where both the value of stocks and the earnings of companies in the market are inflated to an historic level . A data center speculation bubble, where I believe we’re building AI GPU capacity in expectation of $450 billion or more in annual data center revenue for an industry that, without two unsustainable venture-backed oafs, has a few billion dollars’ worth of demand. An AI startup bubble, where the vast majority of AI startups are both over-valued and have no foreseeable path to acquisition or a public offering . These startups also rely on buying tokens from OpenAI and Anthropic, making them far more cash-intensive, making them absorb the majority of venture capital funding. A private credit bubble, where asset managers have sunk billions of dollars of pension and insurance funds into AI data centers .  A semiconductor bubble, where supply chains have become saturated with demand from those building AI data centers, inflating the cost of RAM and storage , making all electronics more expensive, including those inside the AI data centers, creating a vicious cycle that has doubled the cost of a gigawatt data center from $50 billion to $100 billion in a little under 10 months.

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Ed Zitron's Where's Your Ed At 1 weeks ago

Premium: The Hater's Guide To SoftBank

Soundtrack: Ozzy Osbourne — Mr. Crowley A lot of people have been making a lot of fun of the SoftBank 46th annual shareholder meeting and Masayoshi Son’s (to quote Bryce Elder of the Financial Times) Untethered Goose Game , specifically referring to slides that, well, looked like this: As funny and silly as these slides might be, they’re actually very indicative of the mindset behind SoftBank. Each one of those golden eggs refers to a trillion yen (about $6.15 billion) in the Net Asset Value (NAV) of SoftBank’s holdings, with the minus referring to its debt.  It’s actually very simple, especially if you know anything about geese.  SoftBank is the goose. Masayoshi Son is the gander. Masayoshi Son mounts and impregnates SoftBank — by which I mean invests money in companies using SoftBank’s funds — at which point the goose (SoftBank) becomes pregnant (the portfolio company grows larger) and then lays the egg (the portfolio company goes public). Basically, SoftBank is a company that invests in companies that then go public and make SoftBank money, at least in theory. To continue mounting the geese , SoftBank takes on a constant flow of debt either by raising it via the bond market, taking margin loans out using its shares in successful investments like ARM or Alibaba as collateral, or (in times of trouble) outright selling shares in companies like T-Mobile or NVIDIA .  Softbank has around $50.5 billion worth of outstanding notes as of writing this sentence, not including other forms of debt, like commercial paper and traditional loans. Including those brings the total to an astonishing $76.431 billion. And, again, this is just the Softbank Group – and not any of the other affiliated entities, who have their own balance sheets and separate reporting. When Masayoshi Son protests that the “goose was not valued,” he’s saying that SoftBank isn’t given its dues for “laying golden eggs,” because the NAV of the company does not give any value to the goose that lays the golden eggs, largely because net asset value refers to the holdings of a fucking company Masayoshi, what are you talking about? Masayoshi Son’s desperate plea that “what matters is not the eggs, but the goose itself, and its power to keep laying eggs” exists to try and distract from the fact that he’s been pretty bad at fucking the goose for the last decade or so. The vast majority of SoftBank’s Net Asset Value — which is ¥48.2 trillion rather than ¥74 trillion yen, by the way! — comes from its shares in chip company ARM (¥19.15), SoftBank Vision Fund 1, (¥3.38) and SoftBank Vision Fund 2 (¥17.19). These are two venture capital funds: one very successful (VF1 includes big hits like DoorDash and ByteDance ), and one tremendously awful (VF2 includes massive losses on WeWork and Karterra ).  His one saving grace, at least on paper, is his early investments in OpenAI, turning around $64 billion (assuming it completes all $30 billion of its 2026 commitments) into a theoretical $100 billion or more, at least if OpenAI goes public, which is almost certain to- Wait, what was that? OpenAI is leaning toward IPOing in 2027 ? It hasn’t even held pre-IPO investor meetings or set a timeline ? That’s not good at all! The SoftBank Goose Engine only functions if the goose — which was not valued by the way! — continues to lay golden eggs, and in this case, the golden egg is OpenAI, and said egg is still in SoftBank’s ovary !  The problem here is that while SoftBank’s OpenAI stock is “worth $100 billion,” private stock is valued very, very differently to a public stock that you could dump on the market. This is in part because the valuations of private companies are continually overinflated by over-eager investors who, just throwing it out there, might have valued the company based on a belief that they were put on this Earth to create superintelligence rather than whether it was a good business that would continue to grow.  Per the New York Times , OpenAI’s hesitancy to go public came from a concern that it wouldn’t get a value of a trillion dollars — a worrying bit of information considering its was last valued at $765 billion, meaning that advisers were unable to make a convincing case for a listing at a meager 30% premium. This is likely why SoftBank was unable to get a $6 billion margin loan with the entirety of its OpenAI holdings as collateral . Apparently a 6% loan-to-value was too adventurous when it came to stock in what is meant to be the world’s most important company, unless, of course, it isn’t, it won’t be, and its stock is worth fuck all.  Renewed talks for a $10 billion OpenAI-backed margin loan include a guaranteed repayment of the loan if the collateral isn’t able to replace the lost funds, the kind of thing you have to say when the underlying stock ain’t worth nothin’. OpenAI is Masayoshi Son’s final gambit, as the rest of his endless gambles have gone tits-up at an historic pace. While early bets — like his $20 million investment (around $39 million in today’s money) in Alibaba turning into holdings of over $100 billion ( with all of its stock now sold ) — have floated the company for years and helped SoftBank recover from the horrors of its dot com bubble collapse,  SoftBank is now horrendously overleveraged across the board, with 85% of its ARM shares and 70% of its SoftBank Corporation tied up in loans, its entire stakes in Alibaba, T-Mobile and NVIDIA liquidated, and the vast majority of its NAV sitting in the deteriorating value of its Vision Fund 1 and its non-OpenAI Vision Fund 2 holdings. You see, SoftBank is a holding company. It does not have “revenues” or “cashflows” in the traditional sense outside of when it’s able to either sell the things it has or raise debt. As Kakashii put it , Masayoshi Son is a perpetual gambler living in an eternal boom-and-bust cycle, going from losing 96% of his paper wealth after the dot-com bubble burst to sitting at the top of a company with a $200 billion market cap and with golden eggs that are worth, on paper, hundreds of billions of dollars more.  And he’s never, ever gambled more than he has on OpenAI and the greater AI bubble. While SoftBank’s WeWork washout lost it $16 billion , SoftBank has committed or invested over $60 billion in OpenAI, as well as billions more in related counterprojects like a still-pending 75 billion Euro investment in data centers , its $4 billion acquisition of data center firm DigitalBridge , its $1 billion investment in subsidiary SB Energy to build out more data centers , and its planned $3 billion investment in overhauling a Foxconn plant in Lordstown Ohio .  The future of SoftBank relies on both OpenAI’s ability to go public and maintain a high stock price, as any public offering will likely lead to SoftBank immediately looking for a margin loan. To make matters worse, SoftBank’s other bets hinge upon the continued success of the AI industry, which hinge both on the continued success of OpenAI and there being such incredible demand for AI services (in the hundreds of billions of dollars annually). And while the geese might have been a clue, SoftBank is a very, very weird company, and the only thing weirder than SoftBank is Masayoshi Son himself. Yet as goofy and whimsical as this all might seem, SoftBank is also one of the largest companies on the Japanese stock market , valued entirely based on the value of all those golden eggs, and no matter how much value Masayoshi Son might claim his “egg factory” might have, SoftBank’s continued existence relies on its ability to increase its NAV and acquire more debt. My concerns around SoftBank were well-summarized by The Economist back in May : It’s unclear what the future looks like for SoftBank. While death is unlikely given its near-systemic presence in the Japanese economy, its continued existence at its current scale is only made possible as long as the world’s most well-funded gambler can keep his seat at the table. While it’s seen boom and bust cycles in the past, SoftBank has never been this levered, and never gambled so hard on a single entity’s success .  While this is technically a company , SoftBank exists and operates at the whim of a man with questionable idols, insane ideas, and fantastical thinking. At one point during the Dot Com Bubble, Masayoshi Son’s net worth was higher than Bill Gates ’, rising by more than $10 billion a week, before the majority of his net worth in the space of a year and sending SoftBank’s share price crashing by 93%.  Yet even when adjusted for inflation, SoftBank only invested around $2.93 billion ($1.5 billion at the time) in the heights of Dot Com mania , and spread those investments out over multiple startups. Today I’m bringing you a guide to one of the silliest companies ever founded, helmed by one of the goofiest men alive, run in a constant state of brittle leverage.  SoftBank only avoided the void in 2023 by dumping its Alibaba shares , and this time around, Masayoshi Son may have gambled too much, putting all of his eggs in one Altman-shaped basket. Welcome to the Hater’s Guide To SoftBank, or Is Masayoshi Son’s Goose Cooked?

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Ed Zitron's Where's Your Ed At 2 weeks ago

The AI Industry Is Losing

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 (updated to version 3.0 a few weeks ago). My Hater's Guides To the SaaSpocalypse , 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 month, I published a two part series that took a deep-dive into the bubbles-within-a-bubble that make up the AI bubble — from the unsustainable and reckless growth of semiconductor companies, to the cults of personality surrounding Sam Altman and Dario Amodei. On Friday, I’ll publish my long-awaited Hater’s Guide to Softbank. You won’t want to miss it.  Subscribing to premium is both great value and makes it possible to write these large, deeply-researched free pieces every week.  Soundtrack — Queens of the Stone Age - Hideaway (Baloise Orchestral Arrangement) On Sunday, the Bank of International Settlements (BIS) put out its annual report and said, well, a bunch of things that I’ve been saying: As edifying as it is to see the bank for central banks say exactly what I’ve been saying for the last few years, this part is the one that both rocks as far as being right goes and sucks for the world at large: No shit. In April of last year, I wrote a piece called “ AI is a systemic risk to the tech industry, ” where I outlined how the failure of one model lab, OpenAI, would have seismic effects down its supply chain, delivering body blow after body blow to NVIDIA, Oracle, Microsoft, and the various Neoclouds that serve its compute, the most notable of which being CoreWeave.  Since then, OpenAI’s slimy tendrils have sunk into even more facets of the tech industry, and it has signed deals with the likes of Google, Amazon, Cerebras, and Broadcom, while also taking on more investments, including mammoth commitments from Softbank, which is only able to meet them by selling off prized stock in companies like ARM and NVIDIA, and by raising debt.  The idea of systemic risk has never quite left my work, and I’ve spent a lot of time thinking about it over the past year — and, as a result, my writing has examined the potential consequences of an AI spending pullback on those financing the sector, in particular private credit , as well as the semiconductor industry .  The BIS’s concern wasn’t about revenues tanking — which would happen should, as it fears, hyperscalers decide to “slow or halt the aggressive pace of capex development” — but rather revenues tanking and the borrowers within the AI supply chain being unable to service their growing debt burdens.  Again, this is something I’ve raised the alarm bells over a bunch of times. CoreWeave has been a favored popinjay of this newsletter, and in March of 2025, I published CoreWeave Is A Time Bomb, where I focused heavily on the company’s overwhelmingly toxic debt pile and its reliance on OpenAI as a customer.  On a much grander scale, we have Oracle — which I exhaustively profiled in my Hater’s Guide to Oracle newsletter .  Unlike neoclouds like CoreWeave, Oracle’s a much older company, having spent most of its existence selling database and ERP software to some of the world’s largest companies and public sector institutions. Oracle pivoted to serving AI compute at a time when its core business lines had started to stagnate, and thanks to its large scale, it was able to raise insane amounts of debt. And Oracle, as I’ve noted previously, is a company that, even before the AI bubble, was massively indebted. It just so happens that, as a result of its tryst with OpenAI, Larry Ellison saw fit to twist the debt knob to eleven.  Oracle’s spending has already pushed its free cash flow into negative territory — minus $23.7bn, as of the end of FY 2026 — and at the end of May, it had $129.5bn in outstanding debt. This doesn’t include its various lease commitments, which add up to nearly $38bn, nor the additional $260bn in lease commitments that have been signed, but haven’t actually started yet.  All of this is to say that Oracle has massively leveraged itself for the benefit of one company, OpenAI, and if that company can’t pay its bills, it’s fucked. Oracle’s existence — and Larry Ellison’s personal wealth — hinges on whether OpenAI can make good on its promise to spend $300bn in compute.  This is both the most-obvious and under-discussed part of the AI bubble — that the trillion-plus dollars of hyperscaler capex is feeding a massive semiconductor boom based on, at best, the very small likelihood that large language models will turn into something completely different.  If Microsoft, Google, Amazon and Meta decide that it’s time to stop spending $30 billion or more a quarter on GPUs, RAM, storage, and data center construction, that’ll tear a hole in the side of what people assume is a permanent supercycle. I need to state how fucking silly it is that anybody considered said semiconductor boom anything other than a brief chance to fill their boots before a global equity catastrophe so severe that the Futurum Group will be on suicide watch. Hyperscalers — who will see their capex outpace their cashflows as of Q3 2026 — have had such poor returns on their investment in AI that none of them will actually disclose their revenues outside of vague “ run rates ,” which means that all of this investment is effectively based on the idea that something completely different will happen in the future .  Said future will have to make them at least $2 trillion in brand new revenue by 2030 , because if it doesn’t, effectively all of that capex will have been spent to prop up Anthropic, OpenAI, and whatever it is that Meta is doing with its chatbots.  There is no cogent or rational argument in favor of continued capital expenditures, at least not one without a tacit acceptance that much of the current spend has been a waste outside of pumping equities and incubating two different large , unprofitable AI labs. Those millions of H100 and B200 and B300 GPUs are not going to usher in a digital God, they are not going to create recursive self-improvement, they are not going to be the fulcrum to adding $600 billion or more in brand new revenue to current services, and the only revenue they’re generating is compute spend from Anthropic and OpenAI, which I estimate makes up 20% or more of cloud revenues for Google, Amazon, and Microsoft.  I must also be clear that the cost of these companies extends far beyond equity investment. While Microsoft invested $13 billion in funding OpenAI, Microsoft executive Michael Wetter revealed as part of the Musk vs Altman trial that the partnership has cost it more than $100 billion , suggesting infrastructure costs of at least $87 billion just for OpenAI. I imagine Amazon and Google have had to spend similar amounts to handle Anthropic’s similarly-rapacious compute demands, especially given the $11 billion-and-counting cost of Amazon’s Anthropic dedicated Project Rainier data center . This is a criminally-underdiscussed part of the AI bubble. Anthropic and OpenAI have raised a little under $300 billion combined since 2019, but I estimate their true cost is at least $500 billion given hyperscaler capex investments that were necessary for them to exist, and that’s before you consider the $340 billion or more that Oracle is spending to build out the 7.1GW of “Stargate” data centers for OpenAI . These are not startups , but subsidiaries of big tech that only exist as separate arms as a means of pumping equity positions and hiding the truth: that AI capex has been a complete waste of money, even when you include two bulbous failsons that lose tens of billions of dollars a year. As I reported two weeks ago , OpenAI spent $17.2 billion on Microsoft Azure in 2025, a year when it lost $20.9 billion on $13.04 billion in revenue. Even if that were profit (which it is not), that’s $4.2 billion less than the capital expenditures that Microsoft spent in the first quarter of 2025 . Outside of OpenAI, Microsoft may as well not have an AI business. While it boasted back in April about having a $37 billion AI revenue run rate (meaning a non-specific month multiplied by 12), that only works out to about $3.08 billion a month, or less than a tenth of the $31.9 billion that it spent on capital expenditures in the quarter . To make matters worse, Microsoft revealed that number was “up 12% year-over-year,” suggesting that its AI revenue run rate in Q3FY25 was $16.59 billion, or around $1.38 billion a month.   Yet my own reporting on OpenAI’s inference spend from last November showed that it spent $2.947 billion in Q3FY25, representing about $11.7 billion on an annualized basis, meaning that, at least in that quarter, OpenAI likely represented around 70% of Microsoft’s AI revenue , and I’d be surprised if that dramatically changed in the year that followed, given that OpenAI’s inference spend was $3.648 billion in Q1FY26. All of this is to say that the only real outcome from all of this capex spend appears to be propping up Anthropic and OpenAI, two deeply-unprofitable companies, and then receiving a small fraction of it back in the form of revenue that is only made possible through hundreds of billions of dollars of venture capital subsidies.  Now OpenAI and Anthropic represent 50% or more of hyperscaler remaining performance obligations , or around $748 billion. There is simply no logical or rational reason to invest any further capex in AI, outside of the mistaken belief that OpenAI or Anthropic could actually afford to pay without Google , Amazon , or Microsoft handing it to them. Hyperscalers do not have meaningful AI revenues of any kind outside of their own pseudo-startup investments, and it is equal parts ludicrous and irrational that A) they are continuing to invest and B) that the markets, analysts and journalists are acting as if everything is fine. Record sales across NVIDIA, Micron, Sandisk, SK Hynix, and Samsung are a direct result of an entirely speculative asset bubble, driven by the reckless and directionless capital expenditures of some of the largest and richest companies in the world.  Anyone investing in data centers is building speculative capacity for demand that does not exist outside of Anthropic and OpenAI. If said demand existed, AI data center neocloud company CoreWeave would have a healthy and diverse revenue stream, rather than 65% of its revenues coming from Microsoft (for OpenAI) and NVIDIA , and the rest coming from Google (for OpenAI) , Anthropic , Meta , and, of course, OpenAI . There are simply no other massive consumers of AI compute , and the only reason we haven’t hit that harsh reality is that data centers take 18-34 months to finish .  Even if there was, I can find little evidence of anyone but OpenAI, Anthropic and hyperscalers having the demand or funds necessary to substantiate the data center buildout.  I really need to hammer this point home. If we assume that NVIDIA CEO Jensen Huang’s prediction of $1 trillion in Blackwell and Vera Rubin sales comes true, that would be around 40GW of data center capacity with around 30GW of IT load, and if we assume that data centers get about $12 per-megawatt of revenue, that works out to about $435 billion in annual compute demand by, being generous, 2030. Let’s be abundantly clear about something: the only companies that can afford to spend money on compute right now are either hyperscalers or the companies that hyperscalers subsidize. Even then, outside of OpenAI’s $50 billion in 2026 compute spend and what I estimate will be a similar amount from Anthropic, there doesn’t appear to be more than a few billion dollars of demand, and if there were, CoreWeave, IREN, Nebius, Cipher Mining, and other neoclouds would have hundreds of billions of dollars’ worth of remaining performance obligations rather than RPOs that expand only with hyperscaler backstops or the depths of Meta’s Zuckerbergian AI psychosis . Let me put it even simpler: those hundreds of billions of dollars of data centers are being built for no-one, and the only companies that can “afford” to pay for even a fraction of the compute are unprofitable AI companies propped up by hyperscalers.   While this might read as a radical position, I think it’s far more radical to look at the current state of affairs and say “fuck it, I think hyperscalers should spend a trillion dollars next year .” There is no rational justification for doing so out of fantastical thoughts driven by a deranged market desperate to avoid thinking about how tech doesn’t have any hypergrowth ideas left .  The current capital expenditures have, outside of the creation of OpenAI and Anthropic, been a near-complete waste. Microsoft 365 Copilot sucks . GitHub Copilot sucks. Google AI Overviews suck . Google Gemini is an also-ran LLM and thus, as a result, sucks. Meta’s LLMs are horrifyingly dangerous . Amazon Rufus sucks, and Amazon should be investigated by the SEC for suggesting it drove $10 billion in “annualized revenue” in Q3 2025 , because it most assuredly did not. Alexa+ sucks . It all sucks, and it would suck just as badly if big tech had spent a quarter of the capex.  These products are near-universally loathed, barely generate any revenue, and even in the case of the modestly-successful GitHub Copilot (around $1.08 billion in annualized revenue as of end of last year), it was only because users’ compute was heavily-subsidized, leading Microsoft to move users to token-based billing , outraging customers who were used to paying $39 a month to burn thousands of dollars of tokens . Sundar Pichai, Andy Jassy, Satya Nadella, and Mark Zuckerberg are losers. They may have billions of dollars, they may run giant tech companies, but they are losers selling a doomed technology based on unreliable, inefficient and overly-expensive technology ill-suited for the kinds of reliable, deterministic, “set it and forget it” tropes that people actually associate with AI.   The Four Losers are the only reason that anyone has taken these Large Loser Models seriously, which is a sign that the tech industry and our economy are also piloted by losers. Every bit of “progress” that we’ve seen from LLMs has come from aggressively cramming a square peg into a round hole — billions of dollars of training costs, hundreds of billions of dollars of capex, endless harnesses and scripts and wrappers and layers to try and eek out anything approaching the supposed promise of autonomy.  All the king’s horses and all the king’s men have sunk every dollar and ounce of brain matter into trying to make LLMs into something they’re not, and we, as a society, are expected to coddle these things and act like they’re exceptional , and give them credit for things that have yet to take place. I refuse to buy into the premise that LLMs’ ability to generate code or replicate open source software is proof that these things will become a powerful, autonomous tool in the future, and I think those that extrapolate to that point are either intellectually bankrupt, deeply cynical or so easily-fooled that they click every single email claiming their Paypal account has been compromised. I assure you, all this money can be wrong! Hyperscalers can, in fact, spend a trillion dollars on something that doesn’t do what they say, because these companies are more than happy to mislead you, and, to quote Nik Suresh : Why did everybody invest in data centers? Because the hyperscalers did so! Why are Micron and RAM companies selling so much RAM? Because A) GPUs use a ton of high-bandwidth RAM, B) said HBRAM consumes three times as much wafer space as normal DRAM , leaving less space for other kinds of cheaper, lower-margin RAM, and C) because the servers for said AI GPUs are, too, full of RAM!  Those data centers aren’t being built because the creditors have any “insight” into the massive amounts of AI compute that generative AI tools need, and will need. They see the “success” of ChatGPT and Claude (two heavily-subsidized products) and think that because Anthropic and OpenAI need lots of compute, everybody will need lots of compute. And because banks and private credit crave ways to invest their money and everybody is so excited , it’s super easy to get them excited about the prospect of building something big, sexy and costly! It doesn’t help that a lot of the information out there is deeply, deeply flawed. Last week, research firm Exponential View put out a questionable report claiming that AI had $110 billion in trailing 12-month revenues (between what looks like June 2025 and mid-June 2026), and did so by smashing together all AI revenues, including both OpenAI and Anthropic’s customer spend and compute spend , While the report claimed to “deduplicate” the numbers somehow, Exponential View declined to explain how it had done so. It’s also deeply deceptive to include both revenues and compute spend to try and represent the material health of the AI industry. This is because the AI industry is full of losers that cannot win without fiddling with the numbers, and because everybody is so excited, they’re ready to be fooled, and hesitant to dig an inch deeper.  Not me! I don’t give a shit, and I hate the feeling of being lied to, so I dug in. That’s because OpenAI and Anthropic represent as much as 75% of that revenue between their compute spend and revenues. Per The Information ’s and my own reporting , OpenAI had around $8.77 billion in revenue and spent about $17.48 billion on compute in 2025, and per The Information had $5.7 billion in revenue and spent $17.8 billion on compute in the first quarter of 2026, for a total of around $44 billion (40% of Exponential View’s total), which doesn’t include any of OpenAI’s compute spend or revenue for the months of April, May or June, which likely inflates the total further. While Anthropic is a little more-difficult to parse thanks to the Wall Street Journal’s unwillingness to make a readable chart , it had $4.8 billion in revenue in Q1 2026, and spent what I think is at least four billion on inference, and though its training costs are unreported, I think it’s reasonable to assume they’re at least $5 billion, for a total of $14.6 billion. If we, based on The Information’s reporting , take half (being generous, as most of this was weighed toward the end of the year) of Anthropic’s (all numbers are projections) $4.5 billion in 2025 revenue, $2.7 in inference costs and (I seriously question this number) $4.1 billion in training spend, we get $5.65 billion, for a total of $20.25 billion of contributions to Exponential View’s analysis, or around 18.4% of that $110 billion total. So, yeah, not including anything from Q2 2026, Anthropic and OpenAI represent 68% of the $110 billion of AI revenue that Exponential View is trying to get people excited about.  These are the actions of a loser propping up an industry of losers that cannot win by telling you the truth. This report exists entirely to fool the already-fooled and support an existing narrative, which is why Bloomberg covered it in the most obtuse, industry-servile way possible : Here’s two reasons this is fucking silly! Now, you may be wondering how they got that $25 billion number, and that’s because Exponential View gave it to them !   Yeah, but now they’re spending $765 billion on capex . Anyway, as I mentioned above, Exponential View’s Magical Maths magically brings those capex charges down to $25 billion, and entirely removes Meta because "initiatives are focused on ad uplift, so not recognized as pure GenAI revenue, or currently have minimal direct monetization.” What a loser move! Meta has oriented its entire company around AI ! I refuse to waste too much more time on this piece, but I need you to see how deceptively it’s framed this supposed “good news” for the AI industry, comparing its own proprietary depreciation formula against its own proprietary AI revenue formula to get a chart that is built to make the AI industry look good. No need for sourcing! No need for data! Just put the hype in the bag and invest in AI stocks!  I also find it despicable that Exponential View resorted to this weird, confusing “cumulative” AI revenues versus CapEx depreciation chart. The vast majority of this revenue is OpenAI and Anthropic’s compute spend, and I dunno, if you’re trying to do a report that gives the real state of the AI industry, maybe try and represent that anywhere in the report! These are, as I’ve suggested, the acts of losers propping up other losers. In the event that this industry had a fundamentally-sound revenue story, it would be extremely easy to show profits versus losses, track revenue in a transparent way, and produce a report that showed AI’s remarkable ascent. Instead, Exponential View says that AI is “real, big & fast” through a Pee Wee’s Playhouse of undefined models, datasets and alleged “quality grades” that helps feed a dangerous bubble further, and likely cons retail investors into further terrible decisions.  I know it sounds a little mean to call people losers, but what do I call an industry that sells itself on lies and deception? What do I call people that intentionally mislead people about the economics and outcomes of generative AI? If AI is so incredibly successful and impossibly brilliant, why does every explanation sound like it was written by The Riddler or somebody about to chug Jonestown Kool-Aid? Because they’re losers that can’t win by actually winning. Their best (and only) hope is to overwhelm you with a 24/7 marketing campaign (powered by the media) that makes all of this seem inevitable, impossible-to-stop, and a rip-roaring success, even as every company loses money and every product rings with a soulless mediocrity. That’s because LLMs are, while an interesting tool in a vacuum, currently being marketed by losers to losers using a mixture of Doom Trolling , insane extrapolations, and outright lies, manipulating people’s assumption that tech always gets better and that this much money can’t be wrong to create a marketing campaign fueled by deception. While using them doesn’t automatically make you a loser, you become one the very second you aggressively push somebody into doing so, as you have become the acolyte of the Loser Mafia. I have never heard anyone that’s an AI booster advocate for a technology with any level of excitement in their life, because they’re excited about how these tools make them feel and what they represent far more than anything else. They’re also tools intentionally built to produce engagement, and to make you feel you’re productive, even if you’re not. Just listen to this guy in this Bloomberg story about AI making people “productive, anxious and afraid to log off”: I’m sorry man, you have an addiction, and I worry it’s ruining your life. What is this producing? What are you actually doing with this time? Because if you’re allegedly 100 times more productive, wouldn’t that, y’know, produce something fairly incredible? I have no idea — and don’t want to put this man on blast — how significant his commitments on GitHub may or may not be, but the return on investment of “obsessively checking your laptop at all times in case you might not be productive” should be something on the order of curing a disease . The story continues: This man is a victim of a con, an industry-wide psychosis where you’re judged for not constantly dedicating every single second of your existence to prompt a series of chatbots into making something, all under the mistaken belief that at one point it’ll be so smart you…won’t have to prompt them?   Nevertheless, Van Horn is completely right — the sales pitch of AI is that agents were supposed to do the work for you, but billionaire losers are gaslighting you into believing that a digital busybox that requires constant vigilance to make sure it does what you ask or doesn’t spend too much money was somehow “autonomous.” While it’s easy to make fun of Silicon Valley, what we’re witnessing is a widespread mental health epidemic caused by liars like Sam Altman, Dario Amodei, and their wealthy backers lying about the capabilities of AI, creating an abusive culture where humans become subordinate to unthinking, hallucination-prone agents either subsidized by OpenAI or their employer: This is fucking horrible, and every loser who inflated this bubble should be ashamed of themselves.  In fact, fuck it, I want to speak directly to the people working in Silicon Valley and the tech industry who have been ground down by this industry.  I know not all of you are anti-innovation. I know many of you feel suffocated. I see you, I hear from you every day, and I find what is being done to you repulsive. Your industry has abandoned you .  Your investors are lying to you, and are getting rich while you can’t afford a studio apartment in the Tenderloin. AI does not do what you have been promised it does, and those who are excited about it are excited because they believe it will replace you. You are victims of a marketing campaign built to enrich a few people by sacrificing your time and energy to defend a doomed tool.  You are using tools that are built to manipulate you into making you work longer hours in the name of automation. You are being abused. You are being tricked into fighting for the 1% in the name of democratizing software. Your agents are meant to set you free, but they chain your body and mind to a system built to exploit your labor, extract your value and leave you dead. The people who make these agents fantasize about replacing you with them, and want to use your data to do so. They are lying that it is possible, but they want you to be scared so you will use their products more.  They have convinced you to fight on their side in a war where you will lose regardless of the victor.  You are a victim. I am not your enemy. I love technology too, and I want the tech industry to make cool shit again.  That will not happen under its current leadership. This era is built to drain the life out of you, to suffocate you with endless tech chatter, to make technology every part of your life, to somehow sell you the promise of automation, but only a kind of automation that you have to monitor constantly, prompt constantly, built to be addictive and superficially productive, built to fuel a Bay Area culture steeped in a godless version of the Protestant Work Ethic.  You must be a cracked engineer, you must work 15 hour days, you must have 8 subagents beating the absolute shit out of your codebase for one reason or another,  your Calendly must be open 8AM to 8PM, and you must be willing to work yourself to the bone for a chance to escape “The Permanent Underclass,” a misused term to refer to the world after an entirely-imaginary concept of Superintelligence, peddled by people who speak with a smugness that makes me want to spritz them like they jumped on the dinner table .  The grotesque glee that some have at the idea of being the first to announce AI’s destruction of everything you hold dear are your enemy, as are those who are desperate to constantly lick the boots of the Altmans and Amodeis of the world. Do not trust those who say that being part of an in group requires you to use certain kinds of software or attack others in the name of Silicon Valley.  The people encouraging you to work in this way do not care about you, or are being manipulated into believing this is how you all become rich by people exploiting their ignorance, fear or greed.  The people at the top do not care about the future, or progress, or anything other than growth. They are acolytes of a egregore of capital that has no purpose other than to expand and maximum velocity at all times, everything is fine as long as something is always happening, because the moment you stop moving you remember that nothing you’re doing really matters, because you’re making software while working sweatshop hours.  AI agents are built to make you interact with them. They are built to make you burn tokens. They are built to make you apologize for their mistakes and give them credit for your labor. Any “autonomous” tool that requires specific prompting, harnesses, scripts and tooling to make it sometimes work autonomously is conning you.  I’m also sure that there are a few perfectly normal software people using this stuff locally or with an open source model who treat it as normal software, loathe the data centers and see no need for the capex or mass market version of LLMs. These people are drowned out by a worryingly large crowd that speaks like they’re in a cult that exists to prove that OpenAI and Anthropic are somehow something more than SaaS companies. To them, using AI is a way of virtue signaling that they’re a pure, productive spirit, a willing supplicant for a future where they assume they’ll ascend because they told enough people “we’re still early.” The tech industry got taken in by a form of religious con, sold to them wrapped in atheistic “rationalism.”  Some may or may not have AI psychosis — or at the very least a severe addiction — as a result of being forced to interact with these things day-in-day-out, and the easiest way to check is to try not to use them for a day, or to try and solve a problem without them. If this is you, please know that I am not attacking you, and see you as a victim of a con. You are ingesting poison while being told it’s ambrosia. You are being made to work twice as much for roughly the same output, if not less. You are being humiliated or isolated for not using the right tools or saying the right things. Silicon Valley was built on the ideas of individualism and rationality, and the people at the top of your industry are telling you to fall in line and join an illogical consensus. You exist in a monoculture sold as anti-establishment as it mostly enriches Microsoft, Google and Amazon. Your culture is being eroded by people who do not care about technology. You are unwitting pawns in a greater war against innovation, where billions are steered into the hands of those who only ever care about growth and “acceleration” that benefits only a small few. You are not alone if you feel scared, anxious, listless and drained, because you are being worked to the bone building layers on top of AI models owned by subsidiaries of the largest companies in the world.  The fact that so many of you have to orient your products or fundraising around Twitter is a sign that your culture is decaying. A true meritocracy would reject the idea of “going viral on social media” like a virus, because it overwhelmingly benefits a monoculture that suppresses free thought and dissent.  Tech workers are in a constant battle between imbeciles and monsters, or an Arnold Palmer of the two. Those who want to build useful software that customers like you are drowned out by a Greek chorus of unexceptional cretins that think they’re competent because they can bonk an LLM on the head to make an impression of competence.  Generative AI is the Peter Principle on steroids, removing the friction points where a diplomatic moron might get caught out, making them far more mobile and extremely dangerous. Companies are run by men that don’t know what they’re doing, desperate to avoid anybody realizing that we’re at the end of software’s era of hypergrowth, increasingly aware of their own mortality and their lack of a culture that might actually build something a human being would want.  For those of you still hanging in there, I see you and admire you, because if I worked at most tech companies right now I’d fucking quit. Seeing this entire industry bow at the feet of the great unprofitable mediocrity machine is sickening, and based on the many tech workers I talk to every week, the mood effectively everywhere is exhausting, demoralizing, manic, and horrible to watch.  Everything must be done faster, with less people, with less organizational support, but more use of a tool best known for its hallucinations and ruinous cost, which you must use a lot, but also not too much. However much you use it, you must constantly celebrate it for fear a cult of personality and mediocrity will isolate or fire you for the crime of not wanting to “Do AI.” Even if you are still trapped in this world for months or years to come, know that you’re not crazy for finding it revolting, exhausting and debilitating. You do not have to do things this way, but I understand if you’re made to by circumstance or social pressure. The tech industry is in the throes of minor AI psychosis, or, put another way, it’s a way to scale the already-potent sense of make believe that has kept this industry afloat the last decade.  The grander cargo cult of praying at the foot of whatever capital-lust the venture capitalists currently have has led everyone astray, to the point that companies worth billions — or even trillions — of dollars on things based on how they might play out on Twitter, a maligned representation of the tech industry that caters to Silicon Valley gossip and the derangement of the markets, intellectually stunting most who cater their business or marketing to it.  The rest know exactly what they’re doing: appealing to an audience of venture capitalists convinced they’re “in the arena” by posting 12 hours a day writing 2000 word long posts using Claude. You must coddle these rich oafs, because it’s effectively impossible to raise money if you don’t. You must be able to recite the rituals — Hermes! Loops! Permanent underclass! — or you’re considered uncool by the least cool people alive. You, the great individualistic thinker of Silicon Valley, must convince wealthy oafs that you are an independent and rational person, but also that you will follow the greater consensus.  It’s a really unfortunate time to have ideas, dreams or goals outside of some sort of Potemkin agentic startup or if you can do the hocus pocus to con a VC into thinking you — or anyone — will invent recursive self-improvement, or AI that teaches itself.  You’re getting money right now if you can make noises that sound like you’ll be the next Baseten or whatever. It’s the era of inference I guess. Loops too. Keep cheering along! Never stop agreeing with what everyone else is doing, or if you do, only do so in a way that suggests that you all agree on the big stuff, which means you ultimately support either or both OpenAI and Anthropic, who companies that effectively operate as subsidiaries of the largest tech companies in the world.  It will stay this way until something changes.  As if I haven’t made it clear enough, the AI industry is losing. Their plans are not working, their products are not doing the things that they’ve promised, and though they intend to exhaust every available source of capital, they aren’t going to have enough money to do this forever. And no, AI is not “too big to fail.” Everybody makes fun of it. “AI” has become synonymous with generic, ugly, corporate slop. It’s a physical blight on the Earth, pumping horrifying toxins into minority neighborhoods and causing such noise that it makes people physically sick, and to make matters worse, some independent writers have made it their mission to cast doubt on these problems because they do not represent “the aggregate” of data centers. Everyone trying to be the “rational” voice on data centers should know that they’re only helping make the AI industry stronger. If you’re anxious that people are being “unfair” about water use, you’re an active pawn of capital, and exist only to help pump the bags of NVIDIA and the billions of dollars of speculative investment going into these monstrosities.  Without getting into the weeds, know that anyone talking about data center water use in terms of almonds or cattle is an actual industry plant.  California does use a lot of water to make almonds — and also makes 100% of America and 80% of the world’s supply . Cattle and other livestock also take up a lot of water and land, but they also make food for people to eat. You can bicker about how much water a data center may or may not use, and you’re going to sound like a complete loser every second you do so, because you are fighting to make sure that the AI industry can build data centers for the largest companies in the world.   Data centers are a monument to everything wrong with the world — horrifyingly large, loud, demanding of power and water and resources of all kinds. They create very few jobs, and those involved in their construction are usually from out-of-state. Their actual value to the world is largely tied up in their nebulous theoretical contribution to something an AI company does, and they get huge tax breaks, which means they don’t really contribute very much to many of the areas they’re put in. They are intentionally conflated with the smaller, useful data centers we’ve had in the past, all so that pedants can say “ehhmmm, you never had a problem with these before?” I haven’t, because previous data centers haven’t been filled with GPUs or drawn more power than a small town, nor have they been rammed through by a combination of crony capitalism, tax breaks and endless debt. And it’s fundamentally unclear why we need them!  No, really, why do we need these fucking things? So Anthropic and OpenAI can do more of whatever it is they’re doing? Neither appears to be unable to serve customers — other than the lousy uptime of Claude — nor do they appear to improve their products based on the availability of compute.  For such an offensively-large footprint — physically, fiscally and societally — nobody can really explain why the fuck we need all these things, other than the fact that they might make somebody money on a service that is best known for its huge mistakes and lack of profitability.  As I’ve discussed, the demand isn’t there outside of these two companies, and the only reason anyone believes that it does is that the largest tech companies in the world have burned through every dollar they have to hide from you that they’re out of big ideas . The AI industry fights like a bunch of losers because that’s what they are. They cannot win by telling the truth about their products, their infrastructure, the condition of their finances or their overall intentions. They cannot succeed without manipulation and deceit because they know, deep down, that their businesses don’t make sense and their actual products, described in the present tense, are impossible to justify what they’re asking for. They require us to coddle them, to ignore their ruinous cost, avert our eyes when they hallucinate or delete somebody’s database , blame ourselves when they make mistakes and speak entirely in theoretical terms when we describe them because the present kind of fucking sucks.  Absolutely nothing that the AI industry has created is worth even a fraction of the trillion-plus sunk into this industry, and at this point it’s very clear that these models cost about as much as a person and even then are neither capable of replacing one or profitable for the provider.  The best shot the AI industry has is open source models that may only be getting better by distilling American models. At some point Anthropic or OpenAI is going to slow down and then stop making models entirely because it costs too much money to train models, and said costs are only increasing. Even if GLM 5.2 is truly nearly as good as Opus 4.8, it did so by copying its outputs, which means that these models will likely only get as good as long as the foundation model companies keep training, which will only be possible if they can keep raising funding, which will become difficult if open source models eat their lunch in any meaningful way.  Could Anthropic and OpenAI theoretically make better models in a vacuum? Sure! But they’re now going to have to slow-roll them, because Sam and Dario’s four or five-year-long scaremongering campaign has forced them into a situation where the US government demands oversight into their model releases at a time where the AI industry cannot afford to slow down .  Their only option is to sit there and take it or, alternatively, admit that they’re making normal software, which will make the whole “let’s build a trillion dollars of data centers” thing a little harder to justify.  This will also be a tougher sell to Masayoshi Son of SoftBank, who gave a truly demented presentation during the 46th annual SoftBank shareholder meeting , calling the company a “golden egg machine” that’s also a goose that lays eggs that are, at times, undervalued.  Masayoshi Son has sunk $64 billion into OpenAI, and existentially tied a company with a quarter-of-a-trillion dollar market capitalization — the third largest on the Japanese stock market — to whether or not Sam Altman can turn a company that burned $20.9 billion in a single year into a company that makes more than $284 billion in annual revenue by 2030 . If you’re curious, the second-largest is Mitsubishi UFJ Financial Group, a massive Japanese bank with tens of billions of dollars invested in AI data centers , and the first is Kioxia, a memory and storage company that has seen massive revenues as a result of the massive demand for memory and storage for AI data centers.  What do you think happens if AI data center capex slows? What do you think happens when it turns out there’s not enough demand for all those data centers? Even if MUFJ and SMBC (the second-largest Japanese bank, also heavily levered in AI) have sold off part of the risk, their counterparties are still part of the global banking system. Anyway, SoftBank’s glorious, Geese-filled future depends upon OpenAI going public, and the New York Times just reported it’s likely pushed its IPO back to 2027 , because bankers didn’t think it would get a trillion-dollar valuation, which is an absolute disaster considering its pre-money valuation ( as in before the $122 billion it raised ) was around $735 billion. While it's partially blaming the floundering value of SpaceX, I think it’s possible (though I have no privileged knowledge to confirm it) that my story publishing its audited financials had something to do with it.  One can present financial data in all manner of ways, and I have to wonder whether its S-1 might have differed in some way — perhaps how segments were broken down — to what I reported. Perhaps bankers saw the reaction to the numbers, the mess that is SpaceX, the weird state of the market, and said “yeah man you’re gonna be lucky to float at $700 billion.” We may never know. 2027 may as well be in the year 3000 for how far away it is, and how much further OpenAI will have to drag itself to get there.  While it “raised $122 billion” earlier in the year, it’s waiting for two more tranches of $20 billion a piece from NVIDIA and SoftBank, and will now straight up not get the $15 billion that Amazon conditioned on it either going public or reaching AGI. Considering that Mr. Altman can’t even con a bunch of bankers who were dumb enough to believe that SpaceX could 300x its AI revenue by 2030, it’s clear that the jig is up.  Another worrying sign is that SoftBank was unable to raise a $6 billion margin loan with its entire OpenAI stake — likely valued, at least on paper, at over $100 billion — as collateral. This suggests banks have little faith in the company. Some might believe that Anthropic has a better chance, and I’m just not sure there’s much that differentiates it from OpenAI anymore, other than how annoying Dario Amodei is and how much he appears to piss off the Trump administration.  Anthropic is a large language model company that loses billions of dollars that has subsidized accounts that allow users to burn $8,000 a month in tokens for $200 . To paraphrase and build upon something said by Cory Doctorow , if your business is only successful when you give away $40 for $1, that’s not a real business, it’s a way to feed venture capital dollars to hyperscalers and sell a bunch of people a product that doesn’t exist.  Anyone still lazy enough to say “they’ll crank up the price” or use some hackneyed Amazon Web Services or Uber comparison is either deliberately ignorant ( I explain here ) or a loser like the rest of the AI industry. If you’re so confident about this shit, despite all the blaring warning signs, you need to start finding actual, real, tangible evidence, and you need it soon. Every argument in favor of AI requires you to speak in the future tense and ignore your lying eyes. The AI industry will not allow you to discuss LLMs in terms of what they do today without reminding you that progress has been so rapid over the last few years and demanding you immediately acquiesce that something might be good in the future.   Seriously, try and talk to somebody who loves AI sometime and criticize the tech and see how quickly they fall into the tropes of AWS losing money, AI models rapidly getting better ( at benchmarks rigged in their favor because they can’t use a computer like you or me ), about the “cost of intelligence going down” ( when it’s actually going up ), or any number of other tired tropes that mostly rely on you ignoring the present in favor of a billionaire’s dream of the future. These are, as I have been saying, the acts of losers. This is what you do when you do not actually have a compelling story, cannot win by being straightforward or contrite, and have no way to prove yourself valid outside of appealing to cargo cults and doing financial engineering, except you’re such a loser that you’re not even doing it to commit fraud! You’re just writing PDFs so you get shares on Twitter.  Forgive me for being so very brusque , but I have had to prove myself endless the last few years, and when I finally bring you the proof that OpenAI loses a bunch of money, you immediately jump for the first keys jingled above your head. If you truly love the AI industry so much, you should ask it for better proof! You should be enraged that OpenAI’s numbers are so shitty, and that you have to debase yourself by pretending they’re not! How utterly shameful!  That’s loser shit! If you love large language models so much, go out and demand the people making them bring you the answers to my questions. Whenever I’m asked about how I might be wrong it mostly comes down to “but what if something that hasn’t happened happens?”  If your answer is “OpenAI will drive down the cost of its silicon using its “Jalapeño” chip from Broadcom,” you do not have shit! It’s still in early testing ! There is no future for the future these people are building. The demand does not exist for these data centers. It never has. It never will. You can give Baseten as much money as you want, you can talk about the exciting world of open source for hours, but there is not actually enough demand for this stuff unless it becomes something very different, very soon, in a very big way, that likely also involves it getting cheaper.  Anthropic and OpenAI have $1.1 trillion in compute commitments that are contingent on their continued growth, at a time when their customers are protesting their costs, at a time when the market is clearly saying “you are not worth a trillion dollars.”  What do you think changes that?  The halo effect of AI has given way to a societal cynicism, even by the people that love it, who have a sort of vague reticent “I give up” vibe that I find exhausting to watch and will have a great deal of trouble forgetting once the bubble bursts. Even the people who claim to be excited are making jokes about Masayoshi Son and Sam Altman!  Everything about AI has the stench of death and desperation, of losers pretending they’re winners who can only thrive in conditions that reward grifting, specious hype and forward-looking statements that vary from ridiculous to deliberately harmful. It’s ugly, regressive, and when this era ends, I expect financial carnage and chaos that could have easily been avoided had so many people not so readily swallowed poison under the auspices of innovation. Then again, some people might just be born to be regulated by the wallet inspector. 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 10,000 to 18,000 words, including vast, detailed analyses of the biggest events and companies in the AI bubble.  Exponential View’s research rigs the dice using obfuscated, proprietary data. Anthropic and OpenAI represent at least 68% of the supposed $110 billion in AI revenue from the last 12 months. While the report claims to ‘deduplicate’ revenue across the AI stack, it does not provide any source data of any kind, making it impossible to verify. The report uses “annualized run rate” to try and make the AI industry’s revenue seem larger than it is. This report is industry marketing framed as research, but uses deliberately positive framing and questionable data sources. You are comparing costs of the entire industry against depreciation costs of the few companies that actually buy AI GPUs. In Q1 2026, Amazon had $18.94 billion , Microsoft $10.1 billion and Google $4.4 billion in depreciation . That’s $33.44 billion! That’s more than $25 billion! And I haven’t even included Meta, but don’t worry , as I’ll get to, neither does Exponential View!

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Kev Quirk 2 weeks ago

The Laziest Generation

by Ibrahim Diallo Ibrahim talks about house prices in the US, how it's only getting worse, and the perception from previous generations that kids today are somehow lazy because they can't afford a house before the age of 40. Read post ➡ Being the father of 2 young people, this worries me too. Despite this post being US-centric, the script is the same here in the UK. Unless my kids generation come out of school on a 6 figure salary, they don't have a hope in hell of buying a decent house. To put that in context, here in the UK a £100,000 salary puts you in the top 3% of earners . In the late 90's a house would cost around 4x a person's salary on average. Today it's 8x . So most can forget about saving for a deposit. Instead younger generations will have to rely on inheritance, which will only exacerbate the late stages of life in which people are buying houses. Something has to give. Thanks for reading this post via RSS. RSS is ace, and so are you. ❤️ You can reply to this post by email , or leave a comment .

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Krebs on Security 3 weeks ago

Scattered Spider Hackers Plead Guilty on Day 1 of Trial

Two men pleaded guilty in the United Kingdom this week to criminal charges stemming from an August 2024 cyberattack that crippled Transport for London , the entity responsible for the public transport network in the Greater London area. The duo were key members of a prolific cybercrime group known as Scattered Spider , and their guilty pleas came on the first day of what was expected to be a six-week trial. Owen Flowers (left) 18, and Thalha Jubair, 20. Image: UK National Crime Agency (NCA). Thalha Jubair , 20, of East London and 18-year-old Owen Flowers of Walsall admitted conspiring to commit unauthorized acts against Transport for London computer systems and causing risk of serious damage to human welfare. According to a report from the BBC, Flowers alone admitted to being part of a conspiracy to hack into U.S. based healthcare providers SSM Health Care Corporation and Sutter Health in September 2024. Jubair is also wanted by U.S. law enforcement agencies. In September 2025, prosecutors in New Jersey unsealed an indictment alleging Jubair and other Scattered Spider members committed computer fraud, wire fraud, and money laundering in relation to 120 computer network intrusions involving 47 U.S. entities between May 2022 and September 2025, and that the group’s victims paid at least $115 million in ransom payments. In July 2025, KrebsOnSecurity reported that Flowers and Jubair were arrested in the United Kingdom in connection with Scattered Spider ransom attacks  against the retailers  Marks & Spencer  and  Harrods , and the British food retailer  Co-op Group . Multiple sources familiar with those investigations said Flowers was the Scattered Spider member who anonymously gave interviews to the media in the days after the group’s September 2023 ransomware attacks disrupted operations at Las Vegas casinos operated by MGM Resorts  and  Caesars Entertainment . According to prosecutors, Jubair co-ran a bustling Telegram channel called Star Chat , the home of a SIM-swapping group that used voice- and SMS-based phishing attacks to steal credentials from employees at the major wireless providers in the U.S. and U.K. The group would then use that access to sell a service that could redirect a target’s phone number to a device the attackers controlled and intercept the victim’s calls and text messages (including one-time codes for multi-factor authentication). A receipt from Star Fraud Chat’s SIM-swapping service targeting a T-Mobile customer after the group gained access to internal T-Mobile employee tools. “Rocket Ace” was one of Jubair’s hacker handles, according to U.S. prosecutors. New Jersey prosecutors also allege Jubair also was involved in a mass SMS phishing campaign during the summer of 2022 that stole single sign-on credentials from employees at hundreds of companies. That weeks-long SMS phishing campaign led to intrusions and data thefts at more than 130 organizations, including LastPass ,  DoorDash ,  Mailchimp ,  Plex  and  Signal . KrebsOnSecurity reported last year that one of Jubair’s alter egos at age 15 was “ Everlynn ,” a hacker who sold fraudulent “emergency data requests” that used compromised police and government email addresses to demand subscriber data (e.g. username, IP/email address) from major tech companies, claiming the requests concerned urgent matters of life and death and could not wait for a court order. In April 2026, 24-year-old British national and Scattered Spider member Tyler “Tylerb” Buchanan pleaded guilty to wire fraud conspiracy and aggravated identity theft for participating in the group’s SMS phishing spree in the summer of 2022. The government said Buchanan, Jubair and others used the credentials harvested in that phishing campaign to steal at least $8 million in cryptocurrency from victims throughout the United States. Buchanan is currently scheduled to be sentenced on October 2. In August 2025, 20-year-old Scattered Spider member from Florida named Noah Michael Urban was sentenced to 10 years in federal prison and ordered to pay $13 million in restitution, after pleading guilty to charges of wire fraud and conspiracy. The U.S. Department of Justice says three alleged Scattered Spider defendants indicted along with Buchanan still face charges, including Ahmed Hossam Eldin Elbadawy , 24, a.k.a. “AD,” of College Station, Texas; Evans Onyeaka Osiebo , 21, of Dallas, Texas; and Joel Martin Evans , 26, a.k.a. “joeleoli,” of Jacksonville, North Carolina. Flowers and Jubair are slated to be sentenced in a London court on July 15, 2026.

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Ed Zitron's Where's Your Ed At 3 weeks ago

Cargo Culture

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 (updated to version 3.0 a few weeks ago). My Hater's Guides To the SaaSpocalypse , 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 two premium newsletters were a deep-dive into the bubbles-within-a-bubble that make up the AI bubble — from the unsustainable and reckless growth of semiconductor companies, to the cults of personality surrounding Sam Altman and Dario Amodei.  Subscribing to premium is both great value and makes it possible to write these large, deeply-researched free pieces every week.  A few weeks ago, I predicted that the AI industry would start pushing the concept of “loops” — effectively LLMs prompting LLMs and being left to their own, token-intensive devices — as a desperate attempt to get users to burn more tokens, I imagine to create more revenue.  Now Jensen Huang and Claude Code chief Boris Cherny have both, within 24 hours of each other, intimated that the age of prompting models is over, as you’d just be “handling loops,” which conveniently also means burning more tokens. It’s unclear what benefits a loop might have, but at a conference where the first question was mysteriously about “whether loops were for real,” Cherny was emphatic that they were, and attempted to explain why : Pretty convenient for a guy who’s allowed to burn upwards of $130,000 a month in tokens by Anthropic. Burn as many tokens as possible, so that the Great Prophecy might occur! If you don’t endlessly use as many tokens as possible, constantly defend the AI industry, and prove yourself worthy of the Great Beast of Capital, you will never ascend! Use loops now! Agents are out! You’re an ape if you use agents now that we have loops!  I realize I sound like I’m being a little dramatic, but the AI industry moves with an alarming speed and consensus to judge those who are deemed unworthy. Don’t you dare report news that might darken the hearts of the chosen ! Don’t you dare suggest that the Great Prophecy (Anthropic and OpenAI are able to meet their $1.1 trillion in spending commitments) is untrue! Don’t you dare doubt that large language models are the future! This is Silicon Valley, and Silicon Valley stands for rugged individualism, rationality, and meritocracy, unless, of course, everybody in Silicon Valley agrees that an idea is good, at which point all of that bullshit goes out the window. Any singular piece of information that even slightly rings positive is proof of the Grand Prophecy of Growth that AI promises, and Chosen are those that take said proof, and Cursed are those who would question it.  That being said, if you’re an AI booster, I need you to listen to me. It’s very important everybody does the same thing right now. If Saint Tibo resets the Codex limits and you’re not immediately running loops — if we’re all not running loops — then the Great Prophecy won’t be completed. If you hear anybody question the Great Prophecy, you must immediately attack them, ideally finding a single point that you believe to be wrong (having read at most 200 words of what they have written), and then declare your victory. This is now your job.  If you do not write supportive blogs and tweets about the AI industry, you hate the entire tech industry, because Silicon Valley (see: the supposedly individualistic and meritocratic culture built on challenging consensus) needs you to suppress all dissent of any kind and ostracize those who dare to speak ill of it. Do not fret about non-believers who ask about things like “economics.” If you’re worried, re-read AI 2027, a piece of speculative science fiction that the big, serious, rationality-driven tech industry requires you to take seriously. You’re a big boy! You make your own decisions! Unless those decisions run contrary to the consensus of Silicon Valley, which is currently set to “AI is the literal future of everything and can do anything we agree on eventually.”  This is Silicon Valley — a monoculture that sells itself as outliers, putting everything it has into supporting a generative AI industry that sends the vast majority of its value directly to the largest tech companies in the world. The staunch rationalists of the Bay that have built brands convincing people they’re immune to the influence of groupthink need you to think exactly the same way that they were told to.  Why would everybody agree to do something so stupid? Why would everybody act so crazily?  It’s simple: the tech industry has completely run out of ideas, and all that’s left is a cargo cult that hasn’t had a human experience since 2015. Last week, Snap CEO Evan Spiegel debuted Snapchat Specs , a $2195 pair of augmented reality glasses with a demo that makes it apparent that nobody in the C-suite has spoken to a normal person in years. The tech industry desperately craves its next iPhone, but years of growth-at-all-cost management consultancy poison has twisted the already-flimsy mission statements of Silicon Valley from creating societal value and innovation to creating shareholder value and a kind of banal, nihilistic accelerationism that mostly comes down to “how do we make the next thing that will make number go up.” This is, of course, a joke. I have no idea if you’re allowed to look Evan Spiegel in the eye if you work at Snap. I also have no idea if anybody actually considered what a regular human being might do with the product it’s been desperately trying to launch for nearly a decade. A single conversation with a regular person would likely have them tell you that they wish their shit worked better or that the internet wasn’t so full of scams and pop-ups and slop and misinformation. They wish there weren’t so many ads. They wish their apps weren’t confusing and full of dark patterns and ways to trick them into subscriptions or clicking ads or being annoyed. That’s because there’re only so many things you can do for the user until you start doing stuff to the user. Per my piece from the end of 2024 : Despite decades of progress in hardware making computers faster, cameras better, and storage larger, the actual experience of using the computer has gotten materially worse. We’ve hit a wall as far as where mobile and desktop user interfaces can take us, and every attempt at making voice-activated platforms like Alexa replace (or even compete with) them has proven fruitless, with Amazon’s various Echo devices and services losing billions of dollars a year .  This is what I call the Rot-Com Bubble . Big tech has hit the wall of what modern software can do, and in turn run out of hyper-growth ideas. Nobody has the next Google Search, iPhone, cloud computing, mobile app store ,or other idea that would allow Google, Microsoft, Apple, and Amazon to keep growing at a rate that justifies their valuations. While this is partly a natural process — there are only so many ways to do things! — it’s also a direct result of incentivizing and promoting products that create revenue growth or sustain monopolies, which in turn focuses your R&D and hiring efforts toward those who can come up with ways to make Numbers Go Up. Put another way, the tech industry has become the largest cargo cult of all time. Microsoft, Google, Amazon, and Meta sunk what will soon be over a trillion dollars into AI data centers because they don’t have any other ideas, and because the only thing that the MBA’d elites running the tech industry can do is hire people, fire people and spend money. Their (at least in the first three cases) investments in OpenAI and Anthropic were a successful attempt to build both their largest individual customers and a new revenue stream under, I imagine, the mistaken belief that said customers would eventually become independent enough to pay them without continually raising venture capital. I also imagine they believed that AI data centers would actually make a profit at some point, or that said data centers wouldn’t take two or three years to complete , or that AI, as an idea and a tool, would “take off” in a real sense, rather than an imaginary hype cycle in an economy built on speculation.  The problem is that previous eras of innovation and hypergrowth never came from shoving hundreds of billions of dollars into any one thing. The original iPhone took two and a half years to develop, but was the culmination of multiple different innovations in capacitive touchscreens, smaller batteries, and the condensed talent that helped create a touchscreen keyboard that actually worked , and ended up costing about $150 million (or $271 million in today’s money), or a little less than a third of what SoftBank paid OpenAI in 2025 . The reason we haven’t come up with the “next iPhone” is because we’ve maxed out what we can do with the current slate of ways to look at a computer interface, and the next logical step is one that’s effectively screenless, which is an unbelievably big leap, and one that will not be surmounted any time soon. So our only hope is software, and the limits of our current interfaces. Google Search was created by two college students at Stanford . Instagram was a mobile check-in app called Burbn that realized it couldn’t compete with (lol) Foursquare and pivoted to creating a photo-first social network . In fact, most of the historical success stories in the valley are, for the most part, websites that bring together people or services in a way that’s accessible and readily-available, and most of that innovation came from services like Amazon Web Services. Social media companies were the natural next revenue ascent because they were, at least in theory, relatively cheap to run, as the users themselves (and what the platforms could encourage them to do) were the ones that made the reason for you to log onto the site. Except everybody forgets how many dead social networks there are, like iTunes Ping , Google+ , Google Wave , Microsoft SoCl , Meerkat , App.net , Pownce , Orkut , Jaiku , and YikYak . Everybody forgets that just about every other attempt by Meta or Google or Microsoft or Amazon to expand outside of their core competencies (if you can call them that) has failed. Everybody is desperate to ignore the fact that Silicon Valley startups have, for the most part, not done anything particularly new or interesting for over a decade, and that the reason everybody took these people seriously is a result of conflating them with people that have either entirely left the tech industry or have little to no say in its future. There’re only so many ways to solve problems with software, and only so many other ways to solve the problems that doing so creates. Two decades of Silicon Valley “innovation” have come from throwing as much software engineering talent and venture capital at as many problems that could, at least in theory, be solved through a combination of cloud compute, storage and code.  And while there might be more problems that code can solve, they aren’t the kinds that create hundreds of billions of dollars of revenue or massive shareholder returns, nor are they things that big tech can copy and bolt onto their current services to keep them growing either. This is leading to the slow, agonizing collapse of both the software industry’s revenue and the venture capital business model. The “ SaaSpocalypse ” narrative claimed that companies writing their own software was a threat to the business models of SaaS companies (and a justification for their dwindling revenue growth), which was an attempt to paper over the fact that the software industry is in decline , with the growth efficiency (revenue growth versus sales and marketing spend) of software companies declining by half between 2021 and 2023 , with BDO reporting in a 2025 analysis that across 115 publicly-traded SaaS companies, the industry’s revenue had declined by 2% year-over-year, with mid-sized growing companies at a flat 0%. The fact the “SaaSpocalypse” narrative took off is all part of the greater cargo cult of the Valley, and the media’s willingness to buy effectively anything they’re selling. Nobody is actually building their own SAP or Salesforce or Office 365 — that’s a fucking stupid idea! — but because that sounds like a directionally-correct idea that affirms the greater bias of the growth of AI, it set in, which meant some stocks went up and some stocks went down . Did they go up or down based on something that actually happened? God no! The market listens to the media and analysts, who mostly just look at the numbers they’re given and the people they talk to, who more often than not are the CEOs and other executives of the companies that plant these narratives as a means of getting away from an uglier truth. You see, if AI is the reason that the SaaSpocalypse is happening, it fits into the larger imaginary Valley mythology of “disruption,” and gives everybody an excuse to keep believing that every tech company will grow in perpetuity. The cargo cult cannot change its rituals to adapt to a reality that suggests that its gods are dying. Accepting that AI isn’t saving everything means that you have to accept that there might be an end to the era of hypergrowth , which in turn means you have to start thinking about the rationale of, say, venture capital and private equity. Both have seen far better days.  As of the end of last year, the average TVPI (total value put in) of venture capital funds raised between 2017 and 2024 was between 0.8x and 2.0x , meaning you’d get somewhere between 80 cents and $2 for every dollar invested, with 70% of startup exits between 2022 and 2024 netting a loss for their investors , up from 58% between 2009 and 2014, which included much of the bloodbath from the great financial crisis. Per The Economist , the Valley also faces a glut of “Zombie Unicorns,” startups valued at $1 billion that can’t raise money or exit at their current valuation, and a third of all active US unicorns ( per Axios ) haven’t raised any funding in the last three years.  Meanwhile, private equity is facing much the same problem, with more than 16,000 “ zombie companies ” held for more than four years, the longest on record, and holding companies for an average of 7 years in total . Private equity exits have dramatically declined , with a growing amount of exits being funded by “ secondaries ” — venture or private equity funds selling each other their portfolios in the hopes of avoiding having to dump them at a loss. And wouldn’t you know, a big part of the problem is that they piled trillions into software companies assuming they’d all grow forever, massively overvaluing them in the process. Between 2018 and 2022, ( per Apollo ) 30% to 40% of private equity deals were in software companies, with firms taking on debt to buy them and then lending them money in the hopes that they’d all become the next Salesforce. In reality, private equity overvalued the vast majority of its software investments, stuffing them full of debt with payments contingent on near-constant growth, which is why Pluralsight lost its investors $4 billion and Medallia lost Thoma Bravo $5 billion . S&P and 451 Research analyst Scott Denne recently put it bluntly , saying that “..."The holding periods are longer and they're going to get longer because there effectively isn't an exit market for these companies.” It’s almost as if instead of looking at whether the companies were good and making intelligent decisions, private equity instead chose to do what had historically worked and assumed that its investments would continue to grow in perpetuity. You know, vaguely looking at history and doing things in an almost ritualistic way . In venture’s case, while part of the problem was how easy it was to get money in the ZIRP era , the other is that venture capital has been morphing into a cargo cult for a decade, with seed stage financing collapsing since 2015 , and continuing to drop in favor of middle-to-late stage rounds in established players…almost like venture capital just doing stuff in a way that somebody else did because it worked for them in the past. Venture capital no longer really cares about risk at scale, with the vast majority of funds going to late stage, and even “early stage” data poisoned by Series B rounds that are only something you can raise once venture capitalists have arbitrarily decided that you should continue living. As a result, the vast majority of funds do not go into creating the future or taking risks but doing things that resemble success , which usually means following hype cycles and hoping for the best. Baseten, a company that sells AI inference infrastructure, just raised $1.5 billion in a Series F funding round so that people can use or run their own open source AI models, quite literally allowing people to do things that other companies have been doing and train open source models of their own, so that they too can “do AI.”   Its investors include D.E. Shaw Ventures, Greylock and Altimeter Capital, all of whom invested in both Anthropic and OpenAI. Baseten doesn’t own its own infrastructure , renting instead from hyperscalers, which means that that $1.5 billion goes directly into the pockets of Google, Amazon and Microsoft, much like the money raised by OpenAI and Anthropic, which in turn gets spent buying more NVIDIA GPUs. All that “free thinking” and defiance of incumbents always seems to end up as revenue for the largest companies in the world. So much for backing the little guy!  While the Valley’s legend has grown from risk-taking and fostering new ideas, venture capital works in reverse, overwhelmingly funding market consensus and piling into deals after somebody else has risked their capital to keep it alive. Decades of encouraging people to fund startups with the express intention of hypergrowth — with Ben Horowitz suggesting in 2010 that having “zero chance of becoming a high-growth company” was tantamount to “being in purgatory” — has created a startup culture focused entirely on its Total Addressable Market and growth trajectory, which means that companies are founded with that express intention.  Venture capital funds companies that appeal to venture capital, which means Silicon Valley innovation is centered around finding ways to convince venture capital to give it money. While this might have worked a decade ago when there were still hypergrowth companies to build, it intellectually stunted the Valley, promoting and celebrating companies not based on the things they’ve built but the shareholder value they’ve created . A startup is considered a “success” not based on its tangible contribution to the future, but its ability to tick boxes either through funding, revenue growth, acquisitions, or valuation. Everything is about creating the signs that your company is part of the big thing that will supposedly lift every Silicon Valley valuation — after all, 61% of venture capital funding went to AI in 2025 — to the point that it isn’t really clear what anything means or what anybody is doing. Nowhere is this more obvious than the eternal shuffle of different guys between different AI companies. Google paid $2.7 billion in 2024 to acquire Noam Shazeer, one of the authors of the paper that started the generative AI bubble, along with his worthless AI chatbot company Character Dot AI. Two years later, Shazeer is joining OpenAI , and it’s unclear whether his second tenure at Google really did anything, other than helping pad the bags of venture capitalists and possibly having some effect on Google Gemini. It’s unclear what changed at OpenAI when co-founder Andrej Karpathy left in February 2024 , nor is it clear what is happening now he’s joined Anthropic . Barret Zoph left OpenAI in October 2024 to become the CTO of Mira Murati’s Thinking Machines, created absolutely nothing of value, went back to OpenAI in January 2026 as its “GM of B2B,” oversaw an era where its enterprise customers had “huge issues” with its costs , then left again , I assume to another AI lab that will give him lots of stock. I’m going to go out on a limb and suggest none of these guys actually contributed very much in their most-recent tenures, and that their hiring and positions were further cargo cult moves. Noam Shazeer was the original Attention Is All You Need guy! Give him $2.7 billion! Quick, before somebody else does! Quick, hire Andrej Karpathy, a guy who hasn’t worked at OpenAI in years, to do something with your LLMs! His eternal brilliance — which resulted in absolutely nothing since he left OpenAI outside of a placeholder website for a dead education startup with a protected Twitter account — is necessary to doing whatever it is we’re meant to do next! This will help us do hiring too, because everybody wants to work with these great minds that do stuff, somewhere, at some point, or maybe they did stuff, I don’t really know!  Hey, remember when Mark Zuckerberg was paying tens of millions of dollars to hire random AI researchers ? Why do you think he did that, other than the fact that everybody else was hiring lots of AI researchers? Hey, while we’re on the subject, what exactly did they end up doing? That’s right, a mid-tier AI model and an AI app that nobody uses! Sure sounds like Mark Zuckerberg was just doing whatever seemed to work in the past, which was “get smart guy, smart guy do stuff, thing happen,” much like when Microsoft hired Deepmind co-founder Mustafa Suleyman for over a billion dollars , with little to show for it other than mid-tier LLMs, a universally-loathed chatbot , massive capex, and AI revenues that are too small to break out in Microsoft’s earnings.  No, sorry, I forgot the latest cargo cult maneuver — OpenClaw, a product that 99% of people have never heard of other than those who intentionally drown themselves in Silicon Valley cultism, which is why Microsoft , NVIDIA , Meta and Amazon all built OpenClaw bullshit and OpenAI hired its founder . Everybody is moving between various different rituals in the hopes that they’ll be the ones that they’ll be The Great Winner of AI, even if nobody really knows what that is and is only doing all this shit because everybody else is doing it.  That’s because the AI bubble has been part of the greater cargo cult of the Valley. Why did Microsoft buy hundreds of thousands of GPUs? Because an engineer told him that if millions of people used ChatGPT via Bing, they’d need “ every high-end chip the company had .” Why did everybody freak out about ChatGPT? Because it was the first viral product the tech industry had created, and it was truly different. Why does anybody think LLMs are going to change anything? Because everybody vaguely came to the consensus that ChatGPT was trending in the direction that something would change.   And so the greater tech industry moved into full cargo cult mode. Amazon, Google, and Meta had to buy all those GPUs because Microsoft bought a lot of GPUs . Investors piled into various AI companies because when the tech industry does something at the same time, big things happen. Everybody has acted based on reading the signs — ChatGPT’s meteoric growth meant that it could be the next Google, and because the economics had worked out in the past, they would work out here , which is why everybody tells you that it’s just like Uber ( it isn’t ) or AWS ( which cost $52 billion between 2003 and 2017 , or less than a quarter of What OpenAI and Anthropic raised in the last 6 months).  The AI industry is fundamentally judged based on its symbolic similarities to bygone eras. Buying GPUs and building data centers sort of feels like Amazon Web Services, even though the $765 billion that big tech will spend in 2026 will be more than ten times Amazon’s combined capex during the period where AWS was being built. ChatGPT sort of feels like Google Search or Facebook Ads or next app store, but only because it’s a culturally-relevant piece of software, largely driven by the larger cargo cult of tech crystalizing around it.  Most people trying to make these comparisons either don’t remember or are desperate to forget how different the world was when Google Search, the iPhone or Amazon first grew. They don’t want to think too hard about how blatantly obvious the utility of these products was, how they had functional unit economics from their earliest days, or how different their growth stories were. They don’t want you to think about it either, because part of the greater cargo cult is making sure you don’t believe your lying eyes and focus on the greater signs that The Great Prophecy might come true, even if it’s not obvious what that means other than “ChatGPT is the biggest most hugest and most profitable company ever and everybody makes money on their investments.” OpenAI and Anthropic are the height of the Valley’s mysticism. Both are still referred to as startups, despite the fact that Amazon, Google, and Microsoft paid for their entire infrastructure, spending at least $200 billion just on buying GPUs and building capacity for two companies. They have raised — assuming their most-recent rounds fully close — close to $300 billion in the space of two years, and are on course to burn tens of billions of dollars each in 2026.  Neither Anthropic nor OpenAI are actually startups. They have enough money and clout to hire just about anybody, can deploy billions of dollars in stock for acquisitions, have their infrastructure fully paid for by other companies, and because it’s taken so much money to build said infrastructure, effectively nobody else can train models or serve inference at their scale, making them the functional equivalent of a hyperscaler.   And neither company feels anything less than insane outside of outright ignorance or a cargo cult mindset. Both companies have had everything paid for them either by hyperscalers or venture capitalists, and are fundamentally incapable of operating without infinite resources, and the best that anybody has to defend their endless billions of burn is to refer to the 184-year-old railway bubble or the Dot Com Bubble , using them as symbolic proof that everybody can lose a lot of money, and that somehow results in something good, I guess? The logic centers around the idea of “useful infrastructure,” as if railways or telecommunications equipment have any similarity other than that people spent way too much money on them in bygone eras. AI boosters (and the well-meaning and ignorant) return to these bedtime stories as a means of escaping reality and accepting that it’s very possible for everybody to be wrong in a completely new and innovative way. This is the same mystical thinking that gets us to the idea of OpenAI or the greater AI industry being “Too Big To Fail,” an ahistorical trope that ignores the Term Securities Lending and Primary Dealer Credit Facilities that plugged trillions ( no, really! ) of dollars into the side of the banking industry because failing to do so would’ve left America’s financial system insolvent. OpenAI, Anthropic and every AI startup could disappear tomorrow and the world’s financial systems would continue unabated, other than the brutal hit to the stock market and screeching of venture capitalists.  That’s because their actual relevance is, in and of itself, symbolic. OpenAI and Anthropic combined to less than $20 billion in annual revenue in 2025 representing 89% of all AI startup revenues , and spent at least $30 billion on compute on Microsoft Azure, Google Cloud and Amazon Web Services. Their services are sold using the very same cargo cult mentality that got us into this mess — organizations adopting AI at scale and demanding that people use it because “AI is so powerful,” or, put another way, somebody they respect or like suggested it’s the future, and because none of these executives actually build anything or do any work, they have no idea what to do other than whatever it is that everybody else is doing. Our economy is dominated by companies run by people who didn’t build and who don’t participate in the products or services they sell. They have little or no practical experience about what it was that made the company a success, and their “daring” initiatives usually boil down to “fire a bunch of people and flatten the organization” or “spend a bunch of money because it’s the thing to do.” They do not know what AI does other than the fact it can write code or write copy or generate stuff , but because everybody is “doing AI,” they too must “do AI,” which means “everybody that works for me must do this, and also we must add this somewhere, somehow.” But that’s all the modern tech industry can do: an impression of something they think is successful in the hopes that they’ll be successful too. In September 2024, Airbnb CEO Brian Chesky gained an alarming amount of praise for doing “founder mode” at the company : Chesky also notes that he was inspired by “studying Steve Jobs,” a person who has been dead for many years, choosing “not to copy everything, but a lot of how he organized and ran the company.”  Airbnb is most decidedly not Apple, and neither Chesky nor his team are anything close to those who built the original iPod, iPhone, or even the Apple HiFi. Airbnb is a cloud service platform that lets people rent their houses out. When Chesky says he’s “studying Steve Jobs,” he likely means that he watched a few movies, documentaries and videos of Jobs speaking about things that have nothing to do with him, looking for similarities that he could copy — almost like he was copying a successful guy’s moves in the hopes that doing so would give him similar results. Airbnb remains a better-than-the-rest front end for you to rent other people’s houses that provides payment and support layers, and the vast majority of its revenues come from monetizing that process. Airbnb’s stock remains effectively flat since Chesky’s “founder mode” designation, and it remains (extremely) modestly profitable . The irony of the discussion is that it comes from a Paul Graham essay that basically boils down to “the CEO should actually do stuff at the company and know who does stuff at the company,” except written with a Sorkin-esque drama:  No, actually, this shouldn’t be that hard if you actually talk to people at the company, even at a large organization like Apple, if you have any idea what people do for a living. Sure it’d be a lift, but if you can’t organize a 100-person event with a year’s lead time just because you’re too lazy and inert to understand what’s going on, perhaps you shouldn’t be running a company to begin with?  You see, the Valley can’t just say “yeah you should have an active hand in your company and not delegate everything,” it has to be founder mode because everything is special! If tech firms aren’t run by people going founder mode , then they’re just software companies selling software. If OpenAI and Anthropic are just software companies with huge infrastructural costs, then you have to start treating them like normal companies with those kinds of burdens, which would make you start screaming at the top of your lungs. This is the hyperreality (and cargo cult mentality) of Silicon Valley. Apple, Google, Microsoft, and Meta were companies that grew out of relatively boring stories — kids getting internships working at tech companies, computer science graduates coming up with software-driven ideas, and so on — with very few actual lessons to learn other than “you should come up with a really good idea and do it at exactly the right time.” Romanticizing the legend of Steve Jobs or Mark Zuckerberg or Bill Gates, rather than their luck and potential ability to hire people who actually build things for them, allows you to pretend that there are lessons to be learned, and that in turn you too could have these otherworldly riches if you just try hard enough. The success of these large companies has predominantly come from having a few good ideas, great timing, good execution, and building largely-immovable monopolies rather than any incredible acts of genius. Jobs, Zuckerberg, Bezos and Gates all succeeded by finding people who actually did stuff , such as the Sanberg-led growth team that turned Facebook into a monster , and Tony Fadell and Scott Forstall’s hardware and software teams pulling together the original iPhone. Their successes were not the result of some series of things you can mimic or the tone of their voice or a specific series of actions, but being in the right place at the right time with the right idea and the right people, at a point when the underlying hardware or semiconductor infrastructure had reached a point when the idea was possible. Put another way, there was a shit ton of hard work, innovation, and talent that went into these things that you can’t copy, even by working really hard or yourself having a bunch of talent. The ideas must be possible, economically viable, and you must have the people and infrastructure to execute them. Amazon Web Services may have lost money, but lost significantly less than OpenAI or Anthropic, and was significantly more useful than anything the AI industry has ever produced. In 2013 — the year that Amazon Web Services went profitable — Amazon’s total debt was $5.18 billion . And really, there’s nothing more cargo cultish than defending OpenAI burning $21 billion in a single year by saying “this other company burned money too.” Even if the losses were comparable, Amazon was building two very different businesses — a digital store and a cloud compute platform — to OpenAI, which is training and selling access to large language models at a massive loss , does not own its infrastructure, and has absolutely no path to profitability outside of “we keep spending other people’s money.” But that’s all the AI industry is — people doing impressions of things that have worked before in the hopes that they’ll work again. Every AI lab and startup started with cargo cultish subsidized subscriptions , assuming at some point somebody else would solve the problem of costs or that they’d “make it up in volume,” because that’s what worked before. OpenAI and Anthropic threw as much money at pre-training models because a paper had suggested that if they did so there would be infinite gains ( versus diminishing returns ), and when Anthropic worked out that you could add a bunch of scripts on top of an LLM to do coding better with Claude Code, OpenAI immediately copied that and made Codex. Both companies are now jousting to make much the same product by giving away API credits and free weeks of access to create the symbolic aura of an “essential” product to continue convincing VCs and the public markets that they’re “building the future” rather than effectively paying their customers to use their products. The “popularity” of AI has come entirely from social pressure and endlessly-discounted access, and the very second that they charged the actual costs, their customers started freaking out and kvetching about whether AI has ROI .  Our economy is dominated by people who have only a symbolic understanding of the world — Business Idiots with little interaction with productivity or production who do not know how value is created and thus can only create facsimiles of valuable companies. Perhaps they’re lucky enough to have businesses that effectively run themselves, or monopolies that can survive having 98% of their free cash flow spent on AI data centers that only lose money , or are smart enough to stay out of the way of the people who actually do work.  But in many cases, the people running companies — especially those most-obsessed with AI — are cargo cultists following “the most valuable companies in the world” into a void that demands they twist every part of a company they don’t understand into a form that ingratiates them and makes them feel like they’re “doing business.” It’s an obscene and childish way to live one’s life, and typical of an economy that optimizes for growth at all costs thinking and coddles those who think that way. Even the economics of the AI bubble are cargo cultish. The use of annualized revenues ( the single-most easily manipulated metric in Valley history ) as a means of promoting growth only exists as a means of spreading the symbology of hypergrowth, all while deliberately obfuscating the actual financial health of the company by using a single monthly (or weekly) snapshot to extrapolate an annual figure, something that’s particularly egregious when you realize that it involves non-recurring charges like spending money via Anthropic’s API. Yet the Valley either realized (or was fortunate enough to find) that the media had bought into their cargo theology . Much like the Valley craves symbols or prophetic signs that today’s startups will become the next Google, modern tech and business journalism runs not on any scrutiny or skepticism of the future but in finding the “next big thing,” which often requires it to find the very same symbols that the Valley craves, often provided by the executives themselves.  They crave to be the ones to find the next Jobs, Zuckerberg, Bezos, or Gates, and in their crazed search only seek to repeat the same mistakes of every bubble, never noticing that the tech industry has had an astonishingly bad record for more than a decade.  The tech industry must always be framed as an impossible-to-decipher monolith full of troubled geniuses that have good intentions, because when you stop thinking that way, you start seeing it for what it really is — a vehicle for symbolic capital that stymies innovation and promotes growth over everything, funding things based on their similarities to the past and how warm and fuzzy doing so makes them feel. And in its incredible success as a vehicle for capital, tech has managed to beguile society and turn journalists, economists and regulators into cargo cultists that can be easily won over by a smart-sounding guy or an emphatic-enough press release.  AI is the natural endpoint of the Valley’s cargo culture — money-hungry models that can vaguely resemble something that might grow into the future, with opportunities to deploy capital that resemble previous infrastructure movements, all with convenient ways to explain away dissent that mostly boil down to “bad thing happen before but then good thing happen after.” Everybody believes that because AI startups can grow their revenue they’ll grow that revenue forever, that because startups in the past lost money that AI startups will stop doing so, and that because something has a lot of users it can never disappear. I challenge everybody reading this to start living in the present, and to stop taking excuses for the mediocrity of AI. AI boosters are no longer allowed to speak in the future-tense, nor are they allowed to justify AI’s losses based on previous eras.  If you’re an AI booster yourself, know that the AI companies treat you with complete contempt. They force you to defend dogshit, to wheel and deal in dogshit, to celebrate dogshit like it’s caviar, to tell others that they too must defend dogshit, because one day the dogshit will be good.  Nowhere has this been more evident than the response to my exclusive last week . Some have been mighty confident about inference being profitable (due to a $7.5 billion cost of revenue on $13.07 billion in revenue), but overlooked my reporting from last year verified by the Financial Times showed OpenAI spent $8.67 billion on inference in the first nine months of 2025. It’s very clear OpenAI moved around numbers to make things look better than they are, and I believe that inference costs are being dumped in sales and marketing.   How else are you to explain how a company spends more than 43% of its revenue ($5.73 billion) on sales and marketing — more than the Coca-Cola corporation , which has three ad agencies and a vast web of different print, digital, and physical ad spend. Microsoft had $500 million of “sales and marketing” spend too. What do you think that is? OpenAI spending $500 million on sales and marketing through Microsoft? Or itemizing promotional spend or the inference from free users as a sales and marketing cost? If you disagree, please explain in any level of detail how OpenAI has spent $5.67 billion on sales and marketing. Its first major advertising campaign was in September 2025. If it’s spending $250,000 a year on its 500 sales staff, that’s still only $125 million. Unless OpenAI is one of the single largest accounts in digital advertising, I think it’s far more likely that there are actual costs being hidden.  This is the kind of thing a company does when it has utter loathing for its investors and the general public — a brazen attempt to bury costs to make things feel better for an audience that’s directly incentivized to take any shred of proof that things are okay, even if said “thing” is the suggestion that a company that lost $21 billion only actually lost $8 billion .  Alternatively, it’s what an industry does when it believes everybody is gullible enough to accept and promote any rationalization that confirms their beliefs.  So far, they’ve been proven right. Every time I show somebody the kind of tangible proof that these companies are economic septic tanks, somebody uses some sort of theological, mythological or historical statement as proof that what I’m saying doesn’t mean anything. Silicon Valley, the so-called hub of meritocracy and rugged individualism, runs on a kind of empty cultish ephemera that usually ends with sedative-laden Kool Aid. In the end, faith can’t fill your belly, or cover $1.1 trillion in compute commitments . It can’t magic up $2 trillion in revenue by 2030 for an industry that basically doesn’t exist without OpenAI or Anthropic. And however you feel about AI, you should demand better proof of its inevitability than a bunch of mythology, hype, and cargo cult bullshit. 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 10,000 to 18,000 words, including vast, detailed analyses of the biggest events and companies in the AI bubble.

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Ed Zitron's Where's Your Ed At 1 months ago

AI Is Slowing Down

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 (updated to version 3.0 last week). My Hater's Guides To the SaaSpocalypse , 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 . Over a three week period in May , I published an exhaustive three-part guide to how the AI bubble might collapse, the events that might trigger it, and the consequences. For something lighter, check out last week’s premium, where I re-introduce you to the antagonists of the AI bubble (and their fatal weaknesses) in caustic, slightly sweary terms.  Subscribing to premium is both great value and makes it possible to write these large, deeply-researched free pieces every week.  Last week I went on Bloomberg and discussed the state of the AI bubble with a clarity that rattled even the sweatiest boosters, mostly because I spoke with clarity about an investment frenzy whipped up through hype, deceit and mythology. Some were equal parts frustrated and angry that I don’t have money in the market, or, as they’d put it, “skin in the game.” I get it! When your entire worldview is dictated by what a series of venture capitalists and psuedo-journalists on Twitter want you to believe, it must be difficult to imagine someone having “morals” or “beliefs” or that one might hold a position that wasn’t entirely based on greed or tribalism. It must be confusing — upsetting, even! — to hear that somebody is willing to accurately and vociferously tear into a tech industry largely controlled by people with no regard for their users or workers, who are willing to bathe their products in mediocrity all because it’s the thing that everybody else is doing. This is a hysterical era perpetuated by liars, cowards, imbeciles, craven boosters and the easily-fooled. Those excited about generative AI are either the victim or the perpetrator of a con centered around a technology to ingratiate at the highest cost possible. I also think that everybody is a little flippant about what has to happen for me to be wrong. Whatever obtuse fantasies you have about the current state of generative AI are irrelevant to a much larger problem: that the infrastructure being built and compute commitments being made are being done so at a level that demands that generative AI and AI compute generate over $2 trillion in annual revenue by 2030. When I say that, I mean it absolutely has to do that otherwise none of the data center capex makes sense, and neither Anthropic nor OpenAI can pay their commitments. OpenAI expects to spend $50 billion on compute in 2026 , and I wouldn’t be surprised if Anthropic spends anywhere from $30 billion to $50 billion. Between them, Anthropic and OpenAI represent the vast majority of all AI compute demand — at a minimum 70% , if not 80% to 90%.  Put another way, there’s barely a few billion dollars of demand outside of two companies that lose billions — or tens of billions — of dollars a year. Let’s break down these numbers a little further: This is not hyperbole! Every single thing I have stated here precisely maps to the projections and promises of the AI industry. No matter how horny or flaccid you are for the potential of AI, it must grow at an astonishing, unstoppable rate from here until the end of time to be anything close to worthy of its costs. Actually, sorry, let’s put judgments aside for a second, because this isn’t about judgment , but rather the promises that have been made by the software and hardware companies associated with AI. NVIDIA’s place atop the stock market and its ridiculous projections depend on both the continued flow of data center debt and the continued belief that AI services will have the revenue to back it up.  AI cannot, under any circumstances, slow down. In a year, Anthropic and OpenAI’s businesses have to be roughly twice the size they are today, and then double again basically every year until 2029 or 2030. In that time period, they must also both raise hundreds of billions of dollars or, alternatively, turn deeply unprofitable businesses into profitable ones while also doubling their revenues.  Alternatively, both must severely reduce their costs… except if they do that, they won’t have any need for all that compute capacity, which will deprive Oracle, Google, Microsoft, SpaceX, Cerebras, CoreWeave, TeraWulf, Cipher, and Hut8 of the $1.1 trillion in remaining performance obligations. Also, if OpenAI can’t afford — or doesn’t want — its compute, Oracle will simply run out of money . It is spending anywhere from $340 billion to $700 billion (depending on whether you believe Jensen Huang in September 2025 or May 2026) on the 7.1GW of data centers it’s building for OpenAI. These, again, are not hyperbolic statements, but the actual costs associated with Oracle’s massive buildouts in Michigan, New Mexico, Wisconsin and Texas. I didn’t agree to do this! Larry Ellison did!  Apparently, Salesforce is planning to spend $300 million on Anthropic in 2026, to which I say “that’s not nearly enough”! Everybody has to be spending even more than that in the next few years, without fail, no ifs, ands, or buts. It is non-negotiable. Anthropic needs to be making over $100 billion in two years or it can’t afford its commitments, so you filthy token-hogs better slurp up your slop this instant, or Dario Amodei gets made part of the permanent underclass! But seriously folks, the combined compute demand of every single AI company in the world doesn’t currently reach $100 billion — and it needs to be ten times that by 2030 or all those data centers got built for no reason!   And for that to happen, both Anthropic and OpenAI need to be making about $400 billion a year in annual revenue, which means there needs to actually be that much demand for AI services! Right now, Anthropic and OpenAI’s combined projected revenues for 2026 sit somewhere in the region of $60 billion — so, you know, they only need to grow by 496% by the end of 2029!  To make matters worse, it doesn’t seem like anyone else in the AI industry is going to help with the whole “demand for AI services or compute” thing. As The Information reported a few weeks ago, OpenAI and Anthropic make up 89% of all AI startup revenues .  We could include hyperscaler revenues, but that wouldn’t help very much. Microsoft’s $37 billion in AI annual run rate — these fucking cowards never share actual AI revenues! — is predominantly made up of OpenAI’s compute, with the rest of it (maybe $8 billion in annual revenue at best) from Microsoft harassing its permanently-abused customer base into installing Copilot.  Ah, shit, there’s another problem with Microsoft — Microsoft AI CEO Mustafa Suleyman just said that Anthropic’s models were too expensive, and he intended to reduce Microsoft’s use of them to zero ! You can’t do that Mustafa! We need every cent of demand, otherwise everything falls apart!  Anyway, eager math-knowers among you might also notice that even if Anthropic and OpenAI spent $500 billion a year in annual compute — an amount that they can’t afford even if they combined both their unsustainable asses — we’d need at least another $250 billion or more in annual compute revenue to justify it. In other words, they need everybody to be “doing agents” at such a scale that basically every third dipshit you run into on the street is sinking $50 or more a day into them.  I sure hope that’s happe- OH MY GOD! As I discussed last week , you can’t measure the cost of a particular task with AI, nor can you measure its return on investment. The only reason that we’ve been “doing AI” with such ferocity and veracity is that most companies are beholden to Business Idiots disconnected from production who have no real understanding of their underlying firms’ outputs, and thus have very little way of measuring them. These multi-millionaire midwits have been “doing AI” because everybody else is doing it, burning millions of dollars to turn their code into slop ( see: Zillow ) or have their engineers compete to see who can spend the most money ( see: Meta and multiple other companies ). In one case, a company spent $500 million on Anthropic’s models in a month because it didn’t set up spend controls . In Uber’s case, it burned through its entire annual token budget in a single quarter , which led to its COO saying it was harder to justify spending money on AI tokens because it couldn’t show a link between that spend and a meaningful increase in useful features on Uber . Now Uber has capped its employee spend at $1,500 a month per user , with T-Mobile temporarily following at $2,000 a month per user with the intent to move to a tiered system. Over at Brex , engineers are limited to $500 a week in tokens, with non-engineers getting an astonishingly-low $5 a week. These are signs that AI’s revenue growth is slowing, and it’s likely going to slow further, because we currently live in an era where Anthropic and OpenAI are straight-up abusing its clients, providing limited-to-no visibility into spend, per the Wall Street Journal : How utterly ridiculous! Only in the frothiest, most-disconnected economy in history could we have companies spending millions (or tens or hundreds of millions) of dollars on a service without having any visibility into costs until after billing. This is not a sustainable revenue stream under any circumstances, and anybody who says that it is is either ignorant, a mark or a con artist. This is revenue made entirely by convincing your customers that something is true (AI is the most revolutionary thing ever!) and keeping them in the dark as long as humanly possible as they run up ridiculous bills, all in the hopes that you’ve brainwashed the executives /paypigs well enough that they’ll never stop. And really, “paypig” is the accurate term for these cretins: Russell, you may as well let Dario Amodei put a cigarette out on your forehead! This is pathetic! What a fucking loser! Oohhh, I sure hope that the company I pay all this money to lets me see how much I’m spending! I thought Silicon Valley was meant to be all about meritocracy !  Boosters will say that it’s “hard to measure productivity for any job outside of sales,” but that’s simply not true! If you let your engineers spend $1500 or more a month on a service, surely you must have some way of measuring how much actual new stuff went out — new features, customer tickets reduced, projects completed, I don’t know, I’m not the fuckwit spending $1,500 a month per person on this garbage! You’re the one that has to justify it!  But, fundamentally, these are all signs that AI is slowing down.   Remember: Anthropic and OpenAI only moved their customers to token-based billing in Q1 2026 . It only took two or three months for us to get headline after headline of big , serious business publications saying “AI costs a lot of money and companies aren’t sure if there’s a return on investment.”  If things were going well, these stories would be inverted — companies would be boasting about their remarkable token spend and pointing to all the new, incredible things they were shipping. Their products would be spotless, their features sublime, their engineers sliding entire new stacks of impressive software out the door so fast that it would be changing the very nature of software. I mean… someone would be, right? Let’s check out the cha AHHH ! What’s actually happening is that these tools are — at a remarkable price — shoving a lot of stuff out the door. Is the stuff good? No. Do people like or use it? No. Does it make money? Also no. While we’ve discovered the shovelware , that’s all that LLMs have given us — “more” apps, with the vast majority being useless, insecure slopware.  This is meant to be the era of agentic coding! This is meant to be the era where any dickhead with a Codex or Claude Code account with $1,000 of free API credits should be able to create the next Salesforce or whatever it was that dimwit Citrini talked about a few months ago.  I’m sorry to be a little surly and dismissive, but the AI industry has burned over a trillion dollars and I’ve spent two years being told I’m a luddite and an ape for not celebrating it. I don’t care! I’m not impressed! I’m not coddling this mediocre, expensive crap! Like I said earlier: isn’t the tech industry meant to be a glorious bastion of meritocracy? Isn’t this meant to be a cold, harsh community of rationalists?   If so, why are we coddling AI like it’s the kid from that episode of the Twilight Zone ? Has Silicon Valley become so decidedly whipped by the forces of capitalism that it can’t see that none of this makes sense? Or was this always just a culture of lemmings drawn in whatever direction venture capital waved a dollar bill? To make matters worse, both OpenAI and Anthropic are speeding as fast as they can toward IPO — which means that both will have to start looking like real companies , which means both will, inevitably, start charging their customers more and very likely moving the vast majority of them to token-based billing and either kill or vastly limit their subsidized subscriptions . In a mysterious confluence of events, both Claude Code chief Boris Cherny and OpenAI-owned OpenClaw televangelist Peter Steinberger have both said that their users need to be “designing loops for their agents,” meaning “creating ways to make their agents burn a bunch of tokens doing stuff,” I imagine as part of the ongoing campaign by both Anthropic and OpenAI to make people spend lots of money on tokens to keep their enterprises afloat. I expect that “loops” will become the next thing that journalists pick up on and start oinking about. To be clear, “loops” already exist, in that you can make an LLM decide to keep taking actions whether or not a user prompts it for as long as you’d like. Whether the output works at the end isn’t Peter or Boris’ problem, as both of them are allowed to burn anywhere from $130,000 to $1.3 million a month in tokens . As I’ve argued before (though referring to subsidized subscriptions): To be clear, this is both OpenAI and Anthropic’s representative stooges actively suggesting that you “shouldn’t be prompting coding agents anymore,” instead letting LLMs that hallucinate the more they “reason” (IE: make plans for themselves, which is how agents work) do as much reasoning as possible without user input.  These men have complete contempt for their users and customers. They do not give a shit that their models break so often that Notion had to cut access to Anthropic’s for several hours or that the costs are so severe that CFOs are a few bad bills from a trip to Budd Dwyer’s Favorite Lunch Spot. You must burn more tokens, because otherwise you won’t be doing AI coding right, whatever that means. And please, god, stop trying to convince me this shit is impressive. You all sound like you’re in an abusive relationship trying to explain why a guy who rifles through your pockets and half-asses everything he does at an incredible cost is actually super sweet behind closed doors.  I’m distinctly unimpressed!  After hearing a particularly colourful story from Kevin Smith , I came up with the perfect way to explain the AI bubble. Okay, perfect might be a stretch, but I think this gets my point across, and hell, it’s a free newsletter, what’re you going to do? Kill me? Run me over with a truck? Good luck with that, I’m a huge homebody. Anyway, imagine, if you will, a smaller version of the giant mechanical spider from Wild Wild West — a portable one that you sit in like a chair with big arms and big legs. The giant metal spider costs $1 million, and takes up about $40,000 of fuel every time you use it, but it can sometimes pick stuff up and make you dinner.  The problem, however, is that it’s a giant metal spider — sometimes it precisely grabs a diet coke from the fridge, and sometimes it punches a hole clean through it, requiring both a brand new fridge and for me to pay $40,000 regardless. The good news is that the companies that make the giant metal spider from Wild Wild West also subsidize the giant metal spiders at around $200 a month with free insurance, though businesses are forced to pay for its actual costs. As I march it around my apartment, the giant metal spider leaves horrible scratch marks on my floor, it sometimes makes a terrible noise, but I, as the user, barely have to do anything — the spider does everything for me, even though whatever it “does” is incredibly costly, convoluted, and often takes far longer than it should.  Every update to the spider widens what it can allegedly do, but each time I use it it’s just as expensive. Can the spider make me a cup of coffee? Yes. It takes five minutes, which is longer than I’d take, and occasionally it throws the coffee in the air or simply fills the cup full of oil, but most of the time I get a cup of coffee. Isn’t that good? We love the giant metal spider.  When I turn on the news, I see a headline about how “THE GIANT METAL SPIDER FROM WILD WILD WEST WILL CHANGE EVERYTHING.” 30 different guys on Twitter write 800-word-long screeds about how we must redesign apartments and office buildings to cater to the spider, that “it’s inevitable that the metal spider from Wild Wild West will be how everybody does everything in the future,” and one guy even suggests that it’s alive because, after adding a $500,000 add-on, the giant metal spider can be scheduled to get up on its own and make the coffee. Sometimes it does so successfully. Sometimes it smashes the coffee maker up into tiny little pieces.  Sometimes it mashes its legs through the kitchen island. Sometimes the spider opens up my Amazon packages with ease. Sometimes the spider rips them in two.  Thankfully, the companies behind the giant metal spiders subsidize them, so the average person only experiences the occasional act of destruction, but they also lose billions of dollars a year on training the spiders and the constant maintenance required to run them. There are some workplaces full of the giant metal spiders and they’re absolutely insufferable.  Everywhere I go, somebody is telling me the spider is the future. “The giant metal spider from Wild Wild West will eventually stop destroying stuff! Future innovations in giant metal spiders will make them cheaper and more-reliable! Look, we’ve done a study, and the giant metal spider’s ability to complete a task of a certain length 50% of the time has increased !”  Every time they add a new feature to the giant metal spider from Wild Wild West, it requires several hundred million dollars, and it isn’t always clear whether the giant metal spider learned anything new. It’s really good at opening Amazon packages, so they thought it might be able to make a bed, and spent $100 million training it to do so, only to find it kept karate chopping the bed in half approximately 20% of the time. Another time, the giant metal spider from Wild Wild West showed promise at playing Texas Hold ‘Em, successfully getting through an entire game 50% of the time. Unfortunately, the other 50% of the time it smashed the cards into the table. After another $100 million, they were able to reduce that number to 30%. A day later, The Atlantic ran a story: “ Vegas Is Scared Of The Giant Metal Spider From Wild Wild West .”  Technically, the giant metal spider is productive, at least in some households where they give it significant room to maneuver and only give it tasks it’ll excel at. Across the world, private credit funnels billions into giant metal spider factories powered by NVIDIA chips, assuming that everybody will be paying to rent one of them. When you criticize the giant metal spiders, you’re told that you use them in the wrong scenarios, ones where they’re guaranteed to fail. Young graduates are encouraged to learn how to move the giant metal spider, and that if they fail to, they’ll be unable to explore the giant-sized future that will be built for them. Year after year, more people insist that the giant metal spider from Wild Wild West will get cheaper, but the costs only seem to increase along with the vast amounts of damage it causes. It’s undeniable that the giant metal spider from Wild Wild West can do stuff. Sometimes it even does the stuff as well as a person. For some reason, it’s impossible to tell when it’ll get things wrong, and despite everybody saying that the giant metal spider from Wild Wild West is “smart,” it seems to occasionally do things the user didn’t ask for. If you say that the giant metal spider from Wild Wild West isn’t going to be the future of anything due to its massive, unsustainable costs, or suggest that its inconsistencies make it unreliable in some way, you’re told you’re a doomer, a skeptic, a luddite and a rube.  One day, someone using one of the giant metal spiders from Wild Wild West steps on your car. Futurism writes an article laughing at you . You scream so loudly that one of your neighbors calls the police. No matter how much you dress up whatever AI service has gaslit you into believing it’s sentient, generative AI is inherently limited, impossibly expensive and economically unviable. Its services cost too much to run, its progenitors have no path to profitability, and no amount of rigged benchmarks and anecdotal examples of theoretical engineering teams that are “10x’d” can make up for the fact that you can’t measure the cost of an LLM-driven task or its return on investment .  Anyone claiming that you have to “measure AI’s ROI differently” is attempting to con either you or themselves. While it’s tough to measure the ROI of a particular worker or project, most workers and projects don’t increase your operating expenses by anywhere from 10% to 100% under the vaguest of promises that you might be “doing the future. ” AI is calamitously expensive and, despite years of promises of it getting cheaper for both those running AI services and its customers, costs have only ever increased. I think that’s by design. AI labs want their costs to be high so that they can continue growing at ridiculous rates, all so that they can keep feeding money to their hyperscaler compute partners who then invest that money right back into them , creating further reasons to keep buying NVIDIA GPUs, so that NVIDIA can then invest that money back into either AI compute providers (who OpenAI and Anthropic pay) or the AI labs themselves.  Concepts like “efficiency” or “cost reduction” run counter to the greater narrative of AI’s voracious sprawl of data center capex and still-theoretical AI revenue. If OpenAI or Anthropic were to seek profitability or sustainability (assuming these things were possible), that would create less demand for AI compute, which would mean less demand for Azure or Google Cloud or Amazon Web Services or CoreWeave or Oracle Cloud Infrastructure , which would in turn mean less demand for NVIDIA GPUs. The problem with this marvelous plan is that at some point there had to be an honest transaction — real, honest, sustainable demand based on a reliable product that people liked paying for because they understood its value. Right now, AI revenues are either chaotically experimental or so thoroughly-subsidized that labs are giving away hundreds of dollars a user in the hopes that at some point said user might want to pay even more money for measurably less value , the kind of proposition you make when you think your customers are fucking idiots. It only took a few months of token-based billing for the AI conversation to go from “our magical, beautiful agents” to “hmm, are we sure this is worth it?” and I believe it only gets worse from here. AI labs do not have some super secret trick up their sleeves — no, not even Mythos, that was bullshit I’m afraid — that will suddenly provide the kind of ROI that’s impossible to ignore, nor do they have some magical way to bring down their costs while also spending just as much on compute. From here, we basically need to 10x every part of the AI stack based on the projections and commitments made by effectively every AI firm. Anthropic and OpenAI must grow faster than any company has ever grown before in the space of a few years, and suddenly become profitable, all while somehow raising hundreds of billions of dollars. On top of that, we need at least another $250 billion in annual AI compute demand, which likely means at least two other OpenAI or Anthropic-scale companies. If this all sounds unreasonable, don’t blame me. I’m not the stupid fucker that agreed to build 100GW+ of data centers or mortgaged the future of Oracle on the off chance that Sam Altman and Dario Amodei, two craven manipulators , somehow work out how to create Google 2 and Amazon 2 in the space of four fucking years. I won’t tip my hand too much, but I have a story coming out in the next two weeks that will likely confirm the absolute worst fears of the AI industry. Many have been incredibly brazen about the potential losses of particular AI labs to the point that I made it my mission to talk to as many people in the tech industry as humanly possible, in part because some who have suggested that I “don’t speak to people who work in the tech industry.” In truth, I speak with tech workers every single day of the week, and they’re in fucking agony.   If you are someone in the executive team of any major tech company, know that your employees are, for the most part, completely and utterly miserable. Your endless death march of “do as much AI as possible or we’ll fire you” and forcing them to use these tools day-in-day-out has radicalized them against you. Every day I hear from someone who is dealing with the wrath of a different Business Idiot who doesn't do anything other than demand more deliverables in a smaller timeframe with less people because you keep laying people off. If you are a worker at a tech company, I fucking see you. I feel your pain. I hear your sadness. I am enraged and disgusted at the way you are being treated. Reach out to me at ezitron.76 on Signal with anything you’d like to share. I’ll protect your identity, listen to your stories, and if you share something with me that warrants publishing, I’ll make sure I do it justice by understanding the subject matter and reporting it in a way that it never gets back to you.  I’ve done this again and again, and will continue to do so, because I love my sources, I treat them with dignity, respect and empathy, and they, like me, find the current state of the tech industry wretched, its leaders worthless, its road maps directionless and its works mediocre.  Even if I don’t run with the story, I am here to listen, because I hate what you are going through. I feel your pain. So many of you truly love making good software and want to do good things in the world and feel impeded by the Business Idiots and mocked by the boosters who seem to care more about your bosses than anything to do with software or innovation. It sickens me what the industry has done to you and continues to do to you. You deserve better.  I write this newsletter because I deeply enjoy writing and I deeply hate what is being done to the computer. I hate that many people like me are suffering at the hands of the scumbags and freaks birthed from the guts of McKinsey and various MBA programs.  I don’t do this because of a short position. I don’t have one. I don’t hold any stocks, securities, or CFDs.  I do it because it’s my job and because I give a shit. If it’s impossible to comprehend why somebody would do something without a short position, you need to think long and hard about why you bother waking up every morning.  One of my sources has come forward and brought me a story that will possibly burst the AI bubble. The reason they brought this to me is that I’ve shown — and will continue to show — that I actually give a shit about this industry and the people in it.  If you’re wondering what the story is, know that it’s the information I’ve wanted for years, delivered as I have always wanted it, and I will treat it with the reverence it deserves. Imagine what the worst possible thing for me to get would be and you’re probably close. I expect it to be out in the next two weeks, and you’ll know exactly when it runs. There’ll be a podcast and a newsletter, and very likely follow-on coverage elsewhere.  I can guarantee you it’ll be worth it, and you’ll be stunned by what I report.  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 10,000 to 18,000 words, including vast, detailed analyses of the biggest events and companies in the AI bubble.  If we take Sightline Climate’s data from February at face value , there are 190GW of data centers planned. If we take NVIDIA CEO Jensen Huang’s statement that data centers will cost $80 billion to $100 billion a gigawatt at face value, this means that said data centers will cost anywhere from $9.5 trillion to $15 trillion. Bloomberg incorrectly states that this is a “$3 trillion” buildout . This money will have to come from somewhere. The Financial Times reported in May that banks are concerned they might “choke” on data center debt when I estimate there’s barely $250 billion a year being issued. They will, to actually make these data centers happen, have to start issuing anywhere from $500 billion to a trillion a year. Jensen Huang has also said that NVIDIA projects a trillion dollars worth of revenue through the end of 2027 . 54% of NVIDIA’s revenue comes from three clients , which means that NVIDIA’s future largely depends on three unnamed companies — likely Taiwanese ODMs building servers for Microsoft, Google and Meta — and their counterparties’ ability to raise debt on a near-perpetual basis, as the number of firms that can afford to buy thousands of $7.8 million racks of Vera Rubin GPUs is dwindling. Even then, every part of this puzzle requires more and more debt or at the market dumps like Google’s $85 billion equity sale or Meta’s planned multi-billion dollar dump . The fact that hyperscalers are doing equity sales is, as economist Paul Kedrosky raised in our conversation on my show last week , a sign that debt is becoming harder to acquire. Anthropic has made $330 billion in compute and chip commitments between Google, Amazon, and Microsoft , another $30 billion with CoreWeave and another $15 billion with SpaceX. To pay for this compute, Anthropic must meet its projected revenue of $174 billion a year by 2029 . Anthropic has raised $95 billion across rounds in February , April (from Google and Amazon ), and May . These funds will be insufficient to cover Anthropic’s costs, as will Anthropic’s cash flow, meaning that it will have to raise at least another $200 billion in the next year. OpenAI has projected to burn at least $852 billion through the end of 2030 , and has made over $770 billion in compute commitments across Microsoft, Amazon, CoreWeave, Cerebras, and Oracle. The $122 billion funding round from March will be insufficient to cover these costs, and it will require, at the very least, another $250 billion in funding by the end of the year. 190GW of data center capacity assuming a PUE of 1.35 suggests a critical IT load of around 140GW, which, charged at around $12.5 million per megawatt, works out to around $1.75 trillion in annual revenue. If we assume that half of that gets built, that’s still $875 billion in annual revenue that will be needed to keep these data centers from running out of money as their margins are atrocious and they’re all paid for with onerous debt. OpenAI and Anthropic project to make $184 billion and $174 billion in revenue in 2029, for a total of $358 billion in annual revenue. While Anthropic claims it will be profitable by then, I do not believe it will be, nor is it profitable at this point outside of financial engineering .  At present, there are no other major purchasers of AI compute outside of NVIDIA, hyperscalers (who are selling it to Anthropic and OpenAI, or they’re Meta, which has no AI strategy ), OpenAI, and Anthropic. None. I can’t find a single one outside of Jane Street spending more than a few hundred million. We need a few hundred billion. That’s already a huge problem, but the other problem is that we also need companies to spend dramatically more on AI services than they already do. While journalists are currently gooning over OpenAI and Anthropic making $6 billion or $10 billion in a given quarter, that’s just not enough! Both Anthropic and OpenAI need to be making $10 billion or more in monthly revenue by Q1 2028, or their growth rates aren’t going to support their compute commitments.

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Ed Zitron's Where's Your Ed At 1 months ago

AI Doesn't Have ROI

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 the SaaSpocalypse , 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 . Over the last three weeks , I’ve published an exhaustive three-part guide to how the AI bubble might collapse, the events that might trigger it, and the consequences.  Subscribing to premium is both great value and makes it possible to write these large, deeply-researched free pieces every week.  Something changed in the last week. Shortly after Uber COO Andrew Macdonald said that it was “getting harder to justify” spending money on AI as it was “very hard to draw a line” from that spend to useful consumer features ( after its CTO said Uber burned its entire annual token budget in four months ), Axios’ Madison Mills reported that one company had accidentally spent $500 million in the space of a month on Anthropic’s models after failing to set spend limits. A few days later, Mills would report that other companies were now looking for ways to reduce their AI spend . That’s because, as I’ve said before , nobody can actually measure the ROI of AI, or even create a standard measurement of the cost of a task thanks to the inevitable hallucination-prone nature of LLMs and the ever-growing list of different harnesses and “agentic” (sigh) interfaces. Every different prompt and project and interaction can go wrong in a way that is hard to predict or plan for other than having an eternal vigilance that the supposed “intelligence” doesn’t do something catastrophically stupid, because LLMs have no thoughts, consciousness or ability to learn outside of pre and post-training.  If you can’t measure how good something is, how much it might cost, or what your return on investment might be, it’s fair to ask why you’re even paying for it in the first place. People are (reasonably!) harping on about the ROI problem, but I think the “can’t really measure the cost” part is an even bigger problem.  Yesterday, Microsoft’s GitHub Copilot moved all customers to token-based billing from a premium request model ( as I reported a week before everyone ) as users had been allowed to burn thousands of dollars of tokens on a $39-a-month subscription .  Customers are irate. One burned through 50% of their monthly credits in a single prompt , another burned 60% in the space of a few hours , another 31% in a single prompt , another estimated that they’d burn their monthly credits in the space of a single five hour session , another burned nearly half of their credits in eight prompts , another around 14% of their credits in two prompts , and another lamented that GitHub Copilot had gone from their favorite subscription to their most-stressful overnight after burning 33% of their monthly balance in a few hours . And, to be clear, this is during a promotional period where you get $11 or $21 in free monthly credits: These users — much like the users of effectively every subsidized AI subscription — never really knew how much anything they did cost, because Microsoft intentionally hid the actual cost of prompts and allowed users to spend obscene amounts as a way of boosting growth for GitHub Copilot.  This problem is industry-wide. Every single user of every single AI subscription service is having their tokens subsidized and the actual cost of AI obfuscated. As a result, every frothy, fluffy hype-piece about Claude Code or AI in general is a kalopsia — the belief that something is more beautiful than it really is.  Think of it like this: if you’re using an AI subscription with rate limits but no actual costs , any mistakes a model makes — such as getting stuck in a loop or just doing the wrong thing — can be dismissed as the troubled nature of early-stage technology, because the “cost” was $20, $100, or $200 for the entire month. Anthropic, OpenAI and every other AI company deliberately obfuscated these costs because they knew that the second a user actually had to pay for the fuckups of an AI model they’d scream like they were being stung to death by bees. This issue bubbled to the surface in the last few months because Anthropic and OpenAI both quietly moved all of their enterprise customers to token-based billing in Q1 2026 , and because these enterprise customers are run by Business Idiots with no connection to actual work , CEOs encouraged (or actively incentivized ) their workers to use AI as much as possible, in some cases even making one’s AI use a KPI that could cost them their job.  These same workers were conditioned — through their use of AI subscription products that hide the true costs — to use them as if they cost nothing , all while being screamed at by useless middle managers to “make sure to adopt AI at scale,” all while never, ever having any awareness of what a particular unit of work cost. This was always a recipe for destruction. The overwhelming majority of AI users are completely divorced from and actively trained to ignore the true cost of AI tokens, which means they naturally use these services in a way that’s actively uneconomical. Every frothy hype-piece you’ve read has been written by somebody who has been conned into ignoring the true cost of AI, all in service of spreading a technology that’s unreliable, inconsistent and expensive at its core, and never, ever seems to get cheaper.  OpenAI, Anthropic and other AI companies have actively conspired to mislead the world about the true costs of AI, and it was working great right up until they decided to try charging what it actually cost. Less than a quarter into the shift to token-based billing, enterprises are freaking the fuck out, with Walmart setting token limits on its internal “Code Puppy” AI coding tool , with a spokesperson saying that it “wanted employees to apply AI in ways that create value” mere days after Amazon SVP Dave Treadwell told employees to “ not use AI just for the sake of using AI .” The last few years of AI hype have been built on lies. Every company has conspired to make you think that AI is affordable and sustainable, that profitability was possible, that hallucinations were fixable, and that any problems you faced today were a result of being in “ the early innings .” In reality, the AI industry has absorbed over a trillion dollars, effectively all tech talent, the majority of startup funding, the majority of media coverage, the art and work of millions of people, and been given chance after chance after chance to fix the obvious, glaring issues.  Every time a skeptic dared to stand out and say that none of this made sense, they were told that it was just like Uber ( it’s not ) or that Amazon Web Services cost a lot of money ( it cost $52 billion over the course of 14 years and was cash-flow positive in nine ), that “costs always come down,” and that everything would magically be alright as long as they were patient for an indeterminate amount of time. Four years and a trillion dollars in, AI is more expensive, its companies more cash-intensive, its products just as unreliable, and its boosters more desperate than ever to make you ignore reality as a means of empowering one of a few ultra-rich oafs. Products from OpenAI and Anthropic are built to ingratiate and coddle losers while creating work-shaped outputs that are good enough to impress braindead executives, imbeciles and middle management hall monitors that don’t do any real work, and the reason it’s worked this long is that both companies intentionally misled everybody about how much the real costs were. I must repeat myself: AI is more expensive today than it was three years ago, and it is not getting cheaper. Sam Altman’s comments about “ intelligence too cheap to meter ” were lies. NVIDIA’s Blackwell GPUs didn’t make it cheaper, and its Vera Rubin GPUs won’t either. Google’s TPUs won’t do it, Amazon’s Trainium or Inferentia chips won’t do it, Vera Rubin CPUs won’t do it, OpenAI’s chips won’t do it, and no, DeepSeek won’t do it either.  People chose — and still choose — to believe that AI would get cheaper because they think things got cheaper over time in the past, which is sort of true but not remotely similar in any way, because the cost of running and training AI models comes from using the hardware as well as its upfront cost. Large Language Models require expensive GPUs thanks to their reliance on power-intensive parallel processing, and larger, more-complex models in turn require more GPUs to both train and run inference with. And three generations in, NVIDIA GPUs don’t appear to be bringing the cost down at all, which heavily-suggests that the inherent business model of generative AI is broken. People love to compare AI to the Dot Com Bubble ( AI is far, far worse ) because it’s much easier to rationalize bad behavior than accept that we’re facing the largest misallocation of capital of all time. The Dot Com Bubble was really two bubbles — one around eCommerce and internet startups, and one around telecommunications infrastructure. Per Justin Kollar , the telecommunications bubble grew because of a fundamental misunderstanding of demand: As a result, infrastructure was built far in excess of what demand existed, because most people weren’t online, and those who were had very slow internet connections. Per me : Here’s a critical difference between AI and the Dot Com Bubble: when people actually lit up the dark fiber, the underlying internet service was faster, better and cheaper than a dial-up connection. Services like TheGlobe, WebVan, and Pets Dot Com ran businesses that lost incredible sums of money did so not because of the costs associated with accessing their services, but the unrealistic and unsustainable business models themselves.  Their eventual functional forms — Facebook, Instacart, and Chewy — didn’t require fundamental scientific breakthroughs in how goods were delivered or internet services were accessed. Their failures were a result of poorly run businesses that lost money by expanding too rapidly or spending $400 to acquire each customer .   Dell and CoreWeave just turned on the first Vera Rubin GPUs , and you’ll notice nobody is saying the words “profitable” or “sustainable,” because NVIDIA is not interested in making stuff more efficient rather than more expensive.  According to CEO Jensen Huang , AI data centers — which currently cost somewhere in the region of $50 billion per gigawatt — will now cost between $80 billion and $100 billion per gigawatt in the future. Does this sound like it’s getting cheaper to you? Even if said data center packs theoretically more “power,” what does that “power” do for the customer running compute on it? Is it cheaper? More efficient? How do we not have these answers? All of this is to say that the Dot Com Bubble happened due to irrational exuberance and growth lust, and what was recovered at the end came not from scientific breakthroughs but the fact that the useful infrastructure existed and could be adapted and used to make things cheaper and more efficient. That isn’t the case with AI data centers, AI startups or anything else to do with the AI Bubble. Every few days somebody makes a post like this suggesting that “the internet didn’t go away” and “railways didn’t go away” when their bubbles popped, but I think this is a fundamental misunderstanding of what AI is . An AI data center full of AI GPUs is useful for AI and very little else. There are GPU-powered analytics tools, GPU-powered modeling and scientific applications, but the nature of GPUs — good at doing the same thing across big data sets in parallel, but bad at handling many little independent tasks — makes them impractical for most of what modern computing demands. The entire Dot Com Redemption storyline comes from the idea that it “left behind useful infrastructure,” by which they mean “cabling that allowed hundreds of millions of people to use the internet.” While there was some amount of further construction and capex to handle, the end result was useful fiber that connected people with a faster connection at a lower cost. No such story exists for AI. AI data centers are ruinously expensive , requiring billions in upfront funding with operating costs so high that they, at best, run at a loss for the first five or six years of service, if they ever recover their original costs at all. A rack of Vera Rubin or Blackwell GPUs will cost as much to run in five years as they do today, as will an incomplete data center cost just as much to finish construction, connect to the grid or acquire behind-the-meter (IE: generators) power for.  In the aftermath of the Dot Com Bubble, dead startups flooded the market with cheap server and office gear, which allowed plucky founders to cobble together their own services. A single Sun Microsystems Ultra Enterprise 3000 cost $43,000 ($89,000 in today’s money) and had a power draw of between 1,200W and 1,500W, but could run an entire company’s infrastructure . A single B200 Blackwell GPU uses 1,200W , and more-complex AI coding tasks can take up four to twelve of them for a single user’s output. Put simply, you can’t really do very much with a few of these GPUs, and what you can do isn’t profitable, scaleable or valuable. Similarly, dark fiber could be lit up with the right transceivers and networking gear to create internet access. AI data centers are effectively large boxes with custom cooling built for a very limited subset of chips. Adapting them to other uses would require gutting the data center, which would mean that the vast majority of the capital expenditures were wasted.  Even if you were able to buy a hundred Blackwell GPUs from a dead neocloud, you, as a regular person, couldn’t do anything with them. In fact, nobody really could, because you’d still need a physical data center and bespoke cooling , which means that even if the chips were free , the associated construction capex or, at the very least, physical colocation space would still cost a great deal of money The internet and railways didn’t go away because their up front costs were the only real costs that mattered.   Even if somebody were able to pick up a cheap AI data center full of the latest generations of GPUs, the underlying operating expenses are awful, and the only way to make them even close to generating a profit is to have consistent use of all your GPUs. There’s a cost to having them sit idle — both in electricity and personnel — and unless the plan is to have them sit in a data center turned off until you can find somebody else to sell them to, you’ll have to come up with a business model for your AI services that actually makes a profit…which nobody appears to have done, even with unlimited capital and the entire focus of the tech industry. Then there’s the issue of training , which is entirely made up of opex. If you want to train a new model, you’ll likely need thousands — or even tens of thousands — of H100 or H200 GPUs, and they’ll cost just as much electricity whether or not you make anything useful. A failed or unhelpful training run could cost tens of millions or hundreds of millions of dollars , and that will require financial backing that won’t exist. While there could be a theoretical future of LLMs run at their true cost (IE: unaffordable for most) as I covered in last week’s premium newsletter , that would require demand, and as I’ve discussed above, the demand for AI services is a mirage built on subsidized subscriptions, and companies paying the actual costs are already screaming for mercy.  Once the bubble bursts, any excitement for AI — and by extension excitement to spend money on AI — goes out the window. AI startups won’t get funded . AI token budgets won’t get greenlit . AI data centers won’t be able to raise debt .  Every part of this bubble relies upon the momentum of hype to substantiate every link in the chain. Hype must exist around the nebulous concept of an “ AI factory ” to raise debt to buy NVIDIA GPUs and build data centers, hype must exist around AI software to convince enterprises to keep buying services from OpenAI and Anthropic, hype must exist around theoretical demand and outcomes from AI services to fund AI startups, and hype must exist perpetually in the media to make everybody ignore AI’s ruinous costs.  This hype was unsustainable without buckets of lies, misinformation and a captured tech and business media. The value of AI has been inflated by the vagueness of how it’s discussed. For example, major media outlets will gladly write that “AI can build software,” but said sentence suggests that you can just type “build me Slack 2” into Claude and have it fart out a fully-functional, production-ready piece of software, rather than a quasi-functional mound of code-slop that can do enough to trick a business idiot or lazy journalist, but little else.  Said vagueness created a society-wide gravitational pull of consensus that you needed to be behind AI now, because it’s just like the new internet, except bigger, and if you say it’s not you’re going to be really embarrassed.   Creating this pressure was necessary, because without a society-wide aggression against those who didn’t adopt these tools, AI might have actually had to stand on its own merits. That fact AI companies backed by the full manufactured consent of the markets and most of the economy still had to subsidize their products shows exactly how flimsy their value truly is. The only way to inflate the AI bubble both on a hardware and software level was to mislead the general public and investors on the costs and efficacy of AI models.  Now that organizations are having to pay the actual cost of AI, suddenly they’re concerned about its outcomes, and everybody has become a little hysterical. Late last week, SemiAnalysis wrote one of the most insane articles I’ve ever read — AI Dark Output: The Visible Cost of Invisible Output — saying that “AI output will be real before it is measurable,” and, well, whatever the fuck this is: SemiAnalysis is a semiconductor analyst firm with an obvious reason to keep the AI bubble inflated, and if they’re writing a piece that amounts to “AI has a return on investment, you just can’t see it,” things are getting desperate. Here’s how they define “Dark Output”: That “substitution dark output” is explained using a theoretical example of “...a simple legal document which in theoretical GDP should have the same inflation adjusted value to a user whether a lawyer drafts it or AI drafts it,” which is nonsense.   When you pay a lawyer, you don’t pay them to “create an output,” you buy their experience and time and ability to find and adapt case law to reach an outcome, such as in the process of filing stuff, avoiding or actively participating in litigation. Just because AI can fart out an approximation of what a human output may look like — likely riddled with hallucinations — doesn’t mean that said output was created with any “experience.” Models don’t think , they have no experiences , and even if a lawyer is prompting them , that doesn’t mean that the lawyer’s discernment or taste is reflected in the final output. Then there’s this bit: We’re four fucking years into it but we’re still using hypotheticals. Are “...the simplest documents now completed by AI and not lawyers”? You don’t get a lawyer to write a document because they’re the only ones who can write it — you get it to mitigate the risk using the experience of the law firm, both in the associate drafting the document and the partner overseeing it. This flimsy, half-assed logic is how the AI bubble got inflated in the first place. Supposedly smart people continually show a total lack of awareness of how jobs work at basically every level, and in this case — where it should be theoretically possible to find and talk to a lawyer doing this — the supposed “dark output” includes “the research done to complete this article.”  You may be wondering what that “new work done by AI that wasn’t previously being done by humans because AI made it cheap” is, and the answer is “literature reviews” and “summarizing the last six months of email,” and I wish I was kidding. But don’t worry, “...there are anecdotal signs that a large fraction of current token spend is for new work that wasn’t previously paid for rather than replacing existing work.” Have you ever noticed that every story about AI job loss reads like it was written by The Riddler? For example, last year a ton of outlets reported that “Oxford Economics had proven that entry-level workers were being replaced with AI,” but in reality, the study said that “... there are signs that entry-level positions are being displaced by artificial intelligence at higher rates ” with no actual data beyond post-2022 employment declines in some fields that AI might be able to do.  Similarly, CNBC’s brainless headline that an MIT study found that AI “could already replace 11.7% of the US workforce” was entirely based on a labor simulation tool rather than any economic analysis of the actual shit AI can do and what it’s doing in the real world. That’s because AI job loss is a fucking myth. Every company laying off people because of “the power of AI” is doing so because their shareholders are mad and because they know they’ll get headlines.  And if it were actually happening there’d be fucking riots in the streets! Unemployment would be spiking! Things would be burning!  The thing that everybody wants you to avoid thinking about is that if AI worked as advertised, there would be obvious, impossible-to-ignore economic signs: For all of these things to happen, AI would have to be both flawless , hallucination free, a completely different product capable of autonomous intelligence and having unique ideas.  The reason that we can’t measure “AI job loss” is because AI can’t do jobs. It can be used to replace some specific contract positions with extremely shitty versions that don’t scale , but it does not replace jobs because it is incapable of human work. It cannot speak to colleagues, it cannot accrue experience, it does not have instincts or culture or taste or anything other than whatever training data has been crammed up its ass or through endless post-training.  Nevertheless, the threat of AI job loss has been enough to allow both Sam Altman and Dario Amodei to raise hundreds of billions of dollars lying about it, and now that both of them have walked back their job loss scare-propaganda , every oaf and moron that believed them without actually checking should be booted out of their representative industries. It’s fucking embarrassing! You should all be ashamed of yourselves! As I said above, the ROI of AI should be really easy to measure if it actually existed.   If AI was magically able to build and maintain software, we’d have small companies that could build and deploy at the scale of a hyperscaler, and hyperscalers would, in theory, be expanding their margins so aggressively that it would create a new golden age of software revenues…or they’d become entirely infrastructure providers, as anybody else could compete on software. But on a far-simpler level, it would be extremely obvious. Anybody can access ChatGPT, Claude or Gemini, effectively anywhere in the world. The theoretical “power” of AI is that it “just does stuff,” and the proliferation of LLMs would mean that somebody would’ve “done” some “stuff” that we could point at with exceptional ease. Random guys in the midwest would be pumping out profitable, functional, and feature-rich software. Lawsuits would be won by pro se plaintiffs with incredible counsel from a theoretical “ country of geniuses in a data center .” Four years in, we’d have one major AI-powered company demolishing the competition in any industry, or every industry would become so prevalent with (powerful) AI that it would effectively reduce the cost of the service to nothing.  We’d be able to point to companies that adopted AI and then completely fucking exploded. We’d be able to point to useless coworkers who were now doing impressive, meaningful work. There would be widespread economic upheaval, as the concept of a “large company” would lose meaning, because those theoretical “geniuses in the data center” would be automating all the work.” There also wouldn’t be so many pieces insisting that AI is super powerful and so many quotes from Business Idiots saying it’s “ real .” We wouldn’t talk about what AI could do at all. We wouldn’t need Anthropic to lie that Mythos was too powerful to release only to release it several months later .  We wouldn’t have to talk about the fucking potential at all because we’d be able to point to what was going on because it would be obvious! Last week, Bain & Co. released a study of 951 executives from companies with more than $100 million in revenue , and unsurprisingly, the data did not declaratively explain what the ROI of AI was: 10% of…what? What’s the cost you saved on? 10% of $10 million is a lot for a company with $100 million in revenue, but 10% of $1000 isn’t, much like 20% or 30% isn’t either! Yet there are two punchlines to come: This also assumes that those savings are enough to warrant future spending, which…this data does not actually prove. Thankfully, Bain did manage to publish one of the single-funniest quotes of the AI bubble: Put another way, the technology “worked (?),” but did not provide value in doing so. Sounds like it didn’t fuckin’ work to me! Bain had one other crucial bit of advice: Just so we’re clear, Bain & Co, a management consultancy with billions in annual revenue, is advising its clients that they should make sure that they’re getting some sort of return on their investment? And that reinvesting in something that doesn’t have a return on investment would be bad? If AI was real, these fucknuts would be replaced first! They’d replace everybody who wrote this report! You don’t need somebody to tell you this, and if you do you’re a fucking moron!  Thankfully, the AI industry is saved, as Sam Altman had the following to say about AI’s remarkable costs : Motherfucker you are the industry! You are the one that has to work this out! OpenAI is the AI industry ! You are OpenAI’s CEO! You lazy, ignorant, dog-brained loser!  This was an opportunity for “journalist” David Faber to push back, and here’s how that went: This is how the AI bubble inflated! This is how it happened! It happened every time a journalist asked a meaningful question and then immediately diverted to a totally different imaginary topic that made the subject feel good! David Faber, resign and give your job to somebody who has an iota of courage or pride in their work! Unbelievable! Sam Altman is worth billions of dollars, and OpenAI is allegedly worth $852 billion too, and the best he can give us is “teehee, someone else will work it out,” because Sam Altman is a loser that ingrates other losers empowered by losers to sell loser technology to other losers , and the only way that he’s been able to do this is because the people that should know better are sitting around their thumbs up their asses asking him whether there will be data centers in space. If AI had ROI, we wouldn’t be debating whether it had ROI. We wouldn’t discuss its potential, or whether it could, theoretically, under different circumstances, in the future, in a way that nobody can describe be super powerful and do all of the stuff it can’t do today.  If AI had ROI, we’d be able to point with specificity to inarguable examples of economic impacts. AI boosters can jerk their binguses all they like about how Spotify’s CEO said its best engineers don’t write any code anymore . What does that mean? Is Spotify shipping better features, and are those features launching at a rapid clip? Is the software more secure, or stable? Spotify’s design still looks like absolute dogshit ! Most software is worse! Things keep breaking everywhere , and in many cases it’s because of AI coding tools ! In fact, I’d be willing to believe that AI had a negative economic impact, increasing operating expenses across the board and giving some software engineers prompt-based concussions by automating some coding in a way that makes them lazy and bad at writing software by speeding up the process of writing code with so much of it that it’s impossible to review it all ( see Mo Bitar’s video ). LLMs appear to be able to write some code sometimes and do so at high speed , and ingratiates software engineers that don’t really care about writing software by making them feel like they wrote it.  While it might allow some things to go theoretically faster, the overall economic impact of AI-generated code appears to be worse code, worse software, and massive, multi-million dollar bills from Anthropic and Cursor . I will concede that some software engineers seem to like these things, and that many software engineers appear to be using them, but I am yet to see a single one who obsessively posts about their token spend create anything of note or worth, and none of these people appear to be able to point to the actual ROI of all that AI they’re using. I realize I’m painting with a broad brush, so let me get a broader one: I believe anyone who relies on LLMs for anything is a mark.  I don’t give a shit if you use them to spit out a script or do some simple sideline part of your job, or transcribe or dictate into them, or if you’ve used them as a search engine (and even then, you best check every source!), but the moment you rely on and run your entire process on these things, I immediately doubt your ability to do anything, or at the very least wonder how gullible you truly are when somebody ingratiates you enough. Why? Because every single “AI setup” I’ve seen anyone ever use involves a rube goldberg machine of bullshit deterministic scripts to try and bring the hallucination-guaranteed nature of LLMs to heel, usually to the point that you’re doing more work making the LLM work than you did before they existed, and you’re only proud of it because you feel like you’re special. There are, of course, exceptions. I’ve talked to a few people who describe LLMs normally, without hype, who tell very specific stories of very specific outcomes that save indeterminate amounts of time. There are some that have used LLMs to create python scripts to search and organize data, to which I say “you’re impressed with Python, not LLMs.”  If all we’re left with from this era is the ability for some people to write Python scripts without learning Python, this is still an egregious and horrifying waste of capital.  Remember: what you are using is the end result of over a trillion dollars of investment. It is only made possible through manufactured consent that actively misinforms people about the current and future capabilities of LLMs. They didn’t raise hundreds of billions of dollars by talking about any product currently on the market, and that’s because the current products are not very good products. You are all the victims of a con. No matter how “well” your Breakfast Machine of different API calls and if-this-then-that automations may or may not function, you have been sold a bill of goods for “artificial intelligence” that is impossibly stupid. When some of you are pushed to prove the ROI of AI, you immediately return to boring talking points about Uber, or the Dot Com Bubble, or some other slop fed to you by people actively conning you at this very moment.  I mean this with as much empathy as I can muster: if you’re a huge AI booster, why do you defend this so vociferously? What is it about my criticism that hurts? Is it that I’m yucking your yum? Is it that I don’t immediately ingest and regurgitate the theoretical idea that the thing you’re using all the time is or may become sentient? Is it because I’m not impressed?  I think it’s far more likely that people are angry that I’m asking simple questions that should have — and don’t — have satisfying answers. I’m also fundamentally unimpressed with anything I’ve seen an LLM do, because my requirement for software or hardware is that it works as advertised, and the very fundament of the AI con is that LLMs are sold based on their theoretical capabilities. The reason nobody can show you the ROI from AI is that AI does not have a return on investment. Large Language Models can speed up some things in a way that becomes increasingly less-valuable and accurate with the complexity of the task, and more investment in AI data centers does not appear to do anything other than expand the number of tasks that an LLM can attempt.  While some people have been able to get something out of generative AI, that something never seems to be a tangible or impressive achievement. Every “successful” AI story is a result of either ignoring the obvious problems with LLMs or mitigating them at a great cost for an aggressively expensive and mediocre result.  LLMs are sold as “AI,” a technology best-known for automating things, yet they can’t be trusted to run anything on their own.  Instead, they manipulate the user into covering up their errors, explaining away their failures, coddling their meager returns and crediting them with the actual labor that LLMs are meant to automate away.  They do so by their investors and executives conning the media and the markets with outright lies and half-truths that exploit society’s weak points. The media and markets are informed by people that neither understand technology nor history, and Business Idiots that have reached the heights of their careers through diplomacy and ratfucking that care only about attention and adulation for things that other people do.  LLMs coddle the easily-led and narcissistic into believing that the model is doing the work as the human being has to constantly cater to the model’s inefficiencies and inabilities, using more energy and resources than any technology ever made.  And yet with all the money, all the attention, all the resources, all the land, all the power, all the affordances and excuses and endless fucking applause for mediocrity, nobody can actually point to the ROI of AI, because it doesn’t exist outside of it burping out stolen content and enriching and ingratiating billionaire dullards. Even at a hundredth of the price I’d be dismissive, because everything I’ve seen is so decidedly unexceptional. I realize that some will say I’m dismissive of LLMs’ capabilities, and I’m sorry — I’m just not impressed. You spent a trillion dollars to make it somewhat easier to code some things sometimes but not in such a way that it actually results in anything, research reports that nobody will read, shitty powerpoint decks and excel spreadsheets, and art that looks like stock images because that’s exactly what it was trained on.  This shit needs to work every time without fail and be absolutely flawless and autonomous.  You are paying for a tool. You are paying for software. You are a customer. Your job is not to explain to others why this is exciting, nor is it your job to cover up for its mistakes. If you truly love this stuff you should be either secure enough in doing so that you don’t feel compelled to defend it or be demeaning to those that disagree. The fact that I have to write that sentence is proof that something is very, very wrong with the AI industry, and that LLMs are about far more than software.  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 10,000 to 18,000 words, including vast, detailed analyses of the biggest events and companies in the AI bubble.  The foundation of software would be destroyed, as literally anyone could create and maintain any software they desired . Literally nobody would buy any software because they’d just type “computer make me a Slack clone for my organization” and it would magically appear on AWS.  The SaaSpocalypse ( see my premium here ) is a media and market-based hallucination where the collapsing growth of software companies is being explained as “AI taking their business” versus “private equity and venture capital overvalued software companies between 2018 and 2022 to the point that Apollo’s John Zito said “ all the marks are wrong ,” which is very bad, but nothing to do with AI. Accountancy would completely collapse, as nobody would need anyone but ChatGPT to do their taxes. Law schools would collapse, because legal internships would become useless and law firms would no longer have need for the thousands of new associates, because ChatGPT could just draft it all.  Legal salaries would also dramatically collapse. Research in effectively every discipline would collapse, because you could ask for a detailed report and said report would be better than any human being creates. The entirety of scientific research would change, because you could now automate many different disciplines out of existence.

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Ed Zitron's Where's Your Ed At 1 months ago

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?

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Stratechery 1 months ago

The SpaceX IPO and Data Centers in Space

Listen to this post : It’s hardly the biggest problem in the world — or perhaps the height of privilege to consider it a problem at all — but one of the most annoying consumer experiences is booking an Uber Black and realizing you got assigned a Tesla Model Y (Uber finally stopped allowing new Model Y’s onto Black last year ). Buckle up for an uncomfortable back seat, basic plastic finishes, and, all-too-often, potential car sickness from a driver who hasn’t completely mastered the Tesla’s aggressive regenerative braking. Still, the fact that the Model Y ever made it to the Black level is a testament to the brand Elon Musk built. Back in 2016, when 300,000 people dropped $1,000 each in a matter of hours to reserve an as-yet-unreleased Model 3, I explained that the phenomenon was because It’s a Tesla : The real payoff of Musk’s “Master Plan” is the fact that Tesla means something: yes, it stands for sustainability and caring for the environment, but more important is that Tesla also means amazing performance and Silicon Valley cool. To be sure, Tesla’s focus on the high end has helped them move down the cost curve, but it was Musk’s insistence on making “An electric car without compromises” that ultimately led to 276,000 people reserving a Model 3, many without even seeing the car: after all, it’s a Tesla. This is the same brand halo that landed what is, if we’re honest, a pretty basic car on the Uber Black list. What actually makes these cars compelling is the extent to which they are computers on wheels: I know plenty of very rich people who drive a Tesla not for the finishes but rather the Full Self-Driving (Supervised); there is nothing like it on the market, at least when it comes to cars you can own. Tesla appears to be doubling down on this point of differentiation: the company stopped production of the Models S and X earlier this year, focusing production resources on the CyberCab and robots; if you want your car to drive itself, you’ll get the same model as everyone else. It reminds me of Andy Warhol’s famous quote : What’s great about this country is that America started the tradition where the richest consumers buy essentially the same things as the poorest. You can be watching TV and see Coca-Cola, and you know that the President drinks Coke, Liz Taylor drinks Coke, and just think, you can drink Coke, too. A Coke is a Coke and no amount of money can get you a better Coke than the one the bum on the corner is drinking. All the Cokes are the same and all the Cokes are good. Liz Taylor knows it, the President knows it, the bum knows it, and you know it. That “tradition” is scale, and America is indeed better at it than any other country in the world; and, amongst Americans, no one pursues and seeks to leverage scale quite like Musk. From a press release from American Airlines: American Airlines today announced a sweeping modernization of its narrowbody inflight customer experience with the installation of Starlink, the fastest Wi-Fi in the sky, on more than 500 narrowbody aircraft beginning in Q1 2027. Starlink is widely regarded as the world’s most advanced satellite constellation using a low Earth orbit to deliver broadband Internet capable of supporting inflight streaming, online gaming, collaborative meeting tools and more. With thousands of satellites in low Earth orbit, Starlink can deliver multigigabit connectivity to aircraft using its Aero Terminal, which can support up to 1 Gbps per antenna. “As a premium global airline, we are continuously seeking out world-class partners like Starlink to deliver what our customers need and want,” said American Airlines Chief Customer Officer Heather Garboden. “The addition of Starlink solidifies American as a leading airline in keeping passengers connected in flight.” As part of American’s commitment to an elevated onboard experience, Starlink will enable seamless streaming, browsing and real-time communication capabilities across American’s domestic and short-haul international routes. I linked to the press release just for the amusement of American Airlines, which has in recent years built its strategy around offering anything-but-premium on routes you need, billing their Starlink deal as a commitment to “an elevated onboard experience.” That may have been the argument for United’s Starlink deal when it was announced in 2024 , but by this point it’s tablestakes , which is surely exactly how Musk wants it. Starlink is the consumer-facing business of SpaceX, generating $8.7 billion in revenue last year and $4.4 billion in profit; while it’s not totally clear exactly how SpaceX accounts for launch costs, obviously Starlink benefits greatly from the fact that it has access to SpaceX’s launch capacity. That launch capacity has resulted in over ten thousand active satellites in low Earth orbit, delivering low latency high speed Internet anywhere in the world — including in the air. That’s the carrot for airlines; the stick is the prospect of everyone else having the same service, and customers making flight decisions based on the quality of Internet access available. There is a similarity to Tesla in this way. Musk companies at their best don’t win the game; they change the rules through scale, such that billionaires buy economy cars because they actually drive themselves (with supervision), and airlines transform the consumer experience on their own dime. Musk makes all-in bets — whether that be in terms of launch capacity or in autonomous driving — not by making rational short-term business decisions, but by starting with the desired end state and working backwards. Tech has a long history of silly charts — there is an entire category known as Bezos charts — and the SpaceX S-1 has one that made me laugh. It came in the discussion of SpaceX’s total addressable market: We believe we have identified the largest actionable total addressable market (“TAM”) in human history. We estimate that our quantifiable TAM is $28.5 trillion, consisting of $370 billion in Space from space-enabled solutions; $1.6 trillion in Connectivity across $870 billion in Starlink Broadband and $740 billion in Starlink Mobile as well as additional opportunities in enterprise and government; $26.5 trillion in AI across $2.4 trillion in AI infrastructure, $760 billion in consumer subscriptions, $600 billion in digital advertising, and $22.7 trillion in enterprise applications. For illustrative purposes of sizing our addressable market opportunity, we exclude China and Russia from our global estimates. This image is approximately to scale vertically, but certainly not horizontally: I could use the help in really wrapping my mind around the $26.5 trillion AI opportunity, given it’s more than 13 times the space and connectivity opportunity combined! In all seriousness, the numbers are obviously absurd, but then again, everything about this IPO is absurd. SpaceX is seeking a $2 trillion valuation on a mere $18.67 billion in revenue with $4.9 billion in losses last year, and growth actually slowed from 35% to 33%. That slowdown happened despite the addition of xAI (and thus also X), which tipped the company from a small profit to that massive loss, thanks to $5.1 billion in AI R&D expense. That R&D, keep in mind, went towards building a model that is in 5th place, and whose entire founding team recently left the company. But sure, $26.5 trillion AI opportunity! This is not to say that SpaceX won’t get its desired valuation. Tesla’s valuation never made any sense right up until the Models 3 and Y actually worked out, causing Tesla’s share price to soar (and even then it was hard to ever build a financial model that justified the new share price). Musk’s ability to make his own reality starts with investors; from 2021’s Mistakes and Memes and comparing Apple and Tesla: This comparison works as far as it goes, but it doesn’t tell the entire story: after all, Apple’s brand was derived from decades building products, which had made it the most profitable company in the world. Tesla, meanwhile, always seemed to be weeks from going bankrupt, at least until it issued ever more stock, strengthening the conviction of Tesla skeptics and shorts. That, though, was the crazy thing: you would think that issuing stock would lead to Tesla’s stock price slumping; after all, existing shares were being diluted. Time after time, though, Tesla announcements about stock issuances would lead to the stock going up. It didn’t make any sense, at least if you thought about the stock as representing a company. It turned out, though, that TSLA was itself a meme, one about a car company, but also sustainability, and most of all, about Elon Musk himself. Issuing more stock was not diluting existing shareholders; it was extending the opportunity to propagate the TSLA meme to that many more people, and while Musk’s haters multiplied, so did his fans. The Internet, after all, is about abundance, not scarcity. The end result is that instead of infrastructure leading to a movement, a movement, via the stock market, funded the building out of infrastructure. I explained in that Article why I generally did not cover Tesla’s financial results, and the reasoning extends to why I don’t expect to cover SpaceX’s: Musk is the master of memes, and is himself a meme. He offers a dream — Mars, fully autonomous vehicles, an addressable market of $28.5 trillion — and positions his companies and their stock as access to that dream, and through the alchemy of capital markets, transforms shared delusion into mass market reality. Musk’s track record matters in this regard. Building an electric car company was possible, as was full self-driving (supervised); at the same time there were ever increasing government mandates and programs around decreasing emissions that acted as the stick to Tesla’s carrot. Similarly, landing rockets was possible, and the new market creation downstream from correspondingly lower launch costs was comprehensible. That Musk succeeded in both instances gives him the benefit of the doubt. The question that matters, then, is not if the numbers make sense right now (they absolutely do not); what matters is if the dream is even possible, and if there are actual reasons to think it might happen. I think that data centers in space meet these conditions. The first question about data centers in space is if they are even possible, and I think the answer is clearly yes. The key thing to consider is that there is no requirement that these data centers look anything like data centers on earth. On earth we build massive buildings full of GPUs with massive infrastructure for cooling those GPUs and massive power plants (or a connection to a grid which connects to massive power plants) to power those GPUs. The idea of transporting these massive structures to space sounds implausible, and it is! However, there is no reason that space data centers would look like data centers on earth. What makes far more sense is to think about an individual satellite as something akin to a rack. Right now the largest Starlink satellite in orbit is the V2 Mini Direct-to-Cell, which measures 7.4 meters by 2.7 meters by 0.3 meters (estimated); an NVL72 rack from Nvidia, meanwhile, measures 2.2 meters by 1.1 meters by 0.6 meters, so we’re already in the right size range. The V2 Mini Direct-to-Cell consumes (and dissipates) up to an estimated 25kW of energy; the NVL72 up to 135kW, and it can fit a 1 trillion parameter model quantized to FP4. The big shortcoming for a rack-satellite is power and its dissipation, but going from 25kW to 135kW is certainly within the realm of possibility — and given that you don’t need much of the cooling and power distribution usage on earth, something closer to 100kW might deliver similar performance. There are other issues to address, including the problem of radiation screwing with calculations, reliability, etc., although those two concerns could be addressed in part by using larger chips (which are less efficient, but also use less power); these rack-satellites will also be disposable, like Starlink satellites, ameliorating reliability issues. The key factor, however, is that a fleet of racks, interconnected with lasers (as Starlink’s already are), each with their own solar panels and radiator arrays for cooling (deploying 200+ square meters of radiators per rack will be a huge challenge), is possible . The next question about data centers in space is if there is a use case for them — the carrot — and I already made the argument that there is in The Inference Shift . Specifically, there are three types of workloads developing around LLMs: training, answer inference, and agentic inference. From the section making the case for “agentic inference”: Critically, this articulation of an agentic-specific memory hierarchy implies a necessary trade-off of speed for capacity. Here’s the thing, though: lower speed isn’t nearly as important a consideration if there isn’t a human in the loop. If an agent is waiting around for a job that is being run overnight, the agent doesn’t know or care about the user experience impact; what is most important is being able to accomplish a task, and if entirely new approaches to memory make that possible, then delays are fine. If delays are fine, then all of the focus on pure compute power and high-bandwidth memory seems out of place: if latency isn’t the top priority, then slower and cheaper memory — like traditional DRAM, for example — makes a lot more sense. And if the entire system is mostly waiting on memory, then chips don’t need to be as fast as the cutting edge either. This represents a profound shift in future architectures, but it also doesn’t mean that current architectures are going away: At the same time, these categories won’t be equal in size or importance. Specifically, agentic inference will be the largest market by far, because that is the market that won’t be limited by humans or time. Today’s agents are fancy answer inference; in the future true agentic inference will be work done by computers according to dictates given by other computers, and the market size scales not with humans but with compute. It’s agentic inference that makes the most sense for racks in space, and conveniently enough, that is also the market that is likely to be the largest in the long run. The third question about data centers in space is if there is a stick. Specifically, while I think that racks-in-space are both a lot more viable than people think, and a lot more relevant to agentic inference than current modes of compute, it is at the end of the day cheaper and easier to build on earth, all things being equal. All things are not equal, however: right now we are at the very beginning of the AI buildout and already one of the biggest constraints is not just power (expected), but zoning (unexpected). I wrote in an Update last week : That leads to an interesting contrast to globalization: when companies were closing down American factories and laying off workers and moving operations to China, none of the affected towns or workers had a say. They just suddenly no longer had a job, and a huge number of cities across the Rust Belt no longer had a reason to exist. People simply had to move, or worse, retreat to things like alcohol or drugs. AI, however, is the opposite: building data centers requires permission, which is to say that people actually have a say. Again, I am not at all saying that these people are well informed about data centers, or about the economic impact on their communities, much less the economic impact of AI generally; what I am noting is that people who didn’t have a say in globalization are suddenly finding they do have a say about AI, and it’s not a surprise they are expressing their disapproval by blocking data centers. In that Update I made the case that data center builders — and by extension the companies that use them — should straight up pay people for permission to build data centers in their communities. At a minimum, however, that increases the costs of terrestrial data centers. What seems very plausible in the long run is that the demand for compute ends up being so large that there eventually is nowhere left to build, making the vast expanses of space not just an alternative but in fact the only choice. If all of this happens — and there are a lot of “if”s here! — then suddenly that $2 trillion valuation starts looking reasonable. SpaceX is already monetizing xAI’s first data center, Colossus 1, to the tune of $15 billion/year for 300MW of capacity; that’s 3,000 racks-in-space. Anthropic, meanwhile, will probably make 3x the revenue on that capacity; it remains to be seen if xAI can get back in the state-of-the-art game, but if so then the amount of revenue it can generate per rack-in-space will be commensurately higher. Even without xAI, however, SpaceX has the potential to be a monopoly provider of marginal compute capacity. There are, needless to say, a massive number of assumptions baked into this argument, including assuming a huge number of engineering challenges are solved, Starship actually works, SpaceX gets sufficient supply of the right kinds of chips, compute demand is massively larger, agentic inference unbundles current architectures, and data center opponents are successful. The risk attached to all of these assumptions should discount the valuation you put on this business, which is to say I still think this IPO is nuts. At the same time, I’m glad it exists, for multiple reasons. The first one is the most obvious one: Musk, for all of his faults, has already pushed humanity forward on multiple vectors, including electric cars, self-driving, reusable rockets, satellite Internet, etc., and I’m excited to see him try and do more. The second is that I am in fact concerned about our ability to muster enough compute to fully realize the gains from AI, and am very worried about a replay of nuclear power, where our failure to build denied us the opportunity to even imagine what could be invented in a world of unlimited energy; the fact Musk is proposing an alternative path to unlimited compute is a relief. The third is that I appreciate the extent to which this IPO is a return to what an IPO should be: the opportunity for people to contribute capital to actually build the business, and to benefit if it works out. As I noted, I can’t make a financial model that necessarily justifies this valuation, particularly based on current financials, but neither can a VC investing in the Series A of a company. SpaceX has already invented a lot, and its early investors are going to make a lot of money with this IPO; at the same time, there is still so much more to invent that there remains a lot of upside — and, to be very clear, a lot of risk. It’s a testament to SpaceX’s ambitions that retail investors get to play VC. And hey, you get Mars upside for free! Training will continue to matter, and Nvidia’s current architecture, including high-speed compute, large amounts of high-bandwidth memory, and high-speed networking, will likely continue to dominate. Answer inference will be a meaningful market, albeit a relatively small one, and speed from chips like Cerebras or Groq (I explained how Nvidia is deploying Groq’s LPUs here ) will be very useful. Agentic inference will gradually unbundle the GPU, which alternates between stranding high-bandwidth memory (during the prefill process) and stranding compute (during the decode process), in favor of increasingly sophisticated memory hierarchies dominated by high capacity and relatively lower cost memory types, with “good enough” compute; indeed, if anything it will be the speed of CPUs for things like tool use that will matter more than the speed of GPUs.

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Justin Duke 1 months ago

ccusage

Inspired by a recent Simon Willison post , I just ran on my laptop and learned that over the past thirty days, on my sole subscription of $200 a month for Claude, I have consumed $2,422.13 worth of tokens. 1 All of this elides the fact that anchors onto something that is itself slightly lossy: we have to take as given the assumption that the unit cost of tokens through metered billing for companies like Anthropic is, in and of itself, profitable. I suspect that it is. But it's worth calling out, since the entire $2,422.13 number rests on it. Talk about getting my money's worth. It is interesting — and a little bracing — to think about the hypothetical world in which no prosumer subsidy exists, and I would have to actually pay $2,422.13 to receive the output of these tokens. First off, I think my usage patterns would change dramatically . The vast majority of these tokens are spent on Buttondown, and at that price point Anthropic would be the second-largest vendor on the Buttondown books, behind only Stripe. And this doesn't even include anyone else on the team. I would imagine that my actual spend would, in that world, dwindle to a third or a fourth of its current size — not because the marginal cost outweighs the marginal value, but because there is simply so much low-hanging fruit. I am generally in the business of saving $1,000 a month if it's easy to do so. Two other notes prompted by Simon's essay. One: LLM spending, at least from the outside, feels like a bit of a bokeh. I know many companies have grown much more sophisticated about this in the past four years, but during my time at Stripe and Amazon, a lot of the efficiency work 2 "Efficiency" being the buzzword used to mean, roughly, we would like to lower our OpEx in preparation for either the next quarterly earnings report or the next round of layoffs. was not really spent doing fancy backbreaking things — it was spent figuring out which fleets of servers were simply collecting dust because some random team had turned them on six months ago and never spun them down. I joke a lot that my single most meaningful contribution to Amazon was saving us tens of millions of dollars a year because I had the great fortune of realizing one of our ad-hoc clusters for one-off jobs was not scaling down, and we were therefore burning a huge amount of money for no reason in particular. LLM spend, at the org-chart level, smells identical to me. Distributed, badly telemetered, growing fast enough that no one at the top has had time to build intuition for what right looks like. Two: my already fervent interest in local LLMs — and getting to the point where I can run some of the more recent engineering-grade models locally — would, in that hypothetical world, become the single highest-leverage thing I could do from a financial standpoint. The calculus on local inference is not just an aesthetic or moral preference (see VC-subsidized tokens ); it is also an economic one, and one that I suspect will become increasingly load-bearing.

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Ed Zitron's Where's Your Ed At 1 months ago

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.

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Carlos Becker 1 months ago

AI didn't kill portfolios

I used to think that my GitHub profile helped me because people could read my code.

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Gabriel Weinberg 1 months ago

More data supports science funding literally pays for itself

Previously I put out a post explaining “ how science funding literally pays for itself ” that takes you through the math and some data that backs it up. Now two new data points further bolster this claim. First, the Congressional Budget Office (CBO), the nonpartisan federal agency that provides budget and economic information to Congress, published a report entitled “ Estimating the Economic Effects of Federal Investment in Research and Development . ” Usually the CBO only projects out 10 years per their mandate, but because the effects of science funding can take longer to fully manifest, they projected out 30 years. Thanks for reading Gabriel Weinberg! Subscribe for free to receive new posts and support my work. The relevant headline takeaway is highlighted below in their primary table (Table 1), showing that over this period the effects of a $30B increase in science funding for 10 years ($300B in total and about a 33% increase from today) would result in decreasing the overall deficit over 30 years (see green arrows). The decrease is about -2% on average if the “R&D funding increase [is] financed by reducing noninvestment spending” and about -1% on average if the “R&D funding increase [is] financed by borrowing.” This means that the increased science funding would grow the economy so much that the tax revenues received from this growth alone would outweigh the spending increase, leading to an overall decrease in the budget deficit. In other words, increasing science funding (at least by this amount) is a complete no-brainer, so let’s do it already! A few years ago the CBO did a similar report for infrastructure spending and compared the two in this report, finding the ROI effects of science funding to be about seven times greater than infrastructure spending. Again, so let’s do it already! The effect on the present value of GDP over the next 30 years (discounted using Treasury rates) that a dollar increase in deficit-financed R&D spending would have is about seven times larger than the effect that CBO, in its August 2021 report, estimated the same increase in infrastructure spending would have. Second, the Clark Center regularly polls a panel of economists , and recently they asked about this specific topic . The panel essentially universally agreed that historically U.S. science funding has paid for itself. In particular, 82% agreed “historical federal support for scientific research has paid for itself through a substantial positive effect on long-run U.S. productivity growth.” 0% disagreed, with the rest either not answering, or declaring either “no opinion” or “uncertain”. They also ask respondents about the confidence in their answer, and when weighted the results are even more striking with a whopping 97% in the agree category. Are you sold yet? Government science funding, the bulk of which goes to medical research, extends our lifespans and healthspans by inventing new medicines and other technologies that grow our economy so much it literally pays for itself. I get that this is not the most flashy policy area, but it is the most obviously good for our long-term future. Finally, and also new this year, the Pew Research Center put out a survey on Americans’ views of science and science funding , and among other things found broad bipartisan support for government science funding. 84% of U.S. adults say “government investments in scientific research aimed at advancing knowledge are usually worthwhile investments for society over time.” That breaks down by part as 76% of Republicans and 93% of Democrats (including independents who lean one way or the other). Thanks for reading! Subscribe for free to receive new posts or get the audio version .

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Ed Zitron's Where's Your Ed At 1 months ago

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.

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Ed Zitron's Where's Your Ed At 1 months ago

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.

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