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