2026-05-27 00:47:30
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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:
CEOs are uniquely prone to AI psychosis because they’re sufficiently distant from the last mile of work that still has to happen to generate most value with AI.
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:
Patients at the University of Chicago Medicine featured in a promotional video seamlessly refill prescriptions, book appointments and even get parking tips with the help of Agentforce, Salesforce Inc.’s flagship AI tool.
All this is possible, according to the video released in October, with the new technology. But the scenes are largely aspirational — little of that AI functionality is live. Patients who call the hospital system today are greeted with keypad-selection menus and routed to human schedulers. Its chatbot is still being tested and not visible to most web visitors.
The problem is that chunks of the capabilities shown in presentations – including from top customers like Williams-Sonoma Inc. and Finnair Oyj. – are actually mock-ups that aren’t yet widely in use.
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.
Sidenote: Now, I want to preface this by saying that not every LLM user is a loser. There are plenty of people bullied into using these models by their bosses, who use them as a glorified search engine, or who use them to write quick scripts or coding snippets to speed up their work. I think this is pretty clear in the copy, but I’m anticipating somebody reads that title and then stops reading entirely.
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:
Successful deployment of more advanced uses of Agentforce is as much about a customers’ processes and internal compliance as it is about technology, said Madhav Thattai, Salesforce executive vice president of Agentforce. “You’re going to see customers start with simpler use cases,” he said. “As they start to develop and implement more complex things, they kind of realize this full autonomous vision.”
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:
I, however, believe the problem runs a little deeper than the economy, which is a symptom of a bigger, virulent, and treatment-resistant plague that has infected the minds of those currently twigging at the levers of power — and really, the only levers that actually matter.
The incentives behind effectively everything we do have been broken by decades of neoliberal thinking, where the idea of a company — an entity created to do a thing in exchange for money —has been drained of all meaning beyond the continued domination and extraction of everything around it, focusing heavily on short-term gains and growth at all costs. In doing so, the definition of a “good business” has changed from one that makes good products at a fair price to a sustainable and loyal market, to one that can display the most stock price growth from quarter to quarter.
This is the Rot Economy, which is a useful description for how tech companies have voluntarily degraded their core products in order to placate shareholders, transforming useful — and sometimes beloved — services into a hollow shell of their former selves as a means of expressing growth. But it’s worth noting that this transformation isn’t constrained to the tech industry, nor was it a phenomena that occurred when the tech industry entered its current VC-fuelled, publicly-traded incarnation.
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:
When the leader of a company doesn't participate in or respect the production of the goods that enriches them, it creates a culture that enables similarly vacuous leaders on all levels. Management as a concept no longer means doing "work," but establishing cultures of dominance and value extraction. A CEO isn't measured on happy customers or even how good their revenue is today, but how good revenue might be tomorrow and whether those customers are paying them more. A "manager," much like a CEO, is no longer a position with any real responsibility — they're there to make sure you're working, to know enough about your work that they can sort of tell you what to do, but somehow the job of "telling you what to do" doesn't come with it any actual work, and the instructions don’t need to be useful or even meaningful.
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.
Sidenote: As I get into in my coverage, Anthropic’s costs scale linearly with its revenues, and the only reason Q2 was profitable was said costs were artificially suppressed.
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:
To be very clear about what I mean, I think there is currently an AI token binge across both Anthropic and OpenAI. Enterprises do not know the actual value of AI, and do not know how much they should actually be budgeting, which is why Uber and others are running through their token budgets but not, it seems, spending less. We’re currently in an abundance phase — one where nobody is truly thinking about the costs outside of their fear of missing out — but there’s this nasty undercurrent of “wait, how much does that cost?” followed by “oh, fuck, well…you know I love AI but…”
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”:
"That link is not there yet, right?" he said. "I think maybe implicitly there is more that is getting shipped, but it's very hard to draw a line between one of those stats and, 'Okay, now we're actually producing 25% more useful consumer features.’
Macdonald added that AI can seem free if you're "just a user sitting there coming up with interesting use cases" without paying for it. But ultimately, the company foots the bill.
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:
To protect its investment in Abilene, Blue Owl negotiated a completion guarantee that meant it wouldn’t be responsible for finishing the data center should Crusoe go bankrupt. It also got regular payments on its investment during the construction phase, according to a Blue Owl marketing document. (More typically, a firm in Blue Owl’s position would need to wait until the data center is up and running to get paid.)
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.
2026-05-23 00:21:32
Last week I ran the first part of my What If…We’re In An AI Bubble? Series, where I asked questions and posed scenarios as to the consequences of the many, many questions I’ve asked over the last few years. It quickly became one of my most-read articles I’ve ever written, and for those of you who joined me for the first time last week, here’s a quick list of what we’ve covered already:
As I mentioned last week, I believe one of the many problems with the analysis of the AI bubble is that people are willing to consider individual facts — like that AI is too expensive for everybody involved and data centers are not being built at the speed that we believed — but never the gestalt of their consequences.
For example, if data center construction slows to a crawl (as I’ve discussed is already the case) there’s a cascade of events that will occur:
It’s really easy to say “wow, this stuff needs a lot of debt!” and “wow, this stuff takes a while!” but actually sitting and thinking about what that means logically leads you to some gruesome outcomes.
And to be clear, there’s not really an alternative to that scenario if data center construction slows. Even in an optimistic scenario, if data centers that started being built in 2024 don’t get finished until 2027 or 2028, that means that NVIDIA’s “latest” GPUs are perennially two or three years in the future.
While some capacity exists, I believe there are at least one million Blackwell GPUs sitting in warehouses waiting to be installed years into the future, which means that projects are going to launch in a year or two with potentially three-year-old GPUs, or said projects are going to have to either replace their orders with Vera Rubin or dump aged capacity onto a market saturated with Blackwell GPUs.
The argument against what I’m saying is that there’s “insatiable” demand for AI compute — that “any viable compute on the market will be used,” which is true in measurements of days or months, but breaks down in the space of a year. As I mentioned a few weeks ago, AI’s demand story is a lie, because capacity is mostly taken up by Anthropic and OpenAI, creating the illusion of demand by absorbing most available inventory, while simultaneously obfuscating the fact that other sources of demand are simply non-existent in any meaningful numbers..
Many are conflating “there’s not much available” with “there’s so many people that want GPUs” without quantifying what “so many” means or how much they want, when the remaining performance obligations from Google, Amazon, and Microsoft have, outside of OpenAI and Anthropic, effectively plateaued, as is also the case when you remove these companies from CoreWeave order book.
If there were incredible, insatiable, indisputable demand, RPOs would be exploding across the board. Instead, nobody seems interested in buying capacity at scale outside of Anthropic, OpenAI, and the hyperscalers supporting them — or, in some cases, the likes of NVIDIA providing backstops to compute providers, agreeing to buy surplus compute in the case that they’re unable to sell it themselves. This is, to be clear, something that shouldn’t happen if there was genuine, distributed demand.
The sheer scale of the supposed AI data center buildout is in the tens of gigawatts of capacity, which translates to $10 billion to $15 billion per gigawatt in annual revenue. I can find no examples of anybody but Anthropic and OpenAI spending billions on compute.
Both companies need to make or raise a combined $1.25 trillion in the next four years to afford their compute commitments across Oracle, Microsoft, Google, Amazon and CoreWeave.
The counter-argument to everything I’m saying is effectively two points:
The latter is far from compelling, but I can see how somebody would believe it.
So much money appears to be flooding into companies like AMD, Samsung, and Sandisk — tens of billions of dollars to the point that it’s creating shortages across basically every component imaginable — which naturally might make you think that demand would exist at the other end.
For the consumer, that perception becomes even more believable when you notice how consumer electronics are getting more expensive. Certain games consoles, nearly six years after their initial release, are more expensive than they were at launch. Typically, the inverse is true.
Meanwhile, smartphones and PCs are expected to ship with weaker specs or high prices, in part because of shortages of key components, caused by demand for AI data center hardware.
The thing is, demand for AI compute doesn’t have to exist for AI data centers to get built. While some have clients signed up in advance, said deals were signed so many years before construction will complete that it’s hard to guarantee that they’ll be willing — or solvent enough — to pay.
I also imagine most clients have signed contracts that have milestone dates for delivery of compute capacity. If data centers are delayed, clients likely have a contractual out, much like Microsoft does with its $17 billion compute deal with Nebius.
In any case, in a frothy debt market full of desperate speculation, these projects are being funded by the very same private credit firms that piled into SaaS companies between 2018 and 2022 under the assumption that every software company will grow in perpetuity. When due diligence is so weak in private equity and private credit that Apollo’s John Zito says that their valuations are “all wrong,” it’s hard to believe that the same financiers are diligently making sure that enough revenue exists to justify these massive data center debt deals.
The same questionable attention to detail applies to venture capital, which has seen (much like private equity) its investment model slow to a crawl since 2018, with an average TVPI (total value paid in) slow to a horrifying 0.8 to 1.2x since 2018, meaning that for every dollar invested, you’re at best likely to get even money in return.
These are the very same investors telling you that every AI company is worth perpetually-growing amounts of money, that everything will work out perfectly, that somebody will work out how to make AI profitable, and that AI is both here to stay and doing incredible things, even if they can’t really explain what those things might be.
In reality, none of these people have any idea how to turn around these rotten economics. Data centers are massive money-losing operations that in the best case scenario take five years to make a single dollar of margin, and their customers are eternally-unprofitable AI startups that rely on a constant flow of venture capital dollars.
The AI bubble is entirely built by people who hope somebody else will solve their problems. AI labs depend on venture capitalists to fund them, hardware providers to invent silicon that makes their businesses profitable, and their AI startup clients to find ways to make profitable businesses using their APIs. In turn, AI startups rely on AI labs to work out a way to make their models cheaper so that AI startups can make their business models profitable.
Put another way, everybody’s response to “how does this become profitable” is “don’t worry, somebody will work it out, but don’t worry, they’re going to at some point.”
Today, I want to explore what happens if they don’t.
Time. Space. Reality.
It's more than a linear path — it’s a prism of endless possibility. I am the Watcher, and I am well aware of how AI generated that sentence sounds.
I am your guide through these vast new realities.
Follow me and dare to face the unknown.
And ponder the question…
What if…We’re In An AI Bubble?
2026-05-22 22:21:18
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.
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2026-05-22 01:08:19
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:
Anthropic’s revenue is set to more than double to $10.9 billion in the second quarter, an explosive rate of growth that will help it turn an operating profit for the first time.
…
Anthropic generated $4.8 billion in sales in the first quarter. Its quarterly revenue is now growing faster than Zoom did during the pandemic, and Google and Facebook in the run-up to their initial public offerings. It is set to turn an operating profit of $559 million in the June quarter.
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?
The company might not remain profitable for the full year as it plans spending increases due to its vast computing needs.
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!
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:
The company might not remain profitable for the full year as it plans spending increases due to its vast computing needs.
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.
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2026-05-19 23:48:42
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.
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AI is, as it stands, not economically viable for anybody involved other than the construction firms, NVIDIA, and the surrounding hardware companies benefitting from the irrational exuberance of a data center buildout that doesn’t appear to be happening at the speed we believed.
Every AI startup loses millions or billions of dollars a year, and nobody appears to have worked out a way to stop hemorrhaging cash. Hyperscalers have invested over $800 billion in the last three years, with plans to add another $700 billion or so in 2026 and another $1 trillion in 2027, meaning that they need to make at least three trillion dollars in AI specific revenue just to break even, and $6 trillion or more for AI to be anything other than a wash. I went into detail about this (albeit at a lower, pre-2026/2027 capex number) in a premium piece last year.
To give you some context, Microsoft made $281 billion, Meta $200 billion, Amazon $716 billion, and Google $402.8 billion in revenue in their most-recent fiscal years for every single product combined, for a total of $1.599 trillion. None of them will talk about their actual AI revenues. Yes, yes, I know Microsoft said that it had $37 billion in AI revenue run rate ($3.08 billion a month or so) and Amazon had $15 billion, or around $1.25 billion a month, but both of these are snapshots of single months that are meant to make it sound like they’re going to make that much in a year but in the end, you don’t actually know anything about how much money they’ve made from AI.
We do, however, now know that Microsoft has spent an approximate $100 billion on its OpenAI partnership after testimony from an executive during the otherwise-dull Musk-OpenAI trial, per Bloomberg:
That figure includes Microsoft’s original investments in OpenAI, as well as the costs of building infrastructure and hosting OpenAI’s computing, Microsoft deals executive Michael Wetter testified on Monday. It is cumulative through the current fiscal year which ends in June, he said.
This is a fascinating insight for a few reasons:
At the end of 2025, OpenAI claimed that it had 1.9GW of capacity (likely referring to total power draw rather than the actual critical IT of the infrastructure at its disposal), which, per analyst estimates, ($42 to $44 million per megawatt) works out to around $79.8 billion. This claim was made around six months before the release of Microsoft’s most recent quarterly results.
In other words, Microsoft has spent 4 years sinking (either through spending or allocating the capex in advance) nearly $300 billion into…building OpenAI?
Okay, fine. Microsoft also has 20 million Microsoft 365 Copilot subscribers for an absolute maximum revenue of $7.2 billion…if every single one were paying $30 a month, which they are most assuredly not as Microsoft has been offering discounts on it for years.
Based on my reporting from last year, Microsoft made around $7.5 billion from OpenAI’s inference spend and $761 million from its revenue share in Fiscal Year 2025, a year when it invested (either spent or allocated) around $88.2 billion in capital expenditures.
I didn’t report it at the time, but I also had the numbers for all of Microsoft’s revenues for the first three quarters of Fiscal Year 2025 — a total of $8.9 billion of total AI revenue, with around $4.35 billion in revenues when you removed OpenAI’s inference. If we assume that Microsoft’s other AI services grew 10% quarter-over-quarter, I estimate that Microsoft likely made around $17.9 billion in AI revenue in FY2025, or a little under a fifth of its capex.
And let’s be clear: none of these numbers include the actual operating expenses.
Data centers, after all, need electricity to run, and AI data centers in particular need a lot of electricity. And some — though, admittedly, not many — people to handle the things like maintenance, repairs, and operations. And then there are things like taxes, insurance, and the other day-to-day costs that, when you add them all together, make a big, scary number.
You can argue that “actually GPUs are profitable to run” (I disagree!), but for any of this to make sense, four things have to happen:
All four must be true. If AI revenues don’t explode, capex can stop, margins can be positive, and your best-case scenario is…you maybe broke even. If capex never stops being invested, you need revenues to explode dramatically — to the tune of effectively doubling Microsoft, Meta and Google’s entire businesses, and tripling Amazon Web Services’ annual revenue ($128 billion) — and for said revenues to be margin-positive, because if they’re not, eventually other healthy businesses will slow, leaving AI to tear a hole in overall margins. In all cases, AI revenue must stay consistent because, well, you need to get paid.
Sidenote: In all honesty, I have no idea how Meta makes this make sense, as it plans to invest at least $125 billion in capex in 2026 and has, to this point, not shown any actual, real growth in its revenue from AI, and no, those increases in conversion don’t mean actual revenue.
I also cannot find an economic scenario where this pays itself off.
Let’s assume that Anthropic is actually at $45 billion in annualized revenue (I believe it’s doing some very worrisome maths to get there), or around $3.75 billion a month. On an annualized basis, this would not be enough — assuming it had zero operating expenses (rather than losing billions) — to recover a single year of capital expenditures from Microsoft, Google, Meta, or Amazon from 2024 or 2023.
Even if OpenAI’s entire cloud spend ($50 billion) for 2026 went to Microsoft and it doubled its Microsoft 365 Copilot revenue (at full cost) to $14.4 billion, it estimates it will invest $190 billion in capital expenditures this year. Amazon’s $15 billion AI run rate, even if it doubled, wouldn’t put much of a dent in its $200 billion in investment plans. While we don’t know Google’s AI revenues, it plans to invest $185 billion in capex this year.
These AI revenues have to be completely fucking insane and they need to be that way extremely fucking soon, because otherwise the best they’ll be able to say is “our first few years of capex weren’t particularly useful but the stuff we built after it was,” which still works out to a few hundred billion dollars of waste.
Things get even worse when you realize that at least 70% of Microsoft, Google, and Amazon’s compute is dedicated to Anthropic and OpenAI, two companies that burn so many billions of dollars that Microsoft, Google and Amazon have already fed them a combined $54 billion in the last three years, with $28 billion of that coming in the last month and Anthropic due another $50 billion from Google and Amazon if certain performance obligations are met.
And there’s no real sign, outside of Anthropic and OpenAI’s compute spend (which is reliant on hyperscaler and venture capital money), of any real explosion in AI revenue. Per The Information (in a chart I love to share!), more than 50% of hyperscalers’ revenue backlogs comes from these companies:
If massive, incredible demand for AI existed, wouldn’t these remaining performance obligations be near the trillion mark? Wouldn’t there be other Anthropic or OpenAI sized chunks of revenue? There’s allegedly incredible, unstoppable, insatiable demand for compute. Why isn’t it lining up?
Let’s take a look at those RPOs!
That was a lot of numbers, so let me make it simpler: outside of OpenAI and Anthropic, these three companies do not appear to be significantly increasing their revenues, and the only way to get that revenue is to feed money to one or both of these companies.
Put aside all the theoreticals and hypotheticals and metaphors and imaginary future scenarios and tell me: what, in the next year, are Microsoft, Google and Amazon going to do about this problem? How do they solve it?
If we assume the absolute best-case scenario, these companies are making a combined $70 billion in annual revenue on investments that now — including the money invested in the companies themselves — total over $900 billion. Doubling that won’t be enough. Tripling it won’t be enough. In fact, to pay this off, these companies will need to be making over $100 billion each in AI revenue in the next year, because otherwise there is no covering these losses.
And it all comes back to a very simple point: AI is too expensive. If the margins were good, they’d be sharing the margins. If the revenues were good, they’d be sharing the revenues (and no, run rates aren’t revenues). If the business was strong, it would be a separate category in their earnings.
But LLMs are too expensive! They cost too much to run, and said costs appear to increase linearly with revenues. The more a user uses a product, the more it costs the company to run it, and the more capacity they can take up. The only way to capture any growth is to buy and install GPUs, which in turn requires you to build somewhere to put them, which takes time and money.
I’m really struggling to see the argument in favor of continued capex investment. You’re more than $800 billion in the hole with, I estimate, less than half of that resulting in operational GPUs and capacity. Said capacity is mostly taken up by OpenAI and Anthropic, two companies that burn billions of dollars and do not appear to have an answer for how they might stop.
The more you build, the more your infrastructure becomes dependent on the continued existence of two perennially-unprofitable ultra-oafs, as your existent AI product lines are, at best, add-ons to products like Google Workspace or Microsoft 365, or further expansion of cloud compute capacity with lower margins and higher up-front costs than anything you’ve ever built.
Every quarter is an opportunity to put yourself another $30 billion or so in the hole, all in the hopes that, I assume, OpenAI or Anthropic will pay you $100 billion or $200 billion over the course of a few years, because nobody else in the entire universe is spending that much on compute. You are not recovering these investments without either a massive new product line that doesn’t exist today or three or four Anthropic or OpenAI-sized compute contracts.
Put another way, Amazon needs another AWS ($128 billion a year), Microsoft another Azure ($75 billion a year, including OpenAI’s 2025 compute spend) and Google a business line at least half the size of search (around $200 billion a year). These businesses have grown to this size by providing extranormally large amounts of value from the very moment they were created and impenetrable monopolies — and while there are quite literally other cloud providers that can physically provide the infrastructure to OpenAI and Anthropic (Oracle is trying to compete and may die as a result), the actual “monopoly” here is “being able to deploy hundreds of billions of dollars.” Anthropic proved this when it took 300MW of compute from Elon Musk.
Sidenote: I have absolutely no idea what Meta does, and my chaos bet is that it starts renting out its compute to Anthropic or OpenAI when things get rough. Perhaps it does some sort of incestuous deal where Meta gets equity. I really have no idea here! It’s a crazy and stupid company run by a moron.
In Oracle’s case, as I’ve explained at length, it has to successfully build 7.1GW of capacity, have that capacity actually be margin-positive (doubtful!), and then actually get paid for it by the time it’s built in, oh, I dunno, 2032?
Sadly, I have bad news about Oracle, Microsoft, Amazon, and Google’s largest customers.
Here’s a fun game: ask an AI booster how OpenAI or Anthropic becomes profitable!
Here’s what they’ll say:
I must be abundantly clear that nobody has any proof that anyone is profitable on inference, but we have plenty of proof they’re not. They’ll likely cite known liar Sam Altman saying OpenAI is profitable on inference from a party from August 2025, or Dario Amodei saying (in a sentence around “stylized facts” that are “not exact” and are specifically “a toy model” and specifically not about Anthropic) “the inference has some gross margin that’s more than 50%.”
Here’s a really simple way to dispute this: Coatue said that Anthropic’s revenues were 85% API calls in 2025. If it’s profitable on inference, how is it still losing money? You’re gonna say “training,” but that doesn’t actually answer the question: if Anthropic’s process of providing tokens to its models is profitable, how is it losing so much money? Why offer a subscription platform at all?
As I’ll get to, Anthropic has companies paying massive amounts for tokens — hundreds of millions a year in some cases — that’s all inference. Why are you bothering with these stinky, nasty monthly subscriptions?
The “inference is profitable” argument is a bedtime story told to people that can’t reconcile the logic of a company that allows people to burn between $8 and $13.50 of every dollar of their subscription revenue.
Otherwise, you have to reconcile with the fact that both Anthropic and OpenAI are both incinerating money and have no real path to any kind of sustainability other than, well, not doing that.
One very, very specific counter-argument people make is that open source models are cheap, and can somehow be compared to OpenAI and Anthropic’s, despite the fact that we have no idea what the actual parameters of Sonnet, GPT, Opus, or any other of their models actually are.
What we do know is that both of these companies lose billions of dollars.
What we do know is that OpenAI, per The Information, plans to burn $852 billion through the end of 2030, and that as of March 6, 2026 (per CFO Krishna Rao’s sworn affidavit), Anthropic made “exceeding” (sigh) $5 billion in revenue and spent $10 billion on inference and training.
Anthropic has done a great deal of work to obfuscate how much it actually makes or spends, but I think it’s likely it burns even more than OpenAI, given the fact that it’s had to raise $75 billion in the last 6 months (assuming its new $30 billion round closes), and that’s not including an additional $30 billion from Google and Amazon if certain unknown milestones are hit.
Then there’s the issue of those RPOs. Anthropic is now on the hook for $200 billion to Google, $100 billion to Amazon and $30 billion to Microsoft, I assume over the course of the next three or four years.
So let’s lay this out.
Anthropic — based on its own affidavit from March — appears to have spent $3 to make $1 of revenue on a compute basis, and that’s before you include any and all other costs like staff or electricity or the vocal coach that Dario Amodei uses to add that bass to his voice.
Additionally, it needs $330 billion to pay its cloud obligations to Amazon, Google, and Microsoft over the next four years. I’d estimate it needs $5 billion a year for its compute deal xAI (so $20 billion over the total period) and an estimated $30 billion to cover its deal with CoreWeave. That brings us to a total of $380 billion.
It’s hard to estimate the actual costs associated with running Anthropic because so much of the reporting no longer makes sense as a result of that affidavit. Nevertheless, I think it’s fair to assume it will need at least $20 billion of operating expenses across that four year period.
We don’t even need to play in the realm of “what might Anthropic or OpenAI’s revenues be?” to understand the problem here. Both companies aggressively burn money, and neither of them have any answer as to how they might stop. Numerous reports about how Anthropic will turn “cash flow positive” in either 2027 or 2028 are fantastical, illogical, entirely driven by ridiculous projections, and should never have been reported as anything other than an attempt by companies to mislead their investors. In both cases, reporters should’ve had more asterisks on those numbers than Q*Bert reading Frank’s lines from Blue Velvet.
And we have plenty of evidence that they’re losing more money over time. In January 2026, The Information reported that Anthropic’s gross margins were 40% in 2025 — 10% lower than its “optimistic” projections, specifically attributed to “...the costs of running Anthropic models from paying customers, in a process known as inference, on servers from Google and Amazon,” adding that those costs were “23% higher than the company anticipated.”
In February, The Information ran another story saying that OpenAI’s gross margins fell from 40% in 2024 to 33% in 2025, a full 13% lower than its projected margins of 46%, all because (and I quote) “...the company having to buy more expensive compute at the last minute in response to higher than expected demand for its chatbots and models.”
You know, exactly what Anthropic has had to do.
This is what I’ve referred to as the knife-catching problem for compute demand — you either don’t order enough compute and have to rush to buy some last-minute as demand intensifies, or you order too much, and, well, to quote Dario Amodei:
Basically I’m saying, “In 2027, how much compute do I get?” I could assume that the revenue will continue growing 10x a year, so it’ll be $100 billion at the end of 2026 and $1 trillion at the end of 2027. Actually it would be $5 trillion dollars of compute because it would be $1 trillion a year for five years. I could buy $1 trillion of compute that starts at the end of 2027. If my revenue is not $1 trillion dollars, if it’s even $800 billion, there’s no force on earth, there’s no hedge on earth that could stop me from going bankrupt if I buy that much compute.
And right now, as I’ve covered, there’s not enough compute being built to keep up with Anthropic or OpenAI’s voracious demands, meaning that they will both be bartering to buy whatever’s available at whatever price it’s available at. This naturally will savage their already-negative margins…
…and then what?
No, really, and then what? One of you fucking AI boosters, answer me, how does this actual reverse course? Because even if Anthropic were making $100 billion in annual revenue, it would probably be losing $300 billion or more to get there. The fact it had to raise $30 billion in February, $15 billion in April, and now $30 billion more in May all while allegedly pulling in more than $3 billion a month in revenue suggests that its COGS are fucking horrendous, and its growth is coming at a terrible financial cost.
Let’s say that Anthropic keeps growing and (as The Information suggests) hits $100 billion in annualized revenue (around $8.3 billion a month). How, exactly, does it afford to make that much money? Because right now it’s (allegedly) about to hit $45 billion in annualized revenue, and needs so much money that it’s absorbing (along with OpenAI) the majority of venture capital raised this year, and very clearly does not have any path to bring its costs down.
The answer is simple: it can’t! There is no mechanism to do so. More compute does not make OpenAI or Anthropic’s services cheaper to offer. There is no magical silicon coming that will make any of this more affordable, and no, Anthropic is not “profitable on inference,” because if it were, that massive revenue growth would have leveled out its margins rather than require it to raise a little less than the combined value of every Major League Baseball team, or more if you add the other $50 billion that Amazon and Google have promised based on secretly-held performance obligations.
The same goes for OpenAI, which “raised” $122 billion (around $45 to $50 billion in real cash, with the rest either paid in installments or on it IPOing or reaching (sigh) AGI) in February and is now already considering raising more.
Somebody might counter-argue that this is companies raising as a means of boosting their valuations, I think that’s a very convenient way of looking at two extremely problematic companies.
I should also ask why neither of them appear to be seriously considering going public. While both were rumoured earlier in the year to be planning to do so in 2026, both appear poised to raise more private capital.
I think the answer is simple: their CFOs know that doing so would reveal their actual margins, which are hot dogshit with sprinkles on top.
Nobody has a sensible or logical response here.
Which leads us right to our next point!
One important detail to keep in mind here is that as of a month or two ago, Anthropic moved all enterprise customers to token-based-billing, which will begin, I believe, a true stress-test of the true “value” of AI as costs skyrocket.
Just last week I ran the first of a two (or three, potentially) part premium series called “What If We’re In An AI Bubble?” and touched on the gruesome subject of whether organizations could afford to pay for AI long-term:
Per Laura Bratton of The Information, Uber, ServiceNow and multiple other organizations are blowing through their yearly API token budgets in a matter of months, and are currently in the “cope” stage, with Kellie Romack, CIO of ServiceNow, saying the following about a conversation with CFO Gina Mastantuono:
Romack said she recently met with ServiceNow Chief Financial Officer Gina Mastantuono to figure out how to contain costs so employees can keep using their Claude Enterprise accounts for the rest of the year.
“It’s a really hard problem,” Romack said. “She’s worried, I’m worried, and we’re working together to go figure this out.”
Let’s focus on that phrase “...can keep using their Claude Enterprise accounts for the rest of the year,” because it’s important. A public company with a CEO that previously boosted the metaverse and now has profound AI psychosis is saying that it isn’t sure whether it can continue to justify paying for Anthropic’s models through the rest of the year without containing its costs.
Earlier in the week, carnival barker and Salesforce CEO Marc Benioff said his company would spend $300 million on Anthropic tokens in 2026, and as I discussed in my premium from Friday, unrestrained AI spending is inflating the revenues of Anthropic and OpenAI in a way that isn’t sustainable for anybody involved:
For example, sources with direct familiarity with Stripe’s internal cost have told me that its technical staff (a little over 5000 people) are burning an average of $94,000 a day (around $2.8 million a month) in tokens, primarily on Anthropic’s coding models. Stripe’s EBITDA revenue was around $1.6 billion in 2024,so $33.6 million a year isn’t necessarily life-threatening, but if we assume an average salary of $150,000 per member of technical staff, that puts its raw headcount costs at around at least $765 million, making AI costs sit at roughly 4.392% of headcount.
As I said, this is one of the more-normal examples. Goldman Sachs reported a few weeks ago that AI costs are approaching 10% of total headcount costs, and “...could be on track to be on par with headcount costs in the next several quarters based on current trajectories.”
The problem is simple: nobody actually knows how much AI is going to cost them in any given quarter. This means that the current token spend you’re seeing is entirely experimental, which is why organizations keep burning through their tokens so fast.
This massive growth in spend is what underpins the “massive” (I have serious questions about its accounting) growth in Anthropic’s revenue. Executives have, across the board, given their engineers free reign to burn as many tokens as they’d like, and while I severely doubt that Anthropic actually hit $50 billion in annualized revenue outside of not-quite-fraudulent non-GAAP measurements, I believe its revenue growth has come from an artificial boost from a tech industry searching for a reason to pay somebody money.
To be very clear about what I mean, I think there is currently an AI token binge across both Anthropic and OpenAI. Enterprises do not know the actual value of AI, and do not know how much they should actually be budgeting, which is why Uber and others are running through their token budgets but not, it seems, spending less. We’re currently in an abundance phase — one where nobody is truly thinking about the costs outside of their fear of missing out — but there’s this nasty undercurrent of “wait, how much does that cost?” followed by “oh, fuck, well…you know I love AI but…”
Put another way, the current spend on AI tokens is not something that’s indicative of lasting, reliable revenue.
In some cases, the pressure to use AI for everything is turning companies’ software stacks into slop.
Things are worse elsewhere. Something is wrong at Zillow. Something about LLMs has done something to its technical leadership, something that makes them talk strange and send weird slide decks with confusing, slop-ridden sentences.
The real estate tech firm spent over $1 million on AI services in the first quarter of 2026, and in April it spent $749,000 in tokens across Cursor and Anthropic’s services, as well as through AWS Bedrock. As of the end of the month, it was nearly 75% of the way through its annual Cursor token budget of $1.1 million.
As of the middle of May, its total AI spend had already crested over $300,000, and its Cursor budget sat dangerously close to the edge at 85%.
This is particularly-concerning when you consider that Zillow’s net income for Q1 2026 was $46 million, and ranged from $2 million to $10 million each quarter of 2025.
Zillow is currently on course to spend at least $7 million on AI in 2026, and at its current pace might hit as much as $10 million, which would amount to a little less than 50% of its 2025 net income ($23 million).
You’re probably wondering how Zillow manages to spend so much on AI, and the answer — as I’ll get into in next week’s free newsletter — is that its technical executives appear to have AI psychosis, saying that the short-term goal is for “software engineers to never open a code editor again.”
The reality is chaos. In a slide deck that I’ll discuss later, Zillow revealed that while engineering resources have largely stayed the same, outputs requiring human review have increased by nearly 50%. Meanwhile, code deployments and pull requests increased by 39%, and software reviewer load increased by 29,000 hours each month, creating a massive burden on the 1,500 or so engineers working at the company.
In simpler terms, that’s about 19 hours of extra work per engineer that’s literally just looking at extra code written by LLMs.
On Blind, the anonymous social network for tech workers, Zillow workers complain about Zillow’s code “slowly becoming AI slop,” with “much more code getting approved without guardrails or input due to people not being able to keep up the other’s velocity or just not caring anymore.”
One worker claimed that “the slop is job security,” adding that they “don’t want the output to be good or documentation to be clean [as] management will replace [them] with offshore/nearshore/AI agents at the slightest whiff of evidence that the slop cannon is self sustaining.”
Another said that they felt “lost in the agentic world,” and that they “didn’t have full grasp of where we are going or what [their] role is,” with a “lot of overlap in what people are doing.” Another said that “people are burning tokens just to hit internal AI adoption targets,” adding that “this is what happens when leadership ties metrics to usage instead of outcomes,” saying that it “literally subsidized busywork.”
This is all part of what an internal slide deck viewed by this publication called “AI-Native Engineering,” promising a “path to an agentic Zillow” and “faster outcomes for customers,” though customers are never mentioned in any other slide.
The deck — pumped full of AI-generated text — talks about “generic AI being a commodity,” saying that “Zillow-aware AI is a competitive advantage,” and at no point explains what that means. It encourages engineers to go from “AI-Assisted” to “AI-Native,” with “systems enabling org-wide leverage,” with engineers moving from being “soloists” — individual developers with AI tools — to “conductors” that orchestrate AI agents, to “composers” that “define systems AI can safely play,” adding that “2026 is the transition from conductor to composer.”
Yet the strangest part is named “2027: A Tuesday,” discussing a theoretical day in the office for whoever is left at the company.
This theoretical example is, apparently, a process that would take weeks, but now takes under two hours.”
Zillow intends, based on this deck, to sacrifice everything to AI — code review, vulnerability fixes, policy checks, deployments, testing, and basically having agents take over everything, no matter how small, like having an agent do dependency updates and security hotfixes that could be handled with a simple shell script.
To quote Zillow:
Agent capabilities exist across the entire software development lifecycle-from ticket to production-with humans steering and approving rather than executing each step.
In practice, sources at Zillow tell me that there has been no actual movement toward this vision. Software engineers still open IDEs and review code manually, with one describing Zillow’s “vision” as “nonsense,” adding that “you can’t just throw buzzwords on a slide deck and change how all the engineers do their jobs.”
As for why token burn is so high, sources tell me that engineers are actively encouraged to use AI for everything, as much as possible, writing PRD (product requirement documents) in AI, then using the AI to make stuff based on the PRD, then doing a deck with AI, then writing emails with AI, using AI to brainstorm, or create weird, esoteric automations, with some managers pushing workers to have one personal AI “goal” to aspire to.
Zillow’s agentic “vision” is apparently a remit from the C-suite.
It’s hard to tell if this is AI psychosis or just classic Business Idiot bullshit.
Perhaps it’s a little of both.
Every organization I’ve talked to has exceeded or is nearing the edge of their annual token budget barely five months into the year, which means that everybody has suddenly given themselves an extra few million dollars’ worth of operating expenses for reasons that escape effectively everybody I’ve talked to.
Every engineer tells me the same thing: “I’m being made to do this, I don’t want to do this, my managers do not seem to understand, my bosses seem to understand even less than my managers, and if I don’t use AI somebody is going to fire me.”
Put another way, CEOs and CTOs are screeching at their underlings to “use AI as much as possible” to “find its incredible benefits” without anybody really knowing what those are and how much it’ll cost to get there.
This might be because Anthropic obfuscates the data that might tell customers the real costs.
Per Laura Bratton at The Information,
One reason Anthropic costs are tough to predict, ServiceNow chief digital information officer Kellie Romack told me, is that Anthropic doesn’t automatically show customers the kind of granular data that allows them to see which of its users consume which tools; how much they use the tools, and how they’re using them. Software firms such as ServiceNow, SAP, Microsoft and Workday offer such “telemetry” data to their customers, she said.
Bratton’s article has numerous quotes from executives saying that Anthropic lacks transparency and granularity into the ways that tokens are being burned across an organization, in a way that I think sounds very, very suspicious, particularly when you add the following:
Anthropic also doesn’t offer so-called service-level agreements with customers that define how well the product will perform and customer-service response times that the customers should expect, Romack and Mehta said. Such agreements are standard in the software industry.
While I’m not accusing Anthropic of anything untoward, massive, multi-million dollar contracts that involve individuals burning thousands or tens of thousands of dollars’ worth of tokens with no service level agreement, transparency or true granularity into the burn is a perfect setup for a company — not saying it’s Anthropic! — to do something dastardly with those numbers.
While an individual might be able to monitor their own personal usage, in an organization of hundreds or thousands of engineers, who’s to know if, say, the particular token burn is consistent across every member of the company, or that those costs are actually matching up with what the user is doing?
This is a company ostensibly worth $900 billion dollars acting with disregard for the basic measurement of “how much did this cost, and how did it cost so much?”
And in the end, how do you even measure it at scale? Say you’ve got 1,500 engineers, and they’re spending a combined $1 million tokens a month. How the fuck do you actually measure the return on investment for that spend?
How many tokens does it take to do one thing? Is it consistent across every model? Is it consistent across every employee? Are you even measuring how many tokens a task costs? Because if you’re not, that token budget is basically throwing a dart blindfolded.
Okay, now you’ve measured a task, did you make sure to measure it multiple times? Because LLMs can randomly do things differently even with the same prompt and same Claude.MD file and same strictures and same data sources. You’re gonna need at least 10 samples of each task, and you’re gonna need to make sure somebody who actually knows what they’re doing can measure them, because if you get a dimwit, they’re going to say it can do something it can’t.
Unless, of course, you can’t actually measure how many tokens a particular task can take with much accuracy, in which case every single AI token budget is bullshit. And each model does things differently depending on many different variables, some of them a result of the user, some of them a result of the AI labs themselves.
Alright, well, maybe you just need KPIs — measurements you can aspire toward, and by pursuing them you can start working out how much it costs to do stuff.
Wait, which metric works there exactly?
In fact, it’s pretty hard to measure anything like “efficiency” or “productivity” in any business, because every metric connected to them can be gamed, leaving managers and executives with the problematic situation where they have to start learning how things work so they can see if they’re good.
Before AI, this wasn’t as much of a problem, in the sense that inefficiencies and wasted hours weren’t directly connected to a chatbot that is specifically designed to burn money. Managers and executives could come up with whatever deranged, self-gratifying office bullshit they pleased, wasting hours of people’s time in the process, but doing so didn’t immediately connect to a massive, ever-increasing cost.
AI is a perfect storm of failed concepts and organizations, and the apex of the Era of the Business Idiot, an epoch where we’re ruled by people so thoroughly disconnected from the actual workforce that it was inevitable that a technology would be created specifically to grift them.
LLMs are dangerous for many, many reasons, but the under-discussed one is how well they play to a certain kind of executive imbecile. Generative AI is — to quote Mo Bitar — really good at doing an impression of work, much like most managers and c-suite executives, and even if it’s completely incapable of doing something, it’ll absolutely say it can and tell you you’re amazing for suggesting it.
And that’s why Business Idiots love it.
Where regular human beings would say annoying things like “that’s not possible within that timeline” or “we don’t have the resources to do it,” AI will say “of course, right away!” and burn as many tokens as possible.
When it makes mistakes, it’ll apologize — as it should because it failed you — but then promise to do better next time, all while costing so much less, at least in theory, than a regular, stinky human being.
It’ll create a PRD of a theoretical software project with the confident and vigor that you need to take it immediately to a software engineer and say “build this immediately,” and when the software engineer tells you a bunch of bullshit about it not being possible, it’ll spit out several convincing-sounding responses. Fuck, why even bother talking to that engineer at all? Claude Code can mock up a prototype that you can then shove in their fucking face before you fire them for not using AI to do it themselves.
Any executive-level fuckwit you’ve met in your life now has a seemingly-powerful tool that can burp up mimicry of open source software and, if you constantly prompt it, eventually get something half-functional onto some sort of web server. When you face bugs, it’ll try and fix them, sometimes also “fixing” (adding or deleting code) from elsewhere to be helpful, like when Cursor using Anthropic’s Claude Opus 4.6 model deleted an entire production database and all its backups. It will never, ever say no, even if it’s incapable, even if it has no thoughts, even if what you are asking is equal parts impossible and unreasonable in both its timescale and scope.
A Business Idiot, given his druthers, can sit there and fuck around and make an LLM spit out something that makes him feel like he’s coding, which in turn makes him feel that you, a lazy and stupid engineer, could do even more with the power of AI. It doesn’t matter that it costs an absolute shit-ton of money, or that there’s no way to measure its efficacy. The Lion does not concern himself with things like “efficacy” or “productivity,” and the Lion is increasingly tired of your whining! The Lion doesn’t even understand what it is you do every day other than not doing what The Lion is asking for!
You laugh, but this is genuinely how the majority of managers and executives think and act, and now they have a special chatbot that can fart out functional-enough prototypes to convince a Business Idiot they can do anything, because executives and managers do not regularly do much work and thus have no idea what it looks like other than when they look over your shoulder, which is why they wanted you back in the office!
Organizations aren’t burning millions or hundreds of millions of dollars a year on AI because it’s good, they’re doing it because they are run by people who do not know what the fuck they’re doing.
In a sane world, randomly adding a massive, ever-expanding operating expense to your business with the express intent of — to quote IT firm Workato’s CIO, “eating the costs while employees experiment” — would have the board blow up your house. In our world, one dominated by disconnected, self-involved and massively-overpaid dullards, many businesses pushing their workers to use AI are doing so because the other guy is doing it, with about as much strategy and forethought as one would expect from somebody who spends 90% of their life reading emails, going to meetings, or going to lunch.
The majority of those I see trumpeting the so-called benefits of AI do not appear to do anything of note. I have yet to see one so-called multi-agent orchestrator engineer psychopath ship something remarkable or impressive or even functional. I have yet to see any AI-obsessed boss write or create or author or do anything I can remember. I don’t see any of these fuckwits running a company on their own outside of those who have learned to sell stuff to other AI psychosis victims or executive midwits of varying size.
And why oh why is it always the language of inevitability and possessiveness? Nobody who’s this insistent, aggressive and violative with their language of “it’s here and if you don’t adopt it you’re stupid and dead” has ever been right about anything. Nobody this desperate, insistent and forceful has ever had good intentions, good vibes or brought good omens — they are always bearers of some kind of con.
Most technology is sold on elevating and ascending human beings. AI cheapens every interaction by creating a work-shaped product from a person that doesn’t respect you enough to give you work that’s barely fit for a human because it wasn’t made for one.
You must accept becoming a dogshit dealer that loves accepting and receiving low quality goods. You must celebrate intentionless and decaying slop, and defend it and the machine that made it with your entire being. You must sully yourself — treat its unexceptional, sloppy and unreliable outputs as signs of sentience, or at least the proof that digital sentience is possible. You must defend horrible, abrasive, ugly, loud monoliths of steel full of $50,000 graphics cards. You must say they are necessary, and you must aggressively antagonize those who do not.
Every time you defend generative AI you defend a machine of capital that has burned $1 trillion and created one of the most-wasteful products in history. If people disagree with you, you must attempt to harm them somehow — ostracize them, mock them, attack them, denigrate them. You will justify this as moral, because you have been manipulated by a technology built and sold by two of the greatest grifters of all time — Dario Amodei and Sam Altman.
Anything less is opposition to an industry with all the trappings of authoritarianism down to the media toadies, the propaganda and the seizure of land in the name of a nebulous “greater good.”
But man, these men got people good.
Sam Altman helped propagate a technology perfect for conning people with potential, a larger extrapolation of Altman’s own life of taking dogshit — Loopt, for example! — and parlaying it into larger opportunities. It can make a really half-hearted demo of a lot of things, and that’s good enough to sell to Business Idiot.
Dario Amodei took this grift and perfected it. Anthropic is a company purpose-built to con people into giving it by money by making people feel smart. LLMs can do work-shaped stuff, sometimes, as long as you debase yourself to accept mediocre and often-broken stuff that you have to keep a vigilant eye on, and either use a subsided product that loses Anthropic money or pay a shit ton of money as an enterprise to Anthropic and they still lose money.
These companies were only capable of growing in an economy dominated by the gullible and work-shy. Only a capitalist culture dominated by people who don’t actually do or know stuff have let this get so far. Nobody wants this, nobody wanted it since the beginning, it was forced upon everyone, and to pretend otherwise is laughable and offensive. The amount of people who use this shit a bit and become convinced that we’re mere years from it costing over a trillion dollars to somehow making trillions of dollars and being an entirely different and good product should be aware that they are being manipulated. The more you feel compelled to defend AI the more scrutiny you must show it.
I am not your enemy! If you think that I am, you are on the side of a corporation or a product. You can try it, like it, and I don’t really care, but the second I see you trying to be condescending or judgmental or aggressive toward another person for not agreeing with your product choices I immediately feel suspicious. Can’t you see how these people act? Can’t you see how strange it is to defend a thing you pay money for that has terrible economics? If it wasn’t the “in” thing, being an AI person would be considered really weird. I look forward to the day it is. I hope you guys like having the stuff you said since 2022 repeated back to you! I’ve been saving it all. Time is running out for a graceful bow, and you better act quick!
If you feel self conscious while other people dunk on AI, that’s weird! I see people say they don’t like Macs all the time. Who gives a fuck! I’m not going to go to the mat for Tim Cook. People can make their own decisions.
Those comparing AI to AOL mailing CDs to people should feel ashamed of themselves. This is like if every single time you opened a magazine an AOL CD flew at your head, your boss told you he would replace you with a modem if you didn’t go online, and the news constantly ran segments called “I didn’t receive an email: father forgets son forever because he wasn’t online” or panels with “Internet experts” who said “I am on the Internet superhighway right now, and I’m certain that within 10 years AOL Time Warner will be able to email myself to my dad.”
Imagine if Shingy was a billionaire and went on TV every day in 1999 and told you “the world must get ready, because you’re about to get a ICQ message from The Lord.”
Generative AI was purpose-built to grift an economy run by executives and managers who don’t actually do any work. Its success has been driven by a remarkable, society-wide ignorance in the management sect, and its continued proliferation is only possible through the media’s continued trust and faith in the idea that CEOs are busy because they’re actually doing work.
Yet even a Business Idiot eventually realizes that too much money is being spent, and the first one of these dimwits to cut their token budget will send the rest of them running for the doors.
We should lock them. We should make everybody who obsessed over theoretical ideas about what AI can or will do ashamed for their intellectual deceit or constant ignorance.
At the end of the AI era, the only thing that will change the rot at the heart of our economy is the acceptance that the majority of companies are run by lazy, self-involved and ignorant fuckwits, and accountability for those who refused to scrutinize them.
2026-05-16 00:44:27
Every day I read some sort of wrongheaded extrapolation about the future of AI — that today’s models are somehow indicative of AGI creating a “permanent underclass” of people that stops people from building software companies, or really doing any kind of job on the computer:
Hyperbolic? Perhaps. But even those who view the idea of a permanent underclass as overblown tell me that the meme contains a kernel of truth. Yash Kadadi, a 23-year-old start-up founder and Stanford dropout, summarized the sentiment of his peers: “There’s only a matter of time before GPT-7 comes out and eats all software and you can no longer build a software company. Or the best version of Tesla Optimus comes out,” and can perform all physical labor as well. In that world, this year is a human’s “last chance to be a part of the innovation.”
Yash, your peers are fucking idiots. You may as well be talking about breeding Grinches or Ninja Turtles, or kvetching about the upcoming threat from Godzilla. “The best version of Tesla’s Optimus [robot]” suggests that Tesla has released an Optimus robot, or that any prototypes are capable of anything approaching useful work, something that Tesla itself has said isn’t the case.
Every discussion of AI has become a discussion of anywhere between one and a million different theoreticals.
The Information’s headline that OpenAI will “save $97 billion through 2030 in latest Microsoft deal” — one that capped its revenue share (as in the actual money it sends to Microsoft) at $38 billion — hinges on the idea that OpenAI would somehow make $190 billion in revenue, because that’s what it would take to actually max out its revenue share.
The majority of articles about METR’s “time horizon” study of how long models take to complete tasks gush with mindless praise, but regularly leave out two valuable details: that these comparisons are made based on estimates of both human task times, and that the most-commonly shared task is based on how likely it is to complete a task 50% of the time:
The task-completion time horizon is the task duration (measured by human expert completion time) at which an AI agent is predicted to succeed with a given level of reliability. For example, the 50%-time horizon is the duration at which an agent is predicted to succeed half the time.
It’s the Sex Panther joke from Anchorman, except it’s a chart that gets written up in major newspapers and bandied about as proof of models becoming conscious.
Nevertheless, everybody appears to be having a lot of fun making stuff up or making ridiculous assertions based on OpenAI or Anthropic’s predictions. Likely gas leak victim Joseph Jacks posted last week that at its current rate of growth, Anthropic would pass Google’s revenue by 2028. Multiple different people I’d rather not link to are posting benchmarks of Anthropic’s still-to-be-released Mythos model as proof that we’re in the early-to-middle stages of the entirely-fictional AI 2027 “simulation,” despite the entirety of this ridiculous, oafish extrapolation relying on the idea that at some point LLMs become conscious and start doing their own research.
None of these people seem to want to engage with reality, even in their extrapolations.
Whether or not you believe the bubble will burst, it’s hard to argue (not that anybody nobody bothers to try) with my recent reporting about the lack of data centers coming online or the fact that the majority of AI revenue comes from two companies that are, in the end, hyperscalers feeding themselves money. Nobody has presented any real argument as to how Oracle completes its data centers or avoids running out of money given the fact that it needs OpenAI to be able to pay it $70 billion or more a year in the next four years to survive. The lack of any real, thoughtful response to my assertions outside of ultra-centrists and people that can’t count is a sign that I’m onto something, and I take it as a badge of pride.
But what I haven’t done recently — not since AI Bubble 2027, at least — is try my own hand at extrapolating the future based on the things I have read, seen and reported on.
Today, I’m taking a different approach, inspired by one of my favourite comic series. In Marvel’s “What If…?” writers asked questions that would entirely change the course of the Marvel Universe, such as What If The Fantastic Four Didn’t Get Their Powers, or Loki Was Worthy of Mjolnir.
I’ll be honest that there are a lot of unanswered questions I have about the AI bubble that make precise, time-based predictions almost impossible. We’re in the midst of one of the most insane market rallies in history driven around the exploding valuation of NVIDIA and data center related stocks despite there being a great deal of compelling evidence that millions of Blackwell GPUs are sitting in warehouses, meaning that the market is rallying around the idea of data centers getting built without ever confirming whether that’s actually true.
In the past, I’ve approached things from an investigative perspective, proving what I believe to be one of the greatest misallocations of capital in history. Today, I’m going to have a little more fun, exploring both the worrying signs I see and their potential consequences in the form of questions, mixing my own reporting with a little bit of fiction.
My reasoning is simple: I think people are very good at ingesting and remembering specific facts and events, but much worse at understanding their consequences. For example, Dave Lee of Bloomberg — who I adore and admire! — said that An OpenAI Bubble Is Not An AI Bubble and makes numerous correct assertions about OpenAI, but fails to consider that OpenAI accounts for $718 billion of Oracle, Microsoft, and Amazon’s backlogs, meaning that OpenAI’s collapse would leave Oracle destitute, Microsoft and Amazon short-changed, Cerebras without 80%+ of its revenue, and CoreWeave without a major client and in breach of loan covenants guaranteed by OpenAI’s revenue.
Even if Anthropic were able to mop up some of that fallow capacity, it too relies on endless venture capital and hyperscaler welfare to pay, well, increasingly-large shares of hyperscaler revenue.
I feel as if many people are willing to ask if we’re in an AI bubble, but few seem to want to talk about what might happen. It’s really easy to say “stocks are overvalued” or “OpenAI is deeply unprofitable,” but thinking much harder than that starts to make you feel a little crazy. Data center construction now makes up a larger chunk of all construction spending than commercial real estate. OpenAI has made promises that total over a trillion dollars, and Anthropic $330 billion. NVIDIA represents 8% of the value of the S&P 500, and that valuation is based on the idea that it will never, ever stop growing, which is only possible if data center construction never stops. CoreWeave, IREN, Nebius, and Nscale all rely on hyperscaler contracts that are related to OpenAI, and if those contracts go away because OpenAI does, they’re screwed.
Most people can say that these things are true, but very few of them are willing to think about their consequences, because when you do so, things begin feeling completely and utterly fucking insane.
Put another way, for me to be wrong, all of these data centers will have to get built, OpenAI will have to make and raise $852 billion in the next four years, the underlying economics of generative AI will have to improve in a dramatic and unfathomable way, and do so in such a way that it creates hundreds of AI startups that can substantiate $400 billion of annual compute revenue. For NVIDIA to continue growing its revenues at an historic rate, it will also have to, by 2028, be selling over $1 trillion in GPUs, which will require there to be funding to buy these GPUs, at a time when hyperscaler cashflows are dwindling and banks are worried they’re “choking” on AI data center debt.
The AI bubble is supported almost entirely by magical thinking and people ignoring obvious warning signs again and again and again in the hopes that at some point something changes. You can quote whatever story you like about Anthropic’s skyrocketing revenues (which are absolutely inflated) — there’s no getting away from the fact that it loses billions of dollars year, and if your answer is that it will turn profitable in 2028, please tell me how because there is no proof that it’s possible.
I also kind of get why nobody wants to think about this stuff. Even though it’s become blatantly obvious that the economics don’t make sense, the stock market continues to rip based on equities connected to the AI bubble in a way that defies logic but rewards positive speculation. Major media outlets continue publishing positive stories about the power of AI that seem entirely-disconnected from what AI can do, and millions of dollars are being spent by companies based on a theoretical return on investment.
No, really, per The Information’s Laura Bratton quoting PagerDuty CIO Eric Johnson:
“I am preparing myself to be surprised” by the bills, he said. “We believe that there’s a lot of value here. Unfortunately, it’s fairly new technology, so there’s some open questions that we’re gonna be working through” around its costs and getting a return on the investment.
We are fucking years into this man, how is the question of return on investment still an open question?
Okay, we know the answer: we’re in a bubble. Everybody is pressuring everyone else to “integrate AI,” to “get every engineer AI,” to “become more efficient using AI,” with token spend becoming some sort of vulgar status symbol despite the whole point of the AI push being that workers can be replaced, or enhanced, or, I dunno, something measurable. In the end, all that’s being measured is how many tokens employees are burning, leading to Amazon staff deliberately setting up “agents” to burn more tokens to seem more “engaged with AI” than they really are, all because dimwit managers and executives don’t understand what people do at their jobs and can only comprehend Number Go Up.
As a result, it’s far easier to fall in with the groupthink, even if it’s hysterical, nonsensical and based on flimsy ideas like “it’s just like Uber” (it isn’t) or “Amazon Web Services burned a lot of money” (it burned less than half of OpenAI’s $122 billion funding round on capex for the entirety of Amazon in the space of 15 years, adjusted for inflation), because thinking that everybody’s wrong requires you to disagree with the markets, most of social media, your boss, and your most annoying coworkers.
People also don’t really like thinking about bad things happening. They’re happy to make vague leaps in a direction that makes them feel prepared for the worst (such as the specious statements about all of these data centers being for the military or a theoretical bailout), especially if it makes them feel smart, but in doing so they get to avoid the actual bad stuff — the economic ramifications for ordinary people, the years of depression ahead for the tech industry, and the calamitous results for the market.
So, today, I’m going to have a little fun thinking about the actual consequences of everything I’ve been writing. I’m going to thread in both my own and others’ reporting, and take these ideas to their logical endpoints as far as I can.
This is going to be the first of a two-part exploration of what the actual consequences of the AI bubble bursting might be.
I’ll also caveat this by saying that these are, ultimately, explorations of potential future events rather than cast-iron guarantees. People seem to be resistant to being told the truth, so perhaps it’s time to explore these ideas as theoretical — fictional, even — so that people are more willing to take them in.
This series is all about simple scenarios, and one very simple question.
Time. Space. Reality.
It's more than a linear path — it’s a prism of endless possibility. I am the Watcher, and I am well aware of how AI generated that sentence sounds.
I am your guide through these vast new realities.
Follow me and dare to face the unknown.
And ponder the question…
What if…We’re In An AI Bubble?
