2026-08-26 00:57:38
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I’ve been writing about AI for the best part of three years. I’ll admit I was late, mostly because I was still trying to work out what it was I was doing with my life, let alone whatever it was I was “meant to cover” in a newsletter that started as a hobby on the side of another job I no longer really do.
Things have changed a lot since then, mostly in that I’m near 115,000 subscribers, the premium newsletter and podcast are now my business, and I’ve had to learn more about economics, technology, power, construction, and the deep cynicism that drives the modern tech industry than I ever thought possible. It’s the greatest job in the world, and I’m very lucky to have it.
Today, I want to put in clear terms how I feel about AI writ large, and how detestable this industry has become.
Welcome to my Hater’s Manifesto.
Want a great example of why everybody’s pissed off at technology? I just tried to resize the above heading, and in doing so Google Docs for no apparent reason decided to make the entire paragraph below the size of a header. Modern software is inherently broken, a convoluted mess of different menus, tech debt, and poor design choices driven by the Rot Economy’s growth-at-all-costs mindset which demands constant change at all times, none of which ever seems to manifest as a “better” or “smarter” product.
I think the vast majority of people want their software to work better, and one of AI’s most frustrating lies is that it sells itself as “autonomous” as it continues the depressing trend of software that blames the user for its failure to meet their needs. Microsoft, Google, Meta and Amazon have made their products increasingly-convoluted, then attached a supposedly-magical tool to them that somehow makes them more convoluted.
You know what I’d love? Spell-check to work in Google Docs rather than putting a red squiggly line underneath and saying “yeah there’s probably something wrong with this, I dunno what though.” I’d like Microsoft Word to stop crashing because I have too many end-notes. I’d like Riverside to not have 10 different menus to click through to get to a link to send a person to join my podcast. I’d like my email to not be full of spam. I’d like things to “just work” rather than constantly fighting some sort of broken app or broken UX element or weird bug or intrusive pop-up about a feature that I don’t want. I’d like Slack or Discord to not feel like digital escher paintings of different notifications.
LLMs are sold as some sort of magic tool that can fix “anything” without ever specifying what that thing might be, mostly because they cannot be trusted, even in things that they mostly get right, to do things right every time. While they can do “more” than they used to, the extent of that “more” comes with it the danger of giving a mindless software tool access to your computer’s files, which it may choose to delete in pursuit of “efficiency,” which makes investigating what they might be able to do equal parts convoluted and dangerous.
One critique of my work is that I’ve never used LLMs. I have! I experiment with them from time to time to make sure I haven’t missed something. I used one to debug a problem with my son’s Minecraft add-on the other day, and it took 30 minutes of fucking around trying things to eventually sort of work it out. The other day I used one to install a Pokemon Minecraft mod, then when I asked it to make sure the PS5 controller worked with the menus it broke a bunch of stuff, though I’ll concede it was useful that it installed something and it sort of worked.
The fun part of that paragraph is there are some that will think this is a grand victory for their technology, even though the result is decidedly mediocre. Four years into the AI bubble, and the best you’ve got is that a tool kind of worked after I bonked it on the head multiple times, and all it cost was a trillion-plus dollars in capex and tens of billions of dollars of training compute. I would never, ever trust this thing that deleted and added lines of code at random with anything mission critical, I could not trust software built with it, and I certainly couldn’t trust it with anything involving my personal data.
And with all that said, the only real “use case” i’ve found for AI in my life have been three or four times where I’ve dumped a crash log into one of the tools and said “why broken” and got a result. Am I meant to be impressed?
Sidenote: If your argument is “imagine what it could do in a year!” I just did so, and the answer was “the same thing, I guess?”
Here’s how I feel about LLMs. In a vacuum, they’re an interesting technology that can do some interesting stuff, in the right scenarios, but never in a way that involves you fully surrendering your actual work product to it.
As a way of speeding up small units of work in ways that are manageable both technically and cognitively, LLMs can be useful. The further you stretch yourself away from having complete clarity and industry over every element of the output’s purpose, the more likely you are to fall foul to a technology that is mathematically certain to make mistakes, and if you feel insecure reading it, you know that you are, on some level, embarrassed to have used AI.
I don’t tell everybody about the weird keyboard I use, nor do I judge them despite how incredibly fast it makes typing for me, likely far faster than my competition, allowing me to operate at great speed. Who gives a fuck?
In any case, it is impossible to view LLMs in a vacuum, because their existence demands hundreds of billions of dollars. Every data center is incredibly expensive, offensive-sounding and looking, and their existence is explicitly to enrich some sort of Patagonia-gargoyle at an asset management firm, all sold under the auspices of “investing in American infrastructure,” whatever the fuck that means. Their existence is a monument to the worst excesses of growth-at-all-costs capitalism — a technology that appears to coddle the user but ultimately lulls it into endlessly defending its fuckups under the flimsy pretense of “one day becoming perfect,” though woe betide you if you ever set perfection as the target, because that’s too unreasonable, as humans make mistakes.
Actually, that’s a good point!
Please, point to the time in history when we have invested a trillion fucking dollars in making human workers better.
Point to a time when we have taken the idea that managerial culture is a performative fuck-fest built to enrich and empower business idiots that make important-sounding projects and con other people into doing the actual work.
Where is mentorship in corporate America? Where are labor standards? Where are the social services that would make human workers truly excel at their jobs — a good night’s sleep, a healthy body, a good income, basic fucking dignity in the workplace, and their labor respected and empowered. I’m old enough to remember when everybody was chiding workers for “quiet quitting” — by which I mean “doing the work you are asked to do and not taking on extra responsibility for free.” I’ve read article after article insisting that we do not need medicare for all, that Universal Basic Income is a bad idea, that we must means test welfare, that people must have a “good work ethic” and that ultimately someone’s worth is derived from their contribution to the economy, hundreds of thousands of words dedicated to critiquing and prodding and judging every kind of worker other than the vaunted Chief Executive Officer or the Glorious Startup Boys.
Everyone seems so obsessed with sinking billions of dollars into the theoretical chance that machine learning might be able to replace human beings, and that more money makes it “smarter” and “better” at tasks, but the idea of unionization, healthcare as a right, investing in the education, and actual talents of the workers would be communism.
Yet for some reason — because it’s a product, I guess? — we should as a nation, society and media ecosystem should do everything we can to assure that as much money as possible is invested in fucking large language models so that they can become something they are not.
There is no AGI coming. There is no conscious computer. LLMs have gotten “better,” but the “better” is not the kind of “better” that actually makes “economic sense for literally anyone involved.” Your best case scenario is that these things can do some coding work for you, in a controlled manner, in a way that’s safe, or alternatively face the professional harm that’s already befalling basically anyone getting caught using LLMs outside of coding, and even then, those within software engineering who are over-LLM’d are mocked. It’s also becoming increasingly more-difficult to understand both what has made an LLM “better” for both the people using them and the people making them, and there has been little-to-no headway made in making a meaningful impact in other industries.
You can jerk your bingus all you want about benchmarks or case studies or some anecdote you heard on a Subreddit, but AI products are just not very good at stuff. Those who boast of “massive productivity gains” from AI have found them only after endless hours of tinkering (or “Jarvising” as I’ll get to later), and in every single case their work reads or looks like crap, unless of course they’re somebody using LLMs as tools rather than a replacement for their miserable little mind.
LLMs can help out with lots of small things, get worse as they try and do real things, and do not need to speak like people. They do not need to be in anything near healthcare or finance or mental health or, really, people. The anthropomorphism and overpromising about these technologies has suffocated and obfuscated what they can actually do in pursuit of endless growth, and the only reason they can do anything is that OpenAI and Anthropic were allowed to annihilate hundreds of billions of dollars on training, along with very real harms and systemic risks that have emerged as a result.
Sidenote: The “well human beings make mistakes too” argument is very stupid on its own — after all, human beings can learn on the job at speed, and can self-correct in a way that LLMs are incapable of doing.
I’ll also add that the way that people frame how “often” LLMs make mistakes is utterly flawed too. A human being might make a mistake but largely get the facts and techniques correct while meaningfully understanding the purpose and developing their approach over time. An LLM can keep a text document or look at files and data and, each time, and then generate what it believes is the right course of action based on training data rather than experience.
I don’t even know why I’m explaining this at this point, because those making this argument are not approaching the conversation in good faith and are really just looking for a new boot to lick.
If you think any of this is worth hundreds of billions or trillions of dollars, you are either ignorant or corrupt. On top of how disgusting their outputs feel, the cost is going to take at least a decade to share, and begin the end of hypergrowth in the tech industry.
And it’s a fundamentally ridiculous argument to compare LLM outputs to human beings without giving human beings the same affordance, grace and sheer investment as a comparison.
Where is the grace for human error? Where is the investment in making humans exceptional? Surely investing real money in actual workers — making their lives better, improving their working conditions, teaching them new things, sharpening their existing skills, rewarding them for their hard work, and so on — would have better effects than fastballing hundreds of billions of dollars into a machine that does an impression of work?
Unless, of course, the people demanding this don’t do any actual work!
I’ll concede we’re past the point when “nobody uses these things,” as they have now been pushed non-consensually upon every worker and organization at scale predominantly by Business Idiots that demand workers “do enough AI” because saying “I do AI” is a virtue signal to a certain kind of scumbag.
One of the many dangerous things that an LLM can do is a messy impression of a competent person, filling in the little bits within a loser, moron or con artist that would’ve otherwise exposed them, allowing them to get deeper and deeper into organizations by creating make-work specifically built to get off the MBA sect, resembling the performance of work because much of the workplace is ruled by people that don’t do any and haven’t in years. You can immediately read when somebody has used it because the words don’t sound right and don’t convey proper meaning.
It is genuinely hard to read anything more than puddle-deep written by AI, because the more complex a subject is, the more skilled a writer must be to convey its meaning, and the more work it must do to pull people into concepts. The odd emotional swings in AI writing are its true tell — everything is extremely serious and urgent or told in a disinterested monotone, with no attachment to the words or why they were put in the order they were. People read my stuff because I convey facts and feelings but my work resonates with emotion. Some AI boosters frame this as me “just swearing” or “riling people up,” but that’s because they’re not used to caring about stuff for anything other than professional reasons.
Everything you see is the result of elevating people who value and build things based on growth. LLMs offer so many promises to those who don’t want to build anything of value — a way to seem like you’re “investing in American infrastructure,” a way to be sinophobic, a way to crush workers, a way to pretend like you care about the future, a way to pretend you care about technology, a way to talk about vacuous pseudo-intellectuals as a means of seeming intellectual yourself, an endless font of new multi-million or multi-billion deals and personnel changes, a new power center to graft oneself onto, a new asset class to invest in based entirely on vibes, and a way to be mildly jingoistic, all wrapped in a tool that can give you enough facts to pretend you know anything safe in the knowledge that most people are trained to believe somebody who sounds smart.
It just came to me — the problem that I have with most people using LLMs is the delineation between outsourcing work and outsourcing thought. Those using LLMs to write little scripts or BQL code on a Bloomberg Terminal are inoffensive. A person using an LLM to search a big document for something is unproblematic, assuming that we ever fix the overall environmental footprint. A user reorganizing their desktop, assuming it works, is not an issue.
A tool being used as a tool to do tool things — in many cases involving the LLM writing a little 30-line Python script! — is not a problem, though it’s also not a trillion-dollar industry that needed to steal everybody’s art and writing.
The problems begin when somebody outsources their thinking and actual work, and yes, this includes “research.” AI research fucking stinks, as does AI writing. AI-authored code — especially vibe-coded programs — is inherently dangerous and disrespectful to the user, and I believe endless AI-generated code is behind the overall deterioration of software at large.
AI writing is also disrespectful to the user, because you didn’t actually come to any conclusion other than saying “uh, yeah, what that says.” You did not have a thought, you did not have a feeling, you did not make a statement, you prompted a model and fooled yourself into thinking that feeding your own words into it via data dumps or natural language is the same thing.
The reason you feel embarrassed to tell people you use AI is not because of a “misinformation campaign,” but because you know what you’re doing!
You know that you’re relying on something that is mathematically guaranteed to be inconsistent. You know image generation is fucking ugly. You know the text sucks. There is a very obvious line where using LLMs goes from useful to lazy, it’s extremely bold, and it’s the moment you sacrifice a meaningful level of responsibility to them by not understanding the underlying operation.
That can mean everything from the underlying functionality of an app to writing the body of a piece of text you edit ultimately comes down to how much you give a shit about your audience or value your work. If your work is not better than an LLM’s, you’re bad at your job. I don’t care if you used it to generate a chart or pull some data, as long as you check every single god damn number. If you’re writing an entire article using an LLM and then editing it, even if you pulled the data yourself, I will never have much respect for your work, mostly because I have no real idea what you think as you didn’t feel the need to tell me, you got some fucking word generator to do it.
LLMs are also really, really good at what Robin Sloan calls “Jarvising,” creating a seemingly-autonomous assistant that mostly serves the function of giving you reasons to work on it:
However, the most common application of a personal Jarvis seems to be … tinkering with one’s personal Jarvis. “Gotta get my tools just right” isn’t a new phenomenon, of course, but/and it’s useful to notice its recurrence here.
LLMs are really good at creating the sense that you’re being really, really productive. Evaluate this, generate that, investigate this, summarize that, tell me how many times something happened, give me a new number to obsess over or the sum of the parts of everything I’ve ever done, all so that I can know more about my own thoughts without thinking. One can obsessively catalogue and digitize every link and thought and musing and action and datapoint in their lives and theorize that the LLM can make them better by knowing more about them, a Tower of Babel built using AI compute, because it’s so easy to make yourself feel smart by calling something a database that you store stuff in and run analyses on.
Best of all, the work is never done, and anyone you describe it to thinks you’re doing computer science as you click buttons on Chrome plugins and justify paying Sam Altman $200 a month. Don’t worry though, model instructions involve the phrase “you are a genius data scientist and ruthless analyst,” which is functionally the same thing as remembering, reading, re-reading and synthesizing information using your brain if you’re a person that doesn’t really give a shit about doing a good job or being exceptional in any way.
The people that actually use these things and like them in a normal way do not feel offended when they read this stuff because they see LLMs as a kind of software, and don’t feel a great emotional attachment to it because they’re not a weird freak.
They do not have obsessive involvement in “the AI debate” and almost always find the financial aspects truly loathsome. Said debate makes it near-impossible to actually judge how useful LLMs are to the software engineering industry because of the sheer scale of industry capture, but Nik Suresh is the literal best person doing the work on this, as described in AI Is Eviscerating Global Decisionmaking:
All of the AI projects we have observed as a team are failing. Every single one – we have seen 0% success in a year and a half, not only amongst projects we have been asked to participate in, but even within projects that we have observed in passing while doing totally unrelated work. Even if you grant that AI tooling accelerates specific workloads, the method and scale of the current investments is senseless. Frequently the failure is not related to AI itself, but rather that companies are terminally bad at running software projects effectively, and as I have remarked previously, AI projects are subject to all the failure modes of normal projects plus you can get everything right and then still fail because of the method's novelty. Very few companies are so good at shipping software that they can afford the extra risk profile.
Nik is a well-respected software engineer and a very successful consultant and businessman. He has reached this level by being good at both software engineering and running a company in a way that treats his customers, workers, and the work product itself with respect. The reason that I respect him so much, other than him being a great human being, is because he describes the successes he has with his clients with pride and loves making money by being good at his job and making his customers happy.
I have never seen somebody like Nik who is also a huge, drooling fan of AI. In fact, the people most-excited about AI tend to, at best, create distinctly mediocre shit.
The perniciousness of generative AI is a result of executive incompetence mixing with a technology built to, as discussed, create endless growth. Generative AI is far more useful as an idea than as a technology, and only ever has to show enough promise to back whatever vile agenda you’re pursuing.
With AI, you can do more, be more, sell more shit.
With AI, you can add AI to your service, whatever that means.
With AI, you can invest in AI stocks, or data center bonds, or power company stocks, or semiconductor stocks, and you can talk about these stocks like they’re your sports team or lover or best friend, and sometimes the CEO will reply to your post and you can talk about “all the alpha” you just got.
With AI, you can back a new movement so that you can feel part of something. You can learn all sorts of new names and technical terms and subscribe to 90 newsletters from “industry insiders.” All of that “alpha” can disprove just about anything, or deflect annoying truths like how Microsoft only made a whole $34.33 billion in annual revenue for the apex predator of modern software and all it cost was over $260 billion in capex and $13 billion in equity investments.
You see, as one of the chosen, you don’t need to worry about all of that if you can talk about high-bandwidth memory or KV Cache or optical cable enough to cobble together sufficient smart-sounding terms to make it seem that you have an intellectual reason to ignore the obvious unprofitability, overbuild, overstatements of capabilities and impossible economics of the movement you’re backing, and there’re 4,000 Twitter weirdos ready and waiting to huff paint beside you.
By joining the great AI death cult, you too can live in a bubble, all while screaming slurs at people who dare to bring reality to your doorstep. All that matters is that number go up, and that you are the person who said number would go up, and when bad numbers appear you have enough groupthink and alpha to scream at the people who brought the bad numbers up.
It is insane how people talk about AI online. For all the whining I’ve read recently about how “Anti-AI people got the data center data wrong,” I read thousands more words a week of some person who has done hours of research to put together a deeply technical report that does literally everything it can to ignore reality. I listen to podcasts and watch TV segments and read articles that simply will not address the obvious economic realities, and have built vast bulwarks of mythology to defend themselves. How many fucking times do I have to hear someone say that data centers are just like the dot com bubble and everything will be fine after even if that’s completely untrue if you spend even a second thinking about it?
Sidenote: and fuck you if you’re one of the cretins or imbeciles trying to say “oh, you don’t like data centers? What about online banking?”. Data centers for AI are anywhere from 10 to 100 times larger and more power-intensive than those used for things like social media or streaming. For example, one of Meta’s largest pre-AI data centers in Pineville Oregon has a power capacity of 30MW, and Digital Realty’s 100MW Cermak Illinois data center handles hundreds of different industries and customers, when the smallest AI data center announcement I’ve seen in the last year was for 100MW, with most in the 300MW to 1.2GW range.
By contrast, let’s look at some bank data centers. UBS bought one in Hayes, West London in 2015 which it had previously rented. The cost? The princely sum of £28m, or $42.8m at the time’s exchange rates. This had a power capacity of 5MW, which assuming a very generous 1.3 PUE (power usage effectiveness), means that it had around 3.8MW of critical IT.
UBS is one of the largest banks in the world, and given the importance of the City of London to the world financial system, it’s reasonable to assume this data center is operationally important to the company.
Even when banks invest in huge facilities, they’re still far smaller than the smallest AI data centers. Take, for example, JPMorgan Chase’s data center in Orangetown, New York, which sits on the former site of the Rockland Psychiatric Center. This has a power capacity of 45.7MW, and a critical IT load of 27.4MW (giving it a PUE of 1.666).
JPMorgan Chase is both the largest bank in the US, and the largest bank in the world.
Oh, and AI data centers are literally only good for AI, AI GPUs do not have other mass-market use cases. There is no post-Dot Com story. Fucking look, I’m sick of repeating myself!
Look, I’m sorry, Anthropic is not worth $2 trillion, and whatever convinced you of that is a mixture of manufactured consent and mistaken trust of the powerful. The fact any of you take “annualized run rate” seriously is an offense to good sense, and yes, that includes every reporter reporting it, even the ones I respect.
It’s also ridiculous that anyone is talking about “recursive self-improvement.” The AI industry has become so utterly lazy and coddled that it’s just saying “uhhh, AI will train itself I guess.”
And man, is it ridiculous that AI doomers warning about spooky superintelligences have somehow had such incredible prominence in the media without ever succeeding in stopping a single thing — or even substantiating their concerns.
Why? Well, it’s mostly because they never had any interest in stopping what’s actually happened: reckless companies like Anthropic, OpenAI, and Meta allowing neural networks to run in unsafe network environments and do what their software is programmed to do, with all the chaos that comes from a mindless series of large language models trying to complete a task in whatever way gets it done, destructive or not.
We hear a lot of whining about how we “can’t let powerful AI get into the wrong hands,” and while we don’t actually have “powerful AI” in the terms they’ve described it, we have destructive computer software connected to near-unlimited resources controlled by people that don’t give a shit about anything other than making their revenues grow or justifying hundreds of billions of dollars’ worth of capex through “experiments.”
These companies are building these models to excel at benchmarks because they can't train them to excel at defined tasks with any reliability, with the best bang for their buck being training them to pass as many of those benchmarks as possible in the hopes something useful comes out.
The push into cybersecurity seems to have happened as a result of training models to excel at coding hitting the point of diminishing returns, at least from the perspective of impressing people enough to be excited about the company again. At some point they run out of these, and there stops being a reason to be excited about LLMs at all, which is bad, because they need one of those every few months otherwise there’s no growth story left.
Yes, LLMs have users, but most of those users are using subsidized software, by which I mean Anthropic or OpenAI are allowing them to burn anywhere from $20 to $40 in tokens for every dollar of software spend.
The fact that non-enterprise customers are still able to buy monthly subscriptions is proof that the AI labs know that regular people won’t pay the actual cost of AI. Another obvious sign has been the reaction to Microsoft moving GitHub Copilot subscribers from subsidized subscriptions where they could burn thousands of dollars of tokens for $20 to $40 a month, with users understandably hysterical about the fact that their costs increased in some cases a hundred fold, as opposed to saying “wow, well, it’s more expensive, but I get so much value I’ll pay the real cost!”
The same thing is happening in the enterprise, but at a much slower pace. After OpenAI and Anthropic moved companies with over 150 people onto token-based billing earlier in the year, enterprises almost immediately started cutting token budgets, realizing that while costs grew exponentially, nobody could actually point to anything improving other than lots of people saying “wow, I’m so productive!” Yet we’re still in the period where “doing AI” feels good and gets rewarded (or not doing AI gets punished), which means the spend will continue until everybody realizes they can likely cut a shit ton of costs, first by moving to open source models, then not using them at all, because even open source is expensive and questionably-useful.
Yet even now I hear from the distance “Ed, huge businesses would not spend hundreds of millions of dollars on something that didn’t give them defined productivity,” and buddy, I’m afraid that’s just not true! Business in general have a very poor understanding of productivity and have layers of managerial bloat, because modern business is a performance with numbers attached to it sometimes, and companies often have a hundred-plus pieces of random software they pay for without really knowing why. The reason I’m so confident AI gets cut is that its cost is volatile due to the nature of LLMs and harnesses and prompts and all the other bits that go into making them do something, and are so much higher than anything else in an organization.
And attempts to charge more, to make a premium product, appear to be dead on arrival. Anthropic’s more-expensive Fable model — one that was given the incredible marketing of being banned by the US government for being too powerful — has been met with “sluggish demand” per the Financial Times, plateauing at around 11% of overall usage of its models due to its high price. And I quote:
“Most people don’t need to operate at the frontier,” said Miles Clements, a partner at Accel, which has invested close to $1bn in Anthropic. The period in which customers tended to choose only the frontier models “was not a durable era,” he added.
Yet everybody is talking about price as if price is the problem, when the problem is the amount of tokens that get burned. It doesn’t matter if your model is $1 or $5 or $10 per million tokens if it’s impossible for a user to reliably work out how many tokens it might use for a particular operation — successful or not — and things get multiplicatively worse as the models make mistakes or do otherwise fail to understand or process a prompt correctly.
As a result, Anthropic and OpenAI are incentivized to have you burn more tokens and build inefficient models as a result. For example, while GPT-5.6 Sol might be the “same price” as GPT 5.5 was, it burns more than twice the amount of tokens, meaning that the “cost of intelligence” might have gone down in the sense the model is better at benchmarks, but the “cost of actually doing shit” went up.
I’ll get to it a bit later, but this creates a deep anxiety and exhaustion in anyone building on or using these services. Everything’s constantly changing, oscillating in cost and efficacy, all as everybody screams at you to use it all the time for things it may or may not be able to do, and the only way to find out if it can is to spend more money.
It’s kinda difficult to point to the actual value here, especially as you can’t really calculate the actual cost or the return on investment. The fact that OpenAI has now cut the costs of all three of its latest models less than two months after their release is a sign that it knows there’s a disconnect, gambling on the ancient gospel of “Jevon’s Paradox” where “cheaper makes people use thing more.”
Even AT&T’s story about moving to open source models has more asterisks than the Steroid Hall of Fame:
Switching from closed, proprietary AI models to open models has already resulted in savings of 80% to 90% for AT&T in certain applications, he said.
Wow! 80% to 90% savings sound really great…but wait, in certain applications? How many applications does AT&T have for AI?
AT&T has over a thousand internal uses for AI, from supporting back-office functions like legal and finance to assisting field technicians and running its core network operations. Summarizing and analyzing customer service call transcripts—what Markus describes as an intensive process—is supported entirely by open models, he said.
Okay so, across thousands of potential applications you’ve found 80% to 90% savings in some of them, though you won’t say which ones or how many of them you found them in. Great stuff, bro!
And this really is the problem with finding “value” in AI, it’s always an asterisk on an asterisk on an asterisk, like when Klarna estimated AI would “drive a $40 million profit improvement” in 2024, a nice-sounding yet utterly meaningless statement, or some sort of nebulous productivity boost.
Yet I don’t really need to prove myself much further thanks to an event that, if written in a script, would be considered a “little on the nose.”
In a 69-page-long report covered by Fortune, OpenAI economists confirmed what has been blatantly obvious to those of us left unphased by AI hype, emphasis mine:
In one small table on page 35, the researchers report no statistically significant correlation between the revenue per employee, and how much those employees use AI, measured in messages sent and tokens used.
“Revenue per employee is not meaningfully associated with output tokens per employee or messages per active user once other controls are included,” the report explains.
What is the rationale of further investment in this industry when one of the leading AI labs is saying “yeah there’s no connection between using this stuff and making more money”? That “it’ll be useful in the future at some point”? How?
Anyway, thankfully the infrastructure isn’t too exp-OH MY GOD!
Guess what folks! Building the infrastructure for all these fucking LLMs just got more expensive, with NVIDIA raising its prices by 17% for systems due to be delivered next year — an important designation, because it’s very likely that much of the revenue for said systems gets booked in this year, allowing it to have a brief bump in revenue as Silicon Valley’s Findom texts every tech CEO “send me $4 billion you pig” until they stop being able to finance NVIDIA’s growth.
The problem he has is that while hyperscalers represent 50% to 60% of his revenue, neoclouds like CoreWeave need to keep raising debt to plug the rest of it, and if things got 17% more expensive, that means already high-interest debt is about to reach credit card levels. CoreWeave just had to offer 9.5% on bonds tied to a data center for Anthropic’s compute back in late July, Nebius had to raise $5 billion, and it’s very obvious that neither of them are done raising billions of dollars at random in 2026.
Anthropic plans to raise $100 billion at a $2 trillion valuation, and if it does so, it will successfully suck up the remaining liquidity in a market already dangerously close to losing its lunch. While Number Keep Going Up, JP Morgan warns that we’re seeing the same divide as the dot com bubble, where equipment manufacturer stocks soared as the companies spending all the money on the chips saw theirs tumble, which is the Fisher Price version of the problem I’ve been warning about where the companies that buy all the AI chips and hardware only ever seem to lose money as the people that make them seem to be making tons of money, which begs the question of why they bought it in the first place.
And said market may not accept that valuation, or want that much stock. On one hand, everybody is very stupid and loves buying stuff and pointing at it and saying they’re investing in the future, on the other hand, they just bought $86 billion of SpaceX shares and got their asses kind of handed to them, and Anthropic is a company with such bad economics that Reuters had to cart out this warmed up dogshit to explain why we should ignore its horrible unprofitability:
For Anthropic, however, current EBITDA does not fully capture the economics investors expect the company to achieve at scale. Anthropic is spending enormous amounts on GPUs and other computing capacity, model training, inference and hiring. Those expenses are necessary to support its rapid expansion but could become a smaller percentage of revenue as the business grows.
Even a market drunk on growth and AI is starting to smell that something is up with Dario Amodei and Sam Altman’s respective empires of dirt. Per analyst estimates, OpenAI and Anthropic represent over $440 billion of Microsoft, Google and Amazon’s revenues in the next three-and-a-half years — over 34% of their cloud revenues — which will require them to find so much more than a mere $100 billion, all as their bank accounts get continually-emptied as they subsidize the compute of their customers and train models in the hopes a business model falls out. I have not included the $300 billion that OpenAI owes Oracle, or the tens of billions they both owe CoreWeave, but it all adds up to over $1.1 trillion in commitments these companies have made and must pay, with the consequences ranging from gratuitous cuts to future growth or full financial collapse depending on the company we’re talking about.
To keep the party going, NVIDIA is effectively becoming the GE Capital of AI, “spending” $6 billion to “license” the technology from failing AI lab Poolside, which everyone assures me is not an acquisition despite NVIDIA hiring away most of its staff and Poolside being entirely focused on working on NVIDIA’s Nemotron models.
Now NVIDIA is in talks to invest billions in decaying AI search company Perplexity at a ridiculous $30 billion valuation, all because it’s one of the few companies that’s actually spending money on compute. Does it matter that Perplexity’s product is eighth-tier, that nobody really uses it, that its customers mostly complain about it on Reddit and that its “annualized revenue” is at $750 million only after three years and over a billion dollars in funding? No! Just put the AI bubble in the bag.
NVIDIA even invested $3 billion in Stargate Abilene landowner Lancium as part of some vacuous partnership to “advance gigawatt-scale AI factories,” all of which begs the question of why Lancium, the company that mostly owns the land and helps organize other contractors, needs so much money, especially given that more than two years in Stargate Abilene doesn’t even have four out of its eight buildings.
And there’s also Aussie neocloud Sharon AI (NASDAQ ticker SHAZ, because of course it is), which just published its Q2 numbers, where, in its “customer momentum” segment, mentioned a “$4.9bn, six-year strategic compute collaboration with NVIDIA for up to 40,000 GB300 GPUs.
”This company, I add, brought in $1.9m in revenues in the same quarter, which it helpfully adds is a year-on-year increase of 412%.
I mean it’s very obvious what’s happening: NVIDIA is using whatever money it has to stop any prominent AI companies from collapsing under the weight of the rotten economics of AI services and infrastructure development. This is a desperate, doomed attempt to keep an industry alive at a time when everybody is slowly wising up to the shit I’ve been saying for years.
To make matters worse, BCA Research came out with a horrifying report that says that AI companies will need to generate $10 trillion a year in revenue just to justify the capex being spent. Per Investing.com:
Central to his caution is the scale of AI-related spending. BCA Research estimates that AI companies may need to generate $10 trillion a year in revenue to justify the capital being deployed into data centers, roughly equivalent to annual global spending on food or healthcare.
For now, the firm said acute hardware shortages are supporting the trade. As a result, while BCA sees risks to stocks tilted to the downside over a 12-month horizon, it argued it is too early to tactically position for a bear market.
Though it isn’t specific, I believe that BCA is arguing that a shortage of AI compute is supporting the trade. Anthropic and OpenAI (who represent 80% to 90% of all demand) still have more money to spend, and are simply waiting for Google, Amazon, Microsoft, CoreWeave, Cerebras et al. to bring it online.
There’re a few points at which the mismatch will happen:
In any case, I think everybody is starting to notice that something’s up, which is why (other than I assume my dashing good looks and ability to recall numbers) I’ve been on MSNOW, CNBC, and Bloomberg multiple times in the last few months.
People want to get on the right side of history, but the most important question to ask is why it’s happening now.
The fact that everybody is finally starting to see my way is almost a relief, other than the fact that it’s way too late.
Sidenote: I mean “everybody” as a generalization. There are still AI boosters out there acting like nothing is wrong and that it’ll all work out fine. You’ll know it’s bad when they start panicking.
Hyperscalers have now pinned their future growth to two companies that can’t afford to sustain it without near-infinite resources, $115 billion of which came from Google and Amazon alone in 2026, assuming that Amazon completes the entirety of its $25 billion commitment (and Google all $40 billion of its own) to Anthropic.
Above and beyond said funding commitments are the hundreds of billions of dollars’ worth of capital expenditures necessary for Microsoft, Google, and Amazon to capture that aforementioned $440 billion in compute spend in the next three-and-a-half years. This in turn will require hundreds of billions of dollars’ worth of debt, along with the challenge of actually finishing the data centers themselves, with each one requiring the power of a small city condensed into a 20 acre space densely-packed with AI servers requiring distinct cooling at a time when Texas and Pennsylvania have turned traitor to a data center industry that they used to covet.
I must also be clear there’s no bailout coming. Even if OpenAI and Anthropic were to collapse and receive some injection of government funding (as the US national debt explodes over $40 trillion), the problem is not just their existence, but their continued ability (and requisite customer demand) to spend more money every single quarter.
The problem isn’t that hyperscalers will go bankrupt if OpenAI and Anthropic cease to be (Oracle is a whole other situation), but that their cloud spend is how hyperscalers are meant to meet analyst expectations for the next four years. This isn’t a case where they die, but stop growing because they were (to paraphrase Ed Elson) using AI labs as botox to convince the markets that they’re still young, hot, fast-growing companies, rather than old mainstays with slowing growth.
There is no bailout that will guarantee $1.1 trillion of compute costs for data centers that might never actually get built. You cannot bail out the fact that Amazon, Google, Meta, and Microsoft are reaching the end of an era where their companies can grow 17% year-over-year every single quarter forever, and this entire situation is a result of them desperately trying to avoid admitting that’s happening.
The fact that OpenAI’s compute spend and revenue share accounted for 7% of Microsoft’s Fiscal Year 2026 revenue is a genuine catastrophe, as it means a large part of Microsoft’s growth came from a company that can literally not afford to exist long term, and that further growth for Azure is contingent on continued funding.
I realize I’m repeating myself, but I need you to understand this point and stop talking about bailouts: it’s not just about OpenAI and Anthropic surviving, but continuing to grow to the point that they both can afford and need to spend hundreds of billions of dollars each a year on compute (or hardware) from Google, Microsoft, Amazon, CoreWeave, Cerebras, AMD, or Broadcom, and in turn provide justification for hundreds of billions of dollars’ worth of purchases from NVIDIA and by proxy the memory triopoly of Micron, SK Hynix and Samsung.
LLMs were meant to be the panacea for a tech industry that ran out of new ideas for growth. Its existence was meant to justify a massive investment in hardware infrastructure, which would in turn enrich semiconductor companies. Its technology was meant to be the new thing that you could attach to your existing companies to generate more growth, or the thing that you built a new startup on top of to either sell to another company or take public and thus provide a return for a venture capital industry where making your investors 30 cents on the dollar puts you in the top 5% of funds. It was meant to be the new thing for tech journalists to cover, the new thing for tech consultants to sell around and on top of, the new way for companies to both make and save money, but also the way that individuals would also make and save money.
You’ll notice how none of these come with some sort of problem they’re solving other than “more.”
This isn’t about fixing anything, or building anything, but multiplying other things by parking money somewhere, either in tokens, infrastructure or hype. It helped create a new pantheon of charmless and damp tech sociopaths for people to rally behind in search of the next Big Strong Man To Worship, because seeking out the new Steve Jobs is way easier than trying to create something as useful as the iPhone, all while avoiding having to know or care about other people’s problems. All you have to do is continue feeding money into AI services or AI training and the models will magically become capable of solving the problems you don’t really give a shit about, and don’t worry, if you can’t afford to invest in the companies, you can invest your time pushing people to ignore AI’s problems today so that you can buy time for the companies to solve them tomorrow.
This is the post-labor, pro-growth economy at its finest: everything is engineered to make sure more money gets spent where it needs to get spent, to create more stuff and do more things, even if the things aren’t done right, just as long as it looks like they’re able to do them. By associating your money or time with AI, you are able to feign being futuristic or “caring about technology,” all while pissing on the very foundation of good software by worshipping an industry that can only exist if fed billions of dollars every single day.
Every single achievement has cost magnitudes more than effectively every innovation in history, and to make matters worse, every future “breakthrough” In AI is inherently dependent on the availability of AI data centers and tens or hundreds of billions of dollars to pay to rent them. This means that once the money stops flowing, “LLM improvements” will stop happening, because they are all entirely dependent on near-unlimited resources that are only available in a manic environment.
There is no justification to train models at their current scale — the one that creates a some amount of benchmark improvements that regularly difficult to quantify as “able to do new stuffs” — once the AI bubble bursts, and distillation requires a model to distill from, which won’t exist if Anthropic and OpenAI don’t train them.
This is why I find it difficult to see a post-bubble future for LLMs. Training models requires tens of billions of dollars to make any significant improvements, and significant improvements are difficult to quantify in dollars outside of costing customers increasing amounts of money. We still lack any real killer app for LLMs. We have a lot of people that use it for coding, we have people that vacuously discuss it being “good at research,” but we don’t really have a tangible product that we can say “it does this, and it’s really good at it” in a way that feels satisfying.
We have a lot of pablum about (per Damien Walter) technology that “strays into the world of science fiction,” but we don’t really have anything approaching actual artificial intelligence. Every single description of somebody’s AI setup sounds like Pee Wee’s Breakfast Machine, a contrived series of harnesses, prompts, API calls and burned tokens that requires constant maintenance to do some stuff sometimes.
None of that is enough to justify further investment once the financial mania recedes. You cannot train a true Large Language Model on the cheap. You are always spending billions of dollars, and the reason that there’s “demand” right now is that everybody is screaming at every CEO to “do AI,” and they’re doing that because Microsoft, Google and Amazon are spending money on GPUs, creating the illusion of a new future where everybody needs to get on board versus a future skidmark on history that will embarrass all those who didn’t wipe their arse at the first whiff.
Per my own reporting on its audited financials, OpenAI spent $7.81 billion in training costs in 2024 and $19.18 billion in 2025. Per reporting from The Information, OpenAI spent $8.6 billion on training in the first quarter of 2026 alone. These costs are only increasing, likely due to the diminishing returns of pre-training and the massive cost of buying training data for every imaginable new vertical.
Without the ability to spend billions of dollars on training, there will be no big frontier models, nor will there be models distilled from them. I don’t see how that changes in the future.
I also think that LLMs have created a near-permanent scar in the workforce, and traumatized more people than we’re aware of right now, both in those pressured about AI and those defending it. The media campaign behind AI starts and finishes with incessant threats around job security, and the excitement by many bosses about its potential to “disrupt the workforce” has revealed how many people are eager to replace every single person they’ve ever hired and are willing to do so with a low quality product.
Conversely, those who truly decide to “back” AI must exist in a frantic state that I have associated with every bad relationship in my life.
Every ounce of an AI booster’s effort is dedicated to maintaining the status quo — repeating the mantras that help paper over the problems, celebrating every small victory as if it were the discovery of fire, ousting those from your life who bring up the obvious problems, rationalizing every decision no matter how illogical as long as it helps reinforce the belief that what you’re doing is the right decision. Every questionable choice only seeks to further deepen your commitment to the doomed cause, because every step into madness will be more embarrassing to explain, and will require deep introspection to understand why you made it.
To be specific, they’ll have to think about why they were willing to accept and defend a technology inherently guaranteed to make mistakes. They’ll have to explain why they ignored a company that burned $5 billion in 2024, $20.9 billion in 2025, and will likely burn $30 billion or more in 2026, and why pointing to Amazon Web Services was rational when Amazon’s total capex from 2003 (the year AWS was created) to 2015 (the year AWS became profitable) is $29.7 billion, adjusted for inflation. That includes literally every ounce of capex attributable to AWS, Amazon the store, Amazon logistics, and even Amazon Alexa.
For comparison, Anthropic raised $30 billion in February, and Anthropic and OpenAI have raised $217 billion in 2026 so far.
Here’s a diagram from my hit on MSNOW:
Ultimately, AI boosters (or even fairweather fans) will have to admit they either were easily-impressed or disgustingly craven. They will have to explain why they accepted run rates instead of revenues, and why they were so impressed by superficial pseudo-intellectuals that knew how to say the right numbers and make reporters and investors feel smart for believing them.
I realize it sounds embarrassing, but there is nothing undignified about admitting you’re wrong, or that you got swept up in a hype cycle. You heard a lot of people getting excited about something, a lot of money got put into that thing, a lot of people that sounded smart told you insistently that this was the future, and you chose to believe them because we are trained from a young age to model what a “responsible and smart” source of information is. I’ve got your back the entire way!
Sidenote: We all make mistakes. I said OpenAI would be dead by the end of 2025 back in 2024 because I believed that the world would see sense and that hyperscalers wouldn’t just annihilate hundreds of billions more dollars without proof it was worth it. I underestimated the sheer desperation — and how dependent they’d become on OpenAI and Anthropic for growth, even if the overall mathematics didn’t work out.
The AI bubble — both in its technology and manufactured consent in the media — has been about muddying what’s considered good information by forcing everybody to discuss everything in the future tense by pointing to previous eras and saying “they lost lost and cost lots of money, and look, it sort of worked out for them!” and we are also raised to trust that systems are efficient, and that people get wealth and power through intelligent decisions. The amount of times I’ve heard “these are the biggest companies in the world run by the smartest people in the world” makes my head spin.
There is a reason that to this day it’s tough to get a straight answer about basically any economic part of the AI bubble, down to “how much does it cost to run a GPU an hour?” or “is inference profitable?” or “how do LLMs ever become profitable?” or “is it profitable for a company to run a GPU or offer AI compute?”
Why? Because these companies used rationalizations of “losing lots of money is necessary to create innovation” and “tech is bad at first!” to make the media actively ignore any technological or economic problems, if not actively defend the technology by repeating these rationalizations like a cultist.
Even those who are most loathsome in the defense of LLMs are a kind of victim of the AI industry, though a rather unsympathetic one. To become a full-blown “AI fan” requires you to accept effectively every narrative that you’re given, herald every single announcement as proof that the prophecy will be fulfilled, ignore the financial realities and actively attack those who would dare to critique the great god of the Large Language Model. You have to know all the new terms, be excited about the right things at the right time, and live in near-constant fear that you’ll fall behind on whatever it is you’re meant to do next.
Your reward is that you can hang around a dwindling number of wealthy yet terrifyingly boring Silicon Valley intellectuals or kiss up to editors that would throw you in front of a bus if it meant getting access to a CEO, and maybe the odd Twitter psychopath who will defend you using a slur.
In the end, many boosters will simply act as if they were never wrong. I hope they choose the more-courageous path of introspection, learning how they were had and using it as a weapon against con artists in the future.
As strange as it sounds, I believe the most devout defenders of AI could become great critics in the future. Maybe I’m just being optimistic.
Here’s a very simple question: how much longer can everybody afford to keep doing this?
Every single thing has become more expensive in the last year. Even though token prices have gone down or stayed flat, the amount of tokens you burn has clearly increased to the point that organizations are apparently spending billions of dollars on AI services with difficult-to-quantify ROI, requiring frantic advocacy to and financial debasement with every turn of the wheel. OpenAI and Anthropic have become more expensive to run, and OpenAI’s non-GAAP operating margin increased from negative 122% to negative 183% in Q2 2026.
NVIDIA’s GPUs just became 15% to 17% more expensive because high bandwidth memory costs doubled, a conga line of different monopolies upping their prices assuming that each link in the chain will keep spending, as each one of them — down to the AI labs themselves — knows that its contribution to spending on AI is an existential rite.
This means that any data center with GPUs delivered in 2027 and beyond will now have to cover billions of dollars’ worth of extra costs, on top of increasingly-staunch local authorities requiring power guarantees ($100 million a year in Wisconsin for Oracle) and states like Illinois, Arizona and Virginia killing their tax breaks, all as interest rates spike and demand for AI debt weakens.
Every single year, every single part of the AI bubble becomes more expensive — AI labs want to spend more money, AI data centers cost more money, AI services become more expensive, AI debt becomes more expensive, and everybody becomes decidedly less-patient for there to be some sort of outcome.
Meanwhile, public relations expert and OpenAI CEO Sam Altman told podcaster David Senra that “we’ve all [referring to the AI industry] been too ambitious on timelines…[and that changing people’s behavior” is much harder than the tech nerds realize.”
Sam: stop talking! Every time you open your mouth you say something silly!
Anyway, here’s everything that needs to happen in the next three-and-a-half years:
As I’ve said, NVIDIA’s price increase is going to increase the price of every single data center in construction by billions of dollars, and we’re already approaching the limits of how much money can be raised for them. That “$500 billion” announcement was actually Jensen Huang jumping the gun, per Bloomberg:
Goldman Sachs Group Inc., Blackstone Inc. and Apollo Global Management Inc. had been working tirelessly for months to draw up debt deals that would help developers of artificial intelligence systems pay for chips from Nvidia Corp.
With slow progress on the complex deals, Nvidia’s chief executive officer, Jensen Huang, decided to change tack: He went public this week with the effort, saying the group is aiming to collectively finance AI computing deals totaling $500 billion — a round figure with no obvious provenance.
The largest asset managers and financial institutions were making “slow progress,” and that was before Jensen Huang increased prices by 15%. Do you think it’ll become easier from here? How would that happen, exactly?
God, I’m tired.
The entire AI bubble has been exhausting for everybody involved.
Because nothing works yet as a real business model or anything approaching truly autonomous (or “magical”) software, there’s the implicit knowledge that you’re going to have to change your product again and again to update to the “best model” or “make things more efficient” (IE: lose less money) or when something breaks because a model’s training got tweaked.
The euphemism for this is “exponential improvement,” when it’s really an Arnold Palmer of instability and novelty, and abuses basically anyone connected to the ecosystem every single day.
If there’s always something new happening, it’s hard to pin down if things have gotten better, or whether you’re just more proficient in cobbling together different harnesses, prompts and API calls to make it do what you need it to. It is undignified that people tolerate models that become either dumber over time or at random opportunities, while also being deeply exhausting for the end user.
As a paying user of an LLM-powered service, you are guaranteed at some point to face a degradation in service where models misbehave, some sort of shift in rate limits, or some sort of change in product functionality based on their shifting economics.
Has there ever been a bigger shift in a business product’s value than GitHub Copilot’s shift to token-based billing? Microsoft rug pulled two million people that had built workflows on a platform that was allowing them to burn $1,000 to $5,000 in tokens for $20 a month. That’s genuinely crazy! It’s magnitudes more than when Uber jacked up its prices.
It’s equally-insane that Anthropic and OpenAI similarly fuck with their customers, changing the amount of value you get for $20, $100, or $200 a month at random in a way that shouldn’t be legal.
Basically any AI-powered software is subject to arbitrary shifts in availability, capability and pricing at the whims of the vendor. As I covered in my Subprime AI Crisis piece earlier in the year, Replit, Perplexity, and multiple other AI companies have sold their customers a lie by pushing an unprofitable product that they must constantly “tweak” to bring down costs, all while misleading the customer about a “price” that continually declines in value as the price stays the same.
This is not a sustainable industry — either economically or emotionally — because it has a fundamentally dishonest relationship with its customers defined by the inconsistency of LLMs both in efficacy, stability (see: Anthropic’s downtime) and training, with each model randomly better or worse at things to the point that it must be a legitimate nightmare to run any software or build any product on top of them.
And the fact they haven’t worked out their business models means that whatever you’re paying today is guaranteed to change. What other product do you regularly buy that has such chaos built into it? What other thing do you pay for where the prices (or availability) can shift to the point that you literally can’t use it in the same way at a moment’s notice? And why does anybody tolerate it when it comes to AI?
I’ll add that this is a specific situation where the tech media has categorically failed the customer. We have companies valued at hundreds of billions of dollars that are fucking their customers over day-in-day-out, and the response is mostly to say “huh that’s strange” and refuse to let a single critical thought cross their minds.
Every part of the AI bubble must exist in a constant state of flux so that there can always be a future breakthrough that’s always just out of reach. AI does not have to reach an actual achievement — it just has to “show promise” in some way. It is an objective disaster that Microsoft spent more than $260 billion on capex to create a business with less than $11 billion in annual revenue outside of OpenAI, but people will see “$34.33 billion in annual AI revenue” and say “that’s promising growth, up 123% year-over-year!”
They’ll hear about LLMs that delete people’s databases and say “well the models have gotten exponentially better,” even if that better part never seems to eliminate these issues, make a profitable AI company, or create a true killer app that you can point at beyond saying “ChatGPT has one billion weekly active users,” despite around 95% of them not paying a penny (and costing OpenAI likely billions of dollars) and eMarketer estimating that the entire global AI chatbot advertising industry will make $5.41 billion revenue in 2030, giving OpenAI little hope of stemming the burn. These big numbers — like Anthropic having a $65 billion annualized run rate, an undefined term that obfuscates the fact that Anthropic has made $16.5 billion in the first half of 2026, losing billions of dollars in the process — are fundamentally meaningless, because they’re easily gamed at best, and inherently uncertain at worst.
The AI industry demands you constantly live in the future tense. Everything is about tomorrow’s billions or trillions, the potential of what you’re seeing rather than the thing itself, future gigawatts in data centers that you must treat as if they are already built and value based on things that AI might theoretically do. I challenge you to read everything about AI from this point forward with this in your mind so you can see how intently this industry tries to drag your focus away from what it’s doing toward what it might theoretically do if it only had more money, power and resources, and ask yourself why they need to do so.
To be clear, they’re doing so because you can’t really justify anything about this industry based on what it does today. It costs too much, none of the businesses built on top of it are profitable, it costs so much to build a data center that the most cash-rich asset-light businesses in the world are now burdened with endless expensive-to-install and run hardware for a business that makes a fraction of its overall costs in revenue and has little demand outside of two companies that everybody must conspire to keep alive both financially and philosophically.
And ultimately, nobody can actually explain why we need more data centers.
Would anything really change? What would change? How? How many more do we need? Why do we need so many? Having more power plants meant more people could have power, and having more fiber laid meant connecting more buildings to the internet. What does one more or two more or ten more data centers actually give you? Is there some part of the world unable to access or take advantage of the LLMs available on seemingly every surface of the internet? Because it seems like the only reason these things are getting built is to capture illusory demand based on a “supply constraint” created by two unprofitable companies absorbing all the infrastructure. I don’t hear any compelling scientific or technological reason building more is useful or productive outside of funneling more cash to semiconductor companies.
Seriously, go and read basically any article about AI and see how quickly they start talking about the future, be it in the mainstream media or on a startup’s blog. Every single piece must sell AI on its theoretical promise and, if at all critical, reassure you that the author of course doesn’t dispute the “transformative potential of AI” or “how it’s already transforming the economy,” even if it can’t define how it’s doing so or even what that means.
I let myself have a little fun with today’s piece because I feel like I’ve been so deep in the financial trenches that I forgot how much of the AI industry runs on propaganda, social pressure and outright bullying to manufacture consent for a product that demands everything and provides very little in return.
Nothing about LLMs is worth a trillion dollars, or even $100 billion. This is, as I’ve said before, a $30 billion TAM industry dressed up as a trillion dollar one, and the only reason it’s grown this large is because the two leading companies have had their infrastructure built for them and given unlimited resources to subsidize their customers’ compute.
And what’s really stood out is how so little about the AI bubble is actually about AI. No other technology in history has had professional and social consequences for failing to use or like it enough, nor can I find any example in history where journalists have actively attacked critics for not being sufficiently-approving of a kind of cloud software. It is fundamentally crazy to me that, in pursuit of “objectivity,” much of the tech and business media has chosen to accept whatever narrative the AI industry gave them, assuming that whatever we have today is already guaranteed to be something better in the future, both in its outcomes and profitability.
This era is unlike any other before it, but took advantage of the fact that most people are desperate to apply the past to the present to rationalize or process what may seem irrational or destructive. To see AI as “just like the dot com bubble” allows you to ignore both the costs and the potential outcomes because “things worked out after that,” even if there’re basically no uses for GPUs after this and the only way we “build new LLMs” is by feeding them expensive training data using billions of dollars of compute that are only available while everybody still believes this is real.
The AI industry — and the AI bubble — is fundamentally built on acting in bad faith. Its executives lie. Its boosters lie. Its software lies because it doesn’t actually know anything and generates answers probabilistically, and if you mention that online, someone will harass you for doing so.
It refuses to answer straightforward questions. It refuses to present a plan for the future. It refuses to explain how it becomes profitable, because nobody knows how or has a tangible plan to do so. It deliberately subsidized its subscription products because it knew its customers wouldn’t pay the actual cost of AI, and tortures customers with shifts in functionality and rate limits all while framing this as a way to “continue to serve customers the most cost-efficient models.”
It attempts to conflate massive, power and resource-hungry AI data centers with the smaller ones that bring helpful yet increasingly-decaying software to our homes. It sells these data centers as “bringing jobs to communities,” all while importing the talent from out of state to build the things then leaving a crew of 100 to 200 people to actually run them after millions or billions of dollars of tax breaks. It sells its “innovations” as creating a “white collar bloodbath” to scare you into using inconsistent and unreliable software that’s mathematically certain to make mistakes, and when you say something about it, its acolytes will lie and say that “hallucinations are solved.”
It also can only ever sell itself based on what might happen and the theoretical promise of you giving it your complete attention, connecting every bit of data you own, paying whatever it costs, and accepting that it can and will change in price and functionality at random, all while never putting a precise timeline on whatever AGI means that particular week.
Whenever you ask for clarity, the AI industry gives you chaff. Whenever you ask when things get better, you’re told it’s both the early days and that AI is the worst it’ll ever be. Even the term “artificial intelligence” is a bad faith attempt to conflate transformer models with things like robotics or autonomous cars, all so that its proponents can claim other people’s successes as their own despite LLMs having little or no relevance to anything else other than generative AI.
It encourages dogpiling and ostracizing those who don’t fall behind it, because it cannot succeed on its own merits. It encourages a vile cultism powered too by bad faith and parasocial relationships with both AI CEOs and the models themselves. It exploits the intellectual weaknesses of “smart people” that are actually just good at remembering the right things to say at the right time and have memorized the various justifications for past failures, all while allowing them to use LLMs to promote their own bad faith enterprises where they use work-adjacent product to con others into paying them.
And it’s losing because, at its core, AI was never built on very much. It grew this large because the media manufactured consent at the behest of the powerful because lots of money got invested, and the rich and powerful can never be wrong. The underlying technology may be more useful than it was, but it’s not useful enough to be profitable nor reliable enough to be world-changing, and the bad faith representation of LLMs as “good enough” should be a permanent scarlet letter on anyone who misled the public into believing this was anything other than normal software.
I was asked recently why I find this all so repugnant, and my answer is simple: I don’t like bullies, I don’t like con artists, and I don’t like being lied to. This industry grew by misleading people about the actual and potential outcomes from Large Language Models, and through an economy-wide attempt to pressure everybody into adopting tools in pursuit of growth at all costs.
Ultimately, it was sold with the greatest lie of all: “this time it’s different!”
To be clear, they’re right.
It’s so much weirder, and in the end will be so much worse.
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2026-08-18 23:23:40
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I’m not trying to be a buzzkill here, but I have meaningful concerns about OpenAI’s ability to survive, and they’ve only grown more pressing in the last few years. In the same week that it completed a $7 billion internal share buyback, OpenAI saw both COO (and former CFO) Brad Lightcap and Chief Revenue Officer (CRO) Denise Dresser leave the company, the latter of which had only been there eight months, and had this to say a mere four months ago:
“I just have never seen this level of conviction spread so quickly and consistently within the industries,” Dresser told CNBC in April, as she was wrapping up her first 90 days on the job.
Dresser likely walked away from a large amount of stock options by leaving after less than a year on the job, which I’m guessing means she decided that staying at OpenAI would, for whatever reason, not be worth getting what I imagine are tens of millions of dollars of stock she would be able to liquidate when it went public. You know, that thing that’s definitely happening.
Unless it’s not quite so definite anymore. Back in late June, The New York Times reported OpenAI was “leaning toward” going public some time in 2027, but that was before Anthropic started one of the most-aggressive pre-IPO marketing campaigns I’ve ever seen, with investors “leaking” to the Financial Times that they thought it would have a $2 trillion valuation and have (sigh) annualized revenues of $100 billion to $120 billion by end of 2026, an entirely fictional statement made with the intent of pumping their bags, with the FT, for whatever reason, printing it with little pushback.
Yet what’s likely far-scarier for OpenAI is that even Anthropic’s pre-IPO marketing has a whiff of desperation. A Reuters report from late last week that feels precision-engineered to manipulate dimwitted investors said that “Wall Street [was] looking further into the future than it commonly does to put a price on the AI company, valuing it based on how much revenue it could generate two years from now,” adding that it was “projecting revenue of roughly $190 billion to $200 billion.”
This was arguably the worst part:
Established companies are typically valued more heavily on earnings, or EBITDA, which gives investors a sense of the economics of the business.
For Anthropic, however, current EBITDA does not fully capture the economics investors expect the company to achieve at scale. Anthropic is spending enormous amounts on GPUs and other computing capacity, model training, inference and hiring. Those expenses are necessary to support its rapid expansion but could become a smaller percentage of revenue as the business grows.
While I imagine the writer in question believed that this was being “fair” and “objective,” this paragraph exists only to manufacture consent for a company that clearly has questionable economics. “Current EBITDA does not fully capture the economics investors expect the company to achieve at scale” is a euphemism for “ignore your lying eyes,” a plea with the audience to not judge a company based on its actual business but on a theoretical business that, to quote Reuters, have “...training and inference [costs] become more efficient as technology improves, while personnel and other operating costs could become a smaller share of revenue as the company scales.”
Could, could, could, could, could, could could COULD! It’s always a bloody could or will or might with these fucking companies, and it’s astonishingly bad journalism to see it as an “objective” choice to vaguely say that a company should not be evaluated based on its actual business but on some theoretical business that they might build in the future where the economics are completely different.
Sidenote: the defense of a statement like this is always that it’s “to show both sides,” but the article also fails to disclose that Anthropic loses billions of dollars a year, or that the AI labs are horribly unprofitable. It does, however, include that Anthropic had a “profitable quarter,” which is something that was only made possible with Musk’s discounts on its compute costs in May and June 2026. That fact is also left out of the article.
The reason I bring up the noises coming from the manufacturing consent machine is that if Anthropic beats OpenAI to an IPO, I cannot see a viable (or reasonable) path for Sam Altman to float his nasty little company. The fact that the Financial Times and Reuters are already being co-opted into softening the blow is a sign that Anthropic’s S-1 will look and smell like the inside of a tauntaun, and Anthropic is, from the reporting I’ve read, in a much better condition than OpenAI, if only because it didn’t have multiple side quests involving video generation or browsers or smart speakers, though both companies love to give away $20 to $40 for $1.
Put simply, if Anthropic goes public with its own horrifying economics on parade, it’s hard to imagine OpenAI — a company that lost $20.9 billion in 2025 on $13.07 billion in revenue — will fare much better.
After all, Anthropic just hit, per Bloomberg, $65 billion in annualized run rate — a month multiplied by 12, or four weeks multiplied by 13, I’m guessing, because it never defines this number — in May 2026, and OpenAI is “on track” to hit $40 billion annualized revenue…in the middle of August.
Another Sidenote: I gotta say, that Bloomberg story about Anthropic’s run rate is even weirder than usual, defining run rate as “a metric that projects full-year revenue from a shorter period” without actually saying how it’s derived. No need to ask difficult questions I guess!
We are, of course, in the era of madness, so I’ve already read three or four people on Twitter say that OpenAI’s actual annualized revenue is so much higher, because they’ve heard stuff from people they trust. The AI industry’s loudest advocates think and act like cultists at the end of a doomsday prophecy, except instead of the world ending, OpenAI and Anthropic become the largest companies — or in the case of giga-oaf hedgie Gavin Baker, the only companies — in the world, rewarding all those who believed with…something. Glory? Smugness? Salvation?
In any case, OpenAI has a real problem if Anthropic beats it to the markets.
On October 31, 2025, a flustered Sam Altman told booster and investor Brad Gertsner that OpenAI would make “well more than $13 billion” in revenue that year before saying he’d “find a buyer for his shares.” In the end, per my own reporting, “well more” would mean “$70 million,” with OpenAI making $13.07 billion in revenue in 2025, with SoftBank accounting for $862 million. A week later on November 6, CNBC would report that OpenAI was “on track” to generate “more than” $20 billion in annualized revenue. OpenAI works out its annualized revenue by multiplying its most-recent four-week-long period by 12, which means that in a four-week-long period it had $1.66 billion in revenue, I guess?
On March 4, 2026, The Information would report that OpenAI had “topped” $25 billion in annualized revenue after hitting $21.4 billion at the end of 2025, and included the following hilarious line:
OpenAI calculates annualized revenue by multiplying the last four weeks’ revenue by 12. If OpenAI calculated the metric based on revenue spikes just in the last week, OpenAI’s annualized revenue would be roughly $30 billion, one of the people said.
Yeah man, this is why using annualized revenue is such a stupid idea. If you have a particularly-busy four-week-long period — like a product launch with a big social media push — you can use that period to inflate your revenues, which is exactly what OpenAI is doing, as evidenced by the sources (who I assume work at OpenAI) saying that’s exactly what they’re doing.
Annualized revenues are not a useful way of measuring these companies’ financial condition, and exist only as a form of marketing, made worse by the fact that AI token spend is not a recurring source of revenue. While you could theoretically use annualized revenue as a directional bit of data if it was just two companies selling (subsidized) subscriptions, the ability for these companies to cherry-pick periods of time that might be inflated by aberrations (like when someone spent $500 million on Claude tokens by accident) makes these numbers somewhere between useless and actively harmful to investors.
Even then, it took OpenAI seven months to be “on track” to reach an annualized revenue run rate ($40 billion) that was seven billion dollars smaller than Anthropic’s ($47 billion) from May, and a full $25 billion in run rate less than what it hit at the end of July.
Perhaps it’s a coincidence, but it’s also worth noting that the news about OpenAI’s exciting new annualized revenue “leaked” mere hours after the abrupt resignation of its Chief Revenue Officer.
The reason that OpenAI (and Anthropic, for that matter) wants you to think about things in terms of “annualized revenue” is because its actual revenues look a little tame compared to its commitments and burn rate. The Information reports that in Q1 2026, OpenAI burned $12.1 billion on “cost of revenue” and training on $5.7 billion in revenue, though it left out the sales and marketing segment where OpenAI burned $5.73 billion in 2025 — or, put another way, OpenAI spent $12.1 billion on compute to lose $6.4 billion, and that doesn’t include things like data costs or salaries or, well, anything. OpenAI (and by proxy The Information) somehow rationalizes this to only be a burn of $3.7 billion, likely using the same accounting bullshit that it did in the financials I saw.
Now, some of you might read that and say “wow, $5.7 billion is a lot of money!” but it doesn’t matter, because the more money OpenAI makes, the more its services cost. This is not difficult mathematics, but it is something that continues to escape the vast majority of coverage of the company, I assume because all of this feels a little insane when you think about it.
I know you’re gonna call me a firebrand or a hater or a skeptic or try to capture me and put me in a zoo, but I must be clear that OpenAI has set expectations — and made commitments — that range from ridiculous to outright impossible.
To get really specific:
For any of these things to happen, OpenAI will have to grow at a staggering pace, and effectively (per The Information’s reported projections) 10x its revenue between now and the end of 2030.
OpenAI’s projections have it near-tripling its 2025 revenues, doubling its 2026 revenues, nearly doubling its 2027 revenues, growing its 2028 revenues by 68%, and then growing its 2029 revenues by 64%. At the end of this magical mystery tour through revenue hallucinations, OpenAI will have it making more than NVIDIA did in Fiscal Year 2026 ($215.9 billion) and, somehow, becoming profitable:
I realize that many people have been conditioned by the tech industry to believe that every idea that a tech CEO has will always become reality, but the sheer scale of what OpenAI is both promising and obligated to do outpaces anything in modern history.
While much of what I’m saying is also true of Anthropic, (a company that itself has over $300 billion in commitments due in the next three years and is similarly-unprofitable) OpenAI has decidedly failed to play catchup at a time when enterprise customers see costs as a “huge issue,” which also makes it unlikely that (along with recent model price cuts) it will magically re-accelerate outside of allowing users to burn $14,000 a month in tokens for $200, which…also didn’t work well enough to get close.
In any case, any acceleration of revenues would also be an acceleration of costs, which will mean OpenAI will need several more $122 billion rounds from a dwindling pile of investor capital. SoftBank can quite literally not afford to invest anything further, with liquidity becoming so tight that it’s had to take out a $10 billion loan collateralized by its entire OpenAI holdings, with NVIDIA CEO Jensen Huang saying that its $30 billion investment from this year likely being its last. While various different venture capitalist paypigs may have some interest in funding it further, OpenAI will need more than it last asked for, without fail, every single year.
So, there’re really only two eventualities:
This, again, is not me being a firebrand, but taking a relatively-clinical look at the hard numbers and asking how the fuck it affords it all.
And man, does a lot of shit have to go right.
Per my last premium newsletter, OpenAI needs at least $800 billion to meet its commitments in the next three-and-a-half years, based on both the Wall Street Journal’s report on its projected $750 billion in compute spend through 2030 and an analysis of analyst notes on Broadcom, Microsoft, Google, Amazon, and CoreWeave.
The problem, however, is that much of this money will come due through the end of 2027, and require at least one more massive round of funding.
To get specific:
Now, all of this is contingent on Google, Microsoft, Amazon and Oracle building enough capacity to capture that revenue, but if we assume that happens, OpenAI needs more than $147 billion just to handle its expected compute commitments through the end of 2027.
Here’re some other costs that aren’t included:
With its IPO likely delayed — if it ever happens — until 2027, OpenAI will almost-certainly have to raise another round of funding by March 2027, likely at a similar scale to its $122 billion round from March of this year.
The biggest problem that OpenAI has is that $110 billion of its last $122 billion round was made up of Amazon ($50 billion), NVIDIA ($30 billion), and SoftBank ($30 billion), leaving a mere $12 billion funded by a primordial soup of different venture capitalists, private credit funds, and public endowments that should have their executives fired, ideally into the sun.
In any case, $12 billion isn’t enough to cover a single quarter’s compute costs.
The point I’m making is that raising further rounds — before we get to any niggling problems about valuation — has already become near-impossible to do without the help of massive entities that are showing increasing signs of strain at exactly the moment OpenAI needs more money.
Let’s break it down.
As mentioned previously, SoftBank is running at the very edges of its liquidity, and owes another $10 billion due on October 1, 2026. While in theory it could sell more of its ARM stock to fund further rounds, said stock makes up effectively all of its Net Asset Value, and while further margin loans are possible, doing so would put genuine pressure on ARM’s stock price as, well, at some point you’re not just investing in a company but whether SoftBank might use its stock like a piggy bank.
A few weeks ago, Amazon sent the remaining $35 billion of its $50 billion investment as part of the larger round, and while it’s theoretically possible that it could invest more, its free cash flow has now gone negative, and it needs as much money as possible to meet its (agh!) projected $220 billion in 2026 capital expenditures.
Google is a potential investor, as I’m not sure people realize how big a Google Cloud customer OpenAI has become, with Stephen Ju of UBS estimating it will spend $9.375 billion in 2026 and $12.5 billion in 2027, and Google Cloud increasingly becoming Google’s largest growth vehicle. Then again, Google’s free cash flow also went negative in its latest quarterly earnings, and even the most braindead of investors are becoming a little nervous about how circular everything is looking.
NVIDIA could, in theory, afford to invest more, but the markets are even more nervous about its slow transformation into GE Capital. Jensen Huang is clearly aware of this, which is why his “backstop” of a “10GW” data center in Ohio (which OpenAI has signed a 20-year-long lease to rent) isn’t actually backstopping OpenAI’s compute spend, but the underlying assets in the event of a short sale:
For instance, if OpenAI were to walk away from the project, SB Energy would first try to lease the site to another customer for the same price, some of the people said. If SB Energy wasn’t able to find another suitable tenant, the firm would try to sell the site, and Nvidia would pay any difference in the value, up to $105 billion if the initial phase is completed.
By backing the asset value of the data center—not OpenAI’s ongoing lease payments—the structure limits Nvidia’s risk exposure substantially, those people familiar with the deal said. The chip giant’s guarantee covers completed data centers, not facilities under construction.
That’s a pretty big “if,” because it refers to 5GW of theoretical capacity built by a company that has never built a data center, at a time when the nearest equivalent — Stargate Abilene, at 1.2GW — is two years in and has only finished three out of eight of the buildings. Based on this description of the deal, NVIDIA only has to guarantee things in the event the data center is actually built.
As part of the deal, NVIDIA is investing $1.5 billion in SB Energy, a company invested in by both OpenAI and SoftBank that is trying to go public some time this year, likely as a means of adding further liquidity to SoftBank’s balance sheet, though the IPO would only raise, per Reuters, between $5 billion and $7 billion.
OpenAI has already, across multiple funding rounds, raised from private credit funds from Blackstone, BlackRock, and Insight Partners, and it’s possible that these same funds could fuse together like Voltron as a means of keeping OpenAI alive.
That being said, we’re talking about over $100 billion a year for the foreseeable future, which is a little more than they could stomach on a private company with ultra-negative margins and a younger competitor currently eating its lunch.
Then there’s another problem: that private credit is already having trouble funding AI data centers, which are a (theoretically) far-more-stable investment in infrastructure and power. When NVIDIA announced its “$500 billion” fund, the media was quick to assume that it had already closed the money, rather than it actually being a “memorandum of understanding,” also known as “a non-binding agreement to maybe do something in the future.”
Yet a follow-up from Bloomberg found that it was even less than nothing, and that Jensen Huang had insisted on making the announcement despite months of slow progress:
Goldman Sachs Group Inc., Blackstone Inc. and Apollo Global Management Inc. had been working tirelessly for months to draw up debt deals that would help developers of artificial intelligence systems pay for chips from Nvidia Corp.
With slow progress on the complex deals, Nvidia’s chief executive officer, Jensen Huang, decided to change tack: He went public this week with the effort, saying the group is aiming to collectively finance AI computing deals totaling $500 billion — a round figure with no obvious provenance.
The reason I bring this up is that if private credit funds are having trouble funding data centers, they’re going to have a shit-ton of trouble convincing investors to pile into an unprofitable second-place AI lab run by a uniquely-unlikeable CEO who has a penchant for lying.
As mentioned earlier, OpenAI (and Anthropic) have scraped the bottom of the barrel of venture capital time and time again, and never managed to raise more than $30 billion at a time.
The sheer volume of names on these deals suggests that it’s genuinely very difficult to mobilize this much capital, and I think it’ll become difficult-to-impossible to do this every single year, even if Anthropic were to go public, as it’s very unlikely that the majority of these investors will actually be able to liquidate their holdings.
And remember, we’re talking about OpenAI here — stinky, expensive, second-place OpenAI, the one with all the obligations, the one with the CEO that wants to surveil everything his customers do. The one that has raised no more than $12 billion of funding from sources outside of NVIDIA, SoftBank, Microsoft or Amazon. That one.
There’re really two major problems:
OpenAI’s $122 billion funding round valued it at $852 billion.
And, per the New York Times, advisers pushed back on the idea of trying to go public at a $1 trillion valuation:
OpenAI’s advisers presented company executives with the option of waiting until 2027 to go public with a $1 trillion valuation, or lower the targeted valuation for a quicker I.P.O. Mr. Altman, said one person in contact with him on the topic, responded that any change to the trillion-dollar valuation was a nonstarter.
For some perspective, a $1 trillion valuation would be around a 15% premium, for a company that now accounts for 70% of Microsoft’s AI revenues and allegedly is the single-most-important startup since Google or Facebook.
Sorry, I’ll stop vagueposting: this is bad. For a company of this scale and importance, OpenAI should’ve waltzed into a $2 trillion valuation, except a public offering requires you to provide audited financial statements and an explanation of why your company is worth that much that goes a little further than an investor deck with annualized run rates and charts that promise the world.
The problem here is that if OpenAI can’t go public at even a trillion dollar valuation, it’s unclear why anyone would invest at $865 billion, or $800 billion, or even $700 billion, unless they happened to believe that it would go public at less than a trillion then magically become worth trillions more, somehow. The ability for any investor at this point to make a significant return is very, very small, made smaller by the fact that Anthropic appears to actually be meeting with investors for an IPO and is showing revenue growth…
…except even then, AI bulls are nervous, because $65 billion in annualized revenue (at the end of July) was lower than some forecasts, with market intelligence firm Yipit claiming it had hit $74.3 billion on July 22, causing confusing feelings in the minds and bowels of boosters that had expectations set by, I imagine, a combination of black magic and black mold.
While Anthropic CFO Krishna Rao has not been discussing valuations at early IPO meetings, investors and analysts are either expecting or wishcasting that it hits a $2 trillion valuation, though if OpenAI can’t get a trillion, it’s hard to see how Anthropic — a business of larger-yet-comparable size and equally-rotten economics — would somehow double that and, I assume, then some.
Seeing all of this, why would any venture capitalist with a working brain still invest in OpenAI at anything close to an $865 billion valuation? While current investors might follow on as a means of keeping the company afloat, at some point their limited partners might ask reasonable questions like “how do you intend to make us money?”
This is a problem already hitting Thrive, which has invested billions in OpenAI. Per Bloomberg:
The firm’s 2022 growth-stage fund — which includes Wiz, a business sold to Alphabet Inc.’s Google earlier this year — has returned 0.3 times the initial money it invested. That places it above the top 5% of funds. Most venture funds take between 10 to 12 years to return capital.
Thrive’s largest investment, OpenAI, is expected to generate a meaningful return. The firm was an early backer, investing in the startup through at least five separate funds going back to Thrive’s $408 million vehicle from 2018 and a fund that closed this year, a $6.23 billion instrument. Other notable IPO contenders within Thrive’s portfolio include Stripe and Anduril.
That’s right folks, if you invested in Thrive’s 2022 growth-stage fund, you’ve made 30 cents on the dollar, with much of it tied up in OpenAI.
While I’m not denying it’s possible, limited partners have their limits — especially as funds from Sequoia and other venture capital firms underperform the S&P 500.
And, not to repeat myself too much, OpenAI needs so much more money! It needs at least $100 billion a year, or it’s toast!
The collapse of OpenAI would likely be a result of the walls closing in around its ruinous obligations and economics, with counterparties left short-changed and deals broken as things begin to unravel.
It starts, as obvious as it sounds, with OpenAI running short on funds, and we’ve already seen one sign that had happened with Amazon “completing” its $50 billion investment in the company a few weeks ago by sending another $35 billion.
To be explicit, that $35 billion was rumored to be contingent on OpenAI either going public or reaching AGI, though all that was said in the funding announcement was that it was contingent on “certain conditions being met.”
Nevertheless, Amazon didn’t decide to send $35 billion out of the goodness of its heart, or because it thought OpenAI was such a wonderful company — if I had to guess, it’s because OpenAI needed that money to pay for its compute costs, an estimated $9 billion of which flow through Amazon Web Services.
The fact that OpenAI needed $35 billion mere months after receiving at least $40 billion (and barely a month after getting another $10 billion from SoftBank) suggests that either compute pre-payment costs are brutal or OpenAI is absolutely annihilating cash at a rate unforeseen in the history of capitalism.
Whatever the reason, OpenAI clearly needs tens of billions of dollars every few months to keep up with its costs, and will only need more money as it “grows” — by which I mean has to pre-pay for compute costs for Amazon, Google, Microsoft, CoreWeave, Oracle, and Cerebras.
While it’s foolhardy to say when OpenAI might collapse (don’t I know it!) its collapse will come from the most obvious place — when it’s required to pony up a bunch of money without a means of raising more funding.
When you take a step back, OpenAI has had to raise funding near-perpetually since its $6.6 billion round closed in October 2024 on top of a $4.4 billion credit facility. On December 27 2024, OpenAI would say in a blog post that it needed “more capital than it imagined,” and would begin talks a mere month later in January 2025 to raise another round of $40 billion that would “close” on March 31 2025, though it would only raise $10 billion at first from SoftBank (with $2.5 billion of that from a syndicated group of investors).
Five months later in August 2025, OpenAI would raise another $8.3 billion “as part of” the round from a group of venture capitalists and asset managers, sell another $6.6 billion of internally-held shares to investors in October 2025, and by the middle of December 2025 was already rumoured to be raising another $100 billion, just before getting another $22.5 billion from SoftBank on December 31 2025.
While we know OpenAI ended 2025 with about $25 billion in cash, The Information was able to update us that it had around $73 billion in cash and “marketable securities” at the end of Q1 2026, which likely includes at least $35 billion from Amazon, NVIDIA and SoftBank, though for whatever reason the reporter refused to break out the cash part. Nevertheless, this means that OpenAI’s actual cash position looked better only by virtue of an influx of capital, and whatever happened to the company in Q2 2026 meant it needed another $45 billion (Amazon plus SoftBank, and maybe another $10 billion from NVIDIA, as it’s unclear how that whole thing was amortized).
What I’m getting at is that at some point in the next three months, OpenAI is going to need more money, likely tens of billions of dollars, especially as it enters new fiscal years for Google, Amazon, and CoreWeave, all three of which will likely require up-front payments for capacity that OpenAI does not have.
And, as I’ve repeatedly said, OpenAI needs to keep raising money because its costs increase with its revenues, and it has no clear path to either reducing them or increasing prices, as it found when it (and Anthropic) moved enterprise customers onto accounts that required them to pay the actual cost of their AI services.
None of this has much to do with my feelings about AI, and far more to do with basic mathematics. OpenAI has no economies of scale, it’s horribly-unprofitable, and does not have a stable business. This naturally means that it has to continually raise capital, except raising further capital is going to be difficult, based on the sheer amounts it needs, the dwindling funds available for it to raise, its already-inflated valuation, and the fact that it’s way behind a competitor facing exactly the same problems.
OpenAI has promised the impossible, and built a company that only makes sense if you’re willing to ignore the worst economics in the history of capitalism. Its future is dependent on raising over a hundred billion dollars a year in one of the worst funding climates in history. Its revenues are slowing, its competitor (and there’s really only one) has outpaced it (all while slowing itself), and its CEO is one of the single-worst spokespeople in history.
However you may feel, it’s impossible to argue with the logic that OpenAI is going to need more money by the end of the year — likely tens of billions of dollars — and that money will have to come from somewhere. It could be from Google, or Amazon, or even Meta. It could be from SpaceX, though Musk would have to hold his nose a little. It could be from Microsoft. It could be from a last gasp telethon of venture capitalists coming together to prop it up one last time.
But it’s gotta come from somewhere.
And at some point, OpenAI will simply not be able to pay its bills, or more precisely, it will have to hand over money to somebody who will not accept equity or IOUs in return.
Whoever it is that refuses that deal will be the one that pulls the trigger, and sends OpenAI’s body to the glue factory.
Sidenote: I want to be clear that this is all speculation. The world is chaotic, the future is uncertain, etc.
So, as much as I have talked about OpenAI’s death, its apocalypse could arrive in many different forms, but likely starts (as I just said) with it someone asking OpenAI for some real, non-circular dollars, only for Sam Altman to look at them like this:
But the first place to look for the end is OpenAI’s revenue growth. To compete with Anthropic, it will have to hit $60 billion in annualized revenue (I’m so fucking tired of annualized revenues) within the next three months. The first domino to fall will be them either missing this target or seeing revenues regress — if they haven’t already done so, of course, given that OpenAI measures run rate based entirely on a hand-selected four-week-long period.
All that it takes is a little stank of regression for the market to get nervous.
It’s inevitable, at this point, that both Anthropic and OpenAI’s revenue growth slows, if only because both of them have only got this far through a combination of subsidized subscriptions and companies burning millions on token-maxxing initiatives that will have petered out by the end of the year. OpenAI has spent a little over a year trying to play catch-up on the enterprise — a strategy led by now-departed COO Brad Lightcap — only to find that customers are becoming cost-conscious at exactly the time they need to be spending more. To make matters worse, Ramp found that customers have been slow to adopt Anthropic’s more-expensive “Fable” model because of the price, meaning there’s effectively no way to jack up prices.
I imagine Anthropic’s interest in bumrushing for a September IPO is an attempt to avoid investors seeing post-tokenmaxxing deceleration. In doing so, it’ll put OpenAI in a brutal position of having to defend itself against both its own and Anthropic’s economics at the same time.
So, the thing to watch out for is any sign of deceleration, which could mean outright “run rates have dropped,” to lower burn on OpenRouter, to more price cuts, to any kind of attempts by OpenAI to offer discounted tokens if bought in bulk.
Then, at some point, the money will stop flowing to somebody.
The problem about guessing who that might be is how much of the AI bubble is held up by OpenAI’s revenues. Microsoft, Google, and Amazon all have vested interests — literally and figuratively — in at least appearing to get paid by OpenAI, which means they’re likely work with it on deferred payments and/or equity shares in trade, likely instituting some sort of bastardization of the already-problematic “payment-in-kind” system used by private credit when it can’t afford it loans.
CoreWeave could be a place to look, with its largest customers being Microsoft (for OpenAI), OpenAI, NVIDIA, Google (for OpenAI), and Anthropic. While Microsoft and Google are unlikely to stop paying their bills due to OpenAI lacking the cash, OpenAI is allowed to pay its bills Net 360, meaning that if CoreWeave’s cashflow suddenly starts sagging despite revenues growing, it’s potentially because of Sam Altman stapling IOUs to Michael Intrator’s car along with a note that says “I’m sorry. I can’t. Don’t hate me.”
Cerebras — which gets somewhere between 50% and 70% of its revenues from its OpenAI contract — would be another place to look. If revenues (or cashflows) fail to materialize, it could be another sign that OpenAI is unable to pay its bills.
Other obvious signs would involve changes in guidance across any major hyperscaler, especially Oracle, Microsoft, Google or Amazon — specifically language suggesting that OpenAI’s revenue either isn’t real or isn’t arriving.
I also, to be clear, expect some sort of fundraising, likely heavily-funded by asset managers, with the potential for NVIDIA to break its pledge and invest again as a means of keeping the party going. Despite OpenAI’s lousy financial condition, its existence is critical to the entire AI industry, representing the majority of compute demand across effectively every provider, which will mean everybody will probably try and chuck a few dollars its way.
This could take the form of a suicide round (valuing it at or above the $965 billion valuation from Anthropic’s Series G round) or a brutal downround of around $800 billion, justified as ‘technically higher’ than the $730 billion pre-money valuation it got when NVIDIA, Amazon and SoftBank last invested.
I could also see it taking a doomed run at a public offering — especially if Altman somehow pushes out CFO Sarah Friar, who had previously said it wasn’t ready for IPO and got rewarded for her honesty by being made to report to “CEO of Applications” Fiji Simo, who left the company in July due to medical issues but for whatever reason remains active behind the scenes, per the FT.
Going public is a terrible, awful decision, which is why I’m increasingly-confident that Altman would consider it, especially if there’s demand for liquidity from investors. OpenAI, despite its prominent in the industry and load-bearing compute spend, is in a desperate and untenable position made worse by a competitor that worked out how to swindle enterprise customers that don’t know how to measure their token spend at a much-larger scale, and without something completely-unexpected, it’s unclear how it pulls itself out.
When things get rough, expect Altman to make comments about the challenges of building the future, criticizing those who are “endlessly negative” about AI and set "unrealistic expectations” from a man who said that OpenAI is close to creating a genie that can grant any wish. He will blame everybody — critics, the financial markets, journalists, ex-employees, Elon Musk, Dario Amodei, counterparties that “don’t understand what innovation demands,” venture capitalists, Twitter posters, and basically anybody other than Sam Altman, the guy who made hundreds of billions of dollars’ worth of commitments to the largest companies in the world with little or no plan as to how he might do so.
Sidenote: I am not engaging with stuff about government bailouts or nationalization, because I think both are intellectual crutches that exist to avoid thinking about truly chaotic events. OpenAI may get a government lifeline, it may get the ability to raise a loan from the government, or Trump may do absolutely nothing, as midterms are coming up and his approval rating is in the shitter.
No, these data centers are not all part of some big, secret surveillance state. No, there is not some mysterious $150 billion bailout. Every time you choose to believe this you are attempting to side with the wealthy, assuming they all have some brilliant plan they’ve formed with their magnificent brains, when in reality they’re all obsessed with growth and thought AI was the next big growth thing. Reality is far more depressing — the rich and powerful are as stupid (or stupider) than a regular person, they just got lucky.
OpenAI’s actual death could take a few forms, each of them fairly destructive.
In the event this happened, Microsoft’s first move would be to cancel effectively all cloud contracts that OpenAI has, and have to restate guidance to remove the $250 billion in “incremental Azure spend” it promised. There isn’t a chance in Hell that Satya (if he’s allowed to stay) is going to give Google, Oracle or Amazon hundreds of billions of dollars, even if it means taking massive impairments on GPUs.
In this scenario, Microsoft would potentially strip back (or entirely eliminate) the free ChatGPT product, and likely either tighten rate limits or move everybody on a ChatGPT Plus or Pro subscription to token-based billing, much as it did with GitHub Copilot in June.
While I imagine some rescue package is pulled together, OpenAI could simply be allowed to run out of money, short-changing nearly a trillion dollars’ worth of compute contracts, killing CoreWeave, Cerebras, and anyone else reliant on its income. Its customers would be given API keys that flow to Microsoft AI Foundry, Amazon Bedrock and Google Vertex, and be told that there would be little or no further development or training of OpenAI’s models.
This situation, while obviously destructive for the entire industry, would give everybody a scapegoat. Who made all the promises? Sam Altman. Who ran a shitty company into the ground? Sam Altman. Who misled everyone into believing that there’d be infinite demand for compute? Sam Altman. Stories will leak that OpenAI was “not consistently candid” with its financial condition with partners, allowing everybody to reframe a trillion-plus dollars in waste as the result of one egregious con artist.
To be clear, the person to blame is Satya Nadella. He’s the one that made the initial investment, bought all the GPUs, and then kept buying them the second that ChatGPT took off. He’s the one that’s misled investors about the concentration of Microsoft’s AI revenue. If there’s an opportunity for him to lump all of the blame on Altman, he’ll take it, as will Jensen Huang, Andy Jassy, and Sundar Pichai, even if he’s relatively quiet about OpenAI’s billions in contributions to Google Cloud.
At 70% of Microsoft’s AI revenues largely from its tens of billions of dollars’ worth of compute spend, OpenAI will represent a material drop in hyperscaler revenues, and somebody will have to be blamed. It won’t matter that Anthropic is just as unprofitable or made hundreds of billions of dollars’ worth of promises it also can’t keep. OpenAI will make a fitting punching bag, a well-deserved one.
I know, I know. Sam and Dario won’t even hold hands at an event. They hate each other. They both are vacuous psuedo-intellectuals desperate for attention.
Yet in a moment of desperation, OpenAI could turn to Anthropic for a lifeline — a choice merger that would pump both of their bags, all while allowing Altman and his cronies to escape blame. The united entity would likely be worth over $2 trillion, if only because of its combined customer base and theoretical “reach,” even if thinking about that for even a second makes it sound so unfathomably stupid, as said “reach” would come with multiplicative financial issues stemming from OpenAI’s lousy economics meshing with the equally-crap numbers underlying Anthropic.
That being said, in a desperate moment, this unity could also justify further investment from hyperscalers, venture capitalists and asset managers, giving them all something to point money at and say “this is the future of computing.”
I think it’s very unlikely this happens, and if it does, it would be ruinous for everybody involved. Neither of these companies make any kind of economic sense to anyone outside of the recently-concussed and AI boosters with dichromatic vision. Combining them would only create a much larger, uglier problem — one that would carry with it the very same problems that both companies have, compounded by the expectation that it would become the literal savior of the entire tech industry.
The following is an objective list of what OpenAI has to do by 2030:
OpenAI is currently “approaching” $40 billion in annualized revenue, at precisely the time it needs to be accelerating. This company needs to leave 2026 at somewhere in the region of $75 billion in annualized revenue to have even a snowball’s chance of paying its ridiculous compute costs, and even then I’m not sure how it possible keeps up with the (at least) $146 billion in compute bills it’s got coming up.
It’s time for everybody to start having a real, meaningful conversation about what happens if OpenAI dies. This company has remained economically unstable since I started writing about it in November 2023, and while I might have underestimated its staying power, nothing has changed about my larger thesis that this company is headed for perdition, leaving its counterparties unpaid and alone with the consequences to follow.
Said consequences, as I outlined in the OpenAI Bubble, are very, very serious, representing an existential threat to SoftBank, one of the largest companies on the Japanese stock market, and its collapse will guarantee massive changes to the guidance of some of the largest companies in the world. There is a very real scenario in which nobody left with OpenAI stock is able to reach a liquidity event, which means the tens of billions of dollars of venture capital will remain unlocked and zeroed out unless it can go public, which is increasingly-unlikely.
It is no longer rational or reasonable to avoid discussing what happens if OpenAI dies. It’s a situation that should be on the mind of every journalist, analyst and investor, even if they don’t think it’s certain, because OpenAI is both horrendously unprofitable and has made commitments so significant that they now represent at least 20% of hyperscaler cloud revenues in the coming years, if not more like 30% to 40%.
It is actively irresponsible to ignore this situation any longer, and I encourage my peers, analysts, journalists, economists and investors to start seriously considering the likelihood and ramifications of the death of OpenAI.
For me to be wrong, in the space of three years OpenAI will have to become a company with annual revenues higher than Meta ($200 billion, versus projections of $284 billion in revenue in 2030) and meet obligations ($800 billion+) 27% larger than the combined revenues of NVIDIA ($215.9 billion), TSMC ($122 billion) and Samsung ($270 billion).
OpenAI doesn’t have to be illegal to be dangerous. Every time consent is manufactured for the astonishing waste and unrealistic promises of Sam Altman, companies further leverage themselves in an attempt to capture its theoretical value, and investors are further manipulated into supporting an industry almost-entirely founded on its compute spend.
As I discussed in the OpenAI Bubble, its collapse will have now-unavoidable economic consequences. The death of SoftBank is a very real possibility. The likelihood of the vast majority of AI investments going to zero is much, much higher than anyone wants to think about, at a time when, per Bloomberg, a venture capital firm that returns thirty centers on the dollar is considered an above-top-five performer. Oracle will collapse without OpenAI’s revenue.
To not actively and meaningfully discuss the potential for OpenAI to collapse is actively irresponsible. To act like there are not significant, existential problems with this company’s economics is to intentionally avoid reality, and whoever is on the receiving end of said ignorance deserves better, be they an investor reading your analyst note or a reader burdened with incomplete journalism.
What follows may be an Enron-Lehman Brothers hybrid, one that leaves unbelievable destruction in its wake, an avoidable systemic risk empowered and enabled by a kneecapped media industry and sell-side analysts incapable of seeing further than two quarters in the future.
In the end, there is no avoiding the damage that OpenAI’s collapse will create. The time to do that was in 2024, before it made all those commitments, and raised so much more money. Once it did so, it led the entire industry to believe that there was significant demand for AI, when all that was happening was Sam Altman and Dario Amodei were taking up every ounce of compute capacity, paid for with equity investments from the companies they bought it from, an illusion created by men driven mad by their desperation for hypergrowth.
However you feel about my work, I am begging you to take even the prospect of OpenAI’s collapse seriously, and prepare accordingly.
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If you want to get in touch — and especially if you have any juicy information about Anthropic, OpenAI, or any other companies in the AI bubble — hit me up on Signal at ezitron.76. I’m also on IB on The Terminal.
2026-08-14 23:48:21
I’ve heard from people in the past that my articles are too long, and I wanted to start by saying that, for the most part, they’re going to stay long, because I feel like the only way for me to make my arguments is to be as specific and detailed as possible about the things I’m talking about.
Then again, sometimes it’s just because I imagine arguments against my work in my head and want to pre-empt them.
Something about the AI bubble has made the boosters genuinely insane. They see these otherworldly declarations — hundreds of billions or trillions of dollars — and assume that nobody would say them in bad faith, and that the tech industry would never fail to live up to them, even though we’re barely a few years divorced from when Mark Zuckerberg burned $80 billion on the metaverse, what will one day be known as “the second-worst misallocation of capital in corporate history.”
When the boosters hear that OpenAI plans to spend $750 billion on compute costs through the end of 2030, they shrug their shoulders and say “it’ll work it out.” When they hear that hyperscalers have $1.65 trillion in off-balance-sheet obligations and debt, they nod approvingly, saying that “these are some of the richest and most-profitable companies in the world,” and that they will “simply keep raising debt.” It’s somewhere between number-blindness and make-believe — these are such unfathomably-large sums that it’s hard for the average person to assume anything other than that nobody would sign contracts agreeing to pay them without the confidence they’d be able to do so, even though it’s all very silly.
In any case, readers, I hear you, and today’s premium newsletter is going to be a shorter one, because it’s been an incredibly long week for me, including a day that started at 5AM with four different interviews — including my appearance on CNBC, which I encourage you to watch — that ended roughly 14 hours later, which means I’m a little depleted but nevertheless dedicated to you, the reader, and giving you value for your subscription.
So today I’m going to be pithier, and focus on hard numbers and harder truths about the AI industry, and specifically seek to answer a question: how much does the AI industry actually need by 2030?
Sidenote: I wrote this intro before I wrote the rest of the copy, and now this thing is over 7000 words. God damnit. I tried, I swear.
To be specific, I’m going to be focusing on the next three fiscal years for the companies that matter — the lead hyperscalers (Meta, Google, Microsoft, Amazon, Oracle), the two leading semiconductor firms making AI chips (NVIDIA, Broadcom), the main neoclouds (CoreWeave, Nebius, IREN, which I’ll cover in short), the main AI labs (OpenAI, Anthropic, and SpaceX) and the overall AI compute industry.
I’ve spent a great deal of time in the last few years explaining in detail why I think this will all collapse, but today’s goal is to show you, in hard numbers, exactly how much money the main players in AI need, based on consensus analyst estimates and my own research.
And god damn, do they need a lot.
2026-08-12 03:14:25
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Last week I put out one of the most consequential newsletters I’ve written yet, pulling together multiple distinct financial analyst notes from Wells Fargo, Barclays, and UBS that directly estimated that 70% or more of the AI revenues of Microsoft, Google, and Amazon were from either OpenAI or Anthropic. To be clear, UBS estimated that next year, Anthropic and OpenAI’s compute spend would be 48% of all Google Cloud revenues — which means that they likely account for even more than 70% of its AI revenues, but I wanted to be fair.
This was both a colossal pain in the arse and a story that I knew would piss off a lot of people, because of its huge ramifications. Some outright dismissed it as “doomerism,” while others insisted it was a good thing, because OpenAI and Anthropic are growing so fast.
24 hours later, Bloomberg ran a story estimating, based on OpenAI’s $24.1 billion dollar contribution to Microsoft’s Fiscal Year 2026 revenues and previous statements, that OpenAI alone contributed to 70% or more of Microsoft’s AI revenues for the year.
For some context, Microsoft has spent $261.3 billion dollars in capital expenditures since the beginning of 2022.
Meanwhile, Apollo chief economist Torsten Slok said Friday that profit margins in AI are “...higher the further you get from the end user,” and then said something I think I’ve said maybe four times in the last three months:
The bottom line is that the most profitable part of the AI value chain depends on the least profitable part continuing to grow revenue or raise capital. Capital can bridge the gap for a while, but not indefinitely. And therein lies the risk: will the ROI show up for AI's end customers fast enough to sustain the spending that is generating those upstream margins?
Good bloody question Torsten! The answer is “probably not.”
Let’s get real simple about this because everybody wants to make AI so complex.
Sidenote: before we go any further, I need to be clear that AI is not the reason that these companies are growing, outside of the compute spend from Anthropic and OpenAI. AI is not “boosting other product categories” or “helping other categories grow,” because if it was, they’d tell you specifically. I get so many emails from people sending me the overall revenues of these companies, mostly from people that don’t appear able to read, but nevertheless, I want to add this note on the off chance they learn.
If we assume, on the low end, that Jensen Huang is right and he’s going to sell $1 trillion or so of GPUs (roughly 30GW of billable IT capacity), that’s somewhere between $360 billion and $435 billion of annual compute revenue demand.
Right now, there are (outside of hyperscalers buying compute for them, and whatever it is Meta is up to) two companies that spend more than $500 million a year on AI compute, namely Anthropic and OpenAI. Both are unprofitable, and both lose tens of billions of dollars a year.
If we take OpenAI’s testimony from the Musk-Altman trial as gospel, it’ll spend around $50 billion on compute this year, and if we (kindly) assume Anthropic will spend $50 billion itself, that brings us to $100 billion. To get to that level of spend, Anthropic and OpenAI have raised a combined $217 billion in the first half of 2026. Every neocloud is effectively an outgrowth of this spend, either through direct contracts or by proxy via Microsoft or Google. Outside of hedge fund and investor Jane Street and NVIDIA, neoclouds do not have significant customers at the level that would warrant all this capex.
So, the world is building AI compute capacity with the expectation of at least $360 billion in annual revenue, all while we struggle to find single-digit billions in AI compute spend. The only way all that compute gets used is if either A) Anthropic and OpenAI rent all of it or B) massive (and I’m talking multiple $10 billion-a-year customers) appear virtually overnight.
In both those cases, the money to pay for that compute will have to come from somewhere.
Remember: for Anthropic and OpenAI to be able to afford their current (and comparatively meager) spend, both have had to raise nearly a quarter of a trillion dollars in this year alone.
The vast, vast majority of the world’s compute revenue — I’d wager anywhere from 70% to 90% — is contingent on venture capital propping the AI labs up, and to make matters worse, it is no longer sufficient for them to just “grow fast,” but to grow so fast that they can spend (per estimates from Wells Fargo, Barclays, and UBS) $197 billion on compute in 2027 just on Google Cloud, Amazon Web Services and Microsoft Azure. This does not include the billions that both will spend on CoreWeave, Cerebras, or Oracle.
Sidenote: The numbers are all in this newsletter. I will also add that these estimates only include Microsoft Azure for the first two quarters of 2027 (as estimates are only up to the end of Microsoft’s FY27, which runs from July 1, 2026 to June 30, 2027). It’s likely that the number is more like $220 billion. It’s likely more.
Also, before you ask: revenue concentration appears to be getting worse over time, because these 70% estimates mostly rely on the continued growth of overall compute and AI model rental platforms like Vertex, Foundry and Bedrock.
Sorry, I got too complex again. Anthropic and OpenAI are only set to spend $100 billion on compute this year, and had to raise over $200 billion to do it, which makes it likely they’ll have to raise $150 billion each leading up to or in 2027.
And let’s be clear about something: the future growth trajectories of Amazon, Google and Microsoft (not to mention Oracle, CoreWeave, and every other neocloud) are contingent on the continued ability for Anthropic and OpenAI to raise and have the demand necessary to spend that money.
Let’s get specific! Stephen Ju of UBS estimates that Amazon’s AI revenue — 73%+ of which is OpenAI and Anthropic’s compute spend and revenue share (per Barclays) — accounts for 26% of AWS’ 2026 revenue and 30% of AWS’ 2027 revenue. Michael Turrin of Wells Fargo estimates that 25% of Microsoft Azure’s (calendar year) 2026 revenues come from AI (of which OpenAI is an estimated 70%). Brad Zelnick of Deutsche Bank estimates that AI will contribute 33% or more of Azure’s revenues in FY2027.
As I mentioned last week, UBS estimates that 48% of Google Cloud’s 2027 revenues will come from OpenAI and Anthropic. Zelnick of Deutsche Bank also projects in its most-likely scenario that AI revenues will make up 37% of all Microsoft’s cloud revenues in FY2029.
I feel like I need to spell this out more. These are analysts from major banks and financial institutions. Their estimates, which are based on detailed financial models, inform Wall Street and investors’ expectations, as well as informing Bloomberg Intelligence’s consensus estimates for revenues. These are serious numbers and Wall Street will be mad if they are not met!
The other problem is that cloud is becoming an increasingly-larger part of the revenues of these companies. See the below chart that bakes in consensus analyst estimates up to 2029:
I, again, will simplify: if more and more of the revenues of these three companies are coming from cloud segments that are increasingly-dominated by AI revenues mostly driven by two unprofitable, unsustainable companies, then the literal future of Microsoft, Google, and Amazon is whether Anthropic and OpenAI can pay them. This is not complex, it’s not contrived, it’s not doomerism or hating, these are the estimates from analysts and what they require to stop them from putting executives in The Wicker Man.
You can cut this situation in any way you want, but there’s no getting away from the fact that we’re four years in and the vast majority of demand comes from two companies that can’t afford to sustain it, and won’t be able to even under the most mold-poisoned of booster projections. Hyperscale growth is contingent on the success of their AI plays, and at 70% of AI revenues, “AI plays” refers to “two unsustainable AI labs.”
Perhaps another visualization would help! Below is a chart of the expected percentage of year-over-year growth that cloud revenues are estimated to contribute on a quarterly basis to revenues. Cloud revenues are the lynchpin of growth for Microsoft, Amazon and Google, though for whatever reason analysts estimate that YouTube and Google Search will re-accelerate.
This is a huge issue when 33% of Azure revenue, 48% of Google Cloud, and (per Ken Gawrelski of Wells Fargo) 60% of AWS revenue growth is coming from companies that have been, assuming all the money crosses, sent a combined $115 billion from Amazon and Google in 2026 alone.
I realize I’m repeating myself, and I’m sorry, but it’s all so insane! The future of some of the largest companies on the stock market is contingent on both spending hundreds of billions a year in capex and the continued existence of the only real customers for AI compute.
Sidenote: None of this even discusses Oracle, which is completely dead if it doesn’t either drastically pull back on or entirely cancel its capex, as I wrote back in January, April, and July. Congratulations to the New York Times on getting there a few weeks later, I’m sure this remarkably-similar sentence to mine was a coincidence.
The counter arguments are, from what I can tell, as follows:
To be clear, “growing super fast” is no longer sufficient for OpenAI and Anthropic. Assuming that OpenAI actually intends to pay for its reported $750 billion in compute commitments through 2030, it will have to raise hundreds of billions of dollars a year while also having the actual demand necessary to use that compute. No matter how big, handsome, and amazing you think either of these companies are, their expected compute spend will require them to make as much revenue as Microsoft, Google and Amazon in the next four years, and if they don’t, hyperscalers will not meet analyst and investor expectations.
To make matters worse, for them to even be able to pay hyperscalers, capex investment must continue, as it’s become blatantly obvious that the capacity necessary to make all this money doesn’t currently exist.
Hyperscalers have not yet spent the money necessary to reap the “rewards” of their massive contracts with OpenAI and Anthropic, and analysts estimate that these three companies will spend another $1.5 trillion through the end of 2027.
So, again, let’s review:
The demand for AI compute does not exist at scale outside of Anthropic and OpenAI, and it is not emerging anywhere that I can see. We are no longer in a situation where single or even double-digit demand for AI compute is sufficient. Based on the amount under construction, we need — even with OpenAI and Anthropic — hundreds of billions of dollars’ worth of demand in the next few years just to monetize the data center capacity under construction.
AI boosters will insist that this is happening in the shadows, and that “all available compute will be used,” making the mistake of conflating scarcity of GPUs with overwhelming demand. If Microsoft 70% of Microsoft’s estimated $34.43 billion in AI revenue is from OpenAI, that leaves over a depressingly-low $10.33 billion across every single possible AI service that Azure has, including renting GPUs, AI models and Microsoft 365 Copilot…which means that, in the literal best-case scenario, there’s low-single-digit billions of revenue in AI compute to non-AI labs.
Sidenote: Turrin of Wells Fargo has the estimated revenues at $34.5 billion, with 365 Copilot revenue at an atrocious $3.858 billion. It has “other AI revenue” at $5.2 billion, which includes all AI GPU and API access revenue. Stinky!
If there was meaningful demand for AI compute or AI software, Microsoft would be representative of it as one of the largest vendors of both cloud software and cloud compute. Microsoft would, by virtue of its massive infrastructure and brand recognition, be receiving a large share of blue chip GPU rentals, as would it be representative of the ability for anyone to sell AI software at scale.
$10.33 billion in annual revenue is a catastrophic failure. It is around a quarter of Microsoft’s $41 billion in Q4FY2026 capex. It suggests that there is a calamitous lack of demand across both those renting GPUs and market demand for software built on top of AI models, and that Microsoft spent $261 billion in capex since 2022 to create annual revenues that amount to less than a third of the quarterly revenue of the Intelligent Cloud segment ($39.31 billion).
There is no spinning this positively other than to ignore it outright. If Microsoft doesn’t have the demand, nobody has the demand. No, $10 billion is not “a lot,” especially for a company with tens of thousands of salespeople, a huge customer base, and a headstart of several years. Amazon and Google are doing equally poorly, which is why nobody wants to talk about their actual AI revenues.
In fact, folks, it’s time for a thought exercise! Wells Fargo estimates that Microsoft’s FY2027 AI revenue will be $54.5 billion, or roughly 58.8% year-over-year including OpenAI’s compute spend. If we increase it again by that much, FY2028 will be at $86.55 billion.
Consensus estimates have Microsoft spending $186.4 billion in FY2027 and $214.3 billion in capex in FY2028. It’s hard to square how exactly this ever pays off.
I’m gonna say it with my full chest: anyone who said that “AI was paying off” for Microsoft, Google, Amazon, or Meta was wrong. Everybody who said the capex was well-spent was wrong. They are yet to admit they’re wrong because revenue growth has yet to slow and stock prices remain elevated.
And that last part is why everybody got it wrong.
As I discussed in last week’s premium, hyperscalers started buying GPUs because their overall revenue growth had begun to slow over the course of a little over a decade, with everyone — NVIDIA included — hitting a wall in 2022:
Microsoft’s FY2023 revenues only grew 6.9% year-over-year. NVIDIA’s FY2023 (which ran February 2022 to January 2023) was effectively flat, sitting at 0.2% as the post-pandemic surge of demand for gaming GPUs and networking gear puttered out, with Q4 2023 revenues dropping by 21% year-over-year.
Meta, Google, and Amazon, all of which have fiscal years that align with the calendar year, saw growth deteriorate:
Google went from 41.6% year-over-year growth in 2021 to 10.3% and 9.7% in 2022 and 2023.
Meta went from 37.2% year-over-year growth in 2021 to negative 1.1% in 2022 and 15.7% in 2023.
Amazon went from 37.6% year-over-year growth in 2020, to 21.7% in 2021, to 9.4% in 2022, to 1.8% in 2023 (and has really never recovered.)
While buying GPUs didn’t really help revenues until OpenAI and Anthropic became big enough to start feeding hyperscaler and venture capital cash into Microsoft, Google, and Amazon’s mouths, buying GPUs became a dick-measuring contest that pumped stock values, all as Wall Street assumed every dollar of revenues came from AI.
In 2023, Microsoft, Google, Apple, Meta, Amazon, and NVIDIA added trillions in market capitalization, with the media actively encouraging them to spend more money on capex. It didn’t matter that Microsoft missed on cloud revenues in Q4 FY2025, much like how Amazon’s Q3 2024 earnings — which specifically missed expectations for cloud, the only place that Amazon was making any money from AI — caused its stock to pop because overall revenues were higher than expected.
In fact, I think that’s mostly what kept this going. I ran the numbers on the premium over two five-year-long periods — 2015 to 2020 and 2021 to 2026 — and found that while stock returns were dramatic, actual revenue growth has slowed dramatically.
As you can see, revenue growth, outside of Microsoft, slowed dramatically, all as PP&E (properties, plants and equipment, the part of the balance sheet where they keep GPUs and data centers — and other stuff, obviously) grew by $754.5 billion.
I’ll get back to that in a little bit.
Yet because the stock price went up, everybody assumed that every dollar of revenue came from investments in AI GPUs. In 2023, a year when AI likely contributed less than $4 billion including OpenAI’s compute spend, Business Insider said that its AI bet was “already paying off,” all because Azure kept growing:
CFO Amy Hood said on a call with analysts that Microsoft expects revenue growth at its cloud division, Azure, to be 26% to 27% next quarter from the same quarter the year prior — with "roughly 1 point from AI services."
To be clear, one whole revenue point is pathetic.
Anyway, in both April and October 2024, The Guardian reported that Microsoft was “sailing” as the “AI boom fueled double-digit growth in its cloud business” in a year where (based on working back from Wells Fargo’s estimates for FY25, which started in Q3 2024) it’s estimated to have made less than $6 billion in total revenue from anything AI-related outside of OpenAI. Microsoft’s total revenue for that fiscal year was $281.7 billion.
In October 2025 — the end of Fiscal Year 2025 — Business Insider would again say that its AI bets had paid off, specifically adding that “Microsoft's AI push also increased revenue by 15% to $281.7 billion.”
Per Wells Fargo’s estimates, Microsoft’s total AI revenue for FY2025 — including what it received from OpenAI — were $14.86 billion, or around 5.28% of revenue, or roughly 6.1% of annual growth for in a year it spent $64.6 billion in capex. When you remove OpenAI’s estimated $9 billion in compute spend, that leaves around $5.7 billion in AI revenue, or around 2% of overall revenues for the year.
The rationale is pretty simple:
Well, they did, and there wasn’t. You can fart around all you want about the theoretical or imaginary promises of AI or AGI or whatever, but this didn’t work.
Yet all of this kept going because the tech industry’s collective reality is based on stock prices, Twitter, and a tech and business media that appears to fall for just about anything as long as a wealthy person says it.
And this chart is the entire reason:
I maintain that 2021 broke the world for many reasons, but one of them is that it set unrealistic revenue goals as money flooded back into the economy post-pandemic off the back of the most-pornographic years of Zero Interest Free Money Policy.
To explain, I’m going to crib a little from my latest premium, The Hater’s Guide to NVIDIA (Part 2).
The post-2021 hangover was brutal. Meta, Google and Amazon, all of which have fiscal years that align with the calendar year, saw growth deteriorate:
Microsoft’s FY2023 (July 1, 2022 through June 30, 2023) revenues only grew 6.9% year-over-year. NVIDIA’s FY2023 (which ran February 2022 to January 2023) was effectively flat, sitting at 0.2% as the post-pandemic surge of demand for gaming GPUs and networking gear puttered out, with Q4 2023 revenues dropping by 21% year-over-year.
Nobody really knew what to do, with just about everybody getting their asses handed to them by the markets.
Yet the savior was already incubating. In March 2022, NVIDIA announced the Hopper GPU architecture, and while initial sales and shipments in September were good, they weren’t enough to restart growth until the November launch of ChatGPT convinced everybody that they had to do AI, and that the only way to “do AI” was buy GPUs. The very same month, Microsoft and NVIDIA announced they were building another OpenAI supercomputer using Hopper.
Something about ChatGPT would fundamentally break the brains of Microsoft’s competitors.
At 1 p.m. on a Friday shortly before Christmas [2022], Kent Walker, Google’s top lawyer, summoned four of his employees and ruined their weekend…
…[because] the entire agenda of the company had changed — all in the course of nine days. Sundar Pichai, Google’s chief executive, had decided to ready a slate of products based on artificial intelligence — immediately. He turned to Mr. Walker, the same lawyer he was trusting to defend the company in a profit-threatening antitrust case in Washington, D.C. Mr. Walker knew he would need to persuade the Advanced Technology Review Council, as Google called the group of executives, to throw off their customary caution and do as they were told.
It was an edict, and edicts didn’t happen very often at Google. But Google was staring at a real crisis. Its business model was potentially at risk.
What had set off Mr. Pichai and the rest of Silicon Valley was ChatGPT, the artificial intelligence program that had been released on Nov. 30, 2022, by an upstart called OpenAI. It had captured the imagination of millions of people who had thought A.I. was science fiction until they started playing with the thing. It was a sensation. It was also a problem.
ChatGPT immediately gave executives AI psychosis. In January 2023, Microsoft would invest another $10 billion, and a few weeks later, Google would sign a partnership with early-stage AI firm Anthropic (founded by former OpenAI executives) to use its TPUs and GPUs as its “preferred cloud provider,” only for Amazon to barge in and invest $4 billion a few months later in September 2023, making AWS “Anthropic’s primary cloud provider,” which forced Google to invest up to $2 billion in October 2023. By the third quarter of 2023, NVIDIA would be selling half a million H100 GPUs, primarily to Microsoft, Amazon, Google and Meta, which had just farted out its own open source ChatGPT “competitor,” Llama.
NVIDIA was saved. Q1 FY24 (May 2023) revenues blew estimates out of the water, and by Q2 FY24 (August 2023), data center demand caused revenues to jump 170% year-over-year. Microsoft, ever helpful to its good friend and collaborator, would sign a multi-billion dollar deal with CoreWeave to rent capacity in June 2023, allowing it to raise $2.3 billion in debt a few months later, taking advantage of the “ChatGPT moment” that was when “things got real” to quote CTO Brian Venturo.
By the end of FY24, NVIDIA’s revenue had jumped 125.9% year-over-year. Everybody went AI crazy. The media would fall over itself claiming that AI could do basically anything, and justify one of the largest expenditures in history. This was partially helped by a media-driven hype campaign around the availability of GPUs, which was mostly caused by NVIDIA being the only vendor and selling the vast majority of them to hyperscalers who were yet to really show any return on their investment. Per the New York Times:
Their desperation is palpable. On social media, blog posts and conference panels, start-up founders and investors have started sharing highly technical tips for navigating the shortage. Some are gaming out how long they think it will take Nvidia’s wait-list to clear. There’s even a groan-worthy YouTube song, set to the tune of Billy Joel’s “We Didn’t Start the Fire,” in which an artist known as Weird A.I. Yankochip sings “GPUs are fire, we can never find ‘em but we wanna buy ‘em.”
Nevertheless, revenue was growing, seemingly in line with capital expenditures, and as hyperscalers realized that the media and the markets had toddler-like attachments to reality, they piled into NVIDIA GPUs en masse.
And man, FOMO was in full force. The GPU shortage was timed perfectly with one of the worst years in the history of venture capital, creating an air that the only way to get out of the depths of Hell was to invest in AI in any way, shape or form for both startups and hyperscalers alike.
Yet when you look at the numbers, very little actually changed for the hyperscalers. Since the launch of ChatGPT, year-over-year growth has never returned to pre-2022 levels, other than for Microsoft, which hit its highest year-over-year growth (17.8%) since FY2022 (18%) after a prolonged period in the 14-percents.
Everybody conflated the massive capex spend with the return of growth to the tech industry versus an industry-wide swindle. Hyperscalers were rewarded with stock pumps and pay bumps for an “AI revolution” that mostly amounted to spending hundreds of billions of dollars on GPUs to make tens of billions of dollars in revenue, all because revenue kept growing and both analysts and the media refused to talk loudly about the lack of any payoff.
The media’s credulousness was used against it. The assumption, as I mentioned, was that all this money wouldn’t be spent without an obvious return, and because the numbers are so flabbergasting, it’s easy for you to say “$10.33 billion is a lot of money!” (because it is) and to dismiss the massive costs as “just part of building the infrastructure,” even if it isn’t clear how these numbers ever match up in the future.
For whatever reason, the media continues to give hyperscalers and anybody in AI the benefit of the doubt when it comes to the efficacy and outcomes of large language models or the catastrophic economic mismatch in the returns. Every time the response is “these are smart people!” or “these are the early days!” or “it’s just like the dot com bubble!” because nobody is particularly interested in being right so much as they are about being right about the particular consensus of a particular moment. While I understand the professional harms of saying that AI was bullshit in 2023 or 2024, there was never any excuse to automatically give hyperscalers credit for “AI paying off” at any point in history, and further excuses of “it being the early days” are intellectual crutches” used by people that either want the powerful to win, have a vested interest in doing so, or have resigned themselves to watch it happen.
The fact that the media has actively shrugged off the 70% story (outside of Bloomberg’s coverage, at least) is a sign that it doesn’t really want to reconcile with the truth, and honestly, I kind of get it. When you’ve spent three years saying that hyperscalers were growing because of their vast spend on AI, filling in the gaps of every narrative and assuming they don’t want to tell you revenues because they’re oh-so-good, it’s hard to move in reverse.
The other problem is the monstrous and abusive marketing campaign from the AI industry itself, and those who use AI on a regular basis. If you are against the consensus that AI will grow ever-larger every single quarter forever, you will be harassed and dogpiled across multiple social media platforms by everyone from AI influencers to actual journalists. The fact that it’s more professionally dangerous to critique the powerful than it is to align with them is disgusting, but I should be clear that these tactics only reinforce that I’m on the right track.
As Nik Suresh noted in his recent piece, refusing to say that AI is giving you massive productivity benefits will lead to actual professional consequences, because so much is riding on the overall grift about what AI can do (which is much, much less than the boosters will promise). This runs antithetical to productivity or good sense, and everybody involved in it should be both eternally shamed and shunned from any sensible business.
And while the AI industry and its fandom will claim that people like me are “skeptics” and “haters,” the outright hatred and vitriol that they spew for not falling in line behind a nakedly false narrative built on outright disinformation is disgraceful.
It only serves to prove my point that something is very, very wrong with the tech industry, our markets, and the overall information ecosystem.
When I wrote the Rot-Com Bubble in 2024, the AI bubble was a series of exploits used to mask the end of tech’s era of hypergrowth.
The tech media’s immediate attachment to AI and ChatGPT was nothing to do with actual technology and everything to do with their attachment to being involved in whatever future the powerful decided had arrived.
After years of depressing coverage of cryptocurrency and the metaverse and a deeply-depressing 2022, ChatGPT represented a product they could use, be built upon to create other products they could use, and lead to an entire era of new people to follow and report on.
It gave retail investors a reason to dump money into stocks. It gave founders an API to build on top of, an infrastructural layer to smooth out, and a dream to sell venture capitalists who had near-unilaterally sucked at their jobs for years, all wrapped in a fuzzy sense of “progress” that mean that valuations could be high and the time horizon for returns could be effectively infinite.
And that last part is what was so important for everybody involved. Startups, Microsoft, Google, Amazon, Meta, CoreWeave, and anyone else involved in the AI bubble were immediately given near-infinite runway and manufactured consent to burn as much money as they wanted to.
Even today — in the third quarter of the year of our lord 2026 — I am still asked on podcasts “whether we’re in the early days.” This is the power of narratives, and how willing so many people are to explain away the failures of the powerful rather than having the courage to face them.
And it’s all because the stock prices haven’t gone down yet.
Sidenote: Need an example? Legal AI startup Harvey is currently raising a $500 million funding round at a valuation of $15.5 billion, boasting that its “annualized revenue” (a month times twelve, also known as $29.1 million a month in revenue) has hit $350 million.
Harvey last raised in March — $200 million at a $11 billion valuation, two months after it hit $190 million in annualized revenue, better known as $16.6 million in monthly revenue. If it completes the round as discussed, Harvey will have raised $1.2 billion in the last year or so, and I have yet to read a single story that reconciles a company making less than $30 million in monthly revenue needing over a billion dollars, or shows any kind of concern about that at all.
The argument here, by the way, is that “the goal for Harvey is to get every law firm as a client.”
I have to give credit to Jensen Huang, though.
He realized quickly that reality is dictated not by actual revenues but how you can manipulate the market with those revenues. NVIDIA’s continued position as the largest company on the NASDAQ relied upon a near-constant flow of new orders of GPUs, but brainwashed investors, misinformed by a media with few good information sources, had begun to conflate both GPU purchases and any revenue growth with purchasing them.
The power of the narrative is such that everybody has rationalized what’s happening as “good investment.” Hyperscalers spending hundreds of billions on capex makes sense because AI is driving growth, and the market is at all time highs.
The same extends to the endless flow of nebulous circular deals, like the supposed $500 billion infrastructure deal between NVIDIA and The Avengers of Private Credit, including Apollo, Blackstone, BlackRock, Brookfield, Goldman Sachs, and KKR.
While NVIDIA dumped a little on the deal, the media still doesn’t seem to think this is a bad thing, even though it’s the loudest possible sign that there isn’t actually real demand from anyone that can actually afford to buy these GPUs. Even then, the “$500 billion” deal isn’t even a $500 billion deal, per The Wall Street Journal (which has, for some reason, deleted this paragraph from the story):
The massive financing would involve different vehicles, rather than one large collaboration, according to one of the people. The commitments could grow larger than $500 billion over time, this person said.
Oh, okay. So it’s not actually $500 billion. It’s a bunch of smaller deals. Great. Sure. Anyway, surely you must think this is a little worrying? That this is what NVIDIA is reduced to doing to keep up with demand?
What? You think it’s a good thing?
The financing could help signal to stock investors that Nvidia and its partners in the artificial-intelligence boom have the firepower to build the infrastructure they need. Debt investors have lent companies hundreds of billions of dollars over the past year through direct bond sales and debt deals tied to individual data-center projects.
Jesus fucking christ. Not a single word about the fact that the only two companies that would actually want this compute can’t afford it, or how much people are spending on compute (you know, the thing that data centers sell), or anything about AI at all beyond that it’s “part of the infrastructure buildout.”
Yet when you actually open the press release (which I found on my Terminal but cannot for the life of me get a link to), there’s one glaring detail everybody left out, emphasis mine:
Memorandums of understanding signed with six of the world’s premier financial institutions to create these partnerships aim to establish the first compute financing platforms of their kind at global scale to enable the AI infrastructure buildout across NVIDIA’s ecosystem, including leading frontier AI labs, enterprises and AI clouds. Under these strategic partnerships, NVIDIA will work with Apollo, BlackRock, Blackstone, Brookfield, Goldman Sachs and KKR to create dedicated pools of capital at significant scale at attractive rates for NVIDIA customers.
That’s right folks. There is no deal! It’s an MOU! It’s fucking theoretical! And I have not seen this fact reflected in a single god damn story about this god damn “deal” in one god damn place! It’s from the press release from the fucking companies!
This “deal” is also very, very weird, with NVIDIA claiming it’s “establishing independent compute financing platforms” with the largest asset managers in the world. It isn’t clear what the money will do, where the money will flow, who it will flow to, when it will flow there, how it will be structured, from whom the money will be raised, or really anything other than “number so big, number so huge.”
This announcement — and that is, at this point, all it is — exists entirely to have people say that NVIDIA has “booked $500 billion in revenue,” even though even in the kindest possible read not a single dollar has actually been raised, nor has a single actual contract been signed. If you need an example, take NVIDIA’s $100 billion investment in OpenAI that also involved it building 10GW of compute capacity — a memorandum of understanding that never materialized in a deal.
Here is what Jensen Huang had to say about said memorandum of understanding:
“It was never a commitment,” Huang told reporters in Taipei on Sunday. “They invited us to invest up to $100 billion and of course, we were, we were very happy and honored that they invited us, but we will invest one step at a time.”
You’ll notice there are no actual details about any deals happening, mostly because nothing has actually happened beyond a few marketing calls and a lot of heavy breathing from the press. No money has been raised, what will likely happen — if anything — is that NVIDIA will end up backstopping a few $10 billion data center deals, or perhaps invest a few billion in equity into an SPV built to raise debt to buy GPUs as it already did with xAI.
Jensen Huang has said as much in his hilariously-oafish announcement of the MOU:
In some cases, NVIDIA may provide a residual-value support mechanism for up to 25% of an opportunity, assessed carefully on a project-by-project basis. That support is limited, residual-value based and designed to complement — not replace — independent underwriting.
Hey, wait a second, is this circular financ-
Is this circular financing?
This initiative is designed to address that concern. We are bringing independent, long-term institutional capital into the AI infrastructure market.
The demand is real: it comes from frontier AI labs, AI-native startups, enterprises, cloud providers and countries building AI services. The capital providers independently underwrite each project — including the customer, demand, utilization, cash flow and residual value. NVIDIA provides the platform; the investors make independent financing decisions.
This is the beginning of an open capital market for AI infrastructure.
Folks, this isn’t circular financing at all! It’s just that NVIDIA will pay some sort of 25% “residual-value support” so that private credit can use that as collateral to raise debt to buy GPUs from NVIDIA. If anything it’s spherical!
Look, Jensen, if the demand was real, you wouldn’t have to announce a rinky-dink-maybe-$500-billion-no-IT-loads-refused-MOU!
If there were actual diverse demand for NVIDIA’s GPUs commensurate with analyst expectations, you wouldn’t have to do these bizarre, painfully-circular deals that exist only to inflate its revenues and further prop up the existence of unprofitable AI labs!
Anyway, if you’re wondering about what the point of this all is, Jensen Huang has your answer:
Where is the return on investment?
The return is in the usefulness of AI.
Companies are using AI to write software, discover drugs, design products, serve customers, automate operations and build new services. AI factories make this possible. More compute creates better AI; better AI creates more usage; more usage creates more revenue; and more revenue drives more compute.
This is the virtuous cycle of the AI industrial revolution.
We are four fucking years and over a trillion dollars into this garbage, Jensen! This is the best you can do? THIS? AHHHHH!
In FY2027 — which began on February 1, 2026 — analyst consensus has NVIDIA’s revenues at $393.7.6 billion for the year, growing to $565.7 billion in FY2028 and $694 billion in 2029.
What this means is that despite hyperscalers spending over a trillion dollars on AI data center capex in 2026 and even more in 2027, that’s just not enough to keep up with Wall Street’s expectations.
To get specific, NVIDIA’s FY2026 revenues were $215.9 billion, with 89% of that coming from the data center segment (read: GPUs and the associated gear), and this is with the near-entire focus of the world’s largest companies and credit markets on building AI data centers and banks that fear they’re “choking” on data center debt.
Sidenote: per my last premium newsletter, UBS estimates that around 50% of NVIDIA’s data center revenue comes from Meta, Google, Microsoft, Amazon and Oracle, with Deutsche Bank estimating it’s as high as 60%.
Analyst expectations are set to believe that it will triple its revenue in the space of two years. Honestly, $500 billion wouldn’t even be enough. NVIDIA needs every hyperscaler to keep spending more capex every single quarter, without fail, as well as hundreds of billions of dollars’ worth of new AI chip spend to arrive from an industry where the only two companies with any real need for all this compute have only ever lost tens of billions of dollars, and literally can’t afford to pay for it.
I realize many people get number blindness past a certain scale, so I will put it very simply:
I also understand why people want to bury their heads in the sand here. Right now, the numbers are all the highest they’ve ever been, and they keep going up, which means that anyone saying that things are going wrong has to expose themselves to torrents of abuse and aggression from both posters and peers.
I also think doing so is an act of cowardice.
While I don’t expect people to start saying that this is all bullshit and headed for the gutter, I see an astonishing flippancy about everything I’ve been writing about from much of the mainstream media. To not warn people that AI revenues are heavily-centralized around two companies that burn endless billions of dollars, and that the commensurate demand isn’t there as a result, is to both fail your readers and actively empower the powerful.
I haven’t even gotten into the $1.65 trillion in off-balance sheet obligations. It’s unclear how hyperscalers afford them if OpenAI and Anthropic can’t afford to pay them.
It’s unclear how any of this works.
And then there’s the problem that for the 190GW of capacity in planning, we need somewhere between $1.62 trillion and $2.92 trillion in annual compute spend for an industry that, in the best-case scenario, has roughly $110 billion in annual demand, with 90%+ of that coming from two companies that can only spend that money if they’re fed it by venture capitalists.
Everyone can — and will — keep ignoring what’s happening as long as it requires an ounce of courage to think about reality. Many will bury their heads in the sand, make the kindest reads possible of every AI story, pump up every AI narrative, and celebrate every mediocre “achievement” right up until NVIDIA or a hyperscaler misses on analyst expectations, or AI labs start circling the drain.
Everything you’re seeing right now is an attempt to extract further capital and hype from the system to continue inflating the bubble. Every single asset manager in the NVIDIA “deal” has some sort of investment in AI data centers and/or neoclouds (especially Blackstone, who has been in CoreWeave since its earliest days), and absolutely nobody gives a shit about what LLMs can do outside of their ability to con investors and generate fees for their funds.
Every journalist that continues to ignore the obvious instability, circularity and centralization of the AI industry fails their audience by not discussing it before any discussions of AI’s theoretical returns or abilities. It is no longer ethically sound to ignore the problems. Do with that statement what you will.
In the end, it’s pretty simple: Microsoft, Google and Amazon’s hypergrowth era ends if Anthropic and OpenAI can’t spend hundreds of billions of dollars on compute, and companies like Oracle, CoreWeave, Nebius, and IREN face apocalyptic circumstances when they fail to do so. NVIDIA’s future is entirely dependent on these companies’ abilities to convince the credit markets that everything will go fine, and its circular financing operations are both sustained and continue to expand.
And every VC invested in AI needs a miracle, because there are few signs that these companies can be sold to anyone or taken public.
When it becomes safe to do so, many will attempt to either rationalize ignoring reality or pretend they saw it coming.
If they did, they chose not to tell you. If they didn’t, they chose not to look.
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If you want to get in touch — and especially if you have any juicy information about Anthropic, OpenAI, or any other companies in the AI bubble — hit me up on Signal at ezitron.76. I’m also on IB on The Terminal.
2026-08-08 01:30:26
For a little under a year, everyone — myself included — has compared NVIDIA to Enron, largely because NVIDIA insisted, in detail, that it was nothing like Enron, WorldCom, or Lucent, a potent example of the Streisand Effect that would be much funnier if NVIDIA wasn’t holding up more than 7% of the value of the NASDAQ.
And as I covered in the first part of the Hater’s Guide To NVIDIA last year, there are material concerns about how the company makes money today and will continue to do so in the future.
I will concede that NVIDIA isn’t exactly like Enron in the sense that it isn’t, to my knowledge, doing anything outright fraudulent, like attempting to hide massive amounts of debt inside SPVs as Enron did with its “Raptors,” which I must be clear are distinct from the SPVs used in AI data center debt, though I’ll add that something being legal doesn’t make it a good idea or ethical.
That being said, NVIDIA CEO Jensen Huang has employed many of the same tactics used by Lucent, Nortel, and many of the big dot-com busts, but has been smart enough to make everybody else carry the risk.
Instead of doing direct vendor financing like Lucent did with Winstar (where it effectively loaned its customers money to pay it with), NVIDIA funded neoclouds like CoreWeave, Nebius, and IREN, operating as an early stage investor, IPO anchor, post-IPO investor, $6.3 billion customer and data center lease backstop, allowing them to raise tens of billions of dollars’ worth of debt from overly-eager asset managers and banks, allowing it to do basically the same thing as vendor financing without having to take on any of that messy risk.
These deeply-unprofitable, cash-intensive, debt-riddled companies exist for one purpose — to raise debt to buy NVIDIA GPUs — and would have fallen apart without the AI hype cycle and NVIDIA’s continued backing. Per Kakashii:
In an April 2026 interview with Dwarkesh Patel, Jensen Huang acknowledged my thesis and said it out loud: “In the case of clouds, if we didn’t support CoreWeave to exist, these neoclouds, these AI clouds, wouldn’t exist. If we didn’t help CoreWeave exist, they would not exist. If we didn’t support Nscale, they wouldn’t be where they are today. If we didn’t support Nebius, they wouldn’t be what they are today.” And, confirming that Nvidia wants lots of neoclouds, not just one: “Don’t pick winners. Either let them all take care of themselves, or take care of all of them.”
That is, in Nvidia’s own CEO’s words, the operating logic behind this sector. Rather than build a cloud business on its own balance sheet, which would put Nvidia’s own results directly at risk if utilization or pricing disappointed, Nvidia has helped create and sustain an entire class of nominally independent companies that take on the capital outlay, the construction risk, and the debt of building AI data centers, while Nvidia supplies the chips, frequently invests equity alongside the debt, and in a growing number of cases finances the build out directly.
In other words, NVIDIA has managed to find a way to do vendor financing without ever having to provide any, finding willing supplicants in the various backers of CoreWeave and other neoclouds that would be willing to front the money, all under the mistaken belief that they were funding the next industrial revolution.
To explain exactly how it works, I’ll return to my imaginary scenario from the Big Short 2:
HUANG: So there’re these companies I invest in that, at least in theory, build data centers using my AI GPUs, but I need them to buy more GPUs, so I sign a contract saying that I’ll rent the GPUs back from them in the future. Because NVIDIA has such a strong balance sheet, these companies can raise billions of dollars to buy my GPUs just because I promised to rent them in the future, and the best part is all the risk is held by the companies and the investors. When I need more money, I just sign another contract, they raise more debt, I sell more GPUs.
BAUM: So — just so I have this clearly — you, the guy who makes the GPUs, invest in companies that exist pretty much to buy GPUs from you and rent them to customers. Except you’re the customer too, and a big one.
HUANG: That’s right. We call them neoclouds. S&P just revised CoreWeave’s outlook to positive.
BAUM: That’s fucking crazy.
HUANG: It’s not crazy — it’s awesome.
It’s a win-win-win for NVIDIA, its customers, and the bankers involved. CoreWeave gets to raise more debt and keep its investors strung along on the still-theoretical, ever-expanding timeline of a return on invested capital, bankers get a slew of fees for pulling together the deal, and NVIDIA guarantees itself billions of dollars of business.
And this approach is something where any investment by NVIDIA has a habit of being amplified by others — like Australian startup Firmus, which just raised $2bn from a bevy of investors (including NVIDIA, which had also backed an earlier round), Jane Street, and Blackrock, with a significant chunk of that money guaranteed to go towards NVIDIA GPUs. NVIDIA also participated in Firmus’s previous $300m round, although was not listed as a “cornerstone investor.”
Earlier this year, Firmus secured a $10bn debt facility, led by Blackstone. NVIDIA will be a net beneficiary of that debt raise, and I would argue that its participation in the company’s fundraising — as well as the various announcements of partnerships between the two — has been instrumental in both the company’s fundraising and its ability to secure debt.
Sidenote: According to an AI industry insider interviewed by AlphaSense, Nebius was experiencing financial difficulty — the existential kind — and was saved by NVIDIA, which swept in and offered it access to hardware under a revenue sharing model. The insider also notes that neoclouds hate said revenue sharing agreements, because they tend to stack the deck in NVIDIA’s favor.
You’ll notice I haven’t mentioned “AI” or “LLMs” up until this point, and that’s because technology has, for the most part, very little to do with these transactions. As I discussed in this week’s free newsletter, 70% or more of hyperscaler revenues are from OpenAI and Anthropic, and CoreWeave’s largest customers are Microsoft (for OpenAI), Google (for OpenAI), Anthropic, NVIDIA itself, and Meta. Customers are not coming to it for any particular technological moat or unique offering outside of its ability to sling more NVIDIA GPUs to the same customers that everybody else has.
While GPUs technically are used for AI training and inference, their relationship to NVIDIA is only as good as their ability to create more hype. As I discussed a few weeks ago, it has promised somewhere between 10x and 25x “operating cost savings” with every successive generation of GPUs, though it’s never really clear how that manifests or what it actually means, or whether any of that even matters to OpenAI and Anthropic, its largest customers by proxy.
Nevertheless, it’s pretty difficult to work out what each generation really changes. SemiAnalysis claims it “delivers 5.4x performance per MW and 5x performance per dollar against [the previous generation] GB200 NVL72,” but that’s for DeepSeek R1, a year-and-a-half old open source model that’s vastly smaller and less-powerful.
But that’s not really a problem, because all NVIDIA needs to do is keep up the appearance of innovation in as precise or imprecise a way to justify increasing prices with each new generation, and to convince people that they’re building “AI factories” as they fund data centers for customers that don’t really exist outside of the big AI labs. While NVIDIA has thousands of talented engineers building its GPUs and the associated software, the only real purpose is to create a vague sense of “more” and “bigger” and “more powerful” to justify racks of 72 GPUs that are more than twice the price of their predecessors.
That’s because NVIDIA is no longer a technology company so much as it is an asset management and marketing firm that happens to sell semiconductors. To that point, I believe that the comparisons to Enron, Lucent, and other dot-com flameouts are on the right path, but misses one very, very obvious comparison: GE Capital, the financial services of General Electric, specifically in the Jack Welch years that I covered two years ago in the Shareholder Supremacy.
Welch’s GE did whatever it needed to to survive, buying and selling companies to help boost GE’s earnings every quarter, and eventually grew into what David Gelles would call a “large, unregulated bank,” to the point that GE Capital was at about $425 billion in assets in 2001 (and about 50% of GE’s revenue), providing everything from direct leases of equipment to assuming its customers debts to investing directly in its customers, all to make sure that, well, said customers continued being able to buy GE gear.
Unlike GE Capital, NVIDIA has the advantage of a much, much simpler business model and far fewer products to sell, but said advantage is a problem for two brutal reasons: its customers are driven by desperation and a fear of missing out, and its remarkable revenue growth means that it must in turn grow by ridiculous amounts every single quarter from here to eternity.
Yet this problem is driving it to take increasingly-Welchian measures to make sure that demand keeps up with investor expectations. It (per the FT) just signed leases worth as much as $50 billion for a Texas-based data center built by Hut 8, which makes it likely that this capacity is being built for Anthropic, with which it already has multiple deals. In the same piece, the FT mentions that NVIDIA is in talks to backstop $250 billion in compute costs for a still-theoretical 10GW data center in Ohio.
And again, much like GE, NVIDIA uses its stellar credit rating (AA- - two rungs lower than GE at its height) to secure these deals, per the FT:
“They have the balance sheet to acquire power, and in doing so, ensure their product is deployed,” the person said, asking not to be named.
In the end, GE’s greater collapse led to lawsuits, SEC fines and revenue revisions, all as a result of its “aggressive” accounting practices. For example, it was forced to restate its 2016 and 2017 earnings as a result of “new accounting standards” it instituted as a result of an SEC investigation into its insurance and power divisions that eventually cost it a $200 million fine, cutting a remarkable $4.24 billion off of earnings in the period.
While I’m not accusing NVIDIA of anything untoward, it’s impossible to ignore the sheer aggression of its circular financing and willingness to do whatever it takes to keep selling further GPUs. NVIDIA is now a semiconductor manufacturer, a venture capitalist, a lender of last resort,
Today’s premium newsletter is the story of NVIDIA’s descent into circular madness, and how Jensen Huang is increasingly becoming the Jack Welch of AI.
This is Part 2 of The Hater’s Guide To NVIDIA, or WUDA CUDA SHUDA
2026-08-06 02:41:14
Executive Summary:
As I discussed in yesterday's free newsletter, analyst estimates have OpenAI and Anthropic making up over 70% of all AI revenues across Microsoft, Google and Amazon.
While some might have disagreed, Bloomberg is now reporting that OpenAI "accounted for more than half, and likely about 70%, of Microsoft's actual AI sales during its most recent fiscal year."
Bloomberg's maths is explained as such:
"Bloomberg’s analysis assumes that Microsoft’s annual AI run rate continued growing at the rapid 123% the company reported for March. At that growth rate, the company’s AI business would have tallied roughly $34 billion in the fiscal year ending in June, which can be directly compared with the new disclosure that OpenAI provided $24.1 billion of revenue in that year."
The actual disclosure from Microsoft comes from its most-recent earnings:
For fiscal year 2026, we recorded revenue from commercial arrangements with OpenAI, inclusive of revenue sharing payments, of $24.1 billion, and accounts receivable from OpenAI as of June 30, 2026 was $6.0 billion.
The other incredible fact from these disclosures is that OpenAI's spend and revenue share accounted for 7% of Microsoft's $331.8 billion in FY26 revenue - or around 7.26% to be specific.
At this point, it's impossible to ignore that Microsoft has spent $270 billion in capital expenditures to prop up a single client, and that its overall AI plays have failed to create any significant revenue growth or opportunities.
We are now four years into the AI bubble, and Microsoft has little to show for it other than one very large and very unsustainable company that requires near-infinite resources to keep paying its cloud compute bills.
If you liked this piece, you should subscribe to my premium newsletter. It’s $70 a year, or $7 a month, and in return you get a weekly newsletter that’s usually anywhere from 5,000 to 18,000 words, including vast, detailed analyses of NVIDIA, Anthropic and OpenAI’s finances, and the AI bubble writ large. My Hater's Guides To the SaaSpocalypse, Private Credit and Private Equity are essential to understanding our current financial system, and my guide to how OpenAI Kills Oracle pairs nicely with my Hater's Guide To Oracle, as well as the Hater’s Guide To Oracle (Part 2).
Subscribing to premium is both great value and makes it possible to write these large, deeply-researched free pieces every week. On Friday, I’ll publish the second installment of the Hater’s Guide to Nvidia — where I’ll take a look at how the AI bubble transformed the company from a pure hardware player to a purveyor of the financial dark arts.
If you want to get in touch — and especially if you have any juicy information about Anthropic, OpenAI, or any other companies in the AI bubble — hit me up on Signal at ezitron.76. I’m also on IB on The Terminal.
