2026-06-16 11:58:20
Soundtrack: In Flames - Colony
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Today, I can exclusively report, based on audited financial documents viewed by this publication that have been independently verified by the Financial Times, that OpenAI lost around $38.5 billion in 2025, as well as other crucial details about the financial condition of the company.
Due to the seriousness of this story, I am not going to do very much editorializing, as the numbers speak for themselves.
OpenAI’s financial statements tell the story of a company with incredible losses.
Additional factors – including interest income and interest expense – left it with a net loss of $8.84 billion. It then marked $3.74 billion of losses as “net loss attributable to noncontrolling members capital,” leaving the net loss attributable to the company as $5.09 billion.
It’s unclear what this means, nor how OpenAI reconciled the removal of $3.74 billion in costs. I will not speculate further.
Please note that 2025 was the year that OpenAI converted from a non-profit to a for-profit entity, leading to a $41.55 billion loss due to changes in fair value of convertible interests and warrant liability.
Taking into account other minor factors like interest income and interest expense, OpenAI is left with a net loss of $60.35 billion, which it lowered to $38.53 billion by removing $17.87 billion in costs via that “net loss attributable to noncontrolling members capital” and another $3.95 billion via a “net loss attributable to redeemable noncontrolling interests.”
Ultimately, the net loss attributable to OpenAI in 2025 was $38.5 billion.
At the end of the year, OpenAI had just over $50 billion in assets, with almost half of that in cash.
In 2025, SoftBank paid OpenAI $867 million. Microsoft paid it $303 million.
The documents revealed how much OpenAI paid Microsoft for services. In the 2025 calendar year, OpenAI paid Microsoft $10.59 billion for “Research and development” expenses. We believe this most likely refers to the cost of training OpenAI’s models.
The documents also mention a $6.047 billion charge related to “cost of revenue,” a $527 million charge for sales and marketing, and $42 million in “general and administrative expenses.” In total, OpenAI’s expenses to Microsoft amounted to $17.2 billion.
According to the figures, OpenAI had liabilities to Microsoft of $3.64 billion at the close of the calendar year, and additional $21 million in “accrued expenses and other current liabilities.” The documents also mention a further $58 million in non-current liabilities.
I intend to follow up this story in the next month with more in-depth reporting related to the documents. The documents are detailed, and I need time to fully parse them. Once I have done so, you’ll know.
The financial condition of OpenAI is deeply concerning. $38.53 billion in losses are astronomical, and far higher than most believed it would be. Losses also appear to be mounting year-over-year at a dramatic rate, and I’m not sure how this company finds a way toward any kind of sustainability or profitability.
As discussed, I have not editorialized much today. I believe the best thing I can do for the general public is to deliver this news as plainly as possible.
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2026-06-16 03:28:23
If you liked this piece, you should subscribe to my premium newsletter. It’s $70 a year, or $7 a month, and in return you get a weekly newsletter that’s usually anywhere from 5,000 to 18,000 words, including vast, detailed analyses of NVIDIA, Anthropic and OpenAI’s finances, and the AI bubble writ large (updated to version 3.0 last week). My Hater's Guides To the SaaSpocalypse, Private Credit and Private Equity are essential to understanding our current financial system, and my guide to how OpenAI Kills Oracle pairs nicely with my Hater's Guide To Oracle.
Last Friday, I published the first of a two-part series where I explore the many bubbles that form the basis of the AI bubble — including the tokenomics bubble, and the cult of personality bubbles surrounding Sam Altman and Dario Amodei.
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Soundtrack — Local H — Manifest Destiny (Part 2)
We live in a time of deep uncertainty. On Friday, Anthropic was forced to shut off access to its Mythos and Fable models after the US government imposed an export control ban barring any non-US citizens both inside and outside of the country from accessing them.
To explain, Fable is basically Anthropic’s supposedly “too dangerous to release” Mythos model with guardrails forbidding you from what appears to be anything biological weapons and cybersecurity, except it was jailbroken within days by Amazon researchers, leading to Amazon CEO Andy Jassy (and other unnamed companies) reporting it to the US commerce department which gave Anthropic 90 minutes to roll back Fable and Mythos due to “national security risks.” Semafor also reports that this all might have happened because China got access to Mythos.
This situation is a complete mess. PCast co-chair and podcaster David Sacks claimed that Anthropic refused to fix the issue, claiming it wasn’t serious, per Business Insider:
During the calls, Amodei tried to clear up what he assumed was a misunderstanding. He pushed back on the administration's concerns, defended the guardrails, and argued that the type of bypass that occurred, which he believed to be specific, did not pose the same risk as a broader "jailbreak" that would allow it to be used without any of the guardrails put in place by Anthropic.
In a blog post after the export controls were put in place, Anthropic said that "no testers have yet been able to find a universal jailbreak — a jailbreak method that can very broadly bypass the model's safeguards, unblocking a wide range of cyber capabilities," and that total avoidance of any jailbreaks isn't now possible for them or any other companies. They defended their systems, which they said "are so strong that many users have complained that they are overly broad."
A White House official told Business Insider that “export controls were a last resort after begging them for hours to work with us”:
Shortly after the call, the Trump administration imposed its export control on the Fable 5 and Mythos 5 models, citing national security authority and banning their use by foreign nationals, according to Anthropic. The company said the "net effect" of the order was to "abruptly disable" the models for all customers "to ensure compliance."
Anthropic claims no begging occurred, and all it got was (as noted above) 90 minutes. According to Axios, the company has dispatched some of its senior technical staff to D.C to negotiate with the Trump Administration, after virtual meetings with White House officials failed to bear fruit.
In any case, this is a reaping/sowing for the ages. Dario Amodei has spent years selling AI models based on completely fantastical scaremongering about the “rapid advancements” of large language models, cresting the hill in April when he announced Claude Mythos, an LLM that was “too powerful to release” until June 2, when it was released to 150 organizations in 15 countries, and June 9, when it was released with said guardrails under the name “Fable.”
Fable is, of course, just another large language model that’s an indeterminate amount of “better” than the last one. Having talked to multiple people that claim to have used Mythos and deeply enjoyed Davi Ottenheimer’s takedown of its system card, it appears to be much the same model but with security protocols flimsy enough to last only a few days before anonymous researcher Pliny The Liberator broke them. Anthropic has not created recursive self-improvement, nor has it done much more than create a very large language model that gets higher benchmarks in tests built for large language models, wrapped in a veneer of mysticism and panic-hype built to scare organizations in paying them to use it.
The problem with this kind of hype is that you can only use it for so long before somebody believes you. The outright mythology of Mythos existed to scare people and help Anthropic raise at a $965 billion valuation, and because the tech industry has existed fairly divorced from reality, scrutiny, and regulation, Dario Amodei continued to inflate the “Anthropic is too powerful” bubble, believing that all that would happen would that he’d create a new enterprise API business.
Some are attempting to read this story as bullish for Anthropic — that the government will work with it to bring the models back online, creating a proxy marketing campaign for its models — and while I think that’s possible, if not likely, I think there’re many other possibilities.
On Sunday, slopaganidst and Microsoft CEO Satya Nadella posted a mealy-mouthed blog on Twitter that didn’t really say very much of anything, but had two interesting comments:
The last thing any of us want is a world where every company across every sector is ceding value to a few models that eat everything they see. If all the value is accrued by only a few models, the political economy will simply not tolerate it. There is no societal permission for an AI future that hollows out entire industries.
…
In my view, our priority has to be building a frontier ecosystem, not just a frontier model, so value flows broadly across every company, every industry, and every country. One where every organization can own the learning loop that encodes its institutional knowledge, compounding its human and token capital.
This, combined with Microsoft AI CEO Mustafa Suleyman saying Anthropic’s models were too expensive and Andy Jassy likely being part of the reason that Anthropic got banned makes me think that hyperscalers might be trying to cast doubt on the inevitability of AI labs. While Nadella’s piece has clearly gone through 8 PR people and 16 lawyers, it seems to smell of a company saying that no one model actually matters, and given that it was posted on a Sunday, I’m going to guess it’s about the current Anthropic situation.
It’s hard to see how everything goes back to normal from here. Even if Anthropic gets its models greenlit for availability, it’s clear the government has some animus against it after Q1’s battle with the Department of Defense, and may or may not have been waiting for an opportunity to rattle Dario Amodei’s cage.
And, according to Axios, there’s a real animus between the US government and Anthropic, caused in part because of its “inability to communicate effectively,” with one source saying that “Anthropic has not done a great job at trying to speak to the administration and appreciate the ideological differences."
Alternatively, the government has taken Anthropic’s (nonsensical) marketing seriously, and thus decided to take the kind of blunt-force authoritarian position you’d expect — shut the whole thing down, as China might use Mythos to uh, do something!
The other problem is that this is terrible, terrible timing for an AI industry in the throes of a cost crisis. Anthropic and OpenAI’s IPOs depend on myth, hype, and certainty that their growth will never slow. The government’s ability to cut off access at random based on genuine concerns or politicking isn’t a great advertisement at a time when everybody is struggling to find the ROI of AI.
This isn’t a Too Big To Fail or nationalization situation. Amazon and Microsoft are far more scared of the White House than they are of killing their golden goose, and may honestly be relieved to find a reason to bring this era to an end.
You see, Anthropic and OpenAI have much bigger problems than regulation or pissing off Pete Hegseth.
Their business models don’t fucking work.
I’ve been saying for years that the underlying economics of AI don’t make sense — that AI labs were intentionally obfuscating the costs of subscriptions and heavily subsidizing users’ compute, and that the moment that that changed, everything would begin to fall apart, and god damn has it finally begun.
As I discussed in last week’s premium newsletter, the AI Tokenomics Bubble is the simplest and most consequential of them all, because it comes back to something I’ve been saying for years: that the majority of users will refuse to pay the actual cost of AI.
Said bubble inflated through the combined failure of the tech and business media to question AI’s economics and the unprecedented subsidy con perpetuated by Anthropic and OpenAI. Those who dared to suggest that OpenAI burning $5 billion was some sort of problem were dismissed as haters and skeptics that “didn’t care about the future,” with the vast majority of the media completely ignoring the economics until the latter half of 2025.
The Tokenomics Bubble inflated because everybody aggressively ignored the AI industry’s greatest weakness, choosing instead to repeat tired mythologies about how Uber lost a lot of money (which I’ve refuted here) or Amazon Web Services cost a lot of money (Amazon’s total capex between 2003 and 2017 was $52 billion normalized for inflation) instead of being skeptical of…well, anything.
And now it’s bursting because Anthropic and OpenAI’s customers are in revolt, to the point that they’re planning “drastic” price cuts.
Alright, let’s do this one last time.
Sometime early in Q1 2026, Anthropic and OpenAI moved all of their enterprise customers to token-based billing, meaning that instead of using subsidized subscriptions with varying (and ridiculous, as I’ll get into) rate limits, big businesses suddenly had to pay for their AI usage based on the actual tokens they used.
Many hailed this as a masterful gambit, assuming that organizations would have near-infinite budgets for AI services that had yet to prove themselves useful.
It only took a few months for OpenAI and Anthropic’s customers to start sweating.
In the middle of April, The Information’s Laura Bratton likely burst the AI bubble with a piece about how Uber had burned through its entire annual token budget in a single quarter.
This kicked off an industry-wide anxiety about the mounting costs of AI, with multiple other companies burning millions of dollars in the space of a few months, including Zillow, which destroyed its annual Cursor budget by the end of May. What really began the downfall was a comment by Uber COO Andrew Macdonald:
"That link is not there yet, right?" he said. "I think maybe implicitly there is more that is getting shipped, but it's very hard to draw a line between one of those stats and, 'Okay, now we're actually producing 25% more useful consumer features."
He said that the trade-off costs from AI are harder to justify because he can't draw a direct link. Earlier this month, CEO Dara Khosrowshahi said in an earnings call that Uber was slowing hiring to counter its investments in AI.
In a single podcast, Andrew Macdonald gave the entire tech industry permission to say the truth: that nobody was actually able to show any ROI despite its massive costs.
This was always going to be a problem. By starting everybody off with subsidized subscriptions, AI labs shielded users from the costs, training them by proxy to use AI models without any concern for efficiency.
That, and organizations are run by Business Idiots beguiled by a captured tech and business media and a complete disconnection from actual work, meaning that they’d encouraged (or forced) their workers to use AI as much as humanly possible, never once thinking about the costs until they were made to by the AI labs. All it took was a few months of tokenmaxxing to start turning organizations’ stomachs.
This began an increasingly-anxious conversation around AI’s ROI, made worse by the fact that you can’t measure the cost of a task because of the sheer number of models and harnesses, and can’t cleanly translate “AI spend” into “actual financial outcomes.” Toward the end of May, Axios would publish a story about how a company somehow spent $500 million on Anthropic tokens in a single month after failing to set up cost controls.
A few days later, Sam Altman would make a massive fuckup, saying that customers were “totally happy” with their AI spend at the beginning of the year (before token-based billing), and that spend was now a “huge issue,” likely because the costs vastly increased.
Boosters would immediately argue that these massive costs were, in fact, proof that AI was very successful, even if said “success” came from organizations that let their workers burn as many tokens as humanly possible without any consideration of the cost. As I’ve argued previously, the vast majority of Anthropic’s recent surge in revenue comes from experimental revenue from paypigs that it doesn’t deign worthy of clear visibility into their organizational token spend.
In any case, OpenAI and Anthropic need to make a combined $358 billion in annual revenue by 2029 to keep up with their $1.1 trillion in compute commitments. Any slowdown in their growth, as I discussed last week, would be fatal to two companies that have marketed themselves almost entirely by putting the cart before the horse.
It turns out that Altman wasn’t kidding that costs were a “huge issue” for his customers.
Around a week later, The Wall Street Journal reported that OpenAI was planning “drastic” price cuts to its token prices in response to Anthropic potentially doing the same:
OpenAI is considering drastically lowering the prices it charges users as it seeks to win customers from its rival Anthropic.
The company is weighing significant cuts to what it charges for tokens, the unit of measurement artificial-intelligence firms use to bill for their products, according to people familiar with the matter. The move would be in anticipation of similar cuts the company expects at Anthropic, the people said.
Business executives have begun to balk at the high prices for AI usage. OpenAI Chief Executive Sam Altman said at a recent event that costs had become “a huge issue.”
If you’re wondering why they might be doing so, earlier in the day, Cisco President and Chief Product Officer Jeetu Patel said exactly what everybody had been thinking but were too scared to admit: that “...the costs of [AI tokens] are far higher than the actual value that these tokens are generating at scale.
I cannot express how deadly these price cuts would be to the AI industry, and how dangerous this conversation has become. The move to token-based billing has created a revolt in the AI industry’s customer base, coming from (as I’ve discussed) a confusion around the actual ROI and utter despair around the costs.
Depending on how “drastic” these discounts are, any (entirely theoretical) gross margin these companies make on inference will be eaten alive…all so that OpenAI and Anthropic can…uh…decrease their revenues? It’s a desperate strategy being deployed, I imagine, because of a massive wall of customer churn as a result of Business Idiots spunking millions of dollars on tokens they’re no longer able to justify.
Remember: we’re less than three months in to organizations paying the actual costs of LLM-based services, and they’re clearly so outraged at the spiralling costs that both Anthropic and OpenAI are planning to cut the prices of an already-unprofitable service, likely collapsing their revenues while increasing their overall costs.
I anticipate a few booster quips in response, so let’s address them head-on:
Here’s what Jevon’s Paradox means, per Planet Money:
It was within this context that economists rediscovered the Jevons paradox. And they created a modern formulation that's a bit more nuanced. The idea is that making things like cars and appliances more energy efficient creates a "rebound effect." When you make a machine more energy efficient, it effectively lowers the cost of using it. And — hello, the classic law of demand from economics — when stuff gets cheaper, people tend to use or consume more of it.
So, for example, with more-fuel-efficient cars, it gets cheaper to travel every mile, so people drive more miles. Some may decide to stop riding the bus and buy a car. Some families may buy a second car. Others may buy bigger vehicles, like SUVs. With more-efficient light bulbs, people may keep their lights on for longer or build things like the Sphere in Las Vegas.
Newsflash! These price cuts are not happening because Anthropic or OpenAI made their products more efficient! They’re making these price cuts because their customers don’t want to pay their current prices!
In fact, their costs appear to be increasing, which is why they’ve raised (assuming the rounds completely close) over $230 billion in the last six months. You don’t do that unless you think your costs are about to explode or, I dunno, you’re about to massively increase your losses, though the timing and velocity of these price cuts suggests this was a very recent idea.
Oh, right, Jevon’s Paradox! This isn’t that. These companies aren’t getting more efficient. They don’t have any bright ideas to make their businesses lose money, and in fact seem pretty incompetent when it comes to growing their revenues outside of scamming dimwits and selling people $40 for $1.
And that is not hyperbole.
So, you know how I keep going on about “subsidized subscriptions”? And how people online keep saying that they’re not really subsidized?
Well, SemiAnalysis, an extremely pro-AI semiconductor analyst, ran a test made up of random long-horizon coding tasks until they maxed out the limit on OpenAI and Anthropic’s various subscription levels.
Their findings were shocking.
That’s right. Anyone with a $200-a-month Anthropic subscription can burn $8000 in tokens, and with a $200-a-month ChatGPT subscription, you can burn $14,000 in tokens.
This business fucking stinks! It’s not even a real business! OpenAI and Anthropic have to give away somewhere between 20 and 70 times the cost of their subscription in API tokens, which means that they realize that the vast majority of people value these tokens at a fraction of their real cost. This obscene and wasteful subsidy is what you do when you have little to no confidence in the actual value of your product!
Sidenote booster quip: But Ed It’s The Gym Model! Newsflash, chuckles! If you’ve got 2000 people who pay $20 a month but barely cost anything it only takes three people spending $14,000 to eat every single dollar of that revenue! And trust me, I’m about to get to the margins.
SemiAnalysis also modeled out — based on the ridiculous assumption that OpenAI and Anthropic have a 75% gross margin on their tokens — what the margin of a user looks like, and I’m sure it’s f-OH MY GOD!
That’s right folks. With the current subsidies, all it takes for a user to have a gross margin of at best negative 25% is for them to use as little as 25% of their rate limit. And this is based on the generous assumption that they have a 75% gross margin on tokens!
I’ll repeat myself: this is not a real business! This is a joke business, a comedy business, a business invented by the Gods as a means of mocking venture capital! For Sam Altman and Dario Amodei to run a business in this fashion is a sign that they have utter contempt for their investors, the tech media, sell-side analysts, and the general public. If you or I ran our lives in this way, we’d be called fiscally irresponsible millennials that believe the world owes us everything.
This isn’t a real business model because generative AI companies are not real businesses.
Generative AI does not have a business model. It is not a tool with value remotely commensurate with its costs. It isn’t getting cheaper for the providers or the customers. It isn’t becoming “better” in a way that’s measurable using anything other than benchmarks invented specifically for generative AI — an industry-wide coddling of a mediocre technology that only makes money through massive subsidies, FOMO and executive ignorance. It requires endless pre-training, post-training and script-based MacGuffins to do tasks with mathematically-guaranteed hallucinations that burn more tokens, raising costs on customers who are already in mutiny less than a quarter into being forced to pay a cost that is already unprofitable.
Boosters and the recently-concussed will say that these companies can simply stop training, to which I say if that was possible they’d have already done it, and if they stop training, the models will eventually drift into obscurity. If stopping training was all that it’d take to turn these businesses profitable, they’d have done it already, because inference would be a money-printer rather than a cursed object eating away at Altman and Amodei’s souls.
I’ve said it once and I’ll say it again: I believe a large majority of AI token spend — and specifically Anthropic’s revenue growth — has come from Business Idiots disconnected from any real work that have become convinced that “lots of AI” would do something other than rack up massive bills.
And wouldn’t you know I was right!
A little over a month after encouraging its workers to “tokenmaxx,” Meta is now planning to pull back on its AI token spend after realizing it was on track to spend billions on tokens, per The Information:
Meta Platforms plans to clamp down on skyrocketing AI costs inside the company by imposing limits on employees’ token usage, the company told staff in a memo on Tuesday, just weeks after it pushed them to adopt AI tools in their work.
The company is building an internal platform to track employee AI usage and spending in real time, set budgets and establish limits on employees’ token spend, according to an internal memo reviewed by The Information, which Meta shared with about 6,000 staffers earlier this week. The effort is part of a broader efficiency program aimed at cutting costs.
“We’ve seen an exponential increase in AI usage and [we] are tracking to spend billions on internal use alone in 2026,” the memo said. “At the same time, individuals and teams have limited visibility into and control over how they use AI. In 2027, we expect Meta will move toward managing AI tokens in a more structured way—with budgets, allocation decisions, and supporting tools.”
It didn’t even take two months for Meta to go from encouraging its employees to compete to burn the most tokens to talking like a British MP giving a speech about austerity measures.
Meanwhile, The Times reports that banks are running up massive nine-to-ten figure bills from “experiments with artificial intelligence tools”:
Ben Faes, chief executive of RWS, said that businesses were becoming increasingly conscious of the costs involved, without a clear outcome on how it should be used.
“It is very exciting, but you know the cost of playing around with all this AI is rising quite dramatically,” he said.
Faes, 54, said he had spoken to two large banks which between them had racked up $1 billion in costs from experimenting with AI without generating a significant return on investment.
“It is a serious point,” he said. “AI isn’t about generating pictures of cats on skateboards. It’s becoming a serious cost centre for businesses.”
These are the kinds of things you say when you’re planning to drastically cut costs, and I think Uber’s COO gave everybody permission to admit the lack of ROI or, well, any measurable benefits of spending millions of dollars on AI tokens.
Remember: Business Idiots are lemmings! The only reason they wanted to “do AI” was because they read it in the newspaper or heard somebody they thought was intelligent insist that it was the future. These people are extremely sensitive to suggestion (see: Nik Suresh’s Brainwash an Executive Today) and marketing hype, which means they’re also extremely sensitive to peer judgment, meaning that if the worm turns and everybody starts saying “I’m not sure we should spend as much money on AI,” they’ll become anxious to be judged as wasteful for doing something that was considered innovative mere months ago.
Some are suggesting that lower-priced open source models (including some developed in China) for some operations could be the solution, per the Wall Street Journal:
The ecosystem allows autonomous AI systems, or agents, to use cheap models—including those made by Chinese companies like Alibaba and DeepSeek—for many functions. The agents only tap the most capable versions of OpenAI’s ChatGPT and Anthropic’s Claude for more complex tasks. That can reduce costs for some AI-assisted work by as much as 95%, according to executives using the tools.
“Once we find something that is working well and engineers love, we find ways to make it cost effective,” said Dan Robinson, founder of Detail, a startup that identifies bugs. “There’s really an embarrassment of riches right now coming out of the open source labs.”
Robinson shifted 90% of Detail’s workload from Claude and Google’s Gemini to custom models and GLM, a family of models developed in China.
The problem with this argument is that we’re yet to prove if running these models is profitable (or even sustainable) for any provider, nor do we have tangible proof that they can compete at scale with Anthropic or OpenAI’s more-complex LLMs.
Citadel Securities argued late last week that they might be:
For the economy at large, simpler models may be the more cost-effective, productivity-augmenting pathway until physical constraints are eased. We hence see growing signs of a bifurcation in frontier vs “everyday” AI usage.
The problem is that the hundreds of billions of dollars of AI data centers full of NVIDIA GPUs are being built with the expectation that there will be incredible demand — over $150 billion a year just to cover what’s under construction — for very large and compute-intensive models. I am still skeptical that this is a real shift away, if only because using open source models requires you to either work with an inference provider or run your own GPUs.
Nevertheless, even the hint of this migration is enough to start making Business Idiots say “hmmm, what about open source?” even if they don’t know what that means.
But everything comes back to one very simple point: that a lot of AI use (and by extension AI spend) is from the cargo cult mentality of an economy run by the most easily-led dullards in history. They jumped on the AI train because they saw a webinar or read a LinkedIn post or saw a news story about Sam Altman saying his tech was scary or an Atlantic piece saying that Claude Code was ChatGPT 2.0 and thought “fuck, I better throw as much money at this as possible.”
In the end, what is it these organizations are paying for? They’re not replacing anyone, and there isn’t compelling evidence that AI models speed people up. Allowing non-technical people to use LLMs to write code isn’t speeding up the delivery of software in a measurable way, and introduces obvious problems in the sense that, well, you’ve got a bunch of code written by somebody who can’t read or understand it.
People will argue that AI is “really helpful with research,” despite the fact that any research you receive from AI will absolutely have hallucinations, meaning that if you don’t actually know what the answer is to a particular question (which, I assume, is why you researched it), you’re certain to have some sort of small (or large) fuckup.
In a story that’s a little on the nose, The Financial Times reported last week (covering a study by GPTZero) that a KPMG report (that’s now been taken down) about the benefits of AI had exaggerated the scale of its adoption through multiple AI hallucinations:
The October report, “Redefining excellence in the age of agentic AI”, made numerous false claims about the use of AI by organisations including the Swiss bank UBS, the UK’s National Health Service and the public transit groups Swiss Federal Railways and Transport for London.
The inaccuracies were identified as AI hallucinations by the research group GPTZero and verified by the FT. After being alerted to the issue, UBS said it would ask KPMG to remove the false claims, and the Big Four firm on Thursday pulled the report from some of its websites.
The KPMG report claimed global wealth manager UBS “integrates AI agents across investment advisory, risk management and compliance monitoring”. A spokesperson for [UBS] told the FT the assertions were “factually incorrect”.
The report also included hallucinations about AI agent use by Swiss Federal Railways, Transport for London and NHS Greater Manchester, fabricating entire integrations and product lines in a report that was likely used to justify billions of dollars of spend.
Per GPTZero, 40 out of 45 of the report’s citations are either fake, make critical mistakes about the contents, or lack enough detail to be used as proof. They also believe that whoever wrote the report let the AI do most of the work:
Our team suspects that the authors of Total Experience used an AI-powered referencing tool to generate the report’s citations because the errors are both mistakes typical of Large Language Models (LLMs) and consistent throughout the reference list. A human would not consistently paraphrase titles, mistake topics for authors (e.g., citation 9), or repeat information across multiple components (e.g., citation 2).
GPTZero also notes that the report is being cited by LLMs as evidence to prove the success of AI agents, poisoning the already-hallucinatory well of information that these models draw upon.
KPMG has annual revenues of over $39 billion, and sells something called KPMG Workbench which promises to “supercharge your business with [its] multi-agent AI platform, combining advanced, trusted AI agents with insights and deep industry expertise of KPMG professionals.” I assume these are the same professionals that greenlit the report.
It’s likely that this was a mixture of laziness and ignorance, but I also think it might be a situation where the person (or people) writing the report simply couldn’t find any real citations to prove their point, choosing instead to let an LLM crap out some thought-slop in the hopes that nobody would notice.
The fact that Anthropic and OpenAI have any business left after stories like these is proof that the vast majority of companies paying for these services are doing so because they feel pressured to by their peers, investors or the media.
That’s not a tenable business model! You can only get so far on FOMO, gaslighting, and the vague promise that something good will happen if you hand over your credit card.
Hell, let’s take it one step further: neither OpenAI nor Anthropic is a real business.
Let’s cut to the chase: these aren’t real companies!
Their businesses only function by subsidizing or swindling their customer base using deceptive media campaigns that say “let people use as much AI as possible,” and it’s becoming clear that token-based billing might genuinely not work as a viable business line.
The only hope that these companies had was the possibility that they could actually charge something approximating their real costs, though I’d argue that was only the case if there was the option for OpenAI or Anthropic to increase their token costs in the future.
To make matters worse, it’s abundantly clear that the vast majority of people would never actually pay for the tokens they burn. If OpenAI and Anthropic are allowing their customers to burn such egregious amounts of tokens, it’s because they’ve seen that their customers churn when they don’t get to do so. Anthropic’s aggressive rate limiting in March — which still allowed people to burn far more than their subscription cost! — likely scared the everliving shit out of them, to the point that they signed up to pay Elon Musk $1.25 billion a month for access to his Colossus data centers specifically so that they could give people higher usage limits.
The only way that these companies can make money is by giving it away. Both OpenAI and Anthropic have recently started handing out $1000 in API credits to convince people to move over to Codex or Claude Code.
Sidenote: Now OpenAI is allowing its users to “bank” their rate limits — meaning that instead of waiting for the weekly (or hourly) reset, you can choose to save them up and, I assume, use them back to back, allowing power users to effectively double-tap OpenAI’s servers once they’ve run through their usage.
Also, for the next two weeks, anyone they refer gets a free trial of Codex and both of them get another banked reset. This is a transparent attempt to juice user numbers at a cost of hundreds (or thousands) of dollars, and will almost exclusively be used by power users gaming the system.
Their services are not valuable enough for people to cover their business expenses, even if you remove the cost of training, which is so severe it drowns out every dollar of revenue on its own. They cannot raise prices — or even bring them in line with their costs — without their users flipping out. Their training costs are necessary to continue making their models an indeterminate level of “better,” which means that they’re a cost of goods sold, and not a capital expenditure.
As an aside: I’ve been told by somebody that Anthropic has been telling people that they can consider token spend a capital expenditure. I am warning any company in the entire world that if I find out you did this, I will haunt you for the rest of time. I will watch everything you do forever, as this is bullshit accounting that verges on fraudulent, and I can imagine some asshole is going to do it.
Anthropic and OpenAI want you to believe that their businesses can somehow turn profitable, yet neither of them have any explanation as to how. Anthropic negotiated discounted compute for the first two months of its SpaceX deal as a means of pretending to be profitable for a single quarter, but any price cut — or even customer churn! — will immediately put its finances in a kind of red usually reserved for the deeply embarrassed or steroid-enhanced.
They do not have a plan.
You can go on about TPUs, Trainium, Inferentia, and custom silicon for as long as you like — it’s not profitable to run these companies, their costs are too high, and their customers are price-sensitive. Their customers lasted less than three months paying for their actual token burn before crying for mercy. There is no reversing this trend, because if there were, OpenAI and Anthropic would’ve reversed it in any way, shape or form, rather than raising more money than anyone has ever raised before for what appears to be no reason other than to burn it.
OpenAI and Anthropic are unsustainable and recklessly-run companies that do not make sense outside of the broken world of Silicon Valley. The tech industry and venture capital are run by a coterie of has-beens who create no value, and the vague memories of the pre-2015 era, before investors gave up on investing on seed stage companies and decided to joylessly trend-hop for years until they were driven insane by COVID lockdowns and “X: The Everything App.”
The tech industry is run by people who do not experience real problems or have to run real businesses, because a cluster of fellow grifters will vault them back into the black. Investments are no longer made based on rugged meritocracy or any interest in creating the future — it’s only about the Rot Economy’s mediocre growth-at-all-costs accelerationism and making varying numbers go up, though very rarely ones associated with profits.
I think it’s fair at this point to ask whether you could’ve just hired real people to do the shit that AI has done given its enormous cost. $14,000 could probably get you a great deal out of a real software engineer — hell, you could’ve probably hired an agency to do the work for you and actually have someone manage the risk.
The completely imaginary assumption about the AI industry is that it’ll magically get cheaper. That is not something that’s happening. More data centers will not make OpenAI or Anthropic profitable. More data centers will not make customers more willing to pay the actual cost of AI. More venture capital funding will not make Anthropic or OpenAI real businesses.
I agree with anyone saying there should be a pause in the development of generative AI, but I do so based on the belief that this is a doomed grift and science experiment masquerading as an industry that has only gone on this long because it allowed the hardware industry to extract hundreds of billions of dollars from startups, venture capitalists, asset managers, and retirement and insurance funds.
And anyone in Silicon Valley fooling themselves into believing they’re anything other than a corporate stooge is a mark.
The AI industry is the direct opposite of what made Silicon Valley famous.
It is a flattening of everything, absorbing the majority of venture capital funding, media attention, talent, and intellectual oxygen, invading whatever space you’re in because investors insist you must have something to do with AI and because everybody has been convinced they have to use it. It is an intellectual black hole, dragging every conversation toward it, demanding the most money, the most focus, endless justifications and defenses from people that must be rejected for questioning whether LLMs are the future. It debases and humiliates its fans by forcing them to constantly face indignities and embarrassments like it deleting entire databases or breaking AWS. It stunts the intellects of those who use it and, in demanding complete devotion to be considered “part of Silicon Valley,” suffocates the kind of meritocratic skepticism that allegedly got these fuckers so rich.
Silicon Valley was founded on the potentially fictional idea of plucky software developers that rejected the bounds of corporatism. It’s now ingested the worst qualities of corporate America — groupthink, trend-hopping, tribalism, hero-worship, managerial feudalism, and wasteful spending chasing things based on what might make a rich, heavily-coddled oaf smile. There is nothing daring or individualistic about Silicon Valley. At this point, you may as well work at fucking McKinsey.
Silicon Valley is the establishment. OpenAI and Anthropic are effectively owned by Microsoft, Google and Amazon — they do not have infrastructural or financial dependence, they principally run on their hardware, and if anything happens to them, they will likely be absorbed into multiple arms of the Magnificent Seven.
Their financial success benefits only the richest people in Silicon Valley and the wealthiest companies on the stock market. They sell themselves as democratizing software as they extract as many dollars as possible from venture capital, all while selling them back a story of spreading “abundance.”
AI represents the commoditization of startups as a fuel for tech firms with trillion-dollar market caps. AI startups exist only to send money upwards, burning Claude or GPT tokens that run on infrastructure built and owned by the very incumbents that the Valley allegedly takes pride in unseating.
The groupthink and monoculture of the Valley has gaslit these poor individuals into believing that there’s some sort of happy ending rather than a slow descent into insolvency, duping them into defending expensive, unsustainable tools using mythology that benefits only the richest people on Earth.
Someone recently said they think Anthropic and OpenAI are “the last startups,” saying that there was no point in building anything else as “everything has been solved or will be shortly.”
I agree, though for totally different reasons.
Anthropic and OpenAI represent what I believe may be the last hypergrowth startups, and their collapse (however it may happen) will represent the end of the dream of founding a little company that turns into the next Google or Meta.
Neither company was possible without the involvement of Microsoft, Google or Amazon, who provided their earliest funding and, most importantly, their entire physical infrastructure. Anthropic and OpenAI were always entirely dependent on these hyperscalers to shoulder the $100bn+ in infrastructure costs to make training their earliest models or serving inference possible.
The reason there are no other Anthropic or OpenAI-sized startups is that neither of them are actually startups. These are not plucky underdogs who shoulder-barged their way to near-trillion dollar valuations — they’re quite literally subsidiaries of the largest companies in the world, using the mythology of the startup ecosystem to create the mistaken belief that anyone can actually compete with big tech. AI startups are all entirely dependent on big tech, yet sell themselves as rugged individuals.
The fact that Amazon deliberately dobbed Anthropic in to the Commerce Department and neither Microsoft nor Google have shown any interest in defending it suggests that neither really cares if it lives or dies. This would be the exact situation that would prove that Anthropic (or OpenAI) had real leverage over their hyperscale benefactors. Instead, the largest companies in the world have left them to the wolves.
Anthropic believed it was too big to fail, or at least too big to be stopped. It likely believed it would see a flood of support as it did with its argument with the Department of Defense, but nobody seems particularly interested in defending it. Instead, everybody seems kind of confused and annoyed, and the largest companies in the world are making vague statements about how no one model can be “the best.”
Silicon Valley, this is your King — a company that grew through conning and scaring and lying to people at scale, overstating both the capabilities and possibilities of its models in the hopes that everybody would be too scared not to pay for them, only to find its business model collapse because you can’t wish your way to a fucking business model.
While OpenAI is no better, Anthropic is offensive in that it resembles everything that’s ruined the tech industry — a company with a product that costs billions of dollars that can only be sold by talking about what it might do in the future, a masterpiece of grift and hubris that I believe will stumble and crumble in the future.
The next generation of startups will not get built in a system more interested in Twitter clout and trend-chasing than making good software that solves real problems. Braindead, growth-drunk “accelerationists” conflate economic growth with human progress, and as long as they’re in power, the only ones who will build things of note will be the actual outcasts.
You can’t win as a startup anymore. There is no competing with or scaling without the Magnificent Seven, at least not under the current terms of Silicon Valley.
And there never will be again without aggressively flushing away the hubris and ignorance of the current generation of venture capitalists that have abandoned building the future in favor of praying at the feet of management consultants and grifters.
—
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2026-06-13 01:16:51
Friends, I believe we’re approaching the end of this era. Both OpenAI and Anthropic have filed the paperwork to go public, starting a race for exit liquidity for two companies that burn billions of dollars a year and have no path to profitability.
Both of these companies are dogs. No matter how much financial engineering or how many oafish suggestions about the government taking 50% of every AI firm they make, the underlying economics of AI labs simply do not make sense, which is likely why Clammy Sam Altman is so vague about IPO timing, per The Information:
OpenAI CEO Sam Altman told staff in a Slack message on Monday that he expects OpenAI to go public “within the next year” and that “many things could cause it to be sooner or later in that range, but filing now gives us optionality if we want to go sooner.” Another OpenAI leader also teased an upcoming new AI model that the company is preparing to release.
So, yes, OpenAI is expected to go public within the next year, or sooner, or later, at some point it’ll go public, but when it does, well, I dunno. I really don’t know, actually. I have it on good authority that the underlying financials of OpenAI look like the horrible dog from John Carpenter’s The Thing, and any dithering on Altman’s part is an attempt to delay the inevitable, by which I mean “OpenAI needs $865 billion in the next four years to meet its commitments, and the only way to keep raising money is via the public markets”:
However, he said, the magnitude of capital OpenAI needs for its compute and infrastructure buildout could cause it to accelerate IPO plans. (The Information on Tuesday reported on OpenAI’s discussions to lease a proposed Ohio data center campus that would require it to raise or get financing for hundreds of billions of dollars of Nvidia chips, in addition to making substantial lease payments.)
Altman isn’t alone. Anthropic President and Co-Founder Daniela Amodei said at a recent conference that “it’s a very capital-intensive business to train AI models,” adding that the public market is “very well-suited to that.”
As ever, Anthropic is saying one thing and doing another. Last week, Anthropic rankled investors in bonds associated with its $35 billion deal with Broadcom and Google by, to quote Semafor, “resisting sharing financial information” around its section of the bonds:
Some of the lenders being pitched to buy a slice of the $4.6 billion [editor’s note: it was $4.4 billion in the end] notes that don't have a backstop from Broadcom — meaning they are pure exposure to Anthropic — say they haven't received a detailed look at the AI company's numbers, causing some to pass on the deal, the people said. Such disclosures are standard in lending deals.
Hey, quick question: do you think Anthropic is neglecting to share its financials with lenders because they’re good or because they’re bad? As Semafor noted, the standard in basically any lending deal is that you have to share something more robust than the non-GAAPslop investor decks that Anthropic uses to con venture capitalists, but then again, this is private credit, baby! Anthropic can share as much or as little as it wants as long as there are willing marks.
To be explicit, Anthropic is the one on the hook for every payment of this $35 billion debt deal.
According to the Financial Times, asset managers Apollo and Blackstone are finalizing a $35 billion private credit deal to “finance Anthropic’s growth plans,” specifically using the money to buy Google’s TPUs from Broadcom, at which point Google will install them in a data center and rent them back to Anthropic:
The $35bn financing package is the initial step to fund about 1 gigawatt of Broadcom’s planned AI computing capacity, which is expected to expand to 20 gigawatts through 2028, according to a joint statement by Broadcom, Apollo and Blackstone on Tuesday.
A special purpose vehicle formed by Apollo’s Atlas SP Partners named “Compute SPV” would issue the debt, and Anthropic’s five-year lease payments for the chips underpinned the value of the transaction, said people briefed on the matter.
Apollo and Blackstone structured the loan across three tranches, with interest payments on the two senior segments backstopped by Broadcom. The chipmaker is making the so-called tensor processing units, or TPUs, with Google. Its agreement to provide support if Anthropic misses an interest payment helped vastly reduce the costs on the debt.
That’s right — everything sits off balance sheet in a Special Purpose Vehicle (SPV), legally shielding everybody involved…other than Broadcom, which (per Investing dot com) backstopped $30 billion of the debt.
To be specific, if Anthropic defaults on its payments, Broadcom has to step up and pay off bondholders with something called a residual value guarantee, which means that in the event of default, the TPUs would be sold off and Broadcom would cover whatever the difference was between what they cost and what they sold for.
This is some incredibly dodgy shit, but also suggests that Anthropic has abysmal credit, which makes me wonder how Ms. Amodei thinks raising capital in the public markets will go for a company that now very publicly holds $35 billion in off-balance sheet debt to go with its $2.5 billion revolving credit line.
In truth, the Amodei siblings have complete and utter contempt for the media, the markets and their investors. They know that quite literally anything they say will be taken with complete seriousness, to the point that a long winded and specious slog of a blog about “AI that builds itself” is quoted by the media as if recursive self-improvement were anything other than a pipedream.
Yet this still-theoretical concept is being used as a potential excuse for OpenAI to delay its IPO, per The Information:
Altman said that if the company’s technology advances at a rapid pace to the point where the AI itself is able to create new AI—known as recursive self-improvement—that would lessen the chance of a quicker public listing. “The faster the potential RSI takeoff looks like it could be, the more it could be advantageous to delay an IPO,” because the “technology and the world may change in surprising ways, and there might be good reasons to be a private company during that time,” Altman said.
RSI is the wet dream of an AI industry that’s incapable of working out a sustainable or profitable business model. Nobody — not Altman, not Amodei, not Pichai, not Dean, not Hassabis, not Zuckerberg and most certainly not Musk — has managed to work out a viable business model based on large language models, and despite having an effective monopoly over all tech talent and venture capital, the best idea these fucknuts have is “what if we made the LLM work out how to train itself?”
The fact that the media is taking this with any degree of seriousness is one of the loudest bubble indicators we’ve had in a while. If these companies had anything approaching AI that trained itself…they’d be using it. The AI would be training itself. We’d know, because they wouldn’t shut up about it, but instead we have to deal with yet another agonizing, ten-thousand-word-long blog from Dario Amodei (hey, that’s my job!). Ironically, this may be the first time that somebody has ever ripped off Mark Zuckerberg, who wrote his own blog in the middle of 2025 that claimed that Meta had “...seen glimpses of [its] AI systems improving themselves,” which was, of course, a blatant lie that was nevertheless repeated by the media ad verbatim.
RSI is also, I’d argue, a sign that they’re kind of giving up. Instead of talking about things that the thousands of overpaid academics farting around Anthropic or OpenAI are doing, both companies appear to be leaning on the idea that their models are so special that the people themselves don’t matter. RSI is as theoretical as AGI (artificial general intelligence, or a conscious AI), but feels far more tangible because, at least in theory, it’s just a model that’s doing model stuff without a human being.
If I had to guess, the reason that both OpenAI and Anthropic’s representative coding televangelists are talking about creating “loops” where LLMs prompt themselves is to try and claim that they’re on the verge of RSI:
I expect that “loops” will become the next thing that journalists pick up on and start oinking about. To be clear, “loops” already exist, in that you can make an LLM decide to keep taking actions whether or not a user prompts it for as long as you’d like. Whether the output works at the end isn’t Peter or Boris’ problem, as both of them are allowed to burn anywhere from $130,000 to $1.3 million a month in tokens.
Loops are, of course, literally having a hallucination-prone LLM prompt itself or another LLM, with all the chaos and mistakes that you’d expect to follow. Neither Cherny nor Steinberger give a fuck about how much any of this costs as long as it allows their representative CEOs to keep feeding them endless tokens, even if in doing so they inspire a brief and painful bubble of wasteful token spend in the pursuit of “AI that builds itself.”
There’s a very real possibility that the RSI bubble is the last phase of the larger AI bubble. Recursive Superintelligence raised $500 million a month ago without a product or, well, anything other than a vague theory that (to quote VentureBurn) “human intervention is the bottleneck for AI progress”:
“We are building a system that doesn’t just process information; it processes its own logic,” said one source close to the founders. “The goal is an AI stack that designs its own next-generation architecture. If successful, the leap from one version of the model to the next won’t take eighteen months; it could take eighteen hours.”
That’s a load-bearing “if,” buddy. That “if” refers to the idea that they’ll magically create the literal future of computer science — a self-training AI model that, in theory, could sit around and innovate all on its own, which also begs the question as to what all the researchers would do when that happens.
Nevertheless, I expect RSI to become the next — and hopefully the last — hot topic in AI as everybody gives up on coming up with ideas other than “what if the AI came up with the ideas for us.”
The RSI bubble neatly fits into an idea I’ve been working on for a while — that the AI bubble is, in fact, multiple bubbles wrapped into one, led by the largest one of all — The Rot-Com Bubble, my theory that everything we see today is the result of Silicon Valley running out of hypergrowth ideas.
The frenzied, reality-defying hype around Anthropic, OpenAI and Large Language Models is a direct symptom of a tech industry with nowhere else to go. There are no other industries that have any sign of becoming the next Google Search or Facebook or Smartphone, which is why everybody — the media, the markets, and every hyperscaler — is conspiring to try and keep the bubble inflated through accepting effectively any viable narrative and blessing even the most vulgar of circular financing arrangements. If anybody for even a second breaks the kayfabe that AI is the biggest, most hugest, most important bubble of all time, everybody has to accept that none of this makes sense…
…and that there’s nothing else on the other side.
The many bubbles that make up the larger AI bubble all represent different aspects of the same desperation. Microsoft, Google, Amazon and Meta are buying all those GPUs and shoving AI in every crevice of their experience because they know that their core businesses will eventually slow down, with nothing else to replace them. Oracle is spending $340 billion or more on capex entirely for OpenAI because its core business lines are plateauing or collapsing. SoftBank is mortgaging its entire future on OpenAI because it desperately needs another Alibaba or ARM to keep up with its ruinous debt. Broadcom needs to backstop $30 billion in bonds for Anthropic, an unprofitable and unsustainable venture capital welfare recipient, because it knows that its other business units can’t keep up with the Rot Economy’s demands for eternal growth.
AI feels, on some level, like the final stage of the modern era of technology. It’s flattened effectively every startup and tech company into some sort of aberration of Large Language Models, turned every semiconductor firm into an AI firm, made every venture capitalist an AI investor, made every tech journalist an AI journalist, and crowded out effectively every other subject in favor of a larger argument about whether one specific technology is the future.
These many bubbles always come back to a singular point: that the people building modern technology have effectively abandoned innovation, twisted by the Rot Economy’s growth-at-all-costs mindset. The result is that the majority of venture capital goes to latter-stage companies and established founders, turning venture capital into a cult of personality more interested in Twitter clout and view counts than anything to do with the future.
Venture capital is now dominated by people that don’t build anything but worship at the altar of what they imagine a “builder” looks like. As a result, these people flock to founders that confirm their biases — those who are usually men, usually white, usually software engineers from big schools, usually building things that look and sound like everybody else. Seed stage investment is dead, and all that remains is a follower culture.
The AI bubble is sold as the future, but actually resembles the death of Silicon Valley. Only a tech industry dominated by symbolic wealth and value creation would ever abide a trillion dollars of waste for a still-theoretical outcome, and only an intellectually-rotten Valley would be so easily-grifted by people like Dario Amodei and Sam Altman.
The myth of the Valley was always that investors were smart and took risks. In reality, investors follow other investors based on whatever people are excited about in a group chat or on Twitter or TBPN or any other hype-slop they can get their hands on. Modern venture capitalists hate thinking and introspection, invest in basically the same things at the same time, and haven’t done a real job since the early 2000s.
The result is that Silicon Valley stopped building the future, and started investing in its own destruction.
This series will cover the many parts that make up the larger Silicon Valley bubble, and the many collapses that will lead to the end.
This will, as with my What If? Series, be a two-parter, with the option to extend to three.
And man, am I gonna have some fun with it.
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2026-06-08 23:37:39
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Last week I went on Bloomberg and discussed the state of the AI bubble with a clarity that rattled even the sweatiest boosters, mostly because I spoke with clarity about an investment frenzy whipped up through hype, deceit and mythology. Some were equal parts frustrated and angry that I don’t have money in the market, or, as they’d put it, “skin in the game.”
I get it! When your entire worldview is dictated by what a series of venture capitalists and psuedo-journalists on Twitter want you to believe, it must be difficult to imagine someone having “morals” or “beliefs” or that one might hold a position that wasn’t entirely based on greed or tribalism. It must be confusing — upsetting, even! — to hear that somebody is willing to accurately and vociferously tear into a tech industry largely controlled by people with no regard for their users or workers, who are willing to bathe their products in mediocrity all because it’s the thing that everybody else is doing.
This is a hysterical era perpetuated by liars, cowards, imbeciles, craven boosters and the easily-fooled. Those excited about generative AI are either the victim or the perpetrator of a con centered around a technology to ingratiate at the highest cost possible.
I also think that everybody is a little flippant about what has to happen for me to be wrong.
Whatever obtuse fantasies you have about the current state of generative AI are irrelevant to a much larger problem: that the infrastructure being built and compute commitments being made are being done so at a level that demands that generative AI and AI compute generate over $2 trillion in annual revenue by 2030. When I say that, I mean it absolutely has to do that otherwise none of the data center capex makes sense, and neither Anthropic nor OpenAI can pay their commitments.
OpenAI expects to spend $50 billion on compute in 2026, and I wouldn’t be surprised if Anthropic spends anywhere from $30 billion to $50 billion. Between them, Anthropic and OpenAI represent the vast majority of all AI compute demand — at a minimum 70%, if not 80% to 90%.
Put another way, there’s barely a few billion dollars of demand outside of two companies that lose billions — or tens of billions — of dollars a year.
Let’s break down these numbers a little further:
This is not hyperbole! Every single thing I have stated here precisely maps to the projections and promises of the AI industry. No matter how horny or flaccid you are for the potential of AI, it must grow at an astonishing, unstoppable rate from here until the end of time to be anything close to worthy of its costs.
Actually, sorry, let’s put judgments aside for a second, because this isn’t about judgment, but rather the promises that have been made by the software and hardware companies associated with AI. NVIDIA’s place atop the stock market and its ridiculous projections depend on both the continued flow of data center debt and the continued belief that AI services will have the revenue to back it up.
AI cannot, under any circumstances, slow down. In a year, Anthropic and OpenAI’s businesses have to be roughly twice the size they are today, and then double again basically every year until 2029 or 2030. In that time period, they must also both raise hundreds of billions of dollars or, alternatively, turn deeply unprofitable businesses into profitable ones while also doubling their revenues.
Alternatively, both must severely reduce their costs…except if they do that, they won’t have any need for all that compute capacity, which will deprive Oracle, Google, Microsoft, SpaceX, Cerebras, CoreWeave, TeraWulf, Cipher, and Hut8 of the $1.1 trillion in remaining performance obligations.
Also, if OpenAI can’t afford — or doesn’t want — its compute, Oracle will simply run out of money. It is spending anywhere from $340 billion to $700 billion (depending on whether you believe Jensen Huang in September 2025 or May 2026) on the 7.1GW of data centers it’s building for OpenAI. These, again, are not hyperbolic statements, but the actual costs associated with Oracle’s massive buildouts in Michigan, New Mexico, Wisconsin and Texas. I didn’t agree to do this! Larry Ellison did!
Sidenote: Larry Ellison has also got at least $21 billion in loans collateralized by his Oracle shares, and any doubts around Oracle’s ability to pay for its debts or OpenAI’s ability to pay Oracle for its compute will threaten massive margin calls. I wrote about this here. It’s really bad!
Apparently, Salesforce is planning to spend $300 million on Anthropic in 2026, to which I say “that’s not nearly enough”! Everybody has to be spending even more than that in the next few years, without fail, no ifs, ands, or buts. It is non-negotiable. Anthropic needs to be making over $100 billion in two years or it can’t afford its commitments, so you filthy token-hogs better slurp up your slop this instant, or Dario Amodei gets made part of the permanent underclass!
But seriously folks, the combined compute demand of every single AI company in the world doesn’t currently reach $100 billion — and it needs to be ten times that by 2030 or all those data centers got built for no reason!
And for that to happen, both Anthropic and OpenAI need to be making about $400 billion a year in annual revenue, which means there needs to actually be that much demand for AI services! Right now, Anthropic and OpenAI’s combined projected revenues for 2026 sit somewhere in the region of $60 billion — so, you know, they only need to grow by 496% by the end of 2029!
To make matters worse, it doesn’t seem like anyone else in the AI industry is going to help with the whole “demand for AI services or compute” thing. As The Information reported a few weeks ago, OpenAI and Anthropic make up 89% of all AI startup revenues.
We could include hyperscaler revenues, but that wouldn’t help very much. Microsoft’s $37 billion in AI annual run rate — these fucking cowards never share actual AI revenues! — is predominantly made up of OpenAI’s compute, with the rest of it (maybe $8 billion in annual revenue at best) from Microsoft harassing its permanently-abused customer base into installing Copilot.
Ah, shit, there’s another problem with Microsoft — Microsoft AI CEO Mustafa Suleyman just said that Anthropic’s models were too expensive, and he intended to reduce Microsoft’s use of them to zero! You can’t do that Mustafa! We need every cent of demand, otherwise everything falls apart!
Sidenote: Amazon and Meta barely have AI stories. Mark Zuckerberg just said he “thinks” Meta has a use for the vast amount of compute it’s bought or is developing, if you’re wondering how great things are going over there.
A source tells me Meta is working on a Tamagotchi-like pendant that uses OpenClaw, and when I heard that I felt exactly how I felt the first time I heard No Doubt’s Rock Steady — did I really used to like this dogshit when I was young?
Anyway, eager math-knowers among you might also notice that even if Anthropic and OpenAI spent $500 billion a year in annual compute — an amount that they can’t afford even if they combined both their unsustainable asses — we’d need at least another $250 billion or more in annual compute revenue to justify it.
In other words, they need everybody to be “doing agents” at such a scale that basically every third dipshit you run into on the street is sinking $50 or more a day into them.
I sure hope that’s happe-OH MY GOD!
As I discussed last week, you can’t measure the cost of a particular task with AI, nor can you measure its return on investment. The only reason that we’ve been “doing AI” with such ferocity and veracity is that most companies are beholden to Business Idiots disconnected from production who have no real understanding of their underlying firms’ outputs, and thus have very little way of measuring them.
These multi-millionaire midwits have been “doing AI” because everybody else is doing it, burning millions of dollars to turn their code into slop (see: Zillow) or have their engineers compete to see who can spend the most money (see: Meta and multiple other companies). In one case, a company spent $500 million on Anthropic’s models in a month because it didn’t set up spend controls. In Uber’s case, it burned through its entire annual token budget in a single quarter, which led to its COO saying it was harder to justify spending money on AI tokens because it couldn’t show a link between that spend and a meaningful increase in useful features on Uber. Now Uber has capped its employee spend at $1,500 a month per user, with T-Mobile temporarily following at $2,000 a month per user with the intent to move to a tiered system. Over at Brex, engineers are limited to $500 a week in tokens, with non-engineers getting an astonishingly-low $5 a week.
These are signs that AI’s revenue growth is slowing, and it’s likely going to slow further, because we currently live in an era where Anthropic and OpenAI are straight-up abusing its clients, providing limited-to-no visibility into spend, per the Wall Street Journal:
The shift to pricing based on usage, and measured by tokens—the basic unit of measurement for AI computing—is creating new challenges for even the most experienced finance teams. CFOs used to paying flat amounts for technology are finding costs more unpredictable and harder to model as they build agents and embark on ambitious AI investments.
Twenty-six percent of companies say they have a comprehensive view of their AI costs, while 50% have some visibility and 22% report no visibility or visibility after billing, according to an as-yet-unreleased survey from KPMG. “It’s a new resource that needs to be managed that didn’t exist quite that way, and we’re seeing exponential growth,” said Steve Chase, KPMG’s global head of AI.
How utterly ridiculous! Only in the frothiest, most-disconnected economy in history could we have companies spending millions (or tens or hundreds of millions) of dollars on a service without having any visibility into costs until after billing. This is not a sustainable revenue stream under any circumstances, and anybody who says that it is is either ignorant, a mark or a con artist. This is revenue made entirely by convincing your customers that something is true (AI is the most revolutionary thing ever!) and keeping them in the dark as long as humanly possible as they run up ridiculous bills, all in the hopes that you’ve brainwashed the executives/paypigs well enough that they’ll never stop.
And really, “paypig” is the accurate term for these cretins:
Russell Burke, Life360’s finance chief, said the company doesn’t yet have a real-time monitor of its token spending, but he hopes to have one soon. “We hope that’s right around the corner,” he said.
Russell, you may as well let Dario Amodei put a cigarette out on your forehead! This is pathetic! What a fucking loser! Oohhh, I sure hope that the company I pay all this money to lets me see how much I’m spending! I thought Silicon Valley was meant to be all about meritocracy!
Sidenote: I will say that it’s nice after two years of being called a crank and a doomer to read an outlet say exactly what I’ve been saying for years — that businesses will squeal when they are made to pay the true cost of AI.
Boosters will say that it’s “hard to measure productivity for any job outside of sales,” but that’s simply not true! If you let your engineers spend $1500 or more a month on a service, surely you must have some way of measuring how much actual new stuff went out — new features, customer tickets reduced, projects completed, I don’t know, I’m not the fuckwit spending $1,500 a month per person on this garbage! You’re the one that has to justify it!
But, fundamentally, these are all signs that AI is slowing down.
Remember: Anthropic and OpenAI only moved their customers to token-based billing in Q1 2026. It only took two or three months for us to get headline after headline of big, serious business publications saying “AI costs a lot of money and companies aren’t sure if there’s a return on investment.”
Sidenote: What do you think happens when regular people are forced to pay their token-based rates? Do you think they’ll spend more? If so, please read the many, many complaints from users of GitHub Copilot who have been on token-based billing for less than a week.
If things were going well, these stories would be inverted — companies would be boasting about their remarkable token spend and pointing to all the new, incredible things they were shipping. Their products would be spotless, their features sublime, their engineers sliding entire new stacks of impressive software out the door so fast that it would be changing the very nature of software. I mean…someone would be, right?
Let’s check out the chaAHHH!
What’s actually happening is that these tools are — at a remarkable price — shoving a lot of stuff out the door. Is the stuff good? No. Do people like or use it? No. Does it make money? Also no. While we’ve discovered the shovelware, that’s all that LLMs have given us — “more” apps, with the vast majority being useless, insecure slopware.
This is meant to be the era of agentic coding! This is meant to be the era where any dickhead with a Codex or Claude Code account with $1,000 of free API credits should be able to create the next Salesforce or whatever it was that dimwit Citrini talked about a few months ago.
I’m sorry to be a little surly and dismissive, but the AI industry has burned over a trillion dollars and I’ve spent two years being told I’m a luddite and an ape for not celebrating it. I don’t care! I’m not impressed! I’m not coddling this mediocre, expensive crap!
Like I said earlier: isn’t the tech industry meant to be a glorious bastion of meritocracy? Isn’t this meant to be a cold, harsh community of rationalists?
If so, why are we coddling AI like it’s the kid from that episode of the Twilight Zone? Has Silicon Valley become so decidedly whipped by the forces of capitalism that it can’t see that none of this makes sense? Or was this always just a culture of lemmings drawn in whatever direction venture capital waved a dollar bill?
To make matters worse, both OpenAI and Anthropic are speeding as fast as they can toward IPO — which means that both will have to start looking like real companies, which means both will, inevitably, start charging their customers more and very likely moving the vast majority of them to token-based billing and either kill or vastly limit their subsidized subscriptions.
In a mysterious confluence of events, both Claude Code chief Boris Cherny and OpenAI-owned OpenClaw televangelist Peter Steinberger have both said that their users need to be “designing loops for their agents,” meaning “creating ways to make their agents burn a bunch of tokens doing stuff,” I imagine as part of the ongoing campaign by both Anthropic and OpenAI to make people spend lots of money on tokens to keep their enterprises afloat.
I expect that “loops” will become the next thing that journalists pick up on and start oinking about. To be clear, “loops” already exist, in that you can make an LLM decide to keep taking actions whether or not a user prompts it for as long as you’d like. Whether the output works at the end isn’t Peter or Boris’ problem, as both of them are allowed to burn anywhere from $130,000 to $1.3 million a month in tokens. As I’ve argued before (though referring to subsidized subscriptions):
Think of it like this: if you’re using an AI subscription with rate limits but no actual costs, any mistakes a model makes — such as getting stuck in a loop or just doing the wrong thing — can be dismissed as the troubled nature of early-stage technology, because the “cost” was $20, $100, or $200 for the entire month. Anthropic, OpenAI and every other AI company deliberately obfuscated these costs because they knew that the second a user actually had to pay for the fuckups of an AI model they’d scream like they were being stung to death by bees.
To be clear, this is both OpenAI and Anthropic’s representative stooges actively suggesting that you “shouldn’t be prompting coding agents anymore,” instead letting LLMs that hallucinate the more they “reason” (IE: make plans for themselves, which is how agents work) do as much reasoning as possible without user input.
These men have complete contempt for their users and customers. They do not give a shit that their models break so often that Notion had to cut access to Anthropic’s for several hours or that the costs are so severe that CFOs are a few bad bills from a trip to Budd Dwyer’s Favorite Lunch Spot. You must burn more tokens, because otherwise you won’t be doing AI coding right, whatever that means.
And please, god, stop trying to convince me this shit is impressive. You all sound like you’re in an abusive relationship trying to explain why a guy who rifles through your pockets and half-asses everything he does at an incredible cost is actually super sweet behind closed doors.
I’m distinctly unimpressed!
After hearing a particularly colourful story from Kevin Smith, I came up with the perfect way to explain the AI bubble. Okay, perfect might be a stretch, but I think this gets my point across, and hell, it’s a free newsletter, what’re you going to do? Kill me? Run me over with a truck? Good luck with that, I’m a huge homebody.
Anyway, imagine, if you will, a smaller version of the giant mechanical spider from Wild Wild West — a portable one that you sit in like a chair with big arms and big legs. The giant metal spider costs $1 million, and takes up about $40,000 of fuel every time you use it, but it can sometimes pick stuff up and make you dinner.
The problem, however, is that it’s a giant metal spider — sometimes it precisely grabs a diet coke from the fridge, and sometimes it punches a hole clean through it, requiring both a brand new fridge and for me to pay $40,000 regardless. The good news is that the companies that make the giant metal spider from Wild Wild West also subsidize the giant metal spiders at around $200 a month with free insurance, though businesses are forced to pay for its actual costs.
As I march it around my apartment, the giant metal spider leaves horrible scratch marks on my floor, it sometimes makes a terrible noise, but I, as the user, barely have to do anything — the spider does everything for me, even though whatever it “does” is incredibly costly, convoluted, and often takes far longer than it should.
Every update to the spider widens what it can allegedly do, but each time I use it it’s just as expensive. Can the spider make me a cup of coffee? Yes. It takes five minutes, which is longer than I’d take, and occasionally it throws the coffee in the air or simply fills the cup full of oil, but most of the time I get a cup of coffee. Isn’t that good? We love the giant metal spider.
When I turn on the news, I see a headline about how “THE GIANT METAL SPIDER FROM WILD WILD WEST WILL CHANGE EVERYTHING.” 30 different guys on Twitter write 800-word-long screeds about how we must redesign apartments and office buildings to cater to the spider, that “it’s inevitable that the metal spider from Wild Wild West will be how everybody does everything in the future,” and one guy even suggests that it’s alive because, after adding a $500,000 add-on, the giant metal spider can be scheduled to get up on its own and make the coffee. Sometimes it does so successfully. Sometimes it smashes the coffee maker up into tiny little pieces. Sometimes it mashes its legs through the kitchen island. Sometimes the spider opens up my Amazon packages with ease. Sometimes the spider rips them in two.
Thankfully, the companies behind the giant metal spiders subsidize them, so the average person only experiences the occasional act of destruction, but they also lose billions of dollars a year on training the spiders and the constant maintenance required to run them. There are some workplaces full of the giant metal spiders and they’re absolutely insufferable.
Everywhere I go, somebody is telling me the spider is the future. “The giant metal spider from Wild Wild West will eventually stop destroying stuff! Future innovations in giant metal spiders will make them cheaper and more-reliable! Look, we’ve done a study, and the giant metal spider’s ability to complete a task of a certain length 50% of the time has increased!”
Every time they add a new feature to the giant metal spider from Wild Wild West, it requires several hundred million dollars, and it isn’t always clear whether the giant metal spider learned anything new. It’s really good at opening Amazon packages, so they thought it might be able to make a bed, and spent $100 million training it to do so, only to find it kept karate chopping the bed in half approximately 20% of the time. Another time, the giant metal spider from Wild Wild West showed promise at playing Texas Hold ‘Em, successfully getting through an entire game 50% of the time. Unfortunately, the other 50% of the time it smashed the cards into the table. After another $100 million, they were able to reduce that number to 30%. A day later, The Atlantic ran a story: “Vegas Is Scared Of The Giant Metal Spider From Wild Wild West.”
Technically, the giant metal spider is productive, at least in some households where they give it significant room to maneuver and only give it tasks it’ll excel at. Across the world, private credit funnels billions into giant metal spider factories powered by NVIDIA chips, assuming that everybody will be paying to rent one of them. When you criticize the giant metal spiders, you’re told that you use them in the wrong scenarios, ones where they’re guaranteed to fail. Young graduates are encouraged to learn how to move the giant metal spider, and that if they fail to, they’ll be unable to explore the giant-sized future that will be built for them. Year after year, more people insist that the giant metal spider from Wild Wild West will get cheaper, but the costs only seem to increase along with the vast amounts of damage it causes.
It’s undeniable that the giant metal spider from Wild Wild West can do stuff. Sometimes it even does the stuff as well as a person. For some reason, it’s impossible to tell when it’ll get things wrong, and despite everybody saying that the giant metal spider from Wild Wild West is “smart,” it seems to occasionally do things the user didn’t ask for.
If you say that the giant metal spider from Wild Wild West isn’t going to be the future of anything due to its massive, unsustainable costs, or suggest that its inconsistencies make it unreliable in some way, you’re told you’re a doomer, a skeptic, a luddite and a rube.
One day, someone using one of the giant metal spiders from Wild Wild West steps on your car. Futurism writes an article laughing at you. You scream so loudly that one of your neighbors calls the police.
No matter how much you dress up whatever AI service has gaslit you into believing it’s sentient, generative AI is inherently limited, impossibly expensive and economically unviable. Its services cost too much to run, its progenitors have no path to profitability, and no amount of rigged benchmarks and anecdotal examples of theoretical engineering teams that are “10x’d” can make up for the fact that you can’t measure the cost of an LLM-driven task or its return on investment.
Anyone claiming that you have to “measure AI’s ROI differently” is attempting to con either you or themselves. While it’s tough to measure the ROI of a particular worker or project, most workers and projects don’t increase your operating expenses by anywhere from 10% to 100% under the vaguest of promises that you might be “doing the future.” AI is calamitously expensive and, despite years of promises of it getting cheaper for both those running AI services and its customers, costs have only ever increased.
I think that’s by design. AI labs want their costs to be high so that they can continue growing at ridiculous rates, all so that they can keep feeding money to their hyperscaler compute partners who then invest that money right back into them, creating further reasons to keep buying NVIDIA GPUs, so that NVIDIA can then invest that money back into either AI compute providers (who OpenAI and Anthropic pay) or the AI labs themselves.
Concepts like “efficiency” or “cost reduction” run counter to the greater narrative of AI’s voracious sprawl of data center capex and still-theoretical AI revenue. If OpenAI or Anthropic were to seek profitability or sustainability (assuming these things were possible), that would create less demand for AI compute, which would mean less demand for Azure or Google Cloud or Amazon Web Services or CoreWeave or Oracle Cloud Infrastructure, which would in turn mean less demand for NVIDIA GPUs.
The problem with this marvelous plan is that at some point there had to be an honest transaction — real, honest, sustainable demand based on a reliable product that people liked paying for because they understood its value. Right now, AI revenues are either chaotically experimental or so thoroughly-subsidized that labs are giving away hundreds of dollars a user in the hopes that at some point said user might want to pay even more money for measurably less value, the kind of proposition you make when you think your customers are fucking idiots.
It only took a few months of token-based billing for the AI conversation to go from “our magical, beautiful agents” to “hmm, are we sure this is worth it?” and I believe it only gets worse from here. AI labs do not have some super secret trick up their sleeves — no, not even Mythos, that was bullshit I’m afraid — that will suddenly provide the kind of ROI that’s impossible to ignore, nor do they have some magical way to bring down their costs while also spending just as much on compute.
From here, we basically need to 10x every part of the AI stack based on the projections and commitments made by effectively every AI firm. Anthropic and OpenAI must grow faster than any company has ever grown before in the space of a few years, and suddenly become profitable, all while somehow raising hundreds of billions of dollars.
On top of that, we need at least another $250 billion in annual AI compute demand, which likely means at least two other OpenAI or Anthropic-scale companies. If this all sounds unreasonable, don’t blame me. I’m not the stupid fucker that agreed to build 100GW+ of data centers or mortgaged the future of Oracle on the off chance that Sam Altman and Dario Amodei, two craven manipulators, somehow work out how to create Google 2 and Amazon 2 in the space of four fucking years.
I won’t tip my hand too much, but I have a story coming out in the next two weeks that will likely confirm the absolute worst fears of the AI industry. Many have been incredibly brazen about the potential losses of particular AI labs to the point that I made it my mission to talk to as many people in the tech industry as humanly possible, in part because some who have suggested that I “don’t speak to people who work in the tech industry.”
In truth, I speak with tech workers every single day of the week, and they’re in fucking agony.
If you are someone in the executive team of any major tech company, know that your employees are, for the most part, completely and utterly miserable. Your endless death march of “do as much AI as possible or we’ll fire you” and forcing them to use these tools day-in-day-out has radicalized them against you. Every day I hear from someone who is dealing with the wrath of a different Business Idiot who doesn't do anything other than demand more deliverables in a smaller timeframe with less people because you keep laying people off.
If you are a worker at a tech company, I fucking see you. I feel your pain. I hear your sadness. I am enraged and disgusted at the way you are being treated. Reach out to me at ezitron.76 on Signal with anything you’d like to share. I’ll protect your identity, listen to your stories, and if you share something with me that warrants publishing, I’ll make sure I do it justice by understanding the subject matter and reporting it in a way that it never gets back to you.
I’ve done this again and again, and will continue to do so, because I love my sources, I treat them with dignity, respect and empathy, and they, like me, find the current state of the tech industry wretched, its leaders worthless, its road maps directionless and its works mediocre.
Even if I don’t run with the story, I am here to listen, because I hate what you are going through. I feel your pain. So many of you truly love making good software and want to do good things in the world and feel impeded by the Business Idiots and mocked by the boosters who seem to care more about your bosses than anything to do with software or innovation. It sickens me what the industry has done to you and continues to do to you. You deserve better.
I write this newsletter because I deeply enjoy writing and I deeply hate what is being done to the computer. I hate that many people like me are suffering at the hands of the scumbags and freaks birthed from the guts of McKinsey and various MBA programs.
I don’t do this because of a short position. I don’t have one. I don’t hold any stocks, securities, or CFDs.
I do it because it’s my job and because I give a shit. If it’s impossible to comprehend why somebody would do something without a short position, you need to think long and hard about why you bother waking up every morning.
One of my sources has come forward and brought me a story that will possibly burst the AI bubble. The reason they brought this to me is that I’ve shown — and will continue to show — that I actually give a shit about this industry and the people in it.
If you’re wondering what the story is, know that it’s the information I’ve wanted for years, delivered as I have always wanted it, and I will treat it with the reverence it deserves. Imagine what the worst possible thing for me to get would be and you’re probably close.
I expect it to be out in the next two weeks, and you’ll know exactly when it runs. There’ll be a podcast and a newsletter, and very likely follow-on coverage elsewhere.
I can guarantee you it’ll be worth it, and you’ll be stunned by what I report.
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2026-06-05 23:57:04
Last year I wrote one of my favourite pieces ever — The Hater’s Guide To The AI Bubble — and followed it up with The Hater’s Guide To The AI Bubble Volume 2 several months later. Sadly, I’ve realized “volume” is a terrible way to structure something like this, because each volume is more of an update, which is why today’s newsletter will move to a versioning system.
The AI bubble is a psyop, a melodrama, a financial crisis, and a mask-off moment for the Business Idiots that run the vast majority of our economy. It is the largest-scale exploitation of ignorance in history, gnawing at the intellectual weaknesses of society by presenting just enough information or just enough proof to substantiate a trillion-plus dollars of investment and manufactured consent for a technology that, based on how many discuss it, doesn’t actually exist.
And it’s revealed how many rich and powerful people are either (or both) credulous and woefully ignorant.
To be clear, LLMs are real and do some things, but they don’t do any of the things that Dario Amodei is talking about when he says that AI will wipe out 50% of white collar jobs.
We’re four years into this joyless slog and people are still talking about AI’s “potential” and what it “will” do and that we’re in the early innings of a technology that, for the most part, is still doing exactly what it was doing at the beginning with refinements that never come close to reaching the vacuous heights of boosters’ promises.
Markets are moved by poorly-written fan fiction by outright scam artists and deceptive hedge fund gargoyles because those selling AI services have entirely disconnected the minds of the markets and the media from reality. This is because con artists like Amodei and Altman constantly discuss what AI will or might or theoretically could do rather than what it actually does, because if they had to do that they’d have to say it constantly loses money and doesn’t have a measurable return on investment.
As I said on Bloomberg this week, the markets and the media have conflated capital expenditures for data centers with a thriving AI industry. In reality, 89%+ of all AI revenues and 90%+ of all compute demand comes from two companies — OpenAI and Anthropic — largely based on money-losing subsidized AI subscriptions and unrestrained token burn at organizations run by imbeciles that will go away now that executives are having trouble justifying it because there’s no ROI, in part because AI is too inconsistent and unreliable, and in part because you can’t really measure how much a task will cost.
Now enterprises are already capping their AI spend, with many more to follow after multiple companies blew through their annual token budgets in a few months. The sheer volume of the “AI ROI” conversation is remarkable considering that Anthropic and OpenAI only moved enterprises to token-based billing — paying the actual costs of AI — in Q1 of this year.
Remember: the total, actual revenue of the entire AI industry — including OpenAI, Google, Microsoft, Amazon, and Anthropic — has barely reached $100 billion in 2026. That includes every ounce of compute spend, every penny of the $500 million that a single customer accidentally spent on Anthropic’s API, and every cent of NVIDIA’s backstop deal with CoreWeave. More importantly, absolutely nobody is making a profit outside of those selling the bits that go inside a data center.
Both OpenAI and Anthropic lose billions of dollars a year, with no end in sight, though Anthropic did a great job swindling the media by having a single “profitable” quarter thanks to Elon Musk discounting two months of compute. Anthropic has already filed to go public, with OpenAI allegedly not far behind. Neither of these companies are fit for public investors.
Their products are inconsistent, unreliable and only ever seem to get “better” in a kind of wobbly way that can only be measured by increasingly-less-useful benchmarks that they specifically train to beat. Despite many people (and some companies like Spotify) claiming that AI is writing “most” code, nobody can seem to explain what that means. It isn’t saving money, it isn’t saving time, it isn’t making companies ship better or more-functional products, and the only tangible examples of its effects are that it broke AWS several times and deleted a company’s database.
It’s unclear where AI exists outside of coding and the various places companies have shoved it.
I’ve spent years trying to catalogue other, non-coding use cases, and most of what I’ve found are vague descriptions of companies like Goldman Sachs maybe launching agents “soon” at some point to do something maybe and this weird story with Novo Nordisk claiming that it was “integrating ChatGPT’s models to analyze complex data sets” despite them claiming to have done this for years.
That’s because generative AI is, no matter how many hats or harnesses or deterministic processes you add, limited by its mathematically-certain hallucinations. These models are probabilistic, guessing at what the ideal output may be, which means that every bit of information they produce is suspicious and every decision they make is brainless, thoughtless and arbitrary. They do not “know” things, they do not have “thoughts,” and no amount of API connections will fix that problem.
As a result, nobody has really got a clear answer as to what everybody is doing with AI. Code? Image generation? Using it as a shitty search engine? Using it as a companion? You can’t really rely on it to do anything. When a model hallucinates an incorrect answer to something you know is true it’s a problem that can be fixed — when it hallucinates an incorrect decision with your codebase, that’s fucked everything up to a near-permanent end.
This is the ultimate problem with AI. You can try and dress it up with billions of investment and supposed ways to mitigate hallucinations, but it still makes — and will continue to make — mistakes that it has no idea are mistakes.
Well, okay, the other problem is that generative AI just isn’t built to do most jobs. It can generate stuff and summarize stuff at varying degrees of complexity, but the more complex the generation, the more likely it is to hallucinate. The only way to reduce hallucinations is pre-training (shoving stuff into the model at the beginning) and post-training (training it on what “good” looks like), and neither of these actually solve the problem. It is clumsy, inaccurate, unreliable, expensive and cumbersome.
AI cannot do the vast majority of jobs, and the only reason that anybody thinks that it can is that the vast majority of CEOs have no actual connection to the work that enriches them, and because AI can do an impression of something that looks like work, they choose to believe it can do anything. It can burp out a half-functional prototype with the company’s name on it or legitimate-looking legal or financial document, and that’s all it takes for a fuckwit with a high salary and a low IQ to think it’s capable of replacing everybody.
If I were wrong, it would actually be replacing people. You’d be able to point to both the data and the proof. You’d have single-person software companies making billions of dollars, hyperscalers would have their companies destroyed by people copying and bettering their software, accountants and lawyers and writers and every other knowledge work career would be dead, not threatened with constant layoffs that are mostly connected to improving profits, but actually dead, untenable, impossible to work in thanks to the “power of AI.”
In reality, AI is dramatic only in its mediocrity and the ferocity with which it’s proven how ignorant most authority figures and executives have become. Every boss demands you use it, every app screams at you to try its integration, every news story tells you it will replace you imminently, but in the end it doesn’t appear to do very much beyond generating and summarizing at varying levels of complexity.
The media categorically failed to scrutinize an industry built to exploit it, as I said earlier in the week:
This hype was unsustainable without buckets of lies, misinformation and a captured tech and business media. The value of AI has been inflated by the vagueness of how it’s discussed. For example, major media outlets will gladly write that “AI can build software,” but said sentence suggests that you can just type “build me Slack 2” into Claude and have it fart out a fully-functional, production-ready piece of software, rather than a quasi-functional mound of code-slop that can do enough to trick a business idiot or lazy journalist, but little else.
The consent has been manufactured and the markets are engorged with semiconductor stocks running because people keep mistaking the availability of debt for actual, real demand for AI compute. The geniuses in private credit and the greater markets saw the amounts that hyperscalers were spending on data centers and the ascent of OpenAI and thought “fuck me up, grandpa,” leading to $178.5 billion in data center debt deals in the US in 2025 and $50 billion in data center construction in April 2026 alone.
Yet it turns out that data centers take anywhere from 18 to 36 months to build, with Microsoft finishing a grand total of zero of the data centers it broke ground on in 2023, and JP Morgan saying a month ago that 60% of capacity planned for completion in 2027 hasn’t even started construction, with another 7% delayed, per the Wall Street Journal.
And despite the supposed 100GW+ of data center capacity being planned, AI compute demand doesn’t really exist outside of Anthropic and OpenAI, two companies that rely on perpetual flows of venture capital and debt to survive. Between them, they’ve raised over $200 billion in the last six months, and their revenue streams are inherently based on either unprofitable AI startups subsidizing their subscriptions, their own unprofitable subsidized subscriptions, or experimental token spend borne of companies allowing their employees to burn as much as they’d like, which is already coming to an end.
At the top of the pile lies NVIDIA, the largest company on the stock market, which sells GPUs that are so expensive that once cash-rich hyperscalers are now having to take on mountains of debt or, in Google and Oracle’s case, dump tens of billions of dollars of new stock into the markets. NVIDIA’s continued growth relies on a dwindling subset of clients, with 54% of its last quarter’s revenue and 64% of its accounts receivable coming from three customers in its last quarterly earnings.
Demand is somehow both incredibly high for data center components but so low for AI compute that NVIDIA has agreed to spend $30 billion over the next six years to rent GPU capacity.
That’s because the AI buildout is being driven by people who haven’t bothered to check whether the demand is real, much like AI is being adopted by people that don’t bother to do any real work, much like AI is sold based on things that it can’t actually do.
Midwits and the incurious will say this is just like the Dot Com Bubble (it isn’t and won’t leave behind any useful infrastructure), or Uber (it isn’t) or Amazon Web Services (it isn’t) because they want to rationalize the waste. In reality, the people running the tech industry are listless Business Idiots throwing as much cash at the problem as possible rather than facing the fact that they’ve backed a dead-end technology because they’ve run out of hypergrowth ideas.
Today’s piece is an attempt at a little fun — a raucous, aggressive rundown of the major players and stories of the AI Bubble, both as a refresher for those who already know and a guide for those that don’t.
Welcome to the Hater’s Guide To The AI Bubble 3.0.
2026-06-02 22:04:43
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Something changed in the last week.
Shortly after Uber COO Andrew Macdonald said that it was “getting harder to justify” spending money on AI as it was “very hard to draw a line” from that spend to useful consumer features (after its CTO said Uber burned its entire annual token budget in four months), Axios’ Madison Mills reported that one company had accidentally spent $500 million in the space of a month on Anthropic’s models after failing to set spend limits. A few days later, Mills would report that other companies were now looking for ways to reduce their AI spend.
That’s because, as I’ve said before, nobody can actually measure the ROI of AI, or even create a standard measurement of the cost of a task thanks to the inevitable hallucination-prone nature of LLMs and the ever-growing list of different harnesses and “agentic” (sigh) interfaces. Every different prompt and project and interaction can go wrong in a way that is hard to predict or plan for other than having an eternal vigilance that the supposed “intelligence” doesn’t do something catastrophically stupid, because LLMs have no thoughts, consciousness or ability to learn outside of pre and post-training.
If you can’t measure how good something is, how much it might cost, or what your return on investment might be, it’s fair to ask why you’re even paying for it in the first place.
People are (reasonably!) harping on about the ROI problem, but I think the “can’t really measure the cost” part is an even bigger problem.
Yesterday, Microsoft’s GitHub Copilot moved all customers to token-based billing from a premium request model (as I reported a week before everyone) as users had been allowed to burn thousands of dollars of tokens on a $39-a-month subscription.
Customers are irate. One burned through 50% of their monthly credits in a single prompt, another burned 60% in the space of a few hours, another 31% in a single prompt, another estimated that they’d burn their monthly credits in the space of a single five hour session, another burned nearly half of their credits in eight prompts, another around 14% of their credits in two prompts, and another lamented that GitHub Copilot had gone from their favorite subscription to their most-stressful overnight after burning 33% of their monthly balance in a few hours.
And, to be clear, this is during a promotional period where you get $11 or $21 in free monthly credits:
These users — much like the users of effectively every subsidized AI subscription — never really knew how much anything they did cost, because Microsoft intentionally hid the actual cost of prompts and allowed users to spend obscene amounts as a way of boosting growth for GitHub Copilot.
This problem is industry-wide.
Every single user of every single AI subscription service is having their tokens subsidized and the actual cost of AI obfuscated. As a result, every frothy, fluffy hype-piece about Claude Code or AI in general is a kalopsia — the belief that something is more beautiful than it really is.
Educational Sidebar! While many of you may know this, for those just joining me, let me break down how the average AI subscription works. You pay a monthly subscription to, say, Anthropic or OpenAI’s services, and get to use these services as much as you’d like subject to both daily and weekly “rate limits.” None of these companies ever really explain what that rate limit might be, giving users instead a vague percentage gauge and leaving them to work it out on their own.
When you use an AI model, you feed information into it via input tokens (a token is about ¾ of a word) and receive outputs via output tokens, and companies bill on a per-million token basis. While models can “cache” information as a means of avoiding having to read or write it again, every single interaction costs money, regardless of its success or efficacy. This is why every AI startup is inherently unprofitable — they’re literally sending every penny of their venture capital money directly to Anthropic and OpenAI to power their unprofitable services. AI labs may be able to run their own infrastructure and save some costs, but we have no evidence that this makes anything “profitable.”
For example, Anthropic lets you burn anywhere from $8 to $13.50 in tokens for every dollar of subscription cost, and while AI boosters will say that Anthropic is “profitable on inference,” nobody actually has proof outside of theoretical scenarios posed by CEO Dario Amodei.
Think of it like this: if you’re using an AI subscription with rate limits but no actual costs, any mistakes a model makes — such as getting stuck in a loop or just doing the wrong thing — can be dismissed as the troubled nature of early-stage technology, because the “cost” was $20, $100, or $200 for the entire month. Anthropic, OpenAI and every other AI company deliberately obfuscated these costs because they knew that the second a user actually had to pay for the fuckups of an AI model they’d scream like they were being stung to death by bees.
This issue bubbled to the surface in the last few months because Anthropic and OpenAI both quietly moved all of their enterprise customers to token-based billing in Q1 2026, and because these enterprise customers are run by Business Idiots with no connection to actual work, CEOs encouraged (or actively incentivized) their workers to use AI as much as possible, in some cases even making one’s AI use a KPI that could cost them their job.
These same workers were conditioned — through their use of AI subscription products that hide the true costs — to use them as if they cost nothing, all while being screamed at by useless middle managers to “make sure to adopt AI at scale,” all while never, ever having any awareness of what a particular unit of work cost.
This was always a recipe for destruction. The overwhelming majority of AI users are completely divorced from and actively trained to ignore the true cost of AI tokens, which means they naturally use these services in a way that’s actively uneconomical. Every frothy hype-piece you’ve read has been written by somebody who has been conned into ignoring the true cost of AI, all in service of spreading a technology that’s unreliable, inconsistent and expensive at its core, and never, ever seems to get cheaper.
Sidenote: Even with the “cost of intelligence” (the per-million token cost) of models coming down, models are using far, far more tokens for the same task, ultimately raising the cost of inference. Put another way, imagine if the cost of gas got cheaper but the distance between you and your destination kept getting longer.
OpenAI, Anthropic and other AI companies have actively conspired to mislead the world about the true costs of AI, and it was working great right up until they decided to try charging what it actually cost. Less than a quarter into the shift to token-based billing, enterprises are freaking the fuck out, with Walmart setting token limits on its internal “Code Puppy” AI coding tool, with a spokesperson saying that it “wanted employees to apply AI in ways that create value” mere days after Amazon SVP Dave Treadwell told employees to “not use AI just for the sake of using AI.”
The last few years of AI hype have been built on lies. Every company has conspired to make you think that AI is affordable and sustainable, that profitability was possible, that hallucinations were fixable, and that any problems you faced today were a result of being in “the early innings.” In reality, the AI industry has absorbed over a trillion dollars, effectively all tech talent, the majority of startup funding, the majority of media coverage, the art and work of millions of people, and been given chance after chance after chance to fix the obvious, glaring issues.
Every time a skeptic dared to stand out and say that none of this made sense, they were told that it was just like Uber (it’s not) or that Amazon Web Services cost a lot of money (it cost $52 billion over the course of 14 years and was cash-flow positive in nine), that “costs always come down,” and that everything would magically be alright as long as they were patient for an indeterminate amount of time.
Four years and a trillion dollars in, AI is more expensive, its companies more cash-intensive, its products just as unreliable, and its boosters more desperate than ever to make you ignore reality as a means of empowering one of a few ultra-rich oafs. Products from OpenAI and Anthropic are built to ingratiate and coddle losers while creating work-shaped outputs that are good enough to impress braindead executives, imbeciles and middle management hall monitors that don’t do any real work, and the reason it’s worked this long is that both companies intentionally misled everybody about how much the real costs were.
I must repeat myself: AI is more expensive today than it was three years ago, and it is not getting cheaper. Sam Altman’s comments about “intelligence too cheap to meter” were lies. NVIDIA’s Blackwell GPUs didn’t make it cheaper, and its Vera Rubin GPUs won’t either. Google’s TPUs won’t do it, Amazon’s Trainium or Inferentia chips won’t do it, Vera Rubin CPUs won’t do it, OpenAI’s chips won’t do it, and no, DeepSeek won’t do it either.
People chose — and still choose — to believe that AI would get cheaper because they think things got cheaper over time in the past, which is sort of true but not remotely similar in any way, because the cost of running and training AI models comes from using the hardware as well as its upfront cost. Large Language Models require expensive GPUs thanks to their reliance on power-intensive parallel processing, and larger, more-complex models in turn require more GPUs to both train and run inference with.
And three generations in, NVIDIA GPUs don’t appear to be bringing the cost down at all, which heavily-suggests that the inherent business model of generative AI is broken.
People love to compare AI to the Dot Com Bubble (AI is far, far worse) because it’s much easier to rationalize bad behavior than accept that we’re facing the largest misallocation of capital of all time.
The Dot Com Bubble was really two bubbles — one around eCommerce and internet startups, and one around telecommunications infrastructure.
Per Justin Kollar, the telecommunications bubble grew because of a fundamental misunderstanding of demand:
This continental rewiring was also justified by another powerful myth—that internet traffic was doubling every 90 days. The claim spread through analyst reports, earnings calls, and investor presentations like a particularly virulent meme. If true, it meant that demand was growing exponentially, far outpacing any conceivable supply, and that every new trench of fiber would soon pay for itself many times over.
But the mathematics were fiction. Network researchers like Andrew Odlyzko (at AT&T), looking at actual traffic data, found that U.S. backbone traffic was doubling roughly once a year—rapid growth, certainly, but nowhere near the purported 90-day cycle. Meanwhile, advances in fiber technology were making each strand exponentially more powerful. Dense wavelength-division multiplexing allowed dozens of signals to travel simultaneously down the same line at different wavelengths of light, like multiple conversations happening in different colors.
As a result, infrastructure was built far in excess of what demand existed, because most people weren’t online, and those who were had very slow internet connections. Per me:
The similarity everybody points to is that “people doubted the internet at the time,” and people really need to remember their fucking history. In 2000, only 52% of American adults were using the internet, and by 2003, that number had only increased to 61%. Per the World Bank, in 2005 only 16% of the world used the internet, and in 2024, that number had increased to 71%.
Yet the real difference is the access to high speed internet. When the internet was connected to via a 56k modem, access was either charged-by-the-minute or much, much slower. While we’re used to connecting at speeds that make using a web-based app near-indistinguishable from one run on our computer, back in 2000, 2001, or 2002, the average US internet speed was, at best, 400 Kilobits/s, or roughly 50 kilobytes a second, compared to the average US internet speed of over 200Megabits per second, or 25 megabytes a second.
In simpler terms, a website took time to load in a way that feels almost impossible to conceive if you didn’t experience it at the time. We’ve also had dramatic improvements in web design and accessibility, the advent of mobile browsing, and the proliferation of widespread mobile and desktop internet access. In the 2000s, we were at the very early days of eCommerce, and the weird irony of the dot com bubble is that it was actually pretty useful to lay millions of miles of fiber optic.
Here’s a critical difference between AI and the Dot Com Bubble: when people actually lit up the dark fiber, the underlying internet service was faster, better and cheaper than a dial-up connection. Services like TheGlobe, WebVan, and Pets Dot Com ran businesses that lost incredible sums of money did so not because of the costs associated with accessing their services, but the unrealistic and unsustainable business models themselves.
Their eventual functional forms — Facebook, Instacart, and Chewy — didn’t require fundamental scientific breakthroughs in how goods were delivered or internet services were accessed. Their failures were a result of poorly run businesses that lost money by expanding too rapidly or spending $400 to acquire each customer.
Dell and CoreWeave just turned on the first Vera Rubin GPUs, and you’ll notice nobody is saying the words “profitable” or “sustainable,” because NVIDIA is not interested in making stuff more efficient rather than more expensive.
According to CEO Jensen Huang, AI data centers — which currently cost somewhere in the region of $50 billion per gigawatt — will now cost between $80 billion and $100 billion per gigawatt in the future. Does this sound like it’s getting cheaper to you? Even if said data center packs theoretically more “power,” what does that “power” do for the customer running compute on it? Is it cheaper? More efficient? How do we not have these answers?
All of this is to say that the Dot Com Bubble happened due to irrational exuberance and growth lust, and what was recovered at the end came not from scientific breakthroughs but the fact that the useful infrastructure existed and could be adapted and used to make things cheaper and more efficient.
That isn’t the case with AI data centers, AI startups or anything else to do with the AI Bubble.
Every few days somebody makes a post like this suggesting that “the internet didn’t go away” and “railways didn’t go away” when their bubbles popped, but I think this is a fundamental misunderstanding of what AI is.
An AI data center full of AI GPUs is useful for AI and very little else. There are GPU-powered analytics tools, GPU-powered modeling and scientific applications, but the nature of GPUs — good at doing the same thing across big data sets in parallel, but bad at handling many little independent tasks — makes them impractical for most of what modern computing demands.
The entire Dot Com Redemption storyline comes from the idea that it “left behind useful infrastructure,” by which they mean “cabling that allowed hundreds of millions of people to use the internet.” While there was some amount of further construction and capex to handle, the end result was useful fiber that connected people with a faster connection at a lower cost.
No such story exists for AI.
AI data centers are ruinously expensive, requiring billions in upfront funding with operating costs so high that they, at best, run at a loss for the first five or six years of service, if they ever recover their original costs at all. A rack of Vera Rubin or Blackwell GPUs will cost as much to run in five years as they do today, as will an incomplete data center cost just as much to finish construction, connect to the grid or acquire behind-the-meter (IE: generators) power for.
In the aftermath of the Dot Com Bubble, dead startups flooded the market with cheap server and office gear, which allowed plucky founders to cobble together their own services. A single Sun Microsystems Ultra Enterprise 3000 cost $43,000 ($89,000 in today’s money) and had a power draw of between 1,200W and 1,500W, but could run an entire company’s infrastructure. A single B200 Blackwell GPU uses 1,200W, and more-complex AI coding tasks can take up four to twelve of them for a single user’s output. Put simply, you can’t really do very much with a few of these GPUs, and what you can do isn’t profitable, scaleable or valuable.
Similarly, dark fiber could be lit up with the right transceivers and networking gear to create internet access. AI data centers are effectively large boxes with custom cooling built for a very limited subset of chips. Adapting them to other uses would require gutting the data center, which would mean that the vast majority of the capital expenditures were wasted.
Even if you were able to buy a hundred Blackwell GPUs from a dead neocloud, you, as a regular person, couldn’t do anything with them. In fact, nobody really could, because you’d still need a physical data center and bespoke cooling, which means that even if the chips were free, the associated construction capex or, at the very least, physical colocation space would still cost a great deal of money
The internet and railways didn’t go away because their up front costs were the only real costs that mattered.
Even if somebody were able to pick up a cheap AI data center full of the latest generations of GPUs, the underlying operating expenses are awful, and the only way to make them even close to generating a profit is to have consistent use of all your GPUs. There’s a cost to having them sit idle — both in electricity and personnel — and unless the plan is to have them sit in a data center turned off until you can find somebody else to sell them to, you’ll have to come up with a business model for your AI services that actually makes a profit…which nobody appears to have done, even with unlimited capital and the entire focus of the tech industry.
Then there’s the issue of training, which is entirely made up of opex. If you want to train a new model, you’ll likely need thousands — or even tens of thousands — of H100 or H200 GPUs, and they’ll cost just as much electricity whether or not you make anything useful. A failed or unhelpful training run could cost tens of millions or hundreds of millions of dollars, and that will require financial backing that won’t exist.
While there could be a theoretical future of LLMs run at their true cost (IE: unaffordable for most) as I covered in last week’s premium newsletter, that would require demand, and as I’ve discussed above, the demand for AI services is a mirage built on subsidized subscriptions, and companies paying the actual costs are already screaming for mercy.
Once the bubble bursts, any excitement for AI — and by extension excitement to spend money on AI — goes out the window. AI startups won’t get funded. AI token budgets won’t get greenlit. AI data centers won’t be able to raise debt.
Every part of this bubble relies upon the momentum of hype to substantiate every link in the chain. Hype must exist around the nebulous concept of an “AI factory” to raise debt to buy NVIDIA GPUs and build data centers, hype must exist around AI software to convince enterprises to keep buying services from OpenAI and Anthropic, hype must exist around theoretical demand and outcomes from AI services to fund AI startups, and hype must exist perpetually in the media to make everybody ignore AI’s ruinous costs.
This hype was unsustainable without buckets of lies, misinformation and a captured tech and business media. The value of AI has been inflated by the vagueness of how it’s discussed. For example, major media outlets will gladly write that “AI can build software,” but said sentence suggests that you can just type “build me Slack 2” into Claude and have it fart out a fully-functional, production-ready piece of software, rather than a quasi-functional mound of code-slop that can do enough to trick a business idiot or lazy journalist, but little else.
Said vagueness created a society-wide gravitational pull of consensus that you needed to be behind AI now, because it’s just like the new internet, except bigger, and if you say it’s not you’re going to be really embarrassed.
Creating this pressure was necessary, because without a society-wide aggression against those who didn’t adopt these tools, AI might have actually had to stand on its own merits. That fact AI companies backed by the full manufactured consent of the markets and most of the economy still had to subsidize their products shows exactly how flimsy their value truly is.
The only way to inflate the AI bubble both on a hardware and software level was to mislead the general public and investors on the costs and efficacy of AI models.
Now that organizations are having to pay the actual cost of AI, suddenly they’re concerned about its outcomes, and everybody has become a little hysterical.
Late last week, SemiAnalysis wrote one of the most insane articles I’ve ever read — AI Dark Output: The Visible Cost of Invisible Output — saying that “AI output will be real before it is measurable,” and, well, whatever the fuck this is:
We are at risk of having an event on the scale of the Industrial Revolution where most of the new output is invisible even as businesses spend increasingly large amounts on AI services.
SemiAnalysis is a semiconductor analyst firm with an obvious reason to keep the AI bubble inflated, and if they’re writing a piece that amounts to “AI has a return on investment, you just can’t see it,” things are getting desperate. Here’s how they define “Dark Output”:
Dark output is AI-enabled economic value that exists but is not visible, or is badly distorted, in GDP, prices, labor statistics, or industry accounts. We categorize this into two buckets:
1. Substitution dark output is work that was previously done by humans and is now done by AI. In our Dark Output Monitor we have identified roughly $1.5T in tasks that current generation AI could substantially augment or automate.
2. New dark output is new work done by AI that wasn’t previously being done by humans (probably because it was too expensive to do until AI made it cheap). In the long run this is likely to be much larger than the substitution side.
That “substitution dark output” is explained using a theoretical example of “...a simple legal document which in theoretical GDP should have the same inflation adjusted value to a user whether a lawyer drafts it or AI drafts it,” which is nonsense.
When you pay a lawyer, you don’t pay them to “create an output,” you buy their experience and time and ability to find and adapt case law to reach an outcome, such as in the process of filing stuff, avoiding or actively participating in litigation. Just because AI can fart out an approximation of what a human output may look like — likely riddled with hallucinations — doesn’t mean that said output was created with any “experience.” Models don’t think, they have no experiences, and even if a lawyer is prompting them, that doesn’t mean that the lawyer’s discernment or taste is reflected in the final output.
Then there’s this bit:
When AI takes over the task, the receipts vanish as the cost is absorbed in tokens, and when government officials survey lawyers on the cost of services they may find that the average price has gone up, as the simplest documents are now completed by AI and not lawyers. From the perspective of GDP, the transaction has effectively vanished except for a few dollars of tokens sitting in an unrelated sector of the economy.
We’re four fucking years into it but we’re still using hypotheticals. Are “...the simplest documents now completed by AI and not lawyers”? You don’t get a lawyer to write a document because they’re the only ones who can write it — you get it to mitigate the risk using the experience of the law firm, both in the associate drafting the document and the partner overseeing it.
This flimsy, half-assed logic is how the AI bubble got inflated in the first place. Supposedly smart people continually show a total lack of awareness of how jobs work at basically every level, and in this case — where it should be theoretically possible to find and talk to a lawyer doing this — the supposed “dark output” includes “the research done to complete this article.”
You may be wondering what that “new work done by AI that wasn’t previously being done by humans because AI made it cheap” is, and the answer is “literature reviews” and “summarizing the last six months of email,” and I wish I was kidding. But don’t worry, “...there are anecdotal signs that a large fraction of current token spend is for new work that wasn’t previously paid for rather than replacing existing work.”
Have you ever noticed that every story about AI job loss reads like it was written by The Riddler?
For example, last year a ton of outlets reported that “Oxford Economics had proven that entry-level workers were being replaced with AI,” but in reality, the study said that “...there are signs that entry-level positions are being displaced by artificial intelligence at higher rates” with no actual data beyond post-2022 employment declines in some fields that AI might be able to do.
Similarly, CNBC’s brainless headline that an MIT study found that AI “could already replace 11.7% of the US workforce” was entirely based on a labor simulation tool rather than any economic analysis of the actual shit AI can do and what it’s doing in the real world.
That’s because AI job loss is a fucking myth. Every company laying off people because of “the power of AI” is doing so because their shareholders are mad and because they know they’ll get headlines.
And if it were actually happening there’d be fucking riots in the streets! Unemployment would be spiking! Things would be burning!
The thing that everybody wants you to avoid thinking about is that if AI worked as advertised, there would be obvious, impossible-to-ignore economic signs:
For all of these things to happen, AI would have to be both flawless, hallucination free, a completely different product capable of autonomous intelligence and having unique ideas.
The reason that we can’t measure “AI job loss” is because AI can’t do jobs. It can be used to replace some specific contract positions with extremely shitty versions that don’t scale, but it does not replace jobs because it is incapable of human work. It cannot speak to colleagues, it cannot accrue experience, it does not have instincts or culture or taste or anything other than whatever training data has been crammed up its ass or through endless post-training.
Nevertheless, the threat of AI job loss has been enough to allow both Sam Altman and Dario Amodei to raise hundreds of billions of dollars lying about it, and now that both of them have walked back their job loss scare-propaganda, every oaf and moron that believed them without actually checking should be booted out of their representative industries. It’s fucking embarrassing! You should all be ashamed of yourselves!
As I said above, the ROI of AI should be really easy to measure if it actually existed.
If AI was magically able to build and maintain software, we’d have small companies that could build and deploy at the scale of a hyperscaler, and hyperscalers would, in theory, be expanding their margins so aggressively that it would create a new golden age of software revenues…or they’d become entirely infrastructure providers, as anybody else could compete on software.
But on a far-simpler level, it would be extremely obvious.
Anybody can access ChatGPT, Claude or Gemini, effectively anywhere in the world. The theoretical “power” of AI is that it “just does stuff,” and the proliferation of LLMs would mean that somebody would’ve “done” some “stuff” that we could point at with exceptional ease. Random guys in the midwest would be pumping out profitable, functional, and feature-rich software. Lawsuits would be won by pro se plaintiffs with incredible counsel from a theoretical “country of geniuses in a data center.”
Four years in, we’d have one major AI-powered company demolishing the competition in any industry, or every industry would become so prevalent with (powerful) AI that it would effectively reduce the cost of the service to nothing.
We’d be able to point to companies that adopted AI and then completely fucking exploded. We’d be able to point to useless coworkers who were now doing impressive, meaningful work.
There would be widespread economic upheaval, as the concept of a “large company” would lose meaning, because those theoretical “geniuses in the data center” would be automating all the work.”
There also wouldn’t be so many pieces insisting that AI is super powerful and so many quotes from Business Idiots saying it’s “real.” We wouldn’t talk about what AI could do at all. We wouldn’t need Anthropic to lie that Mythos was too powerful to release only to release it several months later.
We wouldn’t have to talk about the fucking potential at all because we’d be able to point to what was going on because it would be obvious!
Last week, Bain & Co. released a study of 951 executives from companies with more than $100 million in revenue, and unsurprisingly, the data did not declaratively explain what the ROI of AI was:
In an April survey of 951 respondents from companies with more than $100 million in revenue, Bain found that 37% said they experienced cost reductions of between 10% and 20%, but a larger 40% saw improvements of 10% or less. Only 4% of global respondents achieved AI-related savings of more than 30%, the survey, shared exclusively with Bloomberg, found.
10% of…what? What’s the cost you saved on? 10% of $10 million is a lot for a company with $100 million in revenue, but 10% of $1000 isn’t, much like 20% or 30% isn’t either! Yet there are two punchlines to come:
Here’s the part that Bain found the most troubling: 44% of large companies that are funding their next wave of AI spending are basing those investments on the last round of savings — savings that haven’t yet materialized for some of them.
This also assumes that those savings are enough to warrant future spending, which…this data does not actually prove.
Thankfully, Bain did manage to publish one of the single-funniest quotes of the AI bubble:
“The technology worked. The value didn’t arrive,” Bain concluded in the report. “Self-funding the next wave from past returns sounds like discipline. In reality, it is a circular bet with a structural leak,” the firm cautioned.
Put another way, the technology “worked (?),” but did not provide value in doing so. Sounds like it didn’t fuckin’ work to me!
Bain had one other crucial bit of advice:
“Companies that don’t validate their reinvestment math against what automation actually returned, rather than what it was supposed to return,” the report concluded, “are compounding risk rather than managing it.”
Just so we’re clear, Bain & Co, a management consultancy with billions in annual revenue, is advising its clients that they should make sure that they’re getting some sort of return on their investment? And that reinvesting in something that doesn’t have a return on investment would be bad?
If AI was real, these fucknuts would be replaced first! They’d replace everybody who wrote this report! You don’t need somebody to tell you this, and if you do you’re a fucking moron!
Thankfully, the AI industry is saved, as Sam Altman had the following to say about AI’s remarkable costs:
FABER: And you think the compute, you know, the last week I was hearing about compute, for example, companies starting to wonder, well, what are we spending it on? Our bills are going through the roof, and it’s not clear to us exactly what – you know, in other words, I know a lot of my spend is going well, but I don’t know which part of it.
ALTMAN: So I think this is the most fair contribution – criticism right now of AI, which is, you hear companies saying, I am spending a ton of money on AI. And I know some great stuff is happening, but I know there’s a ton of waste, and you know, when – how long do I have to wait for it to really show up in revenue, and how long do I have to wait to really get the costs under control? And I assume that the industry will figure that out pretty quickly, but I think that is a fair, a fair issue.
Motherfucker you are the industry! You are the one that has to work this out! OpenAI is the AI industry! You are OpenAI’s CEO! You lazy, ignorant, dog-brained loser!
This was an opportunity for “journalist” David Faber to push back, and here’s how that went:
FABER: You do.
ALTMAN: Yeah.
FABER: Quickly being –
ALTMAN: I would bet that by another year or two from now, there is a much better rationalization of companies’ spend relative to outcomes.
FABER: And finally, Sam, are we ever going to see things like this up in space?
This is how the AI bubble inflated! This is how it happened! It happened every time a journalist asked a meaningful question and then immediately diverted to a totally different imaginary topic that made the subject feel good! David Faber, resign and give your job to somebody who has an iota of courage or pride in their work! Unbelievable!
Sam Altman is worth billions of dollars, and OpenAI is allegedly worth $852 billion too, and the best he can give us is “teehee, someone else will work it out,” because Sam Altman is a loser that ingrates other losers empowered by losers to sell loser technology to other losers, and the only way that he’s been able to do this is because the people that should know better are sitting around their thumbs up their asses asking him whether there will be data centers in space.
If AI had ROI, we wouldn’t be debating whether it had ROI. We wouldn’t discuss its potential, or whether it could, theoretically, under different circumstances, in the future, in a way that nobody can describe be super powerful and do all of the stuff it can’t do today.
If AI had ROI, we’d be able to point with specificity to inarguable examples of economic impacts. AI boosters can jerk their binguses all they like about how Spotify’s CEO said its best engineers don’t write any code anymore. What does that mean? Is Spotify shipping better features, and are those features launching at a rapid clip? Is the software more secure, or stable? Spotify’s design still looks like absolute dogshit! Most software is worse! Things keep breaking everywhere, and in many cases it’s because of AI coding tools!
In fact, I’d be willing to believe that AI had a negative economic impact, increasing operating expenses across the board and giving some software engineers prompt-based concussions by automating some coding in a way that makes them lazy and bad at writing software by speeding up the process of writing code with so much of it that it’s impossible to review it all (see Mo Bitar’s video). LLMs appear to be able to write some code sometimes and do so at high speed, and ingratiates software engineers that don’t really care about writing software by making them feel like they wrote it.
While it might allow some things to go theoretically faster, the overall economic impact of AI-generated code appears to be worse code, worse software, and massive, multi-million dollar bills from Anthropic and Cursor. I will concede that some software engineers seem to like these things, and that many software engineers appear to be using them, but I am yet to see a single one who obsessively posts about their token spend create anything of note or worth, and none of these people appear to be able to point to the actual ROI of all that AI they’re using.
I realize I’m painting with a broad brush, so let me get a broader one: I believe anyone who relies on LLMs for anything is a mark.
I don’t give a shit if you use them to spit out a script or do some simple sideline part of your job, or transcribe or dictate into them, or if you’ve used them as a search engine (and even then, you best check every source!), but the moment you rely on and run your entire process on these things, I immediately doubt your ability to do anything, or at the very least wonder how gullible you truly are when somebody ingratiates you enough.
Why? Because every single “AI setup” I’ve seen anyone ever use involves a rube goldberg machine of bullshit deterministic scripts to try and bring the hallucination-guaranteed nature of LLMs to heel, usually to the point that you’re doing more work making the LLM work than you did before they existed, and you’re only proud of it because you feel like you’re special.
Sidenote: If you’re normal about LLMs I have no beef with you – but something about this technology makes people act irrationally and aggressively to skeptics in a way that requires them to debase themselves. This is a product, and if you feel the need to defend a product you are the victim of a con.
There are, of course, exceptions. I’ve talked to a few people who describe LLMs normally, without hype, who tell very specific stories of very specific outcomes that save indeterminate amounts of time. There are some that have used LLMs to create python scripts to search and organize data, to which I say “you’re impressed with Python, not LLMs.”
If all we’re left with from this era is the ability for some people to write Python scripts without learning Python, this is still an egregious and horrifying waste of capital.
Remember: what you are using is the end result of over a trillion dollars of investment. It is only made possible through manufactured consent that actively misinforms people about the current and future capabilities of LLMs. They didn’t raise hundreds of billions of dollars by talking about any product currently on the market, and that’s because the current products are not very good products.
You are all the victims of a con. No matter how “well” your Breakfast Machine of different API calls and if-this-then-that automations may or may not function, you have been sold a bill of goods for “artificial intelligence” that is impossibly stupid. When some of you are pushed to prove the ROI of AI, you immediately return to boring talking points about Uber, or the Dot Com Bubble, or some other slop fed to you by people actively conning you at this very moment.
I mean this with as much empathy as I can muster: if you’re a huge AI booster, why do you defend this so vociferously? What is it about my criticism that hurts? Is it that I’m yucking your yum? Is it that I don’t immediately ingest and regurgitate the theoretical idea that the thing you’re using all the time is or may become sentient? Is it because I’m not impressed?
I think it’s far more likely that people are angry that I’m asking simple questions that should have — and don’t — have satisfying answers. I’m also fundamentally unimpressed with anything I’ve seen an LLM do, because my requirement for software or hardware is that it works as advertised, and the very fundament of the AI con is that LLMs are sold based on their theoretical capabilities.
The reason nobody can show you the ROI from AI is that AI does not have a return on investment. Large Language Models can speed up some things in a way that becomes increasingly less-valuable and accurate with the complexity of the task, and more investment in AI data centers does not appear to do anything other than expand the number of tasks that an LLM can attempt.
While some people have been able to get something out of generative AI, that something never seems to be a tangible or impressive achievement. Every “successful” AI story is a result of either ignoring the obvious problems with LLMs or mitigating them at a great cost for an aggressively expensive and mediocre result.
LLMs are sold as “AI,” a technology best-known for automating things, yet they can’t be trusted to run anything on their own.
Instead, they manipulate the user into covering up their errors, explaining away their failures, coddling their meager returns and crediting them with the actual labor that LLMs are meant to automate away.
They do so by their investors and executives conning the media and the markets with outright lies and half-truths that exploit society’s weak points. The media and markets are informed by people that neither understand technology nor history, and Business Idiots that have reached the heights of their careers through diplomacy and ratfucking that care only about attention and adulation for things that other people do.
LLMs coddle the easily-led and narcissistic into believing that the model is doing the work as the human being has to constantly cater to the model’s inefficiencies and inabilities, using more energy and resources than any technology ever made.
And yet with all the money, all the attention, all the resources, all the land, all the power, all the affordances and excuses and endless fucking applause for mediocrity, nobody can actually point to the ROI of AI, because it doesn’t exist outside of it burping out stolen content and enriching and ingratiating billionaire dullards. Even at a hundredth of the price I’d be dismissive, because everything I’ve seen is so decidedly unexceptional.
I realize that some will say I’m dismissive of LLMs’ capabilities, and I’m sorry — I’m just not impressed. You spent a trillion dollars to make it somewhat easier to code some things sometimes but not in such a way that it actually results in anything, research reports that nobody will read, shitty powerpoint decks and excel spreadsheets, and art that looks like stock images because that’s exactly what it was trained on.
This shit needs to work every time without fail and be absolutely flawless and autonomous.
You are paying for a tool. You are paying for software. You are a customer. Your job is not to explain to others why this is exciting, nor is it your job to cover up for its mistakes. If you truly love this stuff you should be either secure enough in doing so that you don’t feel compelled to defend it or be demeaning to those that disagree.
The fact that I have to write that sentence is proof that something is very, very wrong with the AI industry, and that LLMs are about far more than software.
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