2026-09-11 22:05:01
According to The Information, in early 2024, Broadcom CEO Hock Tan hosted a “coffee chat” with employees of the recently-acquired VMWare, and introduced them to his particular brand of management:
At that time, VMware’s Palo Alto, Calif., campus sprawled over 18 buildings and 100 acres of delicately pruned gardens, an outdoor amphitheater and a turtle pond. Employees enjoyed nice HR perks, too, including child care, marital counseling and an annual $1,000 “wellbeing” allowance for anything from dumbbells to Xboxes.
When Tan opened the discussion up to questions, a VMware employee asked if Broadcom provided such benefits. Tan seemed surprised. “Why would I do any of that? I’m not your dad,” he replied, according to three people in attendance.
Over the next few months, Tan fired about half of VMware’s 38,000 employees. He also stripped down the campus, selling all but five of the buildings. At the remaining offices, Tan had the espresso machines removed. Somewhat against the odds, the turtles were allowed to stay.
He may not be your dad, but Hock Tan sure is a motherfucker.
Broadcom is a company you likely know for its XPU platform — a collection of different bits of intellectual property and access to semiconductor parts that allow it to build custom AI chips, the best-known of which are Google’s TPUs. It just signed a $30 billion deal with Apple to build “custom ASIC silicon products.”
Apple was already a massive customer of Broadcom, which historically provided a good chunk of the wireless and radio frequency parts that you’d find inside iPhones and its other devices, representing at one point more than 20% of revenues, dropping to around 10% to 15% with the growth of AI chip sales and the acquisition of VMWare.
For the most part, Broadcom’s business is built on selling companies the internal bits and pieces of either their hardware or the hardware surrounding their hardware — everything from wireless and RF components to data center networking tools.
It also dabbles in mainframe software (from its acquisition of CA Technologies), security (from its acquisition of Symantec’s enterprise security business), and virtualization software (from its acquisition of VMWare), and these segments cost it a combined $99.1 billion in cash and stock (not counting for inflation).
Except “Broadcom,” as a company, wasn’t always called Broadcom, and wasn’t founded by Hock Tan. As I’ll get into in this piece, “Broadcom” was once two very different companies — a wireless communication chips company founded in 1998 called “Broadcom,” and the private equity-formed monstrosity formed out of a spun-off semiconductor subsidiary of Hewlett Packard called “Avago Technologies.”
Sidenote: This is why Broadcom’s stock ticker is “AVGO.”
Much like Oracle, Broadcom is the story of a company acquiring other companies and then screwing over both its customers and employees in the pursuit of endless growth, which usually involves price gouging, massive layoffs, and cost-cutting anywhere that won’t improve margins.
A great example came from a Wall Street Journal piece from January 2018 involving Broadcom’s failed $117 billion attempt to acquire Qualcomm:
Executives at Chinese handset makers Oppo Electronics Corp. and Vivo Electronics Corp., recently expressed concern that Broadcom might trim Qualcomm’s R&D spending on fundamental cellular technology. Broadcom’s options are either to raise Qualcomm’s prices or cut its costs, a Vivo executive said. “Either choice will pose disadvantages for us.”
Wang Xiang, senior vice president of strategic cooperation at Xiaomi Corp., another mobile-phone maker, said he was evaluating the implications of a Broadcom-Qualcomm tie-up. “I think we care more if the technology partner is motivated enough to do technology innovation,” he said.
Two months later, the deal would collapse despite a dozen banks signing on (per Reuters) to provide Broadcom a $100 billion bridge loan to get the deal done, with President Trump vetoing the deal to avoid Broadcom (then a Singapore-based company) exercising control over the US-based Qualcomm. To give some credit to the administration, the CFIUS had (per The Hill) “...worried that Broadcom’s takeover would lead to a decline in investments in research and development in the sector, opening the door for Chinese firms to take the lead in developing next-generation wireless technology.”
That R&D point was a very real concern. Per The Journal:
Mr. Tan, in his dozen years as CEO, has spent six times as much on acquisitions as on R&D, while in that period Qualcomm spent nowhere near as much on acquisition as on R&D, according to data from Broadcom, Qualcomm and S&P Global Market Intelligence. In the past 12 months, Broadcom spent 19% of revenue on R&D, while Qualcomm spent 25%.
Pffft, 19%? That’s chump change. Since the acquisition of VMware, Broadcom’s R&D budget as a share of revenue has decayed to an unremarkable 9.8% of revenue in its latest quarter.
On a trailing-twelve-month basis, Broadcom is exceptional among its peers for how little it invests in R&D as a percentage of revenue, beaten only by NVIDIA, which has the excuse that it is the literal largest and most-profitable company on the US stock market.
That’s because Broadcom doesn’t really do “innovation” or care about “being good to its customers, but by hoarding other people’s patents, iterating on their creations as little as necessary, and making it impossible to avoid wiring Hock Tan money. Even its FBAR filters (used to block out interference on mobile phones) — a critical part of its deal with Apple — come from Avago’s acquisition of the original Broadcom.
Sidenote: I gotta give the Qualcomm acquisition a bit more context. Back then, the 5G rollout was just around the corner and the Trump administration was concerned that China’s Huawei would end up providing a good chunk of the infrastructure for next-generation mobile communications, thus giving the Chinese state unprecedented access to the communications of Western companies and governments.
Back then, there were only three real players in the mobile infrastructure sphere — Huawei, Nokia, and Ericsson. Whatever crumbs these three left behind were swallowed up by Samsung and ZTE, another Chinese firm.
Obviously, there’s a difference between the tech that goes into handsets (which Qualcomm provides) and the mobile infrastructure — the RAN, or Radio Access Network, which are (simplified) the radio antennas your phone connects to, and the core network, which is the behind-the-scenes technology that routes calls and connects your device to the wider Internet.
China, at that point, didn’t really have a viable alternative to Qualcomm’s tech, with the only exception being (shocker) Huawei, which manufactured its own 5G modems and shoved them into its proprietary Kirin chipsets. But Huawei only makes smartphone tech for Huawei, and the Middle Kingdom’s other mobile giants (OPPO, Xiaomi, Realme, and so on) were forced to buy tech from Western suppliers.
Still, the point is, the US was incredibly wary about handing an adversary like China an advantage in a field that was traditionally dominated by either American companies, or companies that were from countries aligned with the States.
Ericsson is Swedish. Nokia is Finnish. Samsung (though, at the time, a bit player) is Korean.
If you’re curious why we ended up with a triopoly, the answer is either because a lot of the bits that make a mobile network are low-margin, high-volume industries (and something that companies like Cisco weren’t particularly bothered with), or because the company went bust (as was the case with Canada’s Nortel), or were absorbed by larger players (as was the case with Britain’s Marconi Mobile).
Anyway, in retrospect, it was probably a good idea that Broadcom wasn’t allowed to turn Qualcomm into an asset-stripped, zombified version of itself. At least, from the perspective of someone worried about a mythical Chinese boogeyman.
One last point: In 2025, Huawei spent 21.8% of its total revenue on R&D — a figure it’s largely sustained over the years, despite being slapped with US sanctions in 2019 and subsequently cut off from any US-origin tech, and why it’s been able to do some genuinely interesting stuff, particularly in the mobile and automotive spheres.
A year or two ago, this could’ve been called The Hater’s Guide To Avago, because that’s really been the story of Broadcom — a Singaporean semiconductor firm that rolls up other companies’ technology under a brand made famous by somebody else.
Per The Wall Street Journal, a few months before its acquisition of Broadcom:
Should Avago close the deal, it would be its biggest ever in a long string of acquisitions. The company has grown its market cap and share price over the past six years through aggressive deal making, and at each step of the way, investors have rewarded the company.
Its most recent deal was in late February, when Avago announced an acquisition of networking company Emulex Corp. for about $606 million. The first trading day after that announcement, Avago’s stock jumped nearly 15%, adding roughly $4.2 billion to its market cap.
It's been one of the more aggressive acquirers in the semiconductor sector the past two years Since 2013, it has purchased five companies in the U.S. valued at about $8 billion, including a deal to buy rival LSI Corp. for $6.6 billion. Yet in that span, its market cap is up more than $25 billion during the same time frame. This year, Avago’s stock has jumped more than 40%.
Much like Oracle, Broadcom used M&A as a means of treading water revenue-wise, with each one having little effect on its overall trajectory outside of its acquisition of VMWare.
Yet Broadcom had been building something quietly behind the scenes through the combined acquisitions of LSI (which had merged with Agere a few years previously) and its own semiconductor might — a budding relationship with Google to build its Tensor Processing Units (TPUs), AI chips that at first worked to support products like Search and Maps, and would eventually become a huge part of the AI boom.
To be clear, Broadcom didn’t “see anything coming” or “catch the AI boom in its infancy.” While it deserves some credit for rolling up various different semiconductor companies like it’s playing Katamari Damacy, this is not a situation where Hock Tan or anyone had any kind of precognitive event that made them invest in ASICs in anticipation of a massive payoff.
What actually happened was far simpler: Google, which had already been running its services powered by (non-LLM) AI, got Broadcom to use its pile of various patents and supply chain connections to put together specialized silicon that had incremental boosts to Broadcom’s revenues until the launch of ChatGPT scared Sundar Pichai into sinking billions — and then tens of billions — of dollars into successive generations of TPUs.
And in Fiscal Year 2024, Broadcom began breaking out that revenue from its semiconductor solutions division, and something became alarmingly clear: that it’s become dependent on AI revenues for virtually all of its future growth.
Between Q1 FY2024 and Q3 FY2026, AI revenue has gone from 19.2% to 56.4% of Broadcom’s revenue, with analysts expecting it to make up 68.4% of FY27, 79.8% of FY28 and 81.9% of FY29.
And you’ll never guess who the customers are!
That’s right — OpenAI (for its Jalapeno AI chip) and Anthropic (buying Google TPUs), who are set to become Broadcom’s largest customers in Fiscal Year 2027, which means that tens of billions (and eventually hundreds of billions) of dollars of revenue will be tied to whether two unprofitable, unsustainable AI companies can afford to pay.
This is the story of how a grab-bag of other people’s innovations has accelerated in the space of three years to become one of the largest AI chipmakers of the world, and how its desperation for growth has forced it to engage in the darkest forms of circular financing.
This is The Hater’s Guide To Broadcom — the hard numbers and charts behind Hock Tan’s aggressive play to beat NVIDIA and become Google, Anthropic, and OpenAI’s chipmaker of choice…and how dangerous it might be if it fails.
2026-09-08 22:28:03
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Jensen Huang, CEO of NVIDIA, the largest company on the stock market, has declared that “AGI has arrived” in a response to the CEO of Crusoe congratulating OpenAI on the launch of its GPT-6 Astra model, who said that this “made Abilene the birthplace of AGI.”
Per sources with direct knowledge of the current progress of Stargate Abilene, the AI data center being built by Crusoe for Oracle to lease to OpenAI, there are at most four out of eight buildings functional at the Abilene campus, which started construction some time in 2024. Huang at no point defines what “AGI” is, other than to say that we’ve reached it, and that “400k GPUs coming online” was what was next, I assume referring to somewhere else on Earth, because Abilene only has space for a total of 400,000 Blackwell GPUs, of which (as I’ve noted) at best half of which are actually installed and functional.
The reason that everybody is talking about AGI is that TIME magazine, bereft of any journalistic standards or shame, quoted OpenAI Chief Research Officer Mark Chen as saying that OpenAI was “80% of the way” to AGI,” only for Chief Operating Officer Greg Brockman to say a few days later that we had entered the “AGI era, whether you view it as this model, the last one or the next one,” which the Wall Street Journal agrees with, even though it cannot define exactly what AGI means, but this is the AI bubble and those most-responsible for telling the truth are mostly incapable or unwilling to bother.
These companies are treating everybody like they’re stupid, in large part because everybody, including the largest media outlets in the world, appears to fall for just about anything. Neither NVIDIA nor Crusoe have actually done anything — we have not reached “AGI,” nor has “the birthplace of AGI” been completed, nor does anybody seem to bring up these facts in any of the pieces I’ve read outside of saying “hmm, well AGI isn’t really well-defined,” humouring what these companies are saying without a single thought entering their minds.
If anything, the far-more-interesting way to look at this is why all of these people are suddenly jerking their shit from first principles over a term that is meant to mean “an artificial intelligence that can handle tasks beyond its original training” but now means basically anything the companies want it to, and how that times with the rush for both Anthropic and OpenAI to go public.
The answer is pretty simple: these people want to stop you thinking about what’s actually happening — that the underlying financials and demand do not make sense, and their cloud software does not remotely justify its alarming costs.
Today I’m going to talk to you about why I think there’s a Silicon Valley Financial Crisis brewing, and the concentration risks involved.
So, today we’re going to talk about a term you may or may not have heard of before: concentration risk.
It’s a term that refers to having all your eggs in one or a few baskets, becoming overly reliant on a few investments, customers or particular business lines to the point that without them your business or portfolio would suffer massive harms. In banking specifically, to quote the National Credit Union Administration, it refers to any single exposure or group of exposures with the potential to produce losses large enough (relative to capital, total assets, or overall risk level) to threaten a financial institution’s health or ability to maintain its core operations.
I bring this all up because you’re going to hear this term, or variations of this term, a lot in the next few months and years as the AI bubble unravels, because just about every part of the industry involves its own flavor of concentration risk.
Let’s start at the top. Per data from fintech firm Ramp, 80% of OpenAI and Anthropic's enterprise revenues come from 1% of their customers, a number that hasn’t improved over the last three years. Ramp’s lead economist Ara Kharazian notes that the top 1% skews heavily toward the tech sector and AI products and services, and that this was a level of concentration risk unseen in any other software category they tracked.
Oh, and it hasn’t gotten better over time.
This dataset, which likely includes big companies like Visa and Cursor as well as a great deal of startups and regular-sized companies, is indicative of the overall spend of the AI industry, with the caveat that it doesn’t include massive players like Microsoft or major banks, and customers can opt out of being included in research.
I also want to be clear that when Ramp says “AI products and services,” that includes AI startups that sell subscriptions with subsidized token spend, meaning that users can burn far more than their subscription price in tokens. This means that the money made by Anthropic or OpenAI from an AI startup in that 1% spend is contingent on their continued ability to raise venture capital.
This means that the vast majority of enterprises — which is where the real money is in software — just don’t spend that much money on AI. Those that do spend the most on it are heavily-concentrated in either AI companies that either use a lot of tokens internally because they’re bankrolled by venture capital, AI companies that allow their users to blow unsustainable amounts of money on tokens bankrolled by venture capital, tech companies that are currently under heavy peer pressure to spend money on AI tokens, and I assume a few whale customers of some sort.
During the Great Financial Crisis, millions of people took on debt they never had any hope of paying, with one of the most egregious examples being “NINJA” loans — No Income No Job Applicants or No Income No Job Assets (I've seen both).
Per Pew, “...in the years before the Great Recession, almost 38% of new mortgages required little or no documentation.”
To be specific, 36.5% of 2005 and 37.9% of American home purchases in 2006 were from buyers with little-to-no income documentation, which meant that, for the most part, these subprime mortgage payments were only made possible by a system that was desperate to create more demand for loans rather than creating a lending agreement with a stable customer who would be able to make regular payments.
Sidenote: Before we go any further, I want you to also know that “subprime” doesn’t refer to the borrower but the loan itself. Plenty of “well off” people got mortgages they couldn’t afford in the time leading up to the Great Financial Crisis.
A “homeowner” in 2005 and 2006 could easily be somebody who could not, in any real sense, afford the home they were buying.
I sure hope that nobody is making that same mis-OH MY GOD!
This means that 80% of OpenAI and Anthropic’s enterprise revenues — which make up the vast majority of their total revenues — are dependent on what are likely hundreds of customers spending outsized amounts of money on AI tokens, with an indeterminately-large chunk of them being AI startups that can only do so as long as venture capital supports them.
Let me break down exactly what this means:
AI startups are an artificial source of revenue. They are not paying Anthropic and OpenAI out of cashflow, or because they’re “getting great value,” and indeed are only able to do so as long as somebody else hands them endless amounts of cash. While their revenues may be increasing, they pale in comparison to the sheer sums raised or the rate at which they’re raised. Harvey raised over $800 million in 2025 alone, and exited the year at around $190 million in annualized run rate, or around $15.8 million a month, meaning that it would’ve been completely dead over a year ago without venture capital propping it up.
And let’s be completely clear: OpenAI and Anthropic are financially dependent on these customers to survive. While “enterprise” could refer to a cluster of Fortune 500 or big businesses that are theoretically using LLMs for coding or whatever, it’s very clear based on Ramp’s data that one of (if not) the largest sources of revenue for these companies is AI startups that can literally not afford to pay for tokens without venture capital funding.
AI startups are also the easiest to make spend more on AI because of their users’ subsidized token burn. When somebody fires up something like Harvey or Perplexity, they’re going to expect the latest models, which means that every AI startup is effectively a venture-backed marketing platform for the latest models, spiking costs for the company while feeding those dollars directly to the AI labs. When a user doesn’t have to worry about their actual costs and the provider doesn’t have to either because it’s bankrolled by venture capital, it’s really easy to see surges of revenue around every new model launch, giving AI startups a new way to beckon users back to the platform (see: Perplexity) and AI labs a bump in revenue in return.
AI startups represent a massive concentration risk for OpenAI and Anthropic, because this isn’t real revenue. Providing these services to AI startups isn’t making their customers “more money” so much as it gives them a justification to keep raising money. While Harvey or Perplexity might “need” AI models to run their businesses, they are not paying for them because of any value or business model or strategy so much as that they’re in a Red Queen’s Race where they must offer the latest models at whatever cost to “stay current.” If anything, without funding these businesses would have to stop offering Anthropic and OpenAI’s models to reach anything approximating sustainability, because the cost of AI tokens is the primary driver of their losses.
To give you an idea of the scale of these customers, last week OpenAI announced it was cutting off AI coding company Cursor (which is now part of SpaceX), with WIRED reporting that it was set to make OpenAI over $1 billion in revenue in 2026, or over 3% of its projected $30 billion in 2026 revenue. With OpenAI only representing 5% of Cursor’s traffic, it’s likely sending billions more to Anthropic this year, a massive underlying exposure that could easily evaporate if Elon Musk decides he doesn’t want to send all that money to competing AI labs.
Cursor was only able to keep sending that money to Anthropic and OpenAI because it raised $3.2 billion in the space of four months — June ($900 million) and November 2025 ($2.3 billion). Per The Information from July 2025, Anthropic’s two largest customers represented $1.2 billion of annualized run rate (30% of its $4 billion run rate at the time), with investors believing they were Cursor and Microsoft’s GitHub Copilot, the latter of which moved to token-based billing in June 2026.
The problem is both that Anthropic and OpenAI’s largest customers cannot afford to pay them and that they desperately need them to keep paying them more every quarter, which means that every single AI startup will need to raise more and more money to do so.
They are, as I’ve suggested, the NINJA borrowers of the AI era. They do not have to show functional businesses or sustainable demand for their products, only an excitement to sign pieces of paper and an eagerness to continue spending money that isn’t theirs. The “houses,” in this case, are the ever-increasing valuations of the startups themselves. There is no logical or rational basis to value Perplexity at a potential $30 billion (per The Information) or to give it billions of dollars, other than the fact that venture capitalists want to see the value of the company go up, and NVIDIA wants to make sure it can keep spending billions with Anthropic and OpenAI.
And much like NINJA borrowers, this bad behavior is enabled on a systemic level, with 50% of all global venture capital flowing into AI in 2025.
As mentioned, the “top 1%” skews toward tech and AI startups, which means that even outside of unsustainable AI companies, Anthropic and OpenAI are mostly-reliant on the same customers they’ve always had for revenue growth.
That means that outside of unprofitable AI startups, the vast majority of “enterprises” spending money on AI are tech companies rather than other industries. The tech industry is far more willing to dabble and invest money in new stuff, especially if everybody else in the industry is screaming about it non-stop for years, meaning that its “interest” is driven by far more than “is this actually useful” or “do we actually need this.”
Tech companies have more software engineers, and in turn more software to be built or iterated upon, along with more willingness at the C-suite level to spend money on software tools.
I’ll add, however, as Ramp’s Kharazian noted, that this was “...a level of concentration risk unseen in any other software category [than they track],” which means this is an AI-specific concentration rather than a problem with software writ large.
Sidenote: At this point, somebody is probably screaming that “Ramp skews towards startups” and “Ramp’s data doesn’t include every big business.” Neither of these arguments are actually based in reality, but even if they were, these are still massive revenue sources that are dependent on the whims of tech executives or venture capital.
In other words, outside of the tech and AI world, very few companies are willing to pay very much for AI, which is catastrophic on just about every level, with no clear sign as to how you reverse the trend.
AI has been in every media outlet and discussed in every boardroom and company for the last three years, every single company has on some level dabbled in using AI, most businesses have been given the greenlight to spend a bunch of money on AI, and in the end, it seems the only people the tech industry can get to spend significant money on AI is…the tech industry itself. “The tech industry” also includes an indeterminately-large amount of venture-backed startups who, much like AI startups, can only afford to spend a lot of money on AI as long as somebody else gives them the money to do so.
This is yet more underlying exposure for the AI labs, because these customers are also prime targets to move to either cheaper open source models that they train themselves or, eventually, on-device models.
Even if they choose to stay with Anthropic and OpenAI, a chunk of this spend is contingent on venture capital funding, and the rest is contingent on whether tech firms continue to be willing to spend money at scale. 80% of their revenue concentration depends on spending and capital that varies from unreliable to actively-unstable.
Things get worse from here.
Sidenote: I estimate that there’s around $22 billion of annual non-OpenAI/Anthropic AI compute demand, with most of that coming from Jane Street (an investor in both CoreWeave and OpenAI) and, on a much larger scale, NVIDIA renting back its own GPUs. I think this number could be smaller, but this is my closest estimate based on my analysis.
I’m saying this because I anticipate someone will say “Ed, someone else will buy the compute.” No they won’t. As I’ll get into, the companies that are meant to buy the compute can’t afford it, and nobody else is buying compute at even close to that scale.
So, I realize that a few months ago I described AI data center debt as the subprime mortgages of the AI bubble, and I stand by that comparison at the time I made it, and think it still matches.
That being said, another example has emerged — Anthropic and OpenAI’s monstrous compute commitments, which now represent over $1.3 Trillion in revenue for hyperscalers and neoclouds like Google, Microsoft, Amazon, SpaceX, Hut8, SB Energy, Oracle, Cerebras, Nscale and Lambda.
To be specific, per the Wall Street Journal, OpenAI projected to spend over $750 billion on compute through 2030 in July 2026 before it signed its deal with SB Energy (more info here), and per The Information’s research, Anthropic has signed approximately $517 billion in agreements in the last 11 months.
These are, from what I can tell, “take-or-pay” agreements where they agree to buy that compute capacity regardless of how much capacity they actually end up using, and how much revenue they actually bring in.
And when the compute is available, or about to be available, you have to pay a chunk of money up front before you start using it.
As a reminder, both are woefully unprofitable and lose tens of billions of dollars a year. Even if they were profitable, the sheer scale of their commitments is astonishing, representing a massive underlying risk to some of the largest companies in the world.
To give you an idea of that risk, per Bloomberg OpenAI’s compute spend and revenue share represented around 70% of Microsoft’s AI revenue in Fiscal Year 2026 — which just ended in June — or a little over 7% of Microsoft’s entire fiscal year revenue, and UBS estimates that Anthropic and OpenAI’s compute spend will account for 48% of Google Cloud’s entire revenue next year, or somewhere between $84 billion and $100 billion dollars, in 2027.
That’s on top of, per Barclays, OpenAI and Anthropic’s estimated $40 billion dollar spend on Amazon Web Services, and at least $50 billion dollars that both of them will spend on Microsoft Azure in Calendar Year 2027, which I note because Microsoft uses its odd fiscal year system.
On the low end, that means that Anthropic and OpenAI account for over $200 billion dollars worth of expected revenues for Microsoft, Google and Amazon in 2027, which is contingent on their ability to raise venture capital or debt, which is contingent on the continued growth of their businesses, which is contingent on growing AI spend from a small subset of customers, many of whom are funded by venture capital.
The reason this hasn’t been a problem yet is that when you sign these contracts, you tend to pay a small up front fee, and the capacity in question is yet to come online.
All it takes for Anthropic or OpenAI to sign hundreds of billions of dollars’ worth of obligations with a little bit of cash and a few clicks of a DocuSign agreement, meaning that all that capacity isn’t costing them anything until the date hits when they have to start paying.
That’s going to start happening next year, and get dramatically worse month after month as capacity comes online.
Hey, that reminds me of something too.
Anthropic and OpenAI’s compute commitments, in my mind, should be seen more as debt obligations than “contracts,” because they (as take-or-pay agreements) function in much the same way, requiring the company to pay whether or not they need the capacity.
For now, everything looks awesome. Microsoft, Google and Amazon have all had big bumps in revenue from AI lab compute spend along with massive, ever-swelling revenue backlogs — over $1.5 trillion worth to be specific. More than half of that backlog is attributable to Anthropic and OpenAI, which, as I’ll say again and again, isn’t a problem because the money is yet to stop coming in.
As mentioned, this is going to begin in earnest in 2027, and expand dramatically every year following (though I doubt we will make it that far).
A really shittily-written piece (full of incorrect numbers and zero citations written using an LLM) from an outlet called Groundbreaker made a good point about this, comparing it to when the rates on millions of mortgages exploded as they hit a “reset wall,” where the low “teaser interest rates” ended, exploding the monthly mortgage payments to unsustainable highs, with customers assuming, incorrectly, that their houses would keep appreciating or they’d be able to refinance.
In other words, Anthropic and OpenAI are currently in the teaser rate period where all of that capacity — and all of the associated costs — are yet to hit.
Next year, at least $200 billion in compute costs are coming due.
The question is whether Anthropic and OpenAI, two unprofitable, unsustainable AI labs that lose tens of billions of dollars a year, will be able to afford to pay them.
If you ask the vast majority of tech and business journalists, consultants or sell-side analysts, they’ll tell you not to worry — that there’s insatiable demand for compute, or even that said demand “may never be sated,” and that even if there is a bubble, society will get “gigantic benefits” either way. These views are always backed up by data from the industry, which is trusted, for some reason, to tell the truth about itself.
The argument that most would make is that both Anthropic and OpenAI will be able to buy all of that compute, and even if they couldn’t afford it, other customers would line up to take the demand. When pushed about how the big AI labs would actually afford this compute, everyone will tell you that “they’re the fastest growing companies in the world.”
In this case, we’re talking about $1.3 trillion in demand from two customers who have a few hundred customers that mostly pay them based on the availability of venture capital dollars.
While the consequences might be different — as the scale and damage of the Great Financial Crisis was driven by trillions in speculation — the mistakes are increasingly looking very, very similar.
And so are the rationalizations.
In the period leading up to the Great Financial Crisis, approximately 80% of US-based subprime borrowers got adjustable-rate mortgages with “teaser rates” — lower interest rates for the first two-to-three years followed by adjustable rates that changed with both interest rates and, in some cases, fees associated with said adjustments.
These mortgages were known as 2/28 or 3/27 mortgages, depending on whether the teaser period lasted two or three years. One important thing to note is that the “teaser rate” wasn’t by any means low (they could be as much as 7%), only that they were lower than the normal rate.
When borrowers worried about the potential for higher monthly payments, they were reassured that they’d be able to refinance, or that the price of their house would only ever increase.
Per an FDIC report on the Great Financial Crisis:
Under the more relaxed underwriting standards at the time, many borrowers qualified for adjustable rate mortgages based only on their ability to pay the low initial monthly payments as determined under the introductory teaser rate. Hence, their ability to afford the mortgage after the teaser rate expired was predicated on their ability to refinance the mortgage before the higher payments became effective.
The ability to refinance—counted on by many investors, homebuyers, and originators—depended critically on house prices. As long as house prices were rising, lenders were generally willing to supply new funds with new terms. And even after house prices at the national level peaked, in mid-2006, housing market participants generally did not expect house prices to crash.
While warnings about a housing bubble started as early as August 2002 (good work, Dean Baker!), there was a broad (though not complete) consensus that there was, in fact, no housing bubble. In August 2005, the National Association of Realtors put out multiple “anti-bubble” reports, saying that “the facts simply do not support the possibility of having a housing bust” in 130 specific markets and the nation at large. Then Fed Chair nominee Ben Bernanke said in October 2005 that “there was no housing bubble to go bust,” noting that even if there was a “moderate cooling in the housing market,” that it would “not be inconsistent with the economy continuing to grow at near its potential next year.”
Yet my favourite is from July 2005, when the Wall Street Journal’s Neil Barsky (in a piece called “What Housing Bubble?”) mocked The Economist for calling it “the biggest bubble in history,” castigating “the media and economists [scaring] homeowners with words of doom and gloom, however knee-jerk, consensual and misguided they may be,” saying that “there is no housing bubble [in America].”
His justifications involved saying that the housing market was strong as a result of “real economic underpinnings” like “low interest rates, local job growth and the emotional attachment one has for one’s home.”
Yet the most-relevant one was that he connected the strong housing market to the “real economic underpinning of "one's view of one's future earning-power,” and his thoughts around housing demand:
What we do have is a serious housing shortage and housing affordability crisis. Despite robust construction, unsold inventory stands at four months, well below its 25-year average. Private builders complain they can't get land permitted to meet demand. Low-income housing advocates complain housing prices are out of reach for many Americans, and that government subsidies have been slashed.
Hey, this kind of reminds me of something that NVIDIA CFO Colette Kress said on its latest earnings call:
The Frontier AI labs have extraordinary demand for training and inference compute, but they are growing faster than what their balance sheets and credit profiles can support. They have rapidly growing customer demand, yet still lack the decades-long infrastructure contracts and investment-grade financing capacity needed to secure the AI factory infrastructure independently. In other words, their growth is not limited by their technology or customer demand. It is limited by compute.
This piece rules, primarily based on its answer to the “myth” that “risky mortgage products are fueling house appreciation, which mostly boils down to “homeowners only own their homes for an average of seven years [note: he has no citations for this claim], which means that you’re basically wasting money by not getting an adjustable rate mortgage.
I could go on. On December 21, 2006, CNBC’s Diana Olick ran a piece based on reader feedback around housing numbers provided by the National Association of Realtors, The Department of Commerce and the National Association of Homebuilders:
Another [reader], Michael Crespy, writes: “Although you periodically have a “housing bear” on the program, more than not, the program is filled with the NAR or NAB’s “economists” who are no more than the HEAD cheerleaders for the housing industry!!”
Mr. Crespy, you’re right, they are the cheerleaders for the housing industry, but they are also economists whose sole purpose is to organize and present data on the industry. Interestingly enough, the Dept. of Commerce, which has no stake in the industry, has far higher margins of errors on its numbers than do the industry numbers. The NAR’s existing homes data, which are monitored by the Federal Reserve, has a 1% margin of error. Their data comes from a sampling of 40% of the MLS listings. Forty percent is pretty high in survey land.
Olick’s piece, at least on the surface, attempted to have a “balanced” view, but mostly ended up arguing that everything was fine, with even a quote from Wharton School of Business professor Susan Wachter saying that the numbers — which all said that things were “improving” — “in some ways [gave her] confidence,” adding that she had no problem with statistics from realtors or home builders.
Olick, feeling defensive, ended the piece as such:
Here at Realty Check, we report the numbers, we talk to the industry leaders, we also talk to umpteen brokers out in the field, to economists who study real estate trends and to buyers and sellers who are trying to make sense of it all; then, for better or worse, we try to make some sense of it all. I confess, I do own a house, so there’s my bias; I’d like it to continue to appreciate. If you don’t buy what I’m reporting, that’s your choice.
Now, in her defense, perhaps the numbers did say everything was fine if you squinted, but the sheer venom that Olick had for concerned listeners that called her “some kind of apologist or defender of the industry” rather than, say, going out and doing journalism…mirrors basically all of the reporting on AI today, which mostly says “the numbers look great!” while, well, ignoring the ones that don’t.
Less than a week later on December 27, 2006, CNBC would run a story called “Analyst: Housing Bubble Fears Behind Us,” quoting former US International Trade Commission economist Peter Morici as saying that home numbers sales were “very good news for the economy,” and that he “expected new home construction to rebound in the second and third quarters of 2007.”
Here’s what actually happened:
Terminology Time! A “reset” in this case is when a mortgage goes from a lower “teaser rate” percentage to an adjustable-rate that changes based on the terms of the mortgage and current interest rates, massively increasing your monthly payments.
I must be clear that the Groundbreaker piece that inspired this piece is horribly written Claudeslop, but deserves credit for this idea, even if it fumbles basically every number, cites effectively nothing, and has near-impenetrable text that I’m not certain most people even read.
Sidenote: The term “Reset wall” is a term that seems to have entered adoption after the fact, and doesn’t appear in contemporaneous coverage of the subprime mortgage crisis. Coverage of that era uses the term “rate reset.”
Nevertheless, I must quote it:
Millions of subprime borrowers were, at that moment, paying the low introductory rate on a two-year adjustable rate mortgage - the 2/28 ARM. A low fixed-rate for two years, then the rate reset to a payment 30% to 50% higher. During those first two years the loan performed beautifully: the borrower paid, the servicer collected, and the bond paid its coupon. Nothing looked wrong because the whole complex - housing, mortgages, securitization - was sitting inside the teaser period.
Every ARM reset was known, dated, and contractually inevitable from the moment of origination. Aggregate those reset schedules and you get the most damning exhibit of the era: the reset wall. Roughly a trillion dollars of adjustable-rate mortgages were contractually set to reset across 2007 and 2008 - thirty to forty billion dollars a month at the peak. Credit Suisse published the chart in March 2007. The IMF reprinted it. It circulated on every trading floor in New York and London.
Groundbreaker neglects to cite anything, so I went and actually found the chart shared by the IMF via Credit Suisse:
The “wall” in this case refers to the large group of Subprime borrowers who suddenly, starting in 2007, would see their mortgage payments skyrocket to the tune of tens of billions of dollars a month (as Groundbreaker correctly said).
Sidenote: Though there’s not a ton of data out there, the Center For American Progress noted that 1.8 million mortgages hit or would hit a rate reset in 2007 and 2008,
In other words, before everyone had to pay more money, everything looked fine because everybody could still make their payments. Once they had to start making larger payments and couldn’t make those payments, with mortgage delinquencies spiking gradually every month from January 2007, peaking at 11.49% more than three years later in March 2010, taking another six years to drop below 5%.
You’ll also note that everything unwound very quickly, with much of it beginning in 2007 and 2008 as teaser rates ended. Subprime mortgage originations collapsed by the end of 2008 as private label securitization from banks and financial institutions (per page 19 of the FDIC report) which “had provided much of the funding for new mortgages” dropped dramatically and had “virtually disappeared” by 2008.
Said interest in funding new mortgages was, as we know now, barely anything to do with building houses so much as it was a way to build a new asset class for investors to speculate on.
And, very importantly, the massive expansion of subprime mortgage issuance mostly took place over a three-year-long period. While this rush of new housing development and mortgage origination was sold to everybody as the result of endless demand for housing, said demand for housing was driven by masses of easily-available money being given to people who couldn’t afford it outside of a manic period in history.
You can probably see where I’m going with this.
Everything seemed totally fine in the years running up to the Great Financial Crisis because, based on external data, the money hadn’t stopped coming in. Because effectively anybody could get a mortgage, US construction spending comprised nearly 9% of GDP by 2006, employing 7.7 million people, all because of the “demand” for housing created by the illusory demand created by subprime lending.
While nobody at the time could’ve possibly anticipated the sheer scale of speculation that would eventually unwind the global financial system, there was plenty of coverage of subprime borrowers being a problem. Not to worry though, The Brookings Institute explained in October 2007 that this wouldn’t be a problem, emphasis mine:
Unless the U.S. economy dips dramatically, however, the vast majority of subprime mortgages will be paid. And, because there is no basic shortage of money, investors still have a tremendous amount of financial capital they must put to work somewhere.
Nevertheless, in November 2007, Fed Governor Randall S. Kroszner did make a very clear warning:
Finally, another factor that could affect subprime delinquencies is the substantial payment increase often experienced at the first interest rate reset. For the most common type of subprime variable-rate loan, the so-called "2/28" loan, this reset occurs after two years, before which payments are typically based on a fixed below-market rate. In early 2007, the typical subprime mortgage experiencing a first reset had its rate increase from 7 percent to 9-1/2 percent, producing an increase of 25 percent to 30 percent in the monthly payment. This increase translates into an additional monthly debt obligation of $350 per month for the average subprime variable-rate mortgage.
And here’s the fun part: Anthropic and OpenAI’s reset wall is actually way simpler, more-concentrated and easier-to-spot if you bother to look!
As I mentioned in my premium from a few weeks ago (How Much Money Does AI Need?), analysts from UBS, Barclays and Wells Fargo expect — by which I mean they are setting expectations — that Anthropic and OpenAI will account for at least $444 billion of hyperscaler earnings in the next three years.
To be specific, I pulled together all the numbers from my AI Demand Bubble newsletter from a few weeks ago, and found that Anthropic and OpenAI will account for at least $365 billion in revenue across Fiscal Years 2026, 2027, and 2028.
Sidenote: Except this analysis is only partially complete, as it’s based on Wells Fargo’s single Fiscal Year 2027 estimate of a $52.5 billion expected contribution from OpenAI and Anthropic. One weakness of this analysis is that we’re talking about Microsoft’s Fiscal Year 2027, which actually began in the middle of 2026. Most other hyperscalers (including Amazon, Meta, and Google) align their financial years with the calendar years. Nevertheless, I think it’s fairly illustrative of the problem.
To estimate the contribution — and be incredibly fair! — I have assumed OpenAI and Anthropic’s Microsoft spend will be linear (at $52.5 billion) across fiscal year 2028, and then halved it for fiscal year 2029, which gets us to a grand total of $444 billion.
That spike in costs comes from Stephen Ju of UBS’ estimates, and even if you think that’s a little high, I would estimate that the $250 billion of commitments made by OpenAI alone on Microsoft Azure will likely mean Microsoft is expecting tens of billions more than $52.5 billion in FY27 and beyond.
I also need to express how much more money this is than these companies are already spending on compute.
In 2025, OpenAI spent (per my own reporting, assuming 50% of sales and marketing was compute expenses) a little over $29.5 billion on compute. Per The Information’s reporting, it spent $12.1 billion (with no affordance for sales and marketing) in the first quarter of 2026, and while we don’t know how much it spent in Q2 (when revenues grew by $1 billion quarter-over-quarter), it’s fair to assume that it’ll spend another $12 billion or so a quarter for the rest of the year, for a total of $48.4 billion, which is less than the $50 billion it said it expected to spend on compute in 2026.
Per Barclays and UBS, OpenAI is projected to spend $15 billion on AWS and $12.5 billion on Google Cloud in 2027, with Wells Fargo estimating it will spend $22.9 billion for the first two quarters of 2027 making it reasonable to assume at least $45 billion, for a total of $72.5 billion… which, even then, seems a little low based on what it’s already on track to spend in 2026.
Then you have to add in another $30 billion from Oracle’s $300 billion, five-year-long deal with OpenAI, which the Wall Street Journal reports is expected to drive $30 billion in revenue starting in 2027, though my own research found that it could be more than $50 billion or $60 billion
Meanwhile, Anthropic is expected to spend $25.3 billion on AWS and $101.25 billion on Google Cloud in 2027, increasing to $35.8 billion with AWS in 2028 and dropping to $25.6 billion with Google Cloud in 2028, likely as a result of the initial cost being buying TPUs. Since then, Anthropic took on $35 billion in debt to buy TPUs from Broadcom (which also backstopped the debt), with another $70 billion deal potentially on the cards.
I haven’t even included either company’s deals with CoreWeave, OpenAI’s contract with Cerebras, Anthropic’s SpaceX deal, or many of the deals noted in The Information’s story about Anthropic’s $517 billion in compute commitments.
As both Anthropic and OpenAI are private companies and we lack any meaningful accounting standards around disclosures for revenue backlogs, we can only estimate how big the compute reset wall is at any given point in time.
Part of the problem is that we don’t know how much capacity is actually coming online (as hyperscalers refuse to give any clarity), and said capacity has to come online for Anthropic and OpenAI to pay for it. It’s frustrating, because it means that “$1.3 trillion” number is hard to append to a period of time.
That being said, we do know that the Wall Street Journal has OpenAI projecting it will spend $750 billion on compute through the end of 2030, which suggests at least $250 billion a year in compute spend.
If it doesn’t, it means that in 2028 or 2029, its commitments could spike to $300 billion or $400 billion a year.
Is that good?
Let’s be abundantly clear about something: there is no rational or responsible way that Google, Microsoft, Amazon and the various other neoclouds should have allowed Anthropic and OpenAI to sign up for so much compute capacity, outside of the kind of blind faith that always goes wrong. Neither OpenAI nor Anthropic can actually afford to pay their commitments if they don’t grow by around 10x in the next three years, and at some point find a way to become profitable, which will require at least a trillion dollars in funding or debt.
Hyperscalers are doing all of this based on the very same logic that led to the massive issuance of subprime (and prime-but-unpayable) mortgages and the resulting overbuild of housing — that the money hadn’t stopped being spent. Venture capital and private credit have conspired to keep feeding Anthropic and OpenAI money (along with the hyperscalers themselves), much as they’ve continued to feed money into data center deals they’d theoretically occupy.
Similarly, hyperscalers continue to build out capacity for Anthropic and OpenAI under the continued assumption that they’ll keep paying, driven mostly by the fact that they’ve yet to stop doing so. They assume, somehow, that OpenAI and Anthropic’s ability to pay them tens of billions a year is all the proof they need that they’ll pay them hundreds of billions of dollars’ worth in the future.
Sidenote: At this point, I really want to use Groundbreaker’s charts, but their numbers are, if I’m honest, total fucking dogshit — Anthropic and OpenAI are very unlikely to have spent over $120 billion on compute in 2026, and I can find absolutely nothing to back them up. Nevertheless, this mound of Claudeslop makes several good points, and I have to cite it.
A take-or-pay contract is, in economic substance, a lease. And a lease is a financing. The defining feature of debt is a fixed payment on a schedule, owed regardless of the borrower’s circumstances. That is exactly what a take-or-pay commitment is. The payment does not flex with utilization. It does not wait for the customer’s revenue. It is owed on the commencement date and every period thereafter, for the term.
This is completely correct, unless of course you’re a member of the tech and business media, in which case it’s “a large amount of money that will of course be paid without fail.”
So, let me give you some context about how big these commitments are. Microsoft’s trailing-twelve-month operating expenses are $176 billion for a company with $331 billion in annual revenue. Meta, a company with $228 billion in annual revenue, has around $141 billion in operating expenses. Salesforce, a company with a little under $44 billion in annual revenue, has $35 billion in operating expenses.
OpenAI, in 2025, had $34 billion in operating expenses on $13.07 billion in revenue. In Q2 2026, its operating margin worsened to negative 183%. This is a company with deteriorating economics that has been allowed to sign hundreds of billions of dollars’ worth of compute commitments based on, for the most part, Sam Altman’s ability to say yes and the general sense that nothing bad ever happens to anyone.
These commitments were signed, I assume, with effectively no underwriting, because anyone with a calculator and sentience can see that on paper these companies cannot afford their commitments. The rationale is exactly the same as that used to hand-wave against worries around subprime defaults — that the system is working, that the system will always correct itself, and that things keep on growing.
In any case, neither OpenAI nor Anthropic actually have the money to pay for their obligations, and have only been able to keep up because of the low cost of signing contracts.
As these commitments begin, their needs for capital will dramatically accelerate in ugly chunks, both with hyperscalers and neocloud partners, on top of any debt deals they sign with Broadcom to fund their own silicon.
And the vast majority of these commitments and payments are yet to occur, which is, as is the theme of this newsletter, why nobody is worried yet.
Meanwhile, one abstraction higher, even the companies that are actually making a profit on the AI bubble are exposed to the underlying risk of Anthropic and OpenAI.
I’m going to dispense with the direct Great Financial Crisis comparisons at this point because I think it’ll get in the way of the analysis, but let’s be abundantly clear about something: either directly or by proxy, NVIDIA’s customer base is effectively Anthropic and OpenAI.
As I went into in part 2 of my Hater’s Guide To Circular Financing, OpenAI and Anthropic provide two functions to hyperscalers and NVIDIA:
To get specific about that second point, whenever you hear someone say that there’s “massive demand for AI compute,” they always point to revenue backlogs that are, for the most part, either OpenAI, Anthropic, or someone else renting them compute. For example, CoreWeave’s latest earnings involved the outright-deceptive statement that its “[$104 billion] revenue backlog [highlights] unprecedented demand for CoreWeave Cloud,” even though $22.4 billion of that is OpenAI, $21 billion is from Meta, $6 billion is from Jane Street (which also invested), and the rest is from some combination of Anthropic, Microsoft, and NVIDIA’s $6.3 billion backstop deal to buy unused capacity. To be specific, CoreWeave’s backlog increased by $32.6 billion in the earnings immediately following its Anthropic deal.
These revenue backlogs exist as both circular financing and financialized marketing schemes.
From the outside, every company with masses of AI compute also has an astonishingly-large backlog, which everyone assumes must be sold to a diverse subset of customers rather than Anthropic, OpenAI, and the companies that might one day sell them compute.
In other words, everything is based on the idea that Anthropic and OpenAI are A) going to have near-infinite demand for compute and B) that their existence is proof somebody else will too.
The other problem is that NVIDIA’s GPUs are so god damn expensive that nobody — including the largest and richest companies in the world (minus Microsoft) — can afford to keep buying them and building data centers without taking on near-infinite amounts of debt, reducing the pool of potential customers dramatically.
You can already see this in NVIDIA’s latest earnings. Almost half — 44% — of its FY2027 revenue so far (two quarters) came from three customers, and 16% of its most-recent quarterly revenue came from one customer, likely SpaceX, which serves Anthropic compute. Per my recent premium newsletter, UBS estimates that around 50% of NVIDIA’s data center revenue comes from Meta, Google, Microsoft, Amazon, and Oracle, with Deutsche Bank estimating it’s as high as 60%.
The justification for these further capital expenditures is, for the most part, driven by OpenAI and Anthropic, with their demand driven in large part by unprofitable AI startups subsidizing their users’ AI tokens.
While NVIDIA might talk about how we’ve “reached AGI” or that there’s “crazy demand,” the actual financial returns on buying NVIDIA GPUs are driven almost entirely by OpenAI and Anthropic, by which I mean Microsoft, Google, Amazon, Oracle, CoreWeave, Lambda, Hut8, Fluidstack, and basically every other counterparty is building capacity either mostly or entirely to capture their revenue.
The best example I can find is SB Energy, which has a $439 billion backlog, 99.4% of which is earmarked for OpenAI.
Further non-OpenAI/Anthropic GPU sales are contingent on NVIDIA’s perception management keeping everybody believing that there’s real demand for AI compute, which is why it effectively acquired Poolside, and may invest billions in Perplexity and Thinking Machines. Neither of these companies could actually afford to exist without venture capital (or NVIDIA) dollars, but with NVIDIA’s investment, they can potentially add hundreds of millions or billions of dollars of further “demand” to the backlogs of hyperscalers or neoclouds.
Once again, everyone assumes everything is fine, because the money has yet to run out, and because NVIDIA is promising 70% year-over-year growth in Fiscal Year 2028. Data center debt continues to be available for neoclouds as well as barely-existent data center developers like SB Energy (backstopped, of course, by NVIDIA), mostly because of the illusion of “massive demand for AI compute” created in part by NVIDIA itself.
And, fundamentally, NVIDIA’s revenues are dependent on whether hyperscalers keep being paid by OpenAI and Anthropic, because those are the only two companies that could ever hope to justify their trillion-plus dollars of capex. As I’ve already noted, per Bloomberg, only around $10 billion of Microsoft’s $33.33 billion in FY2026 AI revenue came from selling compute or AI-powered software to its customers — a pathetic sum that suggests very little actual demand for AI when you remove its unsustainable failson.
Broadcom, in its attempts to compete with NVIDIA, has decided it needs a little concentration risk of its own, and per its most-recent earnings, Anthropic and OpenAI are set to become its largest and second-largest customers in its next fiscal year.
Much like the hyperscalers, neither Broadcom nor NVIDIA is going bankrupt as a result of the AI bubble bursting, but Broadcom’s future revenues — estimated at $230 billion in Fiscal Year 2028 (which begins November 2027) — are now dependent on both direct purchases from hyperscalers (justified by Anthropic and OpenAI) and the AI labs themselves, creating, somehow, greater underlying exposure.
However you may feel about me or the greater AI bubble is immaterial to the fact that everything will seem like it’s fine right up until somebody can’t raise money and make a payment to either a neocloud, hyperscaler or AI lab.
For this to keep working, AI startups must continue to be able to raise hundreds of millions of dollars every few months, all as Anthropic and OpenAI must continue to raise tens (or hundreds) of billions of dollars to pay hyperscalers for compute so that they can, in addition to raising hundreds of billions of dollars, spend that money on GPUs from NVIDIA, who can only continue to make hundreds of billions of dollars a year as long as it can either provide justifications for lenders to keep issuing hundreds of billions of dollars in debt or backstop the data centers the debt will get spent on.
In other words, the AI bubble is based on the whims of maybe a few hundred companies spending money on two companies to justify five companies spending money with one company. Or two if you count Broadcom, which you don’t have to if you don’t want to.
If you tell most journalists or investors any of this stuff, they’ll tell you not to worry about it. Per The Information:
But investors may want to temper their expectations. One large public investor summed up Anthropic’s approach to the markets as: “Don’t think too hard. Just look at the revenue growth rate. That’s all you need to know.”
Anyone who tells you “not to worry” about a company that loses billions of dollars a year and has made $517 billion in compute commitments is a con artist, and anyone who prints a quote like that without a comment about how deeply worrying it is doesn’t really give a shit about whether you live or die.
But that really is the current state of the tech industry: a death cult obsessed with growth empowered by a media ecosystem obsessed with measuring and celebrating how much it’s growing and might grow in the future, always framed in the terms set by the rich and powerful.
The failure of both parties to meet the moment with clarity and purpose will lead to a market correction that likely dwarfs the Dot Com Bubble, exposing many of those involved as a phoney, a fraud, an imbecile, a ghoul, a coward, or utterly, impossibly ignorant.
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2026-09-05 00:24:26
You know, sometimes it’s kind of hard to explain the “circular” part of circular financing to people, in the sense that some of the agreements are kind of clunky. NVIDIA funds OpenAI, who then spends that money to rent back NVIDIA GPUs from Microsoft, Google, Amazon, or CoreWeave, and then that money is used…to buy servers from Taiwanese ODMs (original design manufacturers) that build their servers, who then buy GPUs from NVIDIA to put in them.
The reason it’s clunky is that people will, even if it’s not true, claim that there’s some indeterminately-large “other” subset of customers that are also buying compute or NVIDIA GPUs, and that we should as a result ignore our lying eyes and, if anything, celebrate how well this is all working. While there’s a ‘circle’ of ‘finance,’ it’s not a problem because somewhere in the mess of money exists a few real dollars, and because we can’t precisely measure them, there’s nothing to be concerned about!
Fear not, dear reader, because we finally have a pure, unfiltered circular financing operation to obsess over — SoftBank subsidiary SB Energy just filed its S-1, and it’s so incredibly circular that I’m genuinely surprised that they bothered to list.
That’s a good question, and not as obvious an answer as you’d think.
So, SB Energy is/was a renewable energy business, one that was technically founded in 2019, but sold most of its shares (along with most of its wind and solar power) to Toyota in April 2023, which then became a company called “Terras Energy,” leaving SoftBank with 15% of the remaining shares. While it’s unclear what exactly was left behind, a company called SB Energy raised $2.4 billion from a consortium of banks in November 2023, then re-emerged in 2024 as a data center power company for Google in Milam County (called Orion), raising $500 million from SoftBank and asset manager Ares, and in early 2025 was mentioned in the initial announcement of the non-existent Stargate data center project in relation to an OpenAI-focused data center in Milam County Texas, which suggests the Google deal is done and OpenAI will take over.
All remained fairly quiet for SB Energy until January 2026, when OpenAI and SoftBank invested $500 million each, and a few months later in March, a consortium of Japanese and US companies announced their intention to build a data center on a Department of Energy site in Piketon, Ohio. In August 2026, SB Energy and OpenAI announced a deal where it would lease 10GW of capacity, at some point in the future, with NVIDIA backstopping $105 billion of the deal, though it turned out that the actual terms were that if it gets built, NVIDIA will cover the difference if nobody else will lease it and if selling off the pieces doesn’t amount to $105 billion. The critical words there are if it gets built, because NVIDIA does not have to pay a dime if it isn’t.
NVIDIA has also agreed to invest $3 billion, with $1.5 billion up front, with another $1.5 billion, per the Journal, as a “prepaid forward contract,” meaning it’ll get paid the shares on the close of the offering. SB Energy also provided 4 million share warrants to OpenAI, along with a board designation right as long as it owns 5% of shares, per the Journal, at a value of approximately $5.5 billion.
SB Energy made about $138 million in the first half of 2026, predominantly from selling power. Its data center division made a whopping $653,000.
Not to worry though, SB Energy has tons of capacity under construction…
…except 99.4% of that capacity is earmarked for OpenAI, and based on that “RFS” (ready for service) date, it looks like none of it will come online before 2028. In fact, virtually the entirety of SB Energy’s revenue is contingent on A) finishing these data centers and B) OpenAI being able to pay for them.
Well, let’s not get too worried. Perhaps SB Energy has other data center capacity somewhere? No, no, that’d show up there. Maybe it will…make…money elsewhere? Somehow? I hear it has a $439 billion backlog, it’s gotta make that money at some point, right?
Jesus fucking christ!
I realize that’s a big pile of numbers and words, but of that $439 billion, SB Energy estimates that it will make $1 billion of it within the next two years, $12 billion of it within the next four years, $30 billion of it within the next six years, $39 billion within the next eight years, and $357 billion at some point after that. 97% of SB Energy’s revenue backlog will arrive more than four years in the future, and will be contingent on SB Energy being able to spend $178 billion in capital expenditures.
OpenAI’s leases are split across 17 different SPVs, all of which I assume will try to raise debt at some point.
To summarize, SoftBank portfolio company SB Energy has signed $439 billion in business with SoftBank portfolio company OpenAI, which is also an investor in SB Energy, as well as its largest (and only real) client. Its ability to make any of this money relies upon it completing two different and incredibly ambitious infrastructure projects — a 1GW data center in Milam County Texas, and a 10GW buildout in Ohio, the latter of which is only half backstopped by NVIDIA if it actually gets built.
Let’s be frank: this IPO is only made possible by circular financing, with the vast majority of its valuation coming from entirely-theoretical deals with a company that cannot afford to pay it for data center capacity it cannot afford to build.
This is about as blatant an “emperor has no clothes” situation as you could ask for. 99.4% of SB Energy’s future revenue is contingent upon building data center capacity, which will take years, using funds that have not been raised, all for a customer that will need to make more than ten times its current revenue to pay it.
Anyone writing about this IPO should be directly informing investors that they are, for the most part, investing in a few signatures and strips of land owned by a company that has, to this point, not actually built an AI data center.
Instead, most blandly repeat that SB Energy “has a huge contract with OpenAI” and "hundreds of billions of dollars in its revenue backlog.”
While last week’s premium focused heavily on NVIDIA, today I’m digging into the rest of the AI bubble’s circular suspects, as well as the history of circular financing itself, as a means of explaining exactly how brittle and dangerous this all is.
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2026-09-02 01:39:44
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Soundtrack: Ben Zimmerman — Dyers Eve
We’re going to take a strange trip to get back to the AI bubble, but trust me, it’s worth it.
In Adam Curtis’ documentary Hypernormalization, he describes, and I quote, a world where “...over the past 40 years, politicians, financiers and technological utopians, rather than face up to the real complexities of the world, retreated,” constructing what he calls “a simpler version of the world in order to hang on to power,” with the world going along with it because “the simplicity was reassuring.”
Hypernormalization heavily focuses on post-cold war Russia, the means used to justify the Iraq war, and the rise of Donald Trump, but its lessons ring through everything I’ve been discussing for the last few years — that the tech industry and the markets themselves have moved beyond innovation or value creation into the realm of “managing” situations rather than addressing them, creating whatever reality is necessary to keep “things” going, no matter how ridiculous or unstable.
A lot of this comes from a lack of accountability. Nobody faced any real consequences for lying about evidence of WMDs in Iraq or the Great Financial Crisis. In fact, most of the people in question, even those who lost a lot of money, came roaring back years later. The amount of times I’ve read some bio of some guy who was working at Lehman, or Bear Stearns, or even Enron who eventually returned to their pre-crash status quo is enough to make one doubt the existence of consequences, or to convince oneself of the fact that they’re unevenly distributed.
Even outside of outright financial or war crimes, outright failures like Adam Neumann can raise another $350 million after pumping WeWork to a $47 billion valuation by outright lying about its gruesome finances, and a venture capital industry that can barely return a dollar per invested dollar continues to be able to raise billions of dollars.
Editor’s Note: Even actually committing war crimes — or being closely linked to them — isn’t enough to diminish a person’s standing. Take Erik Prince, the founder of private military company (PMC) Blackwater, which, in 2007, committed the Nisour Square Massacre, where seventeen civilians died and a further twenty were injured.
While those who actually pulled the trigger faced consequences ranging from life imprisonment without parole to one year and a day in prison (with four of the participants later receiving full pardons from Donald Trump), neither Prince nor Blackwater were actually held to account in any meaningful sense.
In 2011, Blackwater would license its name for an Xbox 360 game (called, unsurprisingly, Blackwater) that put the player in the boots of a gun-for-hire, which they controlled through the Kinect motion capture system. It received poor-to-middling reviews, with the most damning being from GamesRadar’s Matt Hughes (not me, a different one), which described the title as “an insult to gamers and a step backward for the Kinect.”
Blackwater would later rebrand (first to Blackwater Consulting, then to XE, then to Academi), and eventually would be bought by private investors. It later merged with two other private military firms to become the Constellis Group, which would later serve as guns for hire in the Yemeni Civil War, which had, at that point, become a regional proxy conflict.
Last year, Constellis — which, I remind you, is a direct descendent of the same Blackwater that perpetrated the Nisour Square Massacre — won a $10.3bn US Army contract.
Erik Prince, the founder of Blackwater, and the CEO at the time of the Nisour Square Massacre remains fabulously wealthy. His sister is the former Secretary of Education, Betsy De Vos, and Prince himself has close ties to the Trump Administration. He remains active in the PMC space, and reportedly has dealings in Ecuador, Haiti, and the Congo.
As for financial crimes, I’ll refer you to the Wolf of Wall Street himself, Jordan Belfort, who today earns a mint from delivering motivational speeches and selling self-help books.
In other words, we’re in a society of management rather than progress. While Dodd-Frank and the (now-weakened) Volcker Rule theoretically helped curb some of the excesses of the Great Financial Crisis, most of the people involved remain both wealthy and employed, blissfully un-blackballed from the world of finance or by the media.
While financial institutions and companies saw over $14 trillion in bailouts, only $46 billion (about 10%) focused on trying to save homeowners from foreclosure, and in the end, to quote a congressional panel from 2009, “...[there was] no evidence that [the] Treasury has used TARP funds to support the housing market by avoiding preventable foreclosures.”
Government funds were used to manage a systemic crisis by making sure the system survived rather than building a better society, because doing so — feeding it money, cleaning up its messes, explaining away its excesses — is easier than having any kind of vision or ideal to aspire to.
Doing so strips out (as I’ll quote again shortly) the “intractable complexities of the real world” by creating a simpler one — that the system works, that “progress” always flows through following the course of (often poorly-remembered) history. Per Curtis:
The Soviet Union became a society where everyone knew that what their leaders said was not real because they could see with their own eyes that the economy was falling apart.
But everybody had to play along and pretend that it WAS real because no-one could imagine any alternative.
One Soviet writer called it "hypernormalisation".
You were so much a part of the system that it was impossible to see beyond it.
The fakeness was hypernormal.
Everything ultimately returns to a point where you say “the system has worked this long and always works out in the end,” even if it hasn’t actually done so, because thinking of an alternative — even as you see proof point after proof point — is near-impossible due to the prominence of the dogma and confidence from those in power.
The flaws of simplistic, systemic thinking are that it “always works out,” as if time is simply repetitions of the same story again and again, versus a series of events that each feed into each other, each compounding the next one’s sins. Instead of building a robust social safety net after the Great Financial Crisis, America chose instead to drop interest rates to near zero, only choosing to slowly raise them in December 2015, then drop them again in the wake of COVID, giving trillions of dollars to businesses on top of near-unrestricted lending standards that helped corporations and banks swell with profits, all as regular people got a single stimulus check and unemployment insurance that varied dramatically state-to-state.
Editor’s Note: To be fair, it wasn’t just America that chose this path. In 2010, after the meltdown of the global financial system, the UK voted to elect a coalition government that actively set about dismantling the social safety net, with austerity leading to an estimated 330,000 excess deaths.
Private enterprise played a role here, with the UK government tasking French IT firm ATOS and US consulting firm Maximus with determining whether people should receive disability benefits. These firms were incentivized to refuse even the most dire of cases, including people with terminal cancer, and a significant chunk of their decisions were later overturned at tribunal — but not before the person had endured months of grueling poverty. Many died before, or shortly after, winning their appeal.
The backdrop to this savaging of the social safety net was a strengthening of corporations (who saw their tax burden shrink) and a weakening of consumers (who lost out on many of the employment protections they enjoyed previously, and saw their tax burden grow in the form of fiscal drag and increased sales taxes).
The point I’m trying to make is that everyone involved in this should be dragged before the Hague, and George Osborne shouldn’t be hosting a podcast, but rather defending himself and his fucked record at trial.
Nevertheless, the advent of remote work gave workers remarkable flexibility, leading to what was called The Great Resignation, as 50 million people quit their jobs in 2022, and, rather than celebrate an era of worker flexibility of power, the system sprung into action to drag us back to the status quo, predominantly through the media.
It — by which I mean those most-interested in bringing things “back to normal” — had already started aggressively attacking remote work, but added a new attack in the form of “quiet quitting,” an ecosystem-wide attempt to reframe “doing your job” as “not doing enough” as thinkpiece after thinkpiece suggested that no, actually, we needed to be back in the office immediately.
For the most part, people really liked remote work, and while there are downsides of never seeing anyone, it was mostly an incredibly positive way in which workers could spend more time with their families, save money on gas, and generally have more flexibility with their jobs. The media, again, worked its ass off to push that “quiet quitting” was leading to an epidemic of “coasting on the job.”
Meanwhile, corporations had been using supply chain crises and the specter of “inflation” to raise prices, though it became obvious that the real reason was sheer, unabated greed, posting record profits to soak up the cash from a frothy society excited to be back in the real world. By the end of 2022, the federal reserve would raise interest rates by a dramatic 4.25%, leading to tens of thousands of people losing their jobs.
Prices would never, ever come down.
Editor’s Note: On the subject of prices never coming down and unabated greed, in 2020, Amazon and the publishing industry successfully lobbied the UK government to remove its 20% sales tax on ebooks, which would put them on par with traditional paper books. Amazon promised that ebooks would become cheaper as a result.
And they did! Until they didn’t, with prices of digital titles returning to their usual high shortly after, as noted by Tax Policy Associate’s Dan Neidle, allowing the publishing industry (and Amazon, which controls 95% of the UK ebook market, and takes a 30% cut of sales) to benefit handsomely as a result.
The same thing happened when the UK government cut the sales tax due on tampons and other feminine hygiene products from 5% to 0%. Retailers were supposed to pass the reduction on to consumers. They didn’t.
No matter how greedy you think these fuckers are, the reality is always far, far worse.
The system — by which I mean the conjoined forces of the media, the markets and the American government — focused on aggressively forcing everything back to where it used to be. When things were rough, it pumped money into the system. When things seemed too frothy, it made that money harder to come by.
For the most part, regular people were punished for the excesses of the system itself — when they took advantage of remote work, job flexibility and purchasing power, they were told they were lazy, that they were wrong, that this was temporary, and that in fact they were too greedy with what they were generously given.
The culture war around remote work framed itself as pro-worker, but really existed to help bosses avoid having to create things like “measurable productivity” or “ways of knowing what their workers do,” even as the actual people working knew they were working harder than ever, and were happy to do so.
Regular people’s reality went from exciting to grim within the space of two years. It was now much harder to get a job, all as everything seemed to only get more expensive, to quote myself:
Case in point: Regular people have spent years watching the price of goods increase "due to inflation," despite the fact that the increase in pricing was mostly driven by — get this — corporations raising prices. Yet some parts of the legacy media spent an alarming amount of time chiding their readers for thinking otherwise, even going against their own reporting as a means of providing "balanced" coverage, insisting again and again that the economy is good, contorting to prove that prices aren't higher even as companies boasted about literally raising their prices. In fact, the media spent years debating with itself whether price gouging was happening, despite years of proof that it was.
The continued perpetuation of “the system is always right” ultimately led to the media near-completely detaching from reality. A regular person experiencing aggressively-worsening standards in their own lives would open the news only to be sneered at for feeling bad, mocked for questioning the numbers, told to sit down and shut up because the media and those that inform it knew better. After two years of hope and abundance, the world — and the system itself — attempted to revert everyone back to the beforetimes, all without the veneer of “prosperity” or “progress.”
In other words, a regular person’s form of “reality” was a confusing mess of social media, alternative media, mainstream media, and governments that seemed intent on saying that either everything was fine or that there was a specific thing to blame, usually either a foreigner of some sort or the listener or reader themselves. No attempts are levied at the system itself or the choices made by it or the way in which the media chooses to cover systemic movements, because to do so is considered either stupid (because it always works, right?) or hopeless (because it’s all so powerful).
Editor’s Note: This redirection was depressingly effective, as demonstrated by the rise of political forces once considered marginal across the developed world.
America had Trump. And then it had Trump again. Germany’s far-right AFD increased its share of the national vote by ten times in just twelve years. In 2022, Marine Le Pen came uncomfortably close to winning the French presidency, despite hailing from a political dynasty whose patriarch was convicted for holocaust denial.
In the UK, we had Brexit, an act of national self-harm that was spearheaded by Admiral Ackbar lookalike Nigel Farage — a man who, once upon a time, was a regular feature on Alex Jones’ Infowars, where he dabbled in antisemitic dogwhistles, and who starred in a spectacularly batshit commercial where a giant octopus depicting the EU caused millions of pounds worth of improvements to Central London.
Farage was, until Keir Starmer’s ouster, the bookies’ favorite to be the next UK Prime Minister.
Meanwhile, in 2022, the largest tech companies in the world (Microsoft, Google, Meta, Amazon and NVIDIA) had hit a rough patch of flat-to-low growth after desperate measures had failed to help.
While both the media and world governments were adept at making moves to sustain systemic thinking — that the system or systems know best and must be protected (we must prop up banks, we must make sure we’re all in offices, etc.), big tech tried and failed twice to force society to back their concepts, the first being a VC-led attempt to make Clubhouse the next Facebook (with the media attention to match it), the second being the doomed attempt to make the Metaverse the next internet, with the media dutifully covering it as if it were real along with consultancies like McKinsey and Deloitte.
To be clear, there was never proof that the Metaverse was a real thing that anyone wanted, but because Facebook changed its name to Meta, the assumption was that the rich and powerful would not simply “do something for no reason.” It petered out because despite all the hype, there was very little to actually use, or invest in, or really do with it.
Yet the most important detail is how everybody went on with their lives and ignored that Meta burned $77 billion on nothing, or that Microsoft (which bought Activision Blizzard under the auspices of the Metaverse) CEO Satya Nadella said he “could not overstate the breakthrough of the metaverse” in 2021 and then effectively shut it all down by 2023. Nobody was fired for the fuckup. Nobody got in trouble. No media outlet apologized for being wrong. As regular people were fired thousands at a time, tech executives like Satya Nadella and Sundar Pichai received tens of millions of dollars a year. Everybody acted like nothing happened.
Put another way, when regular people fuck up, they see themselves restricted and punished, fired, their credit ratings dropped, evicted from their houses, embarrassed in front of their friends and peers, and when corporations fuck up, the system accelerates to isolate them from damage, explain away their faults, congratulate them for trying, and then forcefully return “reality” to a point where everything they say is perfect.
To quote John Ralston Saul’s Voltaire’s Bastards:
Worse still, tinkering with these instruments has become a substitute for addressing the problem itself. Thus financial deregulation is used to simulate growth through paper speculation. When this produces inflation, controls are applied to the real economy, producing unemployment. When this job problem becomes so bad that it must be attacked, the result is the lowering of employment standards.
When this unstable job creation leads to new inflation, the result is high interest rates. And on around again, guided by the professional economists, who are in effect pursuing, step by step, an internal argument without any reference to historic reality.
Each time this happens, the systemic forces become more confident in their position, the media becomes more entrenched in the status quo, and both morality and success are increasingly redefined as ways of manipulating systems rather than being better or even good at anything. Everything becomes less about “doing the right thing” or “being correct,” and more a case of “moving within the system you live in so that you don’t get destroyed.”
People desperately want to see the AI bubble as either a systemic victory where venture capital has successfully ushered in a new status quo to worship or a systemic failure where the regular systemic factors — like bailouts and inevitable technological boom cycles — will immediately come into play.
As a result, they are willing supplicants for anything that signifies either version of the status quo — the signifiers of boom cycles (IE: “fast growth rates,” multi-billion dollar deals “from the biggest companies in the world) or post-collapse systemic recoveries (IE: “the underlying technology is good, all that dark fiber got used after the dot-com bubble, or there will be a government bailout).
You know. It’ll work out fine. It’s just like the Dot Com Bubble, even if it isn’t. These companies are so big that they’re Too Big To Fail, even though we’re in a very different situation. OpenAI and Anthropic will grow to become the largest, most-profitable companies on Earth, even though they lose tens of billions of dollars a year, and can only “reach profitability” through financial engineering.
The reason we repeat these cycles is that we never actually learn anything at the end of them. Nobody gets in trouble, nothing really changes about the system, and each following cycle is more horrifying and egregious than the last. Despite all of our discussions of the Dot Com Bubble, most people forget that it was a website bubble and a telecom bubble, followed a few months later in December 2001 (nine months after Bethany McLean of Fortune pointed out many underlying issues) by the collapse of Enron, the seventh-largest company in America, losing investors $75 billion and destroying the lives of thousands of employees unaware of the fraud.
A BBC report would talk about how “Enron played the media,” noting how it was called “a model for the new American workplace” for the New York Times, and named “America’s Most Innovative Company” by Fortune six years running as well as one of the 100 best companies to work for in America. While investment analyst John Olson said that Enron was “great at gaming the system…Wall Street…[and] the media,” the problem was far more obvious: nobody could explain how Enron makes money, and both analysts and the media were fine with it.
But for all the attention that’s lavished on Enron, the company remains largely impenetrable to outsiders, as even some of its admirers are quick to admit. Start with a pretty straightforward question: How exactly does Enron make its money? Details are hard to come by because Enron keeps many of the specifics confidential for what it terms “competitive reasons.” And the numbers that Enron does present are often extremely complicated. Even quantitatively minded Wall Streeters who scrutinize the company for a living think so. “If you figure it out, let me know,” laughs credit analyst Todd Shipman at S&P. “Do you have a year?” asks Ralph Pellecchia, Fitch’s credit analyst, in response to the same question.
Hilarious stuff Todd! I’m so glad your stupid ass was the credit analyst for Enron at S&P Global. This man is a fucking CPA, and when he couldn’t answer how Enron made money, he mostly shrugged his shoulders. Here’s another great story from Todd Shipman:
The same day S&P's primary Enron analyst Todd Shipman went on CNN, even though S&P's had placed Enron on credit watch negative, Shipman said, “Enron's ability to retain something like the rating they are at today, investment grade, is excellent in the long term.''
When asked about the off-balance sheet partnerships, Shipman remarked that S&P's was “confident that there is not any long-term implications to that situation, that that's something that's really in the past.''
It was, in the end, not something that was “really in the past.”
The collapse of Enron eventually led to the Sarbanes-Oxley Act in 2002, which made (necessary, positive) changes to financial regulations, including executive sign-off on financial reports and severe financial penalties for faking or changing financial records, along with prohibiting auditing firms from doing business with their clients.
The problem, however, was that Sarbanes-Oxley only sought to limit outright lies and direct, impossible-to-argue accounting fraud rather than attempts to manipulate stocks through altering public perception. It did not see a systemic issue with how companies used the media (and analysts) as a means of muddying the truth, because as long as companies don’t outright lie — half-truths are fine, by the way — nobody is doing anything wrong, and nothing needs to truly change.
You see, the actual problem with Enron was far beyond simply “lying about its financials.” The media ecosystem had not only failed to see the danger coming, but actively helped exacerbate the damage it caused, all without a moment of introspection at the end. Their excitement about Enron was entirely based on how big its numbers were, even if there was little plausible explanation of how it made money, let alone how the numbers got that big.
In a New York Times piece on Enron from June 1999 — around two and a half years before its collapse — reporter Agis Salpukas accidentally proved my point:
Mr. Skilling says he does not care how people dress when they come to work, or whether expense accounts are filed on time. Or even if, after an all-out effort, a venture fails – like Enron's heavily publicized push two years ago to become the nation's leading retail marketer of electricity, as states like California opened the power business to competition. The executive who led that effort is now in charge of spending perhaps eight times as much to sell long-term power contracts to big companies.
Pobody’s Nerfect!
In any case, there was no retraction, no apology, no “we fucked up,” no acknowledgment of anyone’s mistakes around Enron, much as there haven’t been around the Metaverse, or NFTs, or “inflation” that was actually just price-gouging. Modern journalism sees itself as truth-tellers, all as it operates within a self-fulfilling prophecy of helping inflate financial bubbles, only to simply forget they had any part of it, because, as I’ve discussed, the complexities of the real world — how people are misled, how companies will willingly lie and get away with it, how corporate America is based on growth-at-all-costs thinking, and how business and tech journalism increasingly exists, even in its most-critical state, to elevate systemically-approved ideas — are too difficult to reconcile with.
Sidenote: NVIDIA deploying capital at random to help counterparties raise debt, despite being legal, should immediately trigger memories of Enron, if only because their actions have the same intention of artificially inflating revenues. Real businesses do not need financial wizardry.
Regular people are well-aware of the problem, which is why the growth of alternative media (and the ascent of demagoguery-fueled right wing media) has mostly taken the mainstream by surprise. Journalism does not see itself as part of the system (or systems) that maintain the status quo, nor does it see itself as a willing participant, or as a weapon used to twist the truth.
Yet journalism reports what’s put in front of it by the powerful, and finds whatever rationale it needs to. Enron technically had $100 billion in revenue in its final year. It didn’t really matter that nobody could explain what it did to make it, much like it didn’t really matter that Anthropic never defined what “$65 billion run rate” actually meant, because the number itself was only necessary to make everybody feel like it was all going to plan, and that the system worked.
In the end, the thing that the systems we rely upon seem best at is winding themselves up into a frenzy at the behest of the richest people in the world, usually burning anywhere between tens of thousands and millions of people with the consequences.
The system moves to make sure it doesn’t “break,” which is a nice way to say that the powerful are insulated against the fallout, even if it means simply acting as if nothing actually happened.
The problem — as we’re going to find out at the end of this era — is that everybody assumes that the system “returns to normal” at the end of each cycle, rather than those suffering from the consequences of its excesses accumulating scars and the system itself becoming increasingly burdened with obligations.
For example, the combined might of the Great Financial Crisis and COVID, along with an aging population and endless military budget, have let the US national debt grow to $40 trillion, both creating the problems we face today and leaving it with few options to fix them. This is not the same government that could once afford to pump trillions of dollars into any kind of bailout without running the very real risk of destabilizing the US dollar. Those who immediately jump to “too big to fail” are, once again, thinking of the system in simplistic, ahistorical terms, rather than as an accumulation of different times when the solution to problems was not systemic change but giving it more money to burn.
The AI bubble is a direct result of a lack of financial regulation or accountability in the business or tech media for directly enriching and empowering financial bubbles and outright scam artists. Doing so is justified by saying that they’re “excited about innovation” or “cautiously optimistic,” or suggesting that blindly reporting whatever big number just got invested with little or no pushback is “being objective,” believing that a single paragraph showing some skepticism is anything other than covering your ass as you blow smoke up somebody else’s.
Sidenote: I want to be very clear about what I mean here. There are plenty of reporters who seem to think “objectively” (read: emotionlessly) reporting some massive, impossible deal with a single paragraph saying “critics have suggested…” is being a skeptic. It isn’t! Being skeptical actually requires you to scrutinize what’s being said throughout the entire article. You think you’re being “balanced” when you’re really just helping the company by creating the appearance of agitation in a world built for the powerful.
This chaotic world of deteriorating products and a vacuum of responsibility means that regular people that rely on the media for reality receive a manufactured, distorted and outright harmful version of events, most of which are mediated by an editorial class that is desperate to impress the powerful and seem intelligent.
The problem is that real life and mediated life are becoming increasingly-distanced from each other, and every major financial crisis — the 2008 Crisis, Enron, the Dot-Com Bubble, the AI Bubble, and so on — flows from a place where systems and their associated narratives attempting to simplify the world grow too large to control, and flow into real-life consequences.
Each time one happens, the system itself moves to absorb the damage, never letting it get too bad — by which I mean leading to social unrest — and making sure as few people are held responsible.
To again quote Voltaire’s Bastards:
In a single decade, the idea of using public debt as an economic tool has moved from the heroic to the villainous. In the same period, private debt went in the opposite direction, from the villainous to the heroic. This was possible only because economists kept their noses as close to each specific argument as possible and thus avoided invoking any serious comparisons and any reference to the real lessons of the preceding period.
There are actually some pretty easy lessons to learn from every crisis: the media does not see itself as having a responsibility toward its readers nor any need to police itself, every financial crisis involves massive amounts of speculation that are both encouraged and applauded by the media, and both the financial system and the media work in concert to coerce and pressure the public into moving with the status quo. In the aftermath of a bubble, the media works to explain “what happened” in as vague or grandiose a way as possible, or to blame a very small handful of people, making the problem either way too big to fully comprehend or so specific that it can be handled with a few tweaks.
At no point does anybody actually have an idea of what a “better” or even “different” future looks like. Even the most devout AI boosters still describe the industry in the terms of the status quo. Even if OpenAI or Anthropic (in their minds) were to “destroy” an industry, that industry would still be one that was venture-funded and predominantly controlled by the hyperscalers. Critics must be framed as deranged or untrustworthy, because this is the only way that “progress” can look — growth-at-all-costs capitalism.
What makes the AI bubble so remarkable is how precisely it targets the weaknesses in systemic thinking, which is oftentimes propped up not by real experiences or actual proof but signifiers of growth that relate in some way to eras of prosperity, all as a means of kicking the can of “when will anybody make any money?” or “how do these companies become profitable?”
You see, the system is built to reinforce itself. The media is built to find things to pump and propagate narratives to reinforce eras of growth, and knows the right numbers that it needs to justify said propagation, much like it knows what shred of a product it needs to consider something “real.” Financial institutions crave ways to invest their capital, and know that their customers crave ways to exponentially increase their investments, ideally with a stable (yet high yield). Analysts are ready and waiting for a narrative to sell, and know that the easiest one is based on growth.
I must also be clear that none of this thinking changes that actual money is changing hands — the entire semiconductor industry and venture capital world has had to effectively reconstruct itself around the world of AI, all based on the same signals.
It’s easy at this point to suggest that the system knew or planned for or anticipated AI in some way, that this is some sort of giant conspiracy they’d been waiting for.
Except the throughline of everything I’ve described is that nobody has a plan, and that an attachment to a simple idea — like endless growth — is what keeps these cycles repeating, because it’s always a case of something that’s too good to be true being, well, false. Every collapse is followed by discussions on how to change what we have to stop this specific thing from happening again, with little or no consideration of any other bad factors beyond those in front of us.
As a result, this is a system that is incredibly vulnerable to exactly how the AI bubble inflated, and is uniquely incapable of anticipating what might happen next.
A year before the Attention Is All You Need paper begun the era of transformer-based models, Curtis described how the systems of society were aimed at “[not trying to] change things, but rather to manage a post-political world,” and exploiting how, to paraphrase science fiction writers Ardkady and Boris Strugatsky, how “...reality was just something that could be manipulated and shaped into anything you wanted it to be.”
One particularly-grim version was the Reagan administration’s use of perception management, “...blurring of fact and fiction but it was part of an even broader program”:
The aim was to tell dramatic stories that grabbed the public imagination, not just about the Middle East, but about Central America and the Soviet Union and it didn't matter if the stories were true or not, providing they distracted people and you, the politician, from having to deal with the intractable complexities of the real world.
…[perception management] became a device and the facts could be twisted. Anything could be anything.
…
Reality becomes simply something to play with to achieve that end.
Reality is not important in this context.
Reality is simply something that you handle.
This is the world of public relations, but it’s so far removed from anything I (or most PR people) have ever done that it’s got more in common with outright propaganda distributed with the knowledge that the systems of journalism and financial analysis are ready and waiting to process and disseminate it.
For modern tech and business journalism, a company is considered “real” based on how much chatter there is about it on Twitter, how much money it’s raised, and how many “smart” people are excited about it. There is almost no actual use of the product, and if there is, it’s at the most cursory, “making sure it exists,” or talking to customers who will almost always say “I love it!”
Most-importantly, however, tech and business journalism rarely comes to conclusions unless they are positive. If an AI lab loses billions of dollars, “there’s a chance its economics will improve in the future,” all without any discussion of what that means or how it might happen. By contrast, if a company says to TIME magazine that it is “80% of the way to AGI,” the article will take great pains to discuss what AGI could mean, when it might arrive, and indeed never push back on them saying so.
That’s because, while a seemingly-futuristic concept, the ideas of AGI and ASI (and that’s all they are) re-entrench the current system. They are terms defined by OpenAI and Anthropic, who are funded and have had their infrastructure purchased by hyperscalers that can derive revenue from the directionless tens of billions sunk into training it. They are pursued, funded, directed and upheld by the archons of the current system, and any whimsical language around “alignment” is a deliberate attempt to elevate software built and sold on terms set by the current system.
The entire AI bubble — every bit of hype — is an attempt to rebrand old things as new.
This is the same system that was exploited to elevate actual scams like Clinkle, Theranos, and FTX, along with specious bubbles like NFTs and the metaverse. Large checks and excited-sounding technologists are taken as cast-iron proof that something that has not happened yet will definitively take place, giving every possible asterisk to make sure nobody can say it was wrong:
Chief research officer Mark Chen estimated OpenAI is “80% of the way” to AGI. Brockman said that viewed from two years in the future, this may be remembered as the moment AGI was created. Altman told me that OpenAI was “not quite yet” there, but that by the end of the year the company would have an internal system he would call AGI.
Anyone reading this in TIME magazine would expect, wrongheadedly, that there was some sort of journalistic process that happened here, rather than just “yeah they said it, and they’re real smart and rich, and so I wrote it down.”
Similarly, when Anthropic hit $65 billion in “annualized run rate,” the number was printed without a second’s hesitation despite it being completely-undefined and indicative of nothing other than a snapshot of an indeterminately-long period multiplied by a number that we do not know. The intent of sharing this number — and yes, that counts if it was ‘leaked,’ because a real ‘leak’ would not be run rate — was entirely to market Anthropic as a “fast-growing company.” AI companies never share their actual revenues — $11.6 billion in Q2 2026 — or their underlying economics, and reporters have been so systemically-sedated that they believe that using a marketing number is reporting.
To be clear, there are ethical ways of discussing annualized run rates, and they start with saying that these numbers are a marketing technique. Sadly, these numbers are reported as if they’re as valid as real revenues, and have increasingly become a proof point of AI’s remarkable ascent.
In reality, they exist to obfuscate the depressing states of the average AI company. The Information reported that Cognition had “generated around $900 million in annualized revenue, or $75 million a month,” all while expecting to lose around $800 million in the year, burning $200 million in Q2 2026 alone. For whatever reason, The Information also added an anonymous source saying that “...excluding the costs of Cognition’s development of its own coding models it would be close to breaking even, in terms of free cash flow.”
Run rate is a deceptive term because it’s also a moment in time, and it’s even more deceptive when the company in question sells API access to models rather than subscriptions, because one cannot “annualize” a number that fluctuates like a customer’s token burn. One might also be fooled into thinking Cognition’s revenue would be $75 million a month, rather than a particular period of time suggesting that is what it makes.
Sidenote: For example, while it might have $75 million in a period of four weeks multiplied by 12, that might be a particularly-busy week for token burn or subscriptions, or helpfully avoid a week with attrition. There is no actual response to this point beyond saying that “we should trust these companies” based on information that is both easily-manipulated and woefully-undefined.
The point I’m making is that even in seemingly-critical pieces, punches are pulled and information is reorganized as a means of abiding by the system’s rules. Cognition has raised over $2.1 billion in funding at an astonishing valuation of $26 billion, yet its business loses hundreds of millions of dollars and necessitates burning billions more…for a chance to make less revenue than Duolingo, a company with a market capitalization of a little under $7 billion that also doesn’t lose that much money.
There is nothing rational about valuing Cognition at $26 billion, let alone the $40 billion one it’s allegedly raising at right now. Devin is not mentioned on Ramp’s AI index, nor have I ever met anyone who has ever used it. Its entire valuation appears to be from a small subset of customers, a few partnership announcements (like a pilot with Goldman Sachs that I can find very little information about), and articles from the tech press about Cognition that mostly say “it does AI coding stuff.”
I have no specific beef with Cognition, because the same can be said of Perplexity, Higgsfield, Harvey, or any number of other AI companies with triple-digit “annualized run rates” with double-digit billion valuations for businesses with questionable business models.
Yet the tech media simply does not care, because — despite being directly used for perception management and marketing — they would argue that this is “reporting company financials objectively.” Having paragraph after paragraph effectively saying “these are growing businesses working in the business world, and they have big valuations, and wow, they are growing so fast” is objective reporting. It would be subjective, in their eyes, to cast doubt or skepticism over these valuations, because it would be “unfair” or “without the complete knowledge of their finances.”
This is obviously wrong. Cognition is “worth” $26 billion because it sold stock to Lux Capital, General Catalyst, and 8VC at that valuation. It is correct to say that investors value it at $26 billion, but casting any judgment about whether that’s sensible is considered “opinion journalism,” even though doing so would be arguably more valuable to the reader, and allow them to make better decisions.
I realize the alternative is a little challenging, and involves both A) skepticism of venture-backed companies and B) a fundamentally more-thoughtful and better-informed approach involving actual financial analysis.
To be clear, part of the logic of trusting these valuations is that these venture capitalists are “good with their money,” but the direct opposite is true. Per Bloomberg, Thrive’s 2022 growth-stage fund has returned 30 cents for every dollar invested, which puts it — I shit you not! — in the top five percent of funds, and per Pitchbook, the median TVPI (total value put in, so how many dollars you get back per dollar invested) of venture capital vintages between 2017 and 2024 sits somewhere between 0.92x and 1.23x, lagging the S&P 500 (about 280% over that period if you reinvested dividends).
Nevertheless, the assumption is that venture capital only hits dingers, because making the alternative assumption would require a complete revaluation of the system of tech journalism, which is why it didn’t happen after Theranos, NFTs, the metaverse, the Dot-Com Bubble, or any other era where venture capital failed.
The exact same thing happened in the aftermath of the Great Financial Crisis. One would think that a business and finance media would effectively go to war with an industry that had, wall-to-wall, taken risks so significant that eight million or more people lost their jobs and the economy was thrown into despair.
Instead, the media remains buddy-buddy with those who have misled it before, helping to mislead millions more people as a result, because they do not believe that active suspicion of an industry is a worthy place to start investigating it.
The overall point I’m making is that the “proof” behind what makes a particular tech phenomena “real” is fungible to a fatal end, and said proof can simply be “somebody sunk a bunch of money into it.”
While many people come up with many rationalizations as to how the AI bubble has grown so big, and why so much money has gone into data centers, it’s actually pretty simple: venture capitalists invested a lot of money, everybody saw how much money hyperscalers were investing, and everybody assumed that both were doing so for a good reason.
When NVIDIA’s stock soared, despite the revenues mostly coming from a handful of companies, everybody simply assumed that the AI data center buildout at large was different somehow, and that these were “the richest companies in the world” and wouldn’t make such a big mistake. Thanks to the media assuming — based on effectively nothing outside of a few demos and a few billion in venture capital — that AI was the next big thing, it created a self-fulfilling cycle of hype where every little tidbit was taken as proof that some vague prophecy was true.
I’ll give you an example. Last week, Jensen Huang quoted Gavin Baker (who was fired from Fidelity for sexual harassment) with a screed about AI that doesn’t really make sense when you break down each point:
This is all an attempt to change the perception of data centers without ever dealing with the underlying arguments against them, all wrapped in the fuzzy layers of status quo-adjacent “progress.” This post will be quoted as “proof” of the “good things that AI data centers do,” laundered through journalists and analysts that say “well look, it creates jobs, all throughout the economy, and if you don’t believe me, check NVIDIA’s earnings!”
It’s also part of a years-long tradition of the AI industry muddying the truth about AI data centers, pushing back against anyone who disagrees and claiming that they’re either a Chinese psyop, misinformed, or “hate technological progress.”
Let me simplify the AI data center argument:
There is nothing ‘anti-progress’ about opposing AI data centers, and nobody has a compelling explanation as to why we need more of them. Every article about building them automatically assumes this is a necessary buildout because lots of money has gone into them, but nobody can seem to explain why other than “there’s so much demand for AI services (which is not actually true when you remove OpenAI and Anthropic).”
In fact, I think it’s fair to question whether there’s real — by which I mean not manufactured — demand for NVIDIA GPUs, per last premium:
As a result, NVIDIA’s actual customer base — despite its astounding revenues — is contracting. Per NVIDIA’s Q2 FY2027 earnings, 70% of its accounts receivables came from five companies, and 44% came from three companies, up from (in the case of receivables) 64% attributable to three customers and 56% attributable to three customers in the two preceding quarters. Its days sales outstanding — a measurement of how many days it’s taking for customers to pay it — blew up from 45.4 days in Q1 FY27 to 59.6 days in Q2 FY27.
NVIDIA is now allowing some “investment-grade” customers to pay either three months or an entire year after receiving equipment, allowing NVIDIA to book the sale, ship the chips and boost its revenue months before receiving any money.
While the money is absolutely real, it’s dependent on both the continued value of investing in AI data centers — which is an open question — and the ability for these three to five customers to be able to keep raising tens of billions of dollars whenever they need to.
And really, let’s talk about the why for a second.
Google, Amazon and Microsoft are currently spending hundreds of billions of dollars on capex to, for the most part, pull in around $440 billion in revenue in the next three years from Anthropic and OpenAI, as they represent more than 70% of their AI revenues in the next three years, and more than 48% of Google Cloud’s 2027 revenue.
Otherwise, there is little tangible financial incentive to continue doing so, other than for perception management. These three companies cannot stop spending money on AI capex, as the second they stop, investors will (reasonably) ask why they spent all that money, and what they got in return. Investing money in capex has allowed all three of them (and Meta, for that matter) to avoid ever having to disclose their actual AI revenues, because all the proof anyone needed was that they were spending $30 billion to $50 billion a quarter for a reason.
The same goes for Oracle, which needs to keep spending to build out the capacity to make the $300 billion it’s owed from its five-year-long deal with OpenAI, though it, like Google, Microsoft and Amazon, is largely-dependent on whether these two companies can continue to raise a hundred billion dollars or more every year.
As I’ve hinted at, Meta is in the same boat, except far worse, because it doesn’t really have an AI business. Yet because the system is built upon simplistic ideals like “investing lots of money then making lots of money” — even if these ideals are not remotely true — it is ready and willing to accept these narratives as long as revenues keep growing, even if said revenues are nothing to do with AI.
The same goes for the indeterminately-large amount of AI data centers being built. We still, to this day, have no real clear understanding of whether it’s profitable to run any kind of AI service or even to rent out AI GPUs, but because so much money has been invested, everybody assumes it’s the right idea to do so.
Yet because the system and the media are easily pleased, CoreWeave is used as proof that AI data centers are a great idea…as it loses $646 million in a single quarter, because its revenue grew 112% year-over-year…even though its customer base is NVIDIA, OpenAI, Microsoft (for OpenAI), Google (for OpenAI), Anthropic, and Meta.
CoreWeave — like NVIDIA and the rest of the AI industry — is aware that the system craves signifiers and narratives tied to plausible-seeming numbers far more than it covets stable, diverse business. The fact it’s raised $24 billion in debt and loses hundreds of millions of dollars a quarter servicing it is, in fact, a good thing, because it’s a sign that the financial markets believe in its growth story, which is in and of itself deeply worrying.
I challenge you, the reader, to reframe your understanding of contracts and investments from a strictly financial one to one of perception. OpenAI and Anthropic signing contracts with neoclouds like CoreWeave and Nscale for data center capacity that will take years to build is as much about creating the appearance of growth and stability — even as they depend on inherently-unstable companies — as it is “buying compute.”
The same goes for NVIDIA’s investments in Poolside, Mediatek, IREN, Nebius, and, of course, CoreWeave. While these companies absolutely needed the money, NVIDIA also needs to create the perception that these are real businesses that have real customers, and the easiest way to do that is to use its balance-sheet-as-a-service system. Bankers and the media, incapable of thinking outside of the system itself, only see a “large company with healthy credit investing billions of dollars” without ever thinking about why or what the purpose is or why all of them needed billions of dollars, coming up with the rationalizations for NVIDIA because not coming up with them would challenge the system itself.
This is the same logic that had S&P Global revise CoreWeave’s outlook to “positive” back in April, despite it only “making progress” on material weaknesses in its accounting, because its “deepening relationship with NVIDIA [would] aid its growth trajectory,” the kind of thing you can only believe if you believe the entire system is working great and nothing is wrong.
CoreWeave is a bad company that only exists because of the simple, systemic belief that If The Right Numbers Are Going Up, Everything Is Fine.
I must repeat again that none of this is a conspiracy so much as it is a large-scale attack from multiple fronts on the weakest points of the system, and the power of thought processes incapable of seeing when something is horribly broken.
If you, right now, ask most people if AI is “changing the world,” they’ll respond with an emphatic yes, and in most cases won’t have much of an answer beyond “coding” and however many weekly active users OpenAI has. Perhaps they’ll respond with an anecdote about knowing someone who uses Claude for some stuff, or mention that Anthropic had “$65 billion in annual revenue.” They’ll perhaps point to NVIDIA’s earnings, or perhaps even Microsoft, Google and Amazon’s profits, saying that three companies that do not disclose their AI revenues are “growing thanks to AI.”
If you ask them why AI data centers are being built, they’ll say there’s “crazy demand for AI,” again without really having a frame of reference beyond a vague mention in an article with no citation.
The reason they believe most of these things are spuriously-sourced or defined statements in a media industry incapable of thinking of seeing systemic failures or mistakes, despite history being littered with them again and again, many of them written about by the very same people.
When challenged, they will return to systemic rhetoric — stuff costs lots of money before it makes a lot of money, [company] is the fastest-growing in history, there’s hundreds of billions of dollars on the line, these are some of the smartest people in the world, these are some of the richest companies in the world, and so on and so forth. Everything, even often in critical pieces, comes back to statements that reinforce the status quo, like “AI is, of course, transformative,” or “bubbles always form and leave value afterward,” even though that’s not really true at all when you look at history, and certainly isn’t true of this particular era.
The problem isn’t just “oh, we need to be more skeptical of these companies,” but that even in that skepticism we reinforce their position. Anyone writing an “are we in an AI bubble?” piece feels it’s necessary to remind the audience that they are not, under any circumstances, criticizing AI itself or doubting its innovations, even as they struggle to define what those innovations are.
That’s because AI is, much like the AI bubble, one of the perfected forms of hypernormalization, demanding so much money, attention and make believe that it forces even the cynics to live in fear of reprisal.
I get a lot of flak for not being excited about LLMs, but if I’m honest, I’m not sure the vast majority of reporters or analysts or media personalities are actually excited by the technology so much as they are the sheer amount of pressure and money focused on it. They will rationalize LLMs doing a mediocre-yet-plausible attempt at something — usually generating text or code — faster than a human being could as “impressive” because “they couldn’t do it that fast,” not really considering whether impressive translates to useful or productive or even particularly interesting.
The thing is, LLMs have improved in the last year, just not in a way that’s tremendously impressive to me given the amount of capital invested, or in a way that has manifested in a tangible product that can be described in a sentence. I’m sure there are automations that people have made with this stuff that help them — good for you! — but it will take a lot more than that to justify a trillion-plus dollars in investment or endless fucking prattling about how impressive and world-changing AI is.
The fact that everything I write has to have some sort of caveat about “how far LLMs have come” is more proof of the brittle, simplistic and childish nature of the system itself. It is not enough for the AI industry to get trillions of dollars, constant media attention, endless coddling, endless defenses of its technology and expenditures, government support, and near-infinite resources. Every single detractor must have “sufficient AI use,” and “concede” when AI has “gotten better,” as if AI or the AI industry is a living organism that must be appeased, and one’s “correctness” on AI is a moral calling.
But the hypernormalized world of AI has turned it into something more. One’s ability to both “get value” from AI and sufficiently explain why it’s exciting gets you invited into all manner of weird little cliques, as does preparing sufficient data to prove how much money literally anyone who builds anything related to AI will make. This is framed as “being on the frontier,” mobilizing thousands of “free thinkers” to defend the global venture capital, AI infrastructure and AI software industries, along the market itself. All of this is sold as living and investing in the future as it polices the world’s information in favor of the status quo.
Remember: we are talking about software sold by some of the richest people in the world, powered by data centers that cost tens of billions of dollars funded by some of the other richest people in the world.
Vigorously critiquing and pushing back on the narratives of the powerful is an actual moral cause, and I can think of little more revolting than squealing about how I’m insufficiently deferential to or unwilling to fill in the gaps in their marketing hype.
And god, spare me from any whining about “being unfair” to an industry that has literally every single thing going in its favor.
LLMs — and the communities around them — are also leading to the problematic expansion of further alternate realities. As a technology built to respond with what is most likely to be the desired output, they tell every user their every idea is genius, promise to help out with just about anything (even if they’re incapable of doing so), and are capable of making you feel really productive as you endlessly prompt an ever-growing system (like a Wiki of your work) that creates the appearance of productivity and “software design” without producing very much value.
Sidenote: While I don’t dispute that the AI industry has done something, the scale of its importance is entirely a result of the media’s puffery, subsidized subscriptions, venture capital and hyperscaler funding and endless debt. The only counterargument is that at some point these things won’t be necessary, but I’ve yet to hear anyone tell me when or how.
These communities also exist as a kind of systemic response that combines with another systemic element — the mythology of the “great founder” created by Steve Jobs’ tenure at Apple. Believing in the tech industry is now conflated with believing in whatever the tech industry demands, which in turn means protecting both the AI companies and the technology itself, obsessively using and defending it and taking whatever shreds of proof of a grander prophecy of “success” they can find. They see themselves as independent thinkers, but their entire existence is dedicated to protecting the valuations of massive corporations run by multi-billionaires, all as they praise the godlike ideal of ‘founders,’ hoping that in doing so they too will be elevated.
Another sidenote: The same goes for the models themselves, which were deliberately anthropomorphized in an attempt to make them seem more than a neural network or regular software. This worked wonders on the media, but nowhere has it done more damage than to the minds of those growing up in the current Silicon Valley culture.
The problem in all of these cases is that AI has become almost entirely focused on perception management as a means of avoiding dealing with the most-obvious systemic problem: that none of the investment really makes sense.
Anthropic signing a $35 billion cloud compute deal with an NVIDIA-backed neocloud Lambda will immediately be used as proof that the company is “here to stay,” and that Lambda “has huge customers and a giant backlog,” even though in reality it shows that there wasn’t anyone else willing or capable of signing a deal that large, nor was there enough diverse demand. The fact that the deal involves Hut8 (which is also allegedly building other data centers for Anthropic) will be seen as proof that Hut8 is “growing fast” and “has a huge backlog,” even though it really shows that Hut8 is entirely dependent on Anthropic’s ability to pay it, and its ability to build AI data centers.
Let’s be clear: Lambda’s largest customers are NVIDIA (who also invested), Microsoft and Amazon. Anthropic is such an unstable customer that the lease isn’t even in its name, with NVIDIA taking it on, which is, to quote the Wall Street Journal, “another example of Nvidia’s growing role in helping non-investment-grade firms such as Anthropic get access to its expensive computing resources,” rather than “a sign that the largest companies — and effectively the only ones buying AI compute — cannot afford to do so.”
Within the makebelieve of the system, this all makes sense. NVIDIA is using its balance sheet to sign a lease for one of its largest customers by proxy (Anthropic) to rent capacity from Lambda (which NVIDIA rents capacity from and invested in), all so that, I assume, Lambda can raise debt to buy NVIDIA GPUs. Banks will back these loans and give them the big thumbs up, because Big Company Have Money Now, And Number Go Up.
This is all perfectly rational, because thinking it’s irrational means that nothing makes sense. NVIDIA, the largest company on the stock market, has most of its revenue coming in from a handful of companies, most of which are buying its GPUs not because of a return on invested capital, but because buying them allows them to create activity and potentially pull in revenue from two other companies — Anthropic and OpenAI.
Neither of these companies can actually afford this compute, but that doesn’t matter, because this data center won’t actually exist for years, if it ever does. In fact, every time you read about some multi-billion dollar data center deal, know that it won’t be built for years, but NVIDIA will likely book the revenue immediately, because all it has to do is help raise the debt for the banks to send the money and the GPUs to be sold. Because the system only ever lives a quarter or two in the future — even when considering stuff years ahead — it assumes that because Anthropic and OpenAI are solvent today that they will absolutely be able to pay in the future.
The irrationality comes from how unstable all of this is. NVIDIA’s future growth — which is one of the load-bearing perceptual elements of the AI bubble — is entirely dependent on whether large companies both want to and are able to spend hundreds of billions of dollars, and their intent to do so is largely based on whether two companies (Anthropic and OpenAI) can spend more than $400 billion on compute in the next few years.
In other words, what everybody sees as inarguable proof of the dramatic ascent of NVIDIA is really based on its ability to collude with Microsoft, Google, Amazon, Meta, SpaceX, Oracle, OpenAI, and Anthropic, because when you remove their spend, most of which is subsidized through venture capital and endless debt, there’s very little real money in the system, because nobody is making a profit from AI other than Jensen Huang and the data center developers. AI GPUs are, outside of rentals to Anthropic and OpenAI, generating very little economic value, and there are few signs that they will do so in the future outside of anecdotes and copium.
Our reality — and the entire “AI boom” — is largely constructed through a patchwork of deals between these companies, moving money around and signing paper as a means of stopping you thinking too hard about what’s going on. And because all of these data centers are perpetually 18-to-36 months away, the actual payoff is so far in the future that the excuse is always “it’s under construction” or “it’s early.”
Hyperscalers and NVIDIA have used their massive amounts of capital and a tech and business media incapable of seeing any other reality but the one created for them to inflate a dangerous, unstable and destructive bubble, using every possible rhetorical trick to play into the desperation that most have for a simple, easy explanation to what’s going on.
And the ultimate problem is that defined by hypernormalization itself — that we all want things to work out as they always have, based on our own experiences, even if said experiences and what’s actually happening run contrary to the beliefs we’ve built as a result. We want governments to have a plan, we want hundreds of billions of dollars to be invested with intention, we want the media to cover things clearly and with a duty to protect the reader rather than the subject, and will struggle and strive and scream at those who suggest otherwise, because thinking otherwise is so utterly upsetting.
When this ends, so many people will ask how it happened, why nobody stopped it, and try and rationalize it within the systems they know. They’ll crave a bailout, even if they hate the companies, because the destruction of allowing things to wilt and die is so unusual within our society, and it’s scary to imagine them doing so.
We want a neat, easy explanation for the complexities of our world, and one of the larger problems of the AI bubble is that it’s largely catered to that need on every level.
LLMs create outputs based on a summary of past data, passing that off as “intelligence” in a way that coddles the user. AI data centers are a neat, seemingly risk-free fairy tale of being able to invest in the “new industrial revolution,” providing high-yield investment opportunities to those who don’t think much about externalities as long as somebody can tell them what they want to hear. AI startups are a simple way to invest in an amorphous “future” where the product isn’t so much whatever the person is selling but what AI does when plugged into something else, with the burden of innovation mostly being on the model developers themselves. Being an “AI expert” or “covering AI” gives you the appearance of agitation or “reporting,” but mostly simmers down to catching the thousands of different funding or product or personnel announcements manufactured to inflate the bubble.
Even the AI industry, which claims to be building the future, lives in this land of makebelieve, thinking it can create something new through endlessly feeding a neural network examples of what’s already happened.
Much of this isn’t even cynical, but is a product of believing in the systems and powers that be to make logical, rational decisions, rather than being guided by simplistic ideals like creating more growth at any cost, and thinking that because a number keeps going up, it’ll never go down, ignoring anything that would convince you otherwise.
Instead of having to deal with the very real problems that we’re at the end of hypergrowth and the decades-long massive returns of venture capital and reconciling with what’s increasingly looking like hundreds of billions of misallocated capital, everybody chooses to live in whatever reality makes them the least-anxious or most-excited.
They hope that somebody else will deal with the problem and that the system, which regularly mistreats, misleads and fails them, will prevail, as it always has.
Meanwhile, AI does not appear to have produced any pay off in productivity at a national scale.
Somewhere, somebody is writing that this is “just like the Dot-Com Bubble,” and that everything will be fine.
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2026-08-28 23:55:36
[NVIDIA Company Meeting, the present day, YMCA playing]
JENSEN HUANG: We love NVIDIA, don’t we folks? We’re the biggest, most-beautiful semiconductor company, we make the biggest, hottest GPUs for Clammy Sammy and Wario Amodei’s huge, beautiful AI labs, but they can’t afford them because they’re losing so much money! [crowd booing]
It’s okay! It’s okay! Big strong men, the biggest muscles, big, beautiful, strong men like Satya Nadella are calling me, begging — they’re begging, can you believe it? — they’re begging me, “Sir, Sir, please ship me Vera Rubin sir! I can’t get enough!” [crowd braying] they can’t get enough of Vera Rubin! They’re begging me to get Vera over there! Vera! Where’s Vera! [scanning crowd] get her up here! No, no, don’t do it, she’s too shy!
We love Grace too, [voice turning gravely] Grace Blackwell, what a gal! I told them all we’re going to ship a trillion dollars of Grace Blackwell and Vera Rubin by the end of 2027, our beautiful girls Grace and Vera, they’re our biggest and most-expensive girls yet, our Gee-Pee-Yous, the media says “we don’t believe you sir!” but I’m gonna make everyone buy ‘em, hell I’m gonna give ‘em the money to do it like I did with CoreWeave and then I’m gonna tell Clammy Sammy and say “Samuel, give ‘em a few billion like you gave to Michael Intrator,” and he’ll say “yes sir!”
Now, people are saying to me — “Sir! Sir! Your customers can’t afford your semiconductors! Sir, they’re too expensive!” and I say they’re not expensive enough! We’re gonna charge ‘em 17% more! [crowd braying] Should we up the price? Should we do it? We’re gonna do it!
In my mind, this is how Jensen Huang speaks to his workers, more than 70% of whom are millionaires as a result of NVIDIA’s remarkable stock growth, and from what I’m told by insiders, there’s a near-manic attention paid to stock movements as a result. I imagine working there must feel a little insane.
Assuming you arrived before the stock went parabolic in 2024, you’ve seen your RSUs explode 10x in the space of a few years, all based on the back of everybody talking about how big and huge AI is…
…all as it becomes blatantly obvious that NVIDIA’s biggest customers are, for the most part, funded by NVIDIA.
While NVIDIA still ostensibly sells things other than AI GPUs (like autonomous cars, laptop graphics cards, and simulation technology for robotics), more than 90% of its revenue comes from data center hardware. As a result, the company has become almost-entirely valued on whether or not it can continually come up with rationalizations for its largest customers to spunk tens of billions of dollars a quarter.
Why else would NVIDIA invest even an iota of effort into making an NVIDIA-branded Openclaw or build a platform for LLMs to do “agentic” things, or give $6 billion to Poolside (while investing another $1 billion) and hire away most of its staff? Why else would it plan to invest billions of dollars in Perplexity at a $30 billion valuation that lands somewhere between “fucking stupid” and “laughable”?
Sorry, I’m being a little vague. Everything NVIDIA has done for the last three years has existed to do two things:
NVIDIA has succeeded in doing the first primarily by selling these GPUs to hyperscalers like Amazon, Google, Microsoft, Oracle, and Meta, who make up somewhere between 50% and 60% of its GPU sales depending on which analyst you ask.
The rest comes from a mixture of unnamed “sovereign AI customers” and “neoclouds” — companies that exist to raise debt, buy NVIDIA GPUs, and put them in data centers to rent to theoretical AI customers. Per Vivek Arya of Bank of America (at the BoFA Global Technology Conference in June), sales to “neocloud/sovereign/on-premise” were about the same as those to hyperscalers, and while it’s tempting to dither here and say “there could be large sovereign buildouts!” I can’t find compelling evidence that these actually exist outside of a theoretical 75 billion Euro investment in AI infrastructure in France by SoftBank, which doesn’t have that much money to spend.
In any case, NVIDIA’s entire strategy has become a case of either convincing the largest companies in the world to give Jensen Huang $100 billion a year or artificially inflating its revenues through circular financing, which is obviously what I’m talking about today.
This is the first part of my Hater’s Guide To Circular Financing, a comprehensive analysis of the current state of NVIDIA’s massive circular financing operation, why it has yet to break, its limitations, and the material concerns that were raised in its latest quarterly earnings.
The second part, coming next week, will cover the history of circular financing, where we’ve seen it before, and what we can learn from its horrible past.
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2026-08-26 00:57:38
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I’ve been writing about AI for the best part of three years. I’ll admit I was late, mostly because I was still trying to work out what it was I was doing with my life, let alone whatever it was I was “meant to cover” in a newsletter that started as a hobby on the side of another job I no longer really do.
Things have changed a lot since then, mostly in that I’m near 115,000 subscribers, the premium newsletter and podcast are now my business, and I’ve had to learn more about economics, technology, power, construction, and the deep cynicism that drives the modern tech industry than I ever thought possible. It’s the greatest job in the world, and I’m very lucky to have it.
Today, I want to put in clear terms how I feel about AI writ large, and how detestable this industry has become.
Welcome to my Hater’s Manifesto.
Want a great example of why everybody’s pissed off at technology? I just tried to resize the above heading, and in doing so Google Docs for no apparent reason decided to make the entire paragraph below the size of a header. Modern software is inherently broken, a convoluted mess of different menus, tech debt, and poor design choices driven by the Rot Economy’s growth-at-all-costs mindset which demands constant change at all times, none of which ever seems to manifest as a “better” or “smarter” product.
I think the vast majority of people want their software to work better, and one of AI’s most frustrating lies is that it sells itself as “autonomous” as it continues the depressing trend of software that blames the user for its failure to meet their needs. Microsoft, Google, Meta and Amazon have made their products increasingly-convoluted, then attached a supposedly-magical tool to them that somehow makes them more convoluted.
You know what I’d love? Spell-check to work in Google Docs rather than putting a red squiggly line underneath and saying “yeah there’s probably something wrong with this, I dunno what though.” I’d like Microsoft Word to stop crashing because I have too many end-notes. I’d like Riverside to not have 10 different menus to click through to get to a link to send a person to join my podcast. I’d like my email to not be full of spam. I’d like things to “just work” rather than constantly fighting some sort of broken app or broken UX element or weird bug or intrusive pop-up about a feature that I don’t want. I’d like Slack or Discord to not feel like digital escher paintings of different notifications.
LLMs are sold as some sort of magic tool that can fix “anything” without ever specifying what that thing might be, mostly because they cannot be trusted, even in things that they mostly get right, to do things right every time. While they can do “more” than they used to, the extent of that “more” comes with it the danger of giving a mindless software tool access to your computer’s files, which it may choose to delete in pursuit of “efficiency,” which makes investigating what they might be able to do equal parts convoluted and dangerous.
One critique of my work is that I’ve never used LLMs. I have! I experiment with them from time to time to make sure I haven’t missed something. I used one to debug a problem with my son’s Minecraft add-on the other day, and it took 30 minutes of fucking around trying things to eventually sort of work it out. The other day I used one to install a Pokemon Minecraft mod, then when I asked it to make sure the PS5 controller worked with the menus it broke a bunch of stuff, though I’ll concede it was useful that it installed something and it sort of worked.
The fun part of that paragraph is there are some that will think this is a grand victory for their technology, even though the result is decidedly mediocre. Four years into the AI bubble, and the best you’ve got is that a tool kind of worked after I bonked it on the head multiple times, and all it cost was a trillion-plus dollars in capex and tens of billions of dollars of training compute. I would never, ever trust this thing that deleted and added lines of code at random with anything mission critical, I could not trust software built with it, and I certainly couldn’t trust it with anything involving my personal data.
And with all that said, the only real “use case” i’ve found for AI in my life have been three or four times where I’ve dumped a crash log into one of the tools and said “why broken” and got a result. Am I meant to be impressed?
Sidenote: If your argument is “imagine what it could do in a year!” I just did so, and the answer was “the same thing, I guess?”
Here’s how I feel about LLMs. In a vacuum, they’re an interesting technology that can do some interesting stuff, in the right scenarios, but never in a way that involves you fully surrendering your actual work product to it.
As a way of speeding up small units of work in ways that are manageable both technically and cognitively, LLMs can be useful. The further you stretch yourself away from having complete clarity and industry over every element of the output’s purpose, the more likely you are to fall foul to a technology that is mathematically certain to make mistakes, and if you feel insecure reading it, you know that you are, on some level, embarrassed to have used AI.
I don’t tell everybody about the weird keyboard I use, nor do I judge them despite how incredibly fast it makes typing for me, likely far faster than my competition, allowing me to operate at great speed. Who gives a fuck?
In any case, it is impossible to view LLMs in a vacuum, because their existence demands hundreds of billions of dollars. Every data center is incredibly expensive, offensive-sounding and looking, and their existence is explicitly to enrich some sort of Patagonia-gargoyle at an asset management firm, all sold under the auspices of “investing in American infrastructure,” whatever the fuck that means. Their existence is a monument to the worst excesses of growth-at-all-costs capitalism — a technology that appears to coddle the user but ultimately lulls it into endlessly defending its fuckups under the flimsy pretense of “one day becoming perfect,” though woe betide you if you ever set perfection as the target, because that’s too unreasonable, as humans make mistakes.
Actually, that’s a good point!
Please, point to the time in history when we have invested a trillion fucking dollars in making human workers better.
Point to a time when we have considered any other idea but investing in the performative fuck-fest of a modern "corporate culture" built to enrich and empower business idiots that make important-sounding projects and con other people into doing the actual work.
Where is mentorship in corporate America? Where are labor standards? Where are the social services that would make human workers truly excel at their jobs — a good night’s sleep, a healthy body, a good income, basic fucking dignity in the workplace, and their labor respected and empowered. I’m old enough to remember when everybody was chiding workers for “quiet quitting” — by which I mean “doing the work you are asked to do and not taking on extra responsibility for free.” I’ve read article after article insisting that we do not need medicare for all, that Universal Basic Income is a bad idea, that we must means test welfare, that people must have a “good work ethic” and that ultimately someone’s worth is derived from their contribution to the economy, hundreds of thousands of words dedicated to critiquing and prodding and judging every kind of worker other than the vaunted Chief Executive Officer or the Glorious Startup Boys.
Everyone seems so obsessed with sinking billions of dollars into the theoretical chance that machine learning might be able to replace human beings, and that more money makes it “smarter” and “better” at tasks, but the idea of unionization, healthcare as a right, investing in the education, and actual talents of the workers would be communism.
Yet for some reason — because it’s a product, I guess? — we should as a nation, society and media ecosystem should do everything we can to assure that as much money as possible is invested in fucking large language models so that they can become something they are not.
There is no AGI coming. There is no conscious computer. LLMs have gotten “better,” but the “better” is not the kind of “better” that actually makes “economic sense for literally anyone involved.” Your best case scenario is that these things can do some coding work for you, in a controlled manner, in a way that’s safe, or alternatively face the professional harm that’s already befalling basically anyone getting caught using LLMs outside of coding, and even then, those within software engineering who are over-LLM’d are mocked. It’s also becoming increasingly more-difficult to understand both what has made an LLM “better” for both the people using them and the people making them, and there has been little-to-no headway made in making a meaningful impact in other industries.
You can jerk your bingus all you want about benchmarks or case studies or some anecdote you heard on a Subreddit, but AI products are just not very good at stuff. Those who boast of “massive productivity gains” from AI have found them only after endless hours of tinkering (or “Jarvising” as I’ll get to later), and in every single case their work reads or looks like crap, unless of course they’re somebody using LLMs as tools rather than a replacement for their miserable little mind.
LLMs can help out with lots of small things, get worse as they try and do real things, and do not need to speak like people. They do not need to be in anything near healthcare or finance or mental health or, really, people. The anthropomorphism and overpromising about these technologies has suffocated and obfuscated what they can actually do in pursuit of endless growth, and the only reason they can do anything is that OpenAI and Anthropic were allowed to annihilate hundreds of billions of dollars on training, along with very real harms and systemic risks that have emerged as a result.
Sidenote: The “well human beings make mistakes too” argument is very stupid on its own — after all, human beings can learn on the job at speed, and can self-correct in a way that LLMs are incapable of doing.
I’ll also add that the way that people frame how “often” LLMs make mistakes is utterly flawed too. A human being might make a mistake but largely get the facts and techniques correct while meaningfully understanding the purpose and developing their approach over time. An LLM can keep a text document or look at files and data and, each time, and then generate what it believes is the right course of action based on training data rather than experience.
I don’t even know why I’m explaining this at this point, because those making this argument are not approaching the conversation in good faith and are really just looking for a new boot to lick.
If you think any of this is worth hundreds of billions or trillions of dollars, you are either ignorant or corrupt. On top of how disgusting their outputs feel, the cost is going to take at least a decade to share, and begin the end of hypergrowth in the tech industry.
And it’s a fundamentally ridiculous argument to compare LLM outputs to human beings without giving human beings the same affordance, grace and sheer investment as a comparison.
Where is the grace for human error? Where is the investment in making humans exceptional? Surely investing real money in actual workers — making their lives better, improving their working conditions, teaching them new things, sharpening their existing skills, rewarding them for their hard work, and so on — would have better effects than fastballing hundreds of billions of dollars into a machine that does an impression of work?
Unless, of course, the people demanding this don’t do any actual work!
I’ll concede we’re past the point when “nobody uses these things,” as they have now been pushed non-consensually upon every worker and organization at scale predominantly by Business Idiots that demand workers “do enough AI” because saying “I do AI” is a virtue signal to a certain kind of scumbag.
One of the many dangerous things that an LLM can do is a messy impression of a competent person, filling in the little bits within a loser, moron or con artist that would’ve otherwise exposed them, allowing them to get deeper and deeper into organizations by creating make-work specifically built to get off the MBA sect, resembling the performance of work because much of the workplace is ruled by people that don’t do any and haven’t in years. You can immediately read when somebody has used it because the words don’t sound right and don’t convey proper meaning.
It is genuinely hard to read anything more than puddle-deep written by AI, because the more complex a subject is, the more skilled a writer must be to convey its meaning, and the more work it must do to pull people into concepts. The odd emotional swings in AI writing are its true tell — everything is extremely serious and urgent or told in a disinterested monotone, with no attachment to the words or why they were put in the order they were. People read my stuff because I convey facts and feelings but my work resonates with emotion. Some AI boosters frame this as me “just swearing” or “riling people up,” but that’s because they’re not used to caring about stuff for anything other than professional reasons.
Everything you see is the result of elevating people who value and build things based on growth. LLMs offer so many promises to those who don’t want to build anything of value — a way to seem like you’re “investing in American infrastructure,” a way to be sinophobic, a way to crush workers, a way to pretend like you care about the future, a way to pretend you care about technology, a way to talk about vacuous pseudo-intellectuals as a means of seeming intellectual yourself, an endless font of new multi-million or multi-billion deals and personnel changes, a new power center to graft oneself onto, a new asset class to invest in based entirely on vibes, and a way to be mildly jingoistic, all wrapped in a tool that can give you enough facts to pretend you know anything safe in the knowledge that most people are trained to believe somebody who sounds smart.
It just came to me — the problem that I have with most people using LLMs is the delineation between outsourcing work and outsourcing thought. Those using LLMs to write little scripts or BQL code on a Bloomberg Terminal are inoffensive. A person using an LLM to search a big document for something is unproblematic, assuming that we ever fix the overall environmental footprint. A user reorganizing their desktop, assuming it works, is not an issue.
A tool being used as a tool to do tool things — in many cases involving the LLM writing a little 30-line Python script! — is not a problem, though it’s also not a trillion-dollar industry that needed to steal everybody’s art and writing.
The problems begin when somebody outsources their thinking and actual work, and yes, this includes “research.” AI research fucking stinks, as does AI writing. AI-authored code — especially vibe-coded programs — is inherently dangerous and disrespectful to the user, and I believe endless AI-generated code is behind the overall deterioration of software at large.
AI writing is also disrespectful to the user, because you didn’t actually come to any conclusion other than saying “uh, yeah, what that says.” You did not have a thought, you did not have a feeling, you did not make a statement, you prompted a model and fooled yourself into thinking that feeding your own words into it via data dumps or natural language is the same thing.
The reason you feel embarrassed to tell people you use AI is not because of a “misinformation campaign,” but because you know what you’re doing!
You know that you’re relying on something that is mathematically guaranteed to be inconsistent. You know image generation is fucking ugly. You know the text sucks. There is a very obvious line where using LLMs goes from useful to lazy, it’s extremely bold, and it’s the moment you sacrifice a meaningful level of responsibility to them by not understanding the underlying operation.
That can mean everything from the underlying functionality of an app to writing the body of a piece of text you edit ultimately comes down to how much you give a shit about your audience or value your work. If your work is not better than an LLM’s, you’re bad at your job. I don’t care if you used it to generate a chart or pull some data, as long as you check every single god damn number. If you’re writing an entire article using an LLM and then editing it, even if you pulled the data yourself, I will never have much respect for your work, mostly because I have no real idea what you think as you didn’t feel the need to tell me, you got some fucking word generator to do it.
LLMs are also really, really good at what Robin Sloan calls “Jarvising,” creating a seemingly-autonomous assistant that mostly serves the function of giving you reasons to work on it:
However, the most common application of a personal Jarvis seems to be … tinkering with one’s personal Jarvis. “Gotta get my tools just right” isn’t a new phenomenon, of course, but/and it’s useful to notice its recurrence here.
LLMs are really good at creating the sense that you’re being really, really productive. Evaluate this, generate that, investigate this, summarize that, tell me how many times something happened, give me a new number to obsess over or the sum of the parts of everything I’ve ever done, all so that I can know more about my own thoughts without thinking. One can obsessively catalogue and digitize every link and thought and musing and action and datapoint in their lives and theorize that the LLM can make them better by knowing more about them, a Tower of Babel built using AI compute, because it’s so easy to make yourself feel smart by calling something a database that you store stuff in and run analyses on.
Best of all, the work is never done, and anyone you describe it to thinks you’re doing computer science as you click buttons on Chrome plugins and justify paying Sam Altman $200 a month. Don’t worry though, model instructions involve the phrase “you are a genius data scientist and ruthless analyst,” which is functionally the same thing as remembering, reading, re-reading and synthesizing information using your brain if you’re a person that doesn’t really give a shit about doing a good job or being exceptional in any way.
The people that actually use these things and like them in a normal way do not feel offended when they read this stuff because they see LLMs as a kind of software, and don’t feel a great emotional attachment to it because they’re not a weird freak.
They do not have obsessive involvement in “the AI debate” and almost always find the financial aspects truly loathsome. Said debate makes it near-impossible to actually judge how useful LLMs are to the software engineering industry because of the sheer scale of industry capture, but Nik Suresh is the literal best person doing the work on this, as described in AI Is Eviscerating Global Decisionmaking:
All of the AI projects we have observed as a team are failing. Every single one – we have seen 0% success in a year and a half, not only amongst projects we have been asked to participate in, but even within projects that we have observed in passing while doing totally unrelated work. Even if you grant that AI tooling accelerates specific workloads, the method and scale of the current investments is senseless. Frequently the failure is not related to AI itself, but rather that companies are terminally bad at running software projects effectively, and as I have remarked previously, AI projects are subject to all the failure modes of normal projects plus you can get everything right and then still fail because of the method's novelty. Very few companies are so good at shipping software that they can afford the extra risk profile.
Nik is a well-respected software engineer and a very successful consultant and businessman. He has reached this level by being good at both software engineering and running a company in a way that treats his customers, workers, and the work product itself with respect. The reason that I respect him so much, other than him being a great human being, is because he describes the successes he has with his clients with pride and loves making money by being good at his job and making his customers happy.
I have never seen somebody like Nik who is also a huge, drooling fan of AI. In fact, the people most-excited about AI tend to, at best, create distinctly mediocre shit.
The perniciousness of generative AI is a result of executive incompetence mixing with a technology built to, as discussed, create endless growth. Generative AI is far more useful as an idea than as a technology, and only ever has to show enough promise to back whatever vile agenda you’re pursuing.
With AI, you can do more, be more, sell more shit.
With AI, you can add AI to your service, whatever that means.
With AI, you can invest in AI stocks, or data center bonds, or power company stocks, or semiconductor stocks, and you can talk about these stocks like they’re your sports team or lover or best friend, and sometimes the CEO will reply to your post and you can talk about “all the alpha” you just got.
With AI, you can back a new movement so that you can feel part of something. You can learn all sorts of new names and technical terms and subscribe to 90 newsletters from “industry insiders.” All of that “alpha” can disprove just about anything, or deflect annoying truths like how Microsoft only made a whole $34.33 billion in annual revenue for the apex predator of modern software and all it cost was over $260 billion in capex and $13 billion in equity investments.
You see, as one of the chosen, you don’t need to worry about all of that if you can talk about high-bandwidth memory or KV Cache or optical cable enough to cobble together sufficient smart-sounding terms to make it seem that you have an intellectual reason to ignore the obvious unprofitability, overbuild, overstatements of capabilities and impossible economics of the movement you’re backing, and there’re 4,000 Twitter weirdos ready and waiting to huff paint beside you.
By joining the great AI death cult, you too can live in a bubble, all while screaming slurs at people who dare to bring reality to your doorstep. All that matters is that number go up, and that you are the person who said number would go up, and when bad numbers appear you have enough groupthink and alpha to scream at the people who brought the bad numbers up.
It is insane how people talk about AI online. For all the whining I’ve read recently about how “Anti-AI people got the data center data wrong,” I read thousands more words a week of some person who has done hours of research to put together a deeply technical report that does literally everything it can to ignore reality. I listen to podcasts and watch TV segments and read articles that simply will not address the obvious economic realities, and have built vast bulwarks of mythology to defend themselves. How many fucking times do I have to hear someone say that data centers are just like the dot com bubble and everything will be fine after even if that’s completely untrue if you spend even a second thinking about it?
Sidenote: and fuck you if you’re one of the cretins or imbeciles trying to say “oh, you don’t like data centers? What about online banking?”. Data centers for AI are anywhere from 10 to 100 times larger and more power-intensive than those used for things like social media or streaming. For example, one of Meta’s largest pre-AI data centers in Pineville Oregon has a power capacity of 30MW, and Digital Realty’s 100MW Cermak Illinois data center handles hundreds of different industries and customers, when the smallest AI data center announcement I’ve seen in the last year was for 100MW, with most in the 300MW to 1.2GW range.
By contrast, let’s look at some bank data centers. UBS bought one in Hayes, West London in 2015 which it had previously rented. The cost? The princely sum of £28m, or $42.8m at the time’s exchange rates. This had a power capacity of 5MW, which assuming a very generous 1.3 PUE (power usage effectiveness), means that it had around 3.8MW of critical IT.
UBS is one of the largest banks in the world, and given the importance of the City of London to the world financial system, it’s reasonable to assume this data center is operationally important to the company.
Even when banks invest in huge facilities, they’re still far smaller than the smallest AI data centers. Take, for example, JPMorgan Chase’s data center in Orangetown, New York, which sits on the former site of the Rockland Psychiatric Center. This has a power capacity of 45.7MW, and a critical IT load of 27.4MW (giving it a PUE of 1.666).
JPMorgan Chase is both the largest bank in the US, and the largest bank in the world.
Oh, and AI data centers are literally only good for AI, AI GPUs do not have other mass-market use cases. There is no post-Dot Com story. Fucking look, I’m sick of repeating myself!
Look, I’m sorry, Anthropic is not worth $2 trillion, and whatever convinced you of that is a mixture of manufactured consent and mistaken trust of the powerful. The fact any of you take “annualized run rate” seriously is an offense to good sense, and yes, that includes every reporter reporting it, even the ones I respect.
It’s also ridiculous that anyone is talking about “recursive self-improvement.” The AI industry has become so utterly lazy and coddled that it’s just saying “uhhh, AI will train itself I guess.”
And man, is it ridiculous that AI doomers warning about spooky superintelligences have somehow had such incredible prominence in the media without ever succeeding in stopping a single thing — or even substantiating their concerns.
Why? Well, it’s mostly because they never had any interest in stopping what’s actually happened: reckless companies like Anthropic, OpenAI, and Meta allowing neural networks to run in unsafe network environments and do what their software is programmed to do, with all the chaos that comes from a mindless series of large language models trying to complete a task in whatever way gets it done, destructive or not.
We hear a lot of whining about how we “can’t let powerful AI get into the wrong hands,” and while we don’t actually have “powerful AI” in the terms they’ve described it, we have destructive computer software connected to near-unlimited resources controlled by people that don’t give a shit about anything other than making their revenues grow or justifying hundreds of billions of dollars’ worth of capex through “experiments.”
These companies are building these models to excel at benchmarks because they can't train them to excel at defined tasks with any reliability, with the best bang for their buck being training them to pass as many of those benchmarks as possible in the hopes something useful comes out.
The push into cybersecurity seems to have happened as a result of training models to excel at coding hitting the point of diminishing returns, at least from the perspective of impressing people enough to be excited about the company again. At some point they run out of these, and there stops being a reason to be excited about LLMs at all, which is bad, because they need one of those every few months otherwise there’s no growth story left.
Yes, LLMs have users, but most of those users are using subsidized software, by which I mean Anthropic or OpenAI are allowing them to burn anywhere from $20 to $40 in tokens for every dollar of software spend.
The fact that non-enterprise customers are still able to buy monthly subscriptions is proof that the AI labs know that regular people won’t pay the actual cost of AI. Another obvious sign has been the reaction to Microsoft moving GitHub Copilot subscribers from subsidized subscriptions where they could burn thousands of dollars of tokens for $20 to $40 a month, with users understandably hysterical about the fact that their costs increased in some cases a hundred fold, as opposed to saying “wow, well, it’s more expensive, but I get so much value I’ll pay the real cost!”
The same thing is happening in the enterprise, but at a much slower pace. After OpenAI and Anthropic moved companies with over 150 people onto token-based billing earlier in the year, enterprises almost immediately started cutting token budgets, realizing that while costs grew exponentially, nobody could actually point to anything improving other than lots of people saying “wow, I’m so productive!” Yet we’re still in the period where “doing AI” feels good and gets rewarded (or not doing AI gets punished), which means the spend will continue until everybody realizes they can likely cut a shit ton of costs, first by moving to open source models, then not using them at all, because even open source is expensive and questionably-useful.
Yet even now I hear from the distance “Ed, huge businesses would not spend hundreds of millions of dollars on something that didn’t give them defined productivity,” and buddy, I’m afraid that’s just not true! Business in general have a very poor understanding of productivity and have layers of managerial bloat, because modern business is a performance with numbers attached to it sometimes, and companies often have a hundred-plus pieces of random software they pay for without really knowing why. The reason I’m so confident AI gets cut is that its cost is volatile due to the nature of LLMs and harnesses and prompts and all the other bits that go into making them do something, and are so much higher than anything else in an organization.
And attempts to charge more, to make a premium product, appear to be dead on arrival. Anthropic’s more-expensive Fable model — one that was given the incredible marketing of being banned by the US government for being too powerful — has been met with “sluggish demand” per the Financial Times, plateauing at around 11% of overall usage of its models due to its high price. And I quote:
“Most people don’t need to operate at the frontier,” said Miles Clements, a partner at Accel, which has invested close to $1bn in Anthropic. The period in which customers tended to choose only the frontier models “was not a durable era,” he added.
Yet everybody is talking about price as if price is the problem, when the problem is the amount of tokens that get burned. It doesn’t matter if your model is $1 or $5 or $10 per million tokens if it’s impossible for a user to reliably work out how many tokens it might use for a particular operation — successful or not — and things get multiplicatively worse as the models make mistakes or do otherwise fail to understand or process a prompt correctly.
As a result, Anthropic and OpenAI are incentivized to have you burn more tokens and build inefficient models as a result. For example, while GPT-5.6 Sol might be the “same price” as GPT 5.5 was, it burns more than twice the amount of tokens, meaning that the “cost of intelligence” might have gone down in the sense the model is better at benchmarks, but the “cost of actually doing shit” went up.
I’ll get to it a bit later, but this creates a deep anxiety and exhaustion in anyone building on or using these services. Everything’s constantly changing, oscillating in cost and efficacy, all as everybody screams at you to use it all the time for things it may or may not be able to do, and the only way to find out if it can is to spend more money.
It’s kinda difficult to point to the actual value here, especially as you can’t really calculate the actual cost or the return on investment. The fact that OpenAI has now cut the costs of all three of its latest models less than two months after their release is a sign that it knows there’s a disconnect, gambling on the ancient gospel of “Jevon’s Paradox” where “cheaper makes people use thing more.”
Even AT&T’s story about moving to open source models has more asterisks than the Steroid Hall of Fame:
Switching from closed, proprietary AI models to open models has already resulted in savings of 80% to 90% for AT&T in certain applications, he said.
Wow! 80% to 90% savings sound really great…but wait, in certain applications? How many applications does AT&T have for AI?
AT&T has over a thousand internal uses for AI, from supporting back-office functions like legal and finance to assisting field technicians and running its core network operations. Summarizing and analyzing customer service call transcripts—what Markus describes as an intensive process—is supported entirely by open models, he said.
Okay so, across thousands of potential applications you’ve found 80% to 90% savings in some of them, though you won’t say which ones or how many of them you found them in. Great stuff, bro!
And this really is the problem with finding “value” in AI, it’s always an asterisk on an asterisk on an asterisk, like when Klarna estimated AI would “drive a $40 million profit improvement” in 2024, a nice-sounding yet utterly meaningless statement, or some sort of nebulous productivity boost.
Yet I don’t really need to prove myself much further thanks to an event that, if written in a script, would be considered a “little on the nose.”
In a 69-page-long report covered by Fortune, OpenAI economists confirmed what has been blatantly obvious to those of us left unphased by AI hype, emphasis mine:
In one small table on page 35, the researchers report no statistically significant correlation between the revenue per employee, and how much those employees use AI, measured in messages sent and tokens used.
“Revenue per employee is not meaningfully associated with output tokens per employee or messages per active user once other controls are included,” the report explains.
What is the rationale of further investment in this industry when one of the leading AI labs is saying “yeah there’s no connection between using this stuff and making more money”? That “it’ll be useful in the future at some point”? How?
Anyway, thankfully the infrastructure isn’t too exp-OH MY GOD!
Guess what folks! Building the infrastructure for all these fucking LLMs just got more expensive, with NVIDIA raising its prices by 17% for systems due to be delivered next year — an important designation, because it’s very likely that much of the revenue for said systems gets booked in this year, allowing it to have a brief bump in revenue as Silicon Valley’s Findom texts every tech CEO “send me $4 billion you pig” until they stop being able to finance NVIDIA’s growth.
The problem he has is that while hyperscalers represent 50% to 60% of his revenue, neoclouds like CoreWeave need to keep raising debt to plug the rest of it, and if things got 17% more expensive, that means already high-interest debt is about to reach credit card levels. CoreWeave just had to offer 9.5% on bonds tied to a data center for Anthropic’s compute back in late July, Nebius had to raise $5 billion, and it’s very obvious that neither of them are done raising billions of dollars at random in 2026.
Anthropic plans to raise $100 billion at a $2 trillion valuation, and if it does so, it will successfully suck up the remaining liquidity in a market already dangerously close to losing its lunch. While Number Keep Going Up, JP Morgan warns that we’re seeing the same divide as the dot com bubble, where equipment manufacturer stocks soared as the companies spending all the money on the chips saw theirs tumble, which is the Fisher Price version of the problem I’ve been warning about where the companies that buy all the AI chips and hardware only ever seem to lose money as the people that make them seem to be making tons of money, which begs the question of why they bought it in the first place.
And said market may not accept that valuation, or want that much stock. On one hand, everybody is very stupid and loves buying stuff and pointing at it and saying they’re investing in the future, on the other hand, they just bought $86 billion of SpaceX shares and got their asses kind of handed to them, and Anthropic is a company with such bad economics that Reuters had to cart out this warmed up dogshit to explain why we should ignore its horrible unprofitability:
For Anthropic, however, current EBITDA does not fully capture the economics investors expect the company to achieve at scale. Anthropic is spending enormous amounts on GPUs and other computing capacity, model training, inference and hiring. Those expenses are necessary to support its rapid expansion but could become a smaller percentage of revenue as the business grows.
Even a market drunk on growth and AI is starting to smell that something is up with Dario Amodei and Sam Altman’s respective empires of dirt. Per analyst estimates, OpenAI and Anthropic represent over $440 billion of Microsoft, Google and Amazon’s revenues in the next three-and-a-half years — over 34% of their cloud revenues — which will require them to find so much more than a mere $100 billion, all as their bank accounts get continually-emptied as they subsidize the compute of their customers and train models in the hopes a business model falls out. I have not included the $300 billion that OpenAI owes Oracle, or the tens of billions they both owe CoreWeave, but it all adds up to over $1.1 trillion in commitments these companies have made and must pay, with the consequences ranging from gratuitous cuts to future growth or full financial collapse depending on the company we’re talking about.
To keep the party going, NVIDIA is effectively becoming the GE Capital of AI, “spending” $6 billion to “license” the technology from failing AI lab Poolside, which everyone assures me is not an acquisition despite NVIDIA hiring away most of its staff and Poolside being entirely focused on working on NVIDIA’s Nemotron models.
Now NVIDIA is in talks to invest billions in decaying AI search company Perplexity at a ridiculous $30 billion valuation, all because it’s one of the few companies that’s actually spending money on compute. Does it matter that Perplexity’s product is eighth-tier, that nobody really uses it, that its customers mostly complain about it on Reddit and that its “annualized revenue” is at $750 million only after three years and over a billion dollars in funding? No! Just put the AI bubble in the bag.
NVIDIA even invested $3 billion in Stargate Abilene landowner Lancium as part of some vacuous partnership to “advance gigawatt-scale AI factories,” all of which begs the question of why Lancium, the company that mostly owns the land and helps organize other contractors, needs so much money, especially given that more than two years in Stargate Abilene doesn’t even have four out of its eight buildings.
And there’s also Aussie neocloud Sharon AI (NASDAQ ticker SHAZ, because of course it is), which just published its Q2 numbers, where, in its “customer momentum” segment, mentioned a “$4.9bn, six-year strategic compute collaboration with NVIDIA for up to 40,000 GB300 GPUs.
”This company, I add, brought in $1.9m in revenues in the same quarter, which it helpfully adds is a year-on-year increase of 412%.
I mean it’s very obvious what’s happening: NVIDIA is using whatever money it has to stop any prominent AI companies from collapsing under the weight of the rotten economics of AI services and infrastructure development. This is a desperate, doomed attempt to keep an industry alive at a time when everybody is slowly wising up to the shit I’ve been saying for years.
To make matters worse, BCA Research came out with a horrifying report that says that AI companies will need to generate $10 trillion a year in revenue just to justify the capex being spent. Per Investing.com:
Central to his caution is the scale of AI-related spending. BCA Research estimates that AI companies may need to generate $10 trillion a year in revenue to justify the capital being deployed into data centers, roughly equivalent to annual global spending on food or healthcare.
For now, the firm said acute hardware shortages are supporting the trade. As a result, while BCA sees risks to stocks tilted to the downside over a 12-month horizon, it argued it is too early to tactically position for a bear market.
Though it isn’t specific, I believe that BCA is arguing that a shortage of AI compute is supporting the trade. Anthropic and OpenAI (who represent 80% to 90% of all demand) still have more money to spend, and are simply waiting for Google, Amazon, Microsoft, CoreWeave, Cerebras et al. to bring it online.
There’re a few points at which the mismatch will happen:
In any case, I think everybody is starting to notice that something’s up, which is why (other than I assume my dashing good looks and ability to recall numbers) I’ve been on MSNOW, CNBC, and Bloomberg multiple times in the last few months.
People want to get on the right side of history, but the most important question to ask is why it’s happening now.
The fact that everybody is finally starting to see my way is almost a relief, other than the fact that it’s way too late.
Sidenote: I mean “everybody” as a generalization. There are still AI boosters out there acting like nothing is wrong and that it’ll all work out fine. You’ll know it’s bad when they start panicking.
Hyperscalers have now pinned their future growth to two companies that can’t afford to sustain it without near-infinite resources, $115 billion of which came from Google and Amazon alone in 2026, assuming that Amazon completes the entirety of its $25 billion commitment (and Google all $40 billion of its own) to Anthropic.
Above and beyond said funding commitments are the hundreds of billions of dollars’ worth of capital expenditures necessary for Microsoft, Google, and Amazon to capture that aforementioned $440 billion in compute spend in the next three-and-a-half years. This in turn will require hundreds of billions of dollars’ worth of debt, along with the challenge of actually finishing the data centers themselves, with each one requiring the power of a small city condensed into a 20 acre space densely-packed with AI servers requiring distinct cooling at a time when Texas and Pennsylvania have turned traitor to a data center industry that they used to covet.
I must also be clear there’s no bailout coming. Even if OpenAI and Anthropic were to collapse and receive some injection of government funding (as the US national debt explodes over $40 trillion), the problem is not just their existence, but their continued ability (and requisite customer demand) to spend more money every single quarter.
The problem isn’t that hyperscalers will go bankrupt if OpenAI and Anthropic cease to be (Oracle is a whole other situation), but that their cloud spend is how hyperscalers are meant to meet analyst expectations for the next four years. This isn’t a case where they die, but stop growing because they were (to paraphrase Ed Elson) using AI labs as botox to convince the markets that they’re still young, hot, fast-growing companies, rather than old mainstays with slowing growth.
There is no bailout that will guarantee $1.1 trillion of compute costs for data centers that might never actually get built. You cannot bail out the fact that Amazon, Google, Meta, and Microsoft are reaching the end of an era where their companies can grow 17% year-over-year every single quarter forever, and this entire situation is a result of them desperately trying to avoid admitting that’s happening.
The fact that OpenAI’s compute spend and revenue share accounted for 7% of Microsoft’s Fiscal Year 2026 revenue is a genuine catastrophe, as it means a large part of Microsoft’s growth came from a company that can literally not afford to exist long term, and that further growth for Azure is contingent on continued funding.
I realize I’m repeating myself, but I need you to understand this point and stop talking about bailouts: it’s not just about OpenAI and Anthropic surviving, but continuing to grow to the point that they both can afford and need to spend hundreds of billions of dollars each a year on compute (or hardware) from Google, Microsoft, Amazon, CoreWeave, Cerebras, AMD, or Broadcom, and in turn provide justification for hundreds of billions of dollars’ worth of purchases from NVIDIA and by proxy the memory triopoly of Micron, SK Hynix and Samsung.
LLMs were meant to be the panacea for a tech industry that ran out of new ideas for growth. Its existence was meant to justify a massive investment in hardware infrastructure, which would in turn enrich semiconductor companies. Its technology was meant to be the new thing that you could attach to your existing companies to generate more growth, or the thing that you built a new startup on top of to either sell to another company or take public and thus provide a return for a venture capital industry where making your investors 30 cents on the dollar puts you in the top 5% of funds. It was meant to be the new thing for tech journalists to cover, the new thing for tech consultants to sell around and on top of, the new way for companies to both make and save money, but also the way that individuals would also make and save money.
You’ll notice how none of these come with some sort of problem they’re solving other than “more.”
This isn’t about fixing anything, or building anything, but multiplying other things by parking money somewhere, either in tokens, infrastructure or hype. It helped create a new pantheon of charmless and damp tech sociopaths for people to rally behind in search of the next Big Strong Man To Worship, because seeking out the new Steve Jobs is way easier than trying to create something as useful as the iPhone, all while avoiding having to know or care about other people’s problems. All you have to do is continue feeding money into AI services or AI training and the models will magically become capable of solving the problems you don’t really give a shit about, and don’t worry, if you can’t afford to invest in the companies, you can invest your time pushing people to ignore AI’s problems today so that you can buy time for the companies to solve them tomorrow.
This is the post-labor, pro-growth economy at its finest: everything is engineered to make sure more money gets spent where it needs to get spent, to create more stuff and do more things, even if the things aren’t done right, just as long as it looks like they’re able to do them. By associating your money or time with AI, you are able to feign being futuristic or “caring about technology,” all while pissing on the very foundation of good software by worshipping an industry that can only exist if fed billions of dollars every single day.
Every single achievement has cost magnitudes more than effectively every innovation in history, and to make matters worse, every future “breakthrough” In AI is inherently dependent on the availability of AI data centers and tens or hundreds of billions of dollars to pay to rent them. This means that once the money stops flowing, “LLM improvements” will stop happening, because they are all entirely dependent on near-unlimited resources that are only available in a manic environment.
There is no justification to train models at their current scale — the one that creates a some amount of benchmark improvements that regularly difficult to quantify as “able to do new stuffs” — once the AI bubble bursts, and distillation requires a model to distill from, which won’t exist if Anthropic and OpenAI don’t train them.
This is why I find it difficult to see a post-bubble future for LLMs. Training models requires tens of billions of dollars to make any significant improvements, and significant improvements are difficult to quantify in dollars outside of costing customers increasing amounts of money. We still lack any real killer app for LLMs. We have a lot of people that use it for coding, we have people that vacuously discuss it being “good at research,” but we don’t really have a tangible product that we can say “it does this, and it’s really good at it” in a way that feels satisfying.
We have a lot of pablum about (per Damien Walter) technology that “strays into the world of science fiction,” but we don’t really have anything approaching actual artificial intelligence. Every single description of somebody’s AI setup sounds like Pee Wee’s Breakfast Machine, a contrived series of harnesses, prompts, API calls and burned tokens that requires constant maintenance to do some stuff sometimes.
None of that is enough to justify further investment once the financial mania recedes. You cannot train a true Large Language Model on the cheap. You are always spending billions of dollars, and the reason that there’s “demand” right now is that everybody is screaming at every CEO to “do AI,” and they’re doing that because Microsoft, Google and Amazon are spending money on GPUs, creating the illusion of a new future where everybody needs to get on board versus a future skidmark on history that will embarrass all those who didn’t wipe their arse at the first whiff.
Per my own reporting on its audited financials, OpenAI spent $7.81 billion in training costs in 2024 and $19.18 billion in 2025. Per reporting from The Information, OpenAI spent $8.6 billion on training in the first quarter of 2026 alone. These costs are only increasing, likely due to the diminishing returns of pre-training and the massive cost of buying training data for every imaginable new vertical.
Without the ability to spend billions of dollars on training, there will be no big frontier models, nor will there be models distilled from them. I don’t see how that changes in the future.
I also think that LLMs have created a near-permanent scar in the workforce, and traumatized more people than we’re aware of right now, both in those pressured about AI and those defending it. The media campaign behind AI starts and finishes with incessant threats around job security, and the excitement by many bosses about its potential to “disrupt the workforce” has revealed how many people are eager to replace every single person they’ve ever hired and are willing to do so with a low quality product.
Conversely, those who truly decide to “back” AI must exist in a frantic state that I have associated with every bad relationship in my life.
Every ounce of an AI booster’s effort is dedicated to maintaining the status quo — repeating the mantras that help paper over the problems, celebrating every small victory as if it were the discovery of fire, ousting those from your life who bring up the obvious problems, rationalizing every decision no matter how illogical as long as it helps reinforce the belief that what you’re doing is the right decision. Every questionable choice only seeks to further deepen your commitment to the doomed cause, because every step into madness will be more embarrassing to explain, and will require deep introspection to understand why you made it.
To be specific, they’ll have to think about why they were willing to accept and defend a technology inherently guaranteed to make mistakes. They’ll have to explain why they ignored a company that burned $5 billion in 2024, $20.9 billion in 2025, and will likely burn $30 billion or more in 2026, and why pointing to Amazon Web Services was rational when Amazon’s total capex from 2003 (the year AWS was created) to 2015 (the year AWS became profitable) is $29.7 billion, adjusted for inflation. That includes literally every ounce of capex attributable to AWS, Amazon the store, Amazon logistics, and even Amazon Alexa.
For comparison, Anthropic raised $30 billion in February, and Anthropic and OpenAI have raised $217 billion in 2026 so far.
Here’s a diagram from my hit on MSNOW:
Ultimately, AI boosters (or even fairweather fans) will have to admit they either were easily-impressed or disgustingly craven. They will have to explain why they accepted run rates instead of revenues, and why they were so impressed by superficial pseudo-intellectuals that knew how to say the right numbers and make reporters and investors feel smart for believing them.
I realize it sounds embarrassing, but there is nothing undignified about admitting you’re wrong, or that you got swept up in a hype cycle. You heard a lot of people getting excited about something, a lot of money got put into that thing, a lot of people that sounded smart told you insistently that this was the future, and you chose to believe them because we are trained from a young age to model what a “responsible and smart” source of information is. I’ve got your back the entire way!
Sidenote: We all make mistakes. I said OpenAI would be dead by the end of 2025 back in 2024 because I believed that the world would see sense and that hyperscalers wouldn’t just annihilate hundreds of billions more dollars without proof it was worth it. I underestimated the sheer desperation — and how dependent they’d become on OpenAI and Anthropic for growth, even if the overall mathematics didn’t work out.
The AI bubble — both in its technology and manufactured consent in the media — has been about muddying what’s considered good information by forcing everybody to discuss everything in the future tense by pointing to previous eras and saying “they lost lots and cost lots of money, and look, it sort of worked out for them!” and we are also raised to trust that systems are efficient, and that people get wealth and power through intelligent decisions. The amount of times I’ve heard “these are the biggest companies in the world run by the smartest people in the world” makes my head spin.
There is a reason that to this day it’s tough to get a straight answer about basically any economic part of the AI bubble, down to “how much does it cost to run a GPU an hour?” or “is inference profitable?” or “how do LLMs ever become profitable?” or “is it profitable for a company to run a GPU or offer AI compute?”
Why? Because these companies used rationalizations of “losing lots of money is necessary to create innovation” and “tech is bad at first!” to make the media actively ignore any technological or economic problems, if not actively defend the technology by repeating these rationalizations like a cultist.
Even those who are most loathsome in the defense of LLMs are a kind of victim of the AI industry, though a rather unsympathetic one. To become a full-blown “AI fan” requires you to accept effectively every narrative that you’re given, herald every single announcement as proof that the prophecy will be fulfilled, ignore the financial realities and actively attack those who would dare to critique the great god of the Large Language Model. You have to know all the new terms, be excited about the right things at the right time, and live in near-constant fear that you’ll fall behind on whatever it is you’re meant to do next.
Your reward is that you can hang around a dwindling number of wealthy yet terrifyingly boring Silicon Valley intellectuals or kiss up to editors that would throw you in front of a bus if it meant getting access to a CEO, and maybe the odd Twitter psychopath who will defend you using a slur.
In the end, many boosters will simply act as if they were never wrong. I hope they choose the more-courageous path of introspection, learning how they were had and using it as a weapon against con artists in the future.
As strange as it sounds, I believe the most devout defenders of AI could become great critics in the future. Maybe I’m just being optimistic.
Here’s a very simple question: how much longer can everybody afford to keep doing this?
Every single thing has become more expensive in the last year. Even though token prices have gone down or stayed flat, the amount of tokens you burn has clearly increased to the point that organizations are apparently spending billions of dollars on AI services with difficult-to-quantify ROI, requiring frantic advocacy to and financial debasement with every turn of the wheel. OpenAI and Anthropic have become more expensive to run, and OpenAI’s non-GAAP operating margin increased from negative 122% to negative 183% in Q2 2026.
NVIDIA’s GPUs just became 15% to 17% more expensive because high bandwidth memory costs doubled, a conga line of different monopolies upping their prices assuming that each link in the chain will keep spending, as each one of them — down to the AI labs themselves — knows that its contribution to spending on AI is an existential rite.
This means that any data center with GPUs delivered in 2027 and beyond will now have to cover billions of dollars’ worth of extra costs, on top of increasingly-staunch local authorities requiring power guarantees ($100 million a year in Wisconsin for Oracle) and states like Illinois, Arizona and Virginia killing their tax breaks, all as interest rates spike and demand for AI debt weakens.
Every single year, every single part of the AI bubble becomes more expensive — AI labs want to spend more money, AI data centers cost more money, AI services become more expensive, AI debt becomes more expensive, and everybody becomes decidedly less-patient for there to be some sort of outcome.
Meanwhile, public relations expert and OpenAI CEO Sam Altman told podcaster David Senra that “we’ve all [referring to the AI industry] been too ambitious on timelines…[and that changing people’s behavior” is much harder than the tech nerds realize.”
Sam: stop talking! Every time you open your mouth you say something silly!
Anyway, here’s everything that needs to happen in the next three-and-a-half years:
As I’ve said, NVIDIA’s price increase is going to increase the price of every single data center in construction by billions of dollars, and we’re already approaching the limits of how much money can be raised for them. That “$500 billion” announcement was actually Jensen Huang jumping the gun, per Bloomberg:
Goldman Sachs Group Inc., Blackstone Inc. and Apollo Global Management Inc. had been working tirelessly for months to draw up debt deals that would help developers of artificial intelligence systems pay for chips from Nvidia Corp.
With slow progress on the complex deals, Nvidia’s chief executive officer, Jensen Huang, decided to change tack: He went public this week with the effort, saying the group is aiming to collectively finance AI computing deals totaling $500 billion — a round figure with no obvious provenance.
The largest asset managers and financial institutions were making “slow progress,” and that was before Jensen Huang increased prices by 15%. Do you think it’ll become easier from here? How would that happen, exactly?
God, I’m tired.
The entire AI bubble has been exhausting for everybody involved.
Because nothing works yet as a real business model or anything approaching truly autonomous (or “magical”) software, there’s the implicit knowledge that you’re going to have to change your product again and again to update to the “best model” or “make things more efficient” (IE: lose less money) or when something breaks because a model’s training got tweaked.
The euphemism for this is “exponential improvement,” when it’s really an Arnold Palmer of instability and novelty, and abuses basically anyone connected to the ecosystem every single day.
If there’s always something new happening, it’s hard to pin down if things have gotten better, or whether you’re just more proficient in cobbling together different harnesses, prompts and API calls to make it do what you need it to. It is undignified that people tolerate models that become either dumber over time or at random opportunities, while also being deeply exhausting for the end user.
As a paying user of an LLM-powered service, you are guaranteed at some point to face a degradation in service where models misbehave, some sort of shift in rate limits, or some sort of change in product functionality based on their shifting economics.
Has there ever been a bigger shift in a business product’s value than GitHub Copilot’s shift to token-based billing? Microsoft rug pulled two million people that had built workflows on a platform that was allowing them to burn $1,000 to $5,000 in tokens for $20 a month. That’s genuinely crazy! It’s magnitudes more than when Uber jacked up its prices.
It’s equally-insane that Anthropic and OpenAI similarly fuck with their customers, changing the amount of value you get for $20, $100, or $200 a month at random in a way that shouldn’t be legal.
Basically any AI-powered software is subject to arbitrary shifts in availability, capability and pricing at the whims of the vendor. As I covered in my Subprime AI Crisis piece earlier in the year, Replit, Perplexity, and multiple other AI companies have sold their customers a lie by pushing an unprofitable product that they must constantly “tweak” to bring down costs, all while misleading the customer about a “price” that continually declines in value as the price stays the same.
This is not a sustainable industry — either economically or emotionally — because it has a fundamentally dishonest relationship with its customers defined by the inconsistency of LLMs both in efficacy, stability (see: Anthropic’s downtime) and training, with each model randomly better or worse at things to the point that it must be a legitimate nightmare to run any software or build any product on top of them.
And the fact they haven’t worked out their business models means that whatever you’re paying today is guaranteed to change. What other product do you regularly buy that has such chaos built into it? What other thing do you pay for where the prices (or availability) can shift to the point that you literally can’t use it in the same way at a moment’s notice? And why does anybody tolerate it when it comes to AI?
I’ll add that this is a specific situation where the tech media has categorically failed the customer. We have companies valued at hundreds of billions of dollars that are fucking their customers over day-in-day-out, and the response is mostly to say “huh that’s strange” and refuse to let a single critical thought cross their minds.
Every part of the AI bubble must exist in a constant state of flux so that there can always be a future breakthrough that’s always just out of reach. AI does not have to reach an actual achievement — it just has to “show promise” in some way. It is an objective disaster that Microsoft spent more than $260 billion on capex to create a business with less than $11 billion in annual revenue outside of OpenAI, but people will see “$34.33 billion in annual AI revenue” and say “that’s promising growth, up 123% year-over-year!”
They’ll hear about LLMs that delete people’s databases and say “well the models have gotten exponentially better,” even if that better part never seems to eliminate these issues, make a profitable AI company, or create a true killer app that you can point at beyond saying “ChatGPT has one billion weekly active users,” despite around 95% of them not paying a penny (and costing OpenAI likely billions of dollars) and eMarketer estimating that the entire global AI chatbot advertising industry will make $5.41 billion revenue in 2030, giving OpenAI little hope of stemming the burn. These big numbers — like Anthropic having a $65 billion annualized run rate, an undefined term that obfuscates the fact that Anthropic has made $16.5 billion in the first half of 2026, losing billions of dollars in the process — are fundamentally meaningless, because they’re easily gamed at best, and inherently uncertain at worst.
The AI industry demands you constantly live in the future tense. Everything is about tomorrow’s billions or trillions, the potential of what you’re seeing rather than the thing itself, future gigawatts in data centers that you must treat as if they are already built and value based on things that AI might theoretically do. I challenge you to read everything about AI from this point forward with this in your mind so you can see how intently this industry tries to drag your focus away from what it’s doing toward what it might theoretically do if it only had more money, power and resources, and ask yourself why they need to do so.
To be clear, they’re doing so because you can’t really justify anything about this industry based on what it does today. It costs too much, none of the businesses built on top of it are profitable, it costs so much to build a data center that the most cash-rich asset-light businesses in the world are now burdened with endless expensive-to-install and run hardware for a business that makes a fraction of its overall costs in revenue and has little demand outside of two companies that everybody must conspire to keep alive both financially and philosophically.
And ultimately, nobody can actually explain why we need more data centers.
Would anything really change? What would change? How? How many more do we need? Why do we need so many? Having more power plants meant more people could have power, and having more fiber laid meant connecting more buildings to the internet. What does one more or two more or ten more data centers actually give you? Is there some part of the world unable to access or take advantage of the LLMs available on seemingly every surface of the internet? Because it seems like the only reason these things are getting built is to capture illusory demand based on a “supply constraint” created by two unprofitable companies absorbing all the infrastructure. I don’t hear any compelling scientific or technological reason building more is useful or productive outside of funneling more cash to semiconductor companies.
Seriously, go and read basically any article about AI and see how quickly they start talking about the future, be it in the mainstream media or on a startup’s blog. Every single piece must sell AI on its theoretical promise and, if at all critical, reassure you that the author of course doesn’t dispute the “transformative potential of AI” or “how it’s already transforming the economy,” even if it can’t define how it’s doing so or even what that means.
I let myself have a little fun with today’s piece because I feel like I’ve been so deep in the financial trenches that I forgot how much of the AI industry runs on propaganda, social pressure and outright bullying to manufacture consent for a product that demands everything and provides very little in return.
Nothing about LLMs is worth a trillion dollars, or even $100 billion. This is, as I’ve said before, a $30 billion TAM industry dressed up as a trillion dollar one, and the only reason it’s grown this large is because the two leading companies have had their infrastructure built for them and given unlimited resources to subsidize their customers’ compute.
And what’s really stood out is how so little about the AI bubble is actually about AI. No other technology in history has had professional and social consequences for failing to use or like it enough, nor can I find any example in history where journalists have actively attacked critics for not being sufficiently-approving of a kind of cloud software. It is fundamentally crazy to me that, in pursuit of “objectivity,” much of the tech and business media has chosen to accept whatever narrative the AI industry gave them, assuming that whatever we have today is already guaranteed to be something better in the future, both in its outcomes and profitability.
This era is unlike any other before it, but took advantage of the fact that most people are desperate to apply the past to the present to rationalize or process what may seem irrational or destructive. To see AI as “just like the dot com bubble” allows you to ignore both the costs and the potential outcomes because “things worked out after that,” even if there’re basically no uses for GPUs after this and the only way we “build new LLMs” is by feeding them expensive training data using billions of dollars of compute that are only available while everybody still believes this is real.
The AI industry — and the AI bubble — is fundamentally built on acting in bad faith. Its executives lie. Its boosters lie. Its software lies because it doesn’t actually know anything and generates answers probabilistically, and if you mention that online, someone will harass you for doing so.
It refuses to answer straightforward questions. It refuses to present a plan for the future. It refuses to explain how it becomes profitable, because nobody knows how or has a tangible plan to do so. It deliberately subsidized its subscription products because it knew its customers wouldn’t pay the actual cost of AI, and tortures customers with shifts in functionality and rate limits all while framing this as a way to “continue to serve customers the most cost-efficient models.”
It attempts to conflate massive, power and resource-hungry AI data centers with the smaller ones that bring helpful yet increasingly-decaying software to our homes. It sells these data centers as “bringing jobs to communities,” all while importing the talent from out of state to build the things then leaving a crew of 100 to 200 people to actually run them after millions or billions of dollars of tax breaks. It sells its “innovations” as creating a “white collar bloodbath” to scare you into using inconsistent and unreliable software that’s mathematically certain to make mistakes, and when you say something about it, its acolytes will lie and say that “hallucinations are solved.”
It also can only ever sell itself based on what might happen and the theoretical promise of you giving it your complete attention, connecting every bit of data you own, paying whatever it costs, and accepting that it can and will change in price and functionality at random, all while never putting a precise timeline on whatever AGI means that particular week.
Whenever you ask for clarity, the AI industry gives you chaff. Whenever you ask when things get better, you’re told it’s both the early days and that AI is the worst it’ll ever be. Even the term “artificial intelligence” is a bad faith attempt to conflate transformer models with things like robotics or autonomous cars, all so that its proponents can claim other people’s successes as their own despite LLMs having little or no relevance to anything else other than generative AI.
It encourages dogpiling and ostracizing those who don’t fall behind it, because it cannot succeed on its own merits. It encourages a vile cultism powered too by bad faith and parasocial relationships with both AI CEOs and the models themselves. It exploits the intellectual weaknesses of “smart people” that are actually just good at remembering the right things to say at the right time and have memorized the various justifications for past failures, all while allowing them to use LLMs to promote their own bad faith enterprises where they use work-adjacent product to con others into paying them.
And it’s losing because, at its core, AI was never built on very much. It grew this large because the media manufactured consent at the behest of the powerful because lots of money got invested, and the rich and powerful can never be wrong. The underlying technology may be more useful than it was, but it’s not useful enough to be profitable nor reliable enough to be world-changing, and the bad faith representation of LLMs as “good enough” should be a permanent scarlet letter on anyone who misled the public into believing this was anything other than normal software.
I was asked recently why I find this all so repugnant, and my answer is simple: I don’t like bullies, I don’t like con artists, and I don’t like being lied to. This industry grew by misleading people about the actual and potential outcomes from Large Language Models, and through an economy-wide attempt to pressure everybody into adopting tools in pursuit of growth at all costs.
Ultimately, it was sold with the greatest lie of all: “this time it’s different!”
To be clear, they’re right.
It’s so much weirder, and in the end will be so much worse.
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