2026-08-14 23:48:21
I’ve heard from people in the past that my articles are too long, and I wanted to start by saying that, for the most part, they’re going to stay long, because I feel like the only way for me to make my arguments is to be as specific and detailed as possible about the things I’m talking about.
Then again, sometimes it’s just because I imagine arguments against my work in my head and want to pre-empt them.
Something about the AI bubble has made the boosters genuinely insane. They see these otherworldly declarations — hundreds of billions or trillions of dollars — and assume that nobody would say them in bad faith, and that the tech industry would never fail to live up to them, even though we’re barely a few years divorced from when Mark Zuckerberg burned $80 billion on the metaverse, what will one day be known as “the second-worst misallocation of capital in corporate history.”
When the boosters hear that OpenAI plans to spend $750 billion on compute costs through the end of 2030, they shrug their shoulders and say “it’ll work it out.” When they hear that hyperscalers have $1.65 trillion in off-balance-sheet obligations and debt, they nod approvingly, saying that “these are some of the richest and most-profitable companies in the world,” and that they will “simply keep raising debt.” It’s somewhere between number-blindness and make-believe — these are such unfathomably-large sums that it’s hard for the average person to assume anything other than that nobody would sign contracts agreeing to pay them without the confidence they’d be able to do so, even though it’s all very silly.
In any case, readers, I hear you, and today’s premium newsletter is going to be a shorter one, because it’s been an incredibly long week for me, including a day that started at 5AM with four different interviews — including my appearance on CNBC, which I encourage you to watch — that ended roughly 14 hours later, which means I’m a little depleted but nevertheless dedicated to you, the reader, and giving you value for your subscription.
So today I’m going to be pithier, and focus on hard numbers and harder truths about the AI industry, and specifically seek to answer a question: how much does the AI industry actually need by 2030?
Sidenote: I wrote this intro before I wrote the rest of the copy, and now this thing is over 7000 words. God damnit. I tried, I swear.
To be specific, I’m going to be focusing on the next three fiscal years for the companies that matter — the lead hyperscalers (Meta, Google, Microsoft, Amazon, Oracle), the two leading semiconductor firms making AI chips (NVIDIA, Broadcom), the main neoclouds (CoreWeave, Nebius, IREN, which I’ll cover in short), the main AI labs (OpenAI, Anthropic, and SpaceX) and the overall AI compute industry.
I’ve spent a great deal of time in the last few years explaining in detail why I think this will all collapse, but today’s goal is to show you, in hard numbers, exactly how much money the main players in AI need, based on consensus analyst estimates and my own research.
And god damn, do they need a lot.
2026-08-12 03:14:25
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Subscribing to premium is both great value and makes it possible to write these large, deeply-researched free pieces every week. On Friday, I'm going to pull together exactly how much money is needed to keep the AI bubble inflated in the next three years. It's gonna be a laugh-riot. Or very scary, one of the two.
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Last week I put out one of the most consequential newsletters I’ve written yet, pulling together multiple distinct financial analyst notes from Wells Fargo, Barclays, and UBS that directly estimated that 70% or more of the AI revenues of Microsoft, Google, and Amazon were from either OpenAI or Anthropic. To be clear, UBS estimated that next year, Anthropic and OpenAI’s compute spend would be 48% of all Google Cloud revenues — which means that they likely account for even more than 70% of its AI revenues, but I wanted to be fair.
This was both a colossal pain in the arse and a story that I knew would piss off a lot of people, because of its huge ramifications. Some outright dismissed it as “doomerism,” while others insisted it was a good thing, because OpenAI and Anthropic are growing so fast.
24 hours later, Bloomberg ran a story estimating, based on OpenAI’s $24.1 billion dollar contribution to Microsoft’s Fiscal Year 2026 revenues and previous statements, that OpenAI alone contributed to 70% or more of Microsoft’s AI revenues for the year.
For some context, Microsoft has spent $261.3 billion dollars in capital expenditures since the beginning of 2022.
Meanwhile, Apollo chief economist Torsten Slok said Friday that profit margins in AI are “...higher the further you get from the end user,” and then said something I think I’ve said maybe four times in the last three months:
The bottom line is that the most profitable part of the AI value chain depends on the least profitable part continuing to grow revenue or raise capital. Capital can bridge the gap for a while, but not indefinitely. And therein lies the risk: will the ROI show up for AI's end customers fast enough to sustain the spending that is generating those upstream margins?
Good bloody question Torsten! The answer is “probably not.”
Let’s get real simple about this because everybody wants to make AI so complex.
Sidenote: before we go any further, I need to be clear that AI is not the reason that these companies are growing, outside of the compute spend from Anthropic and OpenAI. AI is not “boosting other product categories” or “helping other categories grow,” because if it was, they’d tell you specifically. I get so many emails from people sending me the overall revenues of these companies, mostly from people that don’t appear able to read, but nevertheless, I want to add this note on the off chance they learn.
If we assume, on the low end, that Jensen Huang is right and he’s going to sell $1 trillion or so of GPUs (roughly 30GW of billable IT capacity), that’s somewhere between $360 billion and $435 billion of annual compute revenue demand.
Right now, there are (outside of hyperscalers buying compute for them, and whatever it is Meta is up to) two companies that spend more than $500 million a year on AI compute, namely Anthropic and OpenAI. Both are unprofitable, and both lose tens of billions of dollars a year.
If we take OpenAI’s testimony from the Musk-Altman trial as gospel, it’ll spend around $50 billion on compute this year, and if we (kindly) assume Anthropic will spend $50 billion itself, that brings us to $100 billion. To get to that level of spend, Anthropic and OpenAI have raised a combined $217 billion in the first half of 2026. Every neocloud is effectively an outgrowth of this spend, either through direct contracts or by proxy via Microsoft or Google. Outside of hedge fund and investor Jane Street and NVIDIA, neoclouds do not have significant customers at the level that would warrant all this capex.
So, the world is building AI compute capacity with the expectation of at least $360 billion in annual revenue, all while we struggle to find single-digit billions in AI compute spend. The only way all that compute gets used is if either A) Anthropic and OpenAI rent all of it or B) massive (and I’m talking multiple $10 billion-a-year customers) appear virtually overnight.
In both those cases, the money to pay for that compute will have to come from somewhere.
Remember: for Anthropic and OpenAI to be able to afford their current (and comparatively meager) spend, both have had to raise nearly a quarter of a trillion dollars in this year alone.
The vast, vast majority of the world’s compute revenue — I’d wager anywhere from 70% to 90% — is contingent on venture capital propping the AI labs up, and to make matters worse, it is no longer sufficient for them to just “grow fast,” but to grow so fast that they can spend (per estimates from Wells Fargo, Barclays, and UBS) $197 billion on compute in 2027 just on Google Cloud, Amazon Web Services and Microsoft Azure. This does not include the billions that both will spend on CoreWeave, Cerebras, or Oracle.
Sidenote: The numbers are all in this newsletter. I will also add that these estimates only include Microsoft Azure for the first two quarters of 2027 (as estimates are only up to the end of Microsoft’s FY27, which runs from July 1, 2026 to June 30, 2027). It’s likely that the number is more like $220 billion. It’s likely more.
Also, before you ask: revenue concentration appears to be getting worse over time, because these 70% estimates mostly rely on the continued growth of overall compute and AI model rental platforms like Vertex, Foundry and Bedrock.
Sorry, I got too complex again. Anthropic and OpenAI are only set to spend $100 billion on compute this year, and had to raise over $200 billion to do it, which makes it likely they’ll have to raise $150 billion each leading up to or in 2027.
And let’s be clear about something: the future growth trajectories of Amazon, Google and Microsoft (not to mention Oracle, CoreWeave, and every other neocloud) are contingent on the continued ability for Anthropic and OpenAI to raise and have the demand necessary to spend that money.
Let’s get specific! Stephen Ju of UBS estimates that Amazon’s AI revenue — 73%+ of which is OpenAI and Anthropic’s compute spend and revenue share (per Barclays) — accounts for 26% of AWS’ 2026 revenue and 30% of AWS’ 2027 revenue. Michael Turrin of Wells Fargo estimates that 25% of Microsoft Azure’s (calendar year) 2026 revenues come from AI (of which OpenAI is an estimated 70%). Brad Zelnick of Deutsche Bank estimates that AI will contribute 33% or more of Azure’s revenues in FY2027.
As I mentioned last week, UBS estimates that 48% of Google Cloud’s 2027 revenues will come from OpenAI and Anthropic. Zelnick of Deutsche Bank also projects in its most-likely scenario that AI revenues will make up 37% of all Microsoft’s cloud revenues in FY2029.
I feel like I need to spell this out more. These are analysts from major banks and financial institutions. Their estimates, which are based on detailed financial models, inform Wall Street and investors’ expectations, as well as informing Bloomberg Intelligence’s consensus estimates for revenues. These are serious numbers and Wall Street will be mad if they are not met!
The other problem is that cloud is becoming an increasingly-larger part of the revenues of these companies. See the below chart that bakes in consensus analyst estimates up to 2029:
I, again, will simplify: if more and more of the revenues of these three companies are coming from cloud segments that are increasingly-dominated by AI revenues mostly driven by two unprofitable, unsustainable companies, then the literal future of Microsoft, Google, and Amazon is whether Anthropic and OpenAI can pay them. This is not complex, it’s not contrived, it’s not doomerism or hating, these are the estimates from analysts and what they require to stop them from putting executives in The Wicker Man.
You can cut this situation in any way you want, but there’s no getting away from the fact that we’re four years in and the vast majority of demand comes from two companies that can’t afford to sustain it, and won’t be able to even under the most mold-poisoned of booster projections. Hyperscale growth is contingent on the success of their AI plays, and at 70% of AI revenues, “AI plays” refers to “two unsustainable AI labs.”
Perhaps another visualization would help! Below is a chart of the expected percentage of year-over-year growth that cloud revenues are estimated to contribute on a quarterly basis to revenues. Cloud revenues are the lynchpin of growth for Microsoft, Amazon and Google, though for whatever reason analysts estimate that YouTube and Google Search will re-accelerate.
This is a huge issue when 33% of Azure revenue, 48% of Google Cloud, and (per Ken Gawrelski of Wells Fargo) 60% of AWS revenue growth is coming from companies that have been, assuming all the money crosses, sent a combined $115 billion from Amazon and Google in 2026 alone.
I realize I’m repeating myself, and I’m sorry, but it’s all so insane! The future of some of the largest companies on the stock market is contingent on both spending hundreds of billions a year in capex and the continued existence of the only real customers for AI compute.
Sidenote: None of this even discusses Oracle, which is completely dead if it doesn’t either drastically pull back on or entirely cancel its capex, as I wrote back in January, April, and July. Congratulations to the New York Times on getting there a few weeks later, I’m sure this remarkably-similar sentence to mine was a coincidence.
The counter arguments are, from what I can tell, as follows:
To be clear, “growing super fast” is no longer sufficient for OpenAI and Anthropic. Assuming that OpenAI actually intends to pay for its reported $750 billion in compute commitments through 2030, it will have to raise hundreds of billions of dollars a year while also having the actual demand necessary to use that compute. No matter how big, handsome, and amazing you think either of these companies are, their expected compute spend will require them to make as much revenue as Microsoft, Google and Amazon in the next four years, and if they don’t, hyperscalers will not meet analyst and investor expectations.
To make matters worse, for them to even be able to pay hyperscalers, capex investment must continue, as it’s become blatantly obvious that the capacity necessary to make all this money doesn’t currently exist.
Hyperscalers have not yet spent the money necessary to reap the “rewards” of their massive contracts with OpenAI and Anthropic, and analysts estimate that these three companies will spend another $1.5 trillion through the end of 2027.
So, again, let’s review:
The demand for AI compute does not exist at scale outside of Anthropic and OpenAI, and it is not emerging anywhere that I can see. We are no longer in a situation where single or even double-digit demand for AI compute is sufficient. Based on the amount under construction, we need — even with OpenAI and Anthropic — hundreds of billions of dollars’ worth of demand in the next few years just to monetize the data center capacity under construction.
AI boosters will insist that this is happening in the shadows, and that “all available compute will be used,” making the mistake of conflating scarcity of GPUs with overwhelming demand. If Microsoft 70% of Microsoft’s estimated $34.43 billion in AI revenue is from OpenAI, that leaves over a depressingly-low $10.33 billion across every single possible AI service that Azure has, including renting GPUs, AI models and Microsoft 365 Copilot…which means that, in the literal best-case scenario, there’s low-single-digit billions of revenue in AI compute to non-AI labs.
Sidenote: Turrin of Wells Fargo has the estimated revenues at $34.5 billion, with 365 Copilot revenue at an atrocious $3.858 billion. It has “other AI revenue” at $5.2 billion, which includes all AI GPU and API access revenue. Stinky!
If there was meaningful demand for AI compute or AI software, Microsoft would be representative of it as one of the largest vendors of both cloud software and cloud compute. Microsoft would, by virtue of its massive infrastructure and brand recognition, be receiving a large share of blue chip GPU rentals, as would it be representative of the ability for anyone to sell AI software at scale.
$10.33 billion in annual revenue is a catastrophic failure. It is around a quarter of Microsoft’s $41 billion in Q4FY2026 capex. It suggests that there is a calamitous lack of demand across both those renting GPUs and market demand for software built on top of AI models, and that Microsoft spent $261 billion in capex since 2022 to create annual revenues that amount to less than a third of the quarterly revenue of the Intelligent Cloud segment ($39.31 billion).
There is no spinning this positively other than to ignore it outright. If Microsoft doesn’t have the demand, nobody has the demand. No, $10 billion is not “a lot,” especially for a company with tens of thousands of salespeople, a huge customer base, and a headstart of several years. Amazon and Google are doing equally poorly, which is why nobody wants to talk about their actual AI revenues.
In fact, folks, it’s time for a thought exercise! Wells Fargo estimates that Microsoft’s FY2027 AI revenue will be $54.5 billion, or roughly 58.8% year-over-year including OpenAI’s compute spend. If we increase it again by that much, FY2028 will be at $86.55 billion.
Consensus estimates have Microsoft spending $186.4 billion in FY2027 and $214.3 billion in capex in FY2028. It’s hard to square how exactly this ever pays off.
I’m gonna say it with my full chest: anyone who said that “AI was paying off” for Microsoft, Google, Amazon, or Meta was wrong. Everybody who said the capex was well-spent was wrong. They are yet to admit they’re wrong because revenue growth has yet to slow and stock prices remain elevated.
And that last part is why everybody got it wrong.
As I discussed in last week’s premium, hyperscalers started buying GPUs because their overall revenue growth had begun to slow over the course of a little over a decade, with everyone — NVIDIA included — hitting a wall in 2022:
Microsoft’s FY2023 revenues only grew 6.9% year-over-year. NVIDIA’s FY2023 (which ran February 2022 to January 2023) was effectively flat, sitting at 0.2% as the post-pandemic surge of demand for gaming GPUs and networking gear puttered out, with Q4 2023 revenues dropping by 21% year-over-year.
Meta, Google, and Amazon, all of which have fiscal years that align with the calendar year, saw growth deteriorate:
Google went from 41.6% year-over-year growth in 2021 to 10.3% and 9.7% in 2022 and 2023.
Meta went from 37.2% year-over-year growth in 2021 to negative 1.1% in 2022 and 15.7% in 2023.
Amazon went from 37.6% year-over-year growth in 2020, to 21.7% in 2021, to 9.4% in 2022, to 1.8% in 2023 (and has really never recovered.)
While buying GPUs didn’t really help revenues until OpenAI and Anthropic became big enough to start feeding hyperscaler and venture capital cash into Microsoft, Google, and Amazon’s mouths, buying GPUs became a dick-measuring contest that pumped stock values, all as Wall Street assumed every dollar of revenues came from AI.
In 2023, Microsoft, Google, Apple, Meta, Amazon, and NVIDIA added trillions in market capitalization, with the media actively encouraging them to spend more money on capex. It didn’t matter that Microsoft missed on cloud revenues in Q4 FY2025, much like how Amazon’s Q3 2024 earnings — which specifically missed expectations for cloud, the only place that Amazon was making any money from AI — caused its stock to pop because overall revenues were higher than expected.
In fact, I think that’s mostly what kept this going. I ran the numbers on the premium over two five-year-long periods — 2015 to 2020 and 2021 to 2026 — and found that while stock returns were dramatic, actual revenue growth has slowed dramatically.
As you can see, revenue growth, outside of Microsoft, slowed dramatically, all as PP&E (properties, plants and equipment, the part of the balance sheet where they keep GPUs and data centers — and other stuff, obviously) grew by $754.5 billion.
I’ll get back to that in a little bit.
Yet because the stock price went up, everybody assumed that every dollar of revenue came from investments in AI GPUs. In 2023, a year when AI likely contributed less than $4 billion including OpenAI’s compute spend, Business Insider said that its AI bet was “already paying off,” all because Azure kept growing:
CFO Amy Hood said on a call with analysts that Microsoft expects revenue growth at its cloud division, Azure, to be 26% to 27% next quarter from the same quarter the year prior — with "roughly 1 point from AI services."
To be clear, one whole revenue point is pathetic.
Anyway, in both April and October 2024, The Guardian reported that Microsoft was “sailing” as the “AI boom fueled double-digit growth in its cloud business” in a year where (based on working back from Wells Fargo’s estimates for FY25, which started in Q3 2024) it’s estimated to have made less than $6 billion in total revenue from anything AI-related outside of OpenAI. Microsoft’s total revenue for that fiscal year was $281.7 billion.
In October 2025 — the end of Fiscal Year 2025 — Business Insider would again say that its AI bets had paid off, specifically adding that “Microsoft's AI push also increased revenue by 15% to $281.7 billion.”
Per Wells Fargo’s estimates, Microsoft’s total AI revenue for FY2025 — including what it received from OpenAI — were $14.86 billion, or around 5.28% of revenue, or roughly 6.1% of annual growth for in a year it spent $64.6 billion in capex. When you remove OpenAI’s estimated $9 billion in compute spend, that leaves around $5.7 billion in AI revenue, or around 2% of overall revenues for the year.
The rationale is pretty simple:
Well, they did, and there wasn’t. You can fart around all you want about the theoretical or imaginary promises of AI or AGI or whatever, but this didn’t work.
Yet all of this kept going because the tech industry’s collective reality is based on stock prices, Twitter, and a tech and business media that appears to fall for just about anything as long as a wealthy person says it.
And this chart is the entire reason:
I maintain that 2021 broke the world for many reasons, but one of them is that it set unrealistic revenue goals as money flooded back into the economy post-pandemic off the back of the most-pornographic years of Zero Interest Free Money Policy.
To explain, I’m going to crib a little from my latest premium, The Hater’s Guide to NVIDIA (Part 2).
The post-2021 hangover was brutal. Meta, Google and Amazon, all of which have fiscal years that align with the calendar year, saw growth deteriorate:
Microsoft’s FY2023 (July 1, 2022 through June 30, 2023) revenues only grew 6.9% year-over-year. NVIDIA’s FY2023 (which ran February 2022 to January 2023) was effectively flat, sitting at 0.2% as the post-pandemic surge of demand for gaming GPUs and networking gear puttered out, with Q4 2023 revenues dropping by 21% year-over-year.
Nobody really knew what to do, with just about everybody getting their asses handed to them by the markets.
Yet the savior was already incubating. In March 2022, NVIDIA announced the Hopper GPU architecture, and while initial sales and shipments in September were good, they weren’t enough to restart growth until the November launch of ChatGPT convinced everybody that they had to do AI, and that the only way to “do AI” was buy GPUs. The very same month, Microsoft and NVIDIA announced they were building another OpenAI supercomputer using Hopper.
Something about ChatGPT would fundamentally break the brains of Microsoft’s competitors.
At 1 p.m. on a Friday shortly before Christmas [2022], Kent Walker, Google’s top lawyer, summoned four of his employees and ruined their weekend…
…[because] the entire agenda of the company had changed — all in the course of nine days. Sundar Pichai, Google’s chief executive, had decided to ready a slate of products based on artificial intelligence — immediately. He turned to Mr. Walker, the same lawyer he was trusting to defend the company in a profit-threatening antitrust case in Washington, D.C. Mr. Walker knew he would need to persuade the Advanced Technology Review Council, as Google called the group of executives, to throw off their customary caution and do as they were told.
It was an edict, and edicts didn’t happen very often at Google. But Google was staring at a real crisis. Its business model was potentially at risk.
What had set off Mr. Pichai and the rest of Silicon Valley was ChatGPT, the artificial intelligence program that had been released on Nov. 30, 2022, by an upstart called OpenAI. It had captured the imagination of millions of people who had thought A.I. was science fiction until they started playing with the thing. It was a sensation. It was also a problem.
ChatGPT immediately gave executives AI psychosis. In January 2023, Microsoft would invest another $10 billion, and a few weeks later, Google would sign a partnership with early-stage AI firm Anthropic (founded by former OpenAI executives) to use its TPUs and GPUs as its “preferred cloud provider,” only for Amazon to barge in and invest $4 billion a few months later in September 2023, making AWS “Anthropic’s primary cloud provider,” which forced Google to invest up to $2 billion in October 2023. By the third quarter of 2023, NVIDIA would be selling half a million H100 GPUs, primarily to Microsoft, Amazon, Google and Meta, which had just farted out its own open source ChatGPT “competitor,” Llama.
NVIDIA was saved. Q1 FY24 (May 2023) revenues blew estimates out of the water, and by Q2 FY24 (August 2023), data center demand caused revenues to jump 170% year-over-year. Microsoft, ever helpful to its good friend and collaborator, would sign a multi-billion dollar deal with CoreWeave to rent capacity in June 2023, allowing it to raise $2.3 billion in debt a few months later, taking advantage of the “ChatGPT moment” that was when “things got real” to quote CTO Brian Venturo.
By the end of FY24, NVIDIA’s revenue had jumped 125.9% year-over-year. Everybody went AI crazy. The media would fall over itself claiming that AI could do basically anything, and justify one of the largest expenditures in history. This was partially helped by a media-driven hype campaign around the availability of GPUs, which was mostly caused by NVIDIA being the only vendor and selling the vast majority of them to hyperscalers who were yet to really show any return on their investment. Per the New York Times:
Their desperation is palpable. On social media, blog posts and conference panels, start-up founders and investors have started sharing highly technical tips for navigating the shortage. Some are gaming out how long they think it will take Nvidia’s wait-list to clear. There’s even a groan-worthy YouTube song, set to the tune of Billy Joel’s “We Didn’t Start the Fire,” in which an artist known as Weird A.I. Yankochip sings “GPUs are fire, we can never find ‘em but we wanna buy ‘em.”
Nevertheless, revenue was growing, seemingly in line with capital expenditures, and as hyperscalers realized that the media and the markets had toddler-like attachments to reality, they piled into NVIDIA GPUs en masse.
And man, FOMO was in full force. The GPU shortage was timed perfectly with one of the worst years in the history of venture capital, creating an air that the only way to get out of the depths of Hell was to invest in AI in any way, shape or form for both startups and hyperscalers alike.
Yet when you look at the numbers, very little actually changed for the hyperscalers. Since the launch of ChatGPT, year-over-year growth has never returned to pre-2022 levels, other than for Microsoft, which hit its highest year-over-year growth (17.8%) since FY2022 (18%) after a prolonged period in the 14-percents.
Everybody conflated the massive capex spend with the return of growth to the tech industry versus an industry-wide swindle. Hyperscalers were rewarded with stock pumps and pay bumps for an “AI revolution” that mostly amounted to spending hundreds of billions of dollars on GPUs to make tens of billions of dollars in revenue, all because revenue kept growing and both analysts and the media refused to talk loudly about the lack of any payoff.
The media’s credulousness was used against it. The assumption, as I mentioned, was that all this money wouldn’t be spent without an obvious return, and because the numbers are so flabbergasting, it’s easy for you to say “$10.33 billion is a lot of money!” (because it is) and to dismiss the massive costs as “just part of building the infrastructure,” even if it isn’t clear how these numbers ever match up in the future.
For whatever reason, the media continues to give hyperscalers and anybody in AI the benefit of the doubt when it comes to the efficacy and outcomes of large language models or the catastrophic economic mismatch in the returns. Every time the response is “these are smart people!” or “these are the early days!” or “it’s just like the dot com bubble!” because nobody is particularly interested in being right so much as they are about being right about the particular consensus of a particular moment. While I understand the professional harms of saying that AI was bullshit in 2023 or 2024, there was never any excuse to automatically give hyperscalers credit for “AI paying off” at any point in history, and further excuses of “it being the early days” are intellectual crutches” used by people that either want the powerful to win, have a vested interest in doing so, or have resigned themselves to watch it happen.
The fact that the media has actively shrugged off the 70% story (outside of Bloomberg’s coverage, at least) is a sign that it doesn’t really want to reconcile with the truth, and honestly, I kind of get it. When you’ve spent three years saying that hyperscalers were growing because of their vast spend on AI, filling in the gaps of every narrative and assuming they don’t want to tell you revenues because they’re oh-so-good, it’s hard to move in reverse.
The other problem is the monstrous and abusive marketing campaign from the AI industry itself, and those who use AI on a regular basis. If you are against the consensus that AI will grow ever-larger every single quarter forever, you will be harassed and dogpiled across multiple social media platforms by everyone from AI influencers to actual journalists. The fact that it’s more professionally dangerous to critique the powerful than it is to align with them is disgusting, but I should be clear that these tactics only reinforce that I’m on the right track.
As Nik Suresh noted in his recent piece, refusing to say that AI is giving you massive productivity benefits will lead to actual professional consequences, because so much is riding on the overall grift about what AI can do (which is much, much less than the boosters will promise). This runs antithetical to productivity or good sense, and everybody involved in it should be both eternally shamed and shunned from any sensible business.
And while the AI industry and its fandom will claim that people like me are “skeptics” and “haters,” the outright hatred and vitriol that they spew for not falling in line behind a nakedly false narrative built on outright disinformation is disgraceful.
It only serves to prove my point that something is very, very wrong with the tech industry, our markets, and the overall information ecosystem.
When I wrote the Rot-Com Bubble in 2024, the AI bubble was a series of exploits used to mask the end of tech’s era of hypergrowth.
The tech media’s immediate attachment to AI and ChatGPT was nothing to do with actual technology and everything to do with their attachment to being involved in whatever future the powerful decided had arrived.
After years of depressing coverage of cryptocurrency and the metaverse and a deeply-depressing 2022, ChatGPT represented a product they could use, be built upon to create other products they could use, and lead to an entire era of new people to follow and report on.
It gave retail investors a reason to dump money into stocks. It gave founders an API to build on top of, an infrastructural layer to smooth out, and a dream to sell venture capitalists who had near-unilaterally sucked at their jobs for years, all wrapped in a fuzzy sense of “progress” that mean that valuations could be high and the time horizon for returns could be effectively infinite.
And that last part is what was so important for everybody involved. Startups, Microsoft, Google, Amazon, Meta, CoreWeave, and anyone else involved in the AI bubble were immediately given near-infinite runway and manufactured consent to burn as much money as they wanted to.
Even today — in the third quarter of the year of our lord 2026 — I am still asked on podcasts “whether we’re in the early days.” This is the power of narratives, and how willing so many people are to explain away the failures of the powerful rather than having the courage to face them.
And it’s all because the stock prices haven’t gone down yet.
Sidenote: Need an example? Legal AI startup Harvey is currently raising a $500 million funding round at a valuation of $15.5 billion, boasting that its “annualized revenue” (a month times twelve, also known as $29.1 million a month in revenue) has hit $350 million.
Harvey last raised in March — $200 million at a $11 billion valuation, two months after it hit $190 million in annualized revenue, better known as $16.6 million in monthly revenue. If it completes the round as discussed, Harvey will have raised $1.2 billion in the last year or so, and I have yet to read a single story that reconciles a company making less than $30 million in monthly revenue needing over a billion dollars, or shows any kind of concern about that at all.
The argument here, by the way, is that “the goal for Harvey is to get every law firm as a client.”
I have to give credit to Jensen Huang, though.
He realized quickly that reality is dictated not by actual revenues but how you can manipulate the market with those revenues. NVIDIA’s continued position as the largest company on the NASDAQ relied upon a near-constant flow of new orders of GPUs, but brainwashed investors, misinformed by a media with few good information sources, had begun to conflate both GPU purchases and any revenue growth with purchasing them.
The power of the narrative is such that everybody has rationalized what’s happening as “good investment.” Hyperscalers spending hundreds of billions on capex makes sense because AI is driving growth, and the market is at all time highs.
The same extends to the endless flow of nebulous circular deals, like the supposed $500 billion infrastructure deal between NVIDIA and The Avengers of Private Credit, including Apollo, Blackstone, BlackRock, Brookfield, Goldman Sachs, and KKR.
While NVIDIA dumped a little on the deal, the media still doesn’t seem to think this is a bad thing, even though it’s the loudest possible sign that there isn’t actually real demand from anyone that can actually afford to buy these GPUs. Even then, the “$500 billion” deal isn’t even a $500 billion deal, per The Wall Street Journal (which has, for some reason, deleted this paragraph from the story):
The massive financing would involve different vehicles, rather than one large collaboration, according to one of the people. The commitments could grow larger than $500 billion over time, this person said.
Oh, okay. So it’s not actually $500 billion. It’s a bunch of smaller deals. Great. Sure. Anyway, surely you must think this is a little worrying? That this is what NVIDIA is reduced to doing to keep up with demand?
What? You think it’s a good thing?
The financing could help signal to stock investors that Nvidia and its partners in the artificial-intelligence boom have the firepower to build the infrastructure they need. Debt investors have lent companies hundreds of billions of dollars over the past year through direct bond sales and debt deals tied to individual data-center projects.
Jesus fucking christ. Not a single word about the fact that the only two companies that would actually want this compute can’t afford it, or how much people are spending on compute (you know, the thing that data centers sell), or anything about AI at all beyond that it’s “part of the infrastructure buildout.”
Yet when you actually open the press release (which I found on my Terminal but cannot for the life of me get a link to), there’s one glaring detail everybody left out, emphasis mine:
Memorandums of understanding signed with six of the world’s premier financial institutions to create these partnerships aim to establish the first compute financing platforms of their kind at global scale to enable the AI infrastructure buildout across NVIDIA’s ecosystem, including leading frontier AI labs, enterprises and AI clouds. Under these strategic partnerships, NVIDIA will work with Apollo, BlackRock, Blackstone, Brookfield, Goldman Sachs and KKR to create dedicated pools of capital at significant scale at attractive rates for NVIDIA customers.
That’s right folks. There is no deal! It’s an MOU! It’s fucking theoretical! And I have not seen this fact reflected in a single god damn story about this god damn “deal” in one god damn place! It’s from the press release from the fucking companies!
This “deal” is also very, very weird, with NVIDIA claiming it’s “establishing independent compute financing platforms” with the largest asset managers in the world. It isn’t clear what the money will do, where the money will flow, who it will flow to, when it will flow there, how it will be structured, from whom the money will be raised, or really anything other than “number so big, number so huge.”
This announcement — and that is, at this point, all it is — exists entirely to have people say that NVIDIA has “booked $500 billion in revenue,” even though even in the kindest possible read not a single dollar has actually been raised, nor has a single actual contract been signed. If you need an example, take NVIDIA’s $100 billion investment in OpenAI that also involved it building 10GW of compute capacity — a memorandum of understanding that never materialized in a deal.
Here is what Jensen Huang had to say about said memorandum of understanding:
“It was never a commitment,” Huang told reporters in Taipei on Sunday. “They invited us to invest up to $100 billion and of course, we were, we were very happy and honored that they invited us, but we will invest one step at a time.”
You’ll notice there are no actual details about any deals happening, mostly because nothing has actually happened beyond a few marketing calls and a lot of heavy breathing from the press. No money has been raised, what will likely happen — if anything — is that NVIDIA will end up backstopping a few $10 billion data center deals, or perhaps invest a few billion in equity into an SPV built to raise debt to buy GPUs as it already did with xAI.
Jensen Huang has said as much in his hilariously-oafish announcement of the MOU:
In some cases, NVIDIA may provide a residual-value support mechanism for up to 25% of an opportunity, assessed carefully on a project-by-project basis. That support is limited, residual-value based and designed to complement — not replace — independent underwriting.
Hey, wait a second, is this circular financ-
Is this circular financing?
This initiative is designed to address that concern. We are bringing independent, long-term institutional capital into the AI infrastructure market.
The demand is real: it comes from frontier AI labs, AI-native startups, enterprises, cloud providers and countries building AI services. The capital providers independently underwrite each project — including the customer, demand, utilization, cash flow and residual value. NVIDIA provides the platform; the investors make independent financing decisions.
This is the beginning of an open capital market for AI infrastructure.
Folks, this isn’t circular financing at all! It’s just that NVIDIA will pay some sort of 25% “residual-value support” so that private credit can use that as collateral to raise debt to buy GPUs from NVIDIA. If anything it’s spherical!
Look, Jensen, if the demand was real, you wouldn’t have to announce a rinky-dink-maybe-$500-billion-no-IT-loads-refused-MOU!
If there were actual diverse demand for NVIDIA’s GPUs commensurate with analyst expectations, you wouldn’t have to do these bizarre, painfully-circular deals that exist only to inflate its revenues and further prop up the existence of unprofitable AI labs!
Anyway, if you’re wondering about what the point of this all is, Jensen Huang has your answer:
Where is the return on investment?
The return is in the usefulness of AI.
Companies are using AI to write software, discover drugs, design products, serve customers, automate operations and build new services. AI factories make this possible. More compute creates better AI; better AI creates more usage; more usage creates more revenue; and more revenue drives more compute.
This is the virtuous cycle of the AI industrial revolution.
We are four fucking years and over a trillion dollars into this garbage, Jensen! This is the best you can do? THIS? AHHHHH!
In FY2027 — which began on February 1, 2026 — analyst consensus has NVIDIA’s revenues at $393.7.6 billion for the year, growing to $565.7 billion in FY2028 and $694 billion in 2029.
What this means is that despite hyperscalers spending over a trillion dollars on AI data center capex in 2026 and even more in 2027, that’s just not enough to keep up with Wall Street’s expectations.
To get specific, NVIDIA’s FY2026 revenues were $215.9 billion, with 89% of that coming from the data center segment (read: GPUs and the associated gear), and this is with the near-entire focus of the world’s largest companies and credit markets on building AI data centers and banks that fear they’re “choking” on data center debt.
Sidenote: per my last premium newsletter, UBS estimates that around 50% of NVIDIA’s data center revenue comes from Meta, Google, Microsoft, Amazon and Oracle, with Deutsche Bank estimating it’s as high as 60%.
Analyst expectations are set to believe that it will triple its revenue in the space of two years. Honestly, $500 billion wouldn’t even be enough. NVIDIA needs every hyperscaler to keep spending more capex every single quarter, without fail, as well as hundreds of billions of dollars’ worth of new AI chip spend to arrive from an industry where the only two companies with any real need for all this compute have only ever lost tens of billions of dollars, and literally can’t afford to pay for it.
I realize many people get number blindness past a certain scale, so I will put it very simply:
I also understand why people want to bury their heads in the sand here. Right now, the numbers are all the highest they’ve ever been, and they keep going up, which means that anyone saying that things are going wrong has to expose themselves to torrents of abuse and aggression from both posters and peers.
I also think doing so is an act of cowardice.
While I don’t expect people to start saying that this is all bullshit and headed for the gutter, I see an astonishing flippancy about everything I’ve been writing about from much of the mainstream media. To not warn people that AI revenues are heavily-centralized around two companies that burn endless billions of dollars, and that the commensurate demand isn’t there as a result, is to both fail your readers and actively empower the powerful.
I haven’t even gotten into the $1.65 trillion in off-balance sheet obligations. It’s unclear how hyperscalers afford them if OpenAI and Anthropic can’t afford to pay them.
It’s unclear how any of this works.
And then there’s the problem that for the 190GW of capacity in planning, we need somewhere between $1.62 trillion and $2.92 trillion in annual compute spend for an industry that, in the best-case scenario, has roughly $110 billion in annual demand, with 90%+ of that coming from two companies that can only spend that money if they’re fed it by venture capitalists.
Everyone can — and will — keep ignoring what’s happening as long as it requires an ounce of courage to think about reality. Many will bury their heads in the sand, make the kindest reads possible of every AI story, pump up every AI narrative, and celebrate every mediocre “achievement” right up until NVIDIA or a hyperscaler misses on analyst expectations, or AI labs start circling the drain.
Everything you’re seeing right now is an attempt to extract further capital and hype from the system to continue inflating the bubble. Every single asset manager in the NVIDIA “deal” has some sort of investment in AI data centers and/or neoclouds (especially Blackstone, who has been in CoreWeave since its earliest days), and absolutely nobody gives a shit about what LLMs can do outside of their ability to con investors and generate fees for their funds.
Every journalist that continues to ignore the obvious instability, circularity and centralization of the AI industry fails their audience by not discussing it before any discussions of AI’s theoretical returns or abilities. It is no longer ethically sound to ignore the problems. Do with that statement what you will.
In the end, it’s pretty simple: Microsoft, Google and Amazon’s hypergrowth era ends if Anthropic and OpenAI can’t spend hundreds of billions of dollars on compute, and companies like Oracle, CoreWeave, Nebius, and IREN face apocalyptic circumstances when they fail to do so. NVIDIA’s future is entirely dependent on these companies’ abilities to convince the credit markets that everything will go fine, and its circular financing operations are both sustained and continue to expand.
And every VC invested in AI needs a miracle, because there are few signs that these companies can be sold to anyone or taken public.
When it becomes safe to do so, many will attempt to either rationalize ignoring reality or pretend they saw it coming.
If they did, they chose not to tell you. If they didn’t, they chose not to look.
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2026-08-08 01:30:26
For a little under a year, everyone — myself included — has compared NVIDIA to Enron, largely because NVIDIA insisted, in detail, that it was nothing like Enron, WorldCom, or Lucent, a potent example of the Streisand Effect that would be much funnier if NVIDIA wasn’t holding up more than 7% of the value of the NASDAQ.
And as I covered in the first part of the Hater’s Guide To NVIDIA last year, there are material concerns about how the company makes money today and will continue to do so in the future.
I will concede that NVIDIA isn’t exactly like Enron in the sense that it isn’t, to my knowledge, doing anything outright fraudulent, like attempting to hide massive amounts of debt inside SPVs as Enron did with its “Raptors,” which I must be clear are distinct from the SPVs used in AI data center debt, though I’ll add that something being legal doesn’t make it a good idea or ethical.
That being said, NVIDIA CEO Jensen Huang has employed many of the same tactics used by Lucent, Nortel, and many of the big dot-com busts, but has been smart enough to make everybody else carry the risk.
Instead of doing direct vendor financing like Lucent did with Winstar (where it effectively loaned its customers money to pay it with), NVIDIA funded neoclouds like CoreWeave, Nebius, and IREN, operating as an early stage investor, IPO anchor, post-IPO investor, $6.3 billion customer and data center lease backstop, allowing them to raise tens of billions of dollars’ worth of debt from overly-eager asset managers and banks, allowing it to do basically the same thing as vendor financing without having to take on any of that messy risk.
These deeply-unprofitable, cash-intensive, debt-riddled companies exist for one purpose — to raise debt to buy NVIDIA GPUs — and would have fallen apart without the AI hype cycle and NVIDIA’s continued backing. Per Kakashii:
In an April 2026 interview with Dwarkesh Patel, Jensen Huang acknowledged my thesis and said it out loud: “In the case of clouds, if we didn’t support CoreWeave to exist, these neoclouds, these AI clouds, wouldn’t exist. If we didn’t help CoreWeave exist, they would not exist. If we didn’t support Nscale, they wouldn’t be where they are today. If we didn’t support Nebius, they wouldn’t be what they are today.” And, confirming that Nvidia wants lots of neoclouds, not just one: “Don’t pick winners. Either let them all take care of themselves, or take care of all of them.”
That is, in Nvidia’s own CEO’s words, the operating logic behind this sector. Rather than build a cloud business on its own balance sheet, which would put Nvidia’s own results directly at risk if utilization or pricing disappointed, Nvidia has helped create and sustain an entire class of nominally independent companies that take on the capital outlay, the construction risk, and the debt of building AI data centers, while Nvidia supplies the chips, frequently invests equity alongside the debt, and in a growing number of cases finances the build out directly.
In other words, NVIDIA has managed to find a way to do vendor financing without ever having to provide any, finding willing supplicants in the various backers of CoreWeave and other neoclouds that would be willing to front the money, all under the mistaken belief that they were funding the next industrial revolution.
To explain exactly how it works, I’ll return to my imaginary scenario from the Big Short 2:
HUANG: So there’re these companies I invest in that, at least in theory, build data centers using my AI GPUs, but I need them to buy more GPUs, so I sign a contract saying that I’ll rent the GPUs back from them in the future. Because NVIDIA has such a strong balance sheet, these companies can raise billions of dollars to buy my GPUs just because I promised to rent them in the future, and the best part is all the risk is held by the companies and the investors. When I need more money, I just sign another contract, they raise more debt, I sell more GPUs.
BAUM: So — just so I have this clearly — you, the guy who makes the GPUs, invest in companies that exist pretty much to buy GPUs from you and rent them to customers. Except you’re the customer too, and a big one.
HUANG: That’s right. We call them neoclouds. S&P just revised CoreWeave’s outlook to positive.
BAUM: That’s fucking crazy.
HUANG: It’s not crazy — it’s awesome.
It’s a win-win-win for NVIDIA, its customers, and the bankers involved. CoreWeave gets to raise more debt and keep its investors strung along on the still-theoretical, ever-expanding timeline of a return on invested capital, bankers get a slew of fees for pulling together the deal, and NVIDIA guarantees itself billions of dollars of business.
And this approach is something where any investment by NVIDIA has a habit of being amplified by others — like Australian startup Firmus, which just raised $2bn from a bevy of investors (including NVIDIA, which had also backed an earlier round), Jane Street, and Blackrock, with a significant chunk of that money guaranteed to go towards NVIDIA GPUs. NVIDIA also participated in Firmus’s previous $300m round, although was not listed as a “cornerstone investor.”
Earlier this year, Firmus secured a $10bn debt facility, led by Blackstone. NVIDIA will be a net beneficiary of that debt raise, and I would argue that its participation in the company’s fundraising — as well as the various announcements of partnerships between the two — has been instrumental in both the company’s fundraising and its ability to secure debt.
Sidenote: According to an AI industry insider interviewed by AlphaSense, Nebius was experiencing financial difficulty — the existential kind — and was saved by NVIDIA, which swept in and offered it access to hardware under a revenue sharing model. The insider also notes that neoclouds hate said revenue sharing agreements, because they tend to stack the deck in NVIDIA’s favor.
You’ll notice I haven’t mentioned “AI” or “LLMs” up until this point, and that’s because technology has, for the most part, very little to do with these transactions. As I discussed in this week’s free newsletter, 70% or more of hyperscaler revenues are from OpenAI and Anthropic, and CoreWeave’s largest customers are Microsoft (for OpenAI), Google (for OpenAI), Anthropic, NVIDIA itself, and Meta. Customers are not coming to it for any particular technological moat or unique offering outside of its ability to sling more NVIDIA GPUs to the same customers that everybody else has.
While GPUs technically are used for AI training and inference, their relationship to NVIDIA is only as good as their ability to create more hype. As I discussed a few weeks ago, it has promised somewhere between 10x and 25x “operating cost savings” with every successive generation of GPUs, though it’s never really clear how that manifests or what it actually means, or whether any of that even matters to OpenAI and Anthropic, its largest customers by proxy.
Nevertheless, it’s pretty difficult to work out what each generation really changes. SemiAnalysis claims it “delivers 5.4x performance per MW and 5x performance per dollar against [the previous generation] GB200 NVL72,” but that’s for DeepSeek R1, a year-and-a-half old open source model that’s vastly smaller and less-powerful.
But that’s not really a problem, because all NVIDIA needs to do is keep up the appearance of innovation in as precise or imprecise a way to justify increasing prices with each new generation, and to convince people that they’re building “AI factories” as they fund data centers for customers that don’t really exist outside of the big AI labs. While NVIDIA has thousands of talented engineers building its GPUs and the associated software, the only real purpose is to create a vague sense of “more” and “bigger” and “more powerful” to justify racks of 72 GPUs that are more than twice the price of their predecessors.
That’s because NVIDIA is no longer a technology company so much as it is an asset management and marketing firm that happens to sell semiconductors. To that point, I believe that the comparisons to Enron, Lucent, and other dot-com flameouts are on the right path, but misses one very, very obvious comparison: GE Capital, the financial services of General Electric, specifically in the Jack Welch years that I covered two years ago in the Shareholder Supremacy.
Welch’s GE did whatever it needed to to survive, buying and selling companies to help boost GE’s earnings every quarter, and eventually grew into what David Gelles would call a “large, unregulated bank,” to the point that GE Capital was at about $425 billion in assets in 2001 (and about 50% of GE’s revenue), providing everything from direct leases of equipment to assuming its customers debts to investing directly in its customers, all to make sure that, well, said customers continued being able to buy GE gear.
Unlike GE Capital, NVIDIA has the advantage of a much, much simpler business model and far fewer products to sell, but said advantage is a problem for two brutal reasons: its customers are driven by desperation and a fear of missing out, and its remarkable revenue growth means that it must in turn grow by ridiculous amounts every single quarter from here to eternity.
Yet this problem is driving it to take increasingly-Welchian measures to make sure that demand keeps up with investor expectations. It (per the FT) just signed leases worth as much as $50 billion for a Texas-based data center built by Hut 8, which makes it likely that this capacity is being built for Anthropic, with which it already has multiple deals. In the same piece, the FT mentions that NVIDIA is in talks to backstop $250 billion in compute costs for a still-theoretical 10GW data center in Ohio.
And again, much like GE, NVIDIA uses its stellar credit rating (AA- - two rungs lower than GE at its height) to secure these deals, per the FT:
“They have the balance sheet to acquire power, and in doing so, ensure their product is deployed,” the person said, asking not to be named.
In the end, GE’s greater collapse led to lawsuits, SEC fines and revenue revisions, all as a result of its “aggressive” accounting practices. For example, it was forced to restate its 2016 and 2017 earnings as a result of “new accounting standards” it instituted as a result of an SEC investigation into its insurance and power divisions that eventually cost it a $200 million fine, cutting a remarkable $4.24 billion off of earnings in the period.
While I’m not accusing NVIDIA of anything untoward, it’s impossible to ignore the sheer aggression of its circular financing and willingness to do whatever it takes to keep selling further GPUs. NVIDIA is now a semiconductor manufacturer, a venture capitalist, a lender of last resort,
Today’s premium newsletter is the story of NVIDIA’s descent into circular madness, and how Jensen Huang is increasingly becoming the Jack Welch of AI.
This is Part 2 of The Hater’s Guide To NVIDIA, or WUDA CUDA SHUDA
2026-08-06 02:41:14
Executive Summary:
As I discussed in yesterday's free newsletter, analyst estimates have OpenAI and Anthropic making up over 70% of all AI revenues across Microsoft, Google and Amazon.
While some might have disagreed, Bloomberg is now reporting that OpenAI "accounted for more than half, and likely about 70%, of Microsoft's actual AI sales during its most recent fiscal year."
Bloomberg's maths is explained as such:
"Bloomberg’s analysis assumes that Microsoft’s annual AI run rate continued growing at the rapid 123% the company reported for March. At that growth rate, the company’s AI business would have tallied roughly $34 billion in the fiscal year ending in June, which can be directly compared with the new disclosure that OpenAI provided $24.1 billion of revenue in that year."
The actual disclosure from Microsoft comes from its most-recent earnings:
For fiscal year 2026, we recorded revenue from commercial arrangements with OpenAI, inclusive of revenue sharing payments, of $24.1 billion, and accounts receivable from OpenAI as of June 30, 2026 was $6.0 billion.
The other incredible fact from these disclosures is that OpenAI's spend and revenue share accounted for 7% of Microsoft's $331.8 billion in FY26 revenue - or around 7.26% to be specific.
At this point, it's impossible to ignore that Microsoft has spent $270 billion in capital expenditures to prop up a single client, and that its overall AI plays have failed to create any significant revenue growth or opportunities.
We are now four years into the AI bubble, and Microsoft has little to show for it other than one very large and very unsustainable company that requires near-infinite resources to keep paying its cloud compute bills.
If you liked this piece, you should subscribe to my premium newsletter. It’s $70 a year, or $7 a month, and in return you get a weekly newsletter that’s usually anywhere from 5,000 to 18,000 words, including vast, detailed analyses of NVIDIA, Anthropic and OpenAI’s finances, and the AI bubble writ large. My Hater's Guides To the SaaSpocalypse, Private Credit and Private Equity are essential to understanding our current financial system, and my guide to how OpenAI Kills Oracle pairs nicely with my Hater's Guide To Oracle, as well as the Hater’s Guide To Oracle (Part 2).
Subscribing to premium is both great value and makes it possible to write these large, deeply-researched free pieces every week. On Friday, I’ll publish the second installment of the Hater’s Guide to Nvidia — where I’ll take a look at how the AI bubble transformed the company from a pure hardware player to a purveyor of the financial dark arts.
If you want to get in touch — and especially if you have any juicy information about Anthropic, OpenAI, or any other companies in the AI bubble — hit me up on Signal at ezitron.76. I’m also on IB on The Terminal.
2026-08-04 23:49:28
If you liked this piece, you should subscribe to my premium newsletter. It’s $70 a year, or $7 a month, and in return you get a weekly newsletter that’s usually anywhere from 5,000 to 18,000 words, including vast, detailed analyses of NVIDIA, Anthropic and OpenAI’s finances, and the AI bubble writ large. My Hater's Guides To the SaaSpocalypse, Private Credit and Private Equity are essential to understanding our current financial system, and my guide to how OpenAI Kills Oracle pairs nicely with my Hater's Guide To Oracle, as well as the Hater’s Guide To Oracle (Part 2).
Subscribing to premium is both great value and makes it possible to write these large, deeply-researched free pieces every week. On Friday, I’ll publish the second installment of the Hater’s Guide to Nvidia — where I’ll take a look at how the AI bubble transformed the company from a pure hardware player to a purveyor of the financial dark arts.
If you want to get in touch — and especially if you have any juicy information about Anthropic, OpenAI, or any other companies in the AI bubble — hit me up on Signal at ezitron.76. I’m also on IB on The Terminal.
Soundtrack: Tool - Forty Six & 2
The question I want to ask anyone reading this who might have invested in or in some way backed the hyperscalers and the greater AI industry:
What is it you think you’ve gotten yourself into? Because I think you’re being sold a lie.
Last week’s tech earnings saw outlet after outlet claim that Amazon, Google, and Microsoft’s AI bets were “paying off” as their respective cloud segments reported record revenue growth, casually ignoring that none of them have broken out their AI revenues. To add insult to injury, Microsoft decided, after sharing that it had a $37 billion AI run rate (about $3.08 billion a month) in Q3 FY2026, that it simply didn’t have to share anything about its actual AI payoff in Q4, realizing that its overall numbers would beguile reporters and analysts — especially those with little interest in what was actually going on as long as the topline stuff looked good.
To be clear, all three of these companies’ cloud platforms have many other customers paying for many other things other than generative AI services or AI GPUs, and they’ve all engaged in a combination of multiple outright price increases and changing their core subscriptions to force AI features on them as a means of boosting revenues and conning the street into believing that “AI is paying off” every time they non-consensually thrust it on their customers, framing higher prices as “better value” in a way that fucks the user to appease Wall Street.
Yet the biggest con of all is that a vast majority of this revenue growth comes from the compute spend of Anthropic and OpenAI, both of whom account for the vast majority of AI revenues and overall cloud growth we’ve seen in the last few years.
Every publication you read right now will tell you that AWS and Azure and Google Cloud are growing like wildfire as a result of the hundreds of billions of dollars they’ve invested in AI GPUs and data centers, when the truth is far simpler: their revenues are being buoyed by two unprofitable, unsustainable AI labs that cannot exist without being funneled tens of billions of dollars each year.
And a decent chunk of that money is coming from the hyperscalers themselves. In the last seven months alone, Google has sunk $10 billion (and up to $30 billion more) into Anthropic, with Amazon funnelling $5 billion to Anthropic within a week of that investment and a total of $50 billion into OpenAI. For all the concern about circular financing in the AI world, it’s astonishing that so much attention has (rightly, to be clear) centered on NVIDIA’s backstopping and funding of neoclouds, and less on the fact that hyperscalers are propping up their now biggest customers, giving them cash that will eventually migrate back to the hyperscaler.
I’d also argue that the vast majority of their capex exists to support these two load-bearing failsons. A few months ago, a Microsoft executive told the judge during the Musk-Altman trial that its OpenAI relationship had cost it “over $100 billion,” including both the $13 billion it sunk into the company and the associated infrastructure.
Microsoft has dedicated its Fairwater data centers (however much actually exists) entirely to OpenAI, much like Amazon has for Anthropic with however much of its massive Indiana-based Project Rainier has actually been turned on, and much like Google is in talks to backstop a $15 billion data center project for Anthropic, along with data centers with Cipher Mining and TeraWulf and a $35 billion private credit-funded Broadcom-backstopped deal where Google will sell Anthropic its TPU AI chips, put them in a Google-built data center, and rent them back to Anthropic.
I want to spell this out: when you remove Anthropic and OpenAI’s compute spend, I am not confident that Google, Microsoft and Amazon have much of an AI business.
While many people believe — largely because the big three refuse to break out their actual AI revenues or disclose their customer concentration — that they have AI revenues coming from a diverse set of different customers, the reality is that their largest cloud customers, let alone AI customers, are two companies that can literally not afford to pay them without a near-infinite flow of venture capital or debt.
Per Ross Sandler of Barclays, Anthropic and OpenAI are estimated to make up 73% of all of Amazon’s AI revenues in both 2026 and 2027 and 75% of AI revenues in 2028, with Anthropic spending $14.1 billion in 2026, $25.3 billion in 2027, and $35.8 billion in 2028, and OpenAI spending $9 billion in 2026, $15 billion in 2027, and $20 billion in 2028.
Amazon plans to spend $220 billion in capital expenditures in 2026 and even more in 2027, and appears to be doing so almost-exclusively to provide compute for a company that had to raise $95 billion in funding in the space of six months, with $5 billion of that coming from Amazon itself.
Editor’s Note: Just before I headed to press on this piece, I found another note from Stephen Ju (who you’re just about to learn about for the first time) about AWS revenues, with the numbers a little different. He has estimated total AI revenues at around $30.9 billion for 2026, with OpenAI and Anthropic’s compute spend sitting at 59% of those revenues ($18.3 billion) and the remaining $12.6 billion coming from Bedrock, the platform from which Amazon sells access to both TPUs and AI models from Anthropic and (more recently) OpenAI. This revenue concentration improves to 55% in the 2027 estimates.
Anyway, the rest of this piece focuses on Sandler’s numbers, as I did not get a ton of time to dig over these. These numbers, while different, do not meaningfully change my perspective.
As I’ll argue about Vertex, making money by proxy of having monopoly permission to sell OpenAI and Anthropic’s models absolutely counts as revenue related to Anthropic and OpenAI to me. A chunk of both labs’ revenue comes from the resale of these models, easy money that also becomes another way in which hyperscalers feed their revenues back into the AI labs so that the AI labs can spend the money on compute. I will add that Microsoft no longer pays a revenue share to OpenAI.
In any case, the viability, efficacy, and attractiveness of these models are still a product of Anthropic and OpenAI’s ongoing work.
Google is in a similar-position. Per Stephen Ju of UBS, “...Anthropic, OpenAI and Meta will account for 21%, 7% and 1% of 2026 Google Cloud revenues, respectively, and 44%, 5% and 1% of 2027 revenues,” or, put another way, 28% of all 2026 and more than 48% of all 2027 Google Cloud revenues are from Anthropic and OpenAI.
Ju also estimates Meta will make up a whopping 1% of Google Cloud revenues in each year, and does not mention a single other customer, which heavily-suggests that there aren’t really any large ones.
Based on Bloomberg Intelligence’s consensus estimates for Google Cloud’s revenues in 2026 ($105.9) and 2027 ($173.8), OpenAI and Anthropic represent $29.4 billion ($7.4bn/$22bn) in 2026 and $84.69 billion ($8.69bn/$76bn) in 2027. To be explicit here, this is all Google Cloud revenues. It is reasonable to believe that this represents at least 75% of Google’s AI revenue, if not more.
What’s crazy is that these numbers are actually lower than UBS’ estimates. As the chart below demonstrates, OpenAI and Anthropic’s spend is estimated to sit at over $35 billion in 2026, larger than both its entire Google Cloud core non-AI business and Vertex AI model rental business that is largely boosted by Google’s ability to sell Anthropic’s models.
Sidenote: Ju and Sandler appear to disagree on how much of Anthropic’s compute spend that Amazon and Google get, which is fair, because both Google and Amazon separately claim to be Anthropic’s primary provider.
Eagle-eyed readers will also see that Google’s non-AI cloud business is estimated to be effectively flat in 2026, 2027, and 2028.
I also don’t think it’s common knowledge that OpenAI is such a large customer of either Google Cloud or Amazon Web Services, spending at least an estimated $52.5 billion in 2026 and at least an estimated $125 billion in 2027.
In the Musk-Altman trial, OpenAI estimated it would spend $50 billion on compute in 2026, and based on those estimates, that gives us about $16.4 billion across Amazon and Google, leaving a likely $33.6 billion in spend left for Microsoft Azure, though I’ll add that OpenAI continually underestimates its own compute spend and losses.
And based on a note from Michael Turrin of Wells Fargo from May 31 2026, things are just as bad for Microsoft, with 70% or more of its AI revenues coming from Anthropic and OpenAI. While Turrin “expects investments at software & models layers [to] pay off in meaningful adoption over time,” it’s difficult to argue that Microsoft has any meaningful AI strategy outside of OpenAI and Anthropic’s compute spend.
To make matters worse, based on Wells Fargo’s estimates, it appears that Microsoft 365’s AI revenues are barely — and I mean barely — beating the revenue share Microsoft gets from OpenAI’s sales.
Wells Fargo also includes a helpful cheat sheet of its estimates for AI contributions, estimating that even at the very end of FY2027 (which began on July 1 2026), OpenAI and Anthropic’s spend will represent a dramatic 74% of all AI revenues. Wells Fargo also estimates that the two AI labs represented 23% of Azure revenue in FY2026, growing to 35% in FY27.
Considering Azure grew 41% year-over-year, this means that 40% or more of Microsoft Azure’s growth came from them — and remember, Azure sells far more than just AI services.
This is an absolute fucking scandal.
The vast majority of Microsoft, Google and Amazon’s AI revenues and revenue growth in their representative cloud platforms are from Anthropic and OpenAI, and they are blatantly, unashamedly misleading investors by not disclosing that this is the case. We’re talking 73% of AWS’ AI revenues, 74% of Microsoft’s, and likely 70%+ of Google Cloud’s considering that just Anthropic and OpenAI’s AI spend is expected to be more than 48% of all cloud revenues.
This is not me being a hater, a skeptic, or a doomer, but the product of actually investigating what’s happening in the real world rather than just looking at whatever numbers the hyperscalers fart out and assuming it’s “all from AI,” and that “AI” means something more than just the two main model labs.
Investors in Amazon, Google and Microsoft have been led to believe that the $994 billion spent on AI GPUs and data centers exists to boost their existing businesses and build what amounts to the next industrial revolution. In fact, this is the line that just about any AI bull will give you about NVIDIA’s GPU sales — that all compute will be used because there’s endless, insatiable demand.
Well, other than the fact there isn’t.
What hyperscalers have actually done is demolish their free cash flow and purchased hundreds of billions of dollars’ worth of GPUs, TPUs, and XPUs to support a customer base dominated by two customers that are now accounting for the vast majority of their revenue growth and quite literally cannot afford to pay their bills without a near-infinite flow of venture capital investments.
Based on these estimates, these analysts also don’t seem to believe that any other large customers are going to emerge, bringing into question both the rationale of their capital expenditures and those of basically anyone building any data center anywhere in the world.
This is all very important, so I want to spell it out really simply for you:
Remember: Microsoft Azure, Google Cloud and Amazon Web Services represent a large chunk of all global cloud spend and AI compute, and thus are a representative sample of all AI compute…and if diverse, “insatiable” demand existed, it would be represented in these estimates.
Sidenote: For the sake of clarity and transparency, Microsoft’s Amy Hood noted in the most recent earnings call that 90% of all cloud spending came from outside the two main frontier AI labs.
The problem is that "cloud revenue," in this case, encompasses a lot of things, including (but not being limited to) "Microsoft 365 Commercial cloud, Azure and other cloud services, the commercial portion of LinkedIn, and Dynamics 365."
With that being said, the fact that frontier spending is just 10% of cloud revenue doesn’t tell us anything — and is arguably a way of obfuscating how dependent Azure is on the two main AI model labs.
This is the single-worst capital misallocation in the history of business. Every single story you’ve read about the “incredible growth” of these cloud platforms is an embarrassing misread of three companies that are misleading investors that will more than likely be forced in the next year or two to have to restate revenues, cut remaining performance obligations, and admit that they’ve drastically overbuilt capacity.
The counterargument to my warnings is always that “this is useful infrastructure that will be used in the future,” or that we’re in an OpenAI Bubble not an AI bubble (which, I argue, is basically the same thing), but when you remove Anthropic and OpenAI, Amazon Web Services and Google Cloud go from exciting growth-engines to chernobyls of capital expenditure.
Without these two “startups,” AI revenues are catastrophically small — for example, Sandler estimates that Amazon Web Services will make a pathetic $8.5 billion in AI revenues in 2026, or roughly 25 times less than the $220 billion Amazon intends to spend this year. While Ju estimates that Google Vertex AI model platform (which is one of the main ways that large enterprises integrate Anthropic’s models) will pull in $28.3 billion in 2026, that’s still a little under $10 billion less than the $35.6 billion that Anthropic and OpenAI will spend on compute.
This needs repeating. Investors and the general public are being lied to. When you remove OpenAI and Anthropic, Amazon, Google and Microsoft’s capex has likely accounted for very little revenue growth, which means that if either or both of them die, the majority of capital expenditures and debt raised as part of the AI bubble have been a waste.
So, let’s go look at the non-Anthropic/OpenAI part of that Barclays note, with each column representing 2025, 2026, 2027 and 2028, with the last three being estimates.
For some context, in the year 2025, Amazon spent $131.8 billion in capex, or roughly 32 times Barclays’ estimates for non-OpenAI/Anthropic revenue — a number that barely improves with the full total ($9.6 billion) to 14 times.
If Amazon has its druthers and invests $220 billion in total capex in 2026, the (pathetic) $8.5bn in non-OpenAI/Anthropic revenue will be roughly 26 times smaller, or 7 times smaller when you use the full $31.6 billion in projected AI revenue for 2026.
If your counterargument here is that “the gap is getting smaller each year,” you are a mark. $31.6 billion is $22.6 billion less than Amazon spent on capital expenditures in its last quarter, or roughly $18.4 billion less than it invested in OpenAI this year. Barclays’ estimates for 2028 have Amazon’s AI revenues — 75% of which are from OpenAI and Anthropic’s compute spend — at around $75 billion, four god damn years into the AI bubble.
Amazon will have, by 2028, likely sunk over $650 billion in capital expenditures into AI, all to earn (and this assumes OpenAI and Anthropic exist and can pay) a little over $171 billion in AI revenue, with the vast majority of it contingent on two entirely venture-backed startups.
Similarly, even if UBS’ estimates come true, Google will have spent roughly $408.5 billion (including consensus estimates of $120.5 billion for the rest of the year) in capital expenditures to create an AI business that makes about $80 billion a year, with most of that coming from either selling Anthropic’s compute or access to its models via Vertex.
Microsoft is in the same position. Wells Fargo’s estimates have its AI revenues for FY2026 (which just ended) at around $34.5 billion, in a year where it spent $115.9 billion in capex, with $41 billion of that in the last quarter, or roughly $6.5 billion more than its entire estimated AI revenues for the god damn fiscal year.
I realize I’m being a little repetitive, but I need you to see that without OpenAI and Anthropic, Microsoft, Google, and Amazon’s AI revenues are absolutely pathetic, and are thus entirely-dependent on their compute spend.
Let’s be serious, and take the absolute kindest read of UBS’ estimates, saying that Google’s Vertex AI platform will make approximately $22.5 billion in annual revenue, and assume, wrongheadedly, that it’s not near-entirely made up of demand for Anthropic’s models…
…
Sundar Pichai, did you spend $288 billion god damn dollars to make an annual business that makes less revenue than YouTube? We haven’t even talked about margins or costs or whether any of this is actually profitable, largely because it’s immaterial, as there is absolutely no way to read this situation as anything other than a historic failure!
Andy Jassy, is that you? Get your country ass over here! You did NOT just go out there and spent $429.5 billion god damn dollars to stand up data centers for a pair of companies you have to literally hand the money to them to pay you, did you? I’m gonna tell momma Jassy what you’ve been up to! She’s gonna paint your back porch red!
Wait, what’s that?
You just gave OpenAI $35 billion dollars? Wasn’t that dependent on it going public or reaching AGI? Are you kidding me man? It’s almost as if you realize that the only way your largest customers are gonna pay y’all is by giving them the money to do so!
Okay, all jokes aside, there’s very clearly a problem here with AI demand, in the sense that it doesn’t really exist without hyperscalers paying themselves to do so.
When you look at these numbers, you see a brutal story of unproductive capex. Looking at Wells Fargo’s estimates, it doesn’t appear that Microsoft 365 Copilot is a meaningful business, hitting a meager estimated $3.859 billion for the entire fiscal year 2026 for a product that allegedly has 30 million paid seats, suggesting massive discounts and questionable value.
Wells Fargo estimates it’ll grow to an unremarkable $10 billion in annual revenue in FY2027 — barely more than OpenAI is estimated to spend in Q1FY2027.
Very Blunt Sidenote: if Microsoft is struggling to sell AI-powered software attached to the literally-most-used enterprise software in the world with a sales team of tens of thousands of people and tens of thousand more resellers, how do you think that the rest of the AI software world is going to do long term?
To be explicit: the demand for AI-powered software is not there, and neither is the demand for selling AI-powered add-ons to other software.
This is an embarrassing accident of an industry with two ticking time bombs underneath it.
There’re really two scenarios:
And, to be explicit, the last part of that sentence is exactly what’s going on. Microsoft, Google and Amazon are have spent over a trillion dollars in capex and equity investments specifically so they can create growth engines that are entirely-dependent on Anthropic and OpenAI, who are entirely-dependent on Microsoft, Google and Amazon to either (or both) feed them money or continually build them more infrastructure.
Sidenote: I haven’t even mentioned how Google’s $99 billion and Amazon’s $53.4 billion in profits were inflated by their stakes in Anthropic (and SpaceX, in Google’s case), because I could write an entire newsletter about how deceptive and ridiculous it is that GAAP allows companies to do this. We need new regulations, and we need them urgently, as investors are being misled.
However you feel about what I’m saying, these estimates also require OpenAI and Anthropic to keep growing at the rate necessary to keep up with expectations for Amazon Web Services, Microsoft Azure and Google Cloud.
The most important question is which part of the machine breaks first.
The wind cannot fall out of the sails OpenAI and Anthropic, as both of them have to keep pace to be able to pay for all this data center capacity, which would mean they would, across Amazon and Google alone, have to produce over $125 billion in 2027, which would require both the actual demand (from customers for inference and for training) to use that much compute and the means to pay for it (from venture capital and the hyperscalers themselves).
Sidenote: To give you some context about how large that amount of money is, Microsoft just announced that its entire fiscal year 2026 revenue for Azure was $100 billion.
For this to be possible, both the demand for access to OpenAI and Anthropic’s models and the money to pay for the inference to serve it must be there to realize these revenues and to keep Google Cloud, Microsoft Azure and Amazon Web Services growing at historical rates.
To even have a shot at doing that, compute capacity must come online fast enough, which is an open question in and of itself. As I covered a few months ago, AI data centers are some of the single-most ambitious construction projects in history, requiring massive amounts of capital, specialist talent, and materials, and execution that includes building decades’ worth of power infrastructure in a few short years, making them take anywhere from 18 to 36 months to complete. If capacity doesn’t come on fast enough, OpenAI and Anthropic can’t pay for it.
It seems very possible that the only reason growth hasn’t stumbled for Microsoft, Google, and Amazon is OpenAI and Anthropic’s compute spend and the ability to sell access to their models, which means that they may see their capital expenditures as existential.
It kind of makes sense. If they fail to build more and more data centers and continue to sink money into Anthropic and OpenAI, growth will slow across both their cloud platforms and associated services, as the two AI labs are the only real aggressive purchasers of AI compute, which makes up the vast majority of hyperscaler AI revenues.
It’s a dangerous game. Without OpenAI and Anthropic, it’s clear that the underlying businesses of the big three hyperscalers are deteriorating, and that their AI plays are a catastrophic failure, because the sheer amount of cash they’ve required to date (and the even greater pile of cash they’ll need in the months and years ahead) demands an outsized return for years to come.
Apparently things are so dire that the only way to patch over slowing growth was to fund two giant startups beholden to massive compute contracts that feed venture capital dollars to hyperscalers in a circular motion that mostly equates to eating poisoned cardboard.
Things look great right now, as long as you avoid thinking too hard about what it means that so much of this revenue growth is coming from Anthropic and OpenAI, and that their other AI plays are producing the lowest end of double digit billions of revenue for something that has cost them over a trillion dollars, their free cash flow, and burdened them with hundreds of billions of dollars of debt, along with off-balance sheet liabilities now totalling over $1.35 trillion (including Meta).
I can already hear the counter-argument that “Anthropic and OpenAI are the fastest-growing companies in history,” and I certainly hope you’re right, because there does not appear to be anyone else who wants to buy compute at their scale other than hyperscalers selling it to them and whatever weird also-ran bullshit Mustafa Suleyman, Demis Hassabis, and Alexandr Wang will be allowed to do until one of the CEOs tries to make them the fall guy.
I really need to be as clear as possible: the current consensus view on AI is entirely divorced from reality. Based on what I’ve shared with you today, it is ridiculous to suggest that hyperscalers are building data centers under the belief that they will make a lot of money or that demand exists. They may believe — or hope — that’s the case, but that doesn’t make it true.
Outside of OpenAI and Anthropic, there appears to be less than $30 billion dollars of non-AI lab compute demand across Amazon, Google and Microsoft. I need to also be clear that this is almost certainly an overestimate, because it includes revenues from Azure Foundry, Amazon Bedrock, and Google Vertex, which includes both compute and API spend on Anthropic and OpenAI’s models.
This means that we are likely overbuilding data center capacity at the scale of hundreds of billions of dollars. As the largest providers of AI compute with the most experience and the biggest brand recognition, it’s hard to argue that there’s pent-up AI demand waiting elsewhere that hyperscalers haven’t realized. If anything, it suggests that everybody else is completely and utterly fucked.
Perhaps you’ll argue that the analyst was wrong or that my analysis is wrong or that demand will magically appear, and you’re welcome to if you want to continue burying your head in the sand.
Let me spell it out for you: if Anthropic and OpenAI each had a run rate of $100 billion, they would still not have the scale to generate the compute demand to cover what their commitments are to Microsoft, Google, Amazon, and, of course, Oracle.
And CoreWeave. And Cerebras. And Cipher Mining and TeraWulf. And IREN. And Nebius. And Broadcom. And AMD. And SpaceX. And maybe Meta, SB Energy, and whoever might build a $30 billion data center in Georgia. While some of these — like Cipher, IREN, Nebius and TeraWulf — will flow revenue directly to Google Cloud or Microsoft Azure, there’s still tens of billions of dollars’ worth of compute revenue that needs to get paid somehow above and beyond OpenAI and Anthropic’s spend on the major platforms.
This is not sustainable. In fact, it’s pretty fucking awful.
Let’s also be blunt about something: neither OpenAI nor Anthropic have worked out their business models. You can fart around claiming that Anthropic was profitable (it wasn’t) for a single quarter or repeat theoretical mantras about “positive gross margins” or say “they can just stop training” all you want. These companies lose tens of billions of dollars, they are horrendously unprofitable, an[d at this time do not have an actual answer to “how do these businesses function without infinite resources?”
Even if they were somehow profitable — which they are not! — they would still need to grow at an impossible rate. Putting aside all of the estimates from this piece, OpenAI projects to spend $750 billion in compute in the next three-and-a-half years, which either means it will need to grow its revenue to hundreds of billions a year very soon or raise half a trillion dollars or more over the next few years, at a time when even hyperscalers are having trouble raising that much money.
And based on both these estimates and the massive amounts hyperscalers are spending on capex, I think they’re well aware that there isn’t diverse demand, and that the only path forward is to continue building capacity specifically for OpenAI and Anthropic, funding them in whatever way possible — either through backstopping the compute costs or helping organize massive private credit deals — to make sure that revenue growth never slows.
This is a doomed mission.
These estimates show that Microsoft, Google and Amazon do not have meaningful AI business outside of the ones they’ve incubated, at least not ones that will pay off their capital expenditures. Consensus estimates for Microsoft’s FY2027 capex are around $186 billion in a year where its non-OpenAI/Anthropic AI revenue is expected to be $18.7 billion, meaning that even if these services had 100% net profit margins (IE: zero costs), it would take a decade of those revenues to pay back the capex.
While you might argue this is unfair — especially as OpenAI and Anthropic are unlikely to die before the fiscal year ends — it is time to start seriously discussing what happens to hyperscaler revenues once they do so.
Put another way, investing in Microsoft, Google, and Amazon as part of the AI trade is an investment in Anthropic and OpenAI’s ability to both survive and grow to become companies of comparative size and revenue growth as their hyperscaler progenitors.
It is clear based on the estimates I’ve shown today that the vast majority of growth in AWS, Google Cloud and Microsoft Azure comes from two companies that can literally not afford to pay their bills.
Jensen Huang has said that he has visibility into $1 trillion in GPU sales through the end of 2027, or, as I estimated, about 40GW of compute capacity requiring $435 billion in annual revenue. Though these estimates do not specifically break out compute demand from Bedrock, Foundry or Vertex, the combined AI revenues — including Anthropic and OpenAI’s compute spend, all API spend run through the platforms, and Microsoft 365 Copilot — for their fiscal years 2027 sits at around $304 billion, with the vast majority of that (around $197 billion) coming from AI lab compute spend.
There is not enough demand. We are overbuilding data centers. If compute demand existed to justify the amount of data center capacity being built — or even close! — then analyst estimates for AI revenues would be both significantly higher and meaningfully diverse rather than centralized around two unprofitable, unsustainable companies.
To be specific, for any of this to “make sense” we’d need to see multiple different companies or groups of companies spending comparable amounts to OpenAI and Anthropic, dramatic amounts of revenue generation from Google Workspace and Microsoft 365, and revenue diversity driven by multiple customers spending billions or tens of billions of dollars at the very least in estimates for 2028.
It’s also likely much worse than I’m explaining because of how the big three bundle every single imaginable AI service inside Foundry, Bedrock, and Vertex, all of which blend direct GPU rentals with API spend on models from Anthropic and OpenAI, which I believe generates a large majority majority of revenue on these platforms rather than diverse interest in renting AI chips or other models.
Microsoft, Google, and Amazon are selling their investors a lie about their AI strategies, and in a properly-regulated market would be forced to file investor disclosures that document the heavy revenue concentration of Anthropic and OpenAI’s compute spend.
In not doing so, they continue to mislead investors and the general public into believing that hyperscalers are funding the next great growth engine in tech, when what they’ve actually done is spend a trillion dollars in capex and investments to make tens of billions of dollars of revenue, much of which came from their own equity investments.
And in doing so, these hyperscalers have mangled their balance sheets, tripling their PP&E, encumbering themselves with over $500 billion in data centers and GPUs that exist mostly to support two companies that can’t afford to pay their bills long term. At the end of this hype cycle, Microsoft, Google and Amazon (and, I guess, Meta) will have left themselves in a much-worse condition than before, with revenue expectations that are overwhelmingly inflated by two unsustainable companies.
As I wrote in the Rot-Com Bubble two years ago, these companies are fundamentally out of hypergrowth ideas, and these analyst estimates confirm my absolute worst fears about the condition of these companies.
AI is not working. A $10 billion or $30 billion-a-year business is not sufficient to justify either the massive capital expenditures or scars on hyperscaler balance sheets. In fact, it’s kind of hard to imagine what that might actually be at this point, because Google, Microsoft and Amazon continue to spend somewhere between $170 billion and $230 billion a year in capital expenditures, and each time they do so, they increase the size of the payback necessary.
Sidenote: I’ve seen a note floating around Twitter from Bank of America that claims there’s $2.3 trillion in backlog across “the top 4 CSPs,” referring to Amazon, Google, Microsoft and Oracle.
Before anyone makes any vivid assumptions about these numbers, know that they refer to literally every dollar of incoming revenue for these companies, including long-standing compute deals, Oracle’s database contracts, and at least (per The Information) $1 trillion in commitments from OpenAI and Anthropic, though remember that we do not actually get a breakdown of RPOs based on segment, company, or related terms around cancellation or amendment.When companies tell you their RPOs, they do so so you’ll believe the revenue is diversified and, ideally, about whatever hype cycle they’re currently focused on (like AI!), which is exactly what’s happening with basically anyone covering them.
Let’s break down when they’ll be realized!
Microsoft: 30% — around $203 billion — will be realized in the next 12 months, for a company with over $300 billion a year in revenue.
Oracle: 12% — around $76.6 billion — will be realized in the next 12 months, for a company with around $62 billion in annual revenue.
Google: 22% or so within the next 24 months, or around $115 billion for a company with over $400 billion in annual revenue.
Amazon: Amazon doesn’t disclose, but it has a backlog of $496 billion, with at least $238 billion of that from OpenAI and Anthropic, and annual revenues of over $700 billion.
At this point, AI would need to become — and this is without OpenAI and Anthropic — a business at the scale of Amazon Web Services ($170 billion, though this number is inflated by OpenAI and Anthropic’s compute spend), Google Search ($200 billion), or at the very least Azure ($100 billion, again inflated by both AI labs’ compute spend) to make sense, and even then, for this to make sense, hyperscalers would have to stop spending money on capex.
Put another way, AI bets cannot “pay off” if hyperscalers continue to funnel three or more times their AI revenues every single year into capital expenditures.
Sidenote: If I had to guess the rationale at this point, it’s that OpenAI and Anthropic have signed up for massive amounts of compute capacity that has yet to be built or invested in, putting hyperscalers in a vicious circle where they must spend more capex to earn revenue from companies that they also must keep alive through either backstops or outright equity investments.
I haven’t even gotten into the other vicious cycle — that the more of these data centers hyperscalers build, the more expensive they become thanks (at least, in part) to the skyrocketing costs of memory that continue to increase primarily because hyperscalers keep buying servers for their AI data centers. As discussed last week, this only increases the amount of debt they’ll need at a time when the market is getting increasingly nervous about AI data center debt.
Yet as we speak, the market is ripping, because hyperscalers have swindled investors, the media, and even the analysts themselves. Article after article after article claims that AI bets have “paid off” because these companies are glazed any time they inflate their earnings using the compute spend of two unstable and unsustainable companies, in part because hyperscalers both refuse to and face no pressure to share their AI revenues, knowing that they’ll get credit as long as the topline numbers look good.
I want to be clear that the air is coming out of these companies, no matter how good these earnings may look.
Everybody is taking the growth of their existing businesses and two AI labs’ compute spend as proof that all this capex is paying off, even though there is now consistent proof that the direct opposite is happening, and that their businesses are becoming increasingly-dependent on that compute spend.
I understand that nobody really wants to think about the logical endpoints of what I’m arguing, so I’m going to do it for them.
To put things really simply, Anthropic and OpenAI are a way that hyperscalers can feed their revenue to themselves by spending money on capex, backstopping compute contracts, or doing direct equity investments.
Their continued existence allows the AI bubble to continue inflating, but this can only continue as long as venture capital and hyperscalers are capable or willing to invest. There is simply not the demand — not from open source, not from other AI labs, not from self-hosting, not from anywhere — to justify the capex or the massive data center buildout.
And for those arguing that there would be a dot-com bubble recovery story, I must be clear that if there isn’t demand today, it won’t magically appear tomorrow. AI GPUs will cost just as much to run in five years as they do today, as will unfinished data centers cost just as much to finish, as will electricity remain expensive, and all this will be happening after it’s easy to raise venture capital to actually buy the compute.
To quote my buddy Kasey, every major cloud compute provider is solely standing on OpenAI and Anthropic.
OpenAI and Anthropic are time bombs, and when either of them explodes, everybody will ask why we didn’t see the brutality that follows coming.
The truth is that nobody wanted to look.
To stare at these numbers and reconcile with their meaning is to acknowledge that the current state of the tech industry is based on mania, deceit, circular financing, and outright cons, and that the ascent of NVIDIA was primarily driven by three companies building compute capacity for two unsustainable companies that became existential to their growth, inspiring hundreds of billions of dollars of waste by obfuscating how little real demand existed.
I realize it’s difficult to think about scary things, and how easy it is to dismiss me as a doomer or a catastrophist, but mine is a logical and rational argument in an era poisoned by hype and grifting at a scale unseen in history.
The greatest lie of this era is that the tech industry is building the next industrial revolution, when what they’re actually building is a monument to everything that’s wrong with modern capitalism — wasteful expenditures disconnected from any real benefits generated as a means of pursuing growth at all costs, setting up a collapse that will tear a hole in the tech industry and the markets, and leave the world full of half-built monoliths sold to local communities as job creators.
The fact we’re talking about compute futures is a joke. The fact we’re talking about AI factories is a joke. Almost every aspect of the AI bubble is a joke, and in the end, investors and the general public will be the punchline. The rich will have gotten richer, the banks will have harvested fees, the hedge funds will have traded and taken profits, the private credit funds will have gotten their fees, and anyone who didn’t have an active inside track will be fucked.
All of this could’ve been avoided, but the world has a cult-like obsession with the wealthy, believing that the CEOs of the largest companies in the world could never make a bad decision, and that any executive is automatically smart by virtue of being rich and powerful.
And oh, how silly that’ll look in retrospect.
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2026-07-31 23:53:49
A great deal of the discussion of the so-called benefits or problems with AI comes down to the theoretical jobs that are (or are not) lost as a result of things LLMs can (or cannot do), or the equally theoretical productivity benefits that’ll come from using LLMs in place of (or in conjunction with) humans.
Anthropic’s Economic Index and OpenAI’s Economic Research Exchange are marketing operations that exist to propagate the (wrongheaded) belief that LLMs are either leading or will soon lead to massive economic or productivity shifts, even though little or no actual evidence exists to show that this is the case, other than the occasional story about LLMs make people worse or slower at their jobs or single lines in studies that are used (incorrectly) to prove that “AI is making it harder to find a job for young people.” In fact, Anthropic’s Head of Economics recently said there was “no material increase in the unemployment rate to date.”
These conversations materially detract from the actual harms or effects of AI, and exist only to make you scared that AI will take your job. They do not have any vested interest in expressing the actual economic effects of AI, which are, at this point, a simmering cauldron of different speculative bets on whether or not LLMs — a definitively niche technology — will create or become general-purpose software (per Roger MacNamee) that scales into the next Google Search, iPhone, or Microsoft 365.
As I’ve argued again and again, the AI industry’s revenues are, outside of Anthropic and OpenAI, incredibly small. Even in Exponential View’s deliberately-pro-industry analysis, there’s only around $110 billion in trailing twelve-month revenues across the entire industry, including OpenAI and Anthropic’s cloud spend.
For those counting at home, that’s $12 billion less than the $122 billion OpenAI raised in March, and a full $145 billion less than all AI startups raised combined in the first quarter of 2026.
Anthropic and OpenAI want you to talk about the theoretical so that you don’t focus on the tangible — their hundreds of billions of dollars’ worth of commitments, said commitments effects on the remaining performance obligations of hyperscalers and chip manufacturers, and the sheer scale of venture capital’s investment in AI, which (as I’ve argued in the past) largely allows for massive on-paper gains with little or no hope of liquidity.
To put this bluntly, I believe the entire conversation around AI’s theoretical relationship to jobs to be masturbatory and a conscious attempt to avoid having a messy conversation about the scale of the actions taken based on the flimsily-founded promises of AI labs and hyperscalers.
Today’s piece will dig into the true scale of the money needed to make AI make sense, by which I mean how much OpenAI and Anthropic will need to meet their commitments, how much money hyperscalers will need to pay off their investments, venture capital’s true exposure to the AI bubble, and what will have to go right for the bubble not to be, well, a bubble.
I’ll also make the case that the longer the bubble continues to inflate, the harder the basic economic puzzle of AI becomes to solve, as creating and deploying infrastructure becomes vastly more expensive — meaning that in order to achieve profitability, hyperscalers and neoclouds need to charge significantly more for compute than before, and the only two real potential customers are ones that cannot pay for it.
This will be a more more-pointed newsletter than usual, focusing on hard numbers and harder truths.
Let’s get it on.
