2026-10-24 00:28:34
For whatever reason, Blackstone founder and CEO Stephen A. Schwarzman has a thirty-chapter-long series called “My Personal History” that is, well, exactly what it sounds like — a financial hagiography that frames him as a noble, sensible man with strong values, like when he sent a letter to “distinguished skull and bones alumni” Averell Harriman, whose father Edward Harriman had crashed the stock market in 1901 in his dogged attempt to monopolize the post-bubble railroad industry. Averell himself had co-founded a bank with Fritz Thyssen, who banked the nazis.
Anyway, seven chapters in, Schwarzman makes a bold claim while telling the story of his first job on Wall Street in 1969, where he saw the oncoming collapse of a nearby student fridge rental and life insurance business (no, really):
I knew at once that the business could not last. Students kick refrigerators, throw them around and break them in no time. And a 20-year-old believes he will live forever; he has no interest in life insurance.
A visit to the company confirmed my suspicions. No one there seemed to know who was sitting in the next room. It looked like a growth company only because the number of students kept rising. Internally, it was out of control. I went back to the office and wrote a report concluding that the company would, in time, go bankrupt. Soon after, it did.
You may not be able to read a financial statement, but you can still recognize madness and folly. When weighing an investment, there are questions one must ask. Does the company have a real reason for being? Are people going to get excited about its products? Are the people running it authentic and focused, or are they merely salespeople trying to persuade you of something that is not real?
Good question, Steve!
While we’re on the subject, why is it that you invested $275 billion of Blackstone’s roughly $1.4 trillion in assets under management into data center and digital infrastructure investments? While people might get excited about their products, I have it on good authority that the $2.3 billion debt financing deal you did for CoreWeave (back when, per The Information, it only had $16 million in revenue) closed despite everybody having serious misgivings about the financial health, ongoing returns and actual viability of the business.
Based on everything I’ve learned about Blackstone, it appears that the only people you do business with are “salespeople trying to persuade you of something that is not real”!
Let’s go down the list:
Blackstone is the world’s largest commercial landlord, the world’s largest private equity firm, and the largest investor in data center infrastructure. It has invested in and/or loaned money at least once to OpenAI, Anthropic, CoreWeave, Google TPUs, Firmus, a private equity vehicle to sell Anthropic’s software, Lambda, and innumerable amounts of random AI startups and AI infrastructure companies at a scale that is hard to quantify for reasons I covered in both the Hater’s Guides to Private Equity and Private Credit, which mostly stem from the word “private.”
Nevertheless, even from the outside, Blackstone’s exposure to AI — and the greater software industry — is distressingly large, and thanks to that privacy, it’s very, very hard to tell where the money is coming from, both in the case of where Blackstone gets its funds and what specific vehicle it’s deploying them from.
Put another way, you can find Blackstone’s fingerprints on almost every big AI deal, yet more often than not have no way of saying what specific fund sunk in the cash.
Blackstone, as the Ur-Private Equitor, is not one company but a labyrinth of different investment funds, each funded by a combination of corporate pension funds, sovereign wealth funds, horrifyingly, public pension funds, which Bloomberg Intelligence estimates accounts for 32% or so of Blackstone’s overall funding.
Today, I’m going to dig into how credit, insurance and big bets on AI have changed one of the most evil companies in the world.
2026-10-21 00:12:06
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In return, you get a weekly premium newsletter that dives deep into the finances of the AI bubble. Over the past few months, I’ve published a two part guide to the world of AI debt, an analysis of the (dubious) impact of generative AI on the global economy, deep analyses of the biggest players in the AI infrastructure space like NVIDIA (part one and two) and Oracle (part one and two), how OpenAI will eventually kill Oracle, and how venture capital is killing itself.
Friday’s piece will be the Hater's Guide to Blackstone — one of the most loathsome companies in existence, and one that’s staked its entire existence on the AI bubble.
If you want to get in touch — and especially if you have any juicy information about Anthropic, OpenAI, Blackstone or any other companies in the AI bubble — hit me up on Signal at ezitron.76. I’m also on IB on your Bloomberg Terminal.
Soundtrack: Royal Blood — Pull Me Through
At the end of July, Apollo Chief Economist Torsten Slok gave a blunt warning to the Washington Post:
“This AI thing better work out because if it doesn’t ... we’re going to have a problem,” said Torsten Slok, chief economist at investment firm Apollo Global Management.
Today, I’m going to quantify exactly how difficult it will be for the “AI thing” to “work out.”
…everybody wants to make the AI bubble really complex, it’s actually super simple: hundreds of billions of dollars are being invested to make single-digit billions of dollars maybe, some day, if AI data centers actually get built at scale and OpenAI and Anthropic can afford to pay for their compute.
The theoreticals and hypotheticals of this industry are so powerful that they’ve created one of the (if not the) largest equities bubbles in modern history, all heavily-centralized around the valuations of a handful of names related to the so-called AI trade, which translates to “buying or selling stuff to do with AI GPUs.”
This has led to the single-highest inflow of foreign investments into US equities in history — larger than the Dot-Com Bubble, the leadup to the Great Financial Crisis, and even the dying gasps of the Zero Interest Free Era in 2021, per Apollo:
However you might hand-wave away the AI bubble’s economic issues by saying it’s “just like the Dot-Com Bubble” — when the NASDAQ 100 fell 77% from its peak — the consequences are now global, at a scale unseen in history, flowing into both direct stocks and indices.
As of typing this sentence, NVIDIA alone makes up 11.31% of the NASDAQ 100, with the combined weight of NVIDIA, Google, Amazon, Meta, Microsoft, Broadcom, Micron, AMD and CoreWeave representing about 46.5% of the index’s value, or 39.3% of the entire 3000-company NASDAQ composite.
These same names — replacing CoreWeave with Oracle — represent around 28.9% of the value of the S&P 500.
To sustain the valuation of these stocks — let alone continue growing — will require every company involved to either spend or make hundreds of billions of dollars in perpetuity, increasing every single quarter, without fail. This will come from either selling ever-increasing amounts of AI chips and related hardware, or buying and using them to generate revenue either through rentals or building their own software products.
While the centralization was more severe at the top of the Dot-Com era NASDAQ — with Cisco, Intel and Microsoft accounting for roughly 74% — the total value of the index was a mere $1.82 trillion ($3.58 trillion adjusted for inflation).
Today’s top three — NVIDIA, Google and Microsoft — account for around 29% of the NASDAQ 100, for a total market capitalization of $13.5 trillion.
I’m afraid things only get worse.
Per Goldman Sachs (via Reuters), "...equity gains have been the dominant driver of household wealth accumulation and the main contributor to a positive wealth effect on consumer spending," and per data from the Fed, corporate equities accounted for 37.9% of all household wealth in Q2 2026, the highest number on record since Q4 2000.
Economist Paul Kedrosky told me it might actually be higher:
Americans are near records in terms of equity exposure, over 50% [as in number of Americans holding stocks], ahead of real estate for the first time since WWII — and Americans are at record levels in terms of equities as a percentage of their liquid net worth — over 60%.
While the Fed’s data only goes back to Q4 2000, corporate equities as a share of household wealth only sat at 25.9% ($11.5 trillion, or $22.7 trillion adjusted for inflation), back at a time when total household wealth sat at $44.4 trillion, or about $85.2 trillion in today’s money. In Q2 2026, total household wealth sits at about $195.8 trillion, with corporate equities sitting at roughly $74 trillion, or roughly 86.8% of the entire US household wealth 26 years ago.
Retails are also making up an historically-large amount of the volume of trades on the market, per Bloomberg Intelligence, making up 20.3% of trading in 2026 to date, approaching the dangerous heights of the worst excesses of the end of the Zero Interest Rate Policy era.
Retail investors also directly own about 29% of Amazon’s, 26.2% of NVIDIA’s, 22.6% of AMD’s, 21.8% of Microsoft’s, and 18.6% of Meta’s stocks, though these numbers are not inclusive of index funds like SPY or QQQ (a fund tied to the NASDAQ). In totality, retail investors own equity assets (at least as of May) equivalent to about 10% of the market.
In fact, Goldman Sachs had retail volumes significantly higher:
In a note to clients, analyst Daniel Chavez explained that Goldman Sachs trading desk data shows retail volumes have risen 28% since mid-April, with a basket of retail-favorite stocks tracked by the firm gaining 29% over the same period.
By comparison, Dot-Com era retail trading was much, much lower — less than 2% of the NYSE’s trading volume according to the University of Michigan’s Alicia J. Davis in a paper that begins with the phrase “the American retail investor is dying.”
When all of this is over, I think retail investors might wish they were dead, as they directly own somewhere in the region of $4.65 trillion worth of NVIDIA, Amazon, Microsoft, Google, Meta, Broadcom, AMD, Micron, Oracle, and CoreWeave stocks. The number is likely much smaller than the true exposure retail investors have, as it isn’t inclusive of investments in index funds.
Sidenote: For some comparison, the total market capitalization of Microsoft, Cisco and Intel on March 10, 2000 (the peak of the market) was around $1.36 trillion.
Another point is that, unlike 2000, retail investors have easy access to exotic securities contracts that weren’t previously available (or, if they were available, were expensive or otherwise obscured from the casual view of most retail investors), and that have the potential to amplify any potential losses.
RobinHood, for example, allows retail investors to buy event contracts (where “investors” speculate on an event with a yes/no outcome), to trade futures, to short stock, and to invest on margin, or with borrowed money. All of this can be done (assuming the investor meets the criteria to access these products) with a few taps on a phone, effectively eliminating the friction associated with making a potentially life-altering financial decision.
Sidenote: CoreWeave isn’t on the S&P 500 and Oracle isn’t on the NASDAQ, just for comprehensiveness.
On April 14, 2000, the NASDAQ composite dropped 9.67% and the S&P 500 5.78%, with a little under 97% of the S&P 500 constituents falling on the day, per TIME and The Washington Post, led by hot inflation numbers and overall anxiety over the tech sector’s valuations:
There were virtually no exceptions to the decline. The correction, analysts said, was taking no prisoners. The crash came at the end of an extraordinary week, where volatility and huge drops were the rule rather than the exception. And market analysts said they expect the declines to continue next week--and possibly in the coming weeks and months--as overvalued stocks come down to earth.
Yet the Post had one telling quote, emphasis mine:
Market momentum--which has trended up for the longest bull market in history for much of the past decade--has now turned down. The recognition that many technology stocks can't possibly grow quickly enough to merit the heights they achieved through momentum investing may have pierced the bubble of overvaluation in recent weeks, analysts said, as momentum changed direction. That turnaround has sent the Nasdaq composite index down 35 percent since its high on March 10.
While it seems unlikely, a drop of around 20% across NVIDIA, Amazon, Google, Meta, Broadcom, AMD, Oracle, Micron, and CoreWeave would have the same effect as the entire market downturn on the worst day of the Dot-Com Bubble crash.
In truth, it wouldn’t even have to be that bad.
Per CNN Moneyline News Hour transcript from April 14, 2000, Qualcomm fell 21%, Yahoo fell 20%, Intel a little over 10%, and Microsoft a mere 5% — and that’s all it took to make the market bottom out.
Brief NVIDIA selloffs have already caused big market declines:
Anyway, remember that quote about momentum investing, overvaluations and growth? Here’s a quote from Bloomberg from last week (October 14), emphasis mine:
Chipmakers fell for an eighth straight day, the longest slide since 2008, on worries over whether the AI infrastructure buildout has outrun the ability of firms to get a return on their investments. CoreWeave Inc., a cloud-computing services provider whose bonds recently hit levels that typically signal distress, sank 12%. The S&P 500 lost 1.1%. Oil climbed, weighing on Treasuries.
And here’s another quote from CNN from a day later, emphasis mine:
The global retreat in chip stocks followed a sharp sell-off on Wall Street on Wednesday, after a strong US inflation report bolstered worries that rising interest rates will make it more expensive for companies to fund AI investments. Many AI companies are already heavily indebted.
History may not repeat, but it rhymes. Every problem that burst the Dot-Com Bubble is here, but it’s worse, bigger and uglier, with global contagion risk that I basically never see discussed.
For example, the ongoing equity price of TSMC on the Taiwanese Stock Exchange makes up about 40% of the entire market, with the ODMs that have seen booming earnings making AI servers for hyperscalers representing another 3% or so. On The Tokyo Stock Exchange, the combined weight of SoftBank, RAM maker Kioxia, and the two major banks (SMBC and MUFG) backing AI data centers make up approximately 11.24% of the TOPIX Index. Samsung and SK Hynix, both of whom have seen remarkable RAM sales (see my Hater’s Guide To The Memory Crisis for more), make up more than 50% of the entire value of Korea’s KOSPI.
Their elevated valuations come from a belief that they’ll grow in perpetuity, much like the American markets.
And let’s be clear about something: all it takes is the vibe souring to the point that people lose faith in the AI trade for everything to start to collapse.
So, what will it take to keep the party going?
The answer is simple: perpetual, parabolic growth in both AI revenues and AI capex.
Per CNBC, well, exactly what I’ve been saying, emphasis mine:
“At the moment, we have more compute capacity than we have demand, and we have a lot of the tokens and processing power being given away in freemium solutions,” [Gartner’s John Lovelock] told CNBC. “Everyone in the AI race is running for a last-man standing position. Pretty much every market that Gartner is tracking in our AI race is likely to collapse down to a duopoly.”
Neoclouds would not be going out of business en masse, Lovelock reiterated, but could be subject to consolidation, such as takeovers and mergers.
As a result of their profitability analysis, Gartner predicted last week that 80% of neoclouds, which are AI-dedicated computing power providers, could cease functioning as independent companies.
Oh!
Are…are you sure about that? Isn’t AI the biggest, most hugest thing ever? It’s transformative! It’ll change everything! Aren’t we all excited-
Lovelock also said Monday that AI is a technology as opposed to a market, comparing it to table salt that will be sprinkled into existing commercial operations rather than creating an inherently new field in which companies will sell differentiated and competing products.
Okay, well, check out Doctor Doom over here, I bet he’s gonna be so surprised when those profitable returns come in-
In their Wednesday report, Gartner researchers found that overall “AI revenue will be insufficient to support profitable returns on all planned AI capital expenditures” between 2027 and 2030.
Well, at least there’s a diverse revenue base for GPU sales-OH MY GOD!
NVIDIA has guided to a 70% increase in its next fiscal year — I assume FY2028, starting February 2027 — or over $670 billion in annual revenue, over three times its FY 2026 sales, with said sales dependent on whether Microsoft, Google, Amazon, Oracle, Meta, SpaceX (who collectively make up 50% to 60% of NVIDIA’s data center revenues), and the various neoclouds like CoreWeave, Nebius, and Nscale keep buying more of them every quarter, or at the very least that a willing vessel can step in to pick up the slack if they stop. If demand wavers, NVIDIA, Broadcom, Micron, AMD, and any associated silicon or hardware counterparties will see their revenue growth stumble, which will have a brutal knock-on effect on their stocks.
NVIDIA already has its own revenue concentration problem — 44% of its first half FY2027 revenues came from three customers, and in Q2 FY2027, 70% of its accounts receivables were from three customers, with 16% of its revenues coming from one. One. How is this not more worrying to people?
Anyway, to buy those GPUs (or TPUs, in Broadcom’s case), everyone outside of Microsoft will have to raise tens of billions of dollars of debt at a time when underlying interest rates are growing and the overall debt markets have chilled around AI data center debt.
Said chill comes from the world finally waking up to what I said in March 2025 — that CoreWeave is a time bomb of expensive debt that’s now selling off as people realize that making any of its supposed $104 billion in backlog (at least 50% of which is OpenAI and Anthropic) will require, per UBS, about $102 billion in debt in the next three years. CoreWeave’s long-dated bonds (by which I mean maturing in 2031) had an effective yield of as much as 16% in the last week.
With endless delays and hundreds of billions of dollars of chips sitting in storage, AI data centers have dramatically turned from the panacea for all infrastructure investment to fixed-income nightmare fuel. For whatever reason, nobody thought that lending hundreds of billions of dollars to a selection of different dodgy SPVs and neoclouds to build the world’s most ambitious infrastructure projects was a problem until, well, everybody, per the WSJ:
There has been a surge in issuance of bonds to finance the construction of data centers for AI computing. This year through Oct. 16, data-center issuers have raised more than $100 billion in the U.S.-dollar bond market, or about 25% of AI-related gross issuance in U.S. currency, according to data from Dealogic and Goldman Sachs credit research analysts. That is up from around 16% the prior year.
Per the Journal, this debt heavily relies on both the continued value of the data center and critical IT gear and whether the bloody thing actually gets built:
The recent AI data center bonds typically have structures that are different from a company’s bonds. They can be secured by the data center being built, while corporate debt is unsecured. How much security that provides, though, also rests on the completion of the data center, and on the value of the data center to another tenant. Effectively, investors are betting that overall demand for data infrastructure means the data center will be of use to someone, even if the current tenant runs into trouble.
In other words, the value of that data center debt is heavily contingent on whether the underlying assets retain their value, which in turn is contingent on whether there are customers that exist. For whatever reason, nobody wants to consider the most obvious problem: that if one customer can’t pay, other customers can’t pay, and if other customers can’t pay, there’s going to be less demand for AI data centers in general, which means there will be a glut of data center infrastructure, which will mean those assets you intend to resell if everything goes poorly won’t be worth as much.
I sure hope that financial institutions have a plan…
“With a data-center bond, you’re underwriting assets, and our view has been that when it gets completed, that can be a meaningful catalyst for spreads to compress,” says Andrew Keches, who is tech, media and telecom credit analyst and head of high-yield research at Barclays.
Oh okay, the problem with overly-expensive AI data center debt is simply that the markets will feel better when the underlying data centers (which are overwhelmingly delayed) get built? Awesome! It’s just that simple! I’m sure somebody out there will deal with the massive delays for the talent, materials and power connections necessary to complete them!
As of writing this sentence, CoreWeave’s bonds are trading somewhere between 14.1% and 15.1%, and the only way it can calm investor anxiety will be building AI data centers at a rate that has proven next to impossible.
Part of the problem is that recent pushes by smaller and somehow-dodgier neoclouds like Firmus (which just pulled its IPO) and Nscale (whose investors are pushing for it to nearly halve its IPO float to $16 billion and just lost Microsoft as a client due to power delays) have made investors intimately aware of the overpromises of AI data center companies.
Both Nscale and Firmus have the same customer concentration problems as CoreWeave, with 85% of Nscale’s backlog coming from Anthropic and Microsoft, and 90% of Firmus’ coming from Meta and OpenAI. Again, these are problems that CoreWeave, IREN and Nebius have had for years, but for whatever reason everybody has decided to wake up. Everybody has the same backlog customers — Microsoft (for OpenAI), Meta, OpenAI, Google (for Anthropic and OpenAI), Amazon (for Anthropic), and, of course, Anthropic.
All this ire towards AI data centers means that the price of borrowing for them is skyrocketing on top of ten-year-dated Treasuries stubbornly sitting above 5%, which makes the overall economics of AI data centers increasingly problematic, as nobody can seem to measure A) how much they make and B) how much they cost.
Or, indeed, how any of this will pay off.
Congratulations to Eugene Kim of Business Insider for getting one of the biggest scoops of the AI bubble, confirming my estimate that billions of dollars of NVIDIA GPUs were being warehoused in this incredible story, emphasis mine:
Some AWS data center sites had room for more racks but not enough available power to turn them on, people familiar with the effort said, leaving the equipment to be staged at intermediary warehouses until the facilities were ready. These people asked not to be identified discussing sensitive matters.
Usually, Amazon doesn't pay for racks until the gear is put into a data center, leaving suppliers to carry the cost of the equipment in transit, according to the internal documents obtained by Business Insider. This helps the company conserve cash.
However, the intensity of the AI data center building boom has made this type of coordination harder. Amazon has to compete more for scarce resources as it jockeys with rivals for access to Nvidia GPUs and other AI components.
Under the new "Early Rack Receipt" process, Amazon now takes ownership of these racks earlier in the supply chain, rather than waiting until the equipment reaches a data center.
That means spending cash sooner. And any delays getting the racks installed and plugged into a reliable power source potentially pushes back the moment when revenue starts flowing from these assets.
At least one AWS supply chain team did not have "a clear forecasted path" for getting racks into data centers, according to one of the emails reviewed by Business Insider.
If you’re wondering exactly how much it’s warehousing…
One Amazon email projected the company would take delivery of about $5 billion of Nvidia GB300 racks in August, followed by another roughly $3 billion each month "for the foreseeable future."
That’s right folks — Amazon, which spent $54 billion in capex last quarter — is taking $3 billion worth of GPU deliveries every month “for the foreseeable future,” despite having such a supply glut that it cannot install them, and in some cases lacks an idea of when it may do so.
As I discussed a few weeks ago, neoclouds and hyperscalers have over $374 billion in “construction in progress” assets, and I estimate that $200 billion or more of them are GPUs, primarily from NVIDIA. One quote from the piece stood out to me, emphasis mine:
Usually, Amazon doesn't pay for racks until the gear is put into a data center, leaving suppliers to carry the cost of the equipment in transit, according to the internal documents obtained by Business Insider. This helps the company conserve cash.
However, the intensity of the AI data center building boom has made this type of coordination harder. Amazon has to compete more for scarce resources as it jockeys with rivals for access to Nvidia GPUs and other AI components.
It appears that NVIDIA is channel stuffing. The phrase “compete more for scarce resources” doesn’t make sense in the context of GPUs that you don’t have enough power or space to install…unless, of course, NVIDIA is saying that if you don’t order and take delivery right now, the GPUs won’t be available by the time you actually build that data center.
The booster counter-argument will be that “Amazon is just planning ahead,” but riddle me this — if Amazon, Google, Microsoft (CEO Satya Nadella said he had chips “sitting in inventory he couldn’t plug in) and many other companies do not have the power to plug in the chips…why are they scarce? What’s making them scarce? If companies cannot install the chips (or, put another way, do not need them yet) what is creating the scarcity, other than something that NVIDIA itself is demanding its partners do?
Then there’s that curious matter of the whole “Amazon doesn't pay for racks until the gear is put into a data center.” How long has that been the case?
Let’s get specific about the verbiage — “...suppliers…carry the cost of the equipment in transit…” heavily suggests that these racks (which had GPUs in them, by the way!) were not actually shipped to customers until the customers needed them…until some indeterminate point at which the “Early Rack Receipt” program took place.
This, if anything, proves that the “demand for AI chips” is far more a result of NVIDIA’s deliberate channel stuffing and antagonistic approach to doing business than anything about how many chips the world needs right now.
Sidenote: Fuck Jensen Huang, man. This means that the increased price of RAM and consumer electronics was a result of an artificial "supply chain crisis" created by Jensen Huang pushing NVIDIA's customers to take early delivery of GPUs. Jensen Huang is responsible for making life worse for everyone. What a greedy, joyless, soulless oaf of a man. Doing this requires you to be a certain kind of low that no money or status can ever fix.
Jensen Huang is a piece of shit. Every hyperscaler CEO is too. These scumbags drove the price of everything up so that they could all help Jensen Huang make more money. We did not need him to build all these GPUs. He just wanted to sell them to boost earnings. Everyone who participated in the early shipment of GPUs is a scumbag.
I would like to take this opportunity to apologize for taking a break from calling these people what they were. I decided to temper some of my more acerbic tendencies because I figured it should take a back-seat to my financial analysis. I won’t make that mistake again.
What these people have done is morally reprehensible, on top of the already-vast damage done by the very existence of generative AI. This destructive, pointless void of an industry has made everything it touches worse, all to prop up the vague dreams and excesses of the wealthiest, most powerful and utterly fucking dull pieces of shit I’ve ever had the misfortune of witnessing. Everything is more annoying, expensive and worse on some level because of generative AI, down to the price of fucking US treasuries. Everyone who aided in the ascent of generative AI should know they have profited off of making the world worse.
A divide has emerged that is much, much larger than data centers. How into AI were you? How defensive of the industry were you? Were you normal about LLMs, or did you act strangely defensive of them? Why did you do that? How gullible were you about the financial or technical promises of AI? These questions will likely be asked of you in the next few years, and I believe your answers will be illuminating. Even if LLMs somehow survive in an open source form, the industry as it exists today is held together through cultism and private equity excess and is rigged to blow. The world will be different afterwards, eventually for the better.
Anyway, even if hyperscalers installed all the fucking things, it’s unclear who would actually pay for them at a volume that would match analyst expectations.
Per Slok at Apollo, financial analysts covering the tech sector seem to be pricing in cash flows that do not match with reality, emphasis mine:
The analysts covering tech expect the sector's operating cash flow to more than double to roughly $2.4 trillion by 2028, an increase of over $1.2 trillion, see chart below. Meanwhile, the analysts covering the other sectors in the S&P 500, which are tech's customers, expect those companies to add much less operating cash flow.
In other words, the tech silo is betting on a future in which demand for AI and tech services explodes, while the silos covering the companies that would pay for those services see a much more modest outlook. Both cannot be right at the same time.
The bottom line is that either tech's customers will generate a lot more cash than their analysts expect, or tech's cash flow forecasts are too optimistic, which raises the question of who exactly will be writing all those checks to buy AI services.
So, in case you didn’t know, every time a company reports earnings, financial analysts at big financial institutions set expectations — sometimes years in advance — of their expected revenues and cashflows. Right now, the analysts covering tech companies are guiding towards them adding $1.2 trillion in operating cash flows, all as the rest of the S&P 500 (ostensibly its customer base) doesn’t add anywhere near that much money.
This is one of those very simple points that nobody seems to be able to answer about AI: where will all the money come from to pay for those revenues?
As Torsten said, tech analysts are expecting a parabolic surge of revenues across the entire tech stack, but the analysts covering them are saying the cash won’t be there.
What’s the plan, exactly?
The answer, it appears, is “Anthropic and OpenAI,” and maybe “open source” sales of indeterminate value.
Sadly, there’s another wrinkle in the plan.
Japanese financial services firm Nomura put out a worrying take a few days ago, emphases mine and from the poster I found it from:
Anthropic hosted a presentation for investors on 15 October. The Financial Times reported sales at Anthropic of $16.8bn in Jul–Sep, versus $11.5bn in Apr–Jun and $4.8bn in Jan–Mar. We have not verified this. We regard ARR at Anthropic as a useful barometer of data center capex, as we think it is currently able to monetize data center capacity better than any other frontier AI lab. For example, (1) assuming that Anthropic has data center capacity of 5GW and ARR of $120bn at end-2026, we estimate that 1GW in compute resources translates into annual sales of $20bn. In addition, (2) if 60% of this revenue is spent on purchasing computing resources, we estimate that it will be able to spend $12bn a year on 1GW of data center capacity, and (3) if data center equipment is depreciated over five years, $60bn per GW represents about the most that can be allocated to data center construction (while noting that data centers also incur operating costs, and that the depreciation scheduled for power infrastructure depreciation is longer than five years). Increased ARR per GW is likely to boost data center spending, and the opposite is likely to hold true too. Assuming that growth in ARR at Anthropic slowed q-q in Jul–Sep, as the Financial Times article suggested, we think this might encourage a slightly more cautious outlook towards data center investment. Moreover, even if rivals were to lose market share to Anthropic, we note that revenues per GW could come under downward pressure across the industry as a whole.
We expect uptake of multi-agent systems (MAS), in which agents collaborate among themselves to execute prompts, and think demand for computing resources will rise once cost-effectiveness has been demonstrated. We think it makes sense to gauge whether the current cost of data center construction is commensurate with the economic benefits that AI models generate by looking at ARR generated by Anthropic and other frontier AI labs, as well as at new services and inventions.
To simmer it down, Nomura is saying based on how much a data center costs both to build and operate, you shouldn’t spend more than $60 billion given the amount of revenue you could hope to generate.
It’s hard to get a precise measurement of how much one costs. The CEO of IBM said $80 billion a gigawatt at the end of 2025. Foxconn said $47 billion per gigawatt in September 2026. Back in June, Jensen Huang said future data centers could cost $80 billion to $100 billion a gigawatt. It is completely insane we do not have a consensus here.
Nomura also throws cold water on the many people excitedly suggesting that Anthropic could make $50 billion to $75 billion a gigawatt, suggesting instead that the number is around $20 billion. In other words, if Anthropic is paying anything more than $20 million a megawatt, it’s losing money hand over fist just on the compute alone.
Anyway, I sure hope that the people building these data centers aren’t dependent on Anthropic’s revenues…
More-importantly, outside of the companies selling the hardware, we know very little about the actual revenue generated from renting AI GPUs. Amazon, Google, Microsoft, and Meta refuse to disclose their actual AI revenues — but analysts estimate that more than 70% of Amazon, Google and Microsoft’s AI revenues come from Anthropic and OpenAI’s compute spend, which is contingent on their continued ability to raise tens of billions of dollars.
As I’ve discussed in the past, I see around $22 billion in non-OpenAI/Anthropic compute spend globally, with an indeterminate amount of that coming from AI startups that, well…
Said billions are contingent on the AI labs’ continued revenue growth, and considering that 80% of Anthropic and OpenAI’s enterprise revenues come from loss-making AI startups allowing their customers to burn many times their subscription price in AI tokens (as much as $1,300 on Cognition’s $200-a-month plan, for example), meaning that Anthropic and OpenAI’s revenue growth is contingent on venture capital’s continued ability to fund their customers.
This is one of the core issues with measuring AI demand — that the vast majority of it comes from selling services that do not make any economic sense.
And their spend is contingent on their backers funding them in perpetuity.
That won’t be easy.
This, in turn, is contingent on venture capital’s continued ability to fund raise, and as I discussed in last week’s premium, VC has been on an historic losing streak since 2017, with the median net TVPI (total value put in, as how much you get in return on a per-dollar basis) sitting between 92 cents and a dollar, with 2025 fundraising dropping by 35%, and 2026 fundraising for first-time funds at its lowest rate in over a decade, per Murph Capital, which adds that despite representing only 6% of funds closed, funds of $500 million or more took 78.1% of all capital raised in 2026 to date.
AI startups, which are predominantly unprofitable, unsustainable, and require near-constant flow of venture capital, represented 64% of all venture capital investment in Q3 2026, with 50% of all global venture flowing into AI in 2025.
As discussed both last year in the Enshittifinancial Crisis and last week’s premium, AI startups represent a devil’s deal for venture — by virtue of being buzzy “AI” companies, their valuations are naturally high, and by virtue of their capital-intensive businesses, they require more rounds of venture capital, with each round increasing valuations to unsustainable heights that make their IP-light, EBITDA-negative businesses impossible for either an IPO or potential acquisition.
In fact, both venture capital and private equity are having real problems selling their investments, with IPOs becoming effectively non-existent as a share of exits for PE, per S&P Global.
Meanwhile, per DealRoom, venture-backed from 2026 seem high, but are inflated by SpaceX’s IPO ($75 billion), the PE-backed take-private of gaming company Electronic Arts ($55 billion), SpaceX’s acquisition of Cursor (all stock, $60 billion), NVIDIA’s acquisition of Hugging Face ($13 billion), and Google’s acquisition of Wiz ($29 billion).
And since 2015, the $2.7 trillion in venture capital invested has only managed to increase its value by about 1.6x — to around $4.4 trillion in value (not returns), or around a third of what you’d get if you’d have invested in the S&P 500.
In other words, venture capital is not particularly good at creating value, and has, in a period of poor value creation, centralized the majority of its investments in a single industry, with the majority of those investments going to companies related to or powered by Large Language Models that have yet to prove their economic worth.
And in this historically-low period of fundraising and exits, venture capital has invested a historically large amount of money, with the vast majority of it going into one bet: AI.
What’s that phrase? “You should always keep your eggs in one basket, so that you know where they all are”? I think that’s it, right?
At this point, the sheer scale of the AI bubble is too large and unwieldy for things, to quote Mr. Slok, “work out,” and yes, I believe we “have a problem.” I say this with no sarcasm, as I think Torsten has done fantastic work posing reasonable, logical questions that nobody seems to want to answer, like “how does this work out?” and “where does the money come from?” even as most media outlets and analysts nod and smile at basically any statement said by an AI lab, hyperscaler, or neocloud.
For everything to “work out,” the current equities surge will have to continue in perpetuity, which requires both hyperscaler AI revenues — which are already depressingly small — and hyperscaler capex to expand infinitely, along with neoclouds, sovereign clouds and AI labs’ vague infrastructure plans to also expand infinitely.
All the while, there can be no perceptual shift against generative AI, because any further souring of the debt markets will have neoclouds and would-be data center operators paying 12% to 15% in interest to build data centers sometime three years in the future for a customer base that’s pretty much entirely made up of Anthropic and OpenAI.
As we’ve seen in the last few weeks, AI debt markets can quickly sour even without any bad news, and conditions will only improve in connection to an incredible earnings season for CoreWeave and other AI infrastructure players.
Said “incredible” earnings would require A) data centers to be built and B) said customers to actually pay for them, which roughly translates to “OpenAI and Anthropic’s continued ability to spend more and more money.” Anthropic’s 47% quarter-over-quarter growth rattled the markets because this company has to grow fast enough to cover $413 billion of non-cancellable compute contracts over the next few years, and everybody kind of knows deep down that the bubble can only continue with parabolic growth, which is why everybody freaked out when OpenAI reported a lower annualized run rate (a made up number) than expected, because growth is the only thing keeping this going.
And said growth rate is predominantly based on whether AI startups continue to let their customers spend anywhere from $2 to $10 in tokens per dollar of subscription, which in turn is dependent on venture capitalists’ continued status as the world’s most braggadocious paypigs, and at some point that venture money will run out, much like it did during the Dot Com Bubble.
I didn’t expect this piece to be so sobering, but when I sat down to write it it just kinda flowed out of me how profoundly difficult the AI industry’s future has become. The fundamental disconnection between the perception of AI growth inflating the value of hyperscaler stocks and the worrying fact that we don’t really know exactly how much AI revenue they make means that the S&P 500 and NASDAQ are held up by a kind of childish excitement that will, at some point, turn to existential fear.
Those reflexively responding with “they’ll get bailed out!” neither understand the markets nor the situation at hand. The AI bubble was inflated by hyperscaler revenue growth (without any disclosure of AI revenue) and NVIDIA’s GPU sales, meaning that the moment one of those slows, everybody freaks out. The US government cannot bail out NVIDIA, Microsoft, Google, Amazon, CoreWeave or Meta’s future misses of analyst expectations, nor can it cover every single bankrupt data center, nor can it bail out investor sentiment when it realizes the AI “boom” was yet another tech-based bust. I went into significantly more detail about it in my premium piece about how AI Isn’t Too Big To Fail.
Remember: even with the bank bailout in 2008, the market still crumbled, and that was before we had over $40 trillion in debt, stubborn inflation, and a stock market that has fundamentally disconnected from reality.
The underlying weakness of the AI trade is that it isn’t really related to anything other than NVIDIA and the hyperscalers’ earnings.
We don’t know how many GPUs are in operation, we don’t know how much money the hyperscalers make from using them or renting them, we don’t know how many active gigawatts of AI capacity they have, we don’t know their actual customer concentration outside of analyst estimates, we don’t really know how much people pay to rent GPUs, and we don’t even know how much it costs to run a GPU on an hourly basis.
OpenAI and Anthropic are horrifyingly unprofitable, deeply unstable, and as I’ve discussed, 80% of their revenues come from companies that literally can’t afford to pay them, and yet the markets never react, because this information doesn’t really matter as long as the right numbers keep going up.
This is why Anthropic’s IPO travails are so profoundly dangerous for the AI trade. Investors are now suggesting that it prices its IPO at $1.5 trillion, meaning a significantly lower payoff for investors and much less capital raised on listing, meaning that it will have to take on more and more debt — like its 10% interest chip financing deal with Broadcom and Blackstone — that will provide a brutal education in what a high-burn-rate, negative-EBITDA company really looks like when you remove the layers of annualized run rates and hype.
And, per Irrational Analysis, Anthropic’s investor day didn’t go particularly well:
Broadly speaking, coding ARR has plateaued, or at least slowed significantly.
OpenAI seems to have no plan, as Sam struggles to raise a new private round at $1.4T valuation. Middle East investors (of all people lol) are pushing back on the valuation.
Anthropic apparently thinks bio (drug discovery) is the next big market for them. Yes, the most regulated and slow-to-revenue industry is what will fund all those "compute commitments”.
I know several people who were at the private Antrhopic (sic) buyside investor day last week. It was apparently a shitshow. Nobody asked an intelligent question. Anthropic was vague and gave little information. Original valuation plan was $2T, dropped to $1.6-$1.8T, and has been dropped again to $1.5T allegedly… in less than 10 days.
Any further delays to Anthropic’s IPO will be somewhere between catastrophic and fatal, and at this point I think it’s fair to question whether it ever makes it to the public markets.
If it does, a $1.5 trillion valuation would spell disaster for OpenAI, which has (as mentioned above) been trying to raise $30 billion at a $1.4 trillion valuation. Closing that round will be difficult to impossible at that price even without Anthropic’s drooping numbers, and if it somehow IPOs at something approaching $1 trillion, I have no idea how Clammy Sammy and OpenAI make it out of this one alive.
And really, does any of this look like the future? Does any of this feel like the next industrial revolution?
Or does it feel like the run-up to another burst bubble and resulting financial crisis?
The difference here is that AI has, for the most part, been sold as a panacea for the rich and powerful — a way to control, to steal, to acquire and to expand, to take and take and take as they always have, occasionally vaguely muttering something about “abundance” as both the media and financial institutions help force through yet another doomed cycle of “innovation.”
The people to blame are the tech CEOs — Sam Altman, Dario Amodei, Sundar Pichai, Andy Jassy, Elon Musk, Hock Tan, Jensen Huang, Sanjay Mehrotra to name a few — and the financiers that have helped them pump this bubble, with the worst of them being that rat fuck Stephen A. Schwarzman, CEO of Blackstone, a private equity (or “alternative asset”) firm that has funded basically every name I just wrote down on some level, funded by everyone from the Saudis to the California state pension system.
This week’s premium will be the Hater’s Guide To Blackstone, a company that vibrates with evil, and has staked its entire future on whether AI works out.
It’s a stupid fucking bet from an evil fucking company, and I can’t wait to tell you all about it on Friday.
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If you want to get in touch — and especially if you have any juicy information about Anthropic, OpenAI, or any other companies in the AI bubble — hit me up on Signal at ezitron.76. I’m also on IB on The Terminal.
2026-10-17 00:11:45
Soundtrack: Spinnerette — A Prescription For Mankind
About fourteen years ago, the government made a mistake that would allow tech companies and venture capitalists to conspire to legally scam millions of retail investors, simultaneously depriving them of good information and spuriously inflating the valuations of every major tech IPO of the last 14 years.
On April 5, 2012, President Obama signed the Jumpstart Our Business Startups (JOBS) Act under the auspices of it being “too difficult” for small businesses and startups — though overwhelmingly the latter — to access capital, all while reducing reporting requirements in ways that mostly helped venture capitalists fund and startup founders run businesses in reckless and destructive ways, all sold as a good thing for society.
In simple terms, while the JOBS Act (which was passed with rare bipartisan support) allowed companies to raise more money from more people, it also incentivized companies staying private for longer by eliminating a rule from the 60s that required companies of a certain size — above 500 shareholders and $10 million in assets — to report as if they were public.
[Editor's Note: It said "public for longer," that's now been fixed.]
In 1964, Congress implemented a rule to securities law called Section 12(g)...[which] said that once a company of any real size had 500 shareholders, it had to start reporting like a public company. This acted as a trigger that pushed private companies into a public reporting regime - the same way a public company reports its financials, a private company would have to file quarterly reports, an annual report, and an update every time something big happened - which was a huge deal for startups, because startups pay people in stock. Every employee who got shares instead of a bigger paycheck was one more shareholder on the way to 500.
As Casual Finance notes, this is the rule that pushed both Facebook and Google to go public — because if you’re going to have to report like a public company, you may as well have access to the public markets:
Back in 2010, Zuck flat out said it. "We aren't planning to go public anytime soon." Then he continued by saying “I tend to think that being private is better for us right now because of some of the big risks we want to take in developing new products.
And Facebook didn't need the money. They were the hottest startup in the world and everyone wanted in…which is how, in January 2011, Facebook announced it had just raised $1.5 billion privately.
But in that same announcement, Facebook said it expected to pass 500 shareholders sometime that year. Which meant they would have to start publishing their financials "no later than April 30, 2012."
So Facebook was about to get the worst of both worlds.
They were about to have to put their numbers out in the open for every competitor to be able to read, just like a public company. Without any of the upside of actually being a public company. Like a large infusion of equity capital and having liquidity for your stock.
So in 2012, Facebook took their stock public.
The JOBS act, amongst other changes, made it so that you could have up to 2,000 shareholders (but no more than 499 “unaccredited” investors) and, crucially, changed the rules so that employees were no longer counted in the calculation, effectively removing any chance that most startups would ever have to deal with reporting like a public company ever again.
The rationale was that having to report like a public company required audited accounts, adding an "unnecessary burden” to fledgling companies, even though “fledgling” here meant “companies worth hundreds of billions of dollars on paper” that absolutely could afford an auditor. Those pushing for the bill also believed that these requirements unfairly punished companies for awarding stock grants, and that public reporting was tantamount to “exposing trade secrets,” as if there was literally no amendment that could’ve been made to anticipate these problems other than “removing reporting anything of any sort.”
However minor this change might have seemed at the time, the net result was one of the largest private valuation bubbles in history, because there was (and is) no longer any incentive for a company to go public before it’s “ready,” with “ready” defined as “as much value has been drained from the company as humanely possible.”
As Casual Finance noted, when Microsoft went public, it had a market cap of around $500 million, Google $23 billion, and Facebook $100 billion, with their total market capitalization as of writing this sentence sitting at around $10.29 trillion. Anyone investing in any of these IPOs in the first five to ten years of their existence had a chance to multiply their money, because you were able to invest before the companies became massive multiconglomerates with multi-trillion dollar market caps, in turn creating value for millions of households that “took the risk” on investing on an earlier-stage company.
These three companies are used as the poster children of why you should invest in a new tech stock — that you’re getting in on the ground floor, with the reward for the risk being that you’d have the chance of a massive multiple.
Except that doesn’t really work if the companies wait as long as humanly possible to go public.
The reason that Microsoft, Google, Meta, Amazon and all the other famous names in “big IPOs that made early investors a lot of money” was that they had to go public before they’d reached the height of their valuation.
And crucially, their valuations are decided by market forces which, while deranged and emotional, at least are somewhat guided by the underlying financial picture of the company.
Today’s startups have their valuations defined entirely based on the whims and fancies of a venture capital sect that makes up a multiple they’re comfortable investing at. That’s how Instinct, a zero-revenue “AI agent” that you text to do stuff, is somehow worth $10 billion — because investors from Sequoia Capital, Benchmark, and Coatue had been willing to give it $1 billion for 10% of the company.
These inflated valuations drain the vast majority of potential opportunity that any investor might have, effectively transferring all the risk for the company’s future onto the public markets while offering very little in return. The days of thousand-percent-or-more returns are long dead, despite every single IPO selling itself on this eventuality, like SpaceX did when it proposed it had a $28.5 trillion total addressable market.
Anthropic’s upcoming IPO has the company demanding a $2 trillion valuation, equivalent to both Meta and SpaceX, based on flimsy promises of $200 billion in annual revenue years in the future and a $30 trillion total addressable market. It is, to quote New Constructs, presenting risks and is positioned to be a far bigger ripoff of the US capital markets, and exists only as a liquidity mechanism so that early investors can multiply their dodgily-valued early investments by selling their shares under the scummiest possible terms.
Anthropic and OpenAI’s IPOs pose a genuine threat to any investor that dares entangle themselves with unprofitable, unsustainable AI companies promising the world (or the world’s destruction), all sold under the auspices of potentially buying into the next Facebook, Google or Microsoft. In reality, the kind of growth you’d need from these companies to reach anything close to the returns of those early-stage tech investments would require them to have trillions of dollars a year in revenue and market capitalizations of $20 trillion or more — an impossibility that nobody seems to want to discuss.
Yet these rotten incentives have also led to a massive valuation bubble in startup valuations writ large, with the AI bubble being the inevitable result.
By allowing private companies to avoid the reporting strictures of the public markets, financial regulations incentivize a kind of venture welfare, keeping companies that cannot borrow money or otherwise sustain themselves through cashflow — you know, bad companies — alive for longer than is natural, never forcing them to become real companies that can survive, with every trip to Sand Hill Road bumping the valuation up further, making any IPO or acquisition much harder to stomach.
This wasn’t as much of a problem when venture capital was pumping up software companies and flogging them to the markets or dimwitted private equity firms, but has become a critical situation for the legions of cashflow-negative AI companies with aggressively-inflated valuations.
In a pre-JOBS environment, many of these companies would’ve wilted in sunlight, and, I’d argue, been forced to run businesses that could stand on their own two feet. Instead, as I discussed in last year’s Enshittifinancial Crisis, these companies are more venture-dependent than any other hype cycle in history, and because venture is so excited about the potential, they’re investing at bigger and bigger rounds, creating the largest valuation bubble in the history of technology, including the Dot Com Bubble.
Today I am going to aggressively advocate for reforms — if not an outright repeal — of the JOBS act, and call for serious legislative efforts to bring the private markets to heel.
My argument is simple: the current system is built to incentivize rich and powerful people inflating private assets with spurious valuations, harming retail investors, reducing the available capital to good startups, and incentivizing the continued development of bad, unsustainable companies. Venture is no longer a trustworthy vehicle for investment, nor can venture capitalists be trusted to value startups any further.
The JOBS act did the literal opposite of what it intended to — fewer tech companies went public, and those that went public did so at oversized valuations pumped up by venture capital, using retail investors as exit liquidity under the flimsy pretense of “getting in on the ground floor.” We are placing outsized trust in a handful of extremely rich and disconnected people and their ability to pick winners, despite the fact that the majority of venture capitalists are fucking terrible at doing so.
For over a decade, venture capital has inflated the value of startup after startup, then been surprised again and again as these companies didn’t work out, with the vast majority of “successes” — SpaceX, Snap, Airbnb, and Uber — mostly existing to dump their horrid bags on an unsuspecting public.
And by repeating the same cycle — inflating companies based on hype and ever-growing valuations — venture capital is setting itself up for a collapse.
Today I’ll explain how it’s happening, and how we might fix what’s left in the end.
2026-10-14 00:03:00
If you liked this piece, you should subscribe to my premium newsletter, and you can subscribe on the following links: $70 a year, $18 a quarter, or $7 a month.
In return, you get a weekly premium newsletter that dives deep into the finances of the AI bubble. Over the past few months, I’ve published a two part guide to the world of AI debt, an analysis of the (dubious) impact of generative AI on the global economy, deep analyses of the biggest players in the AI infrastructure space like NVIDIA (part one and two) and Oracle (part one and two), and the role private credit is playing in funding AI data centers, and how private equity, reeling from years of successive failed software acquisitions, is doubling down on the AI bubble.
On Friday, we’ll take a look at the JOBS Act — or, the Obama-era legislation that created the regulatory environment that would ultimately prove essential for the AI bubble to inflate - and why it might be time to reform or entirely repeal it.
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 your Bloomberg Terminal.
Soundtrack: Cleopatrick - Hometown
"I think the like, state of being an entrepreneur is like, how well do you make decisions in an information-poor environment, right?" - Mark Zuckerberg
The entire AI bubble is inflated based on stupid, made up numbers that only exist to pump the bags of investors and make the AI startups look more successful than they are. It is a failure of journalism that they have been popularized, as they do not tell us very much about anything, other than that there’s a new, bigger number to get excited about.
Last week, the markets dropped on the news that OpenAI’s annualized recurring revenue (ARR) — which was reported as $70 billion by Axios and confirmed by the Financial Times — was actually $50 billion. The number in question was, according to the FT, inflated by “grossing up” the total to include sales through cloud partners, a discrepancy between how OpenAI and Anthropic make up a number that doesn’t really tell anyone anything:
Anthropic recognises gross revenue on sales through cloud partners. That is because Anthropic is the principal in the transaction, according to a person with knowledge of its accounting practices. The Claude maker’s revenue run rate was calculated by annualising the prior 28 days of consumption-based revenue, plus recurring monthly subscription revenue, they added.
OpenAI, meanwhile, counts only its own cut of proceeds from arrangements with partners such as Microsoft.
The $70bn figure reported in September arose from attempts by investors to compare Anthropic and OpenAI performance on a like-for-like basis, according to a person close to OpenAI. However, OpenAI described the $70bn as a “net” revenue metric when it was approached by the FT at that time.
Back in August, Anthropic had told investors that it had hit $65 billion in annualized run rate, but at no point defined what that meant, what period it referred to, what any of the underlying numbers are, or what “gross” versus “net” bollocks it was playing. Per Irrational Analysis the calculus might have been even sillier than that:
Last month, there was a whole kerfuffel in AI/semis/finance circles on Anthropic July ARR. Two numbers were going around. I don’t remember the numbers and frankly it does not matter. You will see.
One ARR number was the traditional “trailing 28 days * 13” number. Personally I hate this venture-capital clown metric but whatever a lot of people use this.
The traditional ARR number was bad and implied deceleration in growth. So the people massively long Anthropic came up with a new ARR number that was July 31st * 365 days.
This is the problem of saying what the revenues of a company might be based on numbers that are not its actual revenues. There is absolutely no value in using “annualized run rates” for anyone other than the company and its investors, and the fact that modern tech and business journalism continues to accept it for what are meant to be the largest and most-important companies in the world is a sign that something is very wrong with how we cover the tech industry.
Sidenote: When one “annualizes” a period of revenue, the intention is usually to take a time period that’s indicative of the consistent performance of the business and extrapolate from there what it might make on an (as you’ve guessed) annual basis. This calculation is useful only when connected to specific periods and forms of revenue — recurring subscriptions and consistent contracts rather than, in the case of the AI bubble, consumption-based revenues that are not ‘recurring’ and thus mean very little when annualized.
The other problem is that these run rates do not measure if revenues go up or down, because you’re choosing the best period possible.
I am not going to spend much time dwelling on gross versus net. It appears that, per Reuters, 47% of Anthropic’s 2025 revenues came from sales made by Google and Amazon, who take a 16% cut as opposed to the roughly 20% that Microsoft takes from OpenAI. It appears that Anthropic for whatever reason counts every dollar of sales as part of its annualized run rate figure and OpenAI does not, for reasons that escape me, as I simply don’t buy that it is “more ethical.”
My evidence? The following quote from the FT, emphasis mine:
The $70bn figure reported in September arose from attempts by investors to compare Anthropic and OpenAI performance on a like-for-like basis, according to a person close to OpenAI. However, OpenAI described the $70bn as a “net” revenue metric when it was approached by the FT at that time.
This is the same OpenAI that tried to claim that a $34 billion loss was actually an $8 billion loss when the FT asked them about my reporting on its audited financials.
I must also say how utterly disappointed I was to see how Bloomberg credulously reported that OpenAI “expected to hit $70 billion in annualized revenue by end of 2026,” a transparent attempt to send the markets back up with a story that tells the world less than nothing.
Wow, OpenAI, by the end of 2026, will increase a number that “represents a projection of [its] yearly sales based on a shorter period” by $20 billion! What is the period in question? Who knows! How is that number derived? Who cares! Number go up! Put the story in the bag! Your job is not to cover the tech industry, it is to celebrate it! Stop thinking!
Sidenote: another very obvious reason these companies use these numbers is SEO! If you try to Google OpenAI’s projected revenues, you’re guaranteed to get one of these stupid annualized figures. In reality, the company projects to make somewhere in the region of $30 billion in 2026.
In other words, OpenAI’s made up number was actually lower than Anthropic’s made up number because of the way it made the number up, but by the end of the year, the made up number would be bigger than the other made up number. These made up numbers are reported by the largest and most well-respected news outlets of the world, who lend said made up numbers respectability despite the numbers in question being utter bullshit.
I’ll give you an example.
For the first half of 2026, OpenAI made $12.4 billion in ($5.7 billion in Q1 and $6.7 billion in Q2) revenue per The Information and the Wall Street Journal, while Anthropic, per the WSJ and the FT, made $15.5 billion ($4.6 billion in Q1 and $10.9 billion in Q2). We do not have consistent coverage of their underlying costs, but in Q1 (per The Information), OpenAI spent $8.6 billion on training and $3.5 billion on “cost of revenue,” with no reporting on its sales and marketing spend where it allocates the cost of serving free users or discounts.
Anthropic said it had hit an annualized run rate (undefined period multiplied by undefined number) of $48 billion on May 28, or roughly 430% more than its entire revenue in the second quarter. In fact, if you added up every dollar that both Anthropic and OpenAI made in the first two quarters, you’d be $19.1 billion short of Anthropic’s May “run rate.”
We do not know how any of these numbers were actually conceived outside of they are somehow derived from a non-specific 28 day period. The information they convey is sparse, if it communicates anything at all.
Reporting “run rates” is a marketing exercise, and if you disagree, please answer me the following: what does a “$65 billion annualized run rate” tell you about Anthropic, exactly? It’s MEANT to make you think it will make $65 billion a year in revenue, but that is not what it actually means. This number tells you nothing, and exists only to poison the well of information.
Without an actual period of time — and I mean an exact one, such as “July 1 to July 31, 2026” — and the exact way it was calculated, this number effectively means nothing, and reporting it without this information only serves OpenAI or Anthropic. You can say “investors use it all the time” all you want. Investors are fucking stupid for using it. If the argument is “it shows how fast OpenAI is growing,” why don’t you find out the actual growth rate of the company?
The fact these numbers moved markets is a reflection of the definitive — and accepted — ignorance built into the entire AI bubble, where despite trillions of dollars on the line, we know very little about the actual underlying numbers, and those numbers we do know are inflated using techniques that would get you shot by an accountant.
In fact, we don’t know most of the numbers that would actually tell us whether any of this is a good idea.
Reporting on the true revenues of the AI industry feels less like financial analysis and more like legal discovery, and it’s fundamentally insane how little tangible, meaningful information that exists to feed the trillion-plus dollars in capital expenditures and endless full-court press in the media about how AI is the next big thing.
We do not know Google, Amazon or Oracle’s AI revenues, and we have a single year — fiscal year 2026 — of Microsoft’s AI revenues based on an estimate from Bloomberg calculated from its disclosure of its OpenAI revenues in an SEC filing. We do not know how much each individual service makes, how much their true revenue shares are with the AI labs or, as I discussed a few weeks ago, how much actual, operational AI capacity exists as a result of the trillion-plus dollars in capital expenditures, though Bloomberg and I have both reported that Microsoft has around 2GW of it, even though it claimed to bring online a gigawatt in each of its last three fiscal quarters.
We do not know the revenues or much about the economics of Perplexity, Harvey, Instinct, Reflection, Cognition, Replit, Lovable, and Factory, all of whom have raised somewhere in the region of $15.8 billion in funding, and have a combined valuation of $215.8 billion. We do not and will never know the true revenues or costs of Cursor’s operations as a result of its acquisition by SpaceX, and as a reminder, annualized revenues are not actual indicators of the economic health — the revenues, costs or cash flows — of any of these companies.
Perhaps this was permissible in the past because so much less capital was being annihilated in the name of startups, but things have escalated so much further than any other era, including the Dot Com bubble.
Look, the entire AI bubble has been inflated based on the implicit belief that there will be demand — from AI startups and elsewhere — for AI compute, to the point that Goldman Sachs says that hyperscalers need AI startups to make $425 billion in annual revenues just for the hyperscalers to break even…and we don’t even know, outside of Anthropic and OpenAI, how much any AI startup actually makes.
What we do have is “annualized run rates” pegged to non-specific time periods and non-specific numbers that tell us, for example, that Cognition hit $1 billion in late September, a number that means next to nothing. Even when The Information was able to report that Cognition would burn $800 million in 2026, it resorted to saying that it was “generating around $900 million in annualized revenue, or $75 million a month” as of August 27 2026 — adding that executives had “projected it would hit $1.5 billion in annualized revenue by end of year,” meaning that two journalists were able to successfully find out its actual expected burn, that it burned $200 million in Q2 2026…but not the actual revenues that the company generated on a quarterly basis.
Let’s be clear about something: the reason we’re not getting these numbers is that these companies are not actually making these amounts on an annual basis. If Cognition was actually going to hit anything close to $1 billion — and let’s be honest, it’s likely less than $500 million! — it’d leak it versus using these weird monthly snapshots.
It is astonishing that we, four years into the AI bubble, do not know the revenues of more than a handful of AI companies outside of OpenAI and Anthropic. All we have is a mishmash of annualized run rates, and at this point I have to wonder if it’s a failure of journalism, successful opsec, even more successful marketing, or that these companies don’t have great accounting.
But really, go and look up basically every AI company. Perplexity? Nothing. Harvey? Nothing. Cursor? Years of reporting — endless thousands of words spent — and nary one report I can find, outside of these fucking annualized run rates.
Part of the reason, I imagine, is this chart from Ramp that I’ve brought up before:
From what I hear, the vast majority of those 1% of customers are AI startups that power their products using OpenAI and Anthropic’s models, predominantly using monthly subscriptions that allow users to burn way more in tokens than their subscriptions actually make.
For example, SemiAnalysis did a deep study into the amount of tokens you could burn on the various different AI subscriptions out there, finding that you could burn anywhere from $83 to $118 a month for $20 and from $1,264 to $1,331 a month for $200 on Cognition, dropping to $46 on a $20-a-month plan and $507 for $200-a-month on Cursor when using Anthropic’s models.
While OpenAI’s models are being dropped from Cursor, SemiAnalysis found you could burn $154 a month using OpenAI’s models on Cognition’s $20-a-month plan, and $1,736 a month on its $200-a-month plan.
The various boosters reading this and making noises like the Skesis from Dark Crystal will say “but these companies get discounts!” as if that makes up for the fact that customers are able to burn two to six times their actual subscription every month. They’ll also argue that “this is the gym model, and most customers don’t spend that much,” a statement that they have no proof for other than that it makes them feel better.
The problem — and the reason I’d argue that the actual revenues of these companies are so shrouded in secrecy — is that the underlying economics do not match up when you consider that effectively every AI startup has to resell tokens at a discount. It doesn’t really matter how much revenue you make if you’re sending twice or thrice or five times that amount out the door to Anthropic and OpenAI, but saying a larger “annualized run rate” makes the amounts you’re raising far less worrying than what I imagine are particularly-shrimpy sales.
Let’s take Harvey — which raised $550 million at a $15.5 billion valuation in September — as an example. As of early August, Harvey had hit $350 million in annualized revenue, up from $190 million in January. If we assume it uses the same “trailing 28 days” model as Anthropic, this means that it made $26.9 million in a 28-day period ending sometime in August, up from $14.6 million in a period that ended some time in January 2026.
Since December 2025, Harvey has raised a total of $910 million across three rounds, or roughly 33.8 times the estimated underlying revenues of its most-recent run rate, which increased by 45.7% in that period.
Bloomberg claimed in late September that Harvey’s Gross Margins had gone from 50% in January 2026 to negative 50% in June 2026 to “positive again” (I imagine in September) due to using open source models, but something about those numbers is deeply weird to me, mostly because I’ve never, ever heard of a company having had that happen.
And none of that explains why a company that makes tens of millions of dollars in revenue needs to raise hundreds of millions of dollars every few months, unless, of course, most of those dollars are flowing immediately to Anthropic and OpenAI, which is, it turns out, exactly what happened:
The $15.6 billion legal startup Harvey built its business around training AI models like OpenAI’s GPT-4 to do specialized work for lawyers. Recently, however, soaring artificial intelligence costs have nudged it to rethink its dependence on the AI giants.
After a March update to its AI agents, Harvey’s customer usage spiked, but its gross margins dropped sharply — from about 50% at the beginning of the year to -50% by June, according to a person familiar with the matter.
This is the antithesis of any good business. In basically any industry, the more demand you have, the more money you make, but in AI, the more demand you get, the more money you lose, and your most engaged customers are also your least-profitable.
However you might rationalize the underlying economics of a company like Harvey, the numbers we do know are extremely worrying — $900 million raised since December 2025, and, in my most-optimistic calculation, around $175 million (though likely less than $150 million) in revenue through the end of August, for a company that raised another $600 million across two other rounds in 2025. That’s $1.66 billion in about a year and a half to make an unprofitable company with less than $30 million in monthly revenue that is somehow worth $15.5 billion.
Harvey has categorically failed to find product-market fit, unless we qualify that as “literally finding a customer.” It’s very clear that its underlying product cannot support itself, and that the company is entirely-dependent on venture capital, raising at least $160 million every three to six months since February 2025. It is abundantly clear that Harvey’s customers — like every AI startup — do not actually want to pay what its services cost, which means it does not have product-market fit, nor what I’d call a “business.”
Then there’s Cognition, which has raised a combined $3 billion in 2026 alone (in May and September), with its run rate going from $492 million to $1 billion in that period. If we assume the same calculation as Anthropic uses, that means revenues were $37.8 million and $76.9 million respectively, and that its last two rounds of funding are roughly 39 times the size of its 28-day trailing revenues. It is unclear what has substantively changed about the company, other than that the $400 million it raised in September 2025 was insufficient to last.
I know there are going to be venture capitalists who argue that “these companies are just raising opportunistically,” but that doesn’t explain why every AI startup raises so much money in so little time, and how one reconciles that distance with any reality outside of “we can raise unlimited money forever.”
And if they can’t do that, large chunks of OpenAI and Anthropic’s revenues disappear.
Let me summarize all of this in simple terms, as it’s all terribly important.
Every AI startup is effectively an extraction mechanism for OpenAI and Anthropic, reselling their tokens through their services at a massive discount because users are unwilling to pay the actual cost of using LLMs.
Using annualized run rates allows everyone to feel like this isn’t a problem, because it’s far easier to rationalize that Cognition has raised $3 billion this year and is worth $48 billion if it has $1 billion in annualized run rate rather than $80 million a month in revenue.
The obfuscation of the actual underlying financial of these companies is intentional, because it makes it harder to do the kinds of comparisons I’ve done today,
If you disagree, start giving me actual revenue numbers for these companies instead of your magic elf number you made up or got told. If you’re an investor in these companies and have not been told the actual most recent quarterly revenue, there’s a reason — the real one sucks!
If you know it, leak it if it’s so impressive. You know where to find me. Only one of us is being weird, and it’s the cloister of you who condone using bad information because you know the real number — the actual quarterly or annual revenue — is probably going to make people panic.
These companies are also, at least in theory, the ones that pay for AI compute, which means that we also have a very, very poor understanding of the actual customers behind those hundreds of billions of NVIDIA GPUs.
In fact, that’s a great place to go next.
People are going to get mad at me for this because they are cowards, but I posit that we have little understanding of the true price that people are paying for AI GPUs, or about the underlying economics of AI data centers writ large.
Yes, yes, I know, you’re going to tell me that SiliconData has an index, and that that index shows “GPU rental prices going up,” but these prices overwhelmingly favor spot pricing — as in what it costs to rent a GPU right now — versus what companies are paying on a long term basis.
It’s kind of like valuing a shipping company based on skyrocketing spot rates during a capacity shortage rather than the year (or years)-long rates that they regularly charge. You know, kind of like annualizing the data to make things look better.
SiliconData claims to create a “single standardized reading,” but does so using a mishmash of non-specific data that “standardizes for rental term length, cluster scale and interconnect” in a way that only ever seems to benefit the providers.
The reason that SiliconData’s index has become so popular is that we otherwise lack many good measurements, which isn’t to say that its measurements are good, just that they’re the best we’ve got at the moment. While I think the company is doing its best with what it’s got, measuring GPU rates in general like this only seeks to confuse the market through abrupt shifts upwards and downwards based on what I imagine are larger contracts ending or beginning — not that we know, because SiliconData does not actually explain what’s changed or why it might have changed.
This is a huge issue, because NVIDIA has sold $535 billion in GPUs since 2022, and we still don’t know on any reliable basis how much their customers can rent them for. We don’t know with much certainty how much it costs to run one, or have a firm grasp on what an hourly comparison would be — as in how many dollars an hour it cost to run one — outside of analyst estimates (and my own model) that all seem to vary wildly and always end up at a place that says “what a great investment!”
There’s also some compelling evidence that the actual long-term rental rates for these things might not be as high as we think.
I realize some people might read what I’m saying and immediately want to pelt me with rocks, but I’m just telling you what I’ve found, and if I’m honest, I think I’m doing a better job than the vast majority of people covering this shit, in that I’m trying to actually show what people are paying versus aggregating various publicly-available sources of information to simmer out info-slop.
Again and again, I’ve found companies paying way, way less than the supposed going rate of compute.
In late April, The Information ran a story about startups renting AI GPUs, specifically focusing on NVIDIA’s B200s, which SiliconData claims rent at $5.94 an hour, emphasis mine:
Six months ago, Krea signed a six-month contract to rent several hundred Nvidia Blackwell chips for $2.80 an hour per chip after several cloud providers competed aggressively for the startup’s business. But in the last month, when the startup began looking for more AI servers to train a model from scratch, some sales representatives at cloud providers wouldn’t pick up the phone, said co-founder and CEO Victor Perez.
[a few months later]…In the end, the founders reached an agreement to pay $3.70 an hour for another several hundred Blackwell chips in a one-year commitment—32% higher than their last deal. And even that price seemed cheap, given the chip prices he had seen from other providers.
To be clear, the B200 GPU, as part of an 8-GPU pod, works out to about $62,500 a GPU. At this rate, 32% of the first six years of its useful life would be absorbed by depreciation, assuming that it was sold in its entirety for every single hour of every single day of those years.
Then there’s Oracle’s under-construction “Stargate Abilene” data center that landowner Lancium believes will make around $10 billion in annual revenue (see slide 5). At $10 billion a year and 450,000 GB200 GPUs, which works out to — if we assume a take-or-pay rate — to about $2.54-per-hour-per-GPU. If we assume that each of these is inside a GB200 NVL72 at about $3.5 million a piece, that’s about $1.61 per-GPU-per-hour of revenue for the first six years before you get to the COGS.
Then there’s this contract between Australian neocloud Sharon AI and ESDS Cloud FZ-LLC, a Dubai-based subsidiary of Indian enterprise cloud company ESDS, renting out 8208 NVIDIA B300 GPUs at $3.30 a month over 60 months (five years). If we assume a cost of $540,000 per eight-GPU B300 cluster, that leaves us with about $1.54 in depreciation over six years, leaving us with about $1.869-per-hour-per-GPU of actual revenue for the first six years, in a contract that is five years long.
Here’s another. A contract I found between neocloud Kidz AI (which rents capacity from a company called Limestone, or, more-specifically, its subsidiary Catalyst Compute) and AI inference company Canopy Wave priced 256 B300 GPUs at $4.30 an hour (per GPU) for the first three years and $3.50 an hour for the last two of the five-year-long contract.
Your depreciation costs here work out to about $1.54-per-GPU-per-hour, meaning that you’re losing nearly half of your revenue for the first six years…of a five year long contract.
Sure hope there aren’t new kinds of GPUs — or an abundance of older GPUs — after these things end!
QumulusAI is a former cryptocurrency mining company that converted into renting GPUs. In this contract, Qumulus is renting out 128 B300 nodes — so around 1,024 B300 GPUs — at $3.70-per-GPU-per-hour, sitting at about $1.284-per-GPU-per-hour of depreciation.
Again, sure hope there aren’t a bunch of data centers under construction that have the same chips as these!
Buried deep in the earnings report of AI infrastructure company WhiteFiber sits a three-year-long contract with buzzy AI inference firm Baseten, which raised $1.5 billion in June:
In August 2026, we entered into a service order with a new customer, BaseTen Labs, Inc., to provide services utilizing a total of 1,392 B300 GPUs. The service order has an initial term of 36 months beginning on the services commencement date, after which it automatically renews for successive one-month periods unless terminated by either party. The service order represents an aggregate revenue opportunity of approximately $165.2 million. The deployment and revenue generation is scheduled to begin in November 2026.
This all works out to about $1.284-per-GPU-per-hour of depreciation on $4.51-per-GPU-per-hour, which is still more than two dollars less than what SiliconData quotes.
As mentioned, BaseTen just raised a ton of money, so it’s willing to pay through the nose, and even then it’s paying so much less than spot.
There’s also another contract with a customer in Iceland that works out to about $3.46-per-GPU-per-hour:
In August 2026, we entered into a new five-year service order with an existing customer to provide services utilizing a total of 576 Nvidia B300s GPUs in Iceland. The service order represents an aggregate revenue opportunity of approximately $87.5 million over its initial term, with additional potential upside through revenue sharing. Service under this agreement is targeted to commence in December 2026.
As I said a few weeks ago, we do not really have any firm understanding of how much AI compute actually exists, to the point that the largest companies in the world (read: Microsoft) are actively misleading the public about how much they’re bringing online.
We also have no clear understanding of how much data center capacity is actually under construction, because nobody actually wants to tell us, and reports like this from Sightline Climate claiming 190GW “in planning” lump in every imaginable kind of data center with ones specializing in AI compute.
As a result, basically any AI compute capacity estimates are guesses. I’ve seen somebody say there’s “50GW of capacity,” I’ve got guys in my mentions saying that actually Bloomberg is wrong in saying that Microsoft has 2GW of capacity and the real number is 5GW based on nebulous “conversations” that only ever end up as a positive for the AI industry.
In a vacuum of good information, people simply start making things up, which is why The Information published one of the more insane things I’ve read in a while, emphasis mine:
In a time of server scarcity, speed matters. The Louisiana campus plans to consume 330 MW of power to produce 245 MW in usable compute capacity. If that capacity can get online even one month faster than its current schedule calls for, Anthropic could generate an extra $1.3 billion or more in revenue, based on investors’ and analysts’ estimates. That assumes it uses all the capacity to serve up answers rather than develop new models.
I asked Okoye for an explanation, and he gave me one in the comments of the article:
All of this hinges on some very wishy-washy numbers about the per-megawatt revenue of AI labs, which in turn hinge on wishy-washy numbers about how much compute labs have. For example, the New York Times reported that Anthropic would have 5GW of capacity by the end of 2026, up from 1.5GW at the end of 2025, according to statements made to its investors, but I can find no compelling evidence that there was even a gigawatt of compute for it to buy.
If we assume these statements are accurate, then:
The peanut gallery will scream and oink at me for not working out how much is inference versus training, to which I say nobody fucking knows how much of its capacity is actually diverted to inference and I don’t even think they know how much capacity Anthropic has writ large!
We are basing the vast majority of words written and billions invested in the AI bubble on assumptions built on assumptions built on assumptions, several of which fall apart when you ask simple questions like “how does that work?” or “how did you get that number?”
This would all be significantly less-worrying if it wasn’t for the fact that everything is built on the idea that AI is going to be the panacea for the entire tech industry!
Put another way, NVIDIA plans to sell $670 billion or more GPUs next year on top of the more than $500 billion it’s already sold and $800 billion has been invested in AI startups mostly by people with little or no idea about the underlying economics of the data centers or their customers!
A few months ago, a dear friend of mine told me that “nobody knows what they’re investing in,” and it’s taken me a while to fully understand what he meant.
The entire world believes we’re in an AI-powered economic boom of data center capex and AI startup growth based on flimsy information sources that vary from fanciful to utterly hysterical, constantly guessing about how much capacity OpenAI or Anthropic or any of their hyperscale sugar daddies actually have operational, or how much a theoretical data center could charge under the best of circumstances to a customer that may or may not actually exist.
We are four fucking years into the AI bubble, have little or no understanding of the actual economics of AI, and that which we do have is extremely grim. Yet when you try and tell an AI booster (or many members of the media!) that Anthropic lost $8 billion and OpenAI lost $20.97 billion in 2025, their immediate response is to say that 2026 will be fine because of quarters with discounted compute costs and adjusted operating profits that don’t actually resolve the underlying problem that these companies have $1.3 trillion in compute commitments, with at least $413 billion of them being uncancellable.
The hysteria has grown so significant that Gavin Baker of investment firm Atreides (invested in both Anthropic and SpaceX) has simply chosen to suggest that “nobody is taking them seriously,” only to disappear into the ether when asked about which of the contracts he believes are “not taken seriously” and “by whom,” probably because he’s talking out of his ass.
This argument is one you make when you are flat out of ideas, and simply hope that people will leave you alone if you sound authoritative enough, and is known in debate circles as the “nuh uh” technique. The reason Baker is deploying it is that he, like every AI booster, knows that while you can fool the press and investors with made up numbers, there’s very little that “annualized run rates” or “Anthropic ending the year with $100 billion in revenue” can do when Broadcom CEO Hock Tan, who recently likened AI investors selling their investments on the news of OpenAI's adjusted financials to degenerate gamblers, asks Dario Amodei to pay some of the $161.2 billion in non-cancellable contracts he signed.
We live in a climate of intense hysteria and profound confusion, made worse by a media ecosystem that currently appears more interested in sustaining narratives than investigating their underlying truths. The vast majority of hyperscaler AI revenues come from Anthropic and OpenAI’s compute spend that is dependent on near-perpetual funding, much like the vast majority of Anthropic and OpenAI’s revenues come from AI startups that depend on near-perpetual funding to continue their spend.
And to make matters worse, we quite literally have no idea about the underlying financial condition of any other AI companies outside of the main AI labs, and no, annualized run rates don’t mean jack shit.
This is extremely bad and extremely important to resolve. Anthropic and OpenAI’s growth depends on the AI startup ecosystem being able to spend increasing amounts on tokens every single month, without fail, for what appears to be the rest of time. Said startups’ ability to keep doing so depends on venture capital being willing to sling them hundreds of millions of dollars a year each, with each investment increasing the paper value of the company but adding little or no actual qualitative improvement other than their ability to keep playing at the Sam Altman Casino.
I genuinely don’t know why most of the tech and business media is ignoring this problem. Is it that it’s too difficult to comprehend? Is it that you, as a writer, truly think that all of this will work out? Is it that you think that Anthropic and OpenAI will just “negotiate out” of their contracts? If that’s the case, what happens to all the data center capacity? Is it that there’s advertiser or editorial pressure to be “positive on AI”? I truly want to know!
Anyway, I’ve realized now that “nobody knows what they’re investing in” applies to basically everything. Most people writing about AI believe it’ll all work out because nothing bad has happened or they have a single statistic that affirms their worldview. Most people are still saying “it’s like Amazon Web Services!” or “it’s like Uber!” or “it’ll be like the Dot Com Bubble,” even though those are not correct or sensible or rational takes that actually mean much other than “money burn good actually,” and that thin level of rationality goes up to the highest rungs of finance and journalism to a degree that turns my fucking stomach.
Most coverage of AI lives in its own bubble where nothing bad has happened or could happen, and that every promise will come true. Every deranged, multi-faceted “model” that shows OpenAI or Anthropic will print money in the future is an attempt to get away from very, very simple mathematical problems.
And, fundamentally, I’m just not sure anyone gives that much of a shit about being correct outside of sounding as close to the consensus as possible, which is why it’s been so profoundly strange hearing people say things recently that I’ve been saying for years.
You can shove your fingers or whatever other objects you’ve got handy in your ears and say “la la la” all you want, but Jefferies’ Chris Wood just said that “the most likely long-term outcome of the AI boom will be massive capital destruction in the US,” and Barclays just said the following (aside: that lab revenue number is completely wrong, but whatever):
…let's face facts; the combined disclosed lab revenues of circa $150bn and total Al revenues running at around $250bn is not going to cut it when the ecosystem needs well in excess of $1trn to run even the maintenance capex of this up front spend and I've yet been presented with a credible framework as to where this money is going to come from other than pie-in-the-sky TAM numbers whimsically inserted in prospectuses.
I want to force boosters to read this like the guy at the end of A Clockwork Orange. I want them to answer me, in detail, why this is actually fine.
I already know the answer: they’d produce some sort of asinine number that shows that growth is parabolic, that there’s insatiable demand for AI compute, that Anthropic and OpenAI are the next Google and Facebook combined, that there’s not enough compute to go around, or another anecdote that is literally anything other than a meaningful response.
There is little difference between how the various run rates, projections, “models” and “frameworks” used to justify future AI investment are used and the underlying tools of a religious con, except the only community you get here is made up of zyned-up twenty-somethings and the elder millennials desperate for their approval.
The AI bubble is inflated by bullshit numbers peddled by the ignorant, craven or easily-led. Your ability to distinguish between which of them you’re talking to is critical to not losing your shirt.
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If you want to get in touch — and especially if you have any juicy information about Anthropic, OpenAI, or any other companies in the AI bubble — hit me up on Signal at ezitron.76. I’m also on IB on The Terminal.
2026-10-09 23:53:00
Ladies and gentlemen, we are in a glorious, splendiferous, outrageous era of crap!
Some call it “high-yield,” some call it “speculative-grade,” but to those of us living in the real world, we know them as junk bonds, the formerly-sleepy world of below-investment grade debt that’s exploded in the last six years.
Correction: the original copy here had data I pulled from Bloomberg which was incorrect, as it was based on me failing to include already-matured bonds, leaving out the vast majority of high-yield issuance and overstating the growth of junk since 2011.
A better source is through SIFMA (the Securities Industry and Financial Markets Association) that has us at $2.19 trillion in total through September of this year, with about $303.4bn in high-yield, with about $222.1bn in total high-yield issuance in 2011, or $329.6bn adjusted for inflation. I regret the error, and appreciate the reader that reached out to correct it! It does not change anything about the rest of the piece.
The high-yield market has continued to balloon over the last few years thanks to the vagaries of the global economy and, in recent years, the arrival of an entirely new category of crap - AI.
And “junk,” in the case of today’s newsletter, is going to extend so much further than simply “high-yield,” and into the murky world of leveraged loans that have been used to fund dodgy software deals and an increasing amount of data center debt deals.
So why should you give a shit about junk?
As I covered in this week’s free newsletter, Anthropic and OpenAI are two of the single-worst businesses in the history of capitalism, with negative EBITDA cashflows and massive customer concentrations, making them likely to be rated toward the lowest tiers of junk — CCC, or the equivalent rating in Moody’s system, Caa:
A CCC credit rating indicates that an entity is currently vulnerable and dependent on favorable business, financial, and economic conditions to meet its financial commitments. This rating suggests a significant risk of default, meaning that the issuer's ability to fulfill its obligations is highly uncertain and contingent on external factors.
While you probably know this, “default” refers to when a company has missed a payment on its debt and the credit agencies don’t believe that it will ever make that payment.
The AI labs will require $50 billion to $100 billion of annual issuance across the worlds of high-yield, private credit, leveraged loans (high-interest loans with floating rates issued to companies with a ton of existing debt), and revolving credit lines that, according to a source in fixed-income, are the kind of thing you only use as a last resort.
They’d also be the first parts of the AI industry to truly touch the world of junk other than the disgraceful AI neoclouds, companies that raise oodles of debt with the vague promise of building AI data centers some time in an indeterminate future, but serve the more immediate benefit of pumping NVIDIA’s revenue numbers by buying GPUs years before they’ll ever dance with the power grid.
Regardless of how shitty it is, this industry needs hundreds of billions of dollars to fund the theoretical buildout of these potentially-possible data centers — CoreWeave alone, per UBS, needs to raise $102 billion in debt through 2030 — at a rate with no historical comparison. Even the railroad bubble of the 1800s had, based on rough calculations of inflation, only around $250 billion in total investment compared to the $400 billion in issuance expected from hyperscalers alone in 2027.
Yet the vast majority of companies building AI data centers range from low credit to no credit, with whatever creditworthiness they may have based near-entirely on their proximity to either NVIDIA or one of the hyperscalers that is invariably going to be their customer.
The problem, you see, isn’t really the AI of it all, but the uniquely stinky business of being in AI. Even under the best possible circumstances, an AI data center will need billions of dollars up front, years in advance of any potential return on investment, buoyed only by the hype around the potential payoff at the other end.
All of this exists in a time in history when more and more companies are demanding more and more debt from the markets — including the large, wet son of Larry Ellison and his $52 billion bond/loan sale to fund their takeover of Warner Brothers Discovery, $12.4 billion of which was funded with junk due to the sheer scale of the buyout, with Paramount/Skydance/Whatever paying as much as 9.1% on the 10-year end of the deal.
And as I’ve discussed in the past, Larry Ellison’s Oracle now sits on the very last rung of investment-grade debt, with any further downgrades dropping it into the junk markets and leading to investment and pension funds legally having to dump its debt en masse, which would be a big problem considering the $30 billion or more of personal loans Ellison has collateralized using Oracle stock will face brutal margin calls in the event the stock dumps…which it will absolutely do in the event of a downgrade.
Its last downgrade came from the most obvious of places, per S&P Global:
OpenAI remains a key credit risk. We estimate that OpenAI makes up roughly half of the $638 billion in RPO. OpenAI’s ability to meet its contractual obligations and raise external financing will be contingent upon AI tailwinds continuing and its models being market leaders. If OpenAI were unable to pay Oracle, we believe Oracle could be left with massive data center leases that it might be unable to exit or have to re-lease to new tenants under less-favorable terms. As a proxy for OpenAI’s future prospects, we’re tracking OpenAI’s financial commitments to data center operators and chip makers to gauge its overall financial exposure and its market share among enterprise and consumers.
Today’s exploration of junk is a culmination of multiple different threads of premium research, ranging from the AI data center bubble to the SaaSpocalypse, with massive amounts of leverage powering some of the worst deals in history, creating a setup for private credit and other investors to lose billions of dollars as a result.
Welcome to the Hater’s Guide To Junk, or Enter The Crapverse.
2026-10-06 22:54:08
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In return you get a weekly premium newsletter including vast, detailed analyses of NVIDIA, Anthropic and OpenAI’s finances, and the AI bubble writ large. It's a great way to support my free work, and you'll get full access to my massive archive of premium analyses of the tech and finance industry. I just did a two part Hater's Guide To AI Debt that's essential reading given the current climate around AI data center loans.
On Friday, I’ll dive into the world of junk debt – or, what happens when a hyperscaler's credit rating slips into the abyss, or what might happen as a certain money-losing AI lab moves into the world of junk, building on the story I'll tell today.
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Every day somebody asks me when or how the AI bubble will burst, what might cause it, what potential avenues I’m missing, begging, pleading for some sort of hole in the argument outside of “but what if all the bad things don’t happen and the good things are even better than we imagined?”
These questions come from everyone ranging from random internet people to hedge fund managers on my Terminal trying to squeeze me for free research, and every one carries with it some thin vein of wrongheaded hope — that there’s some subtle failure in my arguments and, in turn, some way in which this all turns out okay.
Even those actively agreeing with me hesitate to follow my arguments through to their logical endpoint, mostly because doing so can make you feel a little queasy. If you actually sit and think about the consequences of what’s happening rather than just a collection of different events organized in a row that you have to remember to speak about on a podcast, it’s easy to say stuff like “Anthropic has $413 billion in non-cancellable compute contracts” or “$18 billion in Oracle data center debt is trading at 84 cents on the dollar” without ever really thinking about what any of that means.
Everyone acts as if everything in the AI boom is going to go fine, all without much consideration of the real world and its consequences for the greater tech industry. They assume that OpenAI and Anthropic will go public, grow forever, raise whatever debt they need, and that every single data center investment will work out fine.
And make no mistake, these companies will need at least $50 billion or more in debt every single year, all with what will likely be low-grade junk credit ratings.
It’s time to talk about why that’s very, very unlikely.
Alright kids, let’s talk about debt. Some of you are big, strong, and smart and already know this, but some of you don’t, so we’re going to all learn or re-learn together.
I apologize in advance for having to go through all of this, but trust me, you want to know.
Every month, the US Treasury (the part of the US government that manages the country’s money) has an auction for ten-year-dated Treasury Notes, typically referred to as “Ten Year Treasuries.” These auctions then receive bids. Regular people offer something called “non-competitive bids,” meaning they’ll say how much money they want to lend the government, and then there are competitive bids where financial institutions say “we’ll buy in at this specific interest rate.” The US government borrows over other time periods too, but that’s not important for today.
US government bonds are, in general, considered “risk free,” as they’re backed by the full faith and credit of the American government. This status makes them the base for all lending, because anyone borrowing money has to compete with what the US government (or another government) offers to pay.
You’re paid your interest, in the case of the ten-year bill, every six months, with the principal repaid when the bond matures.
Sidenote: “risk-free” in this case is from the perspective of an investor who has to choose between being paid by the government and a riskier investment like lending money to a company. Although both are unlikely, the odds of the US government deliberately defaulting on its bonds is far smaller than even the most iron-clad of companies.
Ten-year treasuries are considered the “benchmark” rate, because it’s the most actively-traded and liquid security in the world, and while mortgages in the US tend to be structured as thirty-year-long loans, most people tend to either sell or refinance their houses in the first ten years of the loan. It also represents, to paraphrase a friend in fixed-income, a period of time that’s both a long way away but not so long as to be impossible to comprehend.
You’ll also notice that the “pricing” of treasuries (and bonds in general) is usually expressed in percentages rather than prices. That’s because the price of a bond doesn’t tell you how much it’ll pay you, how many payments there are left to go, or what its value is relative to other bonds.
As a result, the pricing page for ten-year-dated US Treasury notes shows an interest rate — 5.33%, for example — that represents “how much money would I get on an annual basis if I invested in ten-year Treasuries today?” based on the soup of different notes in the market based on their various maturities and interest rates.
The ten-year is considered the barometer of investor sentiment — how much the market feels comfortable lending to the US government, based on everything they do and do not know, which is why it shifts so often with economic data and the price of oil.
And, importantly, when a bond “gets cheaper,” its effective interest rate goes up, because you’re paying less money for a debt instrument that pays a consistent amount.
Right now, ten-years are “selling off,” meaning that the effective interest rate on them is going up, based on a few different factors:
So, while we can’t point at one reason, there are plenty of reasons that ten-years are selling off, which is in turn raising the cost of borrowing for literally everybody — consumers, hyperscalers and AI data center developers alike — in a way that’s distinctly difficult to calm down.
Sidenote: I’ve mentioned this before, but when you see treasuries measured in “bps,” that refers to basis points, or 0.01% measurements. This is because a move of even 6bps — or 0.06% — has a dramatic effect on global borrowing costs, considering the size of the global bond market.
That last part is very, very important. While stocks can recover based on good news (even if said good news is entirely fictional), the price of ten-year-dated treasuries is reacting to so many different economic indicators that even the things that should calm it down — like lower-than-expected jobs numbers — aren’t stopping them from dumping.
And so when the ten-years dump, the base interest rate of almost everything increases, and some of the things that are becoming more expensive as a result are actively contributing to the problem. The US Government cannot afford to stop issuing debt, the wars in Iran and Ukraine aren’t going anywhere, and hyperscalers expect to issue $400 billion in bonds in 2027 alone.
As a result, everything gets more expensive for everyone, and the worse your credit is, the worse this gets.
So, now that we know all that, we can speak to the larger problem. When somebody borrows money, they do so priced at a “spread” above the equivalent-dated US Treasuries — usually judged based on the underlying economic health of the company, the general vibe about the kind of thing they’re borrowing for, and the current ‘price’ (read: effective yield) of their debt, usually measured based on its “spread” from today’s US Treasury prices.
This means your borrowing prices can go up based on a few factors:
So if your company — say, Oracle — has a bunch of bad press about its debt being distressed, the market will “price” more risk in, selling off the current debt and pricing it as if the effective yield was higher, setting a floor for how expensive your debt will be. This floor will also increase because the price of US treasuries is likely higher today than it was when you raised.
I’ll give you an example. As I discussed last week, Oracle’s $18 billion September bond sale would have (yes, the number has gone up) over $7 billion in added interest over the course of the bonds due to both the sell-off of Oracle’s debt and the overall Treasuries market.
At the time, ten-year-dated US Treasury notes were at a mere 4.13% — as mentioned above, a 0.06% increase is significant, so a 120 basis point difference is gigantic — but Oracle’s overall risk has also exploded along with them.
At the time, spreads were between 105bps and 165bps (so 1.05% to 1.65%) above US Treasuries. Today, those spreads range from 171bps to 282bps, a double dose of pain at a time when the market sentiment is that it doesn’t trust Oracle as much as it did in September — as in how much more it’s demanding over the benchmark rate offered by the government — has increased along with the cost of the benchmark itself.
And so, while I’m not going to repeat everything I went over last week, the point I’m making is that anyone raising any AI-related debt is going to get shafted by both overall Treasury prices, sentiment around AI in general, and their own specific financial situation.
Yet Oracle is, at least for now, “investment grade,” which means that even though its debt “trades like junk” (IE: investors are asking for effective yields of anything around the high-yield index’s average of 8.2%), it’s in a much better position to borrow than the vast majority of AI data center SPVs or neoclouds like CoreWeave, which has Goatse-level spreads of 672bps to 882bps, with the effective yield on its shortest-dated debt (four years) sitting at 11.53% and its longest-dated (a mere six years) sitting at 13.49%.
There are, of course, ways around borrowing on your credit profile. For example, when CoreWeave opened an $8.5 billion delayed draw term loan facility in March 2026, it was able to get it rated as investment-grade and at SOFR (the percentage on overnight borrowing from the Fed for banks) plus 2.25%, all because the counterparty was Meta, and thus the underlying payments would be considered “safe.”
This may seem like the cheat code to get around all these horrifying rates, except CoreWeave, per analysts at UBS, needs to raise $102 billion in debt through 2030, which means it will have to keep raising on its own two feet.
Well, there’s a problem there.
Per The Information, “cracks are beginning to form” in the AI data center debt boom:
Bond markets are getting tough for lower-rated borrowers. One example: CleanSpark, which is developing a data center for Meta Platforms, had to offer investors big concessions to land financing earlier this month.
That deal is among the clearest signs yet that financing the data center boom is getting expensive across the board, and investors are getting picky about which new projects they’ll back. The bank loan market too is showing signs of strain, with some lenders like Société Générale, Sumitomo Mitsui Banking Corp. and Mitsubishi UFJ Financial Group becoming more selective in lending to data center projects, people arranging the deals say. Those dynamics mean companies could struggle to borrow for planned data centers, jeopardizing growth plans for the broader AI industry.
Long-term Zitronistas will remember when I brought up some of these names in my end-of-year-2025 piece The Enshittifinancial Crisis, with SMBC present in seven and MUFJ present in seventeen of the data center deals I analyzed, with one or both of them in effectively every Stargate and CoreWeave debt sale, and both involved in SoftBank’s $15 billion 2025 bridge loan.
“More selective” doesn’t necessarily mean “done investing,” but is more akin to the lights going on in a particularly-rowdy party and seeing who may or may not have pissed themselves. Data center debt is now pricing based on increasingly-sour sentiment driven by power delays, local pushback and a general anxiety that maybe these debts won’t actually get paid.
They also might realize that their due diligence was lacking when it came to building some of the most-ambitious infrastructure projects in history. Here’s an example of how deep the due diligence was for Blue Owl’s $10 billion investment in AI data center projects, per The Information:
Blue Owl’s willingness to make fast decisions has made it a favored partner for the developers racing to produce giant data centers. It took just 15 minutes for Blue Owl executives to agree to invest up to $10 billion in future projects alongside real estate firm Primary Digital Infrastructure during their first in-person meeting two years ago, said Primary chief investment officer Bill Stein.
In any case, AI data center debt is way more expensive now by virtue of the current state of treasuries, with costs compounded by the anxiety around them in general.
This means that any “virgin” projects — those that aren’t directly backstopped or co-signed by hyperscalers — are guaranteed to hit egregiously-high, 9% to 14% rates, which makes (as I discussed a few weeks ago in my two-part debt series) it near-impossible to make the already-questionable economics of running a data center work.
This means that any AI data center debt being raised right now is doing so under stricter credit conditions and doing so at unrealistic, unsustainable prices, if they’re going to be able to raise at all. This makes the $174 billion in debt that SB Energy needs to raise to fund its theoretical data center project with OpenAI — even when backstopped by NVIDIA — either horrendously expensive or impossible to complete, as does it mean that any future large, multi-gigawatt projects will add billions of dollars in interest payments to already-expensive debt.
This all makes pontifications that data center spending will increase to $32 trillion by 2050 equal parts stupid and wasteful, on top of the overall problem that there isn’t even enough demand right now for hyperscalers to break even on their 2026 and 2027 capex plans.
Yet data centers are buildings with stuff in them that, in theory, could be repossessed and leased to another party — or at least sold off — in the event of a default. Investors would theoretically get some sort of return (based on the seniority of their debt, but that’s not important right now) if CoreWeave died, or if Oracle’s Project Jupiter (the one connected to its “Force Majeure” notice) failed to secure power.
We’ve got another problem on the horizon, and nobody seems to be talking about it.
Back in June, Anthropic President Daniela Amodei said the following at a conference:
"It's a very capital-intensive business to train AI models," Amodei said at the Bloomberg Tech conference in San Francisco. She added that the public market is "very well-suited to that."
Quick question, Daniela: are they?
Nobody seems to want to talk about what Anthropic’s plan is once it goes public as far as continuing to raise egregious amounts of capital. Once a public company, Anthropic will no longer be able to raise venture capital at its current scale (over $95 billion in 2026) alone, and if it intends to raise even half that much on a yearly basis, it will become one of the largest issuers of junk-grade debt in history.
In both AI labs’ cases, they are most-decidedly going to be priced like junk, even if the malignant scumbags are able to con ratings agencies into giving them investment-grade ratings, because while the bond markets listen to credit ratings, they price based on what the actual company looks like, guaranteeing Oracle-esque 8% minimum yields on whatever they issue.
The problem is that OpenAI and Anthropic don’t really have assets. They don’t own any of their data center infrastructure, the chips inside, or even their office buildings, and that’s before mentioning their negative cashflows and products under constant threat from cheaper open source alternatives.
This means there’s very little for the company to offer as collateral, and would be raising debt based on a theoretical break-even point somewhere in the future, one that they would both have to actually explain with a level of depth that neither of them have had to deal with.
The big difference between Anthropic/OpenAI and SpaceX is that the debt in question would be raised to fund company operations rather than capital expenditures, as training costs are not capex and at least based on OpenAI’s audited financials are considered operating expenses. While CoreWeave loses a bunch of money, those losses mostly come from the expensive debt it has to take on to fuel its capex ambitions and the depreciation of its GPUs, meaning that it has (if you remove its largest costs!) a positive EBITDA.
Sidenote: CoreWeave is still a massive stinker of a company!
This difference also likely precludes either company from raising capital via an SPV or other off-balance sheet funding, because those are collateralized using the underlying asset, such as Anthropic’s $161.2 billion in non-cancelable contracts to lease back Broadcom’s TPUs. Convertible bonds — low-interest bonds that can convert if a stock price is hit or, at maturity, allow investors to take stock or cash — are an option, but run the real risk that the stock is lower than when the bonds were issued, meaning that Anthropic or OpenAI would have to pony up a ton of cash.
The only exception would be an SPV tied to customer payments which I’ll get to in a bit.
Yet things get a little messier when you factor in non-cancelable contracts, which total $413 billion in Anthropic’s case across the next seven to ten years, guaranteeing financial strain at a time when future cashflows are far from guaranteed. Take-or-pay agreements — the subprime mortgages of the AI bubble — are considered debt equivalents in the eyes of creditors, which would in turn drag on cashflows.
You see, you can jingle the keys of "annualized run rates” and “adjusted operating income” in the faces of venture capitalists and journalists as much as you want, but investors require actual cashflows, even within a frothy market. Anthropic’s vulgar “Earnings Before Training, Interest and Taxes” measurement does not matter when the entire calculation is made on cashflows, and any attempts to act otherwise are either ignorant or deceptive.
EBITDA, of course, refers to Earnings Before Interest, Depreciation and Amortization, which means that all “above the line” costs — such as inference costs, training costs, SG&A and leases — are counted, but the cost of depreciating assets like GPUs (of which Anthropic and OpenAI have none) and interest is left off.
Let me give you a very straightforward example from my own story on OpenAI’s audited 2025 financials, when it had $34 billion of expenses and $13.07 billion in losses, for an EBITDA of $20.92 billion, or an EBITDA margin of negative 160%.
At that EBITDA, it would immediately slam the door shut on an investment-grade rating for OpenAI or Anthropic, and likely get rated between a B (highly speculative) and a CCC+ (substantial credit risk). If ratings agencies push through at that grade, it means they are completely and utterly corrupt, and going against their own guidance about how credit ratings are provided. Every single metric underlying an investment grade rating comes from EBITDA, and SpaceX was only able to qualify through the success of Starlink as a profitable business.
S&P Global also penalizes revenue concentration — specifically referring to customers or products making up a large slice of revenue. In Anthropic and OpenAI’s case, they effectively have two products — the API and subscriptions — and nearly a quarter of Anthropic’s 2025 revenue came from two customers. While SpaceX is a shitty company, it’s a diversified one. OpenAI and Anthropic are not.
In any case, if either gets an investment-grade rating, it’s likely that a hyperscaler has stepped up and guaranteed some or all of the debt, but in doing so, they’d likely sacrifice part of their own credit rating in the process. The same might happen — as hinted at above above — through a rotten kind of SPV, where the hyperscaler guarantees a certain amount of contracted revenue to OpenAI, allowing a connected SPV to raise at an investment-grade interest rate.
Sidenote: Investment grade would allow them to open up much, much longer-rated debt. I’m honest that this happening would require a lot of people to ignore a lot of the very basic mechanisms used to rate and issue debt, which means I’d need some of the rationale behind it to even begin to estimate what might happen.
I’ll add that if this happens, it will be a catastrophic failure of regulation and ratings agencies at a level unseen in the equities markets.
If we assume that OpenAI or Anthropic gets a junk rating, it becomes entirely-unable to raise from the investment grade market, leaving it with dwindling options based on how junky that rating is. The problem they face there is that even the highest-rated level of junk (BB+) requires sustained cashflows, and even lower rungs like B+, B and B- need some sort of path to EBITDA positivity.
Each level of junk grade carries its own limits in both how much money they could raise based on their particular financial profile and the hard-and-fast rules of various funds investing in high yield debt.
When I say “CLO-eligible term loans,” I’m referring to collateralized loan obligation funds that scoop up large buckets of loans and resell them as one investment vehicle, buying somewhere between 60% and 75% of corporate loans. These CLOs also have their own rules about exposure to debt based on various factors, including industries and credit rating, and depending on where OpenAI or Anthropic fell, the demand for their loans — and secondary sales of their loans (because investors LOVE to resell debt!) deteriorate dramatically based on their rating.
The following assumes a $2 trillion valuation, which is far from a foregone conclusion.
If we assume $50 billion is what they need on a yearly basis, this becomes increasingly difficult based on the credit rating.
As you can see, debt alone isn’t getting these companies $50 billion a year outside of a rating that’s near-impossible outside of ratings agencies defaulting on their jobs.
In theory, at a $2 trillion valuation, both Anthropic and OpenAI could sell directly onto the market, but using this as a serious ongoing funding mechanism naturally depresses the price, though the scale of the float — as in how many shares were sold at the IPO.
At that valuation and a planned raise of $60 billion to $100 billion, Anthropic will have a very small float — around 5% — which would mean it was naturally capped on how many shares it could dump in a particular year. It’s hard to gauge this based on the unknowns of its trading volume, but if I had to guess, there’s maybe $20 billion in annual share sales it could do.
AhhhhhHHHH FINE YOU WANT TO TALK ABOUT UBER SO BADLY WE’RE GONNA TALK ABOUT UBER AND AMAZON AND TESLA!
I’ll add that both Anthropic and OpenAI want to raise $50 billion at IPO.
In other words, stop making these comparisons, they are not accurate.
I realize I’ve gone through a lot of technical stuff so far, but the reality is pretty simple: Anthropic and OpenAI do not resemble the financial condition of any company I could find in the history of the stock market outside of WeWork. They are unprofitable, unsustainable, and the only conditions under which they could raise significant debt would involve catastrophic failures of regulatory and ratings bodies.
Sidenote: When WeWork tapped the bond markets in 2018, it received ratings of B (from S&P) and BB- (from Fitch). Both sit below the BBB- threshold for investment-grade debt.
And while WeWork resembles OpenAI and Anthropic, insofar as it didn’t actually own any of the workplaces it rented out to freelancers and startups, it differs in how much it wanted to borrow, seeking only $500m, and the fact that WeWork had a fairly diversified customer base, which is something that (as mentioned) credit ratings, but especially S&P, tend to look favorably on.
I’ll add that the only thing worse than allowing them to go public will be to allow them to raise debt at anything other than the junkiest levels that the market has to offer. These are not stable businesses, nor are they run with much regard for their underlying capital or employees. Both of them have massive amounts of concentration risk, unstable customers, brittle economics, own virtually no assets, and have demonstrated little to no ability to reduce their costs outside of questionable accounting that doesn’t change the fact that they cannot afford their bills.
As a result of their $1.3 trillion in compute commitments, neither company can become “capital efficient” by cutting their training costs, because said training costs are the only means of further growth outside of massive price increases that are unlikely to grow their businesses.
To make matters worse, both are cutting prices to compete with each other, and per Ramp, said price cuts aren’t increasing usage:
Neither company can afford to exist without near-infinite resources, and neither company can afford to slow down due to their massive compute commitments, which have become materially linked to the future growth trajectories of effectively every hyperscaler, as well as Broadcom, which — in pursuit of becoming NVIDIA — has added ruinous amounts of debt at the worst time in history to do so.
And that’s really the biggest problem here.
While Anthropic and OpenAI are yet to pillory the debt markets, their counterparties — and those inspired by them — have been doing so for years with little or no return on investment.
As I discussed a few weeks ago, there are currently over $200 billion of NVIDIA GPUs sitting uninstalled in warehouses, with Morgan Stanley (as found by Bryce Elder of the FT) estimating that more than half of GPUs sold in 2026 through 2028 won’t have anywhere to plug in. Hundreds of billions of dollars have been spent on data center capex for effectively no reason, outside of the belief that there’s “insatiable demand for AI compute” when the reality is that more than 70% of all AI revenues — and I estimate more than 80% of all compute sales — are from Anthropic and OpenAI taking up whatever capacity comes online, leaving very little left for the rest of the world and creating the illusion of massive demand.
Both of these companies want to dump themselves onto the public markets, raise tens of billions of dollars of debt a year, and dump further shares onto unsuspecting investors based on unrealistic revenue projections of hundreds of billions of dollars a year by 2028. Both OpenAI and Anthropic are astonishingly bad businesses, losing $20.92 billion and $8 billion respectively in 2025. The best response that anyone has got to these shocking figures is to vaguely point to adjusted profitability numbers provided by companies that have constantly shared deceptive annualized run rate figures as a means of obfuscating their financial condition.
And these companies account for, per their own obligations, $1.3 trillion of future earnings across Microsoft, Google, SpaceX, Oracle, and Amazon, with $413 billion of Anthropic’s commitments being non-cancellable, and OpenAI projecting to spend at least $750 billion on compute through the end of 2030, with no answer as to how these companies afford to do so.
Analyst expectations have OpenAI and Anthropic contributing at least $444 billion in revenue across hyperscalers in the next three years, and if this revenue fails to arrive — either through insolvency or renegotiation of terms — every connected hyperscaler will see massive revenue misses. These are not hyperbolic, mean-hearted or “skeptical” claims, but the hard mathematics underlying an industry that so often convinces those supposedly analyzing it to ignore good sense and assume that nothing bad will ever happen.
Meanwhile, nobody seems to be taking the shocking financial condition of Oracle very seriously, despite effectively every warning light blinking at once. It is beyond abnormal for a company backing an $18 billion data center project to give a “force majeure” notice no matter what it says on Twitter, and suggests that the $340 billion in data centers it’s building for OpenAI are materially behind schedule, on top of the fact that Oracle is making sounds like it doesn’t intend to pay its debts, which is extremely alarming!
And make no mistake, if Oracle builds these data centers and OpenAI doesn’t pay for them, it will face an existential financial risk unseen in the history of the tech industry. Oracle’s revenue has been flat for fifteen years when adjusted for inflation, with its only growth coming from its wrongheaded acquisition of Cerner in 2021 and selling AI compute that destroy its gross margins, with its largest company being a technically-insolvent startup with volatile economics and a CEO who wants us to accept “bad things will happen” in exchange for whatever ChatGPT is supposed to be.
The problem Oracle also faces is that things don’t have to collapse for a collapse to occur. Chairman and founder Larry Ellison just added another $9.2 billion in stock-backed personal loans to his already-large pile, bringing it (by my count) to around $30 billion, and the margin calls will start somewhere around $60 a share for a stock that pumps and dumps based on any OpenAI news, meaning that anything along the lines of “OpenAI can’t pay Oracle” is guaranteed to start a spiral.
The only reason this hasn’t happened yet is that the media and the markets are unwilling to accept the sheer impossibility of Oracle’s $300 billion, five-year-long deal with OpenAI that neither company can afford and Oracle doesn’t have the capacity to serve. Every one of the “Stargate” data centers is heavily behind schedule, and Stargate Abilene — which Oracle claims is “75% delivered” — has no more than half of its capacity installed, not that anyone bothers to check these things or investigate the claims of anyone connected to the AI bubble.
I roll my eyes at the feint and whiny concerns from Bloomberg about “risk related to Larry Ellison” as a result of Paramount’s huge debt raise. Anyone with a fucking calculator and an interest in the truth could’ve seen last year that none of the underlying economics of Oracle’s situation made much sense, it just required not immediately assuming that every AI data center was a perfect angel that would be birthed without fail onto a world flush with cash.
Then there’s the shocking deterioration of semiconductor firm Broadcom, which is tied to Anthropic for at least $161 billion in non-cancelable chip leases, which has in turn forced Broadcom to raise $60 billion in debt to build them. Broadcom is, as covered in my Premium Hater’s Guide, a company already bathed in debt thanks to its 2023 acquisition of VMware, one that appears to be taking on tens of billions more as a means of selling TPUs to a company that may or may not exist by the time there’s a data center to put them in.
How, exactly, is Anthropic meant to pay for all of those compute leases (or all of that compute) based on its current financial position? Taking away however I may feel about AI in general, for it to reach a size where it can handle even a hundred billion dollars a year in annual operating expenses — Microsoft, by comparison, is at around $176 billion — Anthropic would have to become one of the single-largest cash generators in the history of capitalism, or such a large participant in the world’s debt markets that it starts sucking up cash from the already-distressed and desperate customers of the CCC (lowest tier of junk) bond market.
While a few people have danced with the edges of the potential insolvency of OpenAI and Anthropic, nobody seems to want to talk about the actual consequences, choosing always to take one shot of hopium before getting into the grisly details, with the assumption being that something will go alright — so I’m going to rain on everyone’s parade and go through each point one by one.
The reason that so many of these misunderstandings exist is that people do not, on the whole, are surprisingly optimistic about basically any consensus opinion. Everybody has been saying that AI data centers are the next industrial revolution, NVIDIA’s stock has gone parabolic, every media outlet has constantly discussed Anthropic and OpenAI, and every hyperscaler has sunk hundreds of billions of dollars over the last few years into AI, which in turn makes you believe that everyone must be right and that everything will be alright by extension.
This immediately makes people turn off the parts of their brain that feature critical thinking, because the alternatives are so utterly opposed to what’s been promised by this industry and the media. The assumption is always that this much money can’t be wrong, or that these are the smartest people in the world, or that these are the largest and most-successful companies in the world, even though none of these statements actually answers a single question about “how the fuck does all of this actually work?”
Even if you think AI is the most wonderful, beautiful software tool ever imagined, there is no rational basis under which you can look at the current economic picture and say that everything will be fine.
The future I am talking about — one where most data center debt goes unpaid, where OpenAI and Anthropic fail to meet their obligations, and when AI GPU sales grind to a halt — involves Google, Microsoft and Amazon having catastrophic misses on their earnings expectations, and their future revenue growth stories collapsing, along with NVIDIA’s revenues dropping as much as 90% once the debt-backed AI capex boom ends. It involves CoreWeave, IREN, Nebius, and every other neocloud running out of money, fucking over investors in both their stock and debt some time in the next few years, with the underlying collateral made up of otherwise-useless data center construction and GPUs that will, at that point, be in a supply glut rivaling the Atari video game burial.
I must be clear that I only have to be half right for things to be extremely bad. NVIDIA’s revenue growth cannot be sustained without endless debt issuance at a time when issuing debt is incredibly expensive, all in pursuit of data center construction that takes years to complete for customers that may or may not exist when it does so.
Hyperscalers have no other hypergrowth ideas left — no new Google Search, Microsoft 365, or Facebook — to sell investors, and in pursuit of AI have become the most asset-burdened companies on the Fortune 500, rivaling ExxonMobil, Berkshire Hathaway and Chevron, except instead of oil and diverse stocks they have GPUs that only retain value during a hype cycle.
And there really is no hope for the $800 billion or so invested in AI startups in the last four years, as AI acquisitions are thin thanks to their high costs, miserable revenues and utter lack of intellectual property. I’m not sure venture capital — or anyone covering venture capital — has actually conceptualized how significant the losses may be, because I can see a world where virtually every AI investment goes to zero at a time when venture capital is facing an historic losing streak.
In fact, I’m not sure anyone is trying to conceptualize what actually happens once the bubble bursts, because doing so requires you to think not just in terms of wasted capital, but about hundreds of billions of dollars of unpaid loans, dead AI investments, half-finished data center projects, and a stock market where 24% of the S&P 500’s value comes from five companies with stock prices boosted by theoretical returns on AI investments that are mostly from OpenAI and Anthropic.
The fact we’re living in this bizarre juxtaposition of reality where we can run headlines about OpenAI-connected data centers never getting completed and its massive losses aside headlines about $50 trillion in data center construction by 2050 is a sign that nobody is taking the threat seriously enough.
I would love to say that I think I’m overreacting somehow, but I spend every single week running the numbers and actively looking for evidence that I’m wrong, mostly because the world, despite discussing the fragility of the AI bubble, doesn’t seem to want to think about it actually bursting.
I encourage you to do so, even if you’re pro-AI, even if you truly disagree with me, because this is extremely serious, and you can’t pay $1.3 trillion in commitments with hope. You can truly, madly love LLMs, you can name your dog Dario and your guinea pig Sam, I don’t care, but please, I’m begging you, stop making assumptions based on the best-case scenario, and start taking this seriously, because the consequences of me being right have global stakes.
Anyway, I’ll leave you with a chart from Torsten Slok, Chief Economist at Apollo, and a very reasonable question: if all of these tech companies are expecting record earnings over the next few years, where exactly will the cash come from?
The analysts covering tech expect the sector's operating cash flow to more than double to roughly $2.4 trillion by 2028, an increase of over $1.2 trillion, see chart below. Meanwhile, the analysts covering the other sectors in the S&P 500, which are tech's customers, expect those companies to add much less operating cash flow.
In other words, the tech silo is betting on a future in which demand for AI and tech services explodes, while the silos covering the companies that would pay for those services see a much more modest outlook. Both cannot be right at the same time.
The bottom line is that either tech's customers will generate a lot more cash than their analysts expect, or tech's cash flow forecasts are too optimistic, which raises the question of who exactly will be writing all those checks to buy AI services.
The fact we can’t cleanly answer this question as hundreds of billions of dollars get sunk into AI data centers may be the most glaring miss in the history of finance.
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