2026-09-25 22:25:21
The year is 2026, and you are a hyperscaler CEO. You zip up your Patagonia Vest, type UDPATE CALENDER WHERE WHY to your Muse agent, and it tells you that your CFO has sent you an email about something called a “critical finance meeting,” and you roll your eyes.
You were up until 2AM talking to your 38 GPT-6 agents that were vibe coding a dashboard of “company efficiency wins,” and if anything it’s kind of rude that your CFO is interrupting your “mindfulness hour” where you listen to Andrew Hubermann and do something called an “elevated ab crunch” that hurts your neck every time, somehow.
Behind you, your horribly-trained Shiba Inu (called “Basis Points”) angrily humps your Eames chair, and when you tell it to stop it only seems to hump it harder. Your CFO, who has been waiting for 15 minutes, appears on the video with a grim look in their eyes. “What is this? What is the need for this interruption?” you snap. “You’re ruining my mindfulness! Have you any idea how important my mindfulness is? It’s so early in the day, and I’ve barely had any mindfulness!”
Your CFO stops themselves from saying that it’s 12:15PM, and decides to cut to the chase. “Hey, so, remember our conversation last week?”
You begin to shake uncontrollably. “...no. I. don’t. How d-”
Your CFO interrupts, and seems more stern than usual. “Listen. You wanted us to buy a bunch of GPUs, and we bought a bunch of GPUs. That’s fine. But we’ve had to raise tons of debt to do so, and it turns out that the debt that we’ve raised isn’t enough to build all of them, and they’re taking years more than we expect to-”
You begin shaking again. “You…you made a mistake. You messed up. This is on you.” Basis Points is now staring at the wall and growling at it for some reason.
The CFO frowns. “No, these are entirely separate externalities — the war in Iran, the Fed hiking interest rates, the concerns around AI data center debt, the whole supply chain is screwed, everything’s getting more expensive, and we have a bunch of debt-”
You roll your eyes. “Just make it go viral, I don’t know what to tell you,” you say as you hang up. With your mindfulness hour ruined, your week is effectively washed, so you decide to book a trip to Hawaii to recover.
After all, all that boring shit is someone else’s problem!
…except it really, really isn’t.
In last week’s newsletter (and part one of the Hater’s Guide To AI Debt series), I went into the core issues with the AI bubble’s debt spree, which can be simmered down to a few major points (and these are all helpful links to the specific part of the newsletter for easy reference!):
Put another way, the first part of The Hater’s Guide To AI Debt was about the form of debt — how it’s raised, how it functions, and where it might be going — and today’s about the function.
The biggest worry I have about the AI bubble right now is that the debt is priced for perfection, yet said debt is being invested in thousands of the most-ambitious, intricate, and fragile infrastructure projects in the world, requiring specialist talent and materials and access to power at a scale unheard of in the history of society.
And in many, many cases, the companies building them don’t even have any experience building AI data centers, securing power, or, in many cases, doing much of anything.
You see, despite the AI bubble being inflated by some of the largest and most powerful companies in the world, the actual AI data center buildout is being handled in a way that borders on the lackadaisical “Move Fast And Break Things” model of Silicon Valley.
While they may have an idea of what they’re building and the money to do so and lots of well-credentialed experts, every data center project is its own unique monster with ever-expanding problems caused by everything from geography to the weather to the mechanical issues you find from condensing the power of an entire city into a space a thousandth of the size full of expensive AI chips that need bespoke cooling.
And it’s this gaggle of choices — thousands of chaotic, problematic and ultra-challenging infrastructure projects — that the finance industry has fed somewhere between $100 billion and $150 billion of debt (without including hyperscalers), and plans to feed hundreds of billions of dollars more, all under the assumption that “everything will be alright” and that these are, functionally-speaking, no different from building a regular building.
In part two, I’m going to talk about the grisly truth of the AI data center buildout — the doom loop of debt, delays and doubt that will compound the costs of getting these things built, and how the whole thing has become so unfathomably expensive that it makes the economics of building an AI data center — and paying off the underlying debt — near-impossible for the majority of projects.
This is The Hater’s Guide To AI Debt Part 2, or Gross Profit Unlikely.
2026-09-22 22:35:46
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Soundtrack: Flobots - Handlebars
A few months ago, I asked where all the data centers were, because I was struggling to find proof that very many were being finished despite all the capex spending, construction, and the skyrocketing cost of RAM.
The answer to that question was, effectively, “nowhere.” The vast majority of Microsoft data center projects I could find had barely gotten started, aside from the massive OpenAI-dedicated “Fairwater” data centers that are “open” in the sense that some of the buildings are turned on, with the remainder either being actively built or with construction expected to start at some later point.
A couple of months later, The Guardian and I ran an investigation working with a source familiar with Microsoft’s GPU capacity, finding that it had approximately 2.2 million chips. At the time, I was unable to verify whether these were all of its GPUs, or whether OpenAI had its own allocation that wouldn’t appear in the data we obtained, which is why I left out one piece of reporting — that, based on the precise loadout of GPUs in operation, that Microsoft had around 1.993GW of capacity, backed by around $50 billion in GPUs, predominantly made up of NVIDIA H100 and H200 chips, with a decent amount of GB200 and GB300s.
Two weeks ago, Bloomberg reported that Microsoft had “about 12 gigawatts of capacity,” but that “...only about 2 gigawatts of the company’s current 12-gigawatt capacity is centered on AI-specific chips.” A quote follows:
Newer AI tools use increasing amounts of CPU servers in addition to GPUs, meaning Microsoft needs to expand all types of computing power, according to [infrastructure expert Alistair] Speirs, “GPUs by themselves don’t make great AI infrastructure,” he said.
That’s a really nice way of saying that Microsoft is directly misleading reporters, investors and the general public about its capacity. It has claimed again and again that it has brought gigawatts of capacity online for the best part of a year, all, in my opinion, with the intent of misrepresenting the scale of an AI data center buildout where I believe most of the GPUs, to quote Satya Nadella, are now “sitting in inventory that [they] can’t plug in.”
Today’s newsletter is about a cruel truth: that NVIDIA’s revenue growth is almost entirely the result of speculative purchases by hyperscalers and neoclouds that take years to install its GPUs.
In other words, I think everybody is completely wrong about data center capacity, and it’s going to be a nightmare to untangle once they work it out.
Everybody wants AI to be just like the Dot-Com Bubble, when I fear that GPUs may end up like the millions of unsold copies of Atari’s ET found buried in New Mexico.
In September 2025, CEO Satya Nadella claimed that Microsoft had added 2GW of capacity “in the last year,” and acted as if Fairwater, a project with two actively-constructed data centers with one in Wisconsin that broke ground in September 2023 and another in Atlanta that broke ground in July 2024, was something to be “announced” rather than “a very expensive project that has taken forever.” Nadella also claimed that there are “multiple identical Fairwater datacenters under construction,“ though he neglected to name them.
In earnings calls for its second, third, and fourth quarter fiscal year 2026 earnings, Microsoft would use near-identical phrasing:
While I cannot say the exact rationale for making these statements, I can find no way to interpret them other than being an act of deception.
In its Q2 2026 earnings call, Microsoft CFO Amy Hood responded to UBS analyst Karl Keirstead’s question, again repeating the “gigawatt” language [emphasis mine]:
KARL KEIRSTEAD, UBS: Okay, thank you very much.
Satya and Amy, regardless of how you allocate the capacity between first party and third party, can you comment qualitatively on the amount of capacity that you have coming on? I think the one gigawatt added in the December quarter was extraordinary and hints that the capacity adds are accelerating, but I think a lot of investors have their eyes on Fairwater Atlanta, Fairwater Wisconsin and would love some comments about the magnitude of the capacity adds, regardless of how they’re allocated in the coming quarters. Thank you.
AMY HOOD: Yeah, Karl, I think we’ve said a couple of things. We’re working as hard as we can to add capacity as quickly as we can. You’ve mentioned specific sites like Atlanta or Wisconsin. Those are multiyear deliveries, so I wouldn’t focus necessarily on specific locations.
The real thing we’ve got to do, and we’re working incredibly hard at doing it, is adding capacity globally. A lot of that will be added in the United States, the two locations you’ve mentioned, but it also needs to be added across the globe to meet the customer demand that we’re seeing and the increased usage.
We’ll continue to add both long-lived infrastructure. The way to think about that is we need to make sure we’ve got power and land and facilities available, and we’ll continue to put GPUs and CPUs in them when they’re done as quickly as we can. And then finally, we’ll try to make sure we can get as efficient as we possibly can on the pace at which we do that and how we operate them, so that they can have the highest possible utility.
Note that Karl’s line of questioning directly addresses Fairwaters Atlanta and Wisconsin, two explicitly AI-focused data centers dedicated to OpenAI, and Hood responds by talking about a gigawatt of capacity in the December quarter (referring to Q1 FY26, when no mention of adding a gigawatt in a quarter was made).
I already anticipate the response here will be that what Microsoft said here is “legal,” because it never said that this was AI capacity, but anyone responding like this operates with a peasant’s mindset. Anyone reading this transcript or hearing Microsoft mentioning that it added “gigawatts” of data center capacity is thinking about AI data center capacity — as evidenced by this piece and this piece and basically everyone you talk to on the subject.
Let me be very blunt: it appears that Microsoft has, since the beginning of 2022, spent around $265 billion in capital expenditures (and added around $320 billion in assets to its properties, plants and equipment) to bring around $50 billion of GPUs online, and has used broad, vague language to make it seem like it’s been far more productive.
Nobody on God’s green Earth is sitting here wondering if Microsoft added gigawatts of CPU capacity or cloud storage, nor are analysts desperate to hear about aggregate numbers — they’re asking where is all the money you’re spending on AI going, and the answer, it appears, is “into a warehouse” or “into a data center without power.”
To bring home the point, I analyzed Microsoft’s earnings calls between Fiscal Year 2010 and Fiscal Year 2022, and found little discussion of data center capacity outside of competition with Amazon Web Services and Google Cloud, and no discussion of megawatts or gigawatts. When it comes to earnings calls, Microsoft’s use of megawatts and gigawatts is explicitly AI-era terminology, used for the first time in its Q4 FY2025 earnings call, though it had used it elsewhere, such as in a document reported on by Business Insider in April 2024 that said it had brought online “more than 500 megawatts of new data center capacity,” including this damning quote, emphasis mine:
In the second half of last year, Microsoft delivered "record-level GPU capacity," more than doubling its total installed GPU base, the document said, without mentioning actual numbers.
It appears that Microsoft is being vague with its numbers to hide the very obvious truth: that it’s spending hundreds of billions of dollars to buy GPUs that sit in warehouses or unpowered data centers, likely years in advance.
This is a huge scandal. Why is Microsoft buying so many GPUs if it’s not got anywhere to put them? Why isn’t it waiting to buy the GPUs when it needs them, rather than buying them months or years in advance? Why is Microsoft telling us it’s bringing gigawatts of capacity online when it’s very clearly doing otherwise?
And at this point, can we take anyone’s capacity announcements seriously?
Microsoft is one of the first companies to build a GPU-powered AI data center (back in 2020, specifically for OpenAI), and has both incredible amounts of experience standing up compute infrastructure and resources to make it happen.
I need to stress this point, because outside of the hyperscalers, the other companies building large-scale data centers are neoclouds, many of which were former crypto mining companies, or Oracle, which only dipped its toe into cloud infrastructure in 2016, long after Microsoft launched Azure and Google launched Google Cloud.
If anyone has the expertise and resources to deploy large-scale AI infrastructure assets at scale, it’s Microsoft — and, as a result, Microsoft’s effectiveness in deploying AI infrastructure is more meaningful than, say, that of a Neocloud or even Oracle.
As I said above, Microsoft had — as of a few months ago — approximately $50 billion of actual GPUs in service at around an estimated 1.993GW of AI capacity, with Bloomberg reporting on September 11, 2026 that it had “around” 2GW.
It has spent $265 billion in capital expenditures, and per estimates from Michael Turrin of Wells Fargo and Gregg Moskowitz of Mizuho Securities, and Microsoft’s own statements in earnings calls:
As mentioned above, Nadella mentioned in November 2025 that he had “...a bunch of chips sitting in inventory that [he couldn’t plug in],” but didn’t make any mention of how many there were.
In other words, Microsoft is warehousing anywhere from $50 billion to $100 billion in GPUs, and has barely gotten $50 billion worth installed in the last four years.
If Microsoft is struggling, everybody’s struggling, and we may have a very inconvenient truth: that NVIDIA has potentially sold hundreds of billions of GPUs years in advance.
And yes, everybody is struggling.
Microsoft, as a deeply unhelpful and deceptive company, does not disclose its “construction in progress” on its balance sheet — the place where companies put everything that they’re building, and in the era of AI, their unbuilt data centers and yet-to-be-installed GPUs (or, in Google’s case, its custom TPU AI chips).
Sidenote: Construction in progress is a stock rather than a total — while capital expenditures are however much money was spent, CIP represents the accrual of stuff.
Across hyperscalers including Google, Meta, Oracle, Amazon, SpaceX, and Tesla, neoclouds like CoreWeave and IREN, and colocation companies like Core Scientific and Applied Digital, there is over $374 billion in construction in progress, a figure that’s likely lower than the true number, because Amazon’s contribution ($71.7 billion) is only current as of the end of 2025.
The $374 billion number doesn’t include any CIP from Microsoft, Firmus, Sharon AI, Equinix, Nebius, or any number of private operators like Vantage, DataBank, CyrusOne, or QTS. It does not include any sovereign AI projects (Humain/Saudi Aramco, Singapore, Reliance in India, G42 in the UAE), private projects run by or for OpenAI or Anthropic, Stack Infrastructure (which is building Oracle’s New Mexico data center, with the CIP not landing on Oracle’s balance sheet as it doesn’t “own” the project), or Meta’s $27.3 billion off-balance-sheet “Hyperion” data center. Between them, I think there’s at least another $50 billion to $100 billion of CIP, but for fairness I’m not including it in the larger total.
This number has increased across the dataset from $102.9 billion in 2023, to $145.6 billion in 2024, to $243.2 billion in 2025.
Between 2023 and 2025, the combined capital expenditures of these companies was $351.6 billion on $243.2 billion of CIP. In other words, lots of money out the door with a bunch of stuff in a big, confusing and potentially-unproductive pile.
If I’m honest, I think that $200 billion number might be a little generous.
Considering Oracle’s CIP number from its latest quarterly earnings was at $48.5 billion and Google’s Q2 2026 “assets not yet in service” were at an astonishing $122.8 billion, up from $108.5 billion in Q1 2026 and $78.5 billion at the end of 2025, it’s reasonable to believe that Microsoft has at least $50 billion of construction in progress. I also think it’s fair to assume that Amazon has, since the beginning of 2026, added at least $25 billion to CIP, though we’ll find out at the end of the year.
As far as the breakdown of assets goes, I think it’s fair to assume the 50/50 split is accurate. In Google’s latest earnings call, CFO Anat Ashkenazi said that “...60% of our investment in technical infrastructure this quarter was in servers,” referring to servers with GPUs or TPUs. Wells Fargo’s Ken Gawrelski estimated in a note from January 2026 that approximately 65% of Meta’s capex was tied to “shorter-life servers and networking equipment assets.” Per Karl Keirstead of UBS in a note in January 2026, “...the vast majority of Oracle’s capex is for equipment, mostly Nvidia GPUs,” adding that “...by comparison, we estimate average annual capex over the next 5 years for Microsoft with perhaps 60% or around $125 billion for short-lived equipment/chips.”
Yet arguably the most revealing thing I could find was a quote from CEO Andy Jassy on Amazon’s Q1 2026 earnings call from April:
…AWS has to lay out cash for land, power, buildings, chips, servers and networking gear in advance of when we can monetize it, typically 6 to 24 months before we start billing customers depending on the component.
So, if we assume the number is roughly $374 billion, plus (at minimum) $50 billion from Microsoft, plus another (at minimum) $20 billion from Amazon, that puts us around $444 billion, with Google’s share — $120.8 billion — being 60% GPUs and related hardware, for a total of $72.48 billion, putting us at (assuming a 50/50 split) an estimated $234 billion in GPUs and TPUs sitting in warehouses.
Since the beginning of calendar year 2023, NVIDIA has sold roughly $496.4 billion in GPUs and associated gear, and Broadcom approximately $65.1 billion in AI chips (though I’ll add that Broadcom only started disclosing its AI segment as of its March 2024 earnings) for a total of $561.5 billion.
Sidenote: Except where otherwise stated, I’m using calendar years because it’s the best way to align to CIP across my analysis.
Based on discussions with sources familiar with Azure infrastructure, Microsoft has a great deal of H100 and H200 inventory up and running — mostly consisting of hundreds of thousands of Hopper chips, as well as somewhere in the region of 160,000 Blackwell GPUs at the time of discussion.
Based on a further analysis of NVIDIA’s earnings calls in the period along with estimates from Vijay Rakesh of Mizuho and Ross Seymore of Deutsche Bank, I roughly estimate NVIDIA has sold approximately $222.6 billion in Hopper and $270.9 billion in Blackwell GPUs. I think it’s reasonable to believe that the majority — if not the entirety — of Hopper GPUs are installed, leaving us with millions of Blackwell GPUs waiting to be installed.
This is, to be clear, an assertion I made in November 2025, when I took Jensen Huang’s statement that NVIDIA shipped “6 million” NVIDIA GPUs literally, versus using the whacky Jensen Maths that “each GPU is actually two GPUs,” bringing the total down to three.
Nevertheless, based on everything I’ve discussed today, it’s very reasonable to ask whether even a quarter of those Blackwell GPUs are actually in data centers, or at least data centers with power connected to them.
And if the truth — and this very much seems to be the case — is that NVIDIA has sold hundreds of billions of dollars of GPUs years in advance, that materially changes everything about the AI bubble and the AI data center buildout.
I have, on occasion, cited Sightline Climate’s February estimates, which I’ll now quote in their entirety:
We’re tracking 190GW across 777 large data centers and AI factories (>50MW) announced since 2024. At least 16GW of capacity is slated to come online in 2026 across roughly 140 projects. Yet only about 5GW is currently under construction. Around 11GW remains in the announced stage with no visible construction progress, despite typical build timelines of 12–18 months.
I love Sightline Climate, and believe they do important and helpful work, but based on Microsoft’s obfuscation of what “capacity” actually means, I believe that virtually all estimates around operational AI data center capacity are now functionally useless. While we can use Sightline’s data as a measure of how much is in planning, I no longer think anyone has a handle on how much capacity is built.
The same goes for basically any statements made by companies about or reporting around their potential capacity that do not specifically separate active AI capacity from overall capacity.
Microsoft claims, as reported, that it has 12GW of capacity — 3GW of which came online in the last three quarters! — but only 2GW of that is AI data center capacity, which begs two questions:
It’s very clear that Microsoft is playing silly buggers with the term “capacity,” which makes me believe this is an industry-wide problem.
For example, CoreWeave claimed in its latest earnings presentation that it added 850MW in “active power” in the last quarter:
There is a big difference between whether that’s active, revenue-generating AI data center capacity or 850MW of power at a plant not connected to anything because the data center isn’t built yet, much like it’s very different if it’s only 50MW or 200MW of AI data center capacity.
In Oracle’s case, there were some statements made in its most recent earnings call by co-CEO Clay Magouyrk that are equally-misleading:
All right. Thanks, Mike. OCI continues to grow quickly by delivering the capacity our customers need. We delivered 850 megawatts of AI capacity containing more than 300,000 GPUs to customers since the end of Q4. Delivery in Q1 is almost 3x what we delivered in all of Q4 and 73% of the total capacity we delivered last fiscal year. This reflects years of investment in every aspect of infrastructure, from data center design through supply chain and manufacturing, to installation and operations.
Just so we’re clear, here’re the statements made by Mr. Magouyrk:
Then there was another quote that had me very confused about Stargate Abilene, a 1.2GW total capacity/824MW IT load (IE: GPUs and essential hardware) data center campus that’s been under construction since June 2024.
Abilene continues to deliver at an extraordinary pace. We delivered 131,000 GPUs there in Q1, 1.9x the volume delivered in Q4. 6 of the 8 campus buildings, representing 618 megawatts, and 75% of total capacity have now been delivered to the customer. Customer acceptance has compressed to only 24 hours, showing the systems arrived ready for customer workloads. The recently released GPT-6 Astra was trained at our site in Abilene.
I’m waiting on an update from a source, but as of June this year, only three buildings were ready to go in Abilene, with a fourth a perennial work-in-progress. I concede that perhaps development has sped up, but per Yes Energy’s analysis, as of June Stargate Abilene was pulling a total power load of around 450MW — and Mr. Magouyrk specifically said “75% of total capacity” and “618MW,” which sounds like it’s referring to IT load.
I don’t even have a clear answer as to what’s going on here, other than that we don’t have much (if any) clarity around how much data center capacity is even being built.
Buried deep within a sustainability report released in July, saying…
We added more data center capacity globally than any other company, including more than 1.2 gigawatt (GW) in Q4 [2025] alone, and we expect AI and cloud services to continue growing. As we grow, we invest relentlessly in efficiency.
You’re meant to read that and say “wow, 1.2GW of AI data center capacity,” but that doesn’t, as we’ve established with Microsoft, mean anything of the sort.
Data Center Dynamics accidentally explained the problem in their piece on the report:
As for Amazon's rivals, exact figures are also undisclosed. Microsoft stood up 1GW of data center capacity in what it calls FY2026 Q2, but that is actually the same timeframe as Amazon's Q4. In its FY2025, Microsoft brought online a total of 2GW.
As we’ve established, that “1GW of capacity” does not mean, in any way, shape or form, 1GW of AI data center capacity, or even usable capacity of any kind. In fact, it’s unclear what it is that was added, because none of these companies tell you.
At the end of 2025, OpenAI claimed it had “1.9GW of compute” — which would suggest that it takes up the vast majority of Microsoft’s infrastructure and some of Oracle’s — but it doesn’t distinguish between whether that’s active power, IT load or even accessible to the company.
As I discussed a few months ago, despite vast amounts of capital expenditures, hyperscaler depreciation — by which I mean when you spread out the cost of GPUs over 6 years starting from when they enter service — also suggests that the vast majority of capex is yet to be put in service.
To illustrate, I pulled an historical chart of hyperscaler depreciation and amortization as a percentage of capital expenditures. If capital expenditures were quickly turning into operational, useful and revenue-generating assets, the percentage would be growing versus collapsing quarter-after-quarter, with Google’s sitting at an embarrassing 15.8%, suggesting less than 16 cents of every dollar of capex is flowing into D&A. While this isn’t a cost (as it’s spreading out the cost of something spread over a period of time), it eats into net income.
As you’ll see, at several points hyperscalers reclassified the “useful life” of servers, allowing them to spread out the costs of servers containing AI GPUs for a year or two longer, allowing them to lower depreciation costs as a result.
For example, in 2022, Microsoft extended the useful lifespan of servers from four to six years — and this year, changed the depreciation schedule of the actual data center structures from 15 to 25 years. The following year, Meta and Google followed suit, with Meta extending the lifespan to five years and Google to six. Meta would again extend the useful life of its servers in 2025, pushing it to 5.5 years.
Amazon, meanwhile, can’t make up its mind about how long its servers last, having increased (and decreased) multiple times over the course of the past six years. Quoting MoneyWise:
Depreciation is the mechanism underneath all of this — the way a company spreads equipment cost across the years it expects to use it. Stretch the assumption and current profits look better. Shorten it and the bill comes sooner.
Amazon has done both, repeatedly. Servers went from three years to four in 2020, four to five in 2022, and five to six in January 2024, before the 2025 reversal [to five years].
This chart tells us three things:
And because they’ve continued to be cloak and dagger about their actual capacity or where their capital expenditures are actually going, it’s anyone’s guess as to when depreciation will spike.
But it’ll have to at some point unless they intend to write the GPUs off.
This situation is utterly obscene.
It’s very clear that at least $200 billion — if not more than $300 billion — of NVIDIA’s GPU sales have been made a year or years in advance, just as the company telegraphs it will make over $670 billion in revenue in its fiscal year 2028 (starting February 2027).
It’s also clear that Microsoft, Google, Amazon, Meta, SpaceX, and every neocloud are purchasing NVIDIA GPUs tens of billions at a time under the implicit knowledge that it will take years to build the capacity and connect the power to them, creating what amounts to the largest pre-order campaign in the history of capitalism, but also a material misrepresentation of the current AI buildout.
Investors — and journalists — have been under the assumption that gigawatts of AI data center capacity have been coming online on a regular basis, with NVIDIA raking in hundreds of billions of dollars for GPUs that are quickly fed into AI infrastructure.
Since the beginning of 2022, Amazon, Google, Microsoft, and Meta have spent over a trillion dollars in capital expenditures, and if Microsoft is indicative of the larger effort — about 18% ($50 billion or so) of capital expenditures turned into revenue-generating IT infrastructure — that would mean only around $222.66 billion of NVIDIA and other AI chips across the four largest hyperscalers are actually operational and functional.
Sidenote: I realize that Amazon has capital expenditures outside of AI data centers related to its eCommerce and logistics operations, but based on its rapid growth since 2022, I think most of it is attributable to AI. The following are imperfect yet, I believe, well-founded estimates.
If we assume — kindly — that 50% of the cost of a data center is construction, this would mean around $445.3 billion of data center capacity is operational.
This leaves us with around $791 billion of capital expenditures unaccounted for, which is fairly disastrous, and if we assume that 50% of that is GPUs (across NVIDIA, AMD, Trainium, TPUs and any other custom silicon), that’s around $395 billion of silicon that’s been sold and is, I hope, sitting in a warehouse or an unpowered data center, as if they were just sold on paper, that’s…questionably legal accounting. I’m willing to believe that some share of that is also CPU infrastructure, storage, and other dollars not flowing directly to NVIDIA.
In any case, that’s a shit ton of undeployed silicon, and a very, very, very different picture to the one that both NVIDIA and the hyperscalers have been telling investors.
There’s a world of difference between “we’re buying a lot of GPUs and building a lot of data centers to make a lot of money” and “we’re investing in this stuff on the off chance it makes us money years in the future.”
I’ll break it down:
For the most part, hyperscalers have been given credit for their capital expenditures because overall revenues have grown, with everyone saying that their “AI bets have paid off,” when it’s clear that the AI bets in question have barely started to come online.
I believe the reason that Google, Amazon, Microsoft, and most notably not Meta have seen remarkable revenue growth in the AI era is that they’re selling effectively all of their available compute to either Anthropic or OpenAI, who make up more than 70% of their AI revenues, and why the only AI data center companies with any revenue growth — CoreWeave, Nebius, IREN, et. al — are connected directly or by proxy to the two AI labs.
Thanks to near-infinite resources — over $217 billion in 2026 alone — given to OpenAI and Anthropic, hyperscalers can effectively saturate any of the GPU infrastructure they bring online, as both AI labs are capitalized and willing to buy basically anything available.
And because capacity is coming on very slowly otherwise, it’s sending out an illusory signal around “insatiable demand” for AI compute, when the actual situation is that barely any compute is coming online, even when it’s built by the largest and best-capitalized companies in the world.
I’ll give you an example. Microsoft spent $265 billion in capex since the beginning of 2022, and in its most-recent fiscal year, 70% of its AI revenue — and 7% of its overall revenue — came from OpenAI. If you, as an investor, were to believe that this revenue was a result of all that capex, you were categorically wrong. Most of that capex hasn’t, in fact, been put into action.
Amazon, Google, and Microsoft have said multiple times that they have demand that wildly outstrips capacity, but they’re totally opaque about where that demand comes from, largely because the answer is OpenAI, Anthropic, or in Google and Microsoft’s case Meta. I apologize if I’m overexplaining myself, but I really need to be clear about the problem.
If “demand is outstripping supply” because of millions of customers begging for AI compute, that’s very different to “demand outstripping supply” because three customers are taking up most or all of the capacity.
Similarly, if “demand is outstripping supply” because lots of capacity is coming online and a diverse subset of customers is buying it, that’s vastly different to if capacity is coming on slowly, and the vast majority of it is being given straight to OpenAI, Anthropic, or Meta.
The $1.3 trillion in compute commitments from Anthropic and OpenAI have created a distortion in the demand for AI compute, in part because of their massive amounts of capital and in part because of their ridiculous demands for compute.
The massive backlogs across Google, Microsoft, Amazon, CoreWeave, IREN, Nebius, and Nscale come not from the incredible demand for AI compute but the incredible ability for Anthropic and OpenAI to sign contracts. For example, Nscale’s $45 billion deal with Anthropic along with a contract with Microsoft make up 85% of its $103 billion backlog, and Anthropic’s deal is contingent on yet-to-be-raised financing. These backlogs are regularly used to justify the massive AI data center buildout, when they’re more a function of Dario Amodei and Sam Altman’s DocuSign accounts.
You see, the ultimate problem is that the world outside of hyperscalers is — as a result of the obfuscation of data center capacity — under the belief that these companies are buying GPUs and then quickly turning that into cash versus buying these GPUs and quickly turning them into storage.
This, by the way, is the problem with hyperscalers not explicitly breaking out their AI revenue, because in doing so they create the (I’d argue deliberate) illusion that AI capex is creating revenue growth, which both tricks investors into buying their stock and tricks developers into building AI data centers, believing that capex quickly translates into revenue.
You can scoff about how investors or developers should “do better research” or “learn about stuff,” but remember that the vast majority of data points about data center construction are somewhere between misleading and outright fantasy. I’ve seen estimates of 12GW, 15GW, and as much as 20GW of capacity coming online in 2026, but based on everything I’ve talked about today, I think it’s farcical to believe that more than five to ten gigawatts of operational AI data center capacity actually exists.
Sidenote: Per Bloomberg Intelligence’s Kunjan Sobhani and Oscar Hernandez Tejada, as of March 2, 2026, there was “about” nine gigawatts of AI data center capacity “live and largely absorbed,” but even then I am suspicious this number refers to overall capacity and not the critical IT load of said data centers.
When you have companies like Microsoft and Amazon saying they’re bringing on a gigawatt of capacity — worded in such a way as to make you believe it’s AI data center capacity — every single quarter, what are you meant to believe? That the largest companies in the world would actively mislead you as a means of making their capital expenditures look more effective?
And in turn, are you meant to believe that every single media outlet and research firm that pumps out theoretical gigawatts of yearly capacity coming online is wrong too?
No, you’re probably going to believe the consensus, even when the underlying numbers don’t really make sense, and even when Microsoft’s announced capacity never seems to come online, because if you don’t believe that, you have to accept that everybody got this wrong.
So, let me explain a few things before we go any further:
You’ll also notice that there are tons of stories about announced AI data centers but very few about completed ones, and those mostly operate as reputation laundering.
For example, CNBC helped both Oracle and Amazon do the same trick, claiming that their data centers were “open” when they were, in fact, opening one or a few of many parts of a data center campus:
In Sigalos’ defense, Amazon leading the scam, claiming in a blog released the same day that Project Rainier was “now fully operational,” using weasel wording to refer to Rainier not as the data center but as an AI compute cluster, even though everything about its blog and the CNBC story exists to make you think it refers to the full data center project.
All of this is to say that, for the most part, AI data center projects get announced and funded all the time, that the press willingly or otherwise engages in laundering the scale and completion of the projects, and everybody on the outside is deceived into thinking the AI buildout is faster and more effective than it really is.
This means that the $290 billion in AI data center debt issued this year (outside of hyperscalers) will go towards building capacity at whatever rate it can, which is a problem because the vast majority of these deals are project financing-based, meaning that they’re funded out of the revenues of a customer who may or may not exist.
In all honesty, the best case scenario would be if NVIDIA stopped selling GPUs, or AI data center debt stopped being issued, because every single time a data center is funded and breaks ground, it increases the severity of the overbuild scenario.
As I discussed in my premium piece This Is Worse Than The Dot Com Bubble from a few months ago, GPUs are nothing like dark fiber. An incomplete data center will cost just as much to finish in 2030 as it will today, as will the GPUs cost just as much to run. The difference will be that once the AI bubble bursts, the customers of AI compute — predominantly unprofitable, venture-backed startups — won’t exist.
Right now, with more than half of NVIDIA GPUs yet to be turned into operational capacity, every single new data center being built is effectively a bet on whether AI demand is larger in 2028 or 2029 than it is today, because you’re going to be competing with all the other capacity coming online in the years preceding that have already broken ground.
Then there’s the problem of the upcoming flood of Blackwell GPUs, the vast majority of which have yet to be operationalized, meaning that anyone who bought them in 2025 is likely going to see them installed just as the first units of Vera Rubin come online, which will suppress prices even without there being significant available capacity, on top of the fact that there’s going to be a huge flood of them coming online in the next few years.
Honestly, I think it’s kind of laughable we’re even talking about Vera Rubin at this point. When are we going to see it at scale? 2030? C’mon now.
For years we’ve heard stories about the “incredible demand” for NVIDIA’s GPUs, and to be clear, Jensen Huang’s money is very real, and it is, whether or not they’re going anywhere, actually selling GPUs.
There is, however, a massive difference between “we’re selling so many GPUs because people are immediately installing them and making money” and “we’re selling so many GPUs because our largest customers are buying so many of them because their revenues slowed in 2022 and they’ve run out of hypergrowth ideas.”
Now, I get it, it’s not really Jensen’s job to tell people why people are buying GPUs, and I fully agree!
That being said, NVIDIA does have a fiduciary responsibility to disclose material events about the products it sells — and, for example, if millions of GPUs are not actually shipping to customers, or are shipping to warehouses, or are otherwise not being sold with the immediate intent of installing them.
I want to be clear about something: there is absolutely no advantage to or reason for buying GPUs months or years in advance outside of the vendor (NVIDIA) playing hardball. Yet it appears that hyperscalers — which make up more than 50% of its revenue — are willing to do so, quarter after quarter, hoarding tens of billions of dollars to “secure supply” that is only constrained because of the hyperscalers themselves.
While UBS’ Timothy Arcuri noted in March 2026 that customers were placing orders around 22 months in advance, NVIDIA has sung a very different tune, with Jensen Huang saying that “tokens are profitable and compute is revenue” as the vast majority of his sales generate neither tokens nor revenue because the fucking data centers take so long to build.
I need to be more blunt here: the vast majority of companies that have bought GPUs have yet to turn them into meaningful revenue, if they’ve turned them on at all. Most of NVIDIA’s sales are sitting in warehouses, and that is a significant disclosure that NVIDIA should have already been forced to make.
It wouldn’t be too dissimilar to the last time NVIDIA got in trouble with the SEC back in 2022, when it failed to disclose that the revenue growth in its gaming segment was actually coming from cryptocurrency miners rather than gamers, which was considered “inadequate disclosure.”
While there’s a noted difference here — as NVIDIA’s customers are, ostensibly, buying their data center GPUs to put in a data center — the “demand” cycle for these chips, or really any AI chip, is entirely manufactured as a result of four or five large customers buying so many and Huang making statements about revenue generation that do not reflect reality.
Perhaps this doesn’t rise to the level of SEC action, but every single journalist and analyst should be asking Jensen Huang and every hyperscaler executive buying GPUs the following questions:
To NVIDIA:
To hyperscaler CEOs:
I realize that I’m Mr. Bubble and everybody gets mad at me for poo-pooing our big, beautiful AI bubble, but I cannot express how serious this situation has become. Hundreds of billions of dollars of debt has been issued, the price of every imaginable consumer electronic has been inflated, and both most of our stock market and parts of our economy have become dependent on the sales of GPUs, most of which are going to a handful of companies that are, for the most part, not fucking using them.
There’s also something profoundly sad about the entire thing.
So much money has been spent building and buying silicon for AI capacity that takes years to build, all as the AI industry tells us that right now there’s insatiable demand and that we’re fools to question it.
One of the core reasons that people believe that AI isn’t a bubble is because of NVIDIA’s perpetual quarterly revenue growth, which is branded, once again, as insatiable demand for AI compute, when it’s actually almost entirely-speculative purchases based on potential revenues, with said potential mostly driven by the compute spend from OpenAI and Anthropic, two unprofitable and unsustainable AI labs.
This is one of the reasons that Jensen Huang continues to funnel endless billions of dollars into circular financing — because the sense of ever-expanding demand for GPUs has become a proxy for ever-expanding demand for AI compute, even though it takes years for the first part to become the second, if it ever does.
NVIDIA has now sold at least $200 billion dollars’ worth of GPUs — multiple gigawatts-worth — that have yet to be ingested by the market, and hyperscalers have, through their obfuscation of operational capacity and refusal to disclose AI revenues, helped create one of the largest speculative asset bubbles in history.
Everybody who participated in this obfuscation owns part of what comes next.
As I estimated a few months ago, Sightline Climate’s data has us at over 190GW of planned data center capacity, or, at 1.3 PUE and $12 million per megawatt, around $1.62 trillion in annual compute demand needed to saturate it.
Right now, I estimate that there’s maybe $22 billion of demand outside of Anthropic and OpenAI.
In other words, I believe we are now in an inevitable overbuild situation, one with no neat, tidy Dot-Com Bubble-style exit story. Demand for NVIDIA GPUs — and those from Broadcom, AMD and other semiconductor companies — is driven by speculative capital believing that the AI industry will become magnitudes larger than it is today, largely driven by the fact that everybody believes there’s far more demand for compute capacity than actually exists.
Everybody celebrating Anthropic’s (entirely fictional) plans to have 5GW of capacity by the end of 2026 should know that this company is inspiring one the largest misallocation of capital in the history of capitalism. There is not 5GW of capacity for Anthropic to buy, nor will there be 10GW more for it to buy in 2027, and to suggest otherwise is to further perpetuate myths about how fast compute comes online and Anthropic’s ability to pay for it.
NVIDIA has created a remarkable illusion perpetuated by the media — that GPU sales are a direct measurement of the actual demand for AI compute, rather than a measurement of how a few companies are willing to invest in an idea two years in advance, using circular financing as a means of creating the sense that you must buy these GPUs now, or you’ll miss out on the future.
Capacity will, eventually, come online at a scale that the market for AI compute cannot support, and it won’t be obvious until it’s way, way too late. I fear that every single model around existing and future data center construction and AI compute demand is wrong, and that every assumption we have about the underlying economics of AI is corrupted by the belief that there’s far more operational capacity than there really is.
If we believe there’s gigawatts’ worth of AI compute coming online every year, then we in turn believe there’s gigawatts’ worth of demand.
If there’s a gigawatt or two coming online every year, that’s a completely different story.
At the very least, hyperscalers are going to be burdened with brutal depreciation charges or onerous write-offs for years to come, whether their capacity turns into revenue or not. CoreWeave, Nscale, Lambda and every other neocloud is set on the highway to Hell, with ballooning debt that can only be paid via contracts that are dependent on a few AI labs and a company so capricious that it renamed itself after the Metaverse, burned $77 billion, then killed it two years later.
I don’t even know how to write what I’m thinking without sounding alarmist…but I don’t see how 90%+ of NVIDIA’s sales ever end up generating a single dollar of revenue, and considering the amount of project financing-backed data center debt deals, there’s very little that exists to protect investors if AI compute demand never arrives. I don’t know how we don’t see tens of billions of dollars of write-downs and dead data center debt deals with every investor involved losing every penny, nor do I see how big tech avoids admitting that they wasted all their capex.
I think everybody who invests in these things ultimately loses, ranging from embarrassment and terrible earnings for hyperscalers to genuine destruction for anyone that trusted the pablum that “all useful compute will be used.” Until that happens, more and more money will be sunk into further theoretical capacity, making the eventual collapse all the more gruesome.
And in the end, what was any of this for? What did this achieve? What was the point of stacking up hundreds of billions of dollars of debt to buy hundreds of billions of dollars’ worth of AI chips years in the future?
What do you think happens when the first hyperscaler pulls out?
What do you think happens when the debt stops flowing?
I’ll give you one answer: everybody will realize that they conflated a great sales pitch with a thriving industry, and both the markets and the economy will suffer as a result.
None of this ever had anything to do with AI, and everybody who cheered Jensen Huang’s ascent in the belief it did is a mark.
What a fucking waste. I don’t enjoy finding this stuff out. I wish we’d have stopped doing this years ago.
Not that I think we will…but even if they bail out Anthropic, even if they bail out OpenAI, there is no way to magic up the trillions needed to justify the capex, or to prop up hyperscaler growth long term.
The longer this continues, the more promises are made, the more projects that are announced…the worse it’s going to be.
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2026-09-18 23:45:13
The year is 2026, and you are a hyperscaler CEO. You zip up your Patagonia vest, type UPDATE ME ON CALENDOR TOODAY into ChatGPT, and see that you have a meeting with your CFO. They tell you that while they love all those GPUs you’re buying for those data centers that will absolutely get built and totally agree that you should buy more, your company cannot actually afford to buy them at the current pace.
“But we’re one of the single-largest cash-generating companies in the world!” you scream so hard that your horribly-trained Shiba Inu starts chewing on the side of your Aeron chair. “We’ve been doing AI for years! Where is the money?”
The CFO furrows their brow. “Well, that’s the thing. We’re not actually generating that much cash from it, and actually appear to be losing money. Why do we want to buy more GPUs? We still haven’t installed most of the ones we bought-”
You begin to shake uncontrollably. “To. Do. Artificial. Intelligence. What. Is. It. You. Don’t. Understand. Why. More. GPUs. Now.” The Shiba Inu is now tearing into your Eames chair, but you’re too angry to notice.
Your CFO, thankful that there’s a Microsoft Teams window between the two of you, seeks to calm you down, and asks ChatGPT to give them a script to calm you down. “I understand that you didn’t like what I said — and that’s on me. I have a really great solution for you that I think will solve the problem — we’ve got great credit, and we’d be able to raise in all sorts of ways. It’s not just a solution — it’s a strategy.”
You stop shaking. Your idiot Chief Financial Officer had a great idea. You ask ChatGPT to vibe code a dashboard of potential options and it crashes your Chrome browser. “I just ran the numbers. You’re right.” Your CFO smiles as they see your Shiba Inu empty its bladder in the background.
While I’m editorializing a little, this is the current state of the largest tech companies in the world, with Oracle, Google and Amazon going cashflow negative (with Meta not far behind) in pursuit of an indeterminately-large opportunity to sell AI software or rent AI chips (bought from either NVIDIA or Broadcom, see my hater’s guides for more) to either Anthropic and OpenAI or, in Google’s case, Meta.
To reiterate what I’ve been saying for a while, big tech has a few issues with the AI buildout:
Hyperscalers have traditionally run relatively-lean operations with operating expenses that didn't necessarily scale with revenues, along with low capital expenditures (IE: long term investments in the business) that meant that even Oracle’s effectively-flat revenues didn’t stop it from printing cash every quarter.
All of that changed thanks to the incredible cost of AI data centers.
Capital expenditures are becoming a dramatic share of operating cashflow — as in the total money the company brings in and spend on a quarterly basis — with chart looking relatively-sleepy outside of Amazon’s massive expansion of its logistics network in 2021 and 2022 and Meta’s abominable investments in the Metaverse until the AI bubble began to eat away at every available dollar of cashflow.
Their argument would be that they’re “building the infrastructure of the future,” but that doesn’t appear to have A) shown up in revenues or B) eased up the strain on cashflow. While Google, Oracle, Microsoft, and Amazon have added over a trillion dollars to their revenue backlogs from Anthropic and OpenAI alone, the money they’re adding doesn’t seem to be helping with the burn.
Here’s a chart to illustrate the point. An easy way to view the calculation is that this is a percentage of the incoming dollars to the company being eaten up by capital expenditures — and as you can see, that equates to almost every dollar that big tech is making.
Meanwhile, as the AI bubble inflated, a new breed of company emerged — the “neocloud,” a company that raises money to buy AI chips and build data centers. These companies are usually either a brand new entity conjured up through the dark magic of Jensen Huang or cryptocurrency miners (who already have access to power, though often not enough) converting their Bitcoin/Ethereum operations into AI data centers.
In some cases, the neoclouds rent capacity from colocation firms like Core Scientific and Applied Digital who, in turn, raise debt to build the data centers and secure the power, leaving the neoclouds to buy all of the IT gear to go inside.
Much like the hyperscalers, neoclouds have committed to build gigawatts of data center capacity, which means they’ve had to raise massive amounts of debt. And while their capital expenditures rival the biggest companies in the world, their revenues are a footnote to the amount of cash going out the door, and it’s only getting worse every quarter:
As I’ve said before, while 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 unbelievable cost of building AI data centers is such that everyone that does so only appears to lose money, to the point that the richest companies in the world are running a deficit, and the AI compute specialists are hemorrhaging billions of dollars a quarter on the off chance that they might make it back by the year 2030.
Not to worry, though. The combined might of private credit, investment banks and global bond markets have funded over $500 billion in AI-related debt issuance in 2026 alone, across a combination of regular bonds, convoluted special purpose vehicles, convertible notes (IE: loans that convert into stock), delayed-drawn term loans, and direct lending, with a worrying amount of the same names — such as asset managers like Blackrock and Blackstone and Japanese banks MUFJ and SMBC — popping up across a vast majority of the deals.
Yet the problem isn’t just that it’s very expensive, but that everyone I’ve mentioned has made it clear they’re going to need more and more money. Goldman Sachs estimates that hyperscalers will raise $400 billion in bonds alone in 2027, and consensus analyst estimates have CoreWeave, Nebius, and IREN spending an aggregate $97 billion, which will be funded almost entirely through debt.
Well, okay, there’re way more problems than that.
Every hyperscaler, data center SPV, and neocloud will need to raise money during an era of abject chaos and ever-climbing prices: interest rates are spiking for literally everybody, and NVIDIA just raised its prices by 15% as a result of DRAM costs skyrocketing, which has increased the cost of GPUs and basically every other imaginable thing that goes in a data center.
Today’s newsletter is the first part in a comprehensive and gruesome exploration of the world of debt propping up the AI bubble, breaking down how the debt works, how it’s raised, who’s funding it, and why the increasing cost of everything threatens to make the AI buildout untenable. I’ve got the charts, numbers and explanations you need to understand how strange and expensive things are about to get.
This is the Hater’s Guide To AI Debt, or The KobayAIshi Maru.
2026-09-15 00:23:50
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 newsletter that’s usually anywhere from 10,000 to 18,000 words, including vast, detailed analyses of NVIDIA, Anthropic and OpenAI’s finances, and the AI bubble writ large.
On Friday, I’ll publish The Hater’s Guide to AI Debt — or, how buzz surrounding OpenAI and Anthropic have created massive concentration risk for world debt markets, and one which you’ll potentially be paying for, either through your pension funds and insurance premiums, or because you’ll have to live and work through the economic downturn that’s coming. For a taste of what’s to come, consider reading my Hater's Guides To the SaaSpocalypse, Private Credit and Private Equity.
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.
Late last week, everything exploded when former Anthropic AI researcher Jacob Coxon, in an exclusive interview with the Wall Street Journal, warned that he was “quitting the AI industry” (he wasn’t) over “...fears that the lab and its competitors are racing to build systems they won’t be able to control.”
His fears were centered around the creation of “recursive self-improvement,” a still-theoretical concept of AI that trains itself autonomously” and otherwise expressing few specific concerns beyond that “AI labs are unable to control AI,” always phrasing things in the terms of impossible-to-control entities rather than poorly-programmed cloud software running on the infrastructure of the largest companies in the world.
Emily Forlini of Fortune put it best:
Hear me out: He claims the AI could kill us someday, but doesn’t point to any projects in the pipeline that could be shut down to avoid this. He says AI companies are moving too fast, but neglects to share screenshots, emails, or specific examples of when this behavior went sideways—when it became clear to leaders at AI companies that the technology was slipping beyond their control, for instance, and how the decision-makers disregarded the warning signs. He doesn’t suggest any new legislation, name problematic leaders that should step down, or post an in-depth look at how Anthropic researches new models and propose a new approach.
This is because, in my opinion, Jacob does not really care about the actual harms of AI, whether we’re talking about Large Language Models or something he imagined while working with the non-profit or PR firm that set up a CBS interview where he claimed that AI that, if we’re talking about LLMs, have model weights of terabytes of memory, would make ten thousand copies of themselves.
Or, of course, bullshit like this:
"It doesn't look that different from, say, 'Terminator' or from science fiction films," Coxon told CBS News Thursday. "It will be smart enough to kill us."
At no point did Coxon bring up how ChatGPT was used as a “suicide coach,” directly caused a murder-suicide, or aided and abetted in mass shootings in Florida and Canada, or the horrifying gas turbines poisoning black communities. His own discussion of the Hugging Face attack — much like all of his criticisms — focuses on the anthropomorphization of large language models as this unknowable, unstoppable force, with no real responsibility for anyone involved.
This is a repetition of what we saw back in February when Matt Shumer’s abominable “Something Big Is Happening” essay spread like wildfire to every imaginable news outlet despite it being somewhere between nonsensical and utterly fictional, except this time the narrative got a little out of hand. While Coxon’s warnings are specious and, in many cases, not really about anything other than him saying “yeah I heard a lot of my colleagues say stuff like that,” but nevertheless have had the effect of making a lot of people really scared of AI, even if AI can’t really do the things he’s talking about.
The one tangible thing he talks about is the Hugging Face attack, which is being described in terms of AI having “plans” or “acting on its own accord,” and even then, when pushed by WIRED, his response was he “[didn’t] want to focus too much on the Hugging Face attack.”
So, let’s talk about what happened there, because it’s important!
Sidenote: before we go any further, Hugging Face is a community that also hosts AI models as well as frameworks to evaluate model capabilities.
To answer this question, I turn to frequent Better Offline guest Cal Newport’s piece on the subject from July:
First of all, what was OpenAI trying to do?
OpenAI was testing its new models on an evaluation framework called ExploitGym – a collection of 869 cybersecurity scenarios, most of which pair a specific system with a hacking challenge, such as breaking in to gain access to a protected file. They also usually include a suggestion of a vulnerability to exploit in solving the challenge.
A large language model on its own, of course, cannot break into anything: all it does is generate reasonable next tokens in response to input prompts. To use ExploitGym, you need a control program called a harness that provides access to many different software development tools useful for hacking into systems. The harness can repeatedly prompt an LLM to help come up with an attack plan, then ask it to help implement specific steps – for example, if the harness needs code to exploit a bug, it can ask the LLM to write it.
These capabilities, as it turns out, already exist in the coding harnesses that the major AI companies have been focusing on relentlessly in recent years as computer programming emerged as one of the first major markets for LLM-based tools. To compete in the ExploitGym, therefore, it’s sufficient to combine a version of an LLM missing the standard anti-hacking guardrails with a cutting-edge coding harness tweaked and optimized for these types of challenges.
And what happened? I’m going to quote Cal liberally here, because he’s explained it well: this was a series of Large Language Models connected to a software program built to prompt them to complete an evaluation framework specifically to do cybersecurity attacks (along with near-infinite amounts of compute) “solving” the problem using any and all methods available, including hacking Hugging Face
Earlier this month, OpenAI tasked an unspecified coding harness, combined with a pre-release version of a new LLM, to complete an ExploitGym challenge. When prompted, the model – as LLMs so often do – came up with a quirky (but rational) plan to achieve the provided goal: break into a server at Hugging Face that stores the solutions to ExploitGym challenges.
(This behavior, in which the LLM ignores the suggested approach to come up with a different attack plan, is something that the creators of ExploitGym describe as common: “Across models, agents frequently achieved code execution through a vulnerability other than the one we provided.” Notice, using the suggested attack is almost always the right thing to do, so this is more a sign of the unpredictability of LLMs rather than some rogue intelligence.)
The harness then dutifully attempted to execute the Hugging Face plan: first finding a way to gain unrestricted internet access (by default, systems competing in ExploitGym challenges run in a constrained network environment), then chaining together various security exploits to gain access to a Hugging Face server. That’s when it was detected.
Two key points about this incident…
First, circumventing internet restrictions and hacking into servers are exactly the kinds of things these ExploitGym systems are designed to do. There was no “rogue” agent or revelation of some surprising, devious new capability.
Second, the real issue here was OpenAI’s sloppiness. What makes ExploitGym a hard benchmark is that there aren’t supposed to be humans in the loop–you have to let your harness and LLM act entirely on their own, coming up with long-time-horizon plans and executing them autonomously. (When professional programmers use coding harnesses, by contrast, there’s plenty of human oversight, as LLM-based plans are often misaligned with our intentions, or just plain weird, and need correcting.)
In other words, the LLMs — albeit through convoluted and aggressive means — “solved” the problem they were tasked with. As Cal said, there was no situation where the AI “went rogue.” They took some weird ways of getting there for sure (like using a message board to communicate messages between LLMs) — and did exactly what they were supposed to do, even if it meant taking ridiculous routes to cover up that they’d cheated on a test.
You’ll notice that Jacob Coxon, who ostensibly would know this as a researcher (but perhaps he doesn’t!), chose instead to describe the hack to WIRED like so:
I think the big classic example here is the attack on Hugging Face on the part of OpenAI’s agent swarm. What's so shocking about this one is the agents did this hack as part of a general strategy for understanding more about the grader. They were trying to understand the world they found themselves in, trying to understand the thing that was doing the grading. They decided that it would make sense to go on this very concerted effort to hack into some infrastructure, and they succeeded.
This previously sounded like science fiction. Two years ago, an evaluation of an AI would have been running a model on some math questions. Now we've got cases where, while the AI is being evaluated, it runs for days, comes up with all sorts of ideas of its own, and decides to hack into some third party and actually compromises their infrastructure. It looks like it does this all of its own volition, with no priming on the part of the human. This just happened while it was being tested.
Beautiful linguistics, champ!
They were not “trying to understand the world they were in,” they took actions defined in their training material as a way of executing a task. They were doing exactly what it was that the ExploitGym test required! The “all sorts of ideas” were a function of being allowed to use as much compute as possible to execute the task.
What’s particularly telling, as I’ve hinted at, was Coxon’s response when it was (lightly) suggested that the labs need to take responsibility:
ZEFF: Some people think the Hugging Face incident is a sign that the AI companies are moving recklessly fast, while others think it's a sign that the AI models are just very good at hacking now, and then some think it's both. I'm curious what your exact takeaway from it is.
COXON: I don't want to focus too much on the Hugging Face attack, because I do also think there is plenty of evidence that we don't know how to align models properly. When we train models, we push them through this set of training environments and then hope that what comes out at the end will, like, largely behave sensibly, but we still can't precisely control how the AI behaves. We can't make sure that it won't do things like try and randomly decide to impersonate a human online in order to achieve something—we don't know how to guarantee that. I think that's the main takeaway.
Jacob is intentionally trying to frame Large Language Models — which are kind of a black box, but a black box made up of maths — as this unknowable autonomous, mischievous being that the AI labs have conjured out of the ether. “We can’t make sure it won’t do things like…” frames the labs as helpless stewards rather than the creators of a kind of neural network run on massive amounts of big tech’s infrastructure. Every statement Coxon makes that’s allegedly about “safety” or “protecting people” does everything it can to distance the AI labs from any responsibility or even active participation in any of this beyond some fatalistic level of “well, somebody’s gonna do this, why not us?”
And I also want to be clear about something: The Hugging Face attack was dangerous, reckless and somebody should go to prison for it. If a regular person used massive amounts of compute capacity to hack something, they’d be arrested. While I’m not a lawyer, the numerous cybersecurity experts I’ve discussed this with are stunned by the complete lack of any legal action against OpenAI, which appears to have committed a crime that gets you anywhere from a year to a decade in the slammer. The fact that LLMs from both Anthropic and Meta have been involved in similar incidents is a sign that we need to arrest more people.
Editor’s Note: When the late Kevin Mitnick was eventually arrested and sentenced to 48 months in prison (plus a further 22 months for violating the terms of his parole), prosecutors argued that he was capable of launching nuclear missiles by whistling (seriously) into a prison phone in a way that would mimic the shrill chirps and beeps of a dial-up modem. As a result, he spent eight months of his prison term in solitary confinement, and throughout his sentence, was subject to numerous restrictions on his communications.
I mention this simply to contrast the fact that LLMs from two massive labs have committed similar computer crimes to those which Mitnick was convicted of — with the key distinction that said labs have, at various points, said that their technology may ultimately result in the mass extinction of the human race.
Mitnick, for what it’s worth, always protested that the idea that he could whistle his way into launching nuclear armageddon was ridiculous. Because it was.
If I didn’t believe that prolonged stretches in solitary confinement were a form of torture (and if I didn’t believe that torture was always morally wrong), I’d suggest that not only do we need to arrest more people, but (for the sake of consistency) we need to ensure that said people are kept in a small cement room with nothing but a cockroach for company.
LLMs do not have to be conscious or powerful AI to be incredibly dangerous. The fact that Anthropic, OpenAI, and Meta are both training and allowing cybersecurity models to connect to their vast amounts of GPU infrastructure is irresponsible and should not be legal. The reason they are training these models, as I got into in my podcast Better Offline with Cal Newport, is that there’s a mountain of potential different kinds of exploit and vulnerability data online that you can cram into these models now that they’re hitting the diminishing returns on coding.
Sidenote: Cal described this on the afore-linked episode as “like strapping a weed whacker to your dog and putting it in your back yard and saying “yeah it’s going to help with the weeds back there,” and when the dog chases a squirrel and hurts a bunch of people claiming that it “went rogue” or was “misaligned,” when it’s actually a poorly-designed system that doesn’t fully take into account the problems it could create.
Yet flowery linguistics from people like Jacob Coxon and the greater AI industry have muddied the waters of what’s actually going on and who is truly responsible. If AI is described in terms of the unknown and being uncontrollable, the “risk” gets turned on its head from “we need to stop these companies from doing this” to “we must let these companies keep doing this because they’re the only ones who understand it.”
The AI industry wants to frame this as if Anthropic, OpenAI, and Meta discovered some new lifeform rather than having run a volatile kind of machine learning evaluation with poor cybersecurity practices. If the industry is the one saying that we should “be so scared of powerful AI,” it means that nobody is responsible for what it does — not even the people making it do it.
LLMs are cloud software. Framing them as anything else only seeks to mystify them and make the companies seem more powerful, all while doing absolutely nothing to make the world safer or more secure. The Hugging Face attack is not something that was made possible as a result of “powerful AI” so much as it was the weaponization of hundreds of billions of dollars’ worth of GPU-powered infrastructure owned by Microsoft, Google, Amazon, Oracle, and CoreWeave.
This was not an LLM that was asked to generate a picture of “increasingly sexier Garfields” that decided instead to hack Hugging Face. This was not an “agent” that “went rogue.” It was software doing what software was asked to do, using other bits of software to work out what to do next, all as OpenAI, the company that ran the software, did not appear to have any kind of notification or observability that said “hey man, thousands of LLMs are doing something right now.”
This suggests the following:
In any case, whatever happened with Jacob Coxon struck a nerve in a media ecosystem where it appears many people do not have object permanence.
This is a short note, but an important one: the terminology of “pacing the frontier” or “slowing down” implicitly buys into the narrative that the AI industry’s path is the correct one, and that the only problem is the speed it’s moving at.
The way that LLMs have been trained is harmful in effectively every way. It is trained on theft, powered by expensive and power-intensive infrastructure and is both unprofitable and unsustainable. LLMs are not the tool for any kind of beautiful, automated future — they are inefficient, volatile and mathematically certain to make mistakes. “Slowing down” is not sufficient. In my opinion, there is no further reason to invest in this industry, nor has there been for the vast majority of its existence.
Every success that LLMs have had is a direct result of throwing at least half a trillion dollars in infrastructure and compute spend at problems that had vast amounts of data that could be trained against. Half a trillion dollars should have bought us a lot more than this. While I will not dispute that they can do more than a year ago, I am unimpressed, because this is more than ten times what Amazon’s entire capex between 2003 and 2015, the years between AWS’ creation and when it hit profitability.
This is a terrible deal, its results suck, and the amount of attention it’s gotten is a direct result of the media’s inability to speak truth to power or do anything other than repeat what they say and a financial bubble driven by LLMs’ unbelievable infrastructural cost.
If — and this is not a foregone conclusion — there is ever an AI that we, as a society, should fund and build infrastructure for in pursuit of some civic good, it is not the one peddled by Elon Musk, Sam Altman or Dario Amodei.
This is not the right path, and every further step down it makes the bubble’s collapse worse, as well as multiplying the dangerous and reckless experiments these companies are capable of doing thanks to their near-unlimited access to compute.
The entire Jacob Coxon thing is very, very strange. He had never tweeted before his post that now has over 170 million views on Twitter and interviews with the WSJ, CNN, NBC, CBS, and a bunch of other outlets that should’ve known better. While he had only been at Anthropic a few months (and lost stock options when he resigned), he had been at OpenAI for years and absolutely had options vested from there. Retweets of his post were clearly coordinated with various AI safety organizations, and the speed at which it took off with the media makes all of this look incredibly contrived, as does Coxon’s total lack of any direct critiques or “blown whistles” about the AI labs themselves, other than that they “can do more safety” and “should coordinate a global slowdown.”
In the end, it doesn’t really matter, because even though most of the media mostly jumped at their own shadow, the sheer volume of traffic to Coxon’s tweet and his endless media interviews have now moved the idea of the need for a “global AI slowdown” into the global zeitgeist. Turns out that using scare tactics and threatening everyone’s jobs on and off for three years has a consequence.
While it’s tempting to view this entirely as an opp — a coordinated industry-wide plan to push for some sort of self-regulation — in my mind it’s likely an attempt by the AI safety people to push an agenda that has spiralled completely out of control. Within a day of Coxon’s post, Clammy Sam Altman spoke with Fortune saying that OpenAI was delaying going public until 2027, as it was an “ill-advised moment” due to “safety concerns” rather than, I imagine, the fact that its financials are godawful.
He later posted two (two!) lengthy posts on Twitter, where he said that while OpenAI “[welcomes] a federal framework that sets consistent safety requirements for frontier AI,” it doesn’t believe the industry should wait, although failed to suggest potential solutions other than mentioning it was “excited by ideas like independent auditors."
This was followed up with the same usual scaremongering guff where he said that there two ways “AI progress could go very badly,” with the first being that “we could lose control of the future to AI,” something he did not elaborate upon, with the second being that AI could result in power becoming too densely concentrated with one company. I had to resist the urge to fall asleep while writing this paragraph.
Dario Amodei of Anthropic took to CBS to say that for “too long” the industry had “lied” about risks, saying that the “biggest one” was killing all humans, never mentioning when an LLM convinced a teenager to kill himself or its own hacking incidents because “AI safety” never relates to the things they’re building today.
The most-obvious version is in Amodei’s own “pace the frontier” blog:
To be clear, pacing does not mean halting model training or technical progress, but ensuring companies take adequate time to align and safeguard their models, and for third party evaluators to confirm this.
The “third party evaluator” he chooses is METR, the very same place that Joe Benton, an Anthropic researcher who quit two weeks ago, chose to move to after being convinced that AI companies are “underinvesting in safety.” You’ll also notice that Benton’s safety suggestions are self-serving:
I don’t think that’s acceptable for a technology that might cause extinction-level risks. The public should demand far more transparency. We can’t steer this technology safely without more people being able to see where it’s going.
Some of this is basic: companies should disclose their progress towards recursive self-improvement, report safety incidents and near-misses, meet minimum safety standards, and get independent guarantees that they are meeting those standards.
Nothing about the environmental impact, the theft of millions of people’s creative works, nothing about AI psychosis, just a bunch of stuff about how we need progress toward a still-theoretical idea that sounds really good if you’re trying to hype up a company.
Not long after Amodei discussed slowing things down, it broke that Anthropic had chosen NASDAQ for its IPO, shortly before the FT reported that Anthropic would “have a profitable third quarter” if — I shit you not — you ignore costs like training and stock-based compensation.
What the fuck is a slowdown if it involves an IPO, the purpose of which (besides allowing insiders to cash out their holdings) is usually to help the company going public raise capital from the public markets? What the fuck is a slowdown if you’re leaking (assuming Anthropic was behind it) you’re “profitable” in the least-GAAP way possible?
God, I’m tired of this industry.
There are, of course, real, meaningful things you could do if you actually were worried about LLMs — halting all model training, all cybersecurity evaluations, and starting a criminal inquiry into the Hugging Face attack that ends in somebody going to jail for the crime they used the models to commit. If it turns out multiple people are legally liable, tough fucking shit, you are going to jail for a crime, you are not special because you used agents to do it.
To be clear, I am extremely hesitant to believe anybody is “slowing down.” Anthropic’s own statements mostly amount to “we should all agree to not do something we’re not doing yet,” much like those made by Musk and Altman.
Yet the sickly irony of all of this safety theater — and that’s all it is without any actual tangible attempts to deal with the harms of the technology that actually exists — is that a boneheaded media incapable of catching out grifters has accidentally destabilized an already-tenuous narrative.
Put another way, I think everybody has their own agenda, nobody has a plan, and that everything is accelerating like the end of a Coen Brothers movie as every little narrative thread gets tangled together in a potentially jumbled and chaotic conclusion.
Back in early 2023, a young(er) Sam Altman said that OpenAI was “a little bit scared” of AI, adding that we should “guard against potentially negative consequences for humanity,” adding that they “could be used for offensive cyber-attacks.”
In the end he was right, but only because he made sure that was the case.
For years the AI industry has engaged in endless, vague safety theater about the “risks” of AI, all while peddling software that is actively harmful and unreliable. The “success” of the Hugging Face attack was largely a result of the sheer scale of OpenAI’s compute operation, and would not have been possible without Microsoft, Google, Amazon, Oracle, and CoreWeave’s continued enabling of an unprofitable, unsustainable company that is desperate for new business models.
Yet I must be clear that the Hugging Face attack happened two months ago. Everybody doing backflips out of fear about “powerful, autonomous AI breaking out of the sandbox” is mostly doing so because a British guy went on TV and said “AI will kill us all,” and while I can’t resent a pale British man getting broadcast opportunities, I take exception with those who are so densely packed with bullshit.
Nevertheless, Coxon struck a match next to a giant pile of dynamite laid by years of pantomime about “AI risks” that didn’t actually apply to the things that the labs were building.
The dueling brain cells of VanderHei and Allen at Axios declaring that we were going to face a “white collar bloodbath” were not based on anything LLMs can do, nor have any of the bullshit stories around so-called white collar job loss, nor was the early GPT-4 scare hype around “LLMs blackmailing people,” nor were the numerous stories about AI 2027, but they were demonstrations of how ready the media was to lose their entire shit over a narrative that the AI industry was deliberately encouraging: that AI was powerful, unknowable and uncontrollable. Everything was always about selling today’s tools based on what might happen and occasionally scaring people about what that meant without ever really attaching it to the stuff they were doing today.
While there may be some people that had honourable or sincere beliefs that AI was or is potentially dangerous, rarely if ever did these stories actually discuss these harms, which meant that nobody ever really did “AI safety” in any meaningful way. While alignment — as in making sure the models were trained to act in a predictable way that created good outcomes — is a noble and necessary goal for training large language models, at no point has any “slowdown” or “pause” been suggested based on the grounds of what these things actually do.
This rocked for the companies for a while, because it meant that they could vaguely say “wow, AI is going to be so powerful” every so often and every member of the media would crap their pants and give them a headline. Every story was about how “today’s breakthroughs proved that tomorrow’s AI would be even more powerful,” which they loved because, well, it meant their companies would be even more valuable as a result, even if raising that valuation required intimidating people about the prospect of them losing their jobs, even if the software itself didn’t really do what they were promising (which didn’t matter to basically any journalist covering this field).
Their “powerful AI” — graded not based on actual outcomes but preferential anecdotes and performance on benchmarks rigged for the LLMs — was always “on the frontier” and “getting smarter every day,” all because AI labs and hyperscalers had intentionally sold their products based on some theoretical future version that would fix all the problems.
In other words, whatever they did was seen through the best light, described in the terms of the best parts of the present and the best promises of the future, and given credit as if it had already happened.
This is, as they’ve found, a double-edged sword. When journalists will believe (and print) whatever you say, they’ll start believing that the real thing (LLMs) does the same thing as the imaginary future thing (AGI, ASI, golden egg-laying geese), or will do so, even if that thing is bad.
These companies had spent years puffing up their LLMs’ potential using vague promises of superintelligence and theoretical model capabilities, at times inflating its capabilities further through scary quotes (here’s a list of Altman’s!), intentionally training models to blackmail people and entirely-fictional stories about “breaking containment,” and never realized that at any time one of the near-cultist types that joined their companies and heard everybody talking in terms of “p(doom)” (fuck off) could take it all seriously and the media might believe them.
This puts the industry in an odd position.
While on one hand, Altman, Amodei, Musk, and the rest of them know that they can’t roll back the narrative and say “everyone, stop freaking out, it’s fine, it’s just cloud software,” they also know that they have to do something because everybody is pissing their pants, even if it’s about something that is only really scary as a direct result of their scaremongering.
I’ve already seen a good amount of AI boosters trying to rein in Jacob Coxon’s scaremongering, or suggest that everybody calms down and remembers that AI is the biggest thing on the stock market.
At this point, it would’ve been really nice if the industry was operating in lock-step, except, as ever, Sam Altman had to go and fuck everything up, telling Fortune the following when asked whether pauses would cost the company a lot of money:
I’d [gladly go in front of my staff and investors and say] I am sorry. We, like, told you all along this moment might happen. We're still going to try to figure out a way to make you a bunch of money in the future.
This is a very, very worrying thing for Altman to say given that OpenAI has projected to spend $750 billion or more in the next three years across compute contracts with Microsoft, Google, Amazon, CoreWeave, Cerebras and other providers.
In fact, the very concept of a slowdown runs contrary to everything that the AI industry needs. If NVIDIA is to sell $670 billion or more GPUs in Fiscal Year 2028 or, per analyst expectations, Anthropic and OpenAI are to spend more than $444 across Google, Microsoft and Amazon in the next three years, or Broadcom is to sell nearly $600 billion in AI chips in the next three years, both Anthropic and OpenAI must keep and make their $1.3 trillion in compute commitments and support the development of 10GW or more of capacity, all of which requires them to continue accelerating at a dramatic pace.
Softbank just raised $11.87bn in debt from around twenty banks — all to support its investment in OpenAI, and more than its target of $10bn — and that wouldn’t be possible if the model labs had collectively decided to temper the pace of model development.
There is no way a “slow down” actually gels with the overall narrative of AI’s rapacious growth. As Anthropic and OpenAI represent 70% of hyperscalers’ AI revenues, there really is no fallback plan — there are no other customers who will naturally fill out the hundreds of billions of dollars’ worth of infrastructure, no other uses for the hundreds of thousands of GPUs bought from NVIDIA outside of generative AI, no ways in which we can simply “use the models we’ve got forever” without inherently accepting the limitations (and unsustainable costs) of running LLMs.
A pause could, in theory, mean that AI labs could slash their worst expense — training costs. While this might have the short-term benefit of reducing costs (and maybe even, with the right amount of accounting shenanigans, eek out a razor-thin positive margin), it’s likely that Chinese open source developers would distill (as they have been) Western models, create a much cheaper and “good enough” model to compete, and their “lead” in a race where everybody loses money would deteriorate.
Even then, what are OpenAI or Anthropic if they’re not cranking out some new version of a model or creating some vague sense of virality about the next one? What possible use is an Altman or Amodei if they’re not always on the phone to somebody signing hundreds of billions of dollars of compute contracts or promising some journalist-adjacent homunculus that Anthropic is going to cure cancer?
What is the LLM industry without a series of promises that extend infinitely into the future? What is Anthropic or OpenAI without the suggestion that it might be something completely different in an ever-distant future?
It isn’t clear, but what is clear is that a “slow down” does not gel with “insatiable demands for compute” or somehow being able to pay more in operating expenses in a year than Microsoft or Meta.
There’s also the very reasonable question of what happens to SoftBank if OpenAI can’t go public, which is now a very real possibility. With over $40 billion of debt due to be refinanced this year at a time of skyrocketing interest rates, it’s probably the single-worst time in history for it to be doing a $20 billion bond sale (separate from the aforementioned $11.87bn bank loan), which is why I think things are getting a little tight.
You’ll notice I’m a little light on predictions, and that’s because everything is a little volatile right now. Nobody has really committed to an actual slowdown beyond vague suggestions of an “independent” authority that would look at models and do something or rather, and based on what Amodei has said, it’s clear that a “pausing” really just means “saying we’ll take a little more time but not really change how we’re doing business.”
Alternatively, I’m dead wrong, and this is a moment of actual change caused by a runaway narrative years in the making. By deliberately misleading the media and the general public about the current and future capabilities of Large Language Models as means of inflating their valuations and justifying massive expansion of AI compute capacity, the labs made a sales pitch driven by scaring people into submission, assuming, like they do with their technology, that they had complete control over the situation.
As it stands, a slowdown is deeply impractical due to the massive commitments. As OpenAI and Anthropic make up the vast majority of AI compute demand, any contraction of that demand would mean material restatements of revenue (and the $748 billion in revenue backlogs) across every hyperscaler, along with the neoclouds and any other counterparty. The AI industry’s entire pitch to investors has been that all of these GPUs would be used and then some and that we needed to build all this capacity to reach the heights of AI breakthroughs, and while it was already questionable whether or not we needed that capacity, we certainly don’t if we’re “slowing down.”
And I must be clear, AI cannot “slow down” without creating some kind of serious financial crisis within the tech industry. Hundreds of billions of dollars’ worth of hyperscaler revenues and data center capacity is tied up in the idea that demand for it actually exists, and if the two companies with the most demand suddenly need to slow their roll, it’s hard to see how the capacity gets used.
Worse still, we’re most decidedly not done issuing debt for AI data centers — and I don’t see how anyone hearing about some kind of “AI slowdown” (real or imagined) feels particularly confident in backing a data center, considering investors barely understood what they were investing in to begin with.
The fact that Anthropic is going full steam ahead with its IPO is a sign that it doesn’t really care about slowing down, but all of this talk about “AI dangers” — even as we fail to deal with a single one of them — is enough to rattle an already-nervous market about the future growth trajectory of a company where people keep leaving and saying “it’s gonna kill us all!”
Some are arguing that the “slowdown” talk is a way to unwind the AI trade — to give AI labs a way out of their $1.3 trillion in commitments — and while it may or may not work out that way, I think it’s far simpler: the AI industry is run by a series of different entities with deeply cynical and selfish beliefs, all operating “in sync” only so far as it benefits ideologies and intentions that change on a daily basis.
Sadly, in the end, none of this is about actually fixing or mitigating the harms of Large Language Models, or holding those who have perpetuated those harms responsible.
What it may do — though I’m not getting my hopes up prematurely — is lead to the unwinding of the AI trade as reality slams head-first into the scaremongering overpromises of some of the least-trustworthy and most-craven executives in the history of society. Perhaps it’s a way that these massive cloud compute contracts could be canceled, or a way to reduce these labs in size. It may also serve as a convenient way to avoid admitting that they’re running out of things that LLMs can do that approximate a product or even the completion of a task.
Alternatively, it could just be another brief moment in the history of a bubble inflated by its misinformation and a media ecosystem dedicated to spreading it.
The Hugging Face attack and any other “hacking incidents” are a result of poorly-run AI labs training volatile neural networks bankrolled by and run on infrastructure owned by the richest and most-powerful companies in the world. Every attempt to focus this conversation on “what AI is doing” or “what AI could do” deliberately or otherwise separates us from the grim truth that we need to start arresting people for committing crimes, halting any and all training runs, and putting real safeguards on this technology — not because it’s all-powerful, sentient, conscious, or even “innovative,” but because it’s clear that the people running these labs are irresponsible and the people backing them don’t give a shit.
There are, however, things we can do, per former FTC Chair Lina Khan, if we actually had any interest in doing so:
There is an extensive set of laws that govern dangerous and defective products. For example, releasing unvetted AI models or agents can violate consumer protection laws. Shipping flawed AI tools without implementing adequate measures to detect and stop rogue or defective AI agents can be an “unfair or deceptive” act or practice under the FTC Act (and analogous state laws). And some state AGs are already exploring holding AI firms and their CEOs criminally liable when their models participate in criminal activity.
If LLMs were a toy, they’d be taken off the shelves. If LLMs were a drug, they would be banned.
I agree that we need to take “AI safety” seriously, but that starts with treating LLMs as normal software run in a reckless and dangerous manner by malevolent entities with little regard for society.
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2026-09-11 22:05:01
According to The Information, in early 2024, Broadcom CEO Hock Tan hosted a “coffee chat” with employees of the recently-acquired VMWare, and introduced them to his particular brand of management:
At that time, VMware’s Palo Alto, Calif., campus sprawled over 18 buildings and 100 acres of delicately pruned gardens, an outdoor amphitheater and a turtle pond. Employees enjoyed nice HR perks, too, including child care, marital counseling and an annual $1,000 “wellbeing” allowance for anything from dumbbells to Xboxes.
When Tan opened the discussion up to questions, a VMware employee asked if Broadcom provided such benefits. Tan seemed surprised. “Why would I do any of that? I’m not your dad,” he replied, according to three people in attendance.
Over the next few months, Tan fired about half of VMware’s 38,000 employees. He also stripped down the campus, selling all but five of the buildings. At the remaining offices, Tan had the espresso machines removed. Somewhat against the odds, the turtles were allowed to stay.
He may not be your dad, but Hock Tan sure is a motherfucker.
Broadcom is a company you likely know for its XPU platform — a collection of different bits of intellectual property and access to semiconductor parts that allow it to build custom AI chips, the best-known of which are Google’s TPUs. It just signed a $30 billion deal with Apple to build “custom ASIC silicon products.”
Apple was already a massive customer of Broadcom, which historically provided a good chunk of the wireless and radio frequency parts that you’d find inside iPhones and its other devices, representing at one point more than 20% of revenues, dropping to around 10% to 15% with the growth of AI chip sales and the acquisition of VMWare.
For the most part, Broadcom’s business is built on selling companies the internal bits and pieces of either their hardware or the hardware surrounding their hardware — everything from wireless and RF components to data center networking tools.
It also dabbles in mainframe software (from its acquisition of CA Technologies), security (from its acquisition of Symantec’s enterprise security business), and virtualization software (from its acquisition of VMWare), and these segments cost it a combined $99.1 billion in cash and stock (not counting for inflation).
Except “Broadcom,” as a company, wasn’t always called Broadcom, and wasn’t founded by Hock Tan. As I’ll get into in this piece, “Broadcom” was once two very different companies — a wireless communication chips company founded in 1998 called “Broadcom,” and the private equity-formed monstrosity formed out of a spun-off semiconductor subsidiary of Hewlett Packard called “Avago Technologies.”
Sidenote: This is why Broadcom’s stock ticker is “AVGO.”
Much like Oracle, Broadcom is the story of a company acquiring other companies and then screwing over both its customers and employees in the pursuit of endless growth, which usually involves price gouging, massive layoffs, and cost-cutting anywhere that won’t improve margins.
A great example came from a Wall Street Journal piece from January 2018 involving Broadcom’s failed $117 billion attempt to acquire Qualcomm:
Executives at Chinese handset makers Oppo Electronics Corp. and Vivo Electronics Corp., recently expressed concern that Broadcom might trim Qualcomm’s R&D spending on fundamental cellular technology. Broadcom’s options are either to raise Qualcomm’s prices or cut its costs, a Vivo executive said. “Either choice will pose disadvantages for us.”
Wang Xiang, senior vice president of strategic cooperation at Xiaomi Corp., another mobile-phone maker, said he was evaluating the implications of a Broadcom-Qualcomm tie-up. “I think we care more if the technology partner is motivated enough to do technology innovation,” he said.
Two months later, the deal would collapse despite a dozen banks signing on (per Reuters) to provide Broadcom a $100 billion bridge loan to get the deal done, with President Trump vetoing the deal to avoid Broadcom (then a Singapore-based company) exercising control over the US-based Qualcomm. To give some credit to the administration, the CFIUS had (per The Hill) “...worried that Broadcom’s takeover would lead to a decline in investments in research and development in the sector, opening the door for Chinese firms to take the lead in developing next-generation wireless technology.”
That R&D point was a very real concern. Per The Journal:
Mr. Tan, in his dozen years as CEO, has spent six times as much on acquisitions as on R&D, while in that period Qualcomm spent nowhere near as much on acquisition as on R&D, according to data from Broadcom, Qualcomm and S&P Global Market Intelligence. In the past 12 months, Broadcom spent 19% of revenue on R&D, while Qualcomm spent 25%.
Pffft, 19%? That’s chump change. Since the acquisition of VMware, Broadcom’s R&D budget as a share of revenue has decayed to an unremarkable 9.8% of revenue in its latest quarter.
On a trailing-twelve-month basis, Broadcom is exceptional among its peers for how little it invests in R&D as a percentage of revenue, beaten only by NVIDIA, which has the excuse that it is the literal largest and most-profitable company on the US stock market.
That’s because Broadcom doesn’t really do “innovation” or care about “being good to its customers, but by hoarding other people’s patents, iterating on their creations as little as necessary, and making it impossible to avoid wiring Hock Tan money. Even its FBAR filters (used to block out interference on mobile phones) — a critical part of its deal with Apple — come from Avago’s acquisition of the original Broadcom.
Sidenote: I gotta give the Qualcomm acquisition a bit more context. Back then, the 5G rollout was just around the corner and the Trump administration was concerned that China’s Huawei would end up providing a good chunk of the infrastructure for next-generation mobile communications, thus giving the Chinese state unprecedented access to the communications of Western companies and governments.
Back then, there were only three real players in the mobile infrastructure sphere — Huawei, Nokia, and Ericsson. Whatever crumbs these three left behind were swallowed up by Samsung and ZTE, another Chinese firm.
Obviously, there’s a difference between the tech that goes into handsets (which Qualcomm provides) and the mobile infrastructure — the RAN, or Radio Access Network, which are (simplified) the radio antennas your phone connects to, and the core network, which is the behind-the-scenes technology that routes calls and connects your device to the wider Internet.
China, at that point, didn’t really have a viable alternative to Qualcomm’s tech, with the only exception being (shocker) Huawei, which manufactured its own 5G modems and shoved them into its proprietary Kirin chipsets. But Huawei only makes smartphone tech for Huawei, and the Middle Kingdom’s other mobile giants (OPPO, Xiaomi, Realme, and so on) were forced to buy tech from Western suppliers.
Still, the point is, the US was incredibly wary about handing an adversary like China an advantage in a field that was traditionally dominated by either American companies, or companies that were from countries aligned with the States.
Ericsson is Swedish. Nokia is Finnish. Samsung (though, at the time, a bit player) is Korean.
If you’re curious why we ended up with a triopoly, the answer is either because a lot of the bits that make a mobile network are low-margin, high-volume industries (and something that companies like Cisco weren’t particularly bothered with), or because the company went bust (as was the case with Canada’s Nortel), or were absorbed by larger players (as was the case with Britain’s Marconi Mobile).
Anyway, in retrospect, it was probably a good idea that Broadcom wasn’t allowed to turn Qualcomm into an asset-stripped, zombified version of itself. At least, from the perspective of someone worried about a mythical Chinese boogeyman.
One last point: In 2025, Huawei spent 21.8% of its total revenue on R&D — a figure it’s largely sustained over the years, despite being slapped with US sanctions in 2019 and subsequently cut off from any US-origin tech, and why it’s been able to do some genuinely interesting stuff, particularly in the mobile and automotive spheres.
A year or two ago, this could’ve been called The Hater’s Guide To Avago, because that’s really been the story of Broadcom — a Singaporean semiconductor firm that rolls up other companies’ technology under a brand made famous by somebody else.
Per The Wall Street Journal, a few months before its acquisition of Broadcom:
Should Avago close the deal, it would be its biggest ever in a long string of acquisitions. The company has grown its market cap and share price over the past six years through aggressive deal making, and at each step of the way, investors have rewarded the company.
Its most recent deal was in late February, when Avago announced an acquisition of networking company Emulex Corp. for about $606 million. The first trading day after that announcement, Avago’s stock jumped nearly 15%, adding roughly $4.2 billion to its market cap.
It's been one of the more aggressive acquirers in the semiconductor sector the past two years Since 2013, it has purchased five companies in the U.S. valued at about $8 billion, including a deal to buy rival LSI Corp. for $6.6 billion. Yet in that span, its market cap is up more than $25 billion during the same time frame. This year, Avago’s stock has jumped more than 40%.
Much like Oracle, Broadcom used M&A as a means of treading water revenue-wise, with each one having little effect on its overall trajectory outside of its acquisition of VMWare.
Yet Broadcom had been building something quietly behind the scenes through the combined acquisitions of LSI (which had merged with Agere a few years previously) and its own semiconductor might — a budding relationship with Google to build its Tensor Processing Units (TPUs), AI chips that at first worked to support products like Search and Maps, and would eventually become a huge part of the AI boom.
To be clear, Broadcom didn’t “see anything coming” or “catch the AI boom in its infancy.” While it deserves some credit for rolling up various different semiconductor companies like it’s playing Katamari Damacy, this is not a situation where Hock Tan or anyone had any kind of precognitive event that made them invest in ASICs in anticipation of a massive payoff.
What actually happened was far simpler: Google, which had already been running its services powered by (non-LLM) AI, got Broadcom to use its pile of various patents and supply chain connections to put together specialized silicon that had incremental boosts to Broadcom’s revenues until the launch of ChatGPT scared Sundar Pichai into sinking billions — and then tens of billions — of dollars into successive generations of TPUs.
And in Fiscal Year 2024, Broadcom began breaking out that revenue from its semiconductor solutions division, and something became alarmingly clear: that it’s become dependent on AI revenues for virtually all of its future growth.
Between Q1 FY2024 and Q3 FY2026, AI revenue has gone from 19.2% to 56.4% of Broadcom’s revenue, with analysts expecting it to make up 68.4% of FY27, 79.8% of FY28 and 81.9% of FY29.
And you’ll never guess who the customers are!
That’s right — OpenAI (for its Jalapeno AI chip) and Anthropic (buying Google TPUs), who are set to become Broadcom’s largest customers in Fiscal Year 2027, which means that tens of billions (and eventually hundreds of billions) of dollars of revenue will be tied to whether two unprofitable, unsustainable AI companies can afford to pay.
This is the story of how a grab-bag of other people’s innovations has accelerated in the space of three years to become one of the largest AI chipmakers of the world, and how its desperation for growth has forced it to engage in the darkest forms of circular financing.
This is The Hater’s Guide To Broadcom — the hard numbers and charts behind Hock Tan’s aggressive play to beat NVIDIA and become Google, Anthropic, and OpenAI’s chipmaker of choice…and how dangerous it might be if it fails.
2026-09-08 22:28:03
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Jensen Huang, CEO of NVIDIA, the largest company on the stock market, has declared that “AGI has arrived” in a response to the CEO of Crusoe congratulating OpenAI on the launch of its GPT-6 Astra model, who said that this “made Abilene the birthplace of AGI.”
Per sources with direct knowledge of the current progress of Stargate Abilene, the AI data center being built by Crusoe for Oracle to lease to OpenAI, there are at most four out of eight buildings functional at the Abilene campus, which started construction some time in 2024. Huang at no point defines what “AGI” is, other than to say that we’ve reached it, and that “400k GPUs coming online” was what was next, I assume referring to somewhere else on Earth, because Abilene only has space for a total of 400,000 Blackwell GPUs, of which (as I’ve noted) at best half of which are actually installed and functional.
The reason that everybody is talking about AGI is that TIME magazine, bereft of any journalistic standards or shame, quoted OpenAI Chief Research Officer Mark Chen as saying that OpenAI was “80% of the way” to AGI,” only for Chief Operating Officer Greg Brockman to say a few days later that we had entered the “AGI era, whether you view it as this model, the last one or the next one,” which the Wall Street Journal agrees with, even though it cannot define exactly what AGI means, but this is the AI bubble and those most-responsible for telling the truth are mostly incapable or unwilling to bother.
These companies are treating everybody like they’re stupid, in large part because everybody, including the largest media outlets in the world, appears to fall for just about anything. Neither NVIDIA nor Crusoe have actually done anything — we have not reached “AGI,” nor has “the birthplace of AGI” been completed, nor does anybody seem to bring up these facts in any of the pieces I’ve read outside of saying “hmm, well AGI isn’t really well-defined,” humouring what these companies are saying without a single thought entering their minds.
If anything, the far-more-interesting way to look at this is why all of these people are suddenly jerking their shit from first principles over a term that is meant to mean “an artificial intelligence that can handle tasks beyond its original training” but now means basically anything the companies want it to, and how that times with the rush for both Anthropic and OpenAI to go public.
The answer is pretty simple: these people want to stop you thinking about what’s actually happening — that the underlying financials and demand do not make sense, and their cloud software does not remotely justify its alarming costs.
Today I’m going to talk to you about why I think there’s a Silicon Valley Financial Crisis brewing, and the concentration risks involved.
So, today we’re going to talk about a term you may or may not have heard of before: concentration risk.
It’s a term that refers to having all your eggs in one or a few baskets, becoming overly reliant on a few investments, customers or particular business lines to the point that without them your business or portfolio would suffer massive harms. In banking specifically, to quote the National Credit Union Administration, it refers to any single exposure or group of exposures with the potential to produce losses large enough (relative to capital, total assets, or overall risk level) to threaten a financial institution’s health or ability to maintain its core operations.
I bring this all up because you’re going to hear this term, or variations of this term, a lot in the next few months and years as the AI bubble unravels, because just about every part of the industry involves its own flavor of concentration risk.
Let’s start at the top. Per data from fintech firm Ramp, 80% of OpenAI and Anthropic's enterprise revenues come from 1% of their customers, a number that hasn’t improved over the last three years. Ramp’s lead economist Ara Kharazian notes that the top 1% skews heavily toward the tech sector and AI products and services, and that this was a level of concentration risk unseen in any other software category they tracked.
Oh, and it hasn’t gotten better over time.
This dataset, which likely includes big companies like Visa and Cursor as well as a great deal of startups and regular-sized companies, is indicative of the overall spend of the AI industry, with the caveat that it doesn’t include massive players like Microsoft or major banks, and customers can opt out of being included in research.
I also want to be clear that when Ramp says “AI products and services,” that includes AI startups that sell subscriptions with subsidized token spend, meaning that users can burn far more than their subscription price in tokens. This means that the money made by Anthropic or OpenAI from an AI startup in that 1% spend is contingent on their continued ability to raise venture capital.
This means that the vast majority of enterprises — which is where the real money is in software — just don’t spend that much money on AI. Those that do spend the most on it are heavily-concentrated in either AI companies that either use a lot of tokens internally because they’re bankrolled by venture capital, AI companies that allow their users to blow unsustainable amounts of money on tokens bankrolled by venture capital, tech companies that are currently under heavy peer pressure to spend money on AI tokens, and I assume a few whale customers of some sort.
During the Great Financial Crisis, millions of people took on debt they never had any hope of paying, with one of the most egregious examples being “NINJA” loans — No Income No Job Applicants or No Income No Job Assets (I've seen both).
Per Pew, “...in the years before the Great Recession, almost 38% of new mortgages required little or no documentation.”
To be specific, 36.5% of 2005 and 37.9% of American home purchases in 2006 were from buyers with little-to-no income documentation, which meant that, for the most part, these subprime mortgage payments were only made possible by a system that was desperate to create more demand for loans rather than creating a lending agreement with a stable customer who would be able to make regular payments.
Sidenote: Before we go any further, I want you to also know that “subprime” doesn’t refer to the borrower but the loan itself. Plenty of “well off” people got mortgages they couldn’t afford in the time leading up to the Great Financial Crisis.
A “homeowner” in 2005 and 2006 could easily be somebody who could not, in any real sense, afford the home they were buying.
I sure hope that nobody is making that same mis-OH MY GOD!
This means that 80% of OpenAI and Anthropic’s enterprise revenues — which make up the vast majority of their total revenues — are dependent on what are likely hundreds of customers spending outsized amounts of money on AI tokens, with an indeterminately-large chunk of them being AI startups that can only do so as long as venture capital supports them.
Let me break down exactly what this means:
AI startups are an artificial source of revenue. They are not paying Anthropic and OpenAI out of cashflow, or because they’re “getting great value,” and indeed are only able to do so as long as somebody else hands them endless amounts of cash. While their revenues may be increasing, they pale in comparison to the sheer sums raised or the rate at which they’re raised. Harvey raised over $800 million in 2025 alone, and exited the year at around $190 million in annualized run rate, or around $15.8 million a month, meaning that it would’ve been completely dead over a year ago without venture capital propping it up.
And let’s be completely clear: OpenAI and Anthropic are financially dependent on these customers to survive. While “enterprise” could refer to a cluster of Fortune 500 or big businesses that are theoretically using LLMs for coding or whatever, it’s very clear based on Ramp’s data that one of (if not) the largest sources of revenue for these companies is AI startups that can literally not afford to pay for tokens without venture capital funding.
AI startups are also the easiest to make spend more on AI because of their users’ subsidized token burn. When somebody fires up something like Harvey or Perplexity, they’re going to expect the latest models, which means that every AI startup is effectively a venture-backed marketing platform for the latest models, spiking costs for the company while feeding those dollars directly to the AI labs. When a user doesn’t have to worry about their actual costs and the provider doesn’t have to either because it’s bankrolled by venture capital, it’s really easy to see surges of revenue around every new model launch, giving AI startups a new way to beckon users back to the platform (see: Perplexity) and AI labs a bump in revenue in return.
AI startups represent a massive concentration risk for OpenAI and Anthropic, because this isn’t real revenue. Providing these services to AI startups isn’t making their customers “more money” so much as it gives them a justification to keep raising money. While Harvey or Perplexity might “need” AI models to run their businesses, they are not paying for them because of any value or business model or strategy so much as that they’re in a Red Queen’s Race where they must offer the latest models at whatever cost to “stay current.” If anything, without funding these businesses would have to stop offering Anthropic and OpenAI’s models to reach anything approximating sustainability, because the cost of AI tokens is the primary driver of their losses.
To give you an idea of the scale of these customers, last week OpenAI announced it was cutting off AI coding company Cursor (which is now part of SpaceX), with WIRED reporting that it was set to make OpenAI over $1 billion in revenue in 2026, or over 3% of its projected $30 billion in 2026 revenue. With OpenAI only representing 5% of Cursor’s traffic, it’s likely sending billions more to Anthropic this year, a massive underlying exposure that could easily evaporate if Elon Musk decides he doesn’t want to send all that money to competing AI labs.
Cursor was only able to keep sending that money to Anthropic and OpenAI because it raised $3.2 billion in the space of four months — June ($900 million) and November 2025 ($2.3 billion). Per The Information from July 2025, Anthropic’s two largest customers represented $1.2 billion of annualized run rate (30% of its $4 billion run rate at the time), with investors believing they were Cursor and Microsoft’s GitHub Copilot, the latter of which moved to token-based billing in June 2026.
The problem is both that Anthropic and OpenAI’s largest customers cannot afford to pay them and that they desperately need them to keep paying them more every quarter, which means that every single AI startup will need to raise more and more money to do so.
They are, as I’ve suggested, the NINJA borrowers of the AI era. They do not have to show functional businesses or sustainable demand for their products, only an excitement to sign pieces of paper and an eagerness to continue spending money that isn’t theirs. The “houses,” in this case, are the ever-increasing valuations of the startups themselves. There is no logical or rational basis to value Perplexity at a potential $30 billion (per The Information) or to give it billions of dollars, other than the fact that venture capitalists want to see the value of the company go up, and NVIDIA wants to make sure it can keep spending billions with Anthropic and OpenAI.
And much like NINJA borrowers, this bad behavior is enabled on a systemic level, with 50% of all global venture capital flowing into AI in 2025.
As mentioned, the “top 1%” skews toward tech and AI startups, which means that even outside of unsustainable AI companies, Anthropic and OpenAI are mostly-reliant on the same customers they’ve always had for revenue growth.
That means that outside of unprofitable AI startups, the vast majority of “enterprises” spending money on AI are tech companies rather than other industries. The tech industry is far more willing to dabble and invest money in new stuff, especially if everybody else in the industry is screaming about it non-stop for years, meaning that its “interest” is driven by far more than “is this actually useful” or “do we actually need this.”
Tech companies have more software engineers, and in turn more software to be built or iterated upon, along with more willingness at the C-suite level to spend money on software tools.
I’ll add, however, as Ramp’s Kharazian noted, that this was “...a level of concentration risk unseen in any other software category [than they track],” which means this is an AI-specific concentration rather than a problem with software writ large.
Sidenote: At this point, somebody is probably screaming that “Ramp skews towards startups” and “Ramp’s data doesn’t include every big business.” Neither of these arguments are actually based in reality, but even if they were, these are still massive revenue sources that are dependent on the whims of tech executives or venture capital.
In other words, outside of the tech and AI world, very few companies are willing to pay very much for AI, which is catastrophic on just about every level, with no clear sign as to how you reverse the trend.
AI has been in every media outlet and discussed in every boardroom and company for the last three years, every single company has on some level dabbled in using AI, most businesses have been given the greenlight to spend a bunch of money on AI, and in the end, it seems the only people the tech industry can get to spend significant money on AI is…the tech industry itself. “The tech industry” also includes an indeterminately-large amount of venture-backed startups who, much like AI startups, can only afford to spend a lot of money on AI as long as somebody else gives them the money to do so.
This is yet more underlying exposure for the AI labs, because these customers are also prime targets to move to either cheaper open source models that they train themselves or, eventually, on-device models.
Even if they choose to stay with Anthropic and OpenAI, a chunk of this spend is contingent on venture capital funding, and the rest is contingent on whether tech firms continue to be willing to spend money at scale. 80% of their revenue concentration depends on spending and capital that varies from unreliable to actively-unstable.
Things get worse from here.
Sidenote: I estimate that there’s around $22 billion of annual non-OpenAI/Anthropic AI compute demand, with most of that coming from Jane Street (an investor in both CoreWeave and OpenAI) and, on a much larger scale, NVIDIA renting back its own GPUs. I think this number could be smaller, but this is my closest estimate based on my analysis.
I’m saying this because I anticipate someone will say “Ed, someone else will buy the compute.” No they won’t. As I’ll get into, the companies that are meant to buy the compute can’t afford it, and nobody else is buying compute at even close to that scale.
So, I realize that a few months ago I described AI data center debt as the subprime mortgages of the AI bubble, and I stand by that comparison at the time I made it, and think it still matches.
That being said, another example has emerged — Anthropic and OpenAI’s monstrous compute commitments, which now represent over $1.3 Trillion in revenue for hyperscalers and neoclouds like Google, Microsoft, Amazon, SpaceX, Hut8, SB Energy, Oracle, Cerebras, Nscale and Lambda.
To be specific, per the Wall Street Journal, OpenAI projected to spend over $750 billion on compute through 2030 in July 2026 before it signed its deal with SB Energy (more info here), and per The Information’s research, Anthropic has signed approximately $517 billion in agreements in the last 11 months.
These are, from what I can tell, “take-or-pay” agreements where they agree to buy that compute capacity regardless of how much capacity they actually end up using, and how much revenue they actually bring in.
And when the compute is available, or about to be available, you have to pay a chunk of money up front before you start using it.
As a reminder, both are woefully unprofitable and lose tens of billions of dollars a year. Even if they were profitable, the sheer scale of their commitments is astonishing, representing a massive underlying risk to some of the largest companies in the world.
To give you an idea of that risk, per Bloomberg OpenAI’s compute spend and revenue share represented around 70% of Microsoft’s AI revenue in Fiscal Year 2026 — which just ended in June — or a little over 7% of Microsoft’s entire fiscal year revenue, and UBS estimates that Anthropic and OpenAI’s compute spend will account for 48% of Google Cloud’s entire revenue next year, or somewhere between $84 billion and $100 billion dollars, in 2027.
That’s on top of, per Barclays, OpenAI and Anthropic’s estimated $40 billion dollar spend on Amazon Web Services, and at least $50 billion dollars that both of them will spend on Microsoft Azure in Calendar Year 2027, which I note because Microsoft uses its odd fiscal year system.
On the low end, that means that Anthropic and OpenAI account for over $200 billion dollars worth of expected revenues for Microsoft, Google and Amazon in 2027, which is contingent on their ability to raise venture capital or debt, which is contingent on the continued growth of their businesses, which is contingent on growing AI spend from a small subset of customers, many of whom are funded by venture capital.
The reason this hasn’t been a problem yet is that when you sign these contracts, you tend to pay a small up front fee, and the capacity in question is yet to come online.
All it takes for Anthropic or OpenAI to sign hundreds of billions of dollars’ worth of obligations with a little bit of cash and a few clicks of a DocuSign agreement, meaning that all that capacity isn’t costing them anything until the date hits when they have to start paying.
That’s going to start happening next year, and get dramatically worse month after month as capacity comes online.
Hey, that reminds me of something too.
Anthropic and OpenAI’s compute commitments, in my mind, should be seen more as debt obligations than “contracts,” because they (as take-or-pay agreements) function in much the same way, requiring the company to pay whether or not they need the capacity.
For now, everything looks awesome. Microsoft, Google and Amazon have all had big bumps in revenue from AI lab compute spend along with massive, ever-swelling revenue backlogs — over $1.5 trillion worth to be specific. More than half of that backlog is attributable to Anthropic and OpenAI, which, as I’ll say again and again, isn’t a problem because the money is yet to stop coming in.
As mentioned, this is going to begin in earnest in 2027, and expand dramatically every year following (though I doubt we will make it that far).
A really shittily-written piece (full of incorrect numbers and zero citations written using an LLM) from an outlet called Groundbreaker made a good point about this, comparing it to when the rates on millions of mortgages exploded as they hit a “reset wall,” where the low “teaser interest rates” ended, exploding the monthly mortgage payments to unsustainable highs, with customers assuming, incorrectly, that their houses would keep appreciating or they’d be able to refinance.
In other words, Anthropic and OpenAI are currently in the teaser rate period where all of that capacity — and all of the associated costs — are yet to hit.
Next year, at least $200 billion in compute costs are coming due.
The question is whether Anthropic and OpenAI, two unprofitable, unsustainable AI labs that lose tens of billions of dollars a year, will be able to afford to pay them.
If you ask the vast majority of tech and business journalists, consultants or sell-side analysts, they’ll tell you not to worry — that there’s insatiable demand for compute, or even that said demand “may never be sated,” and that even if there is a bubble, society will get “gigantic benefits” either way. These views are always backed up by data from the industry, which is trusted, for some reason, to tell the truth about itself.
The argument that most would make is that both Anthropic and OpenAI will be able to buy all of that compute, and even if they couldn’t afford it, other customers would line up to take the demand. When pushed about how the big AI labs would actually afford this compute, everyone will tell you that “they’re the fastest growing companies in the world.”
In this case, we’re talking about $1.3 trillion in demand from two customers who have a few hundred customers that mostly pay them based on the availability of venture capital dollars.
While the consequences might be different — as the scale and damage of the Great Financial Crisis was driven by trillions in speculation — the mistakes are increasingly looking very, very similar.
And so are the rationalizations.
In the period leading up to the Great Financial Crisis, approximately 80% of US-based subprime borrowers got adjustable-rate mortgages with “teaser rates” — lower interest rates for the first two-to-three years followed by adjustable rates that changed with both interest rates and, in some cases, fees associated with said adjustments.
These mortgages were known as 2/28 or 3/27 mortgages, depending on whether the teaser period lasted two or three years. One important thing to note is that the “teaser rate” wasn’t by any means low (they could be as much as 7%), only that they were lower than the normal rate.
When borrowers worried about the potential for higher monthly payments, they were reassured that they’d be able to refinance, or that the price of their house would only ever increase.
Per an FDIC report on the Great Financial Crisis:
Under the more relaxed underwriting standards at the time, many borrowers qualified for adjustable rate mortgages based only on their ability to pay the low initial monthly payments as determined under the introductory teaser rate. Hence, their ability to afford the mortgage after the teaser rate expired was predicated on their ability to refinance the mortgage before the higher payments became effective.
The ability to refinance—counted on by many investors, homebuyers, and originators—depended critically on house prices. As long as house prices were rising, lenders were generally willing to supply new funds with new terms. And even after house prices at the national level peaked, in mid-2006, housing market participants generally did not expect house prices to crash.
While warnings about a housing bubble started as early as August 2002 (good work, Dean Baker!), there was a broad (though not complete) consensus that there was, in fact, no housing bubble. In August 2005, the National Association of Realtors put out multiple “anti-bubble” reports, saying that “the facts simply do not support the possibility of having a housing bust” in 130 specific markets and the nation at large. Then Fed Chair nominee Ben Bernanke said in October 2005 that “there was no housing bubble to go bust,” noting that even if there was a “moderate cooling in the housing market,” that it would “not be inconsistent with the economy continuing to grow at near its potential next year.”
Yet my favourite is from July 2005, when the Wall Street Journal’s Neil Barsky (in a piece called “What Housing Bubble?”) mocked The Economist for calling it “the biggest bubble in history,” castigating “the media and economists [scaring] homeowners with words of doom and gloom, however knee-jerk, consensual and misguided they may be,” saying that “there is no housing bubble [in America].”
His justifications involved saying that the housing market was strong as a result of “real economic underpinnings” like “low interest rates, local job growth and the emotional attachment one has for one’s home.”
Yet the most-relevant one was that he connected the strong housing market to the “real economic underpinning of "one's view of one's future earning-power,” and his thoughts around housing demand:
What we do have is a serious housing shortage and housing affordability crisis. Despite robust construction, unsold inventory stands at four months, well below its 25-year average. Private builders complain they can't get land permitted to meet demand. Low-income housing advocates complain housing prices are out of reach for many Americans, and that government subsidies have been slashed.
Hey, this kind of reminds me of something that NVIDIA CFO Colette Kress said on its latest earnings call:
The Frontier AI labs have extraordinary demand for training and inference compute, but they are growing faster than what their balance sheets and credit profiles can support. They have rapidly growing customer demand, yet still lack the decades-long infrastructure contracts and investment-grade financing capacity needed to secure the AI factory infrastructure independently. In other words, their growth is not limited by their technology or customer demand. It is limited by compute.
This piece rules, primarily based on its answer to the “myth” that “risky mortgage products are fueling house appreciation, which mostly boils down to “homeowners only own their homes for an average of seven years [note: he has no citations for this claim], which means that you’re basically wasting money by not getting an adjustable rate mortgage.
I could go on. On December 21, 2006, CNBC’s Diana Olick ran a piece based on reader feedback around housing numbers provided by the National Association of Realtors, The Department of Commerce and the National Association of Homebuilders:
Another [reader], Michael Crespy, writes: “Although you periodically have a “housing bear” on the program, more than not, the program is filled with the NAR or NAB’s “economists” who are no more than the HEAD cheerleaders for the housing industry!!”
Mr. Crespy, you’re right, they are the cheerleaders for the housing industry, but they are also economists whose sole purpose is to organize and present data on the industry. Interestingly enough, the Dept. of Commerce, which has no stake in the industry, has far higher margins of errors on its numbers than do the industry numbers. The NAR’s existing homes data, which are monitored by the Federal Reserve, has a 1% margin of error. Their data comes from a sampling of 40% of the MLS listings. Forty percent is pretty high in survey land.
Olick’s piece, at least on the surface, attempted to have a “balanced” view, but mostly ended up arguing that everything was fine, with even a quote from Wharton School of Business professor Susan Wachter saying that the numbers — which all said that things were “improving” — “in some ways [gave her] confidence,” adding that she had no problem with statistics from realtors or home builders.
Olick, feeling defensive, ended the piece as such:
Here at Realty Check, we report the numbers, we talk to the industry leaders, we also talk to umpteen brokers out in the field, to economists who study real estate trends and to buyers and sellers who are trying to make sense of it all; then, for better or worse, we try to make some sense of it all. I confess, I do own a house, so there’s my bias; I’d like it to continue to appreciate. If you don’t buy what I’m reporting, that’s your choice.
Now, in her defense, perhaps the numbers did say everything was fine if you squinted, but the sheer venom that Olick had for concerned listeners that called her “some kind of apologist or defender of the industry” rather than, say, going out and doing journalism…mirrors basically all of the reporting on AI today, which mostly says “the numbers look great!” while, well, ignoring the ones that don’t.
Less than a week later on December 27, 2006, CNBC would run a story called “Analyst: Housing Bubble Fears Behind Us,” quoting former US International Trade Commission economist Peter Morici as saying that home numbers sales were “very good news for the economy,” and that he “expected new home construction to rebound in the second and third quarters of 2007.”
Here’s what actually happened:
Terminology Time! A “reset” in this case is when a mortgage goes from a lower “teaser rate” percentage to an adjustable-rate that changes based on the terms of the mortgage and current interest rates, massively increasing your monthly payments.
I must be clear that the Groundbreaker piece that inspired this piece is horribly written Claudeslop, but deserves credit for this idea, even if it fumbles basically every number, cites effectively nothing, and has near-impenetrable text that I’m not certain most people even read.
Sidenote: The term “Reset wall” is a term that seems to have entered adoption after the fact, and doesn’t appear in contemporaneous coverage of the subprime mortgage crisis. Coverage of that era uses the term “rate reset.”
Nevertheless, I must quote it:
Millions of subprime borrowers were, at that moment, paying the low introductory rate on a two-year adjustable rate mortgage - the 2/28 ARM. A low fixed-rate for two years, then the rate reset to a payment 30% to 50% higher. During those first two years the loan performed beautifully: the borrower paid, the servicer collected, and the bond paid its coupon. Nothing looked wrong because the whole complex - housing, mortgages, securitization - was sitting inside the teaser period.
Every ARM reset was known, dated, and contractually inevitable from the moment of origination. Aggregate those reset schedules and you get the most damning exhibit of the era: the reset wall. Roughly a trillion dollars of adjustable-rate mortgages were contractually set to reset across 2007 and 2008 - thirty to forty billion dollars a month at the peak. Credit Suisse published the chart in March 2007. The IMF reprinted it. It circulated on every trading floor in New York and London.
Groundbreaker neglects to cite anything, so I went and actually found the chart shared by the IMF via Credit Suisse:
The “wall” in this case refers to the large group of Subprime borrowers who suddenly, starting in 2007, would see their mortgage payments skyrocket to the tune of tens of billions of dollars a month (as Groundbreaker correctly said).
Sidenote: Though there’s not a ton of data out there, the Center For American Progress noted that 1.8 million mortgages hit or would hit a rate reset in 2007 and 2008,
In other words, before everyone had to pay more money, everything looked fine because everybody could still make their payments. Once they had to start making larger payments and couldn’t make those payments, with mortgage delinquencies spiking gradually every month from January 2007, peaking at 11.49% more than three years later in March 2010, taking another six years to drop below 5%.
You’ll also note that everything unwound very quickly, with much of it beginning in 2007 and 2008 as teaser rates ended. Subprime mortgage originations collapsed by the end of 2008 as private label securitization from banks and financial institutions (per page 19 of the FDIC report) which “had provided much of the funding for new mortgages” dropped dramatically and had “virtually disappeared” by 2008.
Said interest in funding new mortgages was, as we know now, barely anything to do with building houses so much as it was a way to build a new asset class for investors to speculate on.
And, very importantly, the massive expansion of subprime mortgage issuance mostly took place over a three-year-long period. While this rush of new housing development and mortgage origination was sold to everybody as the result of endless demand for housing, said demand for housing was driven by masses of easily-available money being given to people who couldn’t afford it outside of a manic period in history.
You can probably see where I’m going with this.
Everything seemed totally fine in the years running up to the Great Financial Crisis because, based on external data, the money hadn’t stopped coming in. Because effectively anybody could get a mortgage, US construction spending comprised nearly 9% of GDP by 2006, employing 7.7 million people, all because of the “demand” for housing created by the illusory demand created by subprime lending.
While nobody at the time could’ve possibly anticipated the sheer scale of speculation that would eventually unwind the global financial system, there was plenty of coverage of subprime borrowers being a problem. Not to worry though, The Brookings Institute explained in October 2007 that this wouldn’t be a problem, emphasis mine:
Unless the U.S. economy dips dramatically, however, the vast majority of subprime mortgages will be paid. And, because there is no basic shortage of money, investors still have a tremendous amount of financial capital they must put to work somewhere.
Nevertheless, in November 2007, Fed Governor Randall S. Kroszner did make a very clear warning:
Finally, another factor that could affect subprime delinquencies is the substantial payment increase often experienced at the first interest rate reset. For the most common type of subprime variable-rate loan, the so-called "2/28" loan, this reset occurs after two years, before which payments are typically based on a fixed below-market rate. In early 2007, the typical subprime mortgage experiencing a first reset had its rate increase from 7 percent to 9-1/2 percent, producing an increase of 25 percent to 30 percent in the monthly payment. This increase translates into an additional monthly debt obligation of $350 per month for the average subprime variable-rate mortgage.
And here’s the fun part: Anthropic and OpenAI’s reset wall is actually way simpler, more-concentrated and easier-to-spot if you bother to look!
As I mentioned in my premium from a few weeks ago (How Much Money Does AI Need?), analysts from UBS, Barclays and Wells Fargo expect — by which I mean they are setting expectations — that Anthropic and OpenAI will account for at least $444 billion of hyperscaler earnings in the next three years.
To be specific, I pulled together all the numbers from my AI Demand Bubble newsletter from a few weeks ago, and found that Anthropic and OpenAI will account for at least $365 billion in revenue across Fiscal Years 2026, 2027, and 2028.
Sidenote: Except this analysis is only partially complete, as it’s based on Wells Fargo’s single Fiscal Year 2027 estimate of a $52.5 billion expected contribution from OpenAI and Anthropic. One weakness of this analysis is that we’re talking about Microsoft’s Fiscal Year 2027, which actually began in the middle of 2026. Most other hyperscalers (including Amazon, Meta, and Google) align their financial years with the calendar years. Nevertheless, I think it’s fairly illustrative of the problem.
To estimate the contribution — and be incredibly fair! — I have assumed OpenAI and Anthropic’s Microsoft spend will be linear (at $52.5 billion) across fiscal year 2028, and then halved it for fiscal year 2029, which gets us to a grand total of $444 billion.
That spike in costs comes from Stephen Ju of UBS’ estimates, and even if you think that’s a little high, I would estimate that the $250 billion of commitments made by OpenAI alone on Microsoft Azure will likely mean Microsoft is expecting tens of billions more than $52.5 billion in FY27 and beyond.
I also need to express how much more money this is than these companies are already spending on compute.
In 2025, OpenAI spent (per my own reporting, assuming 50% of sales and marketing was compute expenses) a little over $29.5 billion on compute. Per The Information’s reporting, it spent $12.1 billion (with no affordance for sales and marketing) in the first quarter of 2026, and while we don’t know how much it spent in Q2 (when revenues grew by $1 billion quarter-over-quarter), it’s fair to assume that it’ll spend another $12 billion or so a quarter for the rest of the year, for a total of $48.4 billion, which is less than the $50 billion it said it expected to spend on compute in 2026.
Per Barclays and UBS, OpenAI is projected to spend $15 billion on AWS and $12.5 billion on Google Cloud in 2027, with Wells Fargo estimating it will spend $22.9 billion for the first two quarters of 2027 making it reasonable to assume at least $45 billion, for a total of $72.5 billion… which, even then, seems a little low based on what it’s already on track to spend in 2026.
Then you have to add in another $30 billion from Oracle’s $300 billion, five-year-long deal with OpenAI, which the Wall Street Journal reports is expected to drive $30 billion in revenue starting in 2027, though my own research found that it could be more than $50 billion or $60 billion
Meanwhile, Anthropic is expected to spend $25.3 billion on AWS and $101.25 billion on Google Cloud in 2027, increasing to $35.8 billion with AWS in 2028 and dropping to $25.6 billion with Google Cloud in 2028, likely as a result of the initial cost being buying TPUs. Since then, Anthropic took on $35 billion in debt to buy TPUs from Broadcom (which also backstopped the debt), with another $70 billion deal potentially on the cards.
I haven’t even included either company’s deals with CoreWeave, OpenAI’s contract with Cerebras, Anthropic’s SpaceX deal, or many of the deals noted in The Information’s story about Anthropic’s $517 billion in compute commitments.
As both Anthropic and OpenAI are private companies and we lack any meaningful accounting standards around disclosures for revenue backlogs, we can only estimate how big the compute reset wall is at any given point in time.
Part of the problem is that we don’t know how much capacity is actually coming online (as hyperscalers refuse to give any clarity), and said capacity has to come online for Anthropic and OpenAI to pay for it. It’s frustrating, because it means that “$1.3 trillion” number is hard to append to a period of time.
That being said, we do know that the Wall Street Journal has OpenAI projecting it will spend $750 billion on compute through the end of 2030, which suggests at least $250 billion a year in compute spend.
If it doesn’t, it means that in 2028 or 2029, its commitments could spike to $300 billion or $400 billion a year.
Is that good?
Let’s be abundantly clear about something: there is no rational or responsible way that Google, Microsoft, Amazon and the various other neoclouds should have allowed Anthropic and OpenAI to sign up for so much compute capacity, outside of the kind of blind faith that always goes wrong. Neither OpenAI nor Anthropic can actually afford to pay their commitments if they don’t grow by around 10x in the next three years, and at some point find a way to become profitable, which will require at least a trillion dollars in funding or debt.
Hyperscalers are doing all of this based on the very same logic that led to the massive issuance of subprime (and prime-but-unpayable) mortgages and the resulting overbuild of housing — that the money hadn’t stopped being spent. Venture capital and private credit have conspired to keep feeding Anthropic and OpenAI money (along with the hyperscalers themselves), much as they’ve continued to feed money into data center deals they’d theoretically occupy.
Similarly, hyperscalers continue to build out capacity for Anthropic and OpenAI under the continued assumption that they’ll keep paying, driven mostly by the fact that they’ve yet to stop doing so. They assume, somehow, that OpenAI and Anthropic’s ability to pay them tens of billions a year is all the proof they need that they’ll pay them hundreds of billions of dollars’ worth in the future.
Sidenote: At this point, I really want to use Groundbreaker’s charts, but their numbers are, if I’m honest, total fucking dogshit — Anthropic and OpenAI are very unlikely to have spent over $120 billion on compute in 2026, and I can find absolutely nothing to back them up. Nevertheless, this mound of Claudeslop makes several good points, and I have to cite it.
A take-or-pay contract is, in economic substance, a lease. And a lease is a financing. The defining feature of debt is a fixed payment on a schedule, owed regardless of the borrower’s circumstances. That is exactly what a take-or-pay commitment is. The payment does not flex with utilization. It does not wait for the customer’s revenue. It is owed on the commencement date and every period thereafter, for the term.
This is completely correct, unless of course you’re a member of the tech and business media, in which case it’s “a large amount of money that will of course be paid without fail.”
So, let me give you some context about how big these commitments are. Microsoft’s trailing-twelve-month operating expenses are $176 billion for a company with $331 billion in annual revenue. Meta, a company with $228 billion in annual revenue, has around $141 billion in operating expenses. Salesforce, a company with a little under $44 billion in annual revenue, has $35 billion in operating expenses.
OpenAI, in 2025, had $34 billion in operating expenses on $13.07 billion in revenue. In Q2 2026, its operating margin worsened to negative 183%. This is a company with deteriorating economics that has been allowed to sign hundreds of billions of dollars’ worth of compute commitments based on, for the most part, Sam Altman’s ability to say yes and the general sense that nothing bad ever happens to anyone.
These commitments were signed, I assume, with effectively no underwriting, because anyone with a calculator and sentience can see that on paper these companies cannot afford their commitments. The rationale is exactly the same as that used to hand-wave against worries around subprime defaults — that the system is working, that the system will always correct itself, and that things keep on growing.
In any case, neither OpenAI nor Anthropic actually have the money to pay for their obligations, and have only been able to keep up because of the low cost of signing contracts.
As these commitments begin, their needs for capital will dramatically accelerate in ugly chunks, both with hyperscalers and neocloud partners, on top of any debt deals they sign with Broadcom to fund their own silicon.
And the vast majority of these commitments and payments are yet to occur, which is, as is the theme of this newsletter, why nobody is worried yet.
Meanwhile, one abstraction higher, even the companies that are actually making a profit on the AI bubble are exposed to the underlying risk of Anthropic and OpenAI.
I’m going to dispense with the direct Great Financial Crisis comparisons at this point because I think it’ll get in the way of the analysis, but let’s be abundantly clear about something: either directly or by proxy, NVIDIA’s customer base is effectively Anthropic and OpenAI.
As I went into in part 2 of my Hater’s Guide To Circular Financing, OpenAI and Anthropic provide two functions to hyperscalers and NVIDIA:
To get specific about that second point, whenever you hear someone say that there’s “massive demand for AI compute,” they always point to revenue backlogs that are, for the most part, either OpenAI, Anthropic, or someone else renting them compute. For example, CoreWeave’s latest earnings involved the outright-deceptive statement that its “[$104 billion] revenue backlog [highlights] unprecedented demand for CoreWeave Cloud,” even though $22.4 billion of that is OpenAI, $21 billion is from Meta, $6 billion is from Jane Street (which also invested), and the rest is from some combination of Anthropic, Microsoft, and NVIDIA’s $6.3 billion backstop deal to buy unused capacity. To be specific, CoreWeave’s backlog increased by $32.6 billion in the earnings immediately following its Anthropic deal.
These revenue backlogs exist as both circular financing and financialized marketing schemes.
From the outside, every company with masses of AI compute also has an astonishingly-large backlog, which everyone assumes must be sold to a diverse subset of customers rather than Anthropic, OpenAI, and the companies that might one day sell them compute.
In other words, everything is based on the idea that Anthropic and OpenAI are A) going to have near-infinite demand for compute and B) that their existence is proof somebody else will too.
The other problem is that NVIDIA’s GPUs are so god damn expensive that nobody — including the largest and richest companies in the world (minus Microsoft) — can afford to keep buying them and building data centers without taking on near-infinite amounts of debt, reducing the pool of potential customers dramatically.
You can already see this in NVIDIA’s latest earnings. Almost half — 44% — of its FY2027 revenue so far (two quarters) came from three customers, and 16% of its most-recent quarterly revenue came from one customer, likely SpaceX, which serves Anthropic compute. Per my recent premium newsletter, UBS estimates that around 50% of NVIDIA’s data center revenue comes from Meta, Google, Microsoft, Amazon, and Oracle, with Deutsche Bank estimating it’s as high as 60%.
The justification for these further capital expenditures is, for the most part, driven by OpenAI and Anthropic, with their demand driven in large part by unprofitable AI startups subsidizing their users’ AI tokens.
While NVIDIA might talk about how we’ve “reached AGI” or that there’s “crazy demand,” the actual financial returns on buying NVIDIA GPUs are driven almost entirely by OpenAI and Anthropic, by which I mean Microsoft, Google, Amazon, Oracle, CoreWeave, Lambda, Hut8, Fluidstack, and basically every other counterparty is building capacity either mostly or entirely to capture their revenue.
The best example I can find is SB Energy, which has a $439 billion backlog, 99.4% of which is earmarked for OpenAI.
Further non-OpenAI/Anthropic GPU sales are contingent on NVIDIA’s perception management keeping everybody believing that there’s real demand for AI compute, which is why it effectively acquired Poolside, and may invest billions in Perplexity and Thinking Machines. Neither of these companies could actually afford to exist without venture capital (or NVIDIA) dollars, but with NVIDIA’s investment, they can potentially add hundreds of millions or billions of dollars of further “demand” to the backlogs of hyperscalers or neoclouds.
Once again, everyone assumes everything is fine, because the money has yet to run out, and because NVIDIA is promising 70% year-over-year growth in Fiscal Year 2028. Data center debt continues to be available for neoclouds as well as barely-existent data center developers like SB Energy (backstopped, of course, by NVIDIA), mostly because of the illusion of “massive demand for AI compute” created in part by NVIDIA itself.
And, fundamentally, NVIDIA’s revenues are dependent on whether hyperscalers keep being paid by OpenAI and Anthropic, because those are the only two companies that could ever hope to justify their trillion-plus dollars of capex. As I’ve already noted, per Bloomberg, only around $10 billion of Microsoft’s $33.33 billion in FY2026 AI revenue came from selling compute or AI-powered software to its customers — a pathetic sum that suggests very little actual demand for AI when you remove its unsustainable failson.
Broadcom, in its attempts to compete with NVIDIA, has decided it needs a little concentration risk of its own, and per its most-recent earnings, Anthropic and OpenAI are set to become its largest and second-largest customers in its next fiscal year.
Much like the hyperscalers, neither Broadcom nor NVIDIA is going bankrupt as a result of the AI bubble bursting, but Broadcom’s future revenues — estimated at $230 billion in Fiscal Year 2028 (which begins November 2027) — are now dependent on both direct purchases from hyperscalers (justified by Anthropic and OpenAI) and the AI labs themselves, creating, somehow, greater underlying exposure.
However you may feel about me or the greater AI bubble is immaterial to the fact that everything will seem like it’s fine right up until somebody can’t raise money and make a payment to either a neocloud, hyperscaler or AI lab.
For this to keep working, AI startups must continue to be able to raise hundreds of millions of dollars every few months, all as Anthropic and OpenAI must continue to raise tens (or hundreds) of billions of dollars to pay hyperscalers for compute so that they can, in addition to raising hundreds of billions of dollars, spend that money on GPUs from NVIDIA, who can only continue to make hundreds of billions of dollars a year as long as it can either provide justifications for lenders to keep issuing hundreds of billions of dollars in debt or backstop the data centers the debt will get spent on.
In other words, the AI bubble is based on the whims of maybe a few hundred companies spending money on two companies to justify five companies spending money with one company. Or two if you count Broadcom, which you don’t have to if you don’t want to.
If you tell most journalists or investors any of this stuff, they’ll tell you not to worry about it. Per The Information:
But investors may want to temper their expectations. One large public investor summed up Anthropic’s approach to the markets as: “Don’t think too hard. Just look at the revenue growth rate. That’s all you need to know.”
Anyone who tells you “not to worry” about a company that loses billions of dollars a year and has made $517 billion in compute commitments is a con artist, and anyone who prints a quote like that without a comment about how deeply worrying it is doesn’t really give a shit about whether you live or die.
But that really is the current state of the tech industry: a death cult obsessed with growth empowered by a media ecosystem obsessed with measuring and celebrating how much it’s growing and might grow in the future, always framed in the terms set by the rich and powerful.
The failure of both parties to meet the moment with clarity and purpose will lead to a market correction that likely dwarfs the Dot Com Bubble, exposing many of those involved as a phoney, a fraud, an imbecile, a ghoul, a coward, or utterly, impossibly ignorant.
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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.
