2026-07-16 00:55:56
Thanks for reading this week’s free Where’s Your Ed At newsletter. As I said last week, I’m taking the rest of this week off, so there won’t be a premium on Friday. That said, if you aren’t already a member, now’s a great time to subscribe.
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In addition to getting access to the entire back catalog of premium posts, you’ll also receive one additional post each week — usually anywhere between 10,000 and 20,000 words — covering the most pressing topics in the AI bubble - the best value in tech analysis. Highlights include last week's Hater's Guide To The Memory Crisis - a guide to how AI made everything more expensive - How OpenAI Kills Oracle (which pairs nicely with the Hater's Guide To Oracle), The Hater's Guide To NVIDIA, The Hater's Guides To Private Credit and Private Equity, and how the entire AI Compute Demand Story Is A Lie.
Today’s piece is one of the largest free newsletters I’ve ever written, and pulls together the last six months of my work.
And it all starts with a question: how much do you trust Sam Altman? The stock market and (to some extent) the global economy rests on your answer.
You see, OpenAI has become one of the largest liabilities in recent economic history. You can argue that OpenAI’s no longer the focal point of the AI bubble — you can talk all you want about open source models or Anthropic or any number of other elements — but without OpenAI, the AI industry doesn’t exist, and the justification for trillions of dollars of capex evaporates.
The AI bubble isn’t a result of any actual return on investment — whether that be in purely monetary terms, like revenue or profitability , productivity gains, or anything tangible or measurable. Rather, it’s an episode of cult-like psychosis that infected the brains of some of the most powerful and wealthy individuals and institutions, where the powerful mythology of a company inspired — and been used to inspire — the greatest capital misallocation in history.
As much as this’ll piss some people off, I fully believe that the only reason this has kept going so long is that OpenAI has yet to collapse. Its failure would be a watershed moment — the Lehman Brothers of the AI bubble, and an event that would define the end of one epoch, the start of another, and that would shake the afflicted out of that psychosis. Absent this wake-up call, NVIDIA has continued to sell GPUs, the coffers of the semiconductor industry have continued to swell, and more and more spending commitments have been made.
Look. OpenAI intends to burn over $852 billion by the end of 2030. It accounts for $748 billion of the remaining performance obligations of Microsoft, Amazon, and Oracle, on top of at least another $70 billion of RPOs across Cerebras, CoreWeave, Nebius, IREN, Lambda, and Nscale (per Kakashii), and plans to spend indeterminate billions’-worth of Broadcom “Jalapeno” chips. It intends to spend $50 billion or more on compute this year, which I estimate is more than 50% of all global AI compute spend (with OpenAI taking up 50%+ of all AI compute infrastructure).
OpenAI can only afford to pay that as a result of its latest (assuming it fully closes) $122 billion funding round, of which it has received at least $50 billion, with $20 billion from SoftBank (of $30 billion, with the third tranche due October 1, 2026). NVIDIA mentioned in its latest quarterly earnings report that it “estimate[d] that one AI research and deployment company contributed to a meaningful amount of [its] revenue by purchasing cloud services from [its] customers in the first quarter of fiscal year 2027,” referring, of course, to OpenAI.
OpenAI is the reason anyone cares about AI. In March 2019 (per JustDario), NVIDIA bought a company called Mellanox that made the high-speed networking tech necessary to create AI GPU clusters, and four months after that, Microsoft invested a billion dollars in OpenAI and started buying AI GPUs and building AI infrastructure for it. By March 2020, NVIDIA would ship its A100 GPU, and in May 2020, Microsoft would announce it had built a supercomputer just for OpenAI with “more than 285,000 CPU cores [and] 10,000 GPUs.”
The launch of ChatGPT in November 2022 came at the perfect time for a tech industry that had run out of ideas and was flirting with a prolonged depression. The IPO market had collapsed, interest hikes killed the Zero Interest Free era dead, pandemic era overhiring began to unwind with some of the worst layoffs in the history of the industry, global venture funding dwindled after historic overinvestment in 2021, and tech stocks took a massive beating.
For the first time, the tech industry was forced to cut its cloth in accordance with its means — something which it has historically been loath to do. Big tech was unpopular, both with investors and the general public. The excesses of the past decade — combined with the growing frustration with, for lack of a better word, “tech exceptionalism,” where it believed that the rules which governed the rest of the world didn’t apply to Silicon Valley — had tested the patience of both regulators and lawmakers. And, in the absence of “one more thing” — a big, splashy, game-changing product category — it no longer had an excuse for its prodigal spending, or its regular breaking of the rules, both written and unwritten, that govern society.
The existence of OpenAI justified an era of mania and opulence. Hyperscalers, bereft of new hypergrowth ideas, were able to point at the fact that ChatGPT had “the fastest growing userbase of all time” and the Microsoft “supercomputer” that built it and tell their investors that if they didn’t invest, they’d be left behind, with Amazon, Meta, and Google announcing their own nebulous “supercomputers” in 2023.
By the end of 2023, NVIDIA had sold 500,000 A100 GPUs, and the only reason it did so was because of ChatGPT’s rapid growth. Sam Altman’s brief ouster only sought to inflate the AI bubble by adding a layer of dull palace intrigue to a tech industry bereft of whimsy or character — and helped further entrench Microsoft’s role as the paternalistic benefactor of OpenAI, which made sure that Altman returned to the helm.
To be clear, when I say “rapid growth,” I mean that OpenAI hit 100 million weekly active users by the end of 2023 and had about $108 million in monthly revenue. Microsoft would invest $10 billion more that year, with the majority of that funding coming in the form of credits to be used on Microsoft Azure.
OpenAI is also the reason that Anthropic exists — not just because multiple founders came from the company, but because both Google and Amazon both agreed to give it a total of $6 billion in 2023 as a means of “competing” with Microsoft’s new obsession, which allowed both to justify spending further hundreds of billions of dollars “to make sure they didn’t miss out on AI.”
When you remove the term “AI” from the equation, this all seems a little ludicrous. $16 billion in equity investment on top of what was, by the end of 2023, over $150 billion in capital expenditures, all of which was pretty much justified by the fact that a single website had been very popular.
And the only reason either of these companies were able to grow was because of hyperscalers bankrolling their entire infrastructure.
In the fourth quarter of 2023, global venture capital funding had dropped to its lowest levels since the third quarter of 2016, with American startups taking up $183.6 billion of the year’s investments. Venture capital alone couldn’t have — and wouldn’t have — actually backed OpenAI or Anthropic at the scale that was necessary to build their infrastructure, nor would there have been any of the hunger from hyperscalers or those providing debt for data centers without hyperscalers inflating both of these companies, almost entirely because of the success of OpenAI.
Remove OpenAI from the years 2020 through 2024 and the AI bubble wouldn’t have inflated at all. No other major AI companies showed any sign of life — not those peddled by hyperscalers, funded by venture capitalists, or those launched by other tech firms.
The only reason that any hyperscaler AI efforts have any revenue — and outside OpenAI and Anthropic it’s pretty meager! — is because they knew they could just sit there and keep saying “AI is the future” until their customers eventually gave in and tried it…largely because everybody was talking about ChatGPT.
Anthropic was considered an also-ran until early 2025, and only continued to get funded because people wanted to invest in the next OpenAI, and Anthropic’s initial funding rounds and infrastructure buildout were only justified in terms of competing with OpenAI.
Those $178.5 billion in US-based data center debt deals in 2025? Pretty much entirely justified by the growth of OpenAI and its rapacious hunger for compute, because outside of OpenAI (and eventually Anthropic), nobody else was using massive clusters of tens of thousands of GPUs, nor does a market for compute at that scale appeared to have popped up in the months and years since.
The largest consumers of compute remain Microsoft (for OpenAI), Google (for Anthropic), Amazon (for OpenAI and Anthropic), CoreWeave (for OpenAI and Anthropic), Meta (which is copying what the other hyperscalers are doing), and Oracle (for OpenAI). Otherwise, there’s very little evidence — and boy, have I looked — that there’s more than a few billion in demand for AI compute, and that’s being generous.
All of those investments — both in AI startups and data centers — existed to fund either the next OpenAI or become the next OpenAI’s landlord.
The assumption — because nobody ever thinks things through — was that because one OpenAI existed, many OpenAIs would bloom. That because one large customer of compute existed, the template had been built for future compute-intensive startups…and, again, because nobody ever thinks about anything, nobody ever stopped to realize that the reason there isn’t another OpenAI is because OpenAI and Anthropic are financial psy-ops by the largest software companies in the world.
The grim truth is that you can’t venture fund an AI lab. While OpenAI and Anthropic have raised nearly $300 billion in the last few years, their actual infrastructure costs — the GPUs and the data centers to power their services — were entirely funded by hyperscalers, likely costing another $250 billion in the process, given that Microsoft has said it spent $100 billion on its OpenAI relationship as of early 2026.
Yet the real cost wasn’t just financial, but the experience and industrial know-how to actually execute on a massive infrastructure bailout. Other than Google, Microsoft, and Amazon, nobody else has the scale or experience to build the kind of AI clusters that OpenAI (and eventually Anthropic) needed.
We know that for a couple of reasons. First, because prior to 2023, there were few — if any — companies actually building AI computing clusters at the kind of scale demanded by OpenAI or Anthropic. The closest thing that one could point to were crypto-mining firms, and it’s telling that many of the neoclouds today (most famously Coreweave) started life running warehouses full of ASICs to mine Bitcoin and Ethereum.
Second, because, based on conversations with people in the data center industry, the whole Overton window of what is considered to be a “big” facility has shifted. Previously, a 50MW data center would have been considered a significant (even noteworthy) development. These were the exception, and not the rule, with most data centers being vastly more modest affairs. The only companies which had any experience building at that scale were, for the most part, hyperscalers.
By treating OpenAI as a “venture backed startup,” hyperscalers created the illusion that this was the next type of big company that would in turn create the next great demand center in cloud computing, except the only reason that these companies existed was because of the hyperscalers themselves willing them into existence, funding them with incredible sums, and allowing them to burn as much money as they’d like.
This is why the idea that OpenAI will continue to grow infinitely is central to the mythology of the AI bubble. The existence of one OpenAI allows others to — no matter how illogical — imagine the existence of more OpenAIs, which in turn means that those OpenAIs will need just as much compute as OpenAI.
The dimwitted investor who believes this tripe can justify it through any number of different buy-side analysts or captured members of the media that talk about the “insatiable demand for compute,” pointing to capacity constraints (caused by slow data center construction and — hah! — OpenAI and Anthropic taking up much of the world’s compute) and increasing GPU prices as proof that actually, there’s tons of demand, all without ever really thinking too hard.
The greatest trick that hyperscalers played was never backing down. By sinking more than a trillion dollars into AI capex without ever showing a single dollar of profit, they justified literally anyone investing in AI data centers under the logic that “the largest companies in the world couldn’t be wrong,” even if the reason they were doing so was to expand capacity for OpenAI and Anthropic, who the hyperscalers themselves incubated.
It is fundamentally illogical and insane for hyperscalers to have spent so much money on AI infrastructure, and the reason that few people will say so is because it was, until recently, considered radical to suggest that this was a waste of money, almost entirely because of the existence and continued growth of OpenAI.
Sidenote: while I realize Anthropic has taken up a lot of attention and grown rapidly in the last year, it’s only been able to do so A) because of the mythology of OpenAI and B) because it too was incubated and allowed to run at a massive loss too.
Whatever utility you may or may not get out of LLMs is irrelevant because it has not, for the most part, been what actually underpins data center investment. While accelerating gains in code generation (itself something that could have only happened without vast subsidies) might have helped grow Anthropic, the vast majority of data center capex has been built chasing the dragon of what AI could be rather than any connection to the revenues or economics of the companies at large — outside, of course, their compute spend.
This is the underlying greed that has driven this wasteful, reckless and destructive era — the belief that there will be another OpenAI and, as I’ve said, the chance to become the next OpenAI’s landlord. And because the media and analysts very rarely have original ideas, everybody justified (and justifies) the waste through the same tired mantras, saying it was “just like Uber (nope!)” or “just like Amazon Web Services (between 2003 and 2015, Amazon spent $29.7 billion on capex, normalized for inflation).”
And like any great investment bubble, the more money that piled in, the greater the fear of missing out, the more dollars that can be justified in turn, and the more-complex and deranged the mythology becomes, which is why you have noted venture capitalists claiming that AI labs have “90%+ inference margins,” a completely unproven statement that AI boosters cling to and repeat often enough that it’s taken as gospel, likely to avoid thinking about the fact that you can burn $14,000 in tokens on a $200-a-month ChatGPT subscription.
This kind of mythology only grows in an environment deliberately deprived of good information. The fact that we’re four years into this horrible bubble and still don’t have consistently-held consensus around the actual costs of large language models is a testament to an industry-wide effort to suppress them.
OpenAI, Anthropic, Microsoft, Google, and Amazon have done everything in their power — based on discussions with sources familiar with their infrastructure — to obfuscate the actual underlying costs of their operations, and Silicon Valley, an industry of alleged free thinkers and individuals, is more than willing to accept whatever convenient myths might sustain their dreams.
And in the end, they all became useful idiots for hyperscalers. Their obsessive attachment to OpenAI — and by extension Anthropic — seems like a decision made under the auspices of “democratizing powerful AI,” all as effectively every dollar flows to either Microsoft, Google, Amazon, or Oracle, who in turn feed that money to NVIDIA or Broadcom, who in turn feeds that money to TSMC, SK Hynix, Samsung, or Micron.
Invest in an AI startup? They’re gonna be paying one of the AI labs, who will in turn pay a hyperscaler. Invest in an AI infrastructure company? That money will flow to NVIDIA, and then upstream to semiconductor companies. In the end, whether they die or get acquired (as none of them are going public), all of the value will end up in the hands of one of the hyperscalers who created this imaginary era, then helped inflate it into something very, very dangerous.
Yet the problem is that this industry cannot, under any circumstances, survive without OpenAI.
When people discuss OpenAI’s potential collapse, they act with pure cowardice either saying “it won’t be that bad” or say something vague about it “being too big to fail.”
If OpenAI — the company with the most money and the most infrastructure and the most attention and the most talent in AI — collapses, it will likely do so after AI data center debt and venture capital funding has been almost entirely exhausted.
You see, Goldman Sachs’ Jeffrey Papai recently noted that it will be “very difficult” to replicate the hundreds of billions of dollars that hyperscalers have raised in the last four years — $244 billion in 2026 alone if you include NVIDIA and SpaceX — which is a problem considering that they can no longer fund their data center capex using their cashflows as of Q3 2026.
And to be clear, hyperscaler capex doesn’t have to stop for NVIDIA to stumble. It just has to slow down meaningfully enough that Jensen Huang can no longer give investors 60%+ year-over-year revenue bumps, because the AI bubble is built on vibes, and it can only survive so long as those vibes don’t become sour.
Yes, yes, I realize there are other customers, but the vast majority of NVIDIA’s demand comes from hyperscalers, who are (for the most part) either building out their operations for OpenAI and Anthropic or simply copying what the other hyperscalers are doing (see: Meta and SpaceX).
Once hyperscalers stop spending money, banks that are afraid of “choking” on data center debt will see that a vast amount of capital is leaving the market and underwrite (or not, as the case may be) deals as such.
This will mean, at some point, that both OpenAI and Anthropic will be walking around with their hands out saying “money please!” at precisely the moment that everybody will be cutting back. While NVIDIA might get a little desperate and throw some extra cash their way, if revenues start collapsing, so too will its interest in further inflating the bubble as investors begin to ask whether any of this was real or one large circular financing scam.
While this is absolutely a problem for Anthropic — especially after its $35 billion debt deal with Broadcom — it’s much, much worse for OpenAI, which has (as mentioned) made $748 billion in compute commitments to some of the largest and well-lawyered companies in the world. OpenAI’s continued marketing efforts involve constantly refreshing rate limits around the launches of its most-expensive models, giving away millions of dollars of tokens to startups, and generally running the “grow as fast as possible and work out a business later” model into the ground at speed, all fueled and funded by Clammy Sammy Altman’s nasty habit of overpromising and underdelivering.
Clamuel’s biggest mistake was leaving the pearly gates of the hyperscalers and dancing with the mortals of Oracle, Cerebras, and CoreWeave. While Microsoft or Amazon might be willing to extend payment terms as a means of saving face and prolonging the inevitable, Oracle — a law firm with a software company attached — is more than capable of loud and aggressive litigation under any contractual breach.
Then there’s the fact that Apple is suing OpenAI after poaching multiple engineers for its hardware efforts and allegedly both coaching and coercing them into stealing trade secrets, which is all but certain to destroy any chance of OpenAI releasing a device in the next few years…and potentially the company itself. These are extremely serious allegations, with Apple also accusing OpenAI of trying to coerce trusted partners into revealing manufacturing techniques for iPhones — the kind of thing that can (and will) lead to brutal discovery and potentially criminal charges.
OpenAI also, as I’ve mentioned, needs to keep growing to keep up with those bills, and at some point will run out of real dollars to pay people, likely at exactly the time that it’s hardest to find more of them. While there might be billions of dollars left to be raised, to pay any of its bills, OpenAI needs tens of billions of dollars multiple times a year. Based on my own reporting on its audited financials from 2024 and 2025, OpenAI will need to raise funding at least three more times in the next decade.
To make matters worse, its free users have become a massive liability. While The Information reported that OpenAI expected to generate $2.4 billion in ad revenue in 2026, and $102 billion in 2030, it turns out that reality is a little harsher, with analyst eMarketer projects that the entire AI chatbot ad industry combined will only make $1 billion this year, with the entire market making $5.41 billion by 2030.
This means that the 900 million weekly active users of ChatGPT will remain a massive drain on the company’s finances, with only 5% or so of them opting to pay, and a projected 80% of its $20-a-month users expected to churn in 2026.
At some point, OpenAI will simply run out of money. It’s nearly exhausted every available source of capital, and now that it’s likely delaying its IPO to 2027 — largely in part because it couldn’t list at a $1 trillion valuation — it will have to raise again, potentially at a down-round valuation or at a modest increase which will, in turn, make it much more difficult for investors to see a return in an IPO.
Investors will likely ask questions like “why couldn’t you go public?” and “what is it that bankers didn’t like?” as Sam Altman looks at them like this:
You see, OpenAI is awesome at selling mythology and hype, but crumbles the second that its numbers have to face the cold, harsh light of day.
While it’s been able to skate by in situations like Altman’s ouster and its conversion to a for-profit, these were strictly legal situations that could be dealt with by lawyers and cheered on by the press. OpenAI has never faced a problem like “not being able to pay its bills” or “breaching a contract with a major company,” and I think these are an inevitability in its future.
Sidenote: Yes, yes, you’re going to say “buhhh, bailout (nope!),” but even if Trump were to funnel another $42 billion to OpenAI, it wouldn’t cover a year’s worth of compute in 2027, the year that I imagine the Hellmouth opens and swallows it whole. If your argument is that OpenAI is going to get nationalized or “the military funds it indefinitely,” you are catastrophizing as a means of pretending you have control over the future in a way that feels intellectually satisfying, all without any of the messy work of interacting with the horrors of reality.
In the end, OpenAI’s collapse will be a dramatic narration of the boring, horrifying economics of the AI bubble.
Let me explain:
The AI bubble is inflated based on hype and hopium rather than tangible proof or substantial revenues driven to anyone outside of the semiconductor industry, and without NVIDIA’s massive returns, I don’t think anybody would’ve taken it seriously past 2024. Any and all achievements of the AI industry are a direct result of market psychosis, a broken media ecosystem, and a trillion dollars that could’ve been sunk into literally anything else, and must be evaluated as such.
The double-edge sword of a mythology-inflated bubble is that it’s much harder to sustain when said mythology dies. The AI bubble was able to grow to such a horrendous size because the markets and the media were willing to accept basically anything that Sam Altman or the greater AI industry said.
By waving away any economic problems as growing pains and dismiss those who would scrutinize it as haters or cynics, reporters and analysts provided investors with the justification to invest again and again in these companies without them ever having to make a real business, which means that, well…they don’t have real businesses, which is a problem when you need to actually pay somebody money that wasn’t given to you by a venture capitalist.
This will leave the AI industry short-changed in its most-desperate times.
The media is important for many, many reasons, but one of the biggest ones is that scrutiny is what keeps capital in check, for the benefit of humanity and at times the companies themselves. By choosing to pull their punches, ignore glaring economic problems and accept every projection with blind faith, the media empowers grifting and suffocates good businesses as a result, encouraging bad behavior and helping them raise unbelievable amounts of money at ridiculous valuations without worrying about having to make a good business. In some cases, the media even encourages them to do so, saying that “all startups lose money at first” instead of thinking about things for a fucking second.
When companies know they won’t face that scrutiny, they engineer themselves as such, putting off ever finding a real business model in favor of whatever will make them buzzy enough to get coverage and raise funding as a result. In a vacuum of skepticism, bubbles inflate, monsters get rich, and regular people always get left holding the bag. As a result, if companies ever bother to become a real business, they only do so at the very last minute, endangering anyone who has backed them and every counterparty in the event they’re incorrect.
When OpenAI dies, it will be after a prolonged period of desperate reorganization and attempts to appeal to investors and the media that it can, in fact, become a real business. These attempts — price increases, price cuts, selling off IP, nebulous circular deals, and so on — will all fail, and by the end, Sam Altman will have run through every single trick imaginable to keep the party going.
And when those fail, what do you think Perplexity does? How about Harvey? Cursor got the last chopper out of ‘Nam with the SpaceX acquisition (assuming it actually happens), but what, exactly, is Cognition, or Glean, or Sierra, or really any AI startup meant to say to compel investors to believe in them once OpenAI dies? That they’re different? That they’re gonna work it out after the company that got given basically everything it needed failed?
The entire AI industry’s sales pitch is that OpenAI opened the world’s eyes to the power of AI, and that giving the AI industry as much money as possible would end in economic abundance the likes of which we’ve never seen. Instead, we’ve got two AI labs that both lose billions of dollars, and the latest model from one of them randomly deletes people’s stuff.
It’s not like any of this was sold on actual ROI or real businesses or returns or productivity or any actual measurable thing other than physical infrastructure erected in its honor.
There are simply no compelling stories about the AI industry that can be told in the present tense. Everything is always based on the theoretical multiplicative power of just waiting a few more years, which becomes much harder to believe if the company with the Mandate of Heaven gets sent to Cocytus.
This will have massive downstream effects on basically everything and everyone connected to the AI industry. You won’t be able to raise money for a startup to spend money on compute, nor will you be able to convince somebody that your LLM wrapper will change the world, nor will you be able to justify a massive valuation. Venture capitalists fancy themselves as brave soldiers of the economy, but are really cowardly lemmings that will sprint for cover the second that things get rough.
I also keep hearing from people that Anthropic is magically safe from the AI bubble’s clutches, or insulated from its rotten economics. The amount of pure mythology and misinformation I read about this company on Twitter is genuinely offensive, and the fact that journalists have categorically failed to push back against it is proof that too few people give a shit about anything other than which boot they get to lick next.
Anthropic faces the same economic realities as OpenAI. It burns billions of dollars on training, it hides inference costs in sales and marketing, and the only real differences are that it focused more on coding and made fewer ridiculous infrastructure commitments…right up until this year, when it committed $200 billion in compute and hardware commitments to Google, raised $35 billion in debt from Apollo to buy Google TPUs, signed a $15 billion a year compute deal with SpaceX, and agreed to a 20-year-long, $19 billion lease with TeraWulf.
Much like OpenAI, Anthropic is also doing way, way too much. There’s Claude for Life Sciences, Claude for Legal, Claude for Small Business, Claude Design, and even, for whatever reason, reports that Anthropic intends to develop its own drugs — and instead of saying “hey man, what the fuck are you doing?” the media falls over itself to repeat and celebrate every single one as if they’re all viable or useful products. Anthropic is as messy, disorderly and unfocused as OpenAI, but has done a better job of convincing people that it’s somehow “ethical” as it fucks over its partners and farts out 200 new products a month.
This is a company that lacks focus or vision other than “more” and “bigger.” The only thing that differentiates OpenAI from Anthropic at this point is the nebulous promises of “AI code” and Dario Amodei’s Doom Trolling and safety theater.
The fact that the majority of the media made no efforts to push back against its shenanigan-rich “profitability” narrative is why we’re in this fucking mess.
Anthropic is an AI lab just like OpenAI. It uses GPUs, TPUs and Trainium chips. It trains models in much the same way to do much the same things, and builds quasi-functional plugins on top of them, just like OpenAI does. It makes big compute commitments, it had its infrastructure built out for it by hyperscalers, its CEO is annoying and beloved by cretins, and its value is largely determined by 1000 people on “X The Everything App” experiencing varying levels of AI psychosis.
Attempts to claim otherwise are tacit admissions that OpenAI is unsustainable.
Please note that when I say “victims,” I don’t always mean “people you should feel sorry for.” In some cases I’ll be talking about real people who are facing the horrible consequences of the OpenAI bubble bursting, and for whom you should feel a degree of sympathy, and in others, I’m referring to various Patagonia gargoyles’ financial woes. I assume you’ll be able to differentiate between them.
My last premium newsletter was the massive Hater’s Guide To The Memory Crisis, or the twisted tale of how three companies — Samsung, SK Hynix and Micron — have diverted meaningful amounts of manufacturing supply away from making the RAM you find in laptops and smartphones toward making the high-bandwidth memory that powers GPUs, jacking up the price of consumer electronics in the process.
To explain:
An AI data center is full of servers, which are in turn full of (for the most part) NVIDIA GPUs. Each NVIDIA GB300 has two B300 GPUs, the two of which have 576GB of High Bandwidth Memory (HBM, or HBM3e to be specific), and a CPU, which has 480GB of lower-power LPDDR5X RAM (the kind usually used in cellphones and other mobile devices). These systems tend to be sold in an NVL72 rack with 18 compute trays, bringing us to 36 GB300s, for a total of 20.7 terabytes of HBM and 17 terabytes of LPDDR5X RAM, and that’s before you get to the RAM associated with the high-speed networking gear and other associated components.
Because HBM takes up more space on a wafer — the slice of semiconductor material that is etched using photolithography (read: molten tin) and then cut into separate dies (individual chips) — and generally has much higher margins (the actual product its more expensive to make, but thanks to the triopoly of Samsung, SK Hynix and Micron, they can charge whatever they like, predominantly to NVIDIA), memory manufacturers are dedicating more space on their manufacturing lines to it than to regular consumer RAM, which allows (thanks to said triopoly) said manufacturers to charge effectively whatever they want for consumer RAM.
To simplify, the AI GPUs in AI data centers require hundreds of gigabytes of high-bandwidth memory, the CPUs attached to them require the same RAM as your smartphone, and the companies making all of this RAM are making huge profits by jacking up the price because of supply chain constraints that they themselves have created. That’s why Micron had 84.9% gross margins in the last quarter. The RAM triopoly controls more than 90% of the world’s memory, and can set prices at whatever rate they want.
These three companies were all fined over $100 million by the Department of justice back in 2002 for price-fixing, with Micron avoiding the fine by turning in its co-conspirators. Five years later in 2007, a Supreme Court judgment and resulting precedent (Bell Atlantic V. Twombly) drastically raised the bar for not simply winning an antitrust case, but even getting one to trial:
The Twombly Test/Rule was designed to raise the bar to civil legislation, so that defendants aren’t forced to comply with discovery in frivolous cases, which can be incredibly expensive.
While this seems reasonable at first glance, it makes it significantly harder to litigate any kind of antitrust action, because the market signals — whether they be pricing, or difficulties in new competitors bringing their products to market — tend to be the starting pistol on any action. The damning evidence — loose-lipped executives talking about their nefarious plans — tends to be something that shows up once the trial has progressed to the discovery stages.
This precedent would kill a 2019 class action case against SK Hynix, Samsung and Micron that alleged they had colluded to tighten the supply of the world’s DRAM, because despite statements from company representatives made at public events, their collective participation in certain industry groups, and observable pricing trends, the precedent set by Twombly meant that the plaintiffs required more than circumstantial evidence to bring something to trial.
Anyway, the reason I bring this up is that while I am not accusing Samsung, SK Hynix, and Micron of price-fixing, a recent lawsuit is accusing them of exactly that:
The suit claims the alleged anti-consumer behavior started in 2022, when the companies began shifting production from SDRAM to HBM — something that, at that point, they made “no economic sense” except as a means to hike prices.
“This plan has thus far succeeded, as consumer purchasers of conventional DRAM and devices incorporating it have paid supracompetitive prices and have otherwise suffered the impacts of a distorted market crippled by the behavior of DRAM oligopolists,” the filing states.
So, what does this have to do with OpenAI?
Well, back on October 1, 2025, OpenAI, Samsung and SK Hynix announced a “strategic partnership” that would involve OpenAI buying 900,000 wafers of DRAM a month (around 40% of the world’s supply at the time) for Stargate data centers — something that never actually happened (it was a memorandum of understanding, and OpenAI also had nowhere to put them), but both SK Hynix and Samsung’s stocks immediately rallied, and Samsung happened to hike prices by 60% a month later, which could be a coincidence, or could have been the company saying “yeah, wow, we’re gonna run out of RAM I guess, better buy now at whatever price we have it!”
Another clue that this might not all have been above board was that Samsung was reportedly doing another deal with OpenAI in March 2026, “...to supply up to 800 million gigabits (Gb) of 12-layer HBM4 chips to OpenAI in the second half of this year” per Reuters, for use with Broadcom’s custom “Jalapeno” chip. Though it’s hard to calculate exactly how much that would be wafer-wise, from what I understand we’re talking in terms of less than 100,000 wafers total after OpenAI, Samsung, and SK Hynix said they’d be taking up 900,000 a month.
Regardless of whether OpenAI ever takes a single wafer of silicon, these deals existed to put the squeeze on any company that uses memory in their products — including NVIDIA, AMD and Broadcom — which in turn led to the most aggressive price increases in the history of consumer electronics. As I said last Friday:
The net result is pretty simple: every single consumer electronic of any kind is getting more expensive. Valve’s Steam Machine console debuted at a 30% higher price point than planned, Apple hiked the prices of its MacBooks and iPads and will likely have to do the same for its next iPhone. Nintendo, Microsoft and Sony increased the cost of their consoles, and the PS5 and Xbox Series now cost more today than they did when they first retailed, almost six years ago.
And yes, OpenAI is responsible, both in its naked collusion with memory manufacturers to push an announcement that never resulted in anything other than price increases and its siren song that made every dimwit with debt desperate to build AI data centers.
Every single consumer suffers as a result. RAM is in everything, and it’s unclear when new manufacturing capacity will actually come online, as fabs are expensive and complex construction efforts and require tons of specialist talent, raw materials, permitting, land and power. SK Hynix Chairman Chey Tae-won said in March that the memory shortage would last until 2030, and he may be right, as a Bank of America report just said that SK Hynix may only be able to add a sixth of its planned capacity by 2028.
This means that the price of consumer electronics will be inflated for the foreseeable future, even if the AI bubble bursts. While capex pullbacks will eventually happen and by extension eventually lead to supply constraints easing, Micron, Samsung, and SK Hynix had sold out their entire 2026 supply by the second week of January, and noted that they’d only be able to handle 60% of “medium-term” customer memory orders, which suggests to me that 2027 might be even worse, with a subtle clue being that SK Hynix CEO Kwak Noh-jung recently told Reuters that 2027 would be “the worst year in the industry’s history from a supply perspective.”
While the memory triopoly has every incentive to make things seem bleak to drum up business and sustain their margins, behind the scenes reports suggest they’re turning the screws on everybody.
Speaking with Steve Burke of GamersNexus for my podcast Better Offline (out next week!), I learned that consumer electronics companies have told him in private that they’ve never seen anything like this — and that the average purchasing experience for buying RAM now involves being told a price that you either accept or never get to do business with the RAM companies again.
This is a graphic example of companies with massive amounts of leverage using it to fuck over both their customers and their customers’ customers.
Who gave them that leverage? The AI industry and Sam fucking Altman.
Hey, remember when I just said that (it seems, but I cannot confirm that) OpenAI helped SK Hynix and Samsung manufacture a supply chain crisis last year using a phoney announcement for a project that would never happen?
That happened three other fucking times in the same three week period, and modern journalism doesn’t seem to give much of a shit!
Let’s review what happened, per my year-ending Enshittifinancial Crisis newsletter:
All four of these companies’ stocks rallied on deals that land somewhere between misleading and fictional, with basically anyone who invested in them being underwater within two months, though all three have recovered thanks to similarly-questionable announcements and deals made by companies with the sole intention of boosting their stocks.
Sidenote: I didn’t even bring up what happened with the entirely-fictional $500 billion “Stargate” Project with Oracle and OpenAI, because I’d write another 200,000 words about how everyone who helped pump it should feel ashamed of themselves.
The same goes for every single analyst who reacted to Oracle’s OpenAI-driven remaining performance obligations like a horny teenager seeing his first boob. The capacity never existed, this deal will never happen, and you all know it and are either hopelessly ignorant or craven beyond words.
Why else would Sam Altman go on CNBC with NVIDIA CEO Jensen Huang on the day of an announcement of a project that was only ever a letter of understanding? Why else would Sam Altman jump on TV with Bob Iger to talk about a Disney deal that clearly never went anywhere?
Spare me any explanations around the “fast-paced dealmaking of AI” or “how deals are complex.” CNBC reported the day after the NVIDIA deal was announced that the first $10 billion tranche would “close within a month or once the transaction had finalized” via a source! It’s blatantly obvious that the intention was to create the appearance that a deal existed that never actually existed at all!
The AI trade is the natural endpoint of an increasingly-enshittified stock market where many analysts and journalists exist only to repeat narratives to influence stock prices. Outside of semiconductors, the AI trade has never, ever been about the actual underlying economics or the actual economic potential of Large Language Models, but projecting shadows on the wall to resemble something that looks like the next generation of technology.
That’s because the AI trade is entirely symbolic and driven by stock prices. When NVIDIA and the rest of the Magnificent Seven (sans Apple) does well, AI is the greatest thing on Earth. When the Magnificent Seven stumbles, everybody worries that they might be overspending on AI. The AI trade exists only to manipulate stock prices through spurious news and smoke signals on social media, and to drag gullible retail investors (who account for 20% of US equity trading volumes, the highest it’s been since 2021) and the rest of the market away from caring about things like “fundamentals” or “reality” toward whatever keys are currently jingling.
My evidence is fairly simple: Google, Meta, Microsoft, and Amazon don’t actually tell you their AI revenues, other than when Microsoft and Amazon have chosen to define it in terms of undefined “run rates.” And why would they? Reporters have been saying that their AI bets have paid off for years without the companies ever having to show it paying off other than their stocks running.
Here’s another example: CoreWeave, a time bomb/AI compute company that only really exists as a revenue source for NVIDIA (per Jensen Huang, if [NVIDIA] didn’t help CoreWeave exist, they would not exist”) by signing contracts with companies for unbuilt capacity that it then takes to banks and uses to raise more money to buy GPUs. NVIDIA knows that analysts and reporters don’t give a shit about the blatant self-dealing and circular financing, all because these deals help the stock price go up, which apparently is the only metric that modern journalism evaluates. That’s why when NVIDIA invested $2 billion in CoreWeave in January 2026 — a warning sign that the company had liquidity problems! — led to endless positive coverage after “the stock popped on the news,” per CNBC.
That’s because the AI trade exists only to extract value and con investors. It is not a trade related to the actual fundamentals of whether AI works or not, whether AI actually makes anyone money, or really anything about AI at all outside of whether mentioning AI or an AI-related company makes a stock number go up or down.
I’ll be blunt: modern journalism has failed the retail investor and directly helped the wallet inspector regulate the stock market. By empowering Sam Altman and the rest of the AI industry’s deliberate attempts to obfuscate the actual economics of generative AI and setting the terms of AI’s success as “how stocks are doing and whether the companies are growing in general,” they have defaulted on their responsibility to the general public and helped the already-rich get richer.
None of this would be possible if business journalism actually saw themselves as having a responsibility to give their audience good information. While one could argue that if you had blindly invested in the AI trade you might have made money, the ability to make money in the AI trade was directly driven by modern journalism’s inability or unwillingness to push back on any corporate narrative. Every major outlet ran a story on every one of the deals I mentioned, and not a single one seemed remotely upset or deterred by the fact they were misled, and in turn misled their audience.
And yes, investment funds can be just as easily manipulated as a retail investor, and will follow whatever trend seems likely to make them money, even if said trend is utterly disconnected from any fundamentals. Tech analysts help do so by creating vast models that give a veneer of respectability, even if their projections mostly amount to “number will always go up in the future.”
This is why Musk was able to dump SpaceX on the public markets. Why SK Hynix chose to list on the NASDAQ. When the entire world is captured by a childlike belief that “AI is good and will be the biggest thing ever,” you empower grifting and swindling at scale.
Well, that and underwriters like Goldman Sachs are so nakedly crooked that they’ll say they expect SpaceX’s AI revenue to grow 100x by 2030. Fuck off!
Yet the memory boom/bust/crisis is where the media has failed investors the most — a final insult before everything collapses.
You see (to quote myself), what makes this particular memory crisis so distinctly dangerous is that it isn’t a result of consumer demand so much as it is capital expenditures from very large companies making bets that don’t connect with reality.
Microsoft, Google, Amazon, and Meta aren’t spending $765 billion in capex in 2026 because of rapid demand by consumers for AI services, but a desperation caused by a lack of hypergrowth ideas, circular financing with Anthropic and OpenAI, and a vague concern that if they stop spending that the other guy will do something as a result.
Anyone blathering on about a “memory supercycle” is intentionally obfuscating where that revenue and demand is coming from — high-bandwidth memory attached to AI GPUs, meaning that this boom cycle only exists as a symptom of a greater hype cycle, meaning that when companies stop buying GPUs, the demand for that (briefly) high-margin high-bandwidth memory goes with it.
To give you some context, a chart from ComputerBase.de showed that high-bandwidth memory demand grew from 681 million gigabits of HBM in 2022 to 29.3 billion gigabits on 2026 — a 40x increase over the course of four years that suggests that once GPU-related capital expenditures stop, high-bandwidth memory demand will effectively disappear.
As I mentioned previously, this isn’t even me being a hater. Hyperscalers are now joining the rest of the world in having to raise debt to buy more GPUs, which means that at some point they aren’t going to be able to afford to buy as much, which will in turn mean that NVIDIA — which accounts for around 65% of all HBM purchasing — won’t need as much.
I have not read a single fucking article that mentions that this is a possibility! Every article about the memory industry right now is about supply constraints and the increasing cost of memory, but none of them warn investors or the general public about what will happen when capex slows, and certainly not the many, many articles in major business publications about SK Hynix, Samsung and Micron’s revenues. In fact, Reuters said that SK Hynix’s “scarcity premium looks built to last.”
The cynical (and boring) response here is that “the market can stay irrational longer than you can stay solvent,” but saying that distracts from the larger point of how said irrationality was manufactured by the media.
Sidenote: There’s plenty of evidence that AI has a pathetic effect on revenues. For example, it took Salesforce three years to get to $1 billion in AI ARR (read: $83 million a month for a company with revenues of $40 billion+ a year), a pathetic amount that should have had people running for the hills…except journalists were saying its AI bet had “paid off” months beforehand. It would be easy — and responsible! — to call this out.
I am not sure what the majority of the media sees as its purpose or responsibility to its readers, so I will speak plainly: the responsibility is to tell them the cold, hard truth, rather than going along with whatever hype cycle is happening out of fear of being wrong or missing out. Skepticism is not doomerism! Being critical is not being negative! These companies are some of the largest and richest enterprises in the world — they should be scrutinized!
And no, scrutiny is not publishing everything they say and then making a vague comment about “whether or not that bet will pay off.” Too often, journalism conflates objectivity with passivity, seeing critiques as “negative” or “biased” when, in fact, repeating everything that corporations say to their benefits is about as biased as it gets.
In the end, the victims are anybody who doesn’t exit the AI trade in time.
Sidenote: It seems increasingly likely that “anybody” will be retail investors, with Citadel Securities noting earlier this week that “retail remains the strongest structural buyer of US equities,” and that it hasn’t seen a single day in the month of July where retail investors were net sellers of stocks, rather than buyers.
Things are similarly bleak in South Korea — where retail investors aren’t just some of the biggest buyers of equities, but they’re doing so on margin.
By the way, there’s no Hell hot enough, by the way, for the people that will read this and smugly say “heh, well, I made money,” or who point to anyone’s returns as evidence that the AI trade is anything other than manufactured consent. The fact that anyone made money on this trade is a sign that the stock market is inherently manipulated to benefit the wealthy at the cost of the many — and when the bubble bursts, the people that will suffer will have suffered because of the media’s participation by helping Sam Altman and the rest of the AI industry obfuscate and twist reality to pump stocks.
Which leads us neatly to our next victim!
In my Hater’s Guide To SoftBank, I told the story of CEO Masayoshi Son, a degenerate gambler who has steered his company through boom and bust cycles only through the grace of whatever God he believes in and sheer luck.
SoftBank Group — the holding company, and not to be confused with Softbank Corp, which runs a bunch of telcos and media companies in Japan — makes money only through either investing in or buying companies, then taking them public or selling them to someone else, and otherwise needs debt for liquidity.
Masayoshi Son makes terrible bet after terrible bet, but his luck always seems to work out for him. His $20 million stake in Alibaba turned into $50 billion at IPO. He bought a 70% stake in Sprint that turned into a 24% holding in T-Mobile. In the early 2000s, Softbank took a 23% stake in Betfair that eventually became part of the $17.7 billion Flutter Entertainment. And then there’s its most-recent and arguably most-impressive (after Alibaba at least) investment, ARM, which it acquired for $32 billion in 2016 and then took it public in 2023 at a valuation of $54.5 billion, and currently sits at around a $300 billion market cap.
Sidenote: SoftBank Group is “valued” based on the “net asset value” of its holdings (which you can see here), and whenever you’ve seen it have “losses,” that’s because the underlying value of its assets (many of which are privately-held companies as part of its disastrous Vision Funds 1 and 2) is what gives it is “returns.”
This is why SoftBank was able to book a yearly gain of $46 billion for its Vision Fund 2 thanks to its investments in OpenAI, despite losses from DiDi Global, Coupang, and Klarna.
Yet his problem has always been his dalliances with whimsical white boys. SoftBank sunk $1.5 billion into dodgy financial services firm Greensill Capital before its collapse, and in the aftermath, it was revealed that Masayoshi Son and CEO Lex Greensill talked on the phone every day, to the point that (per Greensill himself) SoftBank managers felt “threatened” by Greensill’s relationship with Son. It only took Masayoshi Son 28 minutes of conversation with WeWork’s Adam Neumann before he drew up the terms for a $4.4 billion investment on his iPad and signing the deal in the back of a cab, with Son saying that “the last person he felt this with was [Alibaba CEO] Jack Ma.”
And no white boy has ever been more whimsical than Sam Altman.
In 2019, Altman turned down $10 billion from Masayoshi Son (which, ironically, would’ve been an incredible investment at the time), going instead with $1 billion (and full infrastructure support) from Microsoft, and I believe this moment drove Son into a level of madness that will potentially wreck the company.
You see, up until fairly recently, SoftBank had been dragged down by the declining value of its atrocious investments via its two venture capital funds — Vision Fund 1 and 2, the latter of which was self-funded and has mostly gone toward funding OpenAI. Up until recently, SoftBank had quarter after quarter of losses as investment after investment saw its NAV drop because, well, they were overvalued and SoftBank never should’ve invested in them in the first place.
To survive, SoftBank moved into “defense mode” in 2020, slowing investments and selling the vast majority of its Alibaba stock by April 2023, with the ARM IPO and billions of dollars of bond sales helping slow the bleed.
Yet Masayoshi Son knew he was destined for greater things, as he told CNBC in June 2024:
“SoftBank was founded for what purpose? For what purpose was Masa Son born? It may sound strange, but I think I was born to realize ASI. I am super serious about it,” Son said.
OpenAI — and the larger AI trade — had given Masayoshi Son a certain kind of greed-driven mania, where he believed that AI would make SoftBank (as he said recently) “the goose that laid golden eggs,” an eternal money-printer that ostensibly started with the biggest cash-burning machine in history.
Altman, like Neumann, like Greensill, told Masayoshi Son exactly what he wanted to hear: that this would be the biggest thing ever, and that Son would capture all of the value both through his investment in OpenAI and further investments in data centers and other AI infrastructure.
And so began his most vulgar investment yet — OpenAI, sinking $2 billion into the company from Vision Fund 2 in November 2024 — only for Altman to turn around and demand he fund $30 billion of a $40 billion round that would get announced four months later in March 2025.
Masayoshi Son was an emphatic “yes,” except for one little problem: he didn’t have the money, and could only afford the first $7.5 billion (due in April 2025) by taking out a $15 billion, year-long bridge loan, with the rest of it going toward his eventual purchase of Ampere computing.
To fund the remaining $22.5 billion, SoftBank was forced to take out further margin loans on its ARM stock, and sell large chunks of its T-Mobile stock, as well as its entire $5.83 billion stake in NVIDIA.
Yet as soon as the check cleared, Sam Altman was blowing up his phone demanding more money as part of a $110 billion funding round in February 2026 (that eventually became $122 billion in late March).
Masayoshi Son was once again an emphatic yes, except by this point he’d exhausted basically every useful thing left in his coffers outside of around $118 billion in ARM shares that make up around 40% of SoftBank’s net asset value, meaning that selling or using further ARM shares as collateral would directly tank its value — both through the obvious “they have less of a valuable thing” and sales/collateralization of further ARM shares affecting its share price.
So, what did Masayoshi Son do? More debt, baby! More risky debt! You can always refinance it, right?
To pay for its share of OpenAI’s 2026 funding round, SoftBank took out a $40 billion bridge loan (maturing in March 2027), bringing its investment in the company to over $40 billion, with its payments to $10 billion tranches of OpenAI funding due in April, July and October 2026.
A few months later, it tried to raise a $10 billion margin loan using its entire OpenAI investment as collateral, cut the amount it was raising to $6 billion, and when banks remained hesitant to give it the money anyway offered to “guarantee repayment of the loan to address lender concerns,” effectively backing the loan with its own balance sheet (called a recourse loan) because, despite being worth over $100 billion on paper, its lenders had doubts that its OpenAI stake was actually worth that much.
If you’re wondering why it didn’t simply take out more debt, it’s because (as a result of its continuing investments in OpenAI) S&P Global revised SoftBank’s outlook to negative, emphasis theirs:
The liquidity of SoftBank Group's investment portfolio will worsen because OpenAI now accounts for a bigger share of it. OpenAI Group PBC is a privately held U.S.-based AI research and development startup company. SoftBank Group's additional investment amount will be $30 billion (about ¥4.5 trillion).
The creditworthiness of the company's investment assets will also likely deteriorate. We see OpenAI as one of its investments with the weakest credit quality. The company's investments in AI, including OpenAI, mostly involve fledgling startups and private companies that we believe are exposed to significant AI innovation risk and fierce competition.
This has had a knock-on effect on the rating of the telecoms-focused Softbank Corp (as a reminder, Softbank Group is the holding company that owns stock in other companies, Softbank Corp is the energy/telecoms company that actually makes stuff), which is now rated BBB, or the lowest-possible rung of investment-grade financing in the S&P system.
To make matters worse, if SoftBank continues to hold a loan-to-value ratio of above 30% for much longer, it runs the risk of its debt getting downgraded even further, which would slam the door shut on its ability to raise money via bonds, which is…well, basically how SoftBank has functioned for the last 10 or 20 years.
And this is all happening as Japan is determinedly inching away from the era of persistently low interest rates — making debt far more expensive to service.
SoftBank needs OpenAI to IPO so that it can turn that on-paper gain into actual liquid stocks that can be dumped into the market or used for real-life margin loans. SoftBank has jettisoned the vast majority of its heaviest-weight investments, leaving it largely dependent on the continued value of ARM’s stock to keep its seat at the table, and if OpenAI can’t go public, it’ll end up sitting on illiquid stock in a company that will see its value tank as a result.
Yet even if OpenAI does go public, any attempts to get a margin loan will likely be dangerous, as I bet that it will be one of the single-most shorted and volatile stocks in history, which will also be a problem for SoftBank’s underlying net-asset value, which will ebb and flow based on whatever bullshit Altman cooks up every three months.
Masayoshi Son is both a victim of the manufactured consent of the AI trade and an enabler of its worst excesses, empowering and enriching Sam Altman at a time when any kind of financial prudence might have curbed OpenAI’s greed or killed it before it caused further damage.
SoftBank tanking will fuck over anyone invested in the Japanese stock market, where it currently sits as the third-largest company by market cap behind KIOXIA (a memory company booming thanks to the AI trade) and Mitsubishi UFJ Financial (a bank with heavy ties to the AI industry and data center infrastructure). While I severely doubt it’ll die — it’s likely MUFJ and SMBC Bank would extend whatever credit necessary to keep the doors open — OpenAI and the greater AI trade has become a load-bearing toothpick holding up the trillion-ton ass of the world’s most well-funded gambler.
For SoftBank to survive in its current form, OpenAI must go public, become a thriving and profitable business, and have its stock price stay elevated for the foreseeable future. Additionally, ARM must also retain or exceed its current stock price.
Hey, while we’re on the subject of “companies betting the entire future on OpenAI that recently got downgraded by S&P Global…”
Hey! You in the back! Stop laughing! Stop laughing at Larry Ellison! He’s now only the world’s 8th-most-richest guy!
Just kidding, fuck Larry Ellison. What I’m about to tell you might make you laugh, probably because it’s really funny.
Oracle is currently spending over $340 billion to build out over 7.1GW of data center capacity for OpenAI, as part of its $300 billion, five-year-long cloud compute contract that began, at least in theory, on June 1, 2026 at the beginning of its Fiscal Year 2027, though much of the capacity is yet to be built. To fund the buildout, Oracle has had to raise over $50 billion via stock sales and debt, spent $55.7 billion in its last fiscal year, and expects to spend at least $90 billion more in FY2027.
As a result of that, S&P Global downgraded Oracle’s credit rating to BBB/A-2, the literal lowest level before it’ll become junk-grade, meaning that one more downgrade (though it would have to be from two ratings agencies) from here would risk Oracle becoming a “fallen angel,” with investment funds (that can’t hold junk grade debt) having to jettison its debt from indexes, as happened to Ford in March 2020, leading to over $35 billion in debt being dumped and its borrowing costs skyrocketing to between 8.5% and 9.625% when it raised in April 2020. For some context, Ford reported an average interest rate of 5.2% on its long term debt in its 2019 annual report.
You’ll never guess why S&P Global downgraded Oracle! And, once again, the emphasis is theirs:
OpenAI remains a key credit risk. We estimate that OpenAI makes up roughly half of the $638 billion in RPO. OpenAI’s ability to meet its contractual obligations and raise external financing will be contingent upon AI tailwinds continuing and its models being market leaders. If OpenAI were unable to pay Oracle, we believe Oracle could be left with massive data center leases that it might be unable to exit or have to re-lease to new tenants under less-favorable terms. As a proxy for OpenAI’s future prospects, we’re tracking OpenAI’s financial commitments to data center operators and chip makers to gauge its overall financial exposure and its market share among enterprise and consumers.
That’s a load-bearing if, brother!
Anyway, you know who else is trying to warn you about Oracle’s exposure to OpenAI?
Oracle! Per Bloomberg:
Oracle has a new warning for investors: All of the spending on data centers might not pay off.
The disclosures were part of the company’s annual financial report, where Oracle detailed plans to spend big on AI infrastructure for customers like OpenAI. And it noted all of the ways that expensive bet could blow up. Construction of data centers may end up costing more or taking longer than expected, Oracle warned. This could happen due to supply chain hiccups, government restrictions on data center development, or the failure of third parties to complete projects on schedule.
And once the sites are done, major customers might not pay their bills, or opt not to renew their contracts, Oracle said. In this case, the company could be stuck with some very expensive assets, which it “may be unable to re-lease, repurpose or assign such capacity on acceptable terms, if at all.”
As a reminder, the only way that OpenAI will be able to afford to pay its $300 billion cloud compute contract with Oracle will be if it continues to hit revenue projections (per The Information) that have it making $113 billion in 2028, $184 billion in 2029, and $284 billion in 2030, a year when it will magically become profitable, and no, I don’t know how that happens:
Based on my own analysis, assuming that Oracle can successfully build capacity for OpenAI to pay for (a load-bearing assumption), it would have to pay around $75 billion to rent that 7.1GW of capacity. Stargate Abilene, an 8-building, 1.2GW project that broke ground in July 2024, has (per sources familiar with the matter) only built and operationalized three buildings, despite the project having meant to be fully operational by the end of 2025 (per landowner Lancium), or energized by the middle of 2026, it isn’t really clear, and I can’t get a straight answer from anyone about whether the power even exists on site to turn any of it on.
Anyway, for Oracle to make all the rest of that money, it will have to build five more Stargate Abilenes. If you’re wondering how that’s going, Stargate Shackelford only broke ground in December 2025, Stargate Wisconsin appeared to have a single steam beam in March, Stargate Michigan only got its first steel beams two months ago, and Stargate New Mexico is still waiting for permitting to begin construction.
Based on Lancium’s presentation and discussions with sources familiar, Oracle will pull in somewhere in the region of $10 billion in annual revenue from the (assuming it’s ever done), completely-finished 824MW of critical IT infrastructure at Stargate Abilene. It is unclear how Oracle hopes to be paid even a fraction of its $300 billion compute deal, because in its current state, its annual revenue from Stargate projects currently sits in the region of a maximum $5 billion a year, or less than a tenth of its FY2026 capex.
For the most part, Oracle has funded the various Stargate data centers with project financing, meaning that a nebulous SPV will be responsible in the event it defaults on any of these contracts…until Stargate Michigan, which only closed when Oracle agreed to guarantee the $14 billion in bonds raised.
All of this revenue — both theoretical and otherwise — sits in Oracle’s “Cloud” segment, the only part of the business that’s actually growing, as the rest of its business has either been declining or plateauing for about a decade.
In any case, for Oracle to actually get paid its $300 billion, it will have to build upwards of 6GW of data center capacity…in a year and a half? This deal is meant to be worth in the higher range of tens of billions of dollars in annual revenue by FY2028, which begins on June 1 2027! Stargate is horribly, impossibly delayed, to a level that makes me wonder if anybody other than perhaps Anissa Gardizy has bothered to think about Stargate for even a fucking second.
Anyway, Oracle’s entire future rides on this deal. While Oracle Cloud Infrastructure continues to grow, its future growth (and remaining performance obligations) almost entirely hinge on both its ability to build the largest infrastructure project of all time and for OpenAI to continue raising funding for an indefinite amount of time. The rest of that growth comes from Meta and xAI, both of whom are only really “doing AI” because everybody else is.
This puts Oracle in a very, very compromising position on multiple different levels.
Generative AI is the only reason that Wall Street started liking Oracle again as its other business plateaued, even as it burned billions of dollars on capital expenditures and cut its gross margins by a little under 15% since 2022, with the vast majority of that value coming from its revenue from OpenAI and what’s actually active at Stargate Abilene.
Much like the rest of the AI trade, everything about Oracle’s future is sold on potential rather than anybody thinking about reality or things like “whether Oracle can actually build the data centers” or “how Oracle makes any of that revenue if the data centers aren’t built” or “how OpenAI affords to pay for the compute if the data centers get built.”
As Oracle said in its own disclosures, if OpenAI can’t pay, “Oracle could be left with massive data center leases that it might be unable to exit or have to re-lease to new tenants under less-favorable terms,” and there isn’t a single company on Earth who can or would pay for such a large amount of compute, nor is there the aggregate demand to justify it.
Sidenote: No, Anthropic can’t afford it either, and Sundar Pichai would unhinge his jaw and swallow Oracle whole rather than see it move off of its TPU infrastructure.
While its many government contracts and national security significance make it unlikely that Oracle would be allowed to die, the collapse of its only growth segment will likely spell dark times for a company that’s already laid off 21,000 people as a means of funding its AI buildout.
The double-edged sword of the AI trade’s childlike attachment to stock valuations poses an egregious threat to Larry Ellison himself.
Hey — HEY! I said no laughing! Stop it!
This is all very serious! This is a serious situation! You’re laughing about the potential downfall of a guy who once wrote a letter to the New York Times attacking HP for firing former CEO Mark Hurd for repeatedly making sexual advances toward a reality star using HP’s finances!
Sorry, my mistake, you should keep laughing, even the prospect of what I’m about to tell you is hilarious.
As I said in my piece about how OpenAI Kills Oracle:
[The collapse of Stargate and OpenAI would be] a very bad thing for Larry Ellison, who holds around 40% of Oracle’s shares and receives a dividend of around $2.3 billion a year as a result, especially as he’s backed the $111 billion Paramount-Warner Brothers Discovery merger deal with $45.7 billion of that as an equity commitment from the Ellison Trust (the Ellison family investment arm which holds his Oracle shares) with Larry himself guaranteeing the amount, with $24 billion of those funds likely coming from the Middle East.
This leaves the Ellison family with around $12 billion left to fund the deal. Depending on how liquid the trust is, it could foreseeably fund that in cash, but if Ellison is a little light, he might have to take out further margin loans on his Oracle stock.
Yes, I used the word “further.” Ellison has already pledged 346 million shares of his Oracle stock — or around $61.5 billion — “to secure certain personal indebtedness, including various lines of credit,” meaning “many big, beautiful loans against his Oracle shares.” which IFR estimated back in September (when Oracle’s stock price was much higher) could allow him to secure as much as $21.4 billion in debt at a (they say “conservative”) loan-to-value ratio of 20%, and that’s assuming the banks weren’t particularly generous.
One of the consistent themes of this piece is that much of the “value” of AI is hot air — by which I mean whatever people are willing to pay for a stock that’s continually inflated by specious media-driven hype.
Ellison’s wealth is driven by both his share of Oracle’s ongoing yearly dividend, his Oracle shares, and his ability to offer said shares as margin loans, which makes him vulnerable to even a symbolic collapse of OpenAI, which is why it had to tweet in February that “the NVIDIA-OpenAI deal has zero impact on its financial relationship with OpenAI” to calm those dumping the stock.
To be clear, Ellison has around 1.16 billion Oracle shares, leaving him with around 810 million or so left, allowing him to pledge them as further collateral rather than having to either dump them on the market or dip into his reserves of about $10 billion in cash and $15 billion in Tesla stock, with Ellison historically never selling more than about $4.7 billion in stock.
We don’t know the exact scale of terms of his personal loans, but do know that he’s got a shit-ton of them, and that his entire fortune rests on the idea that he never has to sell Oracle stock. That becomes a problem if things drag on with the Warner Bros deal, as he’s also guaranteed $40 billion from the Ellison Trust, effectively barring him from selling or using those shares until the deal clears (and the money from the Middle East arrives to fund the deal).
The amount of shares that Ellison has committed has oscillated on a year-by-year basis, sitting at 305 million in both 2018 and 2019, rising to 317 million in both 2020 and 2021, dropping to its lowest level in 2024 (217 million) before bumping back up to 346 million in 2025. While the board theoretically keeps an eye on his loans and what he’s pledging, he holds 40% of Oracle’s stock and the undying loyalty of veterans like former CEO Safra Catz and co-CEOs Clay Magouyrk and Mike Sicilia.
To get specific about how the Paramount/Warner Bros deal breaks down, $24 billion will be covered by funds from the Middle East (primarily sovereign wealth funds), with Ellison providing $22 billion and bank debt funding the rest.
If the deal doesn’t close by September 30, the Ellisons have to pay around $650 million a quarter in fees.
If it does, Ellison will likely either have to liquidate his Tesla stock, hand over cash, or take out further margin loans on his Oracle stock to fund it. Those would likely increase the amount of shares he’d have to commit somewhere between 150 million and 300 million (at a loan-to-value of 25% to 50%) at whatever price Oracle is currently trading at.
Though it’s hard to tell exactly, the number to look for with Oracle is “below $70.”
Once that happens, Ellison will likely have to proffer more Oracle stock to keep up with his margin calls, which will severely limit his ability to take out further margin loans using his Oracle stock. He will have to renegotiate loans, and if he’s managed to buy Paramount, he’ll be sitting on the stock of a company with $80 billion in debt and constantly loses money, which will be far less-appetizing to potential lenders who are aware that the rest of Ellison’s money is tied up in the plummeting hopes of Oracle.
Things could get much darker if Oracle plunges below $50, as at that point the encumbrances of his various enterprises and his own margin loans could become too much to avoid having to liquidate Oracle stock. If that happens, it creates a vicious cycle that will potentially involve selling off Paramount, dumping further Oracle shares, or even trying to engineer a firesale for the company.
Sidenote: While it’s true that Oracle’s software is economically important — its database systems and ERP platforms power a bunch of big businesses and government organizations — I don’t believe that any external intervention (whether that be an external investor chucking it some cash, or some form of bailout) would be able to stop the pain that’s coming to it.
Simply put, the bets it made are too big — and, economically important Oracle’s software might be, there’s no reason that said software couldn’t continue development under the stead of another company.
All of this was entirely avoidable if he had never met Sam Altman, and never gave in to the temptation of the AI trade.
When the OpenAI Bubble — and OpenAI itself — bursts, many will attempt to eulogize the situation in terms of how we could’ve possibly known this would happen, and I want to be clear that I’m going to be reading and commenting on as many of them as I can find.
I believe that once OpenAI collapses it’ll have a violent, punishing effect on the entire stock market, a precursor to a much greater drawdown as everybody accepts that the AI bubble has burst.
This view is shared by the Bank of England governor Andrew Bailey, who warned that the bursting of the AI bubble would have an effect on the UK economy, even though the UK economy — and the UK financial system — isn’t nearly as exposed to it as that of the United States, and would have significant enough effects to change British monetary policy, specifically, interest rates.
And I continue to stand by my belief that this company will die, though I can’t say when it’ll happen. The promises that Sam Altman has made at the scale that he’s made them are equal parts ridiculous and dangerous, leaving any counterparty somewhere between burned or destitute as a result.
There is no compelling story for any AI company once OpenAI dies. Other AI labs will suddenly have to explain how they avoid the same economical pitfalls while still showing the same aggressive growth projections promised by Sam Altman, and half-measures will no longer be acceptable. Their ability to secure credit — or even venture funding — will be met with impossible-to-answer questions about sustainability and profitability.
Any startup connected to its models will suffer because it’ll be clear that any AI lab is a financial black hole, and it’ll become obvious that basically every AI startup is an unprofitable LLM wrapper. That should be obvious now, but nobody bothers to look.
Any AI infrastructure company will have to pivot aggressively to open source models if they haven’t already, and realize that much of the demand for AI services came from brainless curiosity driven by the AI trade and market hype. CoreWeave, IREN, and the many circular-financed neoclouds will, much like AI labs, find themselves unable to secure funding, as the first question will be “how do you know your customers won’t die?”
NVIDIA just won’t be able to justify selling as many GPUs, as it has repeatedly cited OpenAI (albeit without saying its name) as a proxy driver of sales via counterparties including Microsoft and Amazon.
It’ll be a permanent blemish on a startup ecosystem that helped so many people become rich based on fictional or fanciful promises and projections, enabled and funded by venture capitalists that didn’t force founders to make stable or sustainable companies because it “always worked out before.”
And I genuinely think this will create an accountability crisis in the media.
I speak with readers and listeners every single day that are horrified about how many half-truths and outright lies are published and used as a means of propping up the AI bubble and the larger tech industry. The term “AI” has grown from a kind of technology to a cudgel wielded by the powerful to threaten and terrorize workers, all based on the outcomes from Large Language Models that simply do not do what their progenitors have promised and do not produce ROI or productivity benefits that are in any way measurable.
The OpenAI Bubble inflated not because Sam Altman is a super-genius, but because he’s very, very good at telling people what they want to hear. He’ll give members of the media convincing-enough projections, said with the confidence (or necessary fear) necessary to sway the vast amounts of reporters who are excited to follow the next big hype cycle (or, put another way, are scared to miss out on it).
Altman knows the exact signifiers to use and the minimum viable product necessary to “prove” OpenAI’s worth — however many hundreds of millions of weekly active users, annualized run rates, gigawatts of data centers, vague promises of “abundance” and “intelligence too cheap to meter” that never actually resemble a tangible thing — that work to con reporters and investors who don’t want to think about anything but growth.
He’s also really, really good at playing on people’s greed, be it promising Satya Nadella he can build the next generation of cloud compute cash, Larry Ellison that he can make OCI bigger than Azure, and Masayoshi Son that he can birth a goose that lays golden, AI-labeled eggs.
Altman realized early on that the only way to sell AI was to talk about it in the future tense in a mixture of threats and promises, always subtly suggesting that those who follow the OpenAI gospel will be saved from the permanent underclass.
And that same con worked on the minds of Silicon Valley founders who feel sore that they’ve yet to become an early employee of the next Apple, Google, Amazon or Microsoft, selling the dream of endless wealth under the auspices of “accelerationism” that really means “growth at all costs, usually billed to somebody else.” He and his acolytes have created a palpable mania in the Valley, convincing people that not using his software is a guarantee that they’ll be poverty-stricken imbeciles, and I think he’s fully aware of the fact that Silicon Valley is a dense monoculture that LARPs as a free thinker’s paradise.
In the end, Altman is unlikely to suffer, at least anywhere near as much as those he’s misled or helped mislead. The scale of losses that the stock market may face scare me to the point I’m almost hoping I’m wrong, with the markets heavily dependent on eternal growth of the AI trade, as without NVIDIA selling more GPUs every quarter, it’s unlikely that anybody is going to be excited to invest in tech past the year 2028.
All of this could’ve been stopped if those responsible for scrutinizing the powerful actually did their jobs, and spent more time doing that than critiquing the critics and repeating the promises of craven liars and billionaire scumbags. There were signs from the earliest days that this was all unsustainable, and the only reason it got this big was because the media and the markets fell behind a specious AI trade, empowering and enabling venture capitalists and hyperscalers to sink hundreds of billions of dollars into a doomed industry.
Whatever the AI industry achieves by the end of this farce will pale in comparison to the massive harms it has caused and will cause as a result, and for us to avoid this happening again, we need a fundamental reimagining of how the powerful are covered, how much effort is made to pry apart their plans, and accountability for those who either failed to stop them or actively assisted them.
If I sound salty, it’s because I am worried about the regular people caught up in this madness — the tens of thousands of people that have suffered AI-washed layoffs, the hundreds of millions of people that invest in a global stock market dependent on the AI trade (for a taster, see what’s happened in Korea when the KOSPI dropped earlier in the week, forcing hundreds of thousands of retail investors to face margin calls), those whose retirements and pensions and insurance annuities are tied up in private credit funds invested in AI data centers, and anyone of any kind who built their life around any promise made by Sam Altman and those that followed him.
I challenge those who are glibly dismissive of everything I say — who look for any smidgen of proof to dismiss hard numbers or clear economic issues — to truly think about the consequences of what I’ve written, and take the risk of the OpenAI Bubble seriously. Tech companies are not your friends, venture capitalists are not your saviors, Sam Altman doesn’t care if you live or die, and the AI industry — and Silicon Valley — will dump you the second that you stop being useful as an acolyte or booster.
I love technology, and credit it with making me a success and the person I’ve become, as well as connecting me to many people I love dearly. I believe that tech should be something that empowers, protects and enriches the human experience, something that’s sustainable and reliable and replicable and stable and makes human beings the same as a result.
The tech industry as it stands shows nothing but contempt for the user. Every tech product is somewhere between broken and buggy. The people that write about tech write for the companies far more than they write for those that pay them. Venture capitalists fund companies that they think they can sell to other companies or take public, which in turn means they fund things that are only attractive to people on Twitter or other venture capitalists. Big tech is unregulated, unrestrained, and works entirely to either enrich or fuck over shareholders depending on the day, and because the finance media has little interest in pushing back, they’ll continue to do so to the detriment of the markets and the retail investor.
Everything comes back to a distinct selfishness and lack of responsibility across basically every part of the tech industry. The fact that AI has grown this large is a symptom that Silicon Valley needs to be restrained — that it can and will release dangerous, unreliable, unpredictable and unstable products at scale with little regard for the consequences, in part because it knows the media will celebrate it doing so if it can show user or revenue growth.
OpenAI is the company the tech industry deserves — a directionless company of questionable worth that grew in a vacuum of responsibility that exploits greed and ignorance at scale.
And the tech industry will deserve exactly what it gets for coddling Sam Altman, and letting his empire grow this large.
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2026-07-11 01:51:30
Hi premium readers! I’ll be taking a week off of the premium next week — July 17 — to have some well-earned rest. This will mark only the second time I’ve missed a premium piece since I started this newsletter in June 2025, and I hope you’ll forgive me for the (short) break.
Don’t worry. Today’s piece is also an absolute banger.
Everything’s more expensive, and it’s all AI’s fault.
It really is that simple.
An AI data center is full of servers, which are in turn full of (for the most part) NVIDIA GPUs. Each NVIDIA GB300 has two B300 GPUs, the two of which have 576GB of High Bandwidth Memory (HBM, or HBM3e to be specific), and a CPU, which has 480GB of lower-power LPDDR5X RAM (the kind usually used in cellphones and other mobile devices). These systems tend to be sold in an NVL72 rack with 18 compute trays, bringing us to 36 GB300s, for a total of 20.7 terabytes of HBM and 17 terabytes of LPDDR5X RAM, and that’s before you get to the RAM associated with the high-speed networking gear and other associated components.
Analyst estimates have the cost of the high bandwidth memory of a single NVL72 GB300 at around $15.27 per gigabyte, for a total of around $316,000 of HBM, and while I can’t seem to find a stable source for pricing around LPDDR5X, I think a fair estimate is around $4 per gigabyte based on this piece, so around $68,000 worth per NVL72 rack.
At around 150kW of power draw per NVL72, a 1GW data center (with 740MW of critical IT load) would have around 4,933 NVL7s racks — for a total of $1.894 billion in HBM and LPDDR5X costs, or around $2.559 million of HBM and LPDDR5X RAM per megawatt of IT load.
Oh, and each of these NVL72s can hold as much as a petabyte of expensive solid state storage, costing an additional tens of thousands of dollars.
Because HBM takes up more space on a wafer — the slice of semiconductor material that is etched using photolithography (read: molten tin) and then cut into separate dies (individual chips) — and generally has much higher margins (thanks to the triopoly of Samsung, SK Hynix and Micron), memory manufacturers are dedicating more space on their manufacturing lines to it than to regular consumer RAM, which allows (thanks to said triopoly) said manufacturers to charge effectively whatever they want for consumer RAM.
And thanks to AI — to quote Tom’s Hardware and Counterpoint Research — NVIDIA is buying that LPDDR5X RAM at the scale of an Apple or a Samsung:
"The bigger risk on the horizon is with advanced memory as Nvidia's recent pivot to LPDDR means it is a customer on the scale of a major smartphone maker — a seismic shift for the supply chain which can’t easily absorb this scale of demand," said MS Hwang, research director at Counterpoint Research.
The net result is pretty simple: every single consumer electronic of any kind is getting more expensive. Valve’s Steam Machine console debuted at a 30% higher price point than planned, Apple hiked the prices of its MacBooks and iPads and will likely have to do the same for its next iPhone. Nintendo, Microsoft and Sony increased the cost of their consoles, and the PS5 and Xbox Series now cost more today than they did when they first retailed, almost six years ago.
On the Android front, Samsung has bumped the price of its Galaxy smartphones, and manufacturers in this space (which tends to have smaller margins than those enjoyed by Apple) are likely to limit the number of new devices shipping with 16GB of RAM, as well as re-introduce models with 4GB of RAM .
Meanwhile, memory manufacturers are having record quarters, with Micron’s revenue quadrupling year-over-year in Q3 2026 and its gross margin improving by ten percent (from 74.9% to 84.9%) quarter-over-quarter, and Samsung’s profits growing from $38 billion to $59 billion quarter-over-quarter thanks to the spiralling cost of revenue caused by…well…the companies setting the price of memory at whatever they’d like.
This is a problem caused by the fact that these three companies — SK Hynix, Micron and Samsung — produce more than 90% of the world’s RAM, which is why there’s a price fixing lawsuit against them, per Polygon:
The class action lawsuit filed by 14 individuals and three businesses accuses Samsung, SK Hynix, and Micron of conspiring to fix prices and supply of DDR3 and DDR4 RAM, resulting in higher costs. “This lawsuit seeks to recover for—and stop—concerted anticompetitive behavior by three oligopolists in the market for dynamic random access memory, more commonly called DRAM,” the opening line of the suit reads.
The suit says that the firms have “fixed supply and prices for DRAM, engaging in conduct that makes no economic sense absent collusion and that has driven up the price of conventional DRAM (sometimes called commodity DRAM) approximately 700% in a four-year period.”
To be clear, HBM is more expensive to make than regular RAM, and takes up significantly more space (about 4x more) on the wafer, but because of the incredible demand for AI servers, Samsung, SK Hynix, and Micron can charge effectively whatever they want for it, much like they are for the regular RAM that’s in short supply. The same is becoming increasingly true for the solid state storage that these companies (and others like Sandisk) sell too.
Now, you may think it’s a little rich to suggest that memory manufacturers are colluding to rig their prices, perhaps a little judgmental, and you’d be wrong because they’ve done it before. Quoting Polygon again:
The lawsuit points out that between 1998 and 2002, these same three companies, Samsung, Hynix (the predecessor of SK Hynix), and Micron, took part in a criminal conspiracy to fix the prices of DRAM sold to major American computer companies. After the Department of Justice prosecuted the case, Samsung pleaded guilty and paid a $300 million fine, Hynix pleaded guilty and paid $185 million, and Micron avoided a fine by reporting the conspiracy and cooperating. As a result, several Samsung executives went to prison. The trio of companies was also investigated by the Chinese government during a RAM price spike between 2016 and 2018.
To be clear, I am not saying — nor can I prove — that there is any kind of price-fixing or collusion going on. Nevertheless, there are three companies that effectively make all the world’s RAM, all raising prices at the same time, all seeing record profits, all riding high at a time when everybody else is suffering as a direct result.
The Wall Street Journal put it best:
We are witnessing an enormous transfer of cash from the providers of AI—and, perhaps one day, AI users—to the memory-chip makers. Profit shifts of this scale are rare events, and investors should be paying attention to where the money’s coming from, where it’s being spent and how long it will keep flowing.
In the quarter ending May 28, Micron increased prices for DRAM chips more than 60% on the previous three months, while increasing shipments by a low single-digit percentage, it said this week. Prices for NAND flash memory, also used in data centers, jumped more than 80%.
What makes this particular memory crisis so distinctly dangerous is that it isn’t a result of consumer demand so much as it is capital expenditures from very large companies making bets that don’t connect with reality.
Microsoft, Google, Amazon, and Meta aren’t spending $765 billion in capex in 2026 because of rapid demand by consumers for AI services, but a desperation caused by a lack of hypergrowth ideas, circular financing with Anthropic and OpenAI, and a vague concern that if they stop spending that the other guy will do something as a result.
As I discussed earlier in the week, nobody can make a compelling case for building more data centers other than “we must do so, because of AI.” Nobody is having trouble accessing ChatGPT, Claude or another major AI service because of a lack of compute, outside of Anthropic and OpenAI’s continual rapacious hunger for more compute that doesn’t ever seem to involve them turning away business. While price increases generally help moderate demand for goods or services, none of that matters when you have four companies willing to spend a trillion dollars a year on the off chance that they might get something out of it.
As a result, Micron, Samsung, and SK Hynix can charge effectively as much as they want, and NVIDIA and others building black holes for AI capex can then pass those costs onto Microsoft, Google, Amazon, and Meta, who have given themselves a blank check to build whatever it is that they think will come out of the large language model era.
Put another way, the capex spend of four of the largest companies of the world — all of whom are now funding their capex using debt — has now led to the single-largest increase in the price of consumer electronics in history, for the most part thanks to one company, NVIDIA, becoming the largest purchaser of HBM in the world because those four companies are buying so many GPUs.
To give you an idea of how bad that is, NVIDIA takes up roughly 65% of all high bandwidth memory, with the other 35% (mostly) going to specialist ASICs from Google and Amazon, and AMD’s Instinct line of AI GPUs.
This is a unique — and uniquely dangerous — bubble, because demand isn’t based on actual revenues or events happening outside of those in the imaginations of Sundar Pichai, Mark Zuckerberg, Andy Jassy and Satya Nadella. They didn’t start buying these GPUs because consumers demanded them. In fact, they did so without really checking whether consumers gave a shit, which is why I’m so worried about what comes next.
Only 23% of total DRAM wafers are taken up by HBM, but it’s accounting for a remarkable chunk of revenues, at least for SK Hynix, where it took up 40% of all DRAM sales back in Q3 2025, the most-recent number I can get.
While I can’t find definitive numbers from Samsung or Micron, the situation is bad no matter which way you spin it. Either they’re increasingly-relying on HBM as a revenue driver to the point it’s crowding out the revenue from their other DRAM businesses (making them dependent on GPU and ASIC revenue), or their revenues are spiking because they’re able to crank up the cost of DRAM.
This is setting everybody up for a dramatic and painful collapse, largely based on the strange nature of how memory is built and sold, unless cooler heads prevail and capex doesn’t accelerate based on hopium.
What happens when hyperscalers reduce their capex, or when banks stop issuing data center debt? NVIDIA stops needing all that HBM, which means any and all capex dedicated to expanding manufacturing infrastructure to produce more HBM — which is not particularly valuable outside of AI GPUs — will have been built to capture demand that doesn’t exist. While that capacity could be re-engineered to make useful DRAM with mass appeal, doing so will also drag down the profits of every memory manufacturer in the process, creating a supply glut the likes of which we’ve never seen in history.
The memory industry has gambled its financial future on the idea that there’s near-infinite amounts of capital available for data center capex, adjusting its supply chains and fabs to focus on scooping up demand that’s increasingly only made possible by the availability of debt. Microsoft, Google, Amazon and Meta have turned NVIDIA into a single point of failure for the entire tech industry, creating a painful present for consumers and a brutal future for suppliers, all because they decided to spend more than a trillion dollars on a dead end industry.
The longer it takes for hyperscaler capex to retract, the more expensive everything becomes. The more GPUs that get sold, the more capacity that gets put toward high bandwidth memory, and the more that Micron, SK Hynix and Samsung can charge for it, which makes it more expensive to buy AI GPUs, which increases the amount that hyperscalers are spending on AI capex for effectively the same amount of gear. The longer that hyperscalers sustain this pace, the larger the return needs to be, and at this point, none of them have disclosed their AI revenues, which heavily suggests there’s yet to be a dollar of profit.
Yet the more they commit, the more committed they have to be. Pulling back at this point will prove to the markets that they’ve committed to too much capacity. Yet not pulling back means that hyperscalers will continue to turn their free cash flows negative in pursuit of an indeterminate goal. It’s a vicious cycle made worse by the fact that every spin of the capex wheel increases the price of just about every consumer electronic in the world, creating a market-wide inflation for what amounts to a speculative asset bubble.
And If even one hyperscaler cuts their capex, the cartel-like memory industry is in for a nightmare scenario, one larger and uglier than any they’ve ever faced.
In the end, it all comes down to whose problem this high bandwidth memory becomes. Will SK Hynix, Samsung, and Micron have already built the RAM and face waves of cancellations, resulting in a bunch of fallow inventory it can’t use or sell? Or will they already have shipped it off to NVIDIA and ASIC builders, only for it to sit in warehouses waiting for the day it can finally be melted down?
Who will end up holding the bag? The cartel of horrible fab-gargoyles, Jensen Huang’s Wallet Inspection Firm, one of the four simpleton hyperscalers, Broadcom, or one of the Taiwanese ODMs?
Just to be clear: everybody loses, unless the AI bubble continues in perpetuity.
This is the Hater’s Guide To The Memory Crisis — and the terrible tale of the boom-and-bust memory industry.
2026-07-08 01:09:10
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Soundtrack: Mastodon — Streambreather
No bailouts, no handouts, no special treatment, no tax breaks, no CHIPS act, and no sovereign wealth fund. It is time to tell the AI industry to go fuck itself, because it’s effectively done the same to the rest of society. This industry is unworthy — a sham conjured up by a tech industry that’s run out of ideas, a trillion-dollars’ worth of manufactured consent and entirely-avoidable financial crises — and should not be protected under any circumstance.
Every single time you hear somebody discuss “bailout” or “too big to fail” or “sovereign wealth funds,” know that this is the industry, on some level, attempting to create the air that it cannot die, when in fact every one of these companies is just as weak and brittle as any other startup.
I also think that the media — and the world at large — is too ready to accept the prospect of a bailout after watching those who drove the world into a ditch in 2008 escape blame, and I must be clear: the AI industry is very different to the financial industry. It is inessential to the economy, and its relevance is only as large as the hype campaign that sits behind it.
This is an industry of losers that has inflated only because of the joint manufactured consent of Silicon Valley, the mainstream media, and an enshittified stock market that rewards grifting and circular financing. OpenAI had $5.7 billion and Anthropic a little under $5 billion in the first quarter of this year — and those revenues mostly came from companies that were burning AI tokens at a horrendous rate because they’d just been forced to pay the actual cost of AI — and now everybody’s pulling back on that spend.
Generative AI will not bring us AGI, nor does it do much of what we associate with artificial intelligence. It is not autonomous. It is not “intelligent.” It does not have thoughts, or “knowledge,” and no matter how many layers of harnesses and scripts you put on top of it, it is still (per OpenAI) mathematically certain to hallucinate. I estimate that at least 70% of the entire AI industry’s revenues are made up of OpenAI and Anthropic’s compute spend, and as both companies are horrendously unprofitable, this means that the AI industry is, for the most part, venture capitalists funnelling money to hyperscalers so that they can funnel that money to NVIDIA or data center capex.
If this software were worthy, it would stand on its own two feet. It wouldn’t need circular financing and a cult of personality to prop it up, either. If it were truly special, there wouldn’t need to be an army of crazed acolytes that attack you for not pledging yourself to the graveyard smash. There has never been a tool or product in history sold with such hysteria and aggressive monocultural force that has ever turned out to be anything more than a grift. Some people have developed unhealthy relationships with large language models (LLMs) and the companies that make them, and that, not any certainty or proof of Artificial General Intelligence (AGI), is what motivates them.
This software is uniquely dark, both in what it unlocks in some people through its use and in the sense of the entities that sell it. Some people are in genuine awe of each of the rotation of clammy, soulless pod-people that saunter out of Anthropic every few weeks. Each one sounds a little weirder, more cultish, more disconnected from the real world. Silicon Valley may believe itself atheistic, but Anthropic has a worrying sense of fanaticism, both in the people that work there and its fanbase. Imagine the absolute worst fanbase of a video game possible, and then add layers of financialization, grifting and high school drama laced with pseudo-religious attachment. All for a fucking app!
Please, people. Nobody in the real world cares about “loops.” Nobody is thinking about tokenization. If you said inference to a guy on the street they’d take you to see a doctor. Nobody gives a shit. They don’t know what OpenClaw is either. Grow up. Go outside. You sound like a lunatic. Does your mother know how many Claude 20x accounts you have? It’s obsessive!
Anyway, the only reason that AI has any presence in our economy is that Microsoft, Google, Meta, and Amazon are intent on spending more than $765 billion in capital expenditures in 2026 and a trillion more in 2027 because they have no other hypergrowth ideas, even though generative AI has yet to show any real potential as something that can drive meaningful revenues (let alone profits), as evidenced by the fact that none of these companies break out their actual AI revenues, a point I made on CNBC late last week.
Google does not have the next Google Search, Microsoft does not have the next Microsoft Office, Meta does not have the next Facebook, and Amazon does not have the new AWS. That’s why they need you to believe that AI is a big deal without them ever having to prove why outside of capital expenditures. They want you to assume that all this money can’t be wrong, even though when you remove OpenAI and Anthropic (who represent 89% of the revenues of the largest AI companies) the AI industry is, at best, pulling in $20 billion in annual revenue.
And lord do they want you to say “it’s early,” and that it’s just like the Dot Com Bubble, all so that you’ll either accept AI as your lord and savior or, alternatively, help justify one of the largest misallocations of capital in history as “building useful infrastructure.”
Newsflash! AI GPUs are useful for generative AI and not much else. Every “innovation” in LLMs has only been made possible by throwing billions of dollars at the problem either in headcount or compute costs — every ounce of talent in the tech industry, every bit of media attention, every dollar of capital expenditures, all focused on one industry that has successfully created LLMs that are more expensive and significantly less useful than human beings.
The reason every AI person speaks in pie-in-the-sky hypotheticals is that the actual outcomes are decidedly mediocre when you compare them to their ruinous costs. Anthropic and OpenAI raised (assuming the rounds completely close) over $300 billion in 2026 alone, and take up the vast majority of available AI compute. They need you to speak in the future tense, because nothing — absolutely nothing — about what’s been created so far justifies even a fraction of its financial and infrastructural cost.
When the AI bubble bursts, none of this infrastructure will be particularly useful. As I said in my premium about how this is worse than the Dot Com Bubble, GPUs are not fiber optic cable, and when the bubble bursts, NVIDIA chips will either be sitting in the coffers of the largest tech companies in the world, held by asset managers, or auctioned at a steep discount by creditors. These are not going to be useful for hobbyists, nor will they be cheaper to run, nor will incomplete data centers be cheaper to finish.
The Dot Com era fiber overbuild was a result of a complete misread of demand signals, per Justin Kollar:
This continental rewiring was also justified by another powerful myth—that internet traffic was doubling every 90 days. The claim spread through analyst reports, earnings calls, and investor presentations like a particularly virulent meme. If true, it meant that demand was growing exponentially, far outpacing any conceivable supply, and that every new trench of fiber would soon pay for itself many times over.
But the mathematics were fiction. Network researchers like Andrew Odlyzko (at AT&T), looking at actual traffic data, found that U.S. backbone traffic was doubling roughly once a year—rapid growth, certainly, but nowhere near the purported 90-day cycle. Meanwhile, advances in fiber technology were making each strand exponentially more powerful. Dense wavelength-division multiplexing allowed dozens of signals to travel simultaneously down the same line at different wavelengths of light, like multiple conversations happening in different colors.
While demand doubled annually, supply expanded tenfold or more. Carriers buried the discrepancy under layers of creative accounting that would have impressed medieval alchemists. They sold “indefeasible rights of use”—essentially decades-long leases on fiber capacity—and booked the entire value immediately as revenue. They engaged in elaborate “capacity swaps,” trading bandwidth with competitors and treating each exchange as a sale, manufacturing revenue from thin air.
It’s tempting to compare this to GPUs, but it doesn’t make sense at all!
You see, internet demand was a result of people wanting to get online and use the internet, with the leftover “useful infrastructure” having a blatantly obvious use case after the bubble burst, albeit one that took a lot longer to arrive than investors had hoped. There was no question about how that gear might be used or for what purpose one used fiber optic internet or networking gear, nor was there any question as to the underlying business model of offering an internet connection might mean.
We were also fairly early, and internet speeds were atrocious. In 2000, only 52% of American adults were using the internet, and by 2003, that number had only increased to 61%. Per the World Bank, in 2005 only 16% of the world used the internet, and in 2024, that number had increased to 71%. When the internet was connected to via a 56k modem, access was charged by-the-minute, and obviously much, much slower than even the primitive (though expensive) broadband connections of the day.
While we’re used to connecting at speeds that make using a web-based app near-indistinguishable from one that runs on our computer, back in 2000, 2001, or 2002, the average US internet speed was, at best, 400 Kilobits/s, or roughly 50 kilobytes a second, compared to the average US internet speed of over 200 Megabits per second, or 25 megabytes a second.
Sidenote: Yes, fiber optic internet (and DSL for that matter) was expensive, both for the customers, but also for the providers. Verizon spent $23 billion on bringing FiOS to people’s homes between 2004 and 2010, for example, but the “up front” cost had a defined, obvious return on investment.
Generative AI, on the other hand, is fucking everywhere, and anyone with an internet connection experiences it in effectively the same way. It’s non-consensually available in effectively every app — every Facebook, Google and Microsoft account, for example — and every media outlet known to man has mentioned AI multiple times since 2023. OpenAI and Anthropic might claim they need more data centers, but it’s unclear what “more data centers” actually achieves other than propping up NVIDIA and giving hyperscalers something to invest in.
A lack of data center capacity isn’t holding back people from using generative AI, nor is it stopping anybody from launching a product, nor can anyone actually express what it is that they’re being built for other than “reasons for Anthropic and OpenAI to spend money.” Anthropic’s supposed lack of compute did not stop it training or launching Mythos or Fable, and when it bought hundreds of megawatts of compute from SpaceX, the biggest news was that it expanded rate limits to allow users to burn $8,000 worth of tokens for $200 a month.
Nothing about the painfully slow pace of data center development appears to be restraining a single AI company, outside of hyperscalers complaining they could’ve made more money from either Anthropic or Meta. In fact, the entire argument for more data centers appears to be “we need more compute so that people can buy it” far more than any cogent position around what these capacity shortages actually mean.
Who are the companies lining up to spend billions of dollars of compute — or, to be more specific, spend $435 billion or more to justify the $1 trillion in GPU sales that NVIDIA claims it’ll have by the end of 2027? That’s how much demand we’ll need. As NVIDIA intends to sell over a trillion dollars of Blackwell and Vera Rubin GPUs by the end of 2027, it needs to have around (assuming a PUE of 1.35) 40GW of data center capacity built to support the 30GW+ of GPUs it will have sold. At about $12 a megawatt of critical IT (IE: the stuff in the data center that runs AI compute, and not everything else, like the cooling systems and any transmission loss), that’s $435 billion.
OpenAI estimates it’ll spend $50 billion on compute in 2026, and Anthropic will likely spend comparable amounts. Otherwise, the only other player — outside of Microsoft, Google, and Amazon renting (or backstopping) capacity for Anthropic and OpenAI — with any meaningful compute spend is Meta (with Nebius and CoreWeave)... and Bloomberg is reporting that Meta is planning to start selling its compute because it doesn’t need all of it.
You’ll be shocked to hear that it might be renting some of that capacity…to Anthropic.
Now NVIDIA is agreeing to financially backstop young cloud providers buying their GPUs by promising to rent back any unused capacity, yet another sign that actual, real demand does not exist at scale. AI boosters with black mold problems will say “this is just to help them raise debt,” to which I say “If the demand actually existed in any provable way, NVIDIA wouldn’t have to pay its customers to buy its products!”
Anyway, my larger point is that there was real demand during the dot com bubble, and LLMs’ demand appears decidedly artificial outside of OpenAI and Anthropic, who cannot afford to pay without unlimited venture capital funding.
This shit isn’t going to become magically cheaper once the bubble bursts, and considering the demand doesn’t appear to be there at scale with two-thirds of all venture capital funding focused on AI, I’m not sure what people expect to happen. Right now is the number one time in history where we should see near-infinite demand for compute across every single surface, and way more deals for compute capacity for companies other than the same four or five companies.
Right now, as I’ve discussed before, Anthropic and OpenAI take up the majority of compute, leaving the rest of the world to fight for the leftover scraps, and because data centers take 18 to 36 months to build, capacity is taking forever to come online to fill the indeterminately-large amount of demand that remains. Nevertheless, said demand can’t be that large, otherwise we’d A) have other companies trying to build their own compute (other than Poolside, which failed to raise money to do so) and B) massive remaining performance obligations — hundreds of billions of dollars’ worth — rather than the grim truth that 50% of hyperscaler RPOs are from Anthropic and OpenAI, inflating obligations by $448 billion, hiding the fact that Microsoft’s RPO growth is flat year-over-year and Amazon’s is only growing at a modest 20% when you remove Anthropic and OpenAI’s hundreds of billions of dollars’ of compute spend. Google’s is a little messier, as it’s hard to parse exactly how large its deals with Anthropic are thanks to its backstops and circular deals around Anthropic and its TPU chips.
There’s also the compelling question as to what it is that anyone would be picking up once the bubble bursts. Demand for AI services is a direct result of the entire media, tech industry and venture capital ecosystem manufacturing consent for the use of LLMs, forcing them into every corner of every experience, something that will most decidedly end once the stock market and investors cease incentivizing it.
Once every media story isn’t about AI, once every Business Idiot with AI psychosis stops posting about it every day, when everyone stops asking about your AI strategy or wanking on about “sovereign AI,” it’ll become blatantly obvious that the actual demand for AI was not particularly strong.
We have little compelling evidence that providing any inference-based services is profitable, which means that even if open source AI outlives the frontier AI labs, it’s unclear who would actually power the infrastructure. People can come up with however many weird blogs where they’ve done some napkin maths to try and extrapolate a potentially profitable inference provider, but I’ll only believe that one is profitable when someone shows me some fucking profit.
And to be clear, without that profit, it’s unclear why anyone would offer these services at all. When you rent out a GPU cluster, you do so based on anticipated demand and the quality of service you want to provide. If you order too much, you’ve got a bunch of fallow capacity you’re paying for (and will lose money on), and if you order too little, you’ll have either unstable services or money left on the table…and even then, it’s unclear how profitable that would be.
AI demand is, at this point, a direct result of societal pressure and non-consensually overwhelming customers with AI features. While there are people that like and pay for ChatGPT or Claude, those who do so on a subscription basis are doing so because they can get $30 to $40 of compute for a dollar. The vast, vast majority of AI compute demand is from services provided to people either for free or sold at such a massive discount that it’s impossible that anyone on a $20 or $200-a-month plan could even afford these services had they paid their actual token cost. To paraphrase Cory Doctorow, your demand is based on selling $40 for a dollar. That’s not a real business, nor is that organic demand.
One could argue that “these services will become cheaper,” but that would require them to… become cheaper. More compute isn’t (and hasn’t) lowering the cost of AI. Newer GPUs aren’t lowering the cost. Barely-tested Broadcom GPUs, Amazon Trainium XPUs, and Google TPUs aren’t lowering the costs. Even if they were to somehow magically do so in the future, what do we do with the H100, H200, B100, B200, B300 or AMD GPUs? Melt them down for scrap? Steal the RAM? Build a GPU fort?
The Dot Com (and, by extension, telecom) Bubble was never a question of whether the internet was a useful thing that people would pay for, nor were there journalists and dodgy studies that desperately pleaded with us that AI is here, and it’s real.
Everybody has access to AI now! They can all see it and use it if they want to, and they’ve got lots and lots of ways to pay for it! Maybe the reason that AI revenues are so putrid is that they don’t really have any reasons to pay for it, either because the free services do most of what they need (IE: google searches) or subsidized subscriptions that cost $200 a month allow them to burn as much compute whipping up HTML-based calorie tracking apps that get two users.
Every time I read somebody on Twitter say that “we’re early” or that “most people haven’t even tried agents” I feel like screaming. Motherfucker, everyone is talking about agents in every single media property all the time. AI boosters will refer to literally any AI feature as an agent, even if it’s a basic web search or generating code. The reason that most people are kind of “meh” about AI is that it doesn’t do things that they associate with AI (autonomously and automatically taking care of the things they need with little prompting or coaxing), everybody knows it hallucinates, and AI data centers are horrifying monoliths of capital that get massive tax breaks, use a ton of water, belch toxins into the air, and are being built by faceless corporations, ultra-oafs like Kevin “Mr. Dogshit” O’leary, or charmlessly damp Valley elitists like Altman and Amodei.
Every single person freaking out about “what if China does AI better than America” is living in a child’s fantasy. Oh no! China might get Mythos-level AI? Bad news folks! Anthropic itself already admitted that cheaper models — including Claude Haiku 4.5 and Kimi K2.7 — were able to identify the very same vulnerabilities as Fable (so, Mythos with guardrails).
China has cheap power, data center capacity, and NVIDIA’s Blackwell GPUs. The thing that everybody is scared of has happened already, and you know what else happened? Nothing, because they, like American AI labs, are building LLMs. The only thing that American labs are scared of is cheaper open source Chinese models offering similar performance to their premium products, something that has also already happened.
Remember: the only people that can afford to build data centers are either hyperscalers (that are now having to fund the buildout with debt as their cash flow turns negative), Oracle (which will die if OpenAI can’t pay it), unprofitable neoclouds, and land speculators. AI data centers are massive, expensive operations, and raising money to finish (or furnish) one after the bubble bursts will be very, very difficult.
I realize that everybody wants there to be a happy ending after all of this collapses. I get that it’s easier to think of things in familiar terms — even if said terms involved a 77% drop in the NASDAQ — because there was something good and nice at the end.
But doing so only serves to help protect the interests — and brands! — of venture capitalists, asset managers, private credit funds, hyperscalers, captured tech and business journalists and sell-side analysts that insisted on ignoring every warning sign and waving away problems by saying it was “just like Uber (nope!)” or “just like Amazon Web Services (between 2003 and 2015, Amazon spent $29.7 billion on capex, normalized for inflation),” or simply saying that “yes it’s a bubble, but bubbles lead to great industries.”
GPUs aren’t dark fiber! GPUs aren’t fucking railroads! GPUs are GPUs! They are used for basically one thing! And that one thing lacks meaningful demand outside of subsidized services and circular financing!
And now people are discussing a bailout like this is 2008, and I must be clear how different this is, and how little it resembles the Great Financial Crisis!
The AI industry has demanded everything from us — more money than has ever been invested, more power than anything has ever needed, the stolen works of millions of hard-working creatives, so many GPUs and so many data centers that it’s causing a global supply chain crisis and a new class of RAM and storage-based inflation, the majority of venture capital funding, and constant attention focused on an endless campaign of fear-mongering with the express intention of hyping a technology based on a mixture of mysticism and outright lies — and still, even as we enter the late innings of the bubble, it wants more.
Capital-hog Sam Altman has floated the idea of handing 5% of OpenAI to the US government, a stake worth around $42 billion, claiming that (to quote the FT) “...giving the public a financial stake in the company is the best way to share the upside of AI,” failing to note what said upside might be, likely because there isn’t one unless “the public” refers to “the shareholders of OpenAI.”
It isn’t clear how this would happen, outside of it requiring congressional approval as a result of the Takings Clause of the Fifth Amendment, which states that “private property [can’t] be taken for public use without just compensation,” meaning that the US government would likely have to buy the stock at whatever valuation it considered “just.”
Yet the FT had one other interesting tidbit — that Altman is suggesting that whatever this is would “...would involve other US AI companies handing over a similar stake, although it is not clear if the other labs would be willing to do so”:
Altman and other OpenAI executives have suggested that each of America’s leading AI developers allot 5 per cent of their equity to a vehicle like the Alaska Permanent Fund, a sovereign fund that invests the state’s oil wealth into stocks and pays dividends to the state government and residents.
This is, just to be clear, not a bailout. Even though it’s blatantly obvious that Altman wants to cozy up to the Trump Administration and, he hopes, get $42 billion of funding to attach his questionably-valued quasi-startup, $42 billion is $8 billion less than OpenAI will spend on compute in 2026, and considering OpenAI has projected to burn $852 billion through the end of 2030, that 5% stake would only exist to prolong the inevitable.
You see, a bailout usually has an endpoint — a time at which the company in question no longer needs the funds.
So, let’s be clear about something: we’re actually in several bubbles at once.
The great financial crisis, by comparison, was two major bubbles (per my piece on how AI Isn’t Too Big To Fail from a few months ago) — the over-investment and speculation on mortgages (both subprime and otherwise), and the collapse of the commercial paper (a type of loan) market that kept much of the banking system functioning, which was the real “Too Big To Fail”:
The AI bubble has made us think about corporate debt in terms of Capex — massive loans designed to bring vast data center gigaprojects to life — but in reality, a lot of it goes to small-to-mid-size businesses to cover day-to-day spending like payroll, and where the repayment terms are often measured in months rather than decades.
These loans, issued by banks, money market funds, and other non-traditional lenders, are funded by either repo lending (asset-backed short-term deals where you effectively sell a security and buy it back very quickly at a slightly-inflated price) like Lehman used, or commercial paper — a short-term (usually a month) loan that can, in some cases (such as AIG’s!) be issued without collateral. In others, collateral can be as simple as “we have accounts receivables saying we’ll get paid.”
At its peak, commercial paper was a $2tn global industry.
Federal Reserve Chairman Ben Bernanke noted that around the time of the bailout, AIG had $20 billion of commercial paper — short-term debt for corporations and banks where the maturity can be as low as one day, or as high as 270 days — outstanding, in simple terms meaning it had $20bn of loans it had yet to pay within the coming year.
Commercial paper was, at the time, often paid off using more commercial paper, and when AIG’s credit rating dropped in the middle of September 2008, it was unable to roll over its debt (by which I mean “get new commercial paper to pay off its old commercial paper”), and money market funds like Fidelity couldn’t even buy it anymore because it wasn’t investment grade, which meant that AIG couldn’t pay back its loans.
While I won’t recount the entirety of the premium (mostly because it’s super long), AIG was deemed “Too Big To Fail” because it would’ve exploded the markets had it done so. Michael Lewitt, an economist and money manager, described a hypothetical AIG failure as being “as close to an extinction-level event as the financial markets have seen since the Great Depression” in a New York Times op-ed:
“If A.I.G. had collapsed – and been unable to pay all of its insurance claims – institutional investors around the world would have been instantly forced to reappraise the value of those securities, and that in turn would have reduced their own capital and the value of their own debt. Small investors, including anyone who owned money market funds with A.I.G. securities could have been hurt, too. And some insurance policy holders were worried, even though they have some protections.”
Yet the real “Too Big To Fail” was far quieter and more malignant, taking the form of trillions of dollars funnelled to banks:
A little-discussed part of the scale of the bailout were the liquidity mechanisms created to stop the bleeding — the Primary Dealer Credit Facilities (PDCF) and Term Securities Lending Facilities (TSLF) that provided as much as $100 billion dollars to banks and financial institutions every day.
They existed as short-term lenders of last resort, providing overnight funding to institutions (banks, investment banks, hedge funds) that had become illiquid as their stocks tanked and their stupid, reckless bets came home to roost. The TSLF in particular helped plug the gap in the failing repo market, and I must be clear that everybody who put the US financial system in these conditions should be in prison, or worse.
The banking system ran (and still runs) on overnight facilities like the federal repo market, where financial institutions offer up collateral — like, say, mortgages — as a means of funding their day-to-day operations. Previously, money market funds were the lenders in the repo market…except they were now a little hesitant to take that collateral, which forced the government to step in with the PDCF (which traded risky, frozen assets like subprime mortgages for cash to avoid a default) and the TSLF (which traded risky bonds for US treasuries).
Absolutely nothing about these facilities or anything to do with “too big to fail” were to do with stabilizing the stock market, which was effectively cut in half, with unemployment spiking to 10%. These measures existed exclusively to protect the financial system, with only $46 billion (about 10%) focused on trying to save homeowners from foreclosure, and in the end, to quote a congressional panel from 2009, “...the panel sees no evidence that Treasury has used TARP funds to support the housing market by avoiding preventable foreclosures.”
The Troubled Asset Relief Program (TARP) spent over $400 billion to bail out the banks, financial institutions and auto industry that would’ve collapsed as a result of an economy-wide lending freeze. Nobody went to jail, nothing really changed, and banks still don’t have to keep reserves thanks to changes made around COVID.
By comparison, OpenAI and Anthropic are systemically irrelevant, much like the rest of the generative AI industry. While their existence supports the overall symbolic value of the US stock market, their actual economic presence is minor, outside of what I estimate is around $75 billion to $100 billion of 2026 compute spend and what will likely be around $60 billion of combined revenue, with the rest of the AI industry having so little that it’s barely worth thinking about.
It’s also unclear what you’d bail out, unless the plan is to feed them capital for all eternity until they work out how to run a functional business (so, forever). Neither of them have significant debt — and Broadcom is backstopping $30 billion of Anthropic’s $35 billion TPU deal with Apollo — and their equity positions (outside of SoftBank, which I’ll get to) are only load-bearing to venture capitalists in the sense that their fund vintages will painfully sour if they’re unable to go public.
We Should Talk About SoftBank: There is one company that is systemically dependent on OpenAI — SoftBank. As I covered in this week’s Hater’s Guide, SoftBank has wagered effectively its entire future on $40 billion or more in short-term loans to fund Sam Altman’s No IT Loads Party, and if OpenAI can’t go public, SoftBank will face a legitimate liquidity crisis.
This, again, is nothing compared to what would’ve happened if AIG had collapsed or if the US government hadn’t propped up the liquidity of effectively every major bank. That being said, SoftBank is one of the largest companies on the Japanese stock market, and one of its largest investors is the Japanese government pension investment fund (GPIF), and thus might see some kind of bailout.
There is no avoiding the carnage to come, outside of there being somewhere in the order of ten to a hundred times the demand for AI compute by 2030 that exists today, which would require AI compute to be larger than the $779 billion that the software industry earns annually.
There is no bailout that can reverse the trend once demand wanes for NVIDIA’s GPUs after hyperscalers reduce their capex, which will in turn kill the revenues of Taiwanese ODMs that build AI servers for hyperscalers, which will in turn kill the revenues of RAM and storage companies, which will lead to a prolonged depression throughout a semiconductor industry addicted to hopium peddled by a tech industry ruled by Business Idiots that have no idea what to do other than hire people, fire people and spend money.
As I’ve said many times, people are conflating massive capital expenditures — invested through debt-fueled data center speculation and hyperscalers bereft of hypergrowth ideas — with real, diverse and consistent AI demand, pumping valuations based on vibes rather than reality, which means that when vibes take a violent, permanent shift, nobody has anything to point to as a means of turning people’s frowns upside down.
A sidenote on private credit: I will say that I am deeply worried about the private credit industry and its trillions of dollars of loans, as we don’t really have a firm hold on its exposure to the AI bubble, other than that some indeterminate amount of billions have been sunk into data centers.
Private credit, as mentioned, has sucked up a lot of money from pension funds, insurers, and, ironically, banks themselves who, due to the post-2008 crackdown on speculative bets, are restricted from making massive punts on AI infrastructure companies, but are (thanks to a wonderful loophole) make the same bets by proxy by shuffling cash to a private credit fund.
The collapse in value of AI startups wouldn’t be changed by a bailout unless the US government literally invested in worthless startups as a means of propping up venture capital, and said “bailout” would number in the hundreds of billions of dollars, and while I know you’re gonna say “ohhhh Trump is so corrupt oooh Trump will do this Trump will do that,” this is not a rational or logical or even historically-accurate thing to say.
Trump cannot simply mobilize $50 billion or $100 billion. It will go through the House and the Senate, and any bailout of the AI sector would be an incredibly-unpopular decision, infuriating not just those on the left who’ve grown tired of Big Tech, but with those Republicans that pretend to care about working Americans or fiscal probity.
As a reminder, the first vote of the 2008 bailout failed, with Republicans and Democrats each fairly split on how they felt about the bill — and that rejection happened during a time when the US financial system was quite literally falling to shit.
As far as the data center bubble goes, the government is absolutely willing to let unfinished or abandoned properties lay dormant. In the final quarter of 2008, 11% of US homes were empty, or 15% if you include vacation homes.
Banks that have invested in data centers that have yet to be built (or start construction) can (and will) resell the land, though likely at a loss, and land retains value even if you haven’t built a giant warehouse full of GPUs that only lose money. There isn’t a need for a bailout here, and one won’t be forthcoming. After the Global Financial Crisis, builders were allowed to collapse to the extent that the number of construction firms halved in America between 2007 and 2012.
You could argue that Trump “will just do that this time,” or that he’ll “get a bribe” or something, but is that really the best you’ve got? Scary stories about the President? If every answer you have is “but Trump will just do it,” you’re not analyzing, you’re catastrophizing.
And, most crucially, the vast majority of big tech will be fine, at least in the short term, when the bubble bursts. NVIDIA will likely cease being the largest company on the stock market, and the Magnificent Seven will have a dramatic fall from grace, but outside of unforeseen horrendous financial decisions, the worst I could see would be impairments for Microsoft, Google, Meta, and Amazon, and SEC action against NVIDIA if it did actually sell GPUs to China.
This doesn’t mean that things won’t fucking suck for anyone in the market, nor that the vast majority of people won’t fucking suffer as they always do when bubbles burst.
Which is why I am making a firm, clear statement to end this piece.
I repeat myself:
No bailouts, no handouts, no special treatment, no tax breaks, no CHIPS act, and no sovereign wealth fund. It is time to tell the AI industry to go fuck itself, because it’s effectively done the same to the rest of society. These companies must be forced to stand on their own two feet and die with dignity if their wretched business models can’t keep up.
The world’s governments have rolled on their backs and shown their bellies to the tech industry for far too long, and have been aggressively conned by some of the richest people alive into believing that fucking Sam Altman and Dario Amodei are building anything other than the world’s least-profitable software.
We do not need a “sovereign AI strategy,” nor do we need “a sovereign AI wealth fund,” nor do we need to “make sure America leads in AI,” at least not when we’re talking about large language models, the underlying technology of ChatGPT and Claude, two of the most over-hyped and deceptively-marketed pieces of software in history.
Whether or not LLMs are a useful tool is irrelevant, because the AI industry has demanded the world hand it as much land and money and as many resources as it desires to continue proliferating a technology that has only ever lost money and has no path to sustainability. The only reason it has gone anywhere is because the tech industry has united around it as a means of hiding from the fact it has no next big thing, and nothing — absolutely nothing — that a LLM can do remotely justifies the investment.
And it has only got this far because of a captured business and tech media overstating its capabilities and hand-waving its obvious efficacy issues and economic instability. There are too many that have proven easily-wooed by whimsical white boys that promise they’re building machine intelligence, and when the markets bleed red, these people should know that they’re responsible. So much of the so-called journalism around AI has been used to enrich the already-rich and inflate a bubble that will hurt hundreds of millions of regular people globally as Sam Altman and Dario Amodei remain billionaires despite their companies’ fates.
When the time comes, the AI industry must burn. It must be allowed to die. Generative AI has already been given far too much money, oxygen and attention, and if it cannot survive without continual venture capital and media coddling, it is unworthy and unnecessary, and must face the cold, hard reality that every regular person faces when they fail.
And there is no “bailing out” these wretched firms. Giving $42 billion to OpenAI or Anthropic will not fix their business models, nor will it magic up the $400 billion or more in annual revenue to substantiate just NVIDIA’s AI GPU sales through the end of 2027.
These people are not building the future — they’re finding ways to re-entrench the status quo, to give Microsoft, Google, Amazon and Meta ways to grow their revenues and centralize infrastructure under the auspices of “innovation.”
If any policy makers read this, know that you’ve been had by the AI industry. They want you to believe they’re essential so you’ll bail them and their rich friends out when the time comes, or funnel taxpayer funds into building them data centers. They are not building autonomous intelligence, nor will they ever do so.
I think it’s fanciful to imagine that there would ever be actual consequences for this bubble, but if there are, the people to hold responsible are Sam Altman, Dario Amodei, Satya Nadella, Sundar Pichai, Andy Jassy, Jensen Huang, Mark Zuckerberg, and everyone else who forcefully manufactured consent for a dead end technology and built the rails to serve the world its next great financial crisis.
Until something changes, the tech industry will never be capable of building anything other than consensus and reinforcements of the status quo.
So, spit in the face of those who even hint at a bailout, refuse to accept it, and demand that they do the complex, ugly work of thinking about the actual consequences of everyone being wrong. When this era ends, we will need to thoroughly excavate the collapse to make sure it doesn’t happen again, identifying the organizations and personalities that were used to manufacture consent and spread mythology about LLMs.
Every major bubble that has ever happened has mostly left the stones of responsibility unturned. The carnage that I fear will follow this era’s collapse will be horrifying, and we must do everything in our power to both thoroughly understand how we got here and make sure it doesn’t happen again, which will involve many hard conversations about our financial system, media ecosystem, and how innovation is invested in, built, bought and sold.
The same goes for the acolytes of this era. There are people who have developed a genuine hostility toward those who do not immediately accept a for-profit entity as their lord and savior. This is a sickness within the tech industry that must be put to an end.
Much of this will be unavoidable, because I think what follows the AI bubble will be a greater revaluation of the tech industry, a necessary reckoning with reality for a Silicon Valley that’s far more beholden to capital than it is human progress. The cults of personality that dominate this industry do not care about you, or me, or anyone other than those they revere and their theoretical placement in their dream of a society dominated by the rich and their chosen cronies.
I refuse to accept their future as an inevitability.
The AI bubble is sold as the future, but actually resembles the death of Silicon Valley. Only a tech industry dominated by symbolic wealth and value creation would ever abide a trillion dollars of waste for a still-theoretical outcome, and only an intellectually-rotten Valley would be so easily-grifted by people like Dario Amodei and Sam Altman.
This era must end, and all failures must be allowed to fail.
Let AI burn.
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2026-07-06 22:23:20
Soundtrack: Ozzy Osbourne — Mr. Crowley
A lot of people have been making a lot of fun of the SoftBank 46th annual shareholder meeting and Masayoshi Son’s (to quote Bryce Elder of the Financial Times) Untethered Goose Game, specifically referring to slides that, well, looked like this:
As funny and silly as these slides might be, they’re actually very indicative of the mindset behind SoftBank. Each one of those golden eggs refers to a trillion yen (about $6.15 billion) in the Net Asset Value (NAV) of SoftBank’s holdings, with the minus referring to its debt.
It’s actually very simple, especially if you know anything about geese.
Sidenote: also, before we go on any further, I need to be clear that there are two SoftBanks.
The first is SoftBank Corporation, which has a bunch of consumer businesses, all mostly in the Japanese market. It owns the Softbank-branded mobile provider, sells fixed-line broadband connections, and owns a big chunk of the company that operates the (still wildly relevant) Yahoo! Japan. It also dabbles in household utilities and electronics retail.
The second is the parent company of the Softbank Corporation, which is (confusingly) called the SoftBank Group. This is largely a holding company, and directly operates the various Vision Funds you’ve heard about. the holding company.
That’s not to say there isn’t any overlap. On Thursday, July 2, Softbank announced the launch of Softbank Neo — a US-based neocloud that plans to leverage the group’s “10-gigawatt-scale energy and AI infrastructure currently under development.” That’s a very load-bearing “under development.”
Anyway, Softbank Neo will be 51% owned by Softbank Corp (which, incidentally, has an investment-grade credit rating, whereas its parent does not), and Softbank Group will own the remaining 49%. Because this isn’t confusing enough, Softbank Neo will be, at least organizationally, treated as a subsidiary of Softbank Corp.
Regardless, or the sake of this newsletter, whenever I say “SoftBank,” you should assume that I’m talking about SoftBank Group, unless stated otherwise.
SoftBank is the goose. Masayoshi Son is the gander. Masayoshi Son mounts and impregnates SoftBank — by which I mean invests money in companies using SoftBank’s funds — at which point the goose (SoftBank) becomes pregnant (the portfolio company grows larger) and then lays the egg (the portfolio company goes public). Basically, SoftBank is a company that invests in companies that then go public and make SoftBank money, at least in theory.
To continue mounting the geese, SoftBank takes on a constant flow of debt either by raising it via the bond market, taking margin loans out using its shares in successful investments like ARM or Alibaba as collateral, or (in times of trouble) outright selling shares in companies like T-Mobile or NVIDIA.
Softbank has around $50.5 billion worth of outstanding notes as of writing this sentence, not including other forms of debt, like commercial paper and traditional loans. Including those brings the total to an astonishing $76.431 billion. And, again, this is just the Softbank Group – and not any of the other affiliated entities, who have their own balance sheets and separate reporting.
Sidenote: One thing to note is that describing Softbank’s debt is tricky, insofar as it’s a massive conglomerate with a bunch of different, nominally-independent entities, all of which can raise debt on their own merit (and, in the case of Softbank Corp, have better credit ratings than its parent, the Softbank Group). If we include the debt owed by the various other parts of the business, we end up with a figure that’s much, much bigger than $50.5 billion.
The goose-to-egg process begins to fall apart when SoftBank is unable to convert its investments into a liquid asset or margin loan, as I’ll get to later.
When Masayoshi Son protests that the “goose was not valued,” he’s saying that SoftBank isn’t given its dues for “laying golden eggs,” because the NAV of the company does not give any value to the goose that lays the golden eggs, largely because net asset value refers to the holdings of a fucking company Masayoshi, what are you talking about?
Masayoshi Son’s desperate plea that “what matters is not the eggs, but the goose itself, and its power to keep laying eggs” exists to try and distract from the fact that he’s been pretty bad at fucking the goose for the last decade or so.
The vast majority of SoftBank’s Net Asset Value — which is ¥48.2 trillion rather than ¥74 trillion yen, by the way! — comes from its shares in chip company ARM (¥19.15), SoftBank Vision Fund 1, (¥3.38) and SoftBank Vision Fund 2 (¥17.19). These are two venture capital funds: one very successful (VF1 includes big hits like DoorDash and ByteDance), and one tremendously awful (VF2 includes massive losses on WeWork and Karterra).
His one saving grace, at least on paper, is his early investments in OpenAI, turning around $64 billion (assuming it completes all $30 billion of its 2026 commitments) into a theoretical $100 billion or more, at least if OpenAI goes public, which is almost certain to-
Wait, what was that? OpenAI is leaning toward IPOing in 2027? It hasn’t even held pre-IPO investor meetings or set a timeline? That’s not good at all! The SoftBank Goose Engine only functions if the goose — which was not valued by the way! — continues to lay golden eggs, and in this case, the golden egg is OpenAI, and said egg is still in SoftBank’s ovary!
The problem here is that while SoftBank’s OpenAI stock is “worth $100 billion,” private stock is valued very, very differently to a public stock that you could dump on the market. This is in part because the valuations of private companies are continually overinflated by over-eager investors who, just throwing it out there, might have valued the company based on a belief that they were put on this Earth to create superintelligence rather than whether it was a good business that would continue to grow.
Per the New York Times, OpenAI’s hesitancy to go public came from a concern that it wouldn’t get a value of a trillion dollars — a worrying bit of information considering its was last valued at $765 billion, meaning that advisers were unable to make a convincing case for a listing at a meager 30% premium. This is likely why SoftBank was unable to get a $6 billion margin loan with the entirety of its OpenAI holdings as collateral. Apparently a 6% loan-to-value was too adventurous when it came to stock in what is meant to be the world’s most important company, unless, of course, it isn’t, it won’t be, and its stock is worth fuck all.
Renewed talks for a $10 billion OpenAI-backed margin loan include a guaranteed repayment of the loan if the collateral isn’t able to replace the lost funds, the kind of thing you have to say when the underlying stock ain’t worth nothin’.
OpenAI is Masayoshi Son’s final gambit, as the rest of his endless gambles have gone tits-up at an historic pace. While early bets — like his $20 million investment (around $39 million in today’s money) in Alibaba turning into holdings of over $100 billion (with all of its stock now sold) — have floated the company for years and helped SoftBank recover from the horrors of its dot com bubble collapse,
SoftBank is now horrendously overleveraged across the board, with 85% of its ARM shares and 70% of its SoftBank Corporation tied up in loans, its entire stakes in Alibaba, T-Mobile and NVIDIA liquidated, and the vast majority of its NAV sitting in the deteriorating value of its Vision Fund 1 and its non-OpenAI Vision Fund 2 holdings.
You see, SoftBank is a holding company. It does not have “revenues” or “cashflows” in the traditional sense outside of when it’s able to either sell the things it has or raise debt. As Kakashii put it, Masayoshi Son is a perpetual gambler living in an eternal boom-and-bust cycle, going from losing 96% of his paper wealth after the dot-com bubble burst to sitting at the top of a company with a $200 billion market cap and with golden eggs that are worth, on paper, hundreds of billions of dollars more.
And he’s never, ever gambled more than he has on OpenAI and the greater AI bubble.
While SoftBank’s WeWork washout lost it $16 billion, SoftBank has committed or invested over $60 billion in OpenAI, as well as billions more in related counterprojects like a still-pending 75 billion Euro investment in data centers, its $4 billion acquisition of data center firm DigitalBridge, its $1 billion investment in subsidiary SB Energy to build out more data centers, and its planned $3 billion investment in overhauling a Foxconn plant in Lordstown Ohio.
The future of SoftBank relies on both OpenAI’s ability to go public and maintain a high stock price, as any public offering will likely lead to SoftBank immediately looking for a margin loan. To make matters worse, SoftBank’s other bets hinge upon the continued success of the AI industry, which hinge both on the continued success of OpenAI and there being such incredible demand for AI services (in the hundreds of billions of dollars annually).
And while the geese might have been a clue, SoftBank is a very, very weird company, and the only thing weirder than SoftBank is Masayoshi Son himself.
Yet as goofy and whimsical as this all might seem, SoftBank is also one of the largest companies on the Japanese stock market, valued entirely based on the value of all those golden eggs, and no matter how much value Masayoshi Son might claim his “egg factory” might have, SoftBank’s continued existence relies on its ability to increase its NAV and acquire more debt.
My concerns around SoftBank were well-summarized by The Economist back in May:
Quite how Mr Son will settle these bills puzzles some lenders and frightens others. The cost to insure against a default on its debts has soared. Cashflows from its operating businesses are insufficient. Selling assets would help. But having hawked the family silver (SoftBank sold the last of its Nvidia stock in October), it must now strip metal from the roof of its rusty garage. Its shareholdings in T-Mobile, a telecoms company, Grab, a food-delivery firm, and DiDi, a Chinese ride-hailing platform, are worth much less than they were even a year ago. According to the Financial Times, SoftBank is also considering yet another stockmarket listing, this time made up of an undefined grab bag of loosely AI-related businesses.
The most likely answer is more debt. But from where? The firm already faces a steep wall of maturities: the $40bn bridge loan SoftBank took out to invest in OpenAI matures next March. Selling bonds to Japanese retail investors costs more than it used to. The firm says its level of debt is copacetic, but the “loan-to-value” figure it telegraphs is something of a fantasy, since it ignores an additional $28bn borrowed against stock SoftBank owns in Arm and its Japanese telecoms operation.
It’s unclear what the future looks like for SoftBank. While death is unlikely given its near-systemic presence in the Japanese economy, its continued existence at its current scale is only made possible as long as the world’s most well-funded gambler can keep his seat at the table. While it’s seen boom and bust cycles in the past, SoftBank has never been this levered, and never gambled so hard on a single entity’s success.
While this is technically a company, SoftBank exists and operates at the whim of a man with questionable idols, insane ideas, and fantastical thinking. At one point during the Dot Com Bubble, Masayoshi Son’s net worth was higher than Bill Gates’, rising by more than $10 billion a week, before the majority of his net worth in the space of a year and sending SoftBank’s share price crashing by 93%.
Yet even when adjusted for inflation, SoftBank only invested around $2.93 billion ($1.5 billion at the time) in the heights of Dot Com mania, and spread those investments out over multiple startups.
Today I’m bringing you a guide to one of the silliest companies ever founded, helmed by one of the goofiest men alive, run in a constant state of brittle leverage.
SoftBank only avoided the void in 2023 by dumping its Alibaba shares, and this time around, Masayoshi Son may have gambled too much, putting all of his eggs in one Altman-shaped basket.
Welcome to the Hater’s Guide To SoftBank, or Is Masayoshi Son’s Goose Cooked?
2026-06-30 23:36:38
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Soundtrack — Queens of the Stone Age - Hideaway (Baloise Orchestral Arrangement)
On Sunday, the Bank of International Settlements (BIS) put out its annual report and said, well, a bunch of things that I’ve been saying:
In the near term, the ongoing AI investment boom raises questions about the sustainability of the current economic expansion. The five largest hyperscalers are set to spend over a trillion US dollars on AI-related capital expenditure from 2025 through 2026. These commitments are outpacing earnings and the free cash flow of these firms, leading some to issue debt to raise additional financing.
As edifying as it is to see the bank for central banks say exactly what I’ve been saying for the last few years, this part is the one that both rocks as far as being right goes and sucks for the world at large:
Disappointment in returns could trigger a sudden pullback in financing and turn the capex boom into a protracted investment bust, with potential knock-on effects on financial conditions…should hyperscalers slow or halt the aggressive pace of capex deployment, many borrowers across the supply chain could struggle to replace lost revenue and service their debt.
No shit. In April of last year, I wrote a piece called “AI is a systemic risk to the tech industry,” where I outlined how the failure of one model lab, OpenAI, would have seismic effects down its supply chain, delivering body blow after body blow to NVIDIA, Oracle, Microsoft, and the various Neoclouds that serve its compute, the most notable of which being CoreWeave.
Since then, OpenAI’s slimy tendrils have sunk into even more facets of the tech industry, and it has signed deals with the likes of Google, Amazon, Cerebras, and Broadcom, while also taking on more investments, including mammoth commitments from Softbank, which is only able to meet them by selling off prized stock in companies like ARM and NVIDIA, and by raising debt.
The idea of systemic risk has never quite left my work, and I’ve spent a lot of time thinking about it over the past year — and, as a result, my writing has examined the potential consequences of an AI spending pullback on those financing the sector, in particular private credit, as well as the semiconductor industry.
The BIS’s concern wasn’t about revenues tanking — which would happen should, as it fears, hyperscalers decide to “slow or halt the aggressive pace of capex development” — but rather revenues tanking and the borrowers within the AI supply chain being unable to service their growing debt burdens.
Again, this is something I’ve raised the alarm bells over a bunch of times. CoreWeave has been a favored popinjay of this newsletter, and in March of 2025, I published CoreWeave Is A Time Bomb, where I focused heavily on the company’s overwhelmingly toxic debt pile and its reliance on OpenAI as a customer.
On a much grander scale, we have Oracle — which I exhaustively profiled in my Hater’s Guide to Oracle newsletter.
Unlike neoclouds like CoreWeave, Oracle’s a much older company, having spent most of its existence selling database and ERP software to some of the world’s largest companies and public sector institutions. Oracle pivoted to serving AI compute at a time when its core business lines had started to stagnate, and thanks to its large scale, it was able to raise insane amounts of debt.
And Oracle, as I’ve noted previously, is a company that, even before the AI bubble, was massively indebted. It just so happens that, as a result of its tryst with OpenAI, Larry Ellison saw fit to twist the debt knob to eleven.
Oracle’s spending has already pushed its free cash flow into negative territory — minus $23.7bn, as of the end of FY 2026 — and at the end of May, it had $129.5bn in outstanding debt. This doesn’t include its various lease commitments, which add up to nearly $38bn, nor the additional $260bn in lease commitments that have been signed, but haven’t actually started yet.
All of this is to say that Oracle has massively leveraged itself for the benefit of one company, OpenAI, and if that company can’t pay its bills, it’s fucked. Oracle’s existence — and Larry Ellison’s personal wealth — hinges on whether OpenAI can make good on its promise to spend $300bn in compute.
This is both the most-obvious and under-discussed part of the AI bubble — that the trillion-plus dollars of hyperscaler capex is feeding a massive semiconductor boom based on, at best, the very small likelihood that large language models will turn into something completely different.
If Microsoft, Google, Amazon and Meta decide that it’s time to stop spending $30 billion or more a quarter on GPUs, RAM, storage, and data center construction, that’ll tear a hole in the side of what people assume is a permanent supercycle.
I need to state how fucking silly it is that anybody considered said semiconductor boom anything other than a brief chance to fill their boots before a global equity catastrophe so severe that the Futurum Group will be on suicide watch.
Hyperscalers — who will see their capex outpace their cashflows as of Q3 2026 — have had such poor returns on their investment in AI that none of them will actually disclose their revenues outside of vague “run rates,” which means that all of this investment is effectively based on the idea that something completely different will happen in the future.
Said future will have to make them at least $2 trillion in brand new revenue by 2030, because if it doesn’t, effectively all of that capex will have been spent to prop up Anthropic, OpenAI, and whatever it is that Meta is doing with its chatbots.
There is no cogent or rational argument in favor of continued capital expenditures, at least not one without a tacit acceptance that much of the current spend has been a waste outside of pumping equities and incubating two different large, unprofitable AI labs. Those millions of H100 and B200 and B300 GPUs are not going to usher in a digital God, they are not going to create recursive self-improvement, they are not going to be the fulcrum to adding $600 billion or more in brand new revenue to current services, and the only revenue they’re generating is compute spend from Anthropic and OpenAI, which I estimate makes up 20% or more of cloud revenues for Google, Amazon, and Microsoft.
I must also be clear that the cost of these companies extends far beyond equity investment. While Microsoft invested $13 billion in funding OpenAI, Microsoft executive Michael Wetter revealed as part of the Musk vs Altman trial that the partnership has cost it more than $100 billion, suggesting infrastructure costs of at least $87 billion just for OpenAI. I imagine Amazon and Google have had to spend similar amounts to handle Anthropic’s similarly-rapacious compute demands, especially given the $11 billion-and-counting cost of Amazon’s Anthropic dedicated Project Rainier data center.
This is a criminally-underdiscussed part of the AI bubble. Anthropic and OpenAI have raised a little under $300 billion combined since 2019, but I estimate their true cost is at least $500 billion given hyperscaler capex investments that were necessary for them to exist, and that’s before you consider the $340 billion or more that Oracle is spending to build out the 7.1GW of “Stargate” data centers for OpenAI. These are not startups, but subsidiaries of big tech that only exist as separate arms as a means of pumping equity positions and hiding the truth: that AI capex has been a complete waste of money, even when you include two bulbous failsons that lose tens of billions of dollars a year.
As I reported two weeks ago, OpenAI spent $17.2 billion on Microsoft Azure in 2025, a year when it lost $20.9 billion on $13.04 billion in revenue. Even if that were profit (which it is not), that’s $4.2 billion less than the capital expenditures that Microsoft spent in the first quarter of 2025.
Outside of OpenAI, Microsoft may as well not have an AI business. While it boasted back in April about having a $37 billion AI revenue run rate (meaning a non-specific month multiplied by 12), that only works out to about $3.08 billion a month, or less than a tenth of the $31.9 billion that it spent on capital expenditures in the quarter. To make matters worse, Microsoft revealed that number was “up 12% year-over-year,” suggesting that its AI revenue run rate in Q3FY25 was $16.59 billion, or around $1.38 billion a month.
Yet my own reporting on OpenAI’s inference spend from last November showed that it spent $2.947 billion in Q3FY25, representing about $11.7 billion on an annualized basis, meaning that, at least in that quarter, OpenAI likely represented around 70% of Microsoft’s AI revenue, and I’d be surprised if that dramatically changed in the year that followed, given that OpenAI’s inference spend was $3.648 billion in Q1FY26.
All of this is to say that the only real outcome from all of this capex spend appears to be propping up Anthropic and OpenAI, two deeply-unprofitable companies, and then receiving a small fraction of it back in the form of revenue that is only made possible through hundreds of billions of dollars of venture capital subsidies.
Now OpenAI and Anthropic represent 50% or more of hyperscaler remaining performance obligations, or around $748 billion.
There is simply no logical or rational reason to invest any further capex in AI, outside of the mistaken belief that OpenAI or Anthropic could actually afford to pay without Google, Amazon, or Microsoft handing it to them. Hyperscalers do not have meaningful AI revenues of any kind outside of their own pseudo-startup investments, and it is equal parts ludicrous and irrational that A) they are continuing to invest and B) that the markets, analysts and journalists are acting as if everything is fine.
Sidenote: I haven’t discussed Meta, because Meta does not have an AI story. Mark Zuckerberg has wasted every ounce of its capex, outside of whatever it could get by reselling its capacity to somebody else — but don’t worry, he thinks (that’s a quote!) that Meta has a use for the compute! No, sorry, those GPUs are not driving meaningful increases in ad revenue, I already covered that in the past.
Record sales across NVIDIA, Micron, Sandisk, SK Hynix, and Samsung are a direct result of an entirely speculative asset bubble, driven by the reckless and directionless capital expenditures of some of the largest and richest companies in the world.
Anyone investing in data centers is building speculative capacity for demand that does not exist outside of Anthropic and OpenAI. If said demand existed, AI data center neocloud company CoreWeave would have a healthy and diverse revenue stream, rather than 65% of its revenues coming from Microsoft (for OpenAI) and NVIDIA, and the rest coming from Google (for OpenAI), Anthropic, Meta, and, of course, OpenAI. There are simply no other massive consumers of AI compute, and the only reason we haven’t hit that harsh reality is that data centers take 18-34 months to finish.
Even if there was, I can find little evidence of anyone but OpenAI, Anthropic and hyperscalers having the demand or funds necessary to substantiate the data center buildout.
I really need to hammer this point home.
If we assume that NVIDIA CEO Jensen Huang’s prediction of $1 trillion in Blackwell and Vera Rubin sales comes true, that would be around 40GW of data center capacity with around 30GW of IT load, and if we assume that data centers get about $12 per-megawatt of revenue, that works out to about $435 billion in annual compute demand by, being generous, 2030.
Let’s be abundantly clear about something: the only companies that can afford to spend money on compute right now are either hyperscalers or the companies that hyperscalers subsidize. Even then, outside of OpenAI’s $50 billion in 2026 compute spend and what I estimate will be a similar amount from Anthropic, there doesn’t appear to be more than a few billion dollars of demand, and if there were, CoreWeave, IREN, Nebius, Cipher Mining, and other neoclouds would have hundreds of billions of dollars’ worth of remaining performance obligations rather than RPOs that expand only with hyperscaler backstops or the depths of Meta’s Zuckerbergian AI psychosis.
Let me put it even simpler: those hundreds of billions of dollars of data centers are being built for no-one, and the only companies that can “afford” to pay for even a fraction of the compute are unprofitable AI companies propped up by hyperscalers.
While this might read as a radical position, I think it’s far more radical to look at the current state of affairs and say “fuck it, I think hyperscalers should spend a trillion dollars next year.”
There is no rational justification for doing so out of fantastical thoughts driven by a deranged market desperate to avoid thinking about how tech doesn’t have any hypergrowth ideas left.
The current capital expenditures have, outside of the creation of OpenAI and Anthropic, been a near-complete waste. Microsoft 365 Copilot sucks. GitHub Copilot sucks. Google AI Overviews suck. Google Gemini is an also-ran LLM and thus, as a result, sucks. Meta’s LLMs are horrifyingly dangerous. Amazon Rufus sucks, and Amazon should be investigated by the SEC for suggesting it drove $10 billion in “annualized revenue” in Q3 2025, because it most assuredly did not. Alexa+ sucks. It all sucks, and it would suck just as badly if big tech had spent a quarter of the capex.
These products are near-universally loathed, barely generate any revenue, and even in the case of the modestly-successful GitHub Copilot (around $1.08 billion in annualized revenue as of end of last year), it was only because users’ compute was heavily-subsidized, leading Microsoft to move users to token-based billing, outraging customers who were used to paying $39 a month to burn thousands of dollars of tokens.
Sundar Pichai, Andy Jassy, Satya Nadella, and Mark Zuckerberg are losers. They may have billions of dollars, they may run giant tech companies, but they are losers selling a doomed technology based on unreliable, inefficient and overly-expensive technology ill-suited for the kinds of reliable, deterministic, “set it and forget it” tropes that people actually associate with AI.
The Four Losers are the only reason that anyone has taken these Large Loser Models seriously, which is a sign that the tech industry and our economy are also piloted by losers. Every bit of “progress” that we’ve seen from LLMs has come from aggressively cramming a square peg into a round hole — billions of dollars of training costs, hundreds of billions of dollars of capex, endless harnesses and scripts and wrappers and layers to try and eek out anything approaching the supposed promise of autonomy.
All the king’s horses and all the king’s men have sunk every dollar and ounce of brain matter into trying to make LLMs into something they’re not, and we, as a society, are expected to coddle these things and act like they’re exceptional, and give them credit for things that have yet to take place. I refuse to buy into the premise that LLMs’ ability to generate code or replicate open source software is proof that these things will become a powerful, autonomous tool in the future, and I think those that extrapolate to that point are either intellectually bankrupt, deeply cynical or so easily-fooled that they click every single email claiming their Paypal account has been compromised.
I assure you, all this money can be wrong! Hyperscalers can, in fact, spend a trillion dollars on something that doesn’t do what they say, because these companies are more than happy to mislead you, and, to quote Nik Suresh:
A huge amount of the economy is driven by people who are, simply put, highly suggestible. That is to say that it is very, very easy to get them excited and willing to spend money.
Why did everybody invest in data centers? Because the hyperscalers did so! Why are Micron and RAM companies selling so much RAM? Because A) GPUs use a ton of high-bandwidth RAM, B) said HBRAM consumes three times as much wafer space as normal DRAM, leaving less space for other kinds of cheaper, lower-margin RAM, and C) because the servers for said AI GPUs are, too, full of RAM!
Those data centers aren’t being built because the creditors have any “insight” into the massive amounts of AI compute that generative AI tools need, and will need. They see the “success” of ChatGPT and Claude (two heavily-subsidized products) and think that because Anthropic and OpenAI need lots of compute, everybody will need lots of compute. And because banks and private credit crave ways to invest their money and everybody is so excited, it’s super easy to get them excited about the prospect of building something big, sexy and costly!
It doesn’t help that a lot of the information out there is deeply, deeply flawed.
In Brief:
Last week, research firm Exponential View put out a questionable report claiming that AI had $110 billion in trailing 12-month revenues (between what looks like June 2025 and mid-June 2026), and did so by smashing together all AI revenues, including both OpenAI and Anthropic’s customer spend and compute spend, While the report claimed to “deduplicate” the numbers somehow, Exponential View declined to explain how it had done so. It’s also deeply deceptive to include both revenues and compute spend to try and represent the material health of the AI industry.
This is because the AI industry is full of losers that cannot win without fiddling with the numbers, and because everybody is so excited, they’re ready to be fooled, and hesitant to dig an inch deeper.
Not me! I don’t give a shit, and I hate the feeling of being lied to, so I dug in.
That’s because OpenAI and Anthropic represent as much as 75% of that revenue between their compute spend and revenues. Per The Information’s and my own reporting, OpenAI had around $8.77 billion in revenue and spent about $17.48 billion on compute in 2025, and per The Information had $5.7 billion in revenue and spent $17.8 billion on compute in the first quarter of 2026, for a total of around $44 billion (40% of Exponential View’s total), which doesn’t include any of OpenAI’s compute spend or revenue for the months of April, May or June, which likely inflates the total further.
While Anthropic is a little more-difficult to parse thanks to the Wall Street Journal’s unwillingness to make a readable chart, it had $4.8 billion in revenue in Q1 2026, and spent what I think is at least four billion on inference, and though its training costs are unreported, I think it’s reasonable to assume they’re at least $5 billion, for a total of $14.6 billion. If we, based on The Information’s reporting, take half (being generous, as most of this was weighed toward the end of the year) of Anthropic’s (all numbers are projections) $4.5 billion in 2025 revenue, $2.7 in inference costs and (I seriously question this number) $4.1 billion in training spend, we get $5.65 billion, for a total of $20.25 billion of contributions to Exponential View’s analysis, or around 18.4% of that $110 billion total.
So, yeah, not including anything from Q2 2026, Anthropic and OpenAI represent 68% of the $110 billion of AI revenue that Exponential View is trying to get people excited about.
These are the actions of a loser propping up an industry of losers that cannot win by telling you the truth. This report exists entirely to fool the already-fooled and support an existing narrative, which is why Bloomberg covered it in the most obtuse, industry-servile way possible:
Revenue from artificial intelligence has reached a tipping point, showing that the hundreds of billions of dollars tech companies are spending on it may be economically sustainable, according to a report from research firm Exponential View.
Global AI sales, excluding China, reached $25 billion in the first quarter of 2026, exceeding the industry's estimated $21 billion in depreciation costs tied to investments in data centers and chips for the second consecutive quarter. While the milestone suggests that AI companies are beginning to cover the cost of their capital spending, the margins are thin. Depreciation charges still consume more than two thirds of revenue, leaving a small buffer to cover other costs such as power, labor and financing.
Here’s two reasons this is fucking silly!
Now, you may be wondering how they got that $25 billion number, and that’s because Exponential View gave it to them!
The next question we wanted to track is whether AI revenues can cover the capital investment that’s required to build the infrastructure. Our model separates AI-oriented CapEx from ordinary CapEx across the major hyperscalers and neoclouds, the specialist AI cloud providers. This adjustment is important because hyperscalers were already spending around $120 billion annually on CapEx before ChatGPT.
We capture the additional investment in AI infrastructure, then depreciate compute assets over 6 years and other infrastructure over 14 years. Our modeling shows that revenues attributable to hyperscalers just about clear the depreciation expense.
Yeah, but now they’re spending $765 billion on capex. Anyway, as I mentioned above, Exponential View’s Magical Maths magically brings those capex charges down to $25 billion, and entirely removes Meta because "initiatives are focused on ad uplift, so not recognized as pure GenAI revenue, or currently have minimal direct monetization.” What a loser move! Meta has oriented its entire company around AI!
I refuse to waste too much more time on this piece, but I need you to see how deceptively it’s framed this supposed “good news” for the AI industry, comparing its own proprietary depreciation formula against its own proprietary AI revenue formula to get a chart that is built to make the AI industry look good. No need for sourcing! No need for data! Just put the hype in the bag and invest in AI stocks!
I also find it despicable that Exponential View resorted to this weird, confusing “cumulative” AI revenues versus CapEx depreciation chart. The vast majority of this revenue is OpenAI and Anthropic’s compute spend, and I dunno, if you’re trying to do a report that gives the real state of the AI industry, maybe try and represent that anywhere in the report!
These are, as I’ve suggested, the acts of losers propping up other losers. In the event that this industry had a fundamentally-sound revenue story, it would be extremely easy to show profits versus losses, track revenue in a transparent way, and produce a report that showed AI’s remarkable ascent.
Instead, Exponential View says that AI is “real, big & fast” through a Pee Wee’s Playhouse of undefined models, datasets and alleged “quality grades” that helps feed a dangerous bubble further, and likely cons retail investors into further terrible decisions.
I know it sounds a little mean to call people losers, but what do I call an industry that sells itself on lies and deception? What do I call people that intentionally mislead people about the economics and outcomes of generative AI? If AI is so incredibly successful and impossibly brilliant, why does every explanation sound like it was written by The Riddler or somebody about to chug Jonestown Kool-Aid?
Because they’re losers that can’t win by actually winning. Their best (and only) hope is to overwhelm you with a 24/7 marketing campaign (powered by the media) that makes all of this seem inevitable, impossible-to-stop, and a rip-roaring success, even as every company loses money and every product rings with a soulless mediocrity.
That’s because LLMs are, while an interesting tool in a vacuum, currently being marketed by losers to losers using a mixture of Doom Trolling, insane extrapolations, and outright lies, manipulating people’s assumption that tech always gets better and that this much money can’t be wrong to create a marketing campaign fueled by deception. While using them doesn’t automatically make you a loser, you become one the very second you aggressively push somebody into doing so, as you have become the acolyte of the Loser Mafia.
I have never heard anyone that’s an AI booster advocate for a technology with any level of excitement in their life, because they’re excited about how these tools make them feel and what they represent far more than anything else. They’re also tools intentionally built to produce engagement, and to make you feel you’re productive, even if you’re not.
Just listen to this guy in this Bloomberg story about AI making people “productive, anxious and afraid to log off”:
Matt Van Horn, a serial entrepreneur and father of four, never turns his laptop off anymore. He has more than a half-dozen artificial intelligence agents running at all times in Anthropic's Claude Code.
Every 10 minutes or so, they ask him what to do next.
He keeps his laptop running at his kids' soccer practice, while dropping them off at school and in the hotel during vacations.
When he goes to sleep, one agent steps in to babysit the others.
Van Horn is one of many founders whose work has been transformed by Al. As he builds his latest company, he's used Al agents to help contribute to hundreds of projects on GitHub. But he and many other Al evangelists are also working longer hours than ever before as they grapple with anxieties about how Al might advance without them if they log off.
I’m sorry man, you have an addiction, and I worry it’s ruining your life. What is this producing? What are you actually doing with this time? Because if you’re allegedly 100 times more productive, wouldn’t that, y’know, produce something fairly incredible? I have no idea — and don’t want to put this man on blast — how significant his commitments on GitHub may or may not be, but the return on investment of “obsessively checking your laptop at all times in case you might not be productive” should be something on the order of curing a disease.
The story continues:
After 15 minutes of conversation with a Bloomberg reporter, he notes that most of his agents are probably waiting for his next prompt. "I don't have a therapist, but if I did, they'd be like, 'It's OK, Matt," he says with a laugh. "They said that agents were supposed to do our work for us, but I've never worked harder in my life. I just have 100 times the output that I had before."
This man is a victim of a con, an industry-wide psychosis where you’re judged for not constantly dedicating every single second of your existence to prompt a series of chatbots into making something, all under the mistaken belief that at one point it’ll be so smart you…won’t have to prompt them?
Nevertheless, Van Horn is completely right — the sales pitch of AI is that agents were supposed to do the work for you, but billionaire losers are gaslighting you into believing that a digital busybox that requires constant vigilance to make sure it does what you ask or doesn’t spend too much money was somehow “autonomous.”
While it’s easy to make fun of Silicon Valley, what we’re witnessing is a widespread mental health epidemic caused by liars like Sam Altman, Dario Amodei, and their wealthy backers lying about the capabilities of AI, creating an abusive culture where humans become subordinate to unthinking, hallucination-prone agents either subsidized by OpenAI or their employer:
Engineers are working until 4 a.m. to demonstrate productivity on par with the agents they’re deploying. Startups are creating internal counseling programs so employees can vent about their AI-induced burnout or team up with a self-proclaimed AI ambassador who can help them learn how to better use the technology. In San Francisco, mental health walks are taken in the shadow of small planes flying banners to stop hiring humans, and Friday nights increasingly involve “touch grass” parties — intentional spaces to not talk about AI because everywhere else has been infected.
This is fucking horrible, and every loser who inflated this bubble should be ashamed of themselves.
In fact, fuck it, I want to speak directly to the people working in Silicon Valley and the tech industry who have been ground down by this industry.
I know not all of you are anti-innovation.
I know many of you feel suffocated.
I see you, I hear from you every day, and I find what is being done to you repulsive.
Your industry has abandoned you.
Your investors are lying to you, and are getting rich while you can’t afford a studio apartment in the Tenderloin. AI does not do what you have been promised it does, and those who are excited about it are excited because they believe it will replace you. You are victims of a marketing campaign built to enrich a few people by sacrificing your time and energy to defend a doomed tool.
You are using tools that are built to manipulate you into making you work longer hours in the name of automation. You are being abused. You are being tricked into fighting for the 1% in the name of democratizing software. Your agents are meant to set you free, but they chain your body and mind to a system built to exploit your labor, extract your value and leave you dead. The people who make these agents fantasize about replacing you with them, and want to use your data to do so. They are lying that it is possible, but they want you to be scared so you will use their products more.
They have convinced you to fight on their side in a war where you will lose regardless of the victor.
You are a victim. I am not your enemy. I love technology too, and I want the tech industry to make cool shit again.
That will not happen under its current leadership.
This era is built to drain the life out of you, to suffocate you with endless tech chatter, to make technology every part of your life, to somehow sell you the promise of automation, but only a kind of automation that you have to monitor constantly, prompt constantly, built to be addictive and superficially productive, built to fuel a Bay Area culture steeped in a godless version of the Protestant Work Ethic.
You must be a cracked engineer, you must work 15 hour days, you must have 8 subagents beating the absolute shit out of your codebase for one reason or another, your Calendly must be open 8AM to 8PM, and you must be willing to work yourself to the bone for a chance to escape “The Permanent Underclass,” a misused term to refer to the world after an entirely-imaginary concept of Superintelligence, peddled by people who speak with a smugness that makes me want to spritz them like they jumped on the dinner table.
The grotesque glee that some have at the idea of being the first to announce AI’s destruction of everything you hold dear are your enemy, as are those who are desperate to constantly lick the boots of the Altmans and Amodeis of the world. Do not trust those who say that being part of an in group requires you to use certain kinds of software or attack others in the name of Silicon Valley.
The people encouraging you to work in this way do not care about you, or are being manipulated into believing this is how you all become rich by people exploiting their ignorance, fear or greed.
The people at the top do not care about the future, or progress, or anything other than growth. They are acolytes of a egregore of capital that has no purpose other than to expand and maximum velocity at all times, everything is fine as long as something is always happening, because the moment you stop moving you remember that nothing you’re doing really matters, because you’re making software while working sweatshop hours.
AI agents are built to make you interact with them. They are built to make you burn tokens. They are built to make you apologize for their mistakes and give them credit for your labor. Any “autonomous” tool that requires specific prompting, harnesses, scripts and tooling to make it sometimes work autonomously is conning you.
I’m also sure that there are a few perfectly normal software people using this stuff locally or with an open source model who treat it as normal software, loathe the data centers and see no need for the capex or mass market version of LLMs. These people are drowned out by a worryingly large crowd that speaks like they’re in a cult that exists to prove that OpenAI and Anthropic are somehow something more than SaaS companies. To them, using AI is a way of virtue signaling that they’re a pure, productive spirit, a willing supplicant for a future where they assume they’ll ascend because they told enough people “we’re still early.”
The tech industry got taken in by a form of religious con, sold to them wrapped in atheistic “rationalism.”
Some may or may not have AI psychosis — or at the very least a severe addiction — as a result of being forced to interact with these things day-in-day-out, and the easiest way to check is to try not to use them for a day, or to try and solve a problem without them. If this is you, please know that I am not attacking you, and see you as a victim of a con.
You are ingesting poison while being told it’s ambrosia. You are being made to work twice as much for roughly the same output, if not less. You are being humiliated or isolated for not using the right tools or saying the right things. Silicon Valley was built on the ideas of individualism and rationality, and the people at the top of your industry are telling you to fall in line and join an illogical consensus. You exist in a monoculture sold as anti-establishment as it mostly enriches Microsoft, Google and Amazon.
Your culture is being eroded by people who do not care about technology. You are unwitting pawns in a greater war against innovation, where billions are steered into the hands of those who only ever care about growth and “acceleration” that benefits only a small few. You are not alone if you feel scared, anxious, listless and drained, because you are being worked to the bone building layers on top of AI models owned by subsidiaries of the largest companies in the world.
The fact that so many of you have to orient your products or fundraising around Twitter is a sign that your culture is decaying. A true meritocracy would reject the idea of “going viral on social media” like a virus, because it overwhelmingly benefits a monoculture that suppresses free thought and dissent.
Tech workers are in a constant battle between imbeciles and monsters, or an Arnold Palmer of the two. Those who want to build useful software that customers like you are drowned out by a Greek chorus of unexceptional cretins that think they’re competent because they can bonk an LLM on the head to make an impression of competence.
Generative AI is the Peter Principle on steroids, removing the friction points where a diplomatic moron might get caught out, making them far more mobile and extremely dangerous. Companies are run by men that don’t know what they’re doing, desperate to avoid anybody realizing that we’re at the end of software’s era of hypergrowth, increasingly aware of their own mortality and their lack of a culture that might actually build something a human being would want.
For those of you still hanging in there, I see you and admire you, because if I worked at most tech companies right now I’d fucking quit. Seeing this entire industry bow at the feet of the great unprofitable mediocrity machine is sickening, and based on the many tech workers I talk to every week, the mood effectively everywhere is exhausting, demoralizing, manic, and horrible to watch.
Everything must be done faster, with less people, with less organizational support, but more use of a tool best known for its hallucinations and ruinous cost, which you must use a lot, but also not too much. However much you use it, you must constantly celebrate it for fear a cult of personality and mediocrity will isolate or fire you for the crime of not wanting to “Do AI.”
Even if you are still trapped in this world for months or years to come, know that you’re not crazy for finding it revolting, exhausting and debilitating. You do not have to do things this way, but I understand if you’re made to by circumstance or social pressure.
The tech industry is in the throes of minor AI psychosis, or, put another way, it’s a way to scale the already-potent sense of make believe that has kept this industry afloat the last decade.
The grander cargo cult of praying at the foot of whatever capital-lust the venture capitalists currently have has led everyone astray, to the point that companies worth billions — or even trillions — of dollars on things based on how they might play out on Twitter, a maligned representation of the tech industry that caters to Silicon Valley gossip and the derangement of the markets, intellectually stunting most who cater their business or marketing to it.
Sidenote: You may just be a regular person in an unfortunate situation where your boss (or bosses) are demanding you adopt a tool that, at best, is kind of useful in specific situations. Your performance reviews or continued employment may be dependent on your use of AI tools, and if that’s the case, you must make it your mission to cost your company as much as humanly possible. I call this “rascal’s wager” — in a sufficiently AI-pilled organization you’ll be hailed as a hero, but burn tons of money, and likely get them to reduce their dependence on AI as a result. In a normal one, your CEO will see the astonishing cost of AI and, hopefully, some sense.
The rest know exactly what they’re doing: appealing to an audience of venture capitalists convinced they’re “in the arena” by posting 12 hours a day writing 2000 word long posts using Claude. You must coddle these rich oafs, because it’s effectively impossible to raise money if you don’t. You must be able to recite the rituals — Hermes! Loops! Permanent underclass! — or you’re considered uncool by the least cool people alive. You, the great individualistic thinker of Silicon Valley, must convince wealthy oafs that you are an independent and rational person, but also that you will follow the greater consensus.
It’s a really unfortunate time to have ideas, dreams or goals outside of some sort of Potemkin agentic startup or if you can do the hocus pocus to con a VC into thinking you — or anyone — will invent recursive self-improvement, or AI that teaches itself.
You’re getting money right now if you can make noises that sound like you’ll be the next Baseten or whatever. It’s the era of inference I guess. Loops too. Keep cheering along! Never stop agreeing with what everyone else is doing, or if you do, only do so in a way that suggests that you all agree on the big stuff, which means you ultimately support either or both OpenAI and Anthropic, who companies that effectively operate as subsidiaries of the largest tech companies in the world.
It will stay this way until something changes.
As if I haven’t made it clear enough, the AI industry is losing. Their plans are not working, their products are not doing the things that they’ve promised, and though they intend to exhaust every available source of capital, they aren’t going to have enough money to do this forever. And no, AI is not “too big to fail.”
Everybody makes fun of it. “AI” has become synonymous with generic, ugly, corporate slop. It’s a physical blight on the Earth, pumping horrifying toxins into minority neighborhoods and causing such noise that it makes people physically sick, and to make matters worse, some independent writers have made it their mission to cast doubt on these problems because they do not represent “the aggregate” of data centers.
Everyone trying to be the “rational” voice on data centers should know that they’re only helping make the AI industry stronger. If you’re anxious that people are being “unfair” about water use, you’re an active pawn of capital, and exist only to help pump the bags of NVIDIA and the billions of dollars of speculative investment going into these monstrosities.
Without getting into the weeds, know that anyone talking about data center water use in terms of almonds or cattle is an actual industry plant.
California does use a lot of water to make almonds — and also makes 100% of America and 80% of the world’s supply. Cattle and other livestock also take up a lot of water and land, but they also make food for people to eat. You can bicker about how much water a data center may or may not use, and you’re going to sound like a complete loser every second you do so, because you are fighting to make sure that the AI industry can build data centers for the largest companies in the world.
Data centers are a monument to everything wrong with the world — horrifyingly large, loud, demanding of power and water and resources of all kinds. They create very few jobs, and those involved in their construction are usually from out-of-state. Their actual value to the world is largely tied up in their nebulous theoretical contribution to something an AI company does, and they get huge tax breaks, which means they don’t really contribute very much to many of the areas they’re put in. They are intentionally conflated with the smaller, useful data centers we’ve had in the past, all so that pedants can say “ehhmmm, you never had a problem with these before?”
I haven’t, because previous data centers haven’t been filled with GPUs or drawn more power than a small town, nor have they been rammed through by a combination of crony capitalism, tax breaks and endless debt.
And it’s fundamentally unclear why we need them!
No, really, why do we need these fucking things? So Anthropic and OpenAI can do more of whatever it is they’re doing? Neither appears to be unable to serve customers — other than the lousy uptime of Claude — nor do they appear to improve their products based on the availability of compute.
For such an offensively-large footprint — physically, fiscally and societally — nobody can really explain why the fuck we need all these things, other than the fact that they might make somebody money on a service that is best known for its huge mistakes and lack of profitability.
As I’ve discussed, the demand isn’t there outside of these two companies, and the only reason anyone believes that it does is that the largest tech companies in the world have burned through every dollar they have to hide from you that they’re out of big ideas.
The AI industry fights like a bunch of losers because that’s what they are. They cannot win by telling the truth about their products, their infrastructure, the condition of their finances or their overall intentions. They cannot succeed without manipulation and deceit because they know, deep down, that their businesses don’t make sense and their actual products, described in the present tense, are impossible to justify what they’re asking for.
They require us to coddle them, to ignore their ruinous cost, avert our eyes when they hallucinate or delete somebody’s database, blame ourselves when they make mistakes and speak entirely in theoretical terms when we describe them because the present kind of fucking sucks.
Absolutely nothing that the AI industry has created is worth even a fraction of the trillion-plus sunk into this industry, and at this point it’s very clear that these models cost about as much as a person and even then are neither capable of replacing one or profitable for the provider.
The best shot the AI industry has is open source models that may only be getting better by distilling American models. At some point Anthropic or OpenAI is going to slow down and then stop making models entirely because it costs too much money to train models, and said costs are only increasing.
Even if GLM 5.2 is truly nearly as good as Opus 4.8, it did so by copying its outputs, which means that these models will likely only get as good as long as the foundation model companies keep training, which will only be possible if they can keep raising funding, which will become difficult if open source models eat their lunch in any meaningful way.
Could Anthropic and OpenAI theoretically make better models in a vacuum? Sure! But they’re now going to have to slow-roll them, because Sam and Dario’s four or five-year-long scaremongering campaign has forced them into a situation where the US government demands oversight into their model releases at a time where the AI industry cannot afford to slow down.
Their only option is to sit there and take it or, alternatively, admit that they’re making normal software, which will make the whole “let’s build a trillion dollars of data centers” thing a little harder to justify.
This will also be a tougher sell to Masayoshi Son of SoftBank, who gave a truly demented presentation during the 46th annual SoftBank shareholder meeting, calling the company a “golden egg machine” that’s also a goose that lays eggs that are, at times, undervalued.
Masayoshi Son has sunk $64 billion into OpenAI, and existentially tied a company with a quarter-of-a-trillion dollar market capitalization — the third largest on the Japanese stock market — to whether or not Sam Altman can turn a company that burned $20.9 billion in a single year into a company that makes more than $284 billion in annual revenue by 2030.
If you’re curious, the second-largest is Mitsubishi UFJ Financial Group, a massive Japanese bank with tens of billions of dollars invested in AI data centers, and the first is Kioxia, a memory and storage company that has seen massive revenues as a result of the massive demand for memory and storage for AI data centers.
What do you think happens if AI data center capex slows? What do you think happens when it turns out there’s not enough demand for all those data centers? Even if MUFJ and SMBC (the second-largest Japanese bank, also heavily levered in AI) have sold off part of the risk, their counterparties are still part of the global banking system.
Anyway, SoftBank’s glorious, Geese-filled future depends upon OpenAI going public, and the New York Times just reported it’s likely pushed its IPO back to 2027, because bankers didn’t think it would get a trillion-dollar valuation, which is an absolute disaster considering its pre-money valuation (as in before the $122 billion it raised) was around $735 billion.
While it's partially blaming the floundering value of SpaceX, I think it’s possible (though I have no privileged knowledge to confirm it) that my story publishing its audited financials had something to do with it.
One can present financial data in all manner of ways, and I have to wonder whether its S-1 might have differed in some way — perhaps how segments were broken down — to what I reported. Perhaps bankers saw the reaction to the numbers, the mess that is SpaceX, the weird state of the market, and said “yeah man you’re gonna be lucky to float at $700 billion.”
We may never know. 2027 may as well be in the year 3000 for how far away it is, and how much further OpenAI will have to drag itself to get there.
While it “raised $122 billion” earlier in the year, it’s waiting for two more tranches of $20 billion a piece from NVIDIA and SoftBank, and will now straight up not get the $15 billion that Amazon conditioned on it either going public or reaching AGI. Considering that Mr. Altman can’t even con a bunch of bankers who were dumb enough to believe that SpaceX could 300x its AI revenue by 2030, it’s clear that the jig is up.
Another worrying sign is that SoftBank was unable to raise a $6 billion margin loan with its entire OpenAI stake — likely valued, at least on paper, at over $100 billion — as collateral. This suggests banks have little faith in the company.
Some might believe that Anthropic has a better chance, and I’m just not sure there’s much that differentiates it from OpenAI anymore, other than how annoying Dario Amodei is and how much he appears to piss off the Trump administration.
Anthropic is a large language model company that loses billions of dollars that has subsidized accounts that allow users to burn $8,000 a month in tokens for $200. To paraphrase and build upon something said by Cory Doctorow, if your business is only successful when you give away $40 for $1, that’s not a real business, it’s a way to feed venture capital dollars to hyperscalers and sell a bunch of people a product that doesn’t exist.
Anyone still lazy enough to say “they’ll crank up the price” or use some hackneyed Amazon Web Services or Uber comparison is either deliberately ignorant (I explain here) or a loser like the rest of the AI industry. If you’re so confident about this shit, despite all the blaring warning signs, you need to start finding actual, real, tangible evidence, and you need it soon.
Every argument in favor of AI requires you to speak in the future tense and ignore your lying eyes. The AI industry will not allow you to discuss LLMs in terms of what they do today without reminding you that progress has been so rapid over the last few years and demanding you immediately acquiesce that something might be good in the future.
Seriously, try and talk to somebody who loves AI sometime and criticize the tech and see how quickly they fall into the tropes of AWS losing money, AI models rapidly getting better (at benchmarks rigged in their favor because they can’t use a computer like you or me), about the “cost of intelligence going down” (when it’s actually going up), or any number of other tired tropes that mostly rely on you ignoring the present in favor of a billionaire’s dream of the future.
These are, as I have been saying, the acts of losers. This is what you do when you do not actually have a compelling story, cannot win by being straightforward or contrite, and have no way to prove yourself valid outside of appealing to cargo cults and doing financial engineering, except you’re such a loser that you’re not even doing it to commit fraud! You’re just writing PDFs so you get shares on Twitter.
Forgive me for being so very brusque, but I have had to prove myself endless the last few years, and when I finally bring you the proof that OpenAI loses a bunch of money, you immediately jump for the first keys jingled above your head. If you truly love the AI industry so much, you should ask it for better proof! You should be enraged that OpenAI’s numbers are so shitty, and that you have to debase yourself by pretending they’re not! How utterly shameful!
That’s loser shit! If you love large language models so much, go out and demand the people making them bring you the answers to my questions. Whenever I’m asked about how I might be wrong it mostly comes down to “but what if something that hasn’t happened happens?” If your answer is “OpenAI will drive down the cost of its silicon using its “Jalapeño” chip from Broadcom,” you do not have shit! It’s still in early testing!
There is no future for the future these people are building. The demand does not exist for these data centers. It never has. It never will. You can give Baseten as much money as you want, you can talk about the exciting world of open source for hours, but there is not actually enough demand for this stuff unless it becomes something very different, very soon, in a very big way, that likely also involves it getting cheaper.
Anthropic and OpenAI have $1.1 trillion in compute commitments that are contingent on their continued growth, at a time when their customers are protesting their costs, at a time when the market is clearly saying “you are not worth a trillion dollars.”
What do you think changes that?
The halo effect of AI has given way to a societal cynicism, even by the people that love it, who have a sort of vague reticent “I give up” vibe that I find exhausting to watch and will have a great deal of trouble forgetting once the bubble bursts. Even the people who claim to be excited are making jokes about Masayoshi Son and Sam Altman!
Everything about AI has the stench of death and desperation, of losers pretending they’re winners who can only thrive in conditions that reward grifting, specious hype and forward-looking statements that vary from ridiculous to deliberately harmful.
It’s ugly, regressive, and when this era ends, I expect financial carnage and chaos that could have easily been avoided had so many people not so readily swallowed poison under the auspices of innovation.
Then again, some people might just be born to be regulated by the wallet inspector.
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2026-06-27 02:32:41
It’s been an incredibly long few weeks, and as a result my previously-planned Hater’s Guide just isn’t possible within what little time I have left in this week, which is why I’m starting an ongoing series — Notes From The Bubble — where I’m going to dig into the various stories that have stood out to me in the last few weeks and what they mean for the greater tech ecosystem. It’ll be my weapon of choice going forward for the (few) weeks where a greater narrative is taking longer to pull together than usual.
I also think it’s time for something a little more light-hearted after a few hundred thousand words of deeply-researched financial nightmare fuel. As serious as the tech industry’s descent into cargo cultism has become, it’s really important to laugh at how disordered and goofy everybody has become as they realize that we’re flat out of hypergrowth ideas. Every time you see something stupid, desperate, ridiculous or disconnected from reality, know it’s a symptom of the greater fear that AI isn’t the next big thing, and that everything is an attempt to put off accepting that truth or, alternatively, create another hype cycle so we can avoid talking about it.
I know this all sounds a little reductive, but look at the current state of the tech industry. Meta is creating a Polymarket competitor. Snap is launching its third generation of AR glasses that nobody wants, I assume to compete with Meta’s AI glasses that are exclusively owned by influencers and people that should be banned from public restrooms. Microsoft has gone from loving OpenAI to loving Anthropic to loving open source LLMs and decrying the idea that any one company could control the entire AI ecosystem, somehow missing that Microsoft is the largest AI infrastructure provider in the world and is the reason that this industry exists. Google invested $75 million in movie studio A24 as part of some sort of nebulous AI partnership that will likely result in very little actually happening.
Oh, and you can now watch Instagram on your TV.
This is the modern tech industry: a series of cobbled-together ideas pushed out by also-rans with massive monopolies and talent suffocated by executives that haven’t had a human experience in decades. Can you imagine Satya Nadella or Mark Zuckerberg buying something from a hardware store? Do you think they know how to use a vending machine? When did any of these people last pay a bill, or worry about anything other than shareholder value and stock-based compensation? How often do you think Sundar Pichai actually uses Google, Google Docs, or any other products blighted with a Gemini pop-up?
Today’s newsletter will be a longer-form column, a series of thoughts on the current state of the tech industry.
Welcome…to Notes From The Bubble.
