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.
2026-06-23 23:23:20
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A few weeks ago, I predicted that the AI industry would start pushing the concept of “loops” — effectively LLMs prompting LLMs and being left to their own, token-intensive devices — as a desperate attempt to get users to burn more tokens, I imagine to create more revenue.
Now Jensen Huang and Claude Code chief Boris Cherny have both, within 24 hours of each other, intimated that the age of prompting models is over, as you’d just be “handling loops,” which conveniently also means burning more tokens.
It’s unclear what benefits a loop might have, but at a conference where the first question was mysteriously about “whether loops were for real,” Cherny was emphatic that they were, and attempted to explain why:
Later in the talk [around the 32:00 mark in this YouTube video], Cherny got specific about the loops he keeps running in his own work. One agent is continually looking for ways to improve the code architecture, while another looks for duplicated abstractions that can be unified. They submit pull requests like any other coder, and since the code is constantly changing, they never stop running.
Pretty convenient for a guy who’s allowed to burn upwards of $130,000 a month in tokens by Anthropic. Burn as many tokens as possible, so that the Great Prophecy might occur! If you don’t endlessly use as many tokens as possible, constantly defend the AI industry, and prove yourself worthy of the Great Beast of Capital, you will never ascend! Use loops now! Agents are out! You’re an ape if you use agents now that we have loops!
I realize I sound like I’m being a little dramatic, but the AI industry moves with an alarming speed and consensus to judge those who are deemed unworthy. Don’t you dare report news that might darken the hearts of the chosen! Don’t you dare suggest that the Great Prophecy (Anthropic and OpenAI are able to meet their $1.1 trillion in spending commitments) is untrue! Don’t you dare doubt that large language models are the future! This is Silicon Valley, and Silicon Valley stands for rugged individualism, rationality, and meritocracy, unless, of course, everybody in Silicon Valley agrees that an idea is good, at which point all of that bullshit goes out the window. Any singular piece of information that even slightly rings positive is proof of the Grand Prophecy of Growth that AI promises, and Chosen are those that take said proof, and Cursed are those who would question it.
That being said, if you’re an AI booster, I need you to listen to me. It’s very important everybody does the same thing right now. If Saint Tibo resets the Codex limits and you’re not immediately running loops — if we’re all not running loops — then the Great Prophecy won’t be completed. If you hear anybody question the Great Prophecy, you must immediately attack them, ideally finding a single point that you believe to be wrong (having read at most 200 words of what they have written), and then declare your victory. This is now your job.
If you do not write supportive blogs and tweets about the AI industry, you hate the entire tech industry, because Silicon Valley (see: the supposedly individualistic and meritocratic culture built on challenging consensus) needs you to suppress all dissent of any kind and ostracize those who dare to speak ill of it. Do not fret about non-believers who ask about things like “economics.” If you’re worried, re-read AI 2027, a piece of speculative science fiction that the big, serious, rationality-driven tech industry requires you to take seriously. You’re a big boy! You make your own decisions! Unless those decisions run contrary to the consensus of Silicon Valley, which is currently set to “AI is the literal future of everything and can do anything we agree on eventually.”
This is Silicon Valley — a monoculture that sells itself as outliers, putting everything it has into supporting a generative AI industry that sends the vast majority of its value directly to the largest tech companies in the world. The staunch rationalists of the Bay that have built brands convincing people they’re immune to the influence of groupthink need you to think exactly the same way that they were told to.
Why would everybody agree to do something so stupid? Why would everybody act so crazily?
It’s simple: the tech industry has completely run out of ideas, and all that’s left is a cargo cult that hasn’t had a human experience since 2015.
Last week, Snap CEO Evan Spiegel debuted Snapchat Specs, a $2195 pair of augmented reality glasses with a demo that makes it apparent that nobody in the C-suite has spoken to a normal person in years. The tech industry desperately craves its next iPhone, but years of growth-at-all-cost management consultancy poison has twisted the already-flimsy mission statements of Silicon Valley from creating societal value and innovation to creating shareholder value and a kind of banal, nihilistic accelerationism that mostly comes down to “how do we make the next thing that will make number go up.”
Snapchat Specs are expensive, aesthetically vile, and lack any meaningful use cases. Snap’s own demos are clunky and ugly, looking somewhere between a neutered Vision Pro and the original Google Glass concept video, except even then every example feels like something somebody came up with in a boardroom:
SPIEGEL: What do people do? Drums? They walk around…uh…Tokyo? They look for…uhh…directions, I guess? They look for how to fix a car? People still drive cars, right?
WORMTONGUE-ESQUE PRODUCT MANAGER: Oh yes sir, yes…exactly. Sometimes they even have “boards” of things, for their work, you see.
SPIEGEL: I bet they golf too, right?PM: Yes sir. The person you are imagining — one who can afford a $2195 pair of augmented reality glasses — would play golf, but also be so bad at golf that they need the glass-
SPIEGEL: DID YOU JUST LOOK AT ME? I TOLD YOU NOT TO LOOK ME IN THE EYE! NO EYE CONTACT!
This is, of course, a joke. I have no idea if you’re allowed to look Evan Spiegel in the eye if you work at Snap. I also have no idea if anybody actually considered what a regular human being might do with the product it’s been desperately trying to launch for nearly a decade. A single conversation with a regular person would likely have them tell you that they wish their shit worked better or that the internet wasn’t so full of scams and pop-ups and slop and misinformation. They wish there weren’t so many ads. They wish their apps weren’t confusing and full of dark patterns and ways to trick them into subscriptions or clicking ads or being annoyed.
That’s because there’re only so many things you can do for the user until you start doing stuff to the user.
Per my piece from the end of 2024:
As every single platform we use is desperate to juice growth from every user, everything we interact with is hyper-monetized through plugins, advertising, microtransactions and other things that constantly gnaw at the user experience. We load websites expecting them to be broken, especially on mobile, because every single website has to have 15+ different ad trackers, video ads that cover large chunks of the screen, all while demanding our email or for us to let them send us notifications.
Despite decades of progress in hardware making computers faster, cameras better, and storage larger, the actual experience of using the computer has gotten materially worse. We’ve hit a wall as far as where mobile and desktop user interfaces can take us, and every attempt at making voice-activated platforms like Alexa replace (or even compete with) them has proven fruitless, with Amazon’s various Echo devices and services losing billions of dollars a year.
This is what I call the Rot-Com Bubble. Big tech has hit the wall of what modern software can do, and in turn run out of hyper-growth ideas. Nobody has the next Google Search, iPhone, cloud computing, mobile app store ,or other idea that would allow Google, Microsoft, Apple, and Amazon to keep growing at a rate that justifies their valuations. While this is partly a natural process — there are only so many ways to do things! — it’s also a direct result of incentivizing and promoting products that create revenue growth or sustain monopolies, which in turn focuses your R&D and hiring efforts toward those who can come up with ways to make Numbers Go Up.
Put another way, the tech industry has become the largest cargo cult of all time.
Microsoft, Google, Amazon, and Meta sunk what will soon be over a trillion dollars into AI data centers because they don’t have any other ideas, and because the only thing that the MBA’d elites running the tech industry can do is hire people, fire people and spend money. Their (at least in the first three cases) investments in OpenAI and Anthropic were a successful attempt to build both their largest individual customers and a new revenue stream under, I imagine, the mistaken belief that said customers would eventually become independent enough to pay them without continually raising venture capital. I also imagine they believed that AI data centers would actually make a profit at some point, or that said data centers wouldn’t take two or three years to complete, or that AI, as an idea and a tool, would “take off” in a real sense, rather than an imaginary hype cycle in an economy built on speculation.
The problem is that previous eras of innovation and hypergrowth never came from shoving hundreds of billions of dollars into any one thing. The original iPhone took two and a half years to develop, but was the culmination of multiple different innovations in capacitive touchscreens, smaller batteries, and the condensed talent that helped create a touchscreen keyboard that actually worked, and ended up costing about $150 million (or $271 million in today’s money), or a little less than a third of what SoftBank paid OpenAI in 2025. The reason we haven’t come up with the “next iPhone” is because we’ve maxed out what we can do with the current slate of ways to look at a computer interface, and the next logical step is one that’s effectively screenless, which is an unbelievably big leap, and one that will not be surmounted any time soon.
So our only hope is software, and the limits of our current interfaces. Google Search was created by two college students at Stanford. Instagram was a mobile check-in app called Burbn that realized it couldn’t compete with (lol) Foursquare and pivoted to creating a photo-first social network. In fact, most of the historical success stories in the valley are, for the most part, websites that bring together people or services in a way that’s accessible and readily-available, and most of that innovation came from services like Amazon Web Services. Social media companies were the natural next revenue ascent because they were, at least in theory, relatively cheap to run, as the users themselves (and what the platforms could encourage them to do) were the ones that made the reason for you to log onto the site.
Except everybody forgets how many dead social networks there are, like iTunes Ping, Google+, Google Wave, Microsoft SoCl, Meerkat, App.net, Pownce, Orkut, Jaiku, and YikYak. Everybody forgets that just about every other attempt by Meta or Google or Microsoft or Amazon to expand outside of their core competencies (if you can call them that) has failed. Everybody is desperate to ignore the fact that Silicon Valley startups have, for the most part, not done anything particularly new or interesting for over a decade, and that the reason everybody took these people seriously is a result of conflating them with people that have either entirely left the tech industry or have little to no say in its future.
There’re only so many ways to solve problems with software, and only so many other ways to solve the problems that doing so creates. Two decades of Silicon Valley “innovation” have come from throwing as much software engineering talent and venture capital at as many problems that could, at least in theory, be solved through a combination of cloud compute, storage and code.
And while there might be more problems that code can solve, they aren’t the kinds that create hundreds of billions of dollars of revenue or massive shareholder returns, nor are they things that big tech can copy and bolt onto their current services to keep them growing either.
This is leading to the slow, agonizing collapse of both the software industry’s revenue and the venture capital business model. The “SaaSpocalypse” narrative claimed that companies writing their own software was a threat to the business models of SaaS companies (and a justification for their dwindling revenue growth), which was an attempt to paper over the fact that the software industry is in decline, with the growth efficiency (revenue growth versus sales and marketing spend) of software companies declining by half between 2021 and 2023, with BDO reporting in a 2025 analysis that across 115 publicly-traded SaaS companies, the industry’s revenue had declined by 2% year-over-year, with mid-sized growing companies at a flat 0%.
The fact the “SaaSpocalypse” narrative took off is all part of the greater cargo cult of the Valley, and the media’s willingness to buy effectively anything they’re selling. Nobody is actually building their own SAP or Salesforce or Office 365 — that’s a fucking stupid idea! — but because that sounds like a directionally-correct idea that affirms the greater bias of the growth of AI, it set in, which meant some stocks went up and some stocks went down. Did they go up or down based on something that actually happened? God no! The market listens to the media and analysts, who mostly just look at the numbers they’re given and the people they talk to, who more often than not are the CEOs and other executives of the companies that plant these narratives as a means of getting away from an uglier truth.
You see, if AI is the reason that the SaaSpocalypse is happening, it fits into the larger imaginary Valley mythology of “disruption,” and gives everybody an excuse to keep believing that every tech company will grow in perpetuity. The cargo cult cannot change its rituals to adapt to a reality that suggests that its gods are dying. Accepting that AI isn’t saving everything means that you have to accept that there might be an end to the era of hypergrowth, which in turn means you have to start thinking about the rationale of, say, venture capital and private equity.
Both have seen far better days.
As of the end of last year, the average TVPI (total value put in) of venture capital funds raised between 2017 and 2024 was between 0.8x and 2.0x, meaning you’d get somewhere between 80 cents and $2 for every dollar invested, with 70% of startup exits between 2022 and 2024 netting a loss for their investors, up from 58% between 2009 and 2014, which included much of the bloodbath from the great financial crisis. Per The Economist, the Valley also faces a glut of “Zombie Unicorns,” startups valued at $1 billion that can’t raise money or exit at their current valuation, and a third of all active US unicorns (per Axios) haven’t raised any funding in the last three years.
Meanwhile, private equity is facing much the same problem, with more than 16,000 “zombie companies” held for more than four years, the longest on record, and holding companies for an average of 7 years in total. Private equity exits have dramatically declined, with a growing amount of exits being funded by “secondaries” — venture or private equity funds selling each other their portfolios in the hopes of avoiding having to dump them at a loss.
And wouldn’t you know, a big part of the problem is that they piled trillions into software companies assuming they’d all grow forever, massively overvaluing them in the process.
Between 2018 and 2022, (per Apollo) 30% to 40% of private equity deals were in software companies, with firms taking on debt to buy them and then lending them money in the hopes that they’d all become the next Salesforce. In reality, private equity overvalued the vast majority of its software investments, stuffing them full of debt with payments contingent on near-constant growth, which is why Pluralsight lost its investors $4 billion and Medallia lost Thoma Bravo $5 billion. S&P and 451 Research analyst Scott Denne recently put it bluntly, saying that “..."The holding periods are longer and they're going to get longer because there effectively isn't an exit market for these companies.”
It’s almost as if instead of looking at whether the companies were good and making intelligent decisions, private equity instead chose to do what had historically worked and assumed that its investments would continue to grow in perpetuity. You know, vaguely looking at history and doing things in an almost ritualistic way.
In venture’s case, while part of the problem was how easy it was to get money in the ZIRP era, the other is that venture capital has been morphing into a cargo cult for a decade, with seed stage financing collapsing since 2015, and continuing to drop in favor of middle-to-late stage rounds in established players…almost like venture capital just doing stuff in a way that somebody else did because it worked for them in the past. Venture capital no longer really cares about risk at scale, with the vast majority of funds going to late stage, and even “early stage” data poisoned by Series B rounds that are only something you can raise once venture capitalists have arbitrarily decided that you should continue living.
As a result, the vast majority of funds do not go into creating the future or taking risks but doing things that resemble success, which usually means following hype cycles and hoping for the best. Baseten, a company that sells AI inference infrastructure, just raised $1.5 billion in a Series F funding round so that people can use or run their own open source AI models, quite literally allowing people to do things that other companies have been doing and train open source models of their own, so that they too can “do AI.”
Its investors include D.E. Shaw Ventures, Greylock and Altimeter Capital, all of whom invested in both Anthropic and OpenAI. Baseten doesn’t own its own infrastructure, renting instead from hyperscalers, which means that that $1.5 billion goes directly into the pockets of Google, Amazon and Microsoft, much like the money raised by OpenAI and Anthropic, which in turn gets spent buying more NVIDIA GPUs. All that “free thinking” and defiance of incumbents always seems to end up as revenue for the largest companies in the world.
So much for backing the little guy!
While the Valley’s legend has grown from risk-taking and fostering new ideas, venture capital works in reverse, overwhelmingly funding market consensus and piling into deals after somebody else has risked their capital to keep it alive. Decades of encouraging people to fund startups with the express intention of hypergrowth — with Ben Horowitz suggesting in 2010 that having “zero chance of becoming a high-growth company” was tantamount to “being in purgatory” — has created a startup culture focused entirely on its Total Addressable Market and growth trajectory, which means that companies are founded with that express intention.
Venture capital funds companies that appeal to venture capital, which means Silicon Valley innovation is centered around finding ways to convince venture capital to give it money. While this might have worked a decade ago when there were still hypergrowth companies to build, it intellectually stunted the Valley, promoting and celebrating companies not based on the things they’ve built but the shareholder value they’ve created. A startup is considered a “success” not based on its tangible contribution to the future, but its ability to tick boxes either through funding, revenue growth, acquisitions, or valuation. Everything is about creating the signs that your company is part of the big thing that will supposedly lift every Silicon Valley valuation — after all, 61% of venture capital funding went to AI in 2025 — to the point that it isn’t really clear what anything means or what anybody is doing.
Nowhere is this more obvious than the eternal shuffle of different guys between different AI companies. Google paid $2.7 billion in 2024 to acquire Noam Shazeer, one of the authors of the paper that started the generative AI bubble, along with his worthless AI chatbot company Character Dot AI. Two years later, Shazeer is joining OpenAI, and it’s unclear whether his second tenure at Google really did anything, other than helping pad the bags of venture capitalists and possibly having some effect on Google Gemini. It’s unclear what changed at OpenAI when co-founder Andrej Karpathy left in February 2024, nor is it clear what is happening now he’s joined Anthropic. Barret Zoph left OpenAI in October 2024 to become the CTO of Mira Murati’s Thinking Machines, created absolutely nothing of value, went back to OpenAI in January 2026 as its “GM of B2B,” oversaw an era where its enterprise customers had “huge issues” with its costs, then left again, I assume to another AI lab that will give him lots of stock.
I’m going to go out on a limb and suggest none of these guys actually contributed very much in their most-recent tenures, and that their hiring and positions were further cargo cult moves. Noam Shazeer was the original Attention Is All You Need guy! Give him $2.7 billion! Quick, before somebody else does! Quick, hire Andrej Karpathy, a guy who hasn’t worked at OpenAI in years, to do something with your LLMs! His eternal brilliance — which resulted in absolutely nothing since he left OpenAI outside of a placeholder website for a dead education startup with a protected Twitter account — is necessary to doing whatever it is we’re meant to do next! This will help us do hiring too, because everybody wants to work with these great minds that do stuff, somewhere, at some point, or maybe they did stuff, I don’t really know!
Hey, remember when Mark Zuckerberg was paying tens of millions of dollars to hire random AI researchers? Why do you think he did that, other than the fact that everybody else was hiring lots of AI researchers? Hey, while we’re on the subject, what exactly did they end up doing? That’s right, a mid-tier AI model and an AI app that nobody uses! Sure sounds like Mark Zuckerberg was just doing whatever seemed to work in the past, which was “get smart guy, smart guy do stuff, thing happen,” much like when Microsoft hired Deepmind co-founder Mustafa Suleyman for over a billion dollars, with little to show for it other than mid-tier LLMs, a universally-loathed chatbot, massive capex, and AI revenues that are too small to break out in Microsoft’s earnings.
No, sorry, I forgot the latest cargo cult maneuver — OpenClaw, a product that 99% of people have never heard of other than those who intentionally drown themselves in Silicon Valley cultism, which is why Microsoft, NVIDIA, Meta and Amazon all built OpenClaw bullshit and OpenAI hired its founder. Everybody is moving between various different rituals in the hopes that they’ll be the ones that they’ll be The Great Winner of AI, even if nobody really knows what that is and is only doing all this shit because everybody else is doing it.
That’s because the AI bubble has been part of the greater cargo cult of the Valley. Why did Microsoft buy hundreds of thousands of GPUs? Because an engineer told him that if millions of people used ChatGPT via Bing, they’d need “every high-end chip the company had.” Why did everybody freak out about ChatGPT? Because it was the first viral product the tech industry had created, and it was truly different. Why does anybody think LLMs are going to change anything? Because everybody vaguely came to the consensus that ChatGPT was trending in the direction that something would change.
And so the greater tech industry moved into full cargo cult mode. Amazon, Google, and Meta had to buy all those GPUs because Microsoft bought a lot of GPUs. Investors piled into various AI companies because when the tech industry does something at the same time, big things happen. Everybody has acted based on reading the signs — ChatGPT’s meteoric growth meant that it could be the next Google, and because the economics had worked out in the past, they would work out here, which is why everybody tells you that it’s just like Uber (it isn’t) or AWS (which cost $52 billion between 2003 and 2017, or less than a quarter of What OpenAI and Anthropic raised in the last 6 months).
The AI industry is fundamentally judged based on its symbolic similarities to bygone eras. Buying GPUs and building data centers sort of feels like Amazon Web Services, even though the $765 billion that big tech will spend in 2026 will be more than ten times Amazon’s combined capex during the period where AWS was being built. ChatGPT sort of feels like Google Search or Facebook Ads or next app store, but only because it’s a culturally-relevant piece of software, largely driven by the larger cargo cult of tech crystalizing around it.
Most people trying to make these comparisons either don’t remember or are desperate to forget how different the world was when Google Search, the iPhone or Amazon first grew. They don’t want to think too hard about how blatantly obvious the utility of these products was, how they had functional unit economics from their earliest days, or how different their growth stories were. They don’t want you to think about it either, because part of the greater cargo cult is making sure you don’t believe your lying eyes and focus on the greater signs that The Great Prophecy might come true, even if it’s not obvious what that means other than “ChatGPT is the biggest most hugest and most profitable company ever and everybody makes money on their investments.”
OpenAI and Anthropic are the height of the Valley’s mysticism. Both are still referred to as startups, despite the fact that Amazon, Google, and Microsoft paid for their entire infrastructure, spending at least $200 billion just on buying GPUs and building capacity for two companies. They have raised — assuming their most-recent rounds fully close — close to $300 billion in the space of two years, and are on course to burn tens of billions of dollars each in 2026.
Neither Anthropic nor OpenAI are actually startups. They have enough money and clout to hire just about anybody, can deploy billions of dollars in stock for acquisitions, have their infrastructure fully paid for by other companies, and because it’s taken so much money to build said infrastructure, effectively nobody else can train models or serve inference at their scale, making them the functional equivalent of a hyperscaler.
And neither company feels anything less than insane outside of outright ignorance or a cargo cult mindset. Both companies have had everything paid for them either by hyperscalers or venture capitalists, and are fundamentally incapable of operating without infinite resources, and the best that anybody has to defend their endless billions of burn is to refer to the 184-year-old railway bubble or the Dot Com Bubble, using them as symbolic proof that everybody can lose a lot of money, and that somehow results in something good, I guess?
The logic centers around the idea of “useful infrastructure,” as if railways or telecommunications equipment have any similarity other than that people spent way too much money on them in bygone eras. AI boosters (and the well-meaning and ignorant) return to these bedtime stories as a means of escaping reality and accepting that it’s very possible for everybody to be wrong in a completely new and innovative way.
This is the same mystical thinking that gets us to the idea of OpenAI or the greater AI industry being “Too Big To Fail,” an ahistorical trope that ignores the Term Securities Lending and Primary Dealer Credit Facilities that plugged trillions (no, really!) of dollars into the side of the banking industry because failing to do so would’ve left America’s financial system insolvent. OpenAI, Anthropic and every AI startup could disappear tomorrow and the world’s financial systems would continue unabated, other than the brutal hit to the stock market and screeching of venture capitalists.
That’s because their actual relevance is, in and of itself, symbolic. OpenAI and Anthropic combined to less than $20 billion in annual revenue in 2025 representing 89% of all AI startup revenues, and spent at least $30 billion on compute on Microsoft Azure, Google Cloud and Amazon Web Services. Their services are sold using the very same cargo cult mentality that got us into this mess — organizations adopting AI at scale and demanding that people use it because “AI is so powerful,” or, put another way, somebody they respect or like suggested it’s the future, and because none of these executives actually build anything or do any work, they have no idea what to do other than whatever it is that everybody else is doing.
Our economy is dominated by companies run by people who didn’t build and who don’t participate in the products or services they sell. They have little or no practical experience about what it was that made the company a success, and their “daring” initiatives usually boil down to “fire a bunch of people and flatten the organization” or “spend a bunch of money because it’s the thing to do.” They do not know what AI does other than the fact it can write code or write copy or generate stuff, but because everybody is “doing AI,” they too must “do AI,” which means “everybody that works for me must do this, and also we must add this somewhere, somehow.”
But that’s all the modern tech industry can do: an impression of something they think is successful in the hopes that they’ll be successful too.
In September 2024, Airbnb CEO Brian Chesky gained an alarming amount of praise for doing “founder mode” at the company:
If I could summarize founder mode in a couple sentences, it’s about being in the details. It’s that great leadership is presence, not absence. It’s about a leader being in the details. And if you as a leader aren’t in the details, guess what? Your leaders aren’t in the details, and their leaders aren’t in the details. And one day you’re going to wake up, and you have 50-year-olds managing 40-year-olds, managing 30-year-olds, managing people two years out of college doing all the work with no oversight, and you have these four unnecessary layers. You have no experts in the company.
So, the antidote to this is to try to be as functional as possible. We are a functional organization. Functional just means expertise-based, not general management-based. I’m the only non-functional person in the company; all functions roll up to me. I generally think the CEO should be the chief product officer of the company. The most important thing a company does is make a product. If the CEO is not the expert in the product, then why are they the CEO? Said differently, I should not be the CEO of SpaceX. I couldn’t be the chief product officer because I do not understand rocketry. So maybe I’m a good CEO, but I can’t be the chief product officer. There may be some exceptions, but I generally think that’s the case.
Your leaders shouldn’t just be “managers” (and I put managers in quotes), they should also be in the details. If we were a military, like a battalion, the cavalry general should know how to ride a horse. It’s crazy that they don’t. And leaders shouldn’t be fungible. So it’s really about being in the details.
Chesky also notes that he was inspired by “studying Steve Jobs,” a person who has been dead for many years, choosing “not to copy everything, but a lot of how he organized and ran the company.”
Airbnb is most decidedly not Apple, and neither Chesky nor his team are anything close to those who built the original iPod, iPhone, or even the Apple HiFi. Airbnb is a cloud service platform that lets people rent their houses out. When Chesky says he’s “studying Steve Jobs,” he likely means that he watched a few movies, documentaries and videos of Jobs speaking about things that have nothing to do with him, looking for similarities that he could copy — almost like he was copying a successful guy’s moves in the hopes that doing so would give him similar results. Airbnb remains a better-than-the-rest front end for you to rent other people’s houses that provides payment and support layers, and the vast majority of its revenues come from monetizing that process. Airbnb’s stock remains effectively flat since Chesky’s “founder mode” designation, and it remains (extremely) modestly profitable.
The irony of the discussion is that it comes from a Paul Graham essay that basically boils down to “the CEO should actually do stuff at the company and know who does stuff at the company,” except written with a Sorkin-esque drama:
For example, Steve Jobs used to run an annual retreat for what he considered the 100 most important people at Apple, and these were not the 100 people highest on the org chart. Can you imagine the force of will it would take to do this at the average company? And yet imagine how useful such a thing could be. It could make a big company feel like a startup. Steve presumably wouldn't have kept having these retreats if they didn't work. But I've never heard of another company doing this. So is it a good idea, or a bad one? We still don't know. That's how little we know about founder mode.
No, actually, this shouldn’t be that hard if you actually talk to people at the company, even at a large organization like Apple, if you have any idea what people do for a living. Sure it’d be a lift, but if you can’t organize a 100-person event with a year’s lead time just because you’re too lazy and inert to understand what’s going on, perhaps you shouldn’t be running a company to begin with?
You see, the Valley can’t just say “yeah you should have an active hand in your company and not delegate everything,” it has to be founder mode because everything is special! If tech firms aren’t run by people going founder mode, then they’re just software companies selling software. If OpenAI and Anthropic are just software companies with huge infrastructural costs, then you have to start treating them like normal companies with those kinds of burdens, which would make you start screaming at the top of your lungs.
This is the hyperreality (and cargo cult mentality) of Silicon Valley. Apple, Google, Microsoft, and Meta were companies that grew out of relatively boring stories — kids getting internships working at tech companies, computer science graduates coming up with software-driven ideas, and so on — with very few actual lessons to learn other than “you should come up with a really good idea and do it at exactly the right time.” Romanticizing the legend of Steve Jobs or Mark Zuckerberg or Bill Gates, rather than their luck and potential ability to hire people who actually build things for them, allows you to pretend that there are lessons to be learned, and that in turn you too could have these otherworldly riches if you just try hard enough.
The success of these large companies has predominantly come from having a few good ideas, great timing, good execution, and building largely-immovable monopolies rather than any incredible acts of genius. Jobs, Zuckerberg, Bezos and Gates all succeeded by finding people who actually did stuff, such as the Sanberg-led growth team that turned Facebook into a monster, and Tony Fadell and Scott Forstall’s hardware and software teams pulling together the original iPhone. Their successes were not the result of some series of things you can mimic or the tone of their voice or a specific series of actions, but being in the right place at the right time with the right idea and the right people, at a point when the underlying hardware or semiconductor infrastructure had reached a point when the idea was possible.
Put another way, there was a shit ton of hard work, innovation, and talent that went into these things that you can’t copy, even by working really hard or yourself having a bunch of talent. The ideas must be possible, economically viable, and you must have the people and infrastructure to execute them. Amazon Web Services may have lost money, but lost significantly less than OpenAI or Anthropic, and was significantly more useful than anything the AI industry has ever produced. In 2013 — the year that Amazon Web Services went profitable — Amazon’s total debt was $5.18 billion.
And really, there’s nothing more cargo cultish than defending OpenAI burning $21 billion in a single year by saying “this other company burned money too.” Even if the losses were comparable, Amazon was building two very different businesses — a digital store and a cloud compute platform — to OpenAI, which is training and selling access to large language models at a massive loss, does not own its infrastructure, and has absolutely no path to profitability outside of “we keep spending other people’s money.”
But that’s all the AI industry is — people doing impressions of things that have worked before in the hopes that they’ll work again. Every AI lab and startup started with cargo cultish subsidized subscriptions, assuming at some point somebody else would solve the problem of costs or that they’d “make it up in volume,” because that’s what worked before. OpenAI and Anthropic threw as much money at pre-training models because a paper had suggested that if they did so there would be infinite gains (versus diminishing returns), and when Anthropic worked out that you could add a bunch of scripts on top of an LLM to do coding better with Claude Code, OpenAI immediately copied that and made Codex.
Both companies are now jousting to make much the same product by giving away API credits and free weeks of access to create the symbolic aura of an “essential” product to continue convincing VCs and the public markets that they’re “building the future” rather than effectively paying their customers to use their products. The “popularity” of AI has come entirely from social pressure and endlessly-discounted access, and the very second that they charged the actual costs, their customers started freaking out and kvetching about whether AI has ROI.
Our economy is dominated by people who have only a symbolic understanding of the world — Business Idiots with little interaction with productivity or production who do not know how value is created and thus can only create facsimiles of valuable companies. Perhaps they’re lucky enough to have businesses that effectively run themselves, or monopolies that can survive having 98% of their free cash flow spent on AI data centers that only lose money, or are smart enough to stay out of the way of the people who actually do work.
But in many cases, the people running companies — especially those most-obsessed with AI — are cargo cultists following “the most valuable companies in the world” into a void that demands they twist every part of a company they don’t understand into a form that ingratiates them and makes them feel like they’re “doing business.” It’s an obscene and childish way to live one’s life, and typical of an economy that optimizes for growth at all costs thinking and coddles those who think that way.
Even the economics of the AI bubble are cargo cultish. The use of annualized revenues (the single-most easily manipulated metric in Valley history) as a means of promoting growth only exists as a means of spreading the symbology of hypergrowth, all while deliberately obfuscating the actual financial health of the company by using a single monthly (or weekly) snapshot to extrapolate an annual figure, something that’s particularly egregious when you realize that it involves non-recurring charges like spending money via Anthropic’s API.
Yet the Valley either realized (or was fortunate enough to find) that the media had bought into their cargo theology. Much like the Valley craves symbols or prophetic signs that today’s startups will become the next Google, modern tech and business journalism runs not on any scrutiny or skepticism of the future but in finding the “next big thing,” which often requires it to find the very same symbols that the Valley craves, often provided by the executives themselves.
They crave to be the ones to find the next Jobs, Zuckerberg, Bezos, or Gates, and in their crazed search only seek to repeat the same mistakes of every bubble, never noticing that the tech industry has had an astonishingly bad record for more than a decade.
The tech industry must always be framed as an impossible-to-decipher monolith full of troubled geniuses that have good intentions, because when you stop thinking that way, you start seeing it for what it really is — a vehicle for symbolic capital that stymies innovation and promotes growth over everything, funding things based on their similarities to the past and how warm and fuzzy doing so makes them feel. And in its incredible success as a vehicle for capital, tech has managed to beguile society and turn journalists, economists and regulators into cargo cultists that can be easily won over by a smart-sounding guy or an emphatic-enough press release.
AI is the natural endpoint of the Valley’s cargo culture — money-hungry models that can vaguely resemble something that might grow into the future, with opportunities to deploy capital that resemble previous infrastructure movements, all with convenient ways to explain away dissent that mostly boil down to “bad thing happen before but then good thing happen after.” Everybody believes that because AI startups can grow their revenue they’ll grow that revenue forever, that because startups in the past lost money that AI startups will stop doing so, and that because something has a lot of users it can never disappear.
I challenge everybody reading this to start living in the present, and to stop taking excuses for the mediocrity of AI. AI boosters are no longer allowed to speak in the future-tense, nor are they allowed to justify AI’s losses based on previous eras.
If you’re an AI booster yourself, know that the AI companies treat you with complete contempt. They force you to defend dogshit, to wheel and deal in dogshit, to celebrate dogshit like it’s caviar, to tell others that they too must defend dogshit, because one day the dogshit will be good.
Nowhere has this been more evident than the response to my exclusive last week.
Some have been mighty confident about inference being profitable (due to a $7.5 billion cost of revenue on $13.07 billion in revenue), but overlooked my reporting from last year verified by the Financial Times showed OpenAI spent $8.67 billion on inference in the first nine months of 2025. It’s very clear OpenAI moved around numbers to make things look better than they are, and I believe that inference costs are being dumped in sales and marketing.
How else are you to explain how a company spends more than 43% of its revenue ($5.73 billion) on sales and marketing — more than the Coca-Cola corporation, which has three ad agencies and a vast web of different print, digital, and physical ad spend. Microsoft had $500 million of “sales and marketing” spend too. What do you think that is? OpenAI spending $500 million on sales and marketing through Microsoft? Or itemizing promotional spend or the inference from free users as a sales and marketing cost?
If you disagree, please explain in any level of detail how OpenAI has spent $5.67 billion on sales and marketing. Its first major advertising campaign was in September 2025. If it’s spending $250,000 a year on its 500 sales staff, that’s still only $125 million. Unless OpenAI is one of the single largest accounts in digital advertising, I think it’s far more likely that there are actual costs being hidden.
This is the kind of thing a company does when it has utter loathing for its investors and the general public — a brazen attempt to bury costs to make things feel better for an audience that’s directly incentivized to take any shred of proof that things are okay, even if said “thing” is the suggestion that a company that lost $21 billion only actually lost $8 billion.
Alternatively, it’s what an industry does when it believes everybody is gullible enough to accept and promote any rationalization that confirms their beliefs.
So far, they’ve been proven right. Every time I show somebody the kind of tangible proof that these companies are economic septic tanks, somebody uses some sort of theological, mythological or historical statement as proof that what I’m saying doesn’t mean anything. Silicon Valley, the so-called hub of meritocracy and rugged individualism, runs on a kind of empty cultish ephemera that usually ends with sedative-laden Kool Aid.
In the end, faith can’t fill your belly, or cover $1.1 trillion in compute commitments. It can’t magic up $2 trillion in revenue by 2030 for an industry that basically doesn’t exist without OpenAI or Anthropic.
And however you feel about AI, you should demand better proof of its inevitability than a bunch of mythology, hype, and cargo cult bullshit.
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2026-06-20 01:03:48
So it’s been a big week for me after I published an exclusive covering OpenAI’s audited financials from 2024 and 2025, with reactions ranging from “oh my god, OpenAI spent $34 billion to make $13.07 billion in revenue!” to “actually, it’s good the company lost $21 billion.”
And that, my friends, is the Silicon Valley Bubble writ large – an industry that grew rich and famous off the back of a mythical pragmatism and meritocracy that’s morphed into a pseudo-cult built to protect venture capital investments at the cost of reality.
OpenAI lost $21 billion in 2025. $867 million – or around 6.6% – of its revenue came from SoftBank, I posit (though cannot confirm) is something to do with its supposed “Crystal Intelligence” (formerly Cristal Intelligence) initiative with OpenAI, announced in February 2025 as part of the alleged formation of SB OAI Japan, which only actually formed in November 2025.
I severely doubt that SoftBank was a significant cost center for OpenAI given the timeframe, which means that this likely inflated its revenue significantly, and in a way that was disproportionate to its expenses.
There is no justification for this kind of burnrate. There is no sensible, logical or rational reason to look at this company and think that there’s some magical way it’ll become profitable or sustainable. The fact that OpenAI magicked away billions of dollars of costs using bizarre “net losses attributable to noncontrolling members capital” suggests that there are connected entities that the company has yet to disclose, and nothing about this accountancy voodoo changes the fact that OpenAI spent $34 billion to make $13.07 billion. While I cannot speak to its exact intentions, I can see no reason to do this kind of thing outside of trying to obfuscate the horrible state of the company.
Every attempt to rationalize these losses only serves to prove that Silicon Valley itself is a bubble. This is no longer a community concerned with building the future, but building a capitalized consensus, an idea of where money should flow and to whom it should flow to. Gone are the days when plucky software engineers built “bootstrapped” companies that raised rounds based on their theoretical growth, total addressable market, and potential for industry capture. They’ve been replaced by a pseudo-philosophical belief that spending billions on training large language models will somehow turn into a theoretical computer that does its own research, eliminating the need for Silicon Valley to ever have another idea again.
That’s because the Valley has been captured by people that haven’t done any real work in years, haven’t built very much of anything, and thus will fall, well, for just about anything. They don’t see the problem in describing relatively boring cloud software that can write code based on natural language prompts as the path to a sentient computer, or the fact that these companies have mostly sold their software based on fear-mongering.
Per Cal Newport in the New York Times:
When it comes to A.I., we’ve become so used to this tone of helpless, stress-inducing prognostication that we’ve lost sight of its strangeness. Imagine if the Ford Motor Company put out a report saying that it feared its popular F-150 trucks might soon start bursting into flames, but that there was nothing the company could do about it because automotive technology was too inevitable and important to slow down. You’re probably struggling to picture this scenario because no reasonable consumer product company would ever act like this.
The A.I. companies could start behaving the same way. To do so would require that they stop treating A.I. like some inevitable force that they’re struggling to steward. It’s not. It’s a collection of specific tools that these companies are choosing to design and sell according to specific business plans. Accordingly, they need to talk about their offerings like any other consumer product. This means explaining clearly whom these products are for, justifying their benefits and, critically, taking full responsibility for any harm they might cause. Just because A.I. currently enjoys a high-tech sheen doesn’t make it exceptional with respect to common-sense safety standards.
This kind of specious hype and doom trolling exists to make you ignore the current state of AI models in favor of a theoretical better state that you can extrapolate from what you’re being fed by the companies. If you’re scared of AI, you assume that being able to get Claude Code to barf out a copy of some open source software is merely a precursor to automating all software, or even all jobs. If you’re excited about AI, you’re excited because you believe you’re on the ground floor, which will give you incredible advantages when all the things that Dario Amodei and Sam Altman have vaguely promised have come true.
To engage with AI hype is to become its supplicant. You cannot talk in the present tense. You cannot accept any negativity. You must ignore any signs that things are bad and repeat the necessary shibboleths. You must applaud literally any chart or weird, meandering blog that suggests that at some point something good will happen. The Silicon Valley bubble demands you ignore your lying eyes, because if you start thinking about things rationally — as in talking about the stuff LLMs do today and the underlying economics — things become increasingly more-worrying.
In a conversation with Cal on my podcast Better Offline, he also noted that some have tied their pride to their belief in the “incredible” future of AI, interpreting any naysayers as directly attacking their identity rather than critiquing software and the people building it. Perhaps it’s that they swallowed the hype after a particularly vigorous Claude Code session, perhaps it’s that they want to believe that Silicon Valley has “still got it,” but many AI boosters act as if they’re living in the cold, harsh realm of reality as they desperately grasp at straws.
They don’t actually want to hear contrarian points, nor do they want to know about the financials. All they want are more ephemeral talking points to parrot so that they can fool themselves into believing they’ll be rewarded by an industry built on doomerism, fantasy, deception and outright lies. While this existed in different forms in the past — with cryptocurrency, for example — nothing has ever captured the minds and wallets and hearts and social media presence of Silicon Valley more than AI, a technology that can mean anything you need it to, even if it can’t really do anything you’re promising.
The problem is that the world looks to Silicon Valley to explain what the future might be, and when Silicon Valley is captured by people that are either deranged pseudo-philosophers or cynical growth-drunk egoists, very little actual, real value is created. The stock market depends on Silicon Valley to create the next generation of growth — both in the form of new companies and the next chum for the Magnificent Seven to force upon its monopolized customers — but has never let a hype cycle poison its veins this thoroughly or destructively.
Today’s piece — the second (and final) part of the Silicon Valley Bubble series — is focused on how Silicon Valley’s reality distortion field has escaped containment, exploiting intellectual weaknesses throughout organizations and economies by promising a near-infinite source of capital.
The AI bubble has grown by promising everybody something — a cure for a tech industry that’s run out of hypergrowth ideas, a way for public (and private) companies to promise infinite growth, a way to paper over the collapse of growth throughout the software industry, and a way to convince the general public that the tech industry is an infinite flywheel of ideas rather than a machine custom-tweaked to extract capital through monopolies.
The problem isn’t simply that it will eventually need to make good on those promises, but rather, what those promises do in-and-of-themselves. Like a caustic acid, they’ve deformed and reshaped so much of what we consider to be the tech industry, changing incentives and eliminating what was once considered the guardrails against the kinds of reckless exuberance we’re now seeing.
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