The growing economic concerns surrounding artificial intelligence are not rooted in fears of sentient machines, but in the unsustainable financial practices fueling the current AI investment frenzy. A significant portion of the stock market is now tied to a small number of AI-focused firms that lack viable paths to profitability. These companies rely heavily on massive capital inflows to sustain operations, spending billions on infrastructure like data centers and high-end GPUs, despite having no clear route to generating returns for investors. n nOne central issue lies in what analysts describe as poor unit economics: each new generation of AI technology costs significantly more than the last, and every additional customer deepens the financial losses. Unlike earlier tech booms—such as the early internet or Amazon’s growth phase—where costs decreased with scale, AI systems become more expensive to operate over time. This model is inherently unstable and cannot persist indefinitely. n nRecent reporting by the Wall Street Journal highlights the alarming financial state of the AI sector, comparing its scale to historical bubbles like the WorldCom scandal, but exceeding them in both size and risk. Some data-center firms are even using Nvidia GPUs as collateral for loans, despite the rapid depreciation of such hardware—especially under the intense workloads typical in AI training environments, where thousands of chips can fail within weeks. n nAccounting practices within the industry further distort reality. For example, Microsoft grants OpenAI access to its cloud infrastructure, which OpenAI records as a $10 billion investment. When those credits are used, Microsoft counts the transaction as $10 billion in revenue. Similar circular financial flows occur across major players: Nvidia invests billions in data-center operators, who then use that money to buy Nvidia chips, allowing both sides to book gains without real economic value creation. n nAccording to Bain & Company, the AI industry would need to generate $2 trillion in revenue by 2030 to justify current investment levels—more than the combined annual revenue of Amazon, Google, Apple, Microsoft, Nvidia, and Meta. Yet Morgan Stanley estimates current AI-related income at just $45 billion per year, a figure based on questionable accounting methods such as annualizing a single strong month’s performance. n nFirms like Coreweave, which lease computing capacity, carry enormous debt backed by short-term contracts that expire before obligations are due. Without rapid customer acquisition, defaults are likely. Meanwhile, research from MIT suggests that 95% of businesses adopting AI see no benefit or even suffer losses. A University of Chicago study found no measurable impact of AI on wages, hours worked, or earnings. n nThe inevitable correction may already be unavoidable. When investors eventually demand returns and find none, funding will dry up. The collapse could leave behind widespread job displacement and economic disruption, particularly for workers replaced under the assumption that AI could perform their roles—only for those systems to be shut down when funding vanishes. n nHowever, a post-bubble landscape could offer opportunities: surplus computing power, underutilized talent, and open-source models ripe for refinement. The key lesson is that AI should be viewed not as a revolutionary force, but as a set of tools whose real impact depends on how they are governed and integrated into the economy. The greatest danger isn’t technological—it’s the speculative mania inflating a financial crisis that could affect hundreds of millions. n— news from Cory Doctorow – Medium
— News Original —nThe real (economic) AI apocalypse is nigh n nLike you, I’m sick to the back teeth of talking about AI. Like you, I keep getting dragged into discussions of AI. Unlike you‡, I spent the summer writing a book about why I’m sick of writing about AI⹋, which Farrar, Straus and Giroux will publish in 2026. n n‡probably n n⹋”The Reverse Centaur’s Guide to AI” n nA week ago, I turned that book into a speech, which I delivered as the annual Nordlander Memorial Lecture at Cornell, where I’m an AD White Professor-at-Large. This was my first-ever speech about AI and I wasn’t sure how it would go over, but thankfully, it went great and sparked a lively Q&A. One of those questions came from a young man who said something like “So, you’re saying a third of the stock market is tied up in seven AI companies that have no way to become profitable and that this is a bubble that’s going to burst and take the whole economy with it?” n nI said, “Yes, that’s right.” n nHe said, “OK, but what can we do about that?” n nSo I re-iterated the book’s thesis: that the AI bubble is driven by monopolists who’ve conquered their markets and have no more growth potential, who are desperate to convince investors that they can continue to grow by moving into some other sector, e.g. “pivot to video,” crypto, blockchain, NFTs, AI, and now “super-intelligence.” Further: the topline growth that AI companies are selling comes from replacing most workers with AI, and re-tasking the surviving workers as AI babysitters (“humans in the loop”), which won’t work. Finally: AI cannot do your job, but an AI salesman can 100% convince your boss to fire you and replace you with an AI that can’t do your job, and when the bubble bursts, the money-hemorrhaging “foundation models” will be shut off and we’ll lose the AI that can’t do your job, and you will be long gone, retrained or retired or “discouraged” and out of the labor market, and no one will do your job. AI is the asbestos we are shoveling into the walls of our society and our descendants will be digging it out for generations: n nhttps://pluralistic.net/2025/05/27/rancid-vibe-coding/#class-war n nThe only thing (I said) that we can do about this is to puncture the AI bubble as soon as possible, to halt this before it progresses any further and to head off the accumulation of social and economic debt. To do that, we have to take aim at the material basis for the AI bubble (creating a growth story by claiming that defective AI can do your job). n n“OK,” the young man said, “but what can we do about the crash?” He was clearly very worried. n n“I don’t think there’s anything we can do about that. I think it’s already locked in. I mean, maybe if we had a different government, they’d fund a jobs guarantee to pull us out of it, but I don’t think Trump’ll do that, so –” n n“But what can we do?” n nWe went through a few rounds of this, with this poor kid just repeating the same question in different tones of voice, like an acting coach demonstrating the five stages of grieving using nothing but inflection. It was an uncomfortable moment, and there was some decidedly nervous chuckling around the room as we pondered the coming AI (economic) apocalypse, and the fate of this kid graduating with mid-six-figure debts into an economy of ashes and rubble. n nI firmly believe the (economic) AI apocalypse is coming. These companies are not profitable. They can’t be profitable. They keep the lights on by soaking up hundreds of billions of dollars in other people’s money and then lighting it on fire. Eventually those other people are going to want to see a return on their investment, and when they don’t get it, they will halt the flow of billions of dollars. Anything that can’t go on forever eventually stops. n nThis isn’t like the early days of the web, or Amazon, or any of those other big winners that lost money before becoming profitable. Those were all propositions with excellent “unit economics” — they got cheaper with every successive technological generation, and the more customers they added, the more profitable they became. AI companies have — in the memorable phraseology of Ed Zitron — “dogshit unit-economics.” Each generation of AI has been vastly more expensive than the previous one, and each new AI customer makes the AI companies lose more money: n nhttps://pluralistic.net/2025/06/30/accounting-gaffs/#artificial-income n nThis week, no less than the Wall Street Journal published a lengthy, well-reported story (by Eliot Brown and Robbie Whelan) on the catastrophic finances of AI companies: n nhttps://www.wsj.com/tech/ai/ai-bubble-building-spree-55ee6128?st=efV1EF&reflink=article_email_share n nThe WSJ writers compare the AI bubble to other bubbles, like Worldcom’s fraud-soaked fiber optic bonanza (which saw the company’s CEO sent to prison, where he eventually died), and conclude that the AI bubble is vastly larger than any other bubble in recent history. n nThe data-center buildout has genuinely absurd finances — there are data-center companies that are collateralizing their loans by staking their giant Nvidia GPUs as collateral. This is wild: there’s pretty much nothing (apart from fresh-caught fish) that loses its value faster than silicon chips. That goes triple for GPUs used in AI data-centers, where it’s normal for tens of thousands of chips to burn out over a single, 54-day training run: n nhttps://techblog.comsoc.org/2024/11/25/superclusters-of-nvidia-gpu-ai-chips-combined-with-end-to-end-network-platforms-to-create-next-generation-data-centers/ n nTalk about sweating your assets! n nThat barely scratches the surface of the funny accounting in the AI bubble. Microsoft “invests” in Openai by giving the company free access to its servers. Openai reports this as a ten billion dollar investment, then redeems these “tokens” at Microsoft’s data-centers. Microsoft then books this as ten billion in revenue. n nThat’s par for the course in AI, where it’s normal for Nvidia to “invest” tens of billions in a data-center company, which then spends that investment buying Nvidia chips. The the same chunk of money being energetically passed back and forth between these closely related companies, all of which claim it as investment, as an asset, or as revenue (or all three). n nThe Journal quotes David Cahn, a VC from Sequoia, who says that for AI companies to become profitable, they would have to sell us $800 billion worth of services over the life of today’s data centers and GPUs. Not only is that a very large number — it’s also a very short time. AI bosses themselves will tell you that these data centers and GPUs will be obsolete practically from the moment they start operating. Mark Zuckerberg says he’s prepared to waste “a couple hundred billion dollars” on misspent AI investments: n nhttps://www.businessinsider.com/mark-zuckerberg-meta-risk-billions-miss-superintelligence-ai-bubble-2025-9 n nBain & Co says that the only way to make today’s AI investments profitable is for the sector to bring in $2 trillion by 2030 (the Journal notes that this is more than the combined revenue of Amazon, Google, Microsoft, Apple Nvidia and Meta): n nhttps://www.bain.com/about/media-center/press-releases/20252/$2-trillion-in-new-revenue-needed-to-fund-ais-scaling-trend—bain–companys-6th-annual-global-technology-report/ n nHow much money is the AI industry making? Morgan Stanley says it’s $45b/year. But that $45b is based on the AI industry’s own exceedingly cooked books, where annual revenue is actually annualized revenue, an accounting scam whereby a company chooses its best single revenue month and multiplies it by 12, even if that month is a wild outlier: n nhttps://www.wheresyoured.at/the-haters-gui/ n nIndustry darlings like Coreweave (a middleman that rents out data-centers) are sitting on massive piles of debt, secured by short-term deals with tech companies that run out long before the debts can be repaid. If they can’t find a bunch of new clients in a couple short years, they will default and collapse. n nToday’s AI bubble has absorbed more of the country’s wealth and represents more of its economic activity than historic nation-shattering bubbles, like the 19th century UK rail bubble. A much-discussed MIT paper found that 95% of companies that had tried AI had either nothing to show for it, or experienced a loss: n nhttps://www.technologyreview.com/2019/01/25/1436/we-analyzed-16625-papers-to-figure-out-where-ai-is-headed-next/ n nA less well-known U Chicago paper finds that AI has “no significant impact on workers’ earnings, recorded hours, or wages”: n nhttps://papers.ssrn.com/sol3/papers.cfm?abstract_id=5219933 n nAnything that can’t go on forever eventually stops. Trump might bail out the AI companies, but for how long? They are incinerating money faster than practically any other human endeavor in history, with precious little to show for it. n nDuring my stay at Cornell, one of the people responsible for the university’s AI strategy asked me what I thought the university should be doing about AI. I told them that they should be planning to absorb the productive residue that will be left behind after the bubble bursts: n nhttps://locusmag.com/feature/commentary-cory-doctorow-what-kind-of-bubble-is-ai/ n nPlan for a future where you can buy GPUs for ten cents on the dollar, where there’s a buyer’s market for hiring skilled applied statisticians, and where there’s a ton of extremely promising open source models that have barely been optimized and have vast potential for improvement. n nThere’s plenty of useful things you can do with AI. But AI is (as Princeton’s Arvind Narayanan and Sayash Kapoor, authors of AI Snake Oil put it), a normal technology: n nhttps://knightcolumbia.org/content/ai-as-normal-technology n nThat doesn’t mean “nothing to see here, move on.” It means that AI isn’t the bow-wave of “impending superintelligence.” Nor is it going to deliver “humanlike intelligence.” n nIt’s a grab-bag of useful (sometimes very useful) tools that can sometimes make workers’ lives better, when workers get to decide how and when they’re used. n nThe most important thing about AI isn’t its technical capabilities or limitations. The most important thing is the investor story and the ensuing mania that has teed up an economical catastrophe that will harm hundreds of millions or even billions of people. AI isn’t going to wake up, become superintelligent and turn you into paperclips — but rich people with AI investor psychosis are almost certainly going to make you much, much poorer.