Someone posted a link to an Edward Zitron article here recently, and I figured it might be interesting for a few of you too. Here’s a quick summary: The Subprime AI Crisis Is Here By Edward Zitron — March 31, 2026 Original source: wheresyoured.at/the-subprime-ai-crisis-is-here The Subprime AI Crisis Begins Back in September 2024, Zitron first articulated his thesis of an emerging Subprime AI Crisis: nearly the entire tech industry has bought into a technology sold at heavily discounted rates because it’s massively subsidized by Big Tech. At some point, the toxic burn rate of generative AI will catch up with them. How Money Flows Through the AI Industry The Funders Data centers raise debt from banks, private credit, private equity, or “Business Development Companies.” Recurring names: Blue Owl, MUFG (Mitsubishi), Goldman Sachs, JP Morgan Chase, Morgan Stanley, SMBC (Sumitomo Mitsui), Deutsche Bank. AI labs and startups receive money from venture capitalists (Dragoneer, Founders Fund), hyperscalers (Google, Amazon, NVIDIA, Microsoft — all of which have invested in both OpenAI and Anthropic), sovereign wealth funds (e.g., Singapore’s GIC), and banks via lines of credit. Risk factors: SMBC and MUFG are critical points of failure. Japan is considering rate hikes due to the ongoing Middle East crisis — making debt more expensive. The venture capital industry is in a historic liquidity crisis: it can’t raise its own funds, and its investments aren’t selling. The AI Economy Hierarchy NVIDIA sells GPUs to data centers. At about $42 million per megawatt , these data centers are funded almost entirely with debt. This is the only truly profitable link in the chain. Data center developers rent GPUs to AI labs and hyperscalers. They’ve taken on $178.5 billion in debt in the U.S. alone last year. Many projects are unprofitable even with paying customers. Of more than 200 GW of announced capacity, only 5 GW is actually under construction worldwide. CoreWeave — the largest and best-funded player — had a 2025 operating margin of −6% and a net loss margin of −29% , even though its biggest customers are Microsoft, OpenAI, and NVIDIA. Hyperscalers (Google, Meta, Amazon, Microsoft, Oracle) rent GPUs from data centers and re-rent them to AI labs. They steadfastly refuse to disclose AI revenues. AI labs (OpenAI, Anthropic) rent GPUs but must make massive upfront payments to secure future capacity. Anthropic has generated $5 billion in revenue but spent $10 billion on compute — and had to raise another $30 billion in February 2026 (after raising $16.5 billion in 2025 alone). OpenAI generated $4.3 billion in revenue through September 2025 and spent $8.67 billion on inference alone. Neither company has a path to profitability. AI startups buy API access to models. Every single AI startup is unprofitable and heavily subsidizes its users’ token consumption. Consumers and businesses pay monthly subscriptions ($20 to $200), which in every documented case fail to cover actual token consumption. What Does “Subsidized AI” Mean? AI models charge per million tokens (both input and output). One token is roughly ¾ of a word. With “chain-of-thought” models, token consumption explodes. As a user, you pay a flat monthly fee and see nothing of token consumption. On the back end, AI startups are incinerating cash: until recently, you could burn up to $8 of compute for every dollar of subscription on Anthropic. OpenAI is similar. This is the heart of the problem: when the AI bubble began, venture capitalists flooded startups with money and pushed them to pursue hypergrowth based on subscriptions whose prices came nowhere near covering costs. The result: consumers demand new models constantly. A service that doesn’t offer the latest model at the same price can’t compete — even if the new model is far more expensive to run. Concrete Examples Harvey (AI for lawyers): valued at $11 billion despite a laughably small $190 million ARR ($15.8M/month). Raised capital four times in 2025. Cursor (AI coding tool): has raised a total of $3.36 billion — and turned it into at best $1 billion in revenue. As of March 2026: $2 billion ARR ($166M/month). What Is the Subprime AI Crisis? The Subprime AI Crisis arrives the moment anyone in the chain actually has to start making money — or at least lose less. That’s when it becomes clear that every link in the chain was built on questionable assumptions and short-term thinking. The Sequence of Events Growing AI labs = exploding costs. Both for ongoing compute and upfront payments for future GPU capacity. AI labs hit a cash and compute crunch. They must either limit usage or raise prices. AI labs raise prices on startups , often through “priority tiers” or new models that burn more tokens — the variable-rate mortgage of the AI crisis. AI startups must reduce quality or raise prices → customer churn . AI labs continue subsidizing their own products. Cursor reports that Anthropic at one point allowed users to burn $5,000 worth of tokens per month on a $200 subscription. Eventually, Anthropic and OpenAI must drastically cut token allowances → furious users. The cost of doing business with Anthropic and OpenAI will kill AI startups → which in turn kills the labs’ API revenue. Anthropic and OpenAI are left holding compute reservations they don’t need and can’t pay for. Dario Amodei himself said in February: “There’s no hedge on Earth that could stop me from going bankrupt if I buy that much compute.” Who’s going to pay for all those data centers when the two largest compute customers (OpenAI and Anthropic) collapse? The Venture Capital Problem VCs are sitting on “billions of dollars” of AI companies that lose hundreds of millions. No one is going public, no one is being acquired. Like houses in the financial crisis, AI startups only retain their value as long as the perception of a possible exit exists. It only takes one failed IPO or fire sale to shatter the illusion. Unlike a house, you can’t live in an AI company. Each will be a problem child burning cash on inference, with no real intellectual property, dependent on OpenAI and Anthropic. The Crisis Begins: June 2025 Both OpenAI and Anthropic introduced “Priority Service Tiers,” raising prices on enterprise customers in exchange for guarantees, with 3-12 month upfront commitments. The Fallout at Startups Cursor had to radically restructure its pricing — and even now gives away 16 cents per dollar on its $60 plan and $1 per dollar on its $200 plan. Anthropic introduced weekly limits on July 28, 2025, after quietly tightening other limits a few weeks earlier. Replit moved to “effort-based pricing” and then “Agent 3,” which burns through limits even faster. Augment Code moved to a confusing credit model — users hate it because the company was too cowardly to price transparently. Notion raised its Business Plan from $15 to $20/month over “AI features” — profit margins dropped 10%. Perplexity drastically cut rate limits in February 2026: for some users, deep research queries fell from 600 to 20 per month. Myths Zitron Is Tired Of Zitron dismantles several popular arguments: “But Uber and AWS!” — Wrong. AWS cost roughly $52 billion (inflation-adjusted) to reach profitability (2003–2017). OpenAI alone raised $42 billion last year, Anthropic raised $30 billion in February. Uber had essentially no capex and a completely different business model. “They’re profitable on inference!” — There’s not a single piece of evidence for this. Sam Altman claimed it in August 2025; Dario Amodei spoke of a “stylized fact” that explicitly did not refer to Anthropic. “AI is being funded by healthy balance sheets!” — Microsoft is the only remaining hyperscaler funding the AI buildout without new debt. They collectively need $2 trillion in new AI revenues by 2030. “Costs are falling because token prices are falling!” — The price of tokens is not the same as the cost of serving them. “It’s the gym membership model!” — No. March 2026: The Crisis Hits Anthropic’s Subscribers Both OpenAI and Anthropic are stumbling toward their IPOs and trying to look “respectable.” OpenAI just killed Sora last week — along with a $1 billion Disney investment — because the product was reportedly burning between $1 million and $15 million per day . OpenAI is now pursuing a “Superapp” plan while simultaneously planning to nearly double its workforce from 4,500 to 8,000. Advertising experiments yielded only about $8.3 million over two months. Anthropic’s Months-Long Rugpull In December 2025, a massive media campaign for Claude Code began. Suddenly, posts everywhere claimed Claude Code was “the best thing ever.” Even Dario Amodei claimed some Anthropic coders no longer wrote any code at all. From December 25–31, 2025, Anthropic doubled limits as a “Holiday Promotion.” On January 5, 2026, users complained about brutal new limits — one user reported a 60% reduction in token usage. The media campaign worked: Anthropic closed a $30 billion round on February 12, 2026. The March 2026 Escalation February 18, 2026: Anthropic began banning users with multiple Max accounts. March 26, 2026: Anthropic introduced “peak hours” (5 AM–11 PM Pacific, Mon–Fri) with aggressively reduced limits. The consequences were immediate: A user on the $100 Max plan hit 61% of their session limit after four prompts ($10.26 in tokens). Another on the $200 plan burned 63% of their limit in a single day. Yet another hit 95% after 20 minutes of use. One user hit their Max limit after “two or three things.” OpenAI immediately seized the opportunity and reset its Codex limits to poach angry Anthropic users. Model Quality and “Mythos” Users complain that Claude Opus 4.6 suddenly seems “dumb” — possibly because of Anthropic’s new model Mythos , whose existence was supposedly leaked through an “openly accessible data cache” mysteriously discovered by Fortune. Zitron compares the marketing maneuver to someone deliberately dropping a Magnum condom out of their wallet in front of a woman. Anthropic and OpenAI Have Trained Users Into Unsustainable Habits Zitron’s central thesis: Anthropic and OpenAI are inherently abusive companies built on theft, deception, and exploitation. Users have no idea about “token burn.” Rate limits expand and contract seemingly at random. The whole system was deliberately made opaque because transparency would expose how unsustainable the business models are. No AI company should ever have sold a monthly subscription , because there was never a point at which the economics made sense. Had they charged their true costs, no one would have bothered with AI. The Car Analogy Imagine a $200/month car subscription that lets you drive 50, 25, 100, 4, or 12 miles depending on the day — and never tells you how many miles you have left. Sometimes the car arbitrarily takes a different route, drives you five miles in the wrong direction, or just parks at the curb and still bills you for every mile. That is the reality of using an AI product in 2026. There is no pricing model that makes sense at scale. There is no technical breakthrough waiting in the wings. Vera Rubin (NVIDIA’s next GPU generation) won’t save AI. Nor will a “too big to fail” scenario — the AI industry’s economic footprint is small compared to the financial crisis. The death of the AI industry would be devastating for VCs and would likely kill Oracle — but nothing on the scale of 2008. The Multiple “Strippers With Five Houses” Zitron identifies several groups of actors all living in the same illusion: AI companies that only have customers because they spend $3–10 for every dollar of revenue. Venture capitalists who are paper-rich and have leveraged their funds into companies like Harvey (“worth $11 billion”) and Cursor (“worth $29.3 billion”) — both too large to sell to another company and too poor in quality to take public. AI labs that have built massive businesses on subsidized subscriptions and API access. AI data center companies that, thanks to easy debt, have started 200 GW of projects (only 5 GW actually under construction) — for demand that doesn’t exist. Oracle , taking on hundreds of billions in debt to build data centers for OpenAI, which needs infinite resources to pay its compute bills. AI startup customers building lifestyles, identities, and workflows around unsustainable subscriptions. The Pale Horses of the AI Apocalypse Zitron updates his list of warning signs to watch: Further price hikes or service degradations at Anthropic and OpenAI → cash is running low. Capex reductions at Big Tech → the bubble is bursting. Further price hikes or service degradations at AI startups like Cursor, Perplexity, Harvey, Lovable, Replit. Layoffs at AI companies. Collapse of a data center deal before construction begins. Collapse of a data center already under construction. Collapse of a finished data center. CoreWeave or another major player struggling to raise debt. This has already begun with CoreWeave’s troubles financing its Lancaster, PA data center. Problems at Stargate Abilene (OpenAI’s flagship data center, built by Oracle). Delays or problems with the OpenAI or Anthropic IPOs — both are “the financial equivalent of Chernobyl.” Problems at Blue Owl as a going concern — the loosest lender in the AI bubble. Problems at SoftBank — took on $40 billion in debt (payable in a year) for its OpenAI stake, exceeding its promised 25% loan-to-asset ratio. ARM stock falling — SoftBank has a $15 billion margin loan against ARM stock. Below $80, things get hairy for Masayoshi Son. NVIDIA customers struggling to pay their bills. NVIDIA missing earnings. Zitron’s Conclusion All these actors are operating on a misplaced belief that the world will accommodate them and that nothing will ever change. There are different levels of cynicism — some know about the subsidies but assume they’ll be fine; others, like Sam Altman, are already rich and don’t care. But everyone in the AI industry has convinced themselves they have the mandate of Heaven. The Subprime AI Crisis is the moment when the largest players are finally forced to reckon with their rotten economics — and the downstream consequences that follow. submitted by /u/Good-Fennel-373
Originally posted by u/Good-Fennel-373 on r/ClaudeCode