AI의 수익성 위기: 공짜 점심은 끝났다

Tuesday, June 23, 2026 AI's Affordability Crisis A year ago in The Back Of The AI Envelope I pointed out that the AI platforms were running the drug-dealer's algorithm, "the first one's free". By massively subsidizing the use of their products, they were generating overwhelming demand for them. They used this demand to justify massive investments, in the hope that, by the time they had to show a return on these invetment, the users would be so addicted that they would pay the vastly higher prices needed to generate a return. David Cahn, Sept '23 I have to confess that I was late to the party. The earliest skepticism I've been able to find was from Sequoia Capital's David Cahn in September 2023, entitled AI's $200B Question . Only nine months later Cahn re-ran the same analysis in AI's $600B Question . His estimate of the revenue gap had tripled. Cahn wasn't alone. Independent journalists such as Ed Zitron were flagging this problem long before I was. I started to write this post a couple of months ago when the maiinstream business press began to notice companies complaining about the cost of the tokens their employees were burning. Since then the trickle has turned into a flood, which made finishing the post hard. Below the fold I throw up my hands and dump out a small sample from the flood. One difficulty has been that estimates of the size of the subsidy have varied widely, typically in the range of costing the platforms $8 to $14 to generate $1 in revenue. Two recent posts from Ed Zitron have illuminated this issue. Source First, in AI's Brokenomics Zitron reported that: SemiAnalysis, an extremely pro-AI semiconductor analyst, ran a test made up of random long-horizon coding tasks until they maxed out the limit on OpenAI and Anthropic's various subscription levels. Their findings were shocking. For $200 A Month, You Can Burn $8000 in Anthropic Tokens or $14,000 In OpenAI Tokens That's right. Anyone with a $200-a-month Anthropic subscription can burn $8000 in tokens, and with a $200-a-month ChatGPT subscription, you can burn $14,000 in tokens. Source Zitron's numbers don't tell us the real cost of generating tokens but, subject to the assumption that the platforms are not subsidizing the token price, that means Anthropic is subsidizing their enterprise customers by up to 40 times, and OpenAI up to 70 times. No wonder they are seeing massive demand! But, despite OpenAI's subsidy being 175% of Anthropic's, OpenAI's adoption by businesses has recently been flat while Anthropic's has soared. Source SemiAnalysis also analyzed the platform's gross margins, implausibly assuming that tokens were priced at 4 times the cost of generating them and: With the current subsidies, all it takes for a user to have a gross margin of at best negative 25% is for them to use as little as 25% of their rate limit . Naturally, subsidizing your sales like this means you are feeding cash into the furnace. We have seen OpenAI and Anthropic raising vast sums in equity, but because they both have been private companies we haven't seen the details of their spending or revenue. On June 15 th this changed when Zitron saw OpenAI's 20025 financials and posted OpenAI Losses Increased Nearly 8X in 2025, With Spending Hitting $34 Billion , revealing that: OpenAI Had $13.07 Billion In Revenue, $34 Billion In Costs and Expenses, and $20.92 Billion In Losses, with a net loss attributable to the company of $38.53 Billion The numbers are somewhat complicated because : 2025 was the year that OpenAI converted from a non-profit to a for-profit entity, leading to a $41.55 billion loss due to changes in fair value of convertible interests and warrant liability. ... Ultimately, the net loss attributable to OpenAI in 2025 was $38.5 billion. At the end of the year, OpenAI had just over $50 billion in assets, with almost half of that in cash. Perhaps the most striking of their truly awful numbers were : Revenue: $13.07 billion ... Sales and Marketing: $5.73 billion That is, OpenAI spent 44% of their revenue on sales and marketing! The hype needed to keep the AI bubble inflated is incredibly expensive. Despite this lavish spending, business adoption has been flat. US equity markets are facing three IPOs of AI companies, SpaceX, Anthropic and OpenAI, each led by a world-class bullshitter, each losing tens of billions fo dollars a quarter, and all but SpaceX touting overwhelming demand for their products [ 1 ] . But, after they go public, they will need to charge enough to generate a return on their enormous capital investments. Ideally, they would have postponed the necessary swingeing price increases until the IPO money is in the bank. Alas, their burn rate is so high that they have been forced to make some premature moves toward price sanity. Back in April Ed Zitron reported that Microsoft To Shift GitHub Copilot Users To Token-Based Billing, Tighten Rate Limits : Leaked internal documents viewed by Where's Your Ed At reveal that Microsoft intends to pause new signups for the student and paid individual tiers of AI coding product GitHub Copilot, tighter rate limits, and eventually move users to "token-based billing," charging them based on what the actual cost of their token burn really is. The document says that although token-based billing has been a top priority for Microsoft, it became more urgent in recent months, with the week-over-week cost of running GitHub Copilot nearly doubling since January. The move to token-based billing will see GitHub users charged based on their usage of the platform, and how many tokens their prompts consume — and thus, how much compute they use. Anthropic, OpenAI and Microsoft have all now transitioned customers from subscriptions to token-based pricing. For serious users, this is eye-wateringly expensive. Jamie John, Rafe Rosner-Uddin and Ryan McMorrow's ‘We created a monster': companies rein in AI usage as costs strain budgets quotes a small company's CEO: But the company got a shock when Anthropic switched it over to token-based pricing in May. "Our spend went up 7x the first day and I'm like, oh shit, we created a monster," said Busse. "[Large language model] companies have been subsidising all of our usage and now no longer. User-based pricing shelters you." Thus in recent weeks the idea that Generative AI (LLMs for short) is too expensive has been all over mainstream business media. Examples include Bloomberg's video Major Companies Reconsider AI Costs , Scott Galloway's video AI May Not Be Worth The Cost — Here's Why , Derek Thompson's The AI Boom Has Entered Its 'Wait, Is This Worth It?' Era , and Jowi Morales' AI cost crisis hits tech giants as employee 'tokenmaxxing' backfires, sparking corporate pullback at Microsoft, Meta, and Amazon — agentic AI eats up to 1000x more tokens than standard AI , who notes that: it's now apparent that using AI is more expensive than hiring people, especially since it offers only limited productivity gains at the moment. Lest you think it is only the AI haters complaining about the cost, check out Bruno Ferreira's Nvidia exec says AI is more expensive than actual workers — yet some companies don't see the extra costs as a negative : Bryan Catanzaro, Nvidia's VP of applied deep learning, recently told Axios that "For my team, the cost of compute is far beyond the costs of the employees", quite an interesting statement from the company selling the shovels for the gold rush. That perspective is shared by Uber's CTO Praveen Naga, who "[went] back to the drawing board because the budget [he] thought [he] would need is blown away already" as of two weeks ago. Likewise, Swan AI's Amos Bar-Joseph posted a while back on LinkedIn about how proud he was about a $113k bill from Anthropic (makers of Claude) for a four-person team. Oversimplified math pins that amount that at $28k per person per month, which is likely more than each person's monthly wages. Jokes abound right now that "companies have disco