Fortune published a piece this afternoon connecting Microsoft and Uber's AI cost overruns to token economics, with a headline that lands hard: "Microsoft reports are exposing AI's real cost problem: Using the tech is more expensive than paying human employees." Underneath those headlines, the unit economics tell the story. OpenAI is projected to lose $14 billion in 2026, spending roughly $2 for every dollar of revenue it brings in. Anthropic is in a similar position with break-even not projected until 2028. GPU rental prices for Nvidia's newest Blackwell chips jumped 48% in just two months. OpenAI's response was to close a $122 billion private funding round at an $852 billion valuation, the largest in history.
My Take
The token pricing story is really an IPO timing story. OpenAI, Anthropic, and xAI all need to go public in the next 18 to 24 months because the private market cannot keep absorbing burn rates like these indefinitely. Public markets do not accept "we will figure it out" as a line item on an S-1, they require disclosed unit economics with a credible path to profitability and a date attached. That deadline is why the price increases are happening now rather than next year. The labs need to show declining loss curves before the filings hit, and that means enterprise customers have to start covering more of the actual cost regardless of whether the productivity math holds on their end.
Every token bought over the last two years was effectively subsidized below cost by venture capital and hyperscaler cross-subsidies, and that subsidy has a hard deadline. Uber publicly admitted burning through its entire 2026 AI budget in four months, and CFOs at major enterprises are starting to flag the same pressure. The labs cannot keep losing $2 per dollar of revenue once they file public statements, so the cost transfer to customers accelerates from here. For investors, the question is not whether these companies are valuable. They clearly are. The question is who absorbs the difference between what enterprises can budget and what the models actually consume between now and 2028, and right now the answer is the hyperscalers funding the buildout. That is why I have been watching Microsoft and Amazon capex commentary more closely than the lab announcements themselves.
