Lab economics: gross margins and the training write-off

Plain English. The frontier-lab profit shape: inference — serving tokens — is sold at a positive gross margin, while training runs are enormous upfront costs written off against hoped-for future revenue. The result is fast-growing revenue and deep overall losses at the same time.

Why it moves money. The two headline numbers point in opposite directions and both are real. Anthropic reported preliminary quarterly revenue above US$11.5 billion (reported); the same year's loss forecasts ran to US$14 billion for OpenAI and a reported US$11 billion for Anthropic. Revenue headlines are run rate, not margin: the question a valuation must answer is whether inference gross margin, at scale, ever covers the training treadmill — each generation's write-off is larger, and the pricing power to fund it is exactly what commoditisation attacks. If cognition becomes a commodity, the margin expansion the model assumes never arrives.

What to watch. Gross-margin disclosures as labs approach public markets; whether price cuts read as margin defence or margin confidence; and how training compute is expensed — the depreciation choice that shapes every reported number.

From the signals. OpenAI's $14B and Anthropic's $11B projected 2026 losses meet the cash-burn paradox. Anthropic reports quarterly revenue above US$11.5bn. Mark: the model makers became commodity providers of cognition in August.