Task horizon breaks seat-based pricing — usage scales with workflow depth × length, not headcount

Gupta defines task horizon as “the length dial: how long an AI works on its own before a human steps back in. The unit shifted from the call to the workflow.” The pricing consequence is structural: “Agents run for hours, spawn sub-agents, and burn millions of tokens per decision path, so usage stops scaling with seats. Multiply length by depth and you get the token bill every Fortune 500 CFO now asks about.” Depth (inference-time compute) times length (task horizon) is the new cost surface — and it severs the seat-based logic SaaS was priced on.

This is the demand-side mechanism behind Cap headcount, not compute — token spend per engineer replaces headcount as the scaling unit — when one agent burns millions of tokens per decision path, compute, not seats, is the scaling unit. It explains why Autopilots capture the work budget — six dollars in services for every one in software and Sell the work, not the tool — model improvements compound for services, against software: once usage tracks workflow length rather than logins, you’re selling completed work, not access. Managing long horizons cheaply is exactly the Autonomous coding loops need small stories and fast feedback to work discipline, and the depth half of the multiplication is Inference-time compute makes cost-per-outcome a choice — and that's the application layer's counterattack on the labs.