Build Something Agents Want

Paul Graham’s line — build something people want — was the operating instruction for two decades of product management. It assumed a specific topology: a human sees a product, evaluates it, decides, uses it. Every PM artifact — wireframes, user research, onboarding, retention cohorts — was built around that topology.

The topology is breaking. The evaluator, increasingly, is not a human. It is an agent: a model making a tool call, a planner selecting a sub-skill, an orchestrator routing a task. When the agent is the first-pass user, the PM stack inverts. Wireframes become schemas. Onboarding becomes discoverability in a tool manifest. Retention becomes reuse — does the agent call this tool again?

But the sharper version of the thesis — the one most takes miss — is this:

“Build what agents want” is incomplete. The real problem is designing systems where what agents want locally produces what humans want globally. That is an incentive architecture problem, not an AI problem.