The Pull Signal Still Comes From Humans
Tren Griffin's "12 Things about Product-Market Fit" insists that real PMF is felt as usage pulling the product out of your hands. In an AI-native company, agents can generate activity that looks like pull but isn't. The honest signal is whether customers are coming back because outcomes improved their lives or businesses, not because your system ran a workflow overnight. Human retention is the only retention that counts before you've found the fit.
Before Fit, Speed of Learning Beats Everything
Griffin draws a hard line: before PMF, optimize for learning speed, not growth. For founders building with agents, this means your early agentic workflows should be instrumented to surface what customers actually wanted versus what the agent did. The unscalable work — hand-onboarding the first fifty customers, sitting with them as the agent runs, watching where trust breaks — is market research you cannot buy. Skipping it is how you automate your way to the wrong product at scale.
Distribution Is the Proof, Not the Prize
When software is cheap to produce, PMF and distribution are entangled from the start. The founder who has lived the customer's problem knows which outcomes to promise, which builds the trust that gets the first call returned. That domain expertise is a distribution channel. Agents can compress delivery time to near zero, but they cannot compress the credibility gap. You still have to earn the right to deliver.
After Fit, Don't Fumble the Demand
Griffin's warning about fumbling demand after PMF is especially sharp for agent-powered companies, because the failure mode is invisible. You can scale agentic capacity faster than you can scale human judgment — the oversight, accountability, and tradeoff-making that keep customers trusting you. The work after fit is designing the right boundaries and feedback loops so that more throughput doesn't mean less trust.
