If you're delegating discovery, iteration, and customer touchpoints to agents, you need PMF intuitions baked into the system design, not bolted on later. The signal is the same; the noise arrives faster.
Griffin's guide, originally published through a16z, strips product-market fit down to its mechanical core: it is not a threshold you cross once, it is a condition you either have or you don't, and the market tells you which. Demand that genuinely pulls product from a team feels categorically different from demand you manufacture through effort. Before fit, the only currency worth spending is learning speed. After fit, the mortal sin is operational fumbling — letting supply-side chaos eat the demand you finally earned.
The translation cuts both ways. Agents can compress the pre-fit learning loop dramatically — running more experiments, synthesizing user feedback, iterating copy and flows faster than any human team. But Griffin's framework implies a trap: speed without judgment just accelerates the wrong direction. The pull signal he describes is a human feeling, noticed by someone close enough to customers to feel the difference between traction and noise. Your job as a founder is to keep that receptor alive in the system — probably in yourself — while letting agents handle the throughput on either side of it.
- deploy agents to maximize learning velocity before fit, not to simulate the fit signal itself
- treat the moment someone feels product being pulled from their hands as the one data point no agent should be trusted to interpret alone
- after fit, agents earn their keep by absorbing operational load so demand doesn't die of internal friction.
