Model Market Fit

Summary

In June 2007, Marc Andreessen published what became the defining essay on startup strategy. “The Only Thing That Matters” argued that of the three elements of a startup—team, product, and market—market matters most. A great market pulls the product out of the startup. The product doesn’t need to be great; it just has to basically work.Andreessen’s insight has guided a generation of founders. But nineteen years later, something has changed. A new variable has entered the equation. One that determines whether the market can pull anything at all.That variable is the model.For AI startups, there is a prerequisite layer beneath product-market fit: the degree to which current model capabilities can satisfy what a market demands. I call it Model-Market Fit, or MMF.When MMF exists, Andreessen’s framework applies perfectly. The market pulls the product out. When it doesn’t, no amount of brilliant UX, go-to-market strategy, or engineering can make customers adopt a product whose core AI task doesn’t solve their job to be done.The pattern is unmistakable once you see it. A model crosses a capability threshold. Within months, a vertical that had been dormant for years suddenly explodes with activity.For years, legal tech AI was stuck below scale. There were plenty of companies but none broke through. Document review tools that required more human oversight than they saved. Contract analysis that missed critical clauses. Every legal startup before 2023 struggled to cross $100M ARR.I remember this firsthand. I founded Doctrine in 2016, which grew to become the leading AI legal platform in Europe. But it was incredibly hard to raise money because all companies were sub-scale and the market wasn’t hot at all. Investors saw legal AI as a niche with limited upside.The market existed. Law firms desperately wanted automation. But the state-of-the-art models couldn’t handle the core tasks lawyers needed. BERT and similar transformer models excelled at classification like sorting documen...