Venture capital’s return model was built for a world in which building a company took years and millions of dollars. AI has challenged that assumption, yet much of the industry has not repriced for it.

A paradox is becoming clear. AI is compressing the time and capital needed to reach product-market fit. On the surface, that should be good news for venture capital. But it also changes the nature of defensibility.

When a two-person team can build what once required a funded, twenty-person engineering organisation, the moat that justified venture-scale valuations can become thinner, not stronger. Capital efficiency was meant to be the pitch. It may be becoming the threat.

The mechanics matter. VC economics rely on a small number of exceptional winners to offset a portfolio of failures. That model assumes winners are difficult to replicate once they emerge.

But AI-native competitors can now reproduce a successful product’s feature set in a fraction of the original build time. Moats built primarily on engineering effort are eroding at the same speed that engineering effort itself is becoming cheaper.

None of this means venture capital disappears. It means the model that once priced risk around “can they build it?” must now put much greater weight on “can they defend it?”

And that is a question too few term sheets, investment committees and founders’ narratives are yet asking with sufficient rigour.

#VentureCapital #AIStrategy #StartupEconomics