Verification-Grade Network Effects: The Only Moat That Will Endure AI Competition
As open-weight models close the gap on closed-source AI, traditional moats—routing, distribution, app features—evaporate. Catalini argues the only lasting advantage is a verification-grade network effect: a proprietary loop that decides when machine output is good enough and feeds that judgement back into training. Owning that loop lets founders monetize the last human-in-the-loop step.