Find the Job Nobody Is Hiring Software to Do
Christensen's jobs-to-be-done framework asks what progress a customer is trying to make when no good tool exists yet. AI-native companies are unusually well-positioned here because agents lower the floor on what's economically worth automating. Tasks that required a specialist — and therefore went undone at smaller companies — can now run inside a workflow. The opportunity is to find those neglected jobs and build the operating loop around them before anyone with a legacy architecture can respond.
Stay Integrated Where Performance Still Falls Short
Christensen's prescription is to integrate tightly across the stack whenever performance isn't yet good enough to modularize. AI-native companies face this constantly: model quality, eval reliability, and human-review tooling are all still maturing. Pulling apart too early, outsourcing judgment to an API and calling it done, produces brittle systems. The founders who hold integration — owning the task definition, the context pipeline, and the evaluation layer together — build the compounding data advantage that late entrants cannot easily replicate.
Trust Is the Moat Non-Consumption Creates
Christensen observed that new-market disruptions often win on accessibility and simplicity before they win on raw capability. In a world where software is cheap to create, the scarce thing is trust that a system will behave reliably on consequential work. Founders who design human judgment into their loop from day one — where humans set goals, define boundaries, and own accountability — are not slowing down; they are building the credibility that lets customers hire the product for progressively more important jobs over time.
