AI has made building a startup dramatically easier. That is good news for founders. It is also the reason the bar for what deserves to become a company is going up.

A working prototype is no longer strong evidence. A clever demo is not strong evidence. Even a product that feels magical today can be weak evidence if its advantage exists only because the current generation of models has not yet absorbed the feature.

That changes the founder's job. The question is no longer simply Can AI do this? In many categories, the answer will eventually be yes. The harder question is whether you are building something that remains valuable when AI gets substantially better, cheaper and more available.

If the answer is no, you may still have a useful feature. You may even have a good business for a while. But you should know what you are building before you mistake temporary model limitations for a moat.

There is nothing wrong with shipping a thin AI product. Thin products can acquire users, generate revenue and teach you a market faster than a larger platform can. The mistake is assuming that a wedge and a moat are the same thing.

A wedge gets you in. A moat gives you somewhere to go once everyone else can copy the first feature.

The strongest AI companies are likely to own something deeper than a model call: a workflow, proprietary feedback, distribution, trusted actions, integrations, embedded history or an economic loop that becomes harder to replace with usage. Their advantage is not that they found a model everyone else missed. Their advantage is that better models make the system they own more capable.

Before you build, score the idea against the questions below. The goal is not to talk yourself out of building. The goal is to find the part of the idea that deserves to survive.

Start with the uncomfortable version of the question: if the AI disappeared, would there still be a valuable workflow underneath the product?

Many products begin with a beautiful interaction: ask, generate, summarize, transform. Those interactions can be useful, but they are becoming easier to reproduce. If the user can obtain roughly the same outcome by opening a general-purpose assistant and writing one good prompt, the burden of proof is on the startup.

A stronger opportunity usually appears when AI is not merely changing the interface. It changes the economics or feasibility of the job. It makes a workflow possible at a cost, speed or level of personalization that did not make sense before.

That distinction matters because features are absorbed. Workflows are harder to absorb when they include context, permissions, integrations, decision logic and responsibility for the final outcome.