AI Made Execution Cheaper. It Did Not Make Product Judgment Cheaper.

Execution is getting cheaper, and that part is real. A team can now generate options, draft specs, produce usable code, and assemble first-pass assets in a fraction of the time it used to take.

The problem is that this can be easy to misread.

Cheaper execution does not mean cheaper judgment. The expensive questions in product are still expensive: what should be built first, which customer problem is urgent enough to matter, what tradeoff is acceptable under current constraints, and what evidence would actually cause us to change direction.

If those answers are weak, faster execution does not save us. It just speeds up how quickly we can make costly mistakes.

This is why I do not think product strategy and AI strategy should be split into separate conversations. They are the same system. AI changes the execution layer, but it does not replace the product layer. If anything, it makes product judgment more important, because there are now more plausible paths to pursue, more outputs to evaluate, and more opportunities to confuse activity with progress.

The teams that will win here are rarely the teams with the biggest prompt library. They are the teams with operating discipline: explicit hypotheses, visible decision records, context that survives handoffs, validation that can reject work quickly, and humans who are clearly accountable for approval and escalation.

That is not anti-AI. It is the opposite. It is how AI becomes compounding leverage instead of compounding noise.

When execution is no longer the scarce resource, judgment becomes the bottleneck. In product, bottlenecks usually define the next competitive edge.