When code becomes cheap, good judgement becomes expensive.

The question is less and less: how do we write this code? It is: what should be developed at all, in what order, and when do you stop?

Where would perfection be an unnecessary up-front investment? When does an experiment become a product?

No AI system makes these decisions.

As AI tools become more capable, the relative effort of pure code production falls. With that, the bottleneck moves.

Increasingly, the central question is no longer: how do we produce this code?

It is:

What should be developed at all, in what form and in what order?

Which uncertainty should be reduced first? Which function is essential? Which architecture is appropriate to the current state of development? Where would technical perfection be an unnecessary advance investment? Which dependencies are acceptable? When does a state of development deliver enough insight? When should an experiment become a lasting product? When should development be ended or redirected?

These decisions do not arise automatically from an AI system.

AI can accelerate implementation, generate variants, support technical research and considerably shorten the distance between an idea and a working state of development. Responsibility for selection, architecture, assessment and integration nevertheless remains.

When production becomes faster and more economical, the relative value of good decisions rises.