AI Coding: Architecture or Archaeology?

One of the loudest claims around AI coding tools is that one developer can now do the work of a whole team. Ship features in hours. Replace weeks with prompts. Move 10x faster.

Well, maybe. But only if you pretend that software development is mostly typing code. It isn't.

For any system more complex than basic CRUD, the hard part is not producing source files. The hard part is knowing what the source files must preserve.

The model. The boundaries. The contracts. The states. The invariants. The failure modes. The tests that actually prove something. The decisions that stop the system collapsing under its own ambiguity six months later. AI does not remove that work.

It can help draft it. It can challenge it. It can produce options. It can fill in boilerplate once the shape is clear. But it cannot own it.

If you let the tool discover the model by generating code, you don't get architecture. You get archaeology. Later, someone has to dig through the result and work out what assumptions accidentally became real. That's not acceleration. That's debt with better autocomplete.

That's the difference between using AI as a tool and letting AI smear decisions across a codebase. For production systems, modeling is not optional. Correctness is not optional. Evolvability is not optional.

So no, AI coding agents do not make software engineering disappear. They make weak engineering visible faster. And in the hands of a good engineer, they can be useful. Sometimes very useful.