Real Programmers Don't Use AI

The argument was never AI or no AI.

It was what kind of work you are asking AI to do.

That is why this 1983 Pascal satire still works.

The FORTRAN crowd had reasons. For scientific computing, FORTRAN had the optimizing compilers, the array model, and the libraries. Pascal looked like an academic teaching language. The pushback had a point.

They were right about parts of the tool. They were wrong about the direction of travel. Structure, readability, and compiler checks kept migrating until they became part of serious software engineering.

AI has the same shape.

The slop is real. It is also hard to distinguish from quality work, which dilutes the engineers using AI well. But the answer is not refusal. The answer is discrimination.

Discrimination means refusing the false choice.

Some engineers reject the new layer because the current tools are messy. Some use it everywhere because the current tools are powerful.

Both miss the point.

The real skill is asking what kind of work this is.

If the work needs repeatability, use deterministic tools. If the work needs judgment, search, synthesis, or iteration, AI may earn a place.

That was the Pascal lesson: it could be the wrong tool for a lot of 1983 scientific computing and still carry ideas the industry would eventually absorb.

The same thing is happening now.

The best engineers will not be the people with the purest rejection or the broadest adoption. They will be the people who can tell the difference.

That is the bridge from deterministic scaffolding to the next question: where does probability actually earn its keep?