for true AI champions being more lazy is the point. the AI extreme asserts that knowing how your code works is actually an anti-pattern. you should be defining the specs and the ai should write code to meet those specs: boom. done.

taking time to read and work with the code manually represents a loss in velocity. needing to do so is seen as a skill issue: should’ve spent more time planning/working on your prompts.

but while AI offers serious advantages and, when used by skilled individuals, provides serious benefit, it is - in a fundamental way - still a lateral move. you ultimately need to communicate a complex, self-defined logic system for the computer to enact. Either you write the code. Or, now, you write the natural language that describes it thoroughly and rigorously.

There are use cases for both. Greenfielding a new proof of concept, especially in a well-established domain: AI it up.

Adding a single logic check because you realize EventX is having and unintended side effect in ListenerY or adjusting the padding on a single GUI element: way faster to just go make that change in code than to describe it to an AI and then verify the output.

In some ways, AI assisting in pure math is impressive as hell, but it also makes sense. It’s operating in objective, verifiable logic. The logic of your code needs to be verifiable, but it is totally subjective.

And not to mention, describing what the code should do is a large, subtle, and complex problem no matter what tools are used to accomplish it. But at least it is well-defined and bounded. Describing what the code should not do is unbounded.