[ndriscoll]

You could e.g. write specs and only review high level types plus have deterministic validation that no type escapes/"unsafe" hatches were used, or instruct another agent to create adversarial blackbox attempts to break functionality of the primary artifact (which is really just to say "perform QA").

As a simple use-case, I've found LLMs to be much better than me at macro programming, and I don't really need to care about what it does because ultimately the constraint is just that it bends the syntax I have into the syntax I want, and things compile. The details are basically irrelevant.

[surajrmal]

Code quality will impact the effectiveness of ai. Less code to read and change in subsequent changes is still useful. There was a while where I became more of a paper architect and stopped coding for a while and I realized I wasn't able to do sufficient code reviews anymore because I lacked context. I went back into the code at some point and realized the mess my team was making and spent a long while cleaning it up. This improved the productivity of everyone involved. I expect AI to fall into a similar predicament. Without first hand knowledge of the implementation details we won't know about the problems we need to tell the AI to address. There are also many systems which are constrained in terms of memory and compute and more code likely puts you up against those limits.

[ndriscoll]

I don't disagree that code quality is currently more important than it's ever been (to get the most out of the tools). I expect that quality will increase though as people refine either training or instructions. I was able to get much better (well factored, aligned to business logic) output that I'm generally happy-ish with a couple months ago with some coding guidelines I wrote. It's possible that newer models don't even need that, but they work well enough with it that I haven't touched those instructions since.

[rafterydj]

I mean, sure, for programming macros. Or programming quick scripts, or type-safe or memory-safe programs. Or web frontends, or a11y, or whatever tasks for which people are using AI.

But if you peel back that layer to the point where you are no longer discussing the code, and just saying "code X that does Y"... how big is X going to get without verifying it? This is a basic, fundamental question that gets deflected by evaluating each case where AI is useful.

When you stop being specific about what the AI is doing, and switch to the general tense, there is a massive and obvious gap that nobody is adequately addressing. I don't think anyone would say that details are irrelevant in the case of life-threatening scenarios, and yet no one is acknowledging where the logical end to this line of thinking goes.