> History is littered with similar cases where the abstraction layer ends up getting lifted, and people struggle with getting accustomed to working at that higher abstraction level.

But there's no abstraction layer that ends up getting lifted. When I use a library like SDL or a standard like POSIX, I don't tend to look at the underlying implementation. Instead I work with the high level concepts that they come up with. There's no such things with AI tooling. The most similar is when fully vibing software and everyone knows the quality of the result.

I've learned something at every abstraction layer in computing from electronics (hardware), theory of computation (software) to high level programming languages with their paradigms. Same with several domains embodied by libraries. LLM tooling is more like shamanic ritual than engineering.

> There's no such things with AI tooling. The most similar is when fully vibing software and everyone knows the quality of the result.

This is the same “but the ai can’t even do fingers” argument all over again, and it will age just a well.

The whole vibe coding thing only really started taking hold with the release of opus 4.5. That wasn’t even a year ago. you absolutely can be quality software without looking at the code. The only thing up for debate in my mind is how big that software can get before you need to look under the hood.

But that’s dictated by current model capabilities. And I don’t see any reason to believe that they will suddenly stop improving on this front.

Exactly this. You still have to understand the abstraction layer the LLM is working at in order to be able to form an opinion about the quality of the code. (If you DON’T care about the quality of the code, there’s always been ways to get an extra 1000% short-term speed improvement by just not giving a shit.)

AI isn’t “a code abstraction layer” any more than Jira is, or the engineer themselves is.

>and everyone knows the quality of the result.

Yeah, a bespoke program that does exactly what I need it to do, at a speed that I had forgotten was possible on computers, with customization that is an exact fit to me, at a cost that is smaller than a rounding error.

I get that LLMs struggle with the old paradigm of a single piece of software meant to serve every conceivable use case of every conceivable user, but I kinda hope that paradigm dies.

With AI you learn contributor, project, and product management.