Why are executives enamored with AI, but ICs aren't?

I do not think most executives are particularly enamored with AI. They are being mostly driven by the fear of missing out. More precisely, their thought process is: if they bet on AI and fail, they can plausibly claim that it was the technology's fault (not good enough, poorly suited for the business, etc). But if they skip on AI by choice, and their competition succeeds, they will be blamed personally. The more hyped a technology is, the stronger this calculus is for the managers. It's like Pascal's wager in a way.

My problem with the code the agents produce has nothing to do with style or art. The clearest example of how bad it is was shown by Anthropic's experiements where agents failed to write a C compiler, which is not a very hard programming job to begin with if you know compilers, as the models do, but they failed even with a practically unrealistic level of assistance (a complete spec, thousands of human-written tests, and a reference implementation used as an oracle, not to mention that the models were trained on both the spec and reference implementation).

If you look at the evolution of agent-written code you see that it may start out fine, but as you add more and more features, things go horribly wrong. Let's say the model runs into a wall. Sometimes the right thing to do is go back into the architecture and put a door in that spot; other times the right thing to do is ask why you hit that wall in the first place, maybe you've taken a wrong turn. The models seem to pick one or the other almost at random, and sometimes they just blast a hole through the wall. After enough features, it's clear there's no convergence, just like what happened in Anthropic's experiment. The agents ultimately can't fix one problem without breaking something else.

You can also see how they shoot themselves in the foot by adding layers upon layers of defensive coding that get so think they themselves can't think through them. I once asked an agent to write a data structure that maintains an invariant in subroutine A and uses it in subroutine B. It wrote A fine, but B ignored the invariant and did a brute-force search over the data, the very thing the data structure was meant to avoid. As it was writing it the agent explained that it doesn't want to trust the invariant established in A because it might be buggy... Another thing you frequently see is that the code they write is so intent on success that it has a plan A, plan B, and plan C for everything. It tries to do something one way and adds contingencies for failure.

And so the code and the complexity compound until nothing and no one can save you. If you're lucky, your program is "finished" before that happens. My experience is mostly with gpt5.4 and 5.3-codex, although Anthropic's failed experiment shows that the Claude models suffer from similar problems. What does it say when a compiler expert that knows multiple compilers pretty much by heart, with access to thousands of tests, can't even write a C compiler? Most important software is more complex than a C compiler, isn't as well specified, and the models haven't trained on it.

I wish they could write working code; they just don't.[1] But man, can they debug (mostly because they're tenacious and tireless).

[1]: By which I don't mean they never do, but you really can't trust them to do it as you can a programmer. Knowing to code, like knowing to fly a plane, doesn't mean sometimes getting the right result. It means always getting the right result (within your capabilities that are usually known in advance in the case of humans).

I think ICs are threatened because they're told from day one how they are at will employees that can be terminated at any time with or without cause.

On top of that, places like Amazon extol the virtues of only working on projects that can be completed with entirely fungible staffing and Google tries ever so hard to electroplate this steaming turd of an ideology with iron pyrite calling fungibles "generalists."

So along comes AI coding agents, which I love as an IC because it excels at tedious work I'd rather not have to do in the first place, yet I get why others see it as a threat. But I really think it's no more of a threat than any other empty promise to cut costs with the silver bullet of the month and we just have to let the loudmouths insist otherwise until the industry figures out this isn't a magic black box. They never learn, do they? Maybe their jobs depend on never learning.

Admit to having drank the koolaide, it is the first step. I wrote an entire system with tech I barely understand (duckdb), next.js etc, made 7 to 10 iterations per day, and multiple new functions and integrations in hours all while doing my main job. What does the code look like ???. It works, I do not care. Can the AI modify it in under 5 minutes, yes. New features that would take minimal a week, got done in 2 to 3 minutes. Did the AI ever complain, no it did not. Anyone who thinks they will be hand coding going forward is completely fooling themselves. The AI tests better than most engineers. When asked it builds flawless test harnesses and even suggests better solutions. Never going back.

How is it not? It reads to me as them saying that all these devs have deskilled from "barely competent" to "completely helpless". Or is your claim that they were actually really good devs, and the deskilling has been even more intense than I'm picturing?

Because that also sounds real bad!