I don’t really understand the appeal of AI for code gen. It can do some cool stuff, and I can see it being really useful for things like generating a bunch of hard-coded yaml or whatever, but I’m much faster at writing code than reviewing it, so it feels like a net negative in productivity to outsource the production of the code and take on a full-time reviewer role. Reviewing code is also much more mentally taxing, so it wears you down earlier in the day.

Most of the 20-30+ year veterans I talk to swear that AI is changing everything and always talk about how amazing it is, which I’m guessing is because they have 20+ years of experience in senior roles where they were mostly reviewing code and mentoring other people. I don’t have that level of experience though, and I imagine that if everyone starts with AI, they’ll never develop that experience because reviewing AI code is not the same as reviewing a human’s code. Human code review is a collaborative process that involves mentoring for more junior submitters and design discussion for peers. With AI, it’s just making sure it didn’t hard-code the API keys in the source files again.

Actually, I’m using it/them on a daily basis and they are driving me nuts more often than not. For as long as they’re used as “smart” search engines (a hint book), almost everything is fine. Face them with anything deeper than that and it becomes a waste of time.

The other day I confronted two of them because they had conflicting suggestions. I gave them both a snippet of Elixir code (like 30 LOC) to double check if I did it correctly and analyze against possible race conditions.

At first I was convinced ChatGPT gave me a competent answer. The analysis sounded competent, going through different scenarios and all. Then just in case I also gave it to the newest Grok 4.1 - bam! Equally competent sounding analysis, but with a conflicting conclusion. Then I took the suggestion of one and gave it to the other.
“The other LLM is completely wrong.” was the answer, and then some. As if I hit its ego. Then I did the opposite (gave the suggestion of the second to the first one) - same result if not worse. In the end, I realized they both did the same mistake - neither took the current version of the Elixir Task module source code (until I explicitly copy-pasted it for them in the end) but they did blame each other in the meantime for not doing it.

Anyway, the above is just a drop in the ocean of my dissatisfaction and I truly cannot figure out how on Earth do some people think they can use these tools to write fully operational software.

Lamport begrudgingly wrote “Paxos Made Simple” despite believing that the best way to present an algorithm like Paxos is through proofs (i.e. a TLA+ spec). As it turns out, not only is the paper widely mocked for not being simple, but the explanation in the paper contains a bug which is present depending on your interpretation of the algorithm as specified in “plain English”. Vindication!

Programmers do not understand AI because it’s not for them. These models are a revolutionary technology for people who do not understand how to program computers. They are useful for doing things that we cannot do with computer code.

I remember seeing someone (I think it was antirez?) make a point that all of the hype for MCP (the protocol) was a case of everyone focusing their attention on the most boring and unremarkable part of a fundamentally revolutionary technology.

There is something so comically absurd about taking a technology which is revolutionary specifically because it can do things we cannot do with computer code and then using those models to write the same computer code we were already writing, except badly. Talk about missing the forest for the trees.

But hey, at least we finally managed to put radio on the internet!