I am inclined to agree.

However, I'm not completely sure. Eg object oriented programming was basically a useless fad full of empty, never-delivered-on promises, but software companies still lapped it up. (If you happen to like OOP, you can probably substitute your own favourite software or wider management fad.)

Another objection: even an LLM with limited capabilities and glaring flaws can still be useful for some commercial use-cases. Eg the job of first line call centre agents that aren't allowed to deviate from a fixed script can be reasonable automated with even a fairly bad LLM.

Will it suck occasionally? Of course! But so does interacting with the humans placed into these positions without authority to get anything done for you. So if the bad LLM is cheaper, it might be worthwhile.

This. I think we’ve about reached the limit of the usefulness of anecdata “hey I asked an LLM this this and this” blog posts. We really need more systematic large scale data and studies on the latest models and tools - the recent one on cursor (which had mixed results) was a good start but it was carried out before Claude Code was even released, i.e. prehistoric times in terms of AI coding progress.

For my part I don’t really have a lot of doubts that coding agents can be a useful productivity boost on real-world tasks. Setting aside personal experience, I’ve talked to enough developers at my company using them for a range of tickets on a large codebase to know that they are. The question is more, how much: are we talking a 20% boost, or something larger, and also, what are the specific tasks they’re most useful on. I do hope in the next few years we can get some systematic answers to that as an industry, that go beyond people asking LLMs random things and trying to reason about AI capabilities from first principles.