[jreynar]

Another problem with perception of AI tools, for coding and other things, is that people often adopt a one-size-fits-all view. If Claude/Codex whatever can fix a bug in my tiny hobby project then it's going to revolutionize all software engineering. If it can write a haiku, then it the great American novel will be dead in a few years and the novelists will starve.

There aren't many truly general purpose tools so viewing things this way seems like either a fantasy or an over-reaction. And if nothing else the processes we use will have to change along with the tools.

It's the early days so we still have a lot to figure out but one of the most significant is which tools are appropriate for what sort of tasks. I've had good luck refactoring a small code base, building some small hobby projects and building features for our company's product. But, I've also dodged bullets doing greenfield development on some features where Claude (my default) has made what seemed like sound choices early on, and which I approved of, only to build something fragile or with unforseen consequences. I haven't quite figured out what distinguished those situations from the successful ones but I'm trying. But it's complicated by the fact that things are evolving quickly and yesterday's failure mode isn't the same as today's and, for that matter, yesterday's successes aren't guaranted to be repeatable today.