Comment by lamasery

"AI" tools I've got at work (and am mandated to use, complete with usage tracking) aren't a wide-open field of options like what someone experimenting on their own time might have, so I'm stuck with whatever they give me. The projects are brown-field, integrate with obscure industry-specific systems, are heavy with access-control blockers, are already in-flight with near-term feature completion expectations that leave little time for going back and filling in the stuff LLMs need to operate well (extensive test suites, say), and must not wreck the various databases they need to interact with, most of which exist only as a production instance.

I'm sure I could hack together some simple SaaS products with goals and features I'm defining myself in a weekend with these tools all on my own (no communication/coordination overhead, too!), though. I mean for an awful lot of potential products I could do that with just Rails and some gems and no LLM any time I liked over the last 15+ years or whatever, but now I could do it in Typescript or Rust or Go et c. with LLMs, for whatever that's worth. At work, with totally different constraints, the results are far less dramatic and I can't even feasibly attempt to apply some of the (reputedly) most-productive patterns of working with these things.

Meanwhile, LLMs are making all the code-adjacent stuff like slide decks, diagrams, and ticket trackers, incredibly spammy.

[EDIT] Actually, I think the question "why didn't Rails' extreme productivity boost in greenfield tiny-team or solo projects translate into vastly-more-productive development across all sectors where it might have been relevant, and how will LLMs do significantly better than that?" is one I'd like to see, say, a panel of learned LLM boosters address. Not in a shitty troll sort of way, I mean their exploration of why it might play out differently would actually be interesting to me.