[rafterydj]

Super interesting. I don't know why, but something about this comment made something click for me, as an "AI fatigued" engineer.

From the view you describe, it seems AI just lets you experiment faster, when all you want to do is experiment. You find product market fit easier, you empower designers more, etc. Much easier to iterate and find easy wins from alternative designs - as long as your fundamentals work!

Only problem is that you are experimenting in public, so the massive wave of new AI generated features come to the public from everywhere at once. Hence the widespread backlash.

Not to mention, the core job function when you are experimenting is different from what defines a lot of hard technical progress: creating new technologies, or foundational work that others build on, is naturally harder and slower than building e.g. CRUD services on top of an existing stack. Deep domain expertise matters for selling, deep programming expertise matters for stability. I don't know, curious where the line will end up getting drawn.

[xeromal]

Yeah, the examples I've seen really focus on experimentation which my employers's platform is designed around. We are constantly testing changes in design and copy and hoping that we get small incremental increases in user attention. AI is really suited for these small changes and it allows us developers to build platforms specific stuff instead of working on baby tweaks. We already had a pretty good system where astute business people could tweak HTML and CSS but now their lives are even easier and they can focus on their actual job which is increasing customer sign ups and attention