[iainbryson]

This is very true. And similarly for the recently-passed era of googling, copying and pasting and glueing together something that works. The easy 80% of turning specs into code.

Beyond this issue of translating product specs to actual features, there is the fundamental limit that most companies don't have a lot of good ideas. The delay and cost incurred by "old style" development was in a lot of cases a helpful limiter -- it gave more time to update course, and dumb and expensive ideas were killed or not prioritized.

With LLMs, the speed of development is increasing but the good ideas remain pretty limited. So we grind out the backlog of loudest-customer requests faster, while trying to keep the tech debt from growing out of control. While dealing with shrinking staff caused by layoffs prompted by either the 2020-22 overhiring or simply peacocking from CEOs who want to demonstrate their company's AI prowess by reducing staff.

At least in my company, none of this has actually increased revenue.

So part of me thinks this will mean a durable role for the best product designers -- those with a clear vision -- and the kinds of engineers that can keep the whole system working sanely. But maybe even that will not really be a niche since anything made public can be copied so much faster.

[tombert]

Honestly I think a lot of companies have been grossly overhiring engineers, even well before generative AI; I think a lot of companies cannot actually justify having engineering teams as large as they do, but they have to have all these engineers because OtherBigCo has a lot of engineers and if they have all of them then it must be important.

Intentionally or not, generative AI might be an excuse to cut staff down to something that's actually more sustainable for the company.