I think it's more complicated than that. The projects that are getting the funding are usually the hard, technical ones, but that funding also supports better docs + more time for API design. This doesn't apply to bleeding edge stuff, but look back through the core SciML libraries and there's no shortage of effort directed towards "dull" stuff like docs + improving compile times. Likewise for the core language: a lot of recent work is bread and butter engineering like (again) improving compile times, filing rough edges off of APIs and (gradually) tackling the deployment story.
Now, one area where this dull problem work isn't as noticeable is on the "core" deep learning libraries (Flux and Zygote). AFAICT those two haven't received any significant funding for a couple of years, and there is at most 1 full time, active contributor for both of them. Compare with JAX or even higher-level wrapper libraries like Flax, Haiku or PyTorch Lightning, which have 5-10+ full time core devs. Given this, is it surprising that progress on anything (including docs + interface design) is slow?
Who is doing more than a couple rounds of interviews outside of (pardon the scare quotes) "tech companies"? Has anyone run into, say, a bank pulling 4+ rounds of interviews, or is this limited to FAANG(M), SV companies and startups that seek to emulate them?
Now, one area where this dull problem work isn't as noticeable is on the "core" deep learning libraries (Flux and Zygote). AFAICT those two haven't received any significant funding for a couple of years, and there is at most 1 full time, active contributor for both of them. Compare with JAX or even higher-level wrapper libraries like Flax, Haiku or PyTorch Lightning, which have 5-10+ full time core devs. Given this, is it surprising that progress on anything (including docs + interface design) is slow?