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?
Bronson and Pigott were a nice read, but it's unclear where Föll is getting most of his conjecture from because they certainly don't talk about it. Frankly, the whole piece smacks of the same Diamond-esque "they made fireworks, we made guns" trope that has been thoroughly torn apart since. Now to his credit, he does claim ignorance at the top of the page, but that seems to be quickly forgotten given how much hyperbole is spewed later on.
[1] is a better lay overview of medieval-era steel-making. And for a great breakdown of forces behind European success in the modern era, see Brett Devereaux's series on EU4 [2]
Trying to interpret individual radicals of a character as standalone components using their original meaning is enticing, but more often than not incorrect. For example, the character for maternal aunt uses the same radical. Phonetic-semantic compound characters are very, very, common. The standalone pronunciation of 夷 doesn't appear to have turkic/steppe origins either [1].
Moreover, we know Mongolian writing (because of the geopolitics of the time and its status as a younger written tradition) borrowed quite liberally from its southern neighbours. Including, but not limited to, China [2]. So while Wagner's point about proliferation of ironmaking techniques from outside the (nominal) Chinese state at the time makes sense, the whole phonetic angle doesn't.
As for the points about centralization and family name elitism, the first lasted less than 200 years, by which time many formerly aristocratic family names had become _so_ diluted so as to be almost meaningless. One of the main conceits of a major character in RoTK is that he's an average Joe who only gets a modicum of respect for having the same surname as the dynastic family. It also completely ignores the existence of profession-based surnames like 匠 ("artisan", notably 1/2 of 铁匠/blacksmith).
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?