This makes sense, though. They have to be exceptionally confident in the performance of the product and work to continually improve its quality and efficiency. Many other companies can get by more just as consumers of it, but the makers of the product need some of the best coding skills available.
If they do, they're behind the curve. They don't matter anymore.
Systems design, architecture, database fundamentals, a bit of SRE. That matters. Not coding and syntax and toy problem solving with data structures and algorithms.
I think this is something where definitions and static roles might serve as a useful reference point. Is it really the case that the trillion-dollar frontier AI labs are behind the curve, and only some other group of people knows how to really leverage AI? It's not impossible - Nvidia employees aren't the best in the world at using their chips - but the hypothesis practically demands some analysis of who these clever curve-setters are and why we're hearing so much less about them than about Anthropic.
My own experience is that coding, syntax, and some kinds of toy problem solving are important skills to use AI coding tools most effectively. I routinely get PRs these days whose descriptions seem plausible but are clearly wrong from a glance at the code, and I've had times when I'm unable to communicate to someone why a PR is wrong because they're not able to form a sufficiently detailed mental model of what it's doing.
