One important constraint is this topic of intent (also referred to as alignment). Specifically, when a user requests a feature or change, there is MOUNTAINS of implied context and intent in their comment. Some cultural, religious, social, economic, base on work and personal experience, their use cases, their values, what they intend to get done, etc. Every time these models get smarter, they get a little better at understanding this context. It's combination of reasons. Some of it is parametric. Some is inferred. Some is about selecting the right data to examine and research. As these models get smarter, I spend less time time iterating and educating the model. They reach the intent faster and better.
From what I hear and experience, this is also true in the coding space. LLMs are basically another abstraction level on top of formal languages, which sit on top of compilers and machine code and etched instructions. Smaller and dumber models require a lot more hand holding to achieve the same results. They're less capable of inferring the gaps. Less capable of extrapolating potential negative outcomes of the change they're making. Less/less useful parametric knowledge. Less capable of selecting the right data to use to make the right decisions. They do what they're told.
This has been less of an issue for coders who religiously document, understand the codebase and architecture intimately, and demand to be kept in the loop on every change. So seniors often do not see the utility of smarter models. They know exactly what they want, how to ask, the context to provide, and how to assess if the change worked as expected. The utility is for the 99.9999% of the world which is not a senior developer.
However, your statement that there are only 8300 senior developers in the world does not seem correct to me. That seems like an unreasonably high bar for that title.
I think the issue is fairly fundamental, perhaps because of tuning, perhaps even beyond what they are tuned or trained on, because the bulk of what they can go find and ground into seems to drag them heavily toward implicit biases that split around categories I can’t quite discern. The category boundaries sometimes look like academic fields, but sometimes they seem like more fundamental attractors.