When I refer to improvements, I mean fundamental improvements to the underlying technology, which appear to be at a stubborn plateau.

I believe the improvements you’re referring to are better guardrails. They are still improving the interface with regard to context and scope, as those functionalities are separate from the underlying technology, bolted on top of it to keep it on task and more continually aware of and operating within the defined context.

Underneath, though, each new model appears to be a refactoring of the previous one to get different sometimes better results, but the methodology is the same, and its strengths and weaknesses remain largely unchanged.

So, essentially what my objection to this practice is this:

This technology has led to companies leaning harder on their current people to get more done with the same amount of time with AI tools. That doesn’t seem to be successful at any sort of scale so far, but that’s the plan nonetheless. As a result, new talent is coming into the industry at a much slower rate than before–hiring is on hold while everyone waits to see if these tools really can replace bodies in the workforce in a serious way (again, super inconclusive at this point).

So, looking forward even one single generation, we will have dramatically fewer experts in the field than before, because so many fewer people were able to start in that field last generation. Since the need for programmers is greater every year, either these tools will be a wild success and meet all these business demands, or there will be a crisis of demand with no easy ways out.

Since both of the foreseeable outcomes are detrimental to the workers themselves, what and who exactly are we rooting for? I think that most people, given the choice, would choose the existing cycle with a proven track record, rather than gamble on something so uncertain with no clear economic benefit to the workers themselves.