I'd also say I've worked with many engineers over the years who were more focused on the software engineering aspect that the user experience. Depending on the use case for the software, that can absolutely end up being the limiting factor.

However, if in the future AIs become much more efficient at learning without requiring vast amounts of RL, closer to how humans learn. Then you would have to assume we’d have a real problem.

However, things are changing so rapidly that I can see that starting to change as well. But it would take much better learning efficiency to understand unknown domains, 100% computer use reliability etc. I suspect we’ll see this by the end of the decade.

If this base is then trained using RL towards a different objective (maths and coding), the model becomes fundamentally a different thing and the recent models are clear evidence of that, regardless of they fact they remain autoregressive.

However, over time the complexity of problems where you can get away with less/no oversight is increasing. And the models are already great at solving certain classes of problems where one doesn't really care that much about code quality, that wouldn't have even been attempted in a pre-LLM world. Over the weekend I was using Claude to add features to the compiled (no source available) firmware of one of my audio devices, adding workflow features by patching assembly and custom DSP code.

In coding, as with other areas, what's emerging is jagged intelligence.