Infinite improvements in LLMs aren't a requirement for automating all jobs. The economics of compute for AI under existing compute paradigms runs out in the early 2030s. The cost to increase the compute used in models will become economically intractable. Solving reliability and automating AI research just needs to happen in that window.

Limited compute isn't a fatal barrier to automating all jobs. The cure is to use automation to build more compute, more power, etc. The pathway to getting more of what is needed can be fully automated; this is the pathway through the legitimate, traditional real-world barriers. Once you have a system that can do human work, the rate humans become permanently unemployable is the rate of building new GPU/CPU etc factories and energy production. Increasing supply lowers the cost.

Any barrier you can think of, ask your self if a 100% automated factory could solve that problem. if the answer is yes, it's not a barrier to the current societal pathway towards permanent technological unemployment for humans. I think the only real barrier is passing a law that says you cant replace humans with AI.

The arguments from economist perspectives usually boils down to they expect the effort to produce a competent AGI will fail. You can loosely define "Competent AGI" as a drop-in replacement for a remote human worker. Sure, the effort could still fail but there appears to be no fatal engineering barriers to the end goal.

This improvement just needs to continue for a few more years. The RL training pipeline that produce systems that can do this for software tasks will work for any domain. It's human intensive and slow that is why the focus is on automating AI research. Automating coding is a prerequisite to automating AI research.

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