I am personally of the opinion that ML will end up being 'normal technology', albeit incredibly transformative.

I think you can combine 'Incanters' and 'Process Engineers' into one - 'Users'. Jobs that encompass a role that requires accountability will be directing, providing context, and verifying the output of agents, almost like how millions of workers know basic computer skills and Microsoft Office.

In my opinion, how at-risk a job is in the LLM era comes down to:

1: How easy is it to construct RL loops to hillclimb on performance?

2: How easy is it to construct a LLM harness to perform the tasks?

3: How much of the job is a structured set of tasks vs. taking accountability? What's the consequence of a mistake? How much of it comes down to human relationships?

Hence why I've been quite bullish on software engineering (but not coding). You can easy set up 1) and 2) on contrived or sandboxed coding tasks but then 3) expands and dominates the rest of the role.

On Model Trainers -- I'm not so convinced that RLHF puts the professional experts out of work, for a few reasons. Firstly, nearly all human data companies produce data that is somewhat contrived, by definition of having people grade outputs on a contracting platform; plus there's a seemingly unlimited bound on how much data we can harvest in the world. Secondly, as I mentioned before, the bottleneck is both accountability and the ability for the model to find fresh context without error.

In some sense, technology is "not normal" regardless.

If we think of the digitization tech revolution... the changes it made to the economy are hard to describe well, even now.

In the early days, it was going to turn banks from billion dollar businesses to million dollar ones. Universities would be able to eliminate most of their admin. Accounting and finances would be trivialized. Etc.

Earlier tech revolution s were unpredictable too... But at lest retrospectively they made sense.

It's not that clear what the core activities of our economy even are. It's clear at micro level, but as you zoom out it gets blurry.

Why is accountability needed? It's clearly needed in its context... but it's hard to understand how it aggregates.

>nd historically the courts have not looked

This is dependent on having a court system uncaptured by corruption. We're already seeing that large corporations in the "too big to fail" categories fall outside of government control. And in countries with bribing/lobbying legalized or ignored they have the funds to capture the courts.