The article asserts that the quality of human knowledge work was easier to judge based on proxy measures such as typos and errors, and that the lack of such "tells" in AI poses a problem.

I don't know if I agree with either assertion… I've seen plenty of human-generated knowledge work that was factually correct, well-formatted, and extremely low quality on a conceptual level.

And AI signatures are now easy for people to recognize. In fact, these turns of phrase aren't just recognizable—they're unmistakable. <-- See what I did there?

Having worked with corporate clients for 10 years, I don't view the pre-LLM era as a golden age of high-quality knowledge work. There was a lot of junk that I would also classify as a "working simulacrum of knowledge work."

Everybody's output is someone else's input. When you generate quantity for using an LLM, the other person uses an LLM to parse it and generate their own output from their input. When the very last consumer of the product complains, no one can figure out which part went wrong.

I don't really agree with the premise of the article. Sure proxy measures are everywhere. But for knowledge work specifically you can usually check real quality. Of course it's not as extremely easy as "oh this report contains a few spelling errors", but it is doable. If you accepted work purely based on superficial proxy measures you were not fairly evaluating work at all.

>"is the RLHF judge happy with the answer."

Reinforcement Learning with Verifiable Rewards (RLVR) to improve math and coding success rates seems like an exception.

>We've automated ourselves into Goodhart's law.

Yes.

This does not however mean that progress is not being made.

It just means the progress is happening along such dimensions that are completely illegible in terms of the culture of the early XXI century Internet, which is to say in terms of the values of the society which produced it.