Zero comments there, too. Likely because direct-linking a PDF to a "book" on the multi-multi impacts of something that has barely started and we don't understand... seems less than useful.

My recap of the PDF is something like:

1. There are good books about the near-term economics as AI.

2. There aren't many good books about "what if the AI researchers are right" (e.g. rapid scientific acceleration) and the economic and political impacts of those cases.

3. The Second Machine Age: Digital progress boosts the bounty and widens the spread, more relative inequality. Wrong on speed (e.g. self driving tech vs regulatory change).

4. Prediction Machines: AI = cheaper prediction. Which raises the value of human judgement, because that's a complement.

5. Power and Prediction: Value comes when the whole system is improved not just from smaller fixes. Electrification's benefits arrived when factories reorganized, not just when they added electricity to existing layouts. Diffusion is slow because things need to be rebuilt.

6. The Data Economy: Data is a nonrivalrous asset. As models get stronger and cheaper, unique private data grows in relative value.

7. The Skill Code: Apprenticeship pathways may disappear. E.g. survival robots prevent juniors getting practice reps.

8. Co-Intelligence: Diffusion is slowed by the jagged frontier (AI is spiky). Superhuman at one thing, subhuman at another.

9. Situational Awareness: By ~2027, $1T/yr AI capex spend, big power demand, and hundreds of millions of AI researchers getting a decade of algo progress in less than a year. (Author doesn't say he agrees, but says economists should analyze what happens if it does)

10. Questions: What if the AGI-pilled AI researchers are right, what will the economic and policy implications be?

Going back that far may not be too helpful. Three years ago, ChatGPT wasn't out. A year ago, LLM-type AI only sort of worked. Now, it's reasonably decent in some areas. If you look at AI impacts retrospectively, you probably underestimate them.