Where this was said
Does sample efficiency matter?
At 10:28 · chapter starts 8:46
Dwarkesh argues inefficiency doesn't block white-collar automation because AI's training costs amortize across billions of sessions. [1] — Dwarkesh Patel "AIs can be wildly data-inefficient and still automate white-collar work, because common tasks are common enough to bring into the training …" 07:48 He bets on more human software engineers in 2027, and previews a future post on the intelligence explosion. [2] — Dwarkesh Patel "Current discourse on intelligence explosions is stuck between two bad takes: it's impossible, or a god emerges at the end. Neither is right…" 10:17
Software engineering is supposed to be the first job AI takes. But Dwarkesh bets there will be more demand for human software engineers in 2027 than today — because AI is acting as a complementary input that expands the overall market for software.
Dwarkesh bets there will be overall more demand for human software engineers in 2027 than today, largely due to AI acting as a complementary input rather than a replacement.
Current discourse on intelligence explosions is stuck between two bad takes: it's impossible, or a god emerges at the end. Neither is right. The real question is what a period of faster-than-usual AI progress looks like when it's built atop the particular kind of intelligence LLMs represent.
