
David Reich – Why the Bronze Age was an inflection point in human evolution
Dwarkesh Patel... · 133:20 · ٤٬٥٢٠ استماع
من الألبوم Dwarkesh Podcast
الوصف
David Reich is back.He and collaborator Ali Akbari just published a paper that overturns a long-standing consensus about human evolution — that natural selection has been dormant in our species since the agricultural revolution.By scaling ancient DNA sequencing and developing a new statistical method, they found that selection has actually sped up.Selection went especially bonkers during the Bronze Age (around 3,000 years ago).That’s when gene frequencies for everything from immune function to body fat to intelligence were most in flux.Over the last 10,000 years, selection pushed the genetic predictor of cognitive performance up by roughly a full standard deviation — most of it between 4,000 and 2,000 years ago.After we finished recording, David sketched out on a whiteboard his new heretical model about who the Neanderthals really were. Luckily, I took out my iPhone and managed to record it.He thinks the standard story (that Neanderthals are some separate archaic lineage we interbred with a little) just doesn’t fit the evidence. Instead, he proposes that Neanderthals are essentially genetically-swamped modern humans.A small population somewhere around the Caucasus invented Middle Stone Age technology roughly 300,000 years ago and expanded outward. The ones that moved into Europe interbred with local archaic humans, got genetically swamped, and became Neanderthals. The same expansion went into Africa, met much more diverged archaic Africans, and that mixture became us.This means Neanderthals and modern humans share the same cultural ancestry — the only difference is which archaic humans they mixed with afterward.David is a brilliant and rigorous scholar. It was a real delight to learn from him again.Watch on YouTube; read the transcript.Sponsors* Cursor was super useful as I prepped for this episode. Whenever I had a question, I’d have Cursor kick off a few different models simultaneously and then compare their responses. I found that this led to better results than I could get out of any individual LLM. If you’ve
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