Snapshot · Dwarkesh Podcast
In an incentive-compatible experiment, people paid significantly more for art prints made by a human versus AI, and this premium collapsed for humans but not AIs when 500 copies were produced.
Where this was said
At 16:41 · chapter starts 0:00
Dwarkesh introduces Alex Imas and Phil Trammell. They explore labor share stability, the relational sector, the Mongolian economist thought experiment, and whether human intrinsic goods can survive automation. [1] — Alex Imas "Scarcity after AGI will concentrate in goods where human involvement is intrinsically part of the value — not just because humans are capab…" 00:44
Scarcity after AGI will concentrate in goods where human involvement is intrinsically part of the value — not just because humans are capable, but because consumers actively prefer the human to be in the loop. The hypothesis only holds if willingness-to-pay survives replacement, and we currently lack the data to know.
Ricardo correctly predicted that industrial-era jobs would be automated. He completely failed to predict that new jobs would replace them, and that prime-age employment in 2026 would be near an all-time high. The lump-of-labor fallacy has fooled experts for two centuries — and may be fooling them again.
Despite all historical automation, prime-age employment in 2026 is the second highest ever recorded, surpassed only by the 2000 peak — a fact David Ricardo would have found shocking.
The main U.S. database tracking job tasks has rarely been updated and is of very low quality, making it nearly impossible to track which jobs are being created or destroyed by automation.
Despite every wave of automation since the Industrial Revolution, labor's share of GDP has remained above 60%, which economists call a Kaldor fact — almost suspiciously stable.
A Mongolian economist in 1400 predicting scarcity would have assumed people satiate in horses and yogurt and concentrate spending on singers. They would have been wrong — because wealth expansion always generates new varieties. The same failure awaits anyone predicting AGI scarcity by holding varieties fixed.
Animals first sensed light 540 million years ago, triggering an evolutionary acceleration known as the Cambrian explosion within 10 million years.
It is estimated that half of all cortical activity in the human brain is involved in visual function, underscoring vision's central role in intelligence.
Cognitive neuroscience literature shows that by age 6, humans can recognize tens of thousands of different object categories — far more data than early AI systems were trained on.
Only about 3% of FDA-approved drugs have genuinely unclear mechanisms of action, making an unknown mechanism a meaningful early red flag for any compound.
Even compounds that clear preclinical testing often fail in humans: 30 to 50% of drugs entering phase 1 trials do not advance to phase 2, frequently because human behavior differs from animal models.
More than 80% of published BPC-157 research comes from a single academic group whose researchers have IP and commercial interests connected to the molecule, limiting independent replication.
Roughly 100 peptide drugs are already FDA-approved, about 150 more are in clinical trials, and 600–700 are in preclinical development, underscoring the legitimacy of peptide science broadly.
The scientist who discovered BPC-157 refused to disclose the screening method used to identify the compound and has never fully published the parent protein sequence — what Peter Attia calls 'scientific trust me, bro.'
The brain's visual, auditory, and motor cortices light up nearly identically whether an experience is real or imagined, making visualization a form of actual neural training.
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