Speaker
Alex Imas
Appearances over time
1 episodes
Episodes
1
Podcasts
Quotes & moments
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.
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.
Political scientist Andy Hall found that even a 2% rise in unemployment is enough to dramatically shift political outcomes, making a slow 'drip' automation scenario particularly dangerous.
Even though the technology to automate telephone operators existed, the transition took 20 years, resulting in workers being reabsorbed into the economy but at lower salaries and mostly underemployed.
For AI automation to cause negative economic growth, wealthy capital owners would need to completely stop investing — not just spend less — a condition economists consider nearly impossible.
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.
The Yale Budget Lab's analysis finds almost no signal of mass AI-driven unemployment across the economy, with junior developer hiring only slightly below trend rather than sharply declining.
Mobile banking is more prevalent in Nigeria than in Germany, illustrating how developing countries can leapfrog stages of technological development with transformative technology.
If AI distributes like electricity, downstream users capture most gains; if it distributes like social media, rents accrue to platforms. This distinction determines whether wealth becomes highly concentrated.
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.
Moore's Law isn't just about supply — it's about demand evaporation. We create new uses for compute so fast that the value of each unit halves every 18 months. For the first time, AI may have broken this: H100 rental prices are higher now than three years ago despite massive supply expansion.
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.
For AI to automate white-collar work without generating redistribution wealth, automation would have to be cheap enough to replace workers but not cheap enough to produce abundance. That's an implausibly narrow window — and history suggests technological frontiers always expand alongside job displacement.
For AGI to cause negative economic growth, rich capital owners would have to stop investing entirely — not just consume less, but refuse to build more data centers even as the technological frontier expands. Abundance causing recession requires conditions that have never existed in history.
O-ring theory explains why automating 9 out of 10 job tasks sometimes lowers output quality enough to make automation counterproductive. But this friction works symmetrically: once AIs are reliable enough, the same logic will make humans — who slow down AI production flows — impossible to integrate.
If even a small number of agents — whether AI firms, humans like Elon Musk, or von Neumann probes — have effectively unlimited demand for capital accumulation, they will compound faster than everyone else and gradually dominate the economy's preference structure. Labor share could go to zero through selection alone.
If AGI diffuses like electricity, every company captures it and index ownership works. If it diffuses like social media, platform rents stay concentrated and ordinary people miss the gains. Which model prevails depends largely on whether open models stay competitive and whether frontier labs go public.
The most robust strategy for developing countries facing AGI is to index into the returns — buy sovereign wealth exposure to the AI supply chain — rather than rely on retraining programs. If AGI diffuses like electricity, index ownership captures the gains. If it concentrates, retraining won't save you anyway.
In an incentive-compatible experiment, people paid significantly more for human-made art prints than AI-made ones — but the human premium collapsed when 500 copies existed, while AI art was already treated as a commodity with no such drop. This is the kind of data needed to validate the entire relational sector hypothesis.
UBI makes basic needs contingent on who holds political power — a dangerous dependency. Universal Basic Capital gives people ownership stakes with property rights, making them normal shareholders rather than welfare recipients. The tradeoff is targeting: which stocks do you put in people's portfolios?
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- Government 17%
- History 17%
- Society & Culture 16%
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