Which AI Application Layers Are Winning?

In this episode of Econ 102, Noah Smith and Erik Torenberg explore AI's effects on productivity, how AI business models will shake out, the US and China's rare earth minerals industries, and China's economic challenges, including demographics, real estate, and involution.

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• AI can affect productivity in multiple ways: replacing humans, enhancing human productivity, creating new tasks, and increasing capital productivity
• AI may follow other essential industries with low profit margins despite creating enormous value:
  • Like farming, solar power, and airlines
• Code-related AI applications are seeing particularly strong adoption
• Vertical AI applications in specific industries (healthcare, legal, real estate) are gaining traction
• China controls the majority of rare earth mining and refining
• The US has sufficient rare earth deposits but faces two challenges:
  • Regulatory barriers to mining
  • Lack of solvent extraction technology and know-how
• China's fertility rate is lower than Japan and Europe
• However, a "baby bulge" (ages 7-22) will support the workforce short-term

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Timestamps:
00:00 — Intro
00:52 — AI’s impact on productivity
02:27 — Debating whether AI will increase productivity
03:11 — Historical analogy: Electricity’s impact on productivity, lessons for AI
07:55 — Sponsors: Notion | Netsuite
09:57 — Application layer companies, AI in coding, vertical AI applications
12:49 — AI bubble vs. CapEx boom/bust, historical parallels (railroads, telecoms)
16:54 — Brand loyalty, price wars, and profitability in AI models
22:26 — US-China trade, rare earths, and supply chain challenges
32:20 — Sponsor: Found
33:33 — China’s demographic and economic challenges, over-competition, and deflation
54:06 — Recommendations for China’s economic policy, rationalizing the economy
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