The Gradient
The Gradient: Perspectives on AI
Arjun Ramani & Zhengdong Wang: Why Transformative AI is Really, Really Hard to Achieve

Arjun Ramani & Zhengdong Wang: Why Transformative AI is Really, Really Hard to Achieve

On why "AI will change the world" narratives might be less true than you think.

In episode 91 of The Gradient Podcast, Daniel Bashir speaks to Arjun Ramani and Zhengdong Wang.

Arjun is the global business and economics correspondent at The Economist.

Zhengdong is a research engineer at Google DeepMind.

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  • (00:00) Intro

  • (03:53) Arjun intro

  • (06:04) Zhengdong intro

  • (09:50) How Arjun and Zhengdong met in the woods

  • (11:52) Overarching narratives about technological progress and AI

  • (14:20) Setting up the claim: Arjun on what “transformative” means

  • (15:52) What enables transformative economic growth?

  • (21:19) From GPT-3 to ChatGPT; is there something special about AI?

  • (24:15) Zhengdong on “real AI” and divisiveness

  • (27:00) Arjun on the independence of bottlenecks to progress/growth

    • (29:05) Zhengdong on bottleneck independence

  • (32:45) More examples on bottlenecks and surplus wealth

  • (37:06) Technical arguments—what are the hardest problems in AI?

    • (38:00) Robotics

    • (40:41) Challenges of deployment in high-stakes settings and data sources / synthetic data, self-driving

    • (45:13) When synthetic data works

    • (49:06) Harder tasks, process knowledge

    • (51:45) Performance art as a critical bottleneck

    • (53:45) Obligatory Taylor Swift Discourse

      • (54:45) AI Taylor Swift???

  • (54:50) The social arguments

    • (55:20) Speed of technology diffusion — “diffusion lags” and dynamics of trust with AI

    • (1:00:55) ChatGPT adoption, where major productivity gains come from

    • (1:03:50) Timescales of transformation

    • (1:10:22) Unpredictability in human affairs

  • (1:14:07) The economic arguments

    • (1:14:35) Key themes — diffusion lags, different sectors

    • (1:21:15) More on bottlenecks, AI trust, premiums on human workers

    • (1:22:30) Automated systems and human interaction

    • (1:25:45) Campaign text reachouts

  • (1:30:00) Counterarguments

    • (1:30:18) Solving intelligence and solving science/innovation

    • (1:34:07) Strengths and weaknesses of the broad applicability of Arjun and Zhengdong’s argument

    • (1:35:34) The “proves too much” worry — how could any innovation have ever happened?

    • (1:37:25) Examples of bringing down barriers to innovation/transformation

  • (1:43:45) What to do with all of this information?

  • (1:48:45) Outro


The Gradient
The Gradient: Perspectives on AI
Deeply researched, technical interviews with experts thinking about AI and technology. Hosted, recorded, researched, and produced by Daniel Bashir.