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.Have suggestions for future podcast guests (or other feedback)? Let us know here or reach us at editor@thegradient.pubSubscribe to The Gradient Podcast:  Apple Podcasts  | Spotify | Pocket Casts | RSSFollow The Gradient on TwitterOutline:* (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) OutroLinks:* Zhengdong’s homepage and Twitter* Arjun’s homepage and Twitter* Why transformative artificial intelligence is really, really hard to achieve* Other resources and links mentioned:* Allan-Feuer and Sanders: Transformative AGI by 2043 is <1% likely* On AlphaStar Zero* Hardmaru on AI as applied philosophy* Robotics Transformer 2* Davis Blalock on synthetic data* Matt Clancy on automating invention and bottlenecks* Michael Webb on 80,000 Hours Podcast* Bob Gordon: The Rise and Fall of American Growth* OpenAI economic impact paper* David Autor: new work paper* Baumol effect paper* Pew research centre poll, public concern on AI* Human premium Economist piece* Callum Williams — London tube and AI/jobs* Culture Series book 1, Iain Banks Get full access to The Gradient at thegradientpub.substack.com/subscribe