Ethan Mollick is the Co-Director of the Generative AI Lab at Wharton, which builds prototypes and conducts research to discover how AI can help humans thrive while mitigating risks. Ethan is also an Associate Professor at the Wharton School of the University of Pennsylvania, where he studies and teaches innovation and entrepreneurship, and also examines the effects of artificial intelligence on work and education. His papers have been published in top journals and his book on AI, Co-Intelligence, is a New York Times bestseller.
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Timestamps:
(00:00) Intro
(02:31) Thoughts on the New Llama 3.1 Model
(05:52) Four Potential Outcomes: A Framework for the Future
(08:24) Will AI Achieve Escape Velocity or Plateau Like the iPhone?
(09:56) Identifying the Core Bottleneck: Compute, Data, or Algorithms?
(13:53) Why Aren’t AI Providers Offering User-Friendly Guides?
(15:28) Should Powerful AI Models Be Open Source or Closed?
(18:49) Will Regulations Limit AI Growth?
(22:10) What Are AI Labs Missing About Business Needs?
(26:00) How Can We Better Harness AI to Drive Productivity?
(28:22) Will AI Redistribute Talent or Eliminate Jobs?
(33:23) AI and Consumers: The Future Interface Experience
(36:09) AI Ambition in Startups: What’s Holding Them Back?
(41:35) Founders’ Diverging Views on AGI Timelines & Funding
(43:33) Will You Thrive or Get Steamrolled?
(49:49) The Future of Education with AI
(57:33) Energy Demands & Compute as Currency
(01:00:00) The Role of AI in Future Electoral Systems & Politics
(01:04:40) Quick-Fire Round
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In Today’s Episode with Ethan Mollick We Discuss:
1. Models: Is More Compute the Answer:
How has Ethan changed his mind on whether we have a lot of room to run in adding more compute to increase model performance?
What will happen with models in the next 12 months that no one expects?
Why will open models immediately be used by bad actors, what should happen as a result?
Data, algorithms, compute, what is the biggest bottleneck and how will this change with time?
2. OpenAI: The Missed Opportunity, Product Roadmap and AGI:
Why does Ethan believe that OpenAI is completely out of touch with creating products that consumers want to use?
Which product did OpenAI shelve that will prove to be a massive mistake?
How does Ethan analyse OpenAI’s pursuit of AGI?
Why did Ethan think Brad, COO @ OpenAI’s heuristic of “startups should be threatened if they are not excited by a 100x improvement in model” is total BS?
3. VCs, Startups and AI Labs: What the World Does Not Understand:
What do Big AI labs not understand about big companies?
What are the biggest mistakes companies are making when implementing AI?
Why are startups not being ambitious enough with AI today?
What are the single biggest ways consumers can and should be using AI today?
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