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20VC: Is More Compute the Answer to Model Performance | Why OpenAI Abandons Products, The Biggest Opportunities They Have Not Taken & Analysing Their Race for AGI | What Companies, AI Labs and Startups Get Wrong About AI with Ethan Mollick

Jul 31, 2024

Ethan Mollick, Co-Director of the Generative AI Lab at Wharton, dives into the evolving world of AI and OpenAI's strategies. He critiques OpenAI for missing key product opportunities and discusses how adding more compute power isn't a panacea for model performance. Mollick highlights the transformative potential of open-source AI while cautioning about its misuse. He shares insights on the intersection of AI and education, the challenges facing startups in an AGI-driven landscape, and the need for coherent strategies as technology advances.

01:08:50

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Ethan Mollick

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Quick takeaways

Ethan Mollick emphasizes the need for companies to realign their product strategies to meet consumer demands amid OpenAI's AGI ambitions.

The rapid advancements in AI models raise questions about their practical implications, highlighting the necessity for ongoing evaluation in transforming industries.

Integrating AI into workplaces requires clear guidelines and training to alleviate employee fears and maximize productivity improvements.

Deep dives

OpenAI's Pursuit of AGI

OpenAI is focusing on achieving Artificial General Intelligence (AGI), prioritizing the development of technologies that could lead to this goal. The company is currently generating substantial revenue, even without a fully developed product offering beyond their chatbot and API. There's a notable contrast between prevailing funding trends and the anticipated emergence of AGI, as many startups appear to be investing in technologies that may not survive an AGI-dominated landscape. This paradox leads to the question of how these companies can thrive if AGI, as perceived by investors, arrives sooner than expected.

Master Tools, Not Just Sell Them

01:55

Innovate Boldly, Not Incrementally

00:52

Embrace Struggle for True Learning

01:08

Innovation Flourishes in Proximity

01:22

Intro

4min

Bridging Entrepreneurship and AI: A Journey of Innovation

2min

The Llama Revolution: Open-Source AI Dynamics

11min

Navigating the AI Landscape

21min

Navigating Innovation and Investment in the Age of AI

6min

AI in Education: Enhancements and Challenges

13min

AI, Energy, and the Future of Work

10min

Wrapping Up Insights on Startups and Sponsor Highlights

3min

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.

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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