The Twenty Minute VC (20VC): Venture Capital | Startup Funding | The Pitch

20VC: AI Scaling Myths: More Compute is not the Answer | The Core Bottlenecks in AI Today: Data, Algorithms and Compute | The Future of Models: Open vs Closed, Small vs Large with Arvind Narayanan, Professor of Computer Science @ Princeton

Aug 28, 2024

Arvind Narayanan, a Princeton Professor and expert in AI and cryptocurrency, debunks the myth that simply adding more compute enhances AI performance. He emphasizes that data quality and algorithms are critical bottlenecks, questioning the future of synthetic data. Arvind draws parallels between AI and crypto hype, expressing skepticism about commoditization. He explores the potential of various small models versus few large ones, while highlighting the ethical implications and challenges in assessing AI's real-world applications and advancements.

51:55

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Arvind Narayanan

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Quick takeaways

Arvind Narayanan argues that adding more compute alone does not guarantee improved AI model performance, as diminishing returns become evident.

The importance of understanding product-market fit in generative AI is highlighted, stressing the need for genuine product development over mere novelty.

Quality of data remains a significant concern in AI development, with a critical focus on effective data gathering over sheer quantity.

Deep dives

The Diminishing Returns of AI Training

The future of large AI models may be constrained by diminishing returns regarding compute performance. Historically, significant increases in model size have led to improved performance, but this trend appears to be plateauing as data becomes a bottleneck. Many companies have already trained models on nearly all accessible data, and while further compute still aids performance, the gains may not be as impactful as before. The current landscape suggests a shift towards smaller models that maintain capability while being cheaper and more manageable.

Technology Creates, Not Just Replaces, Jobs

01:07

AI Deployment Mirrors Slow Evolution

01:31

Diverging Visions on AGI's Timeline

00:52

Innovation Beyond Size

01:27

Intro

3min

Exploring Hype: AI vs. Cryptocurrency

3min

Challenges in Generative AI: Quality vs. Quantity

14min

The Minefield of Evaluating Language Models: Benchmarks vs. Real-World Application

2min

Navigating AI's Future: Challenges and Implications

27min

Innovative Solutions for Product Users and Entrepreneurs

3min

Arvind Narayanan is a professor of Computer Science at Princeton and the director of the Center for Information Technology Policy. He is a co-author of the book AI Snake Oil and a big proponent of the AI scaling myths around the importance of just adding more compute. He is also the lead author of a textbook on the computer science of cryptocurrencies which has been used in over 150 courses around the world, and an accompanying Coursera course that has had over 700,000 learners.

In Today's Episode with Arvind Narayanan We Discuss:

1. Compute, Data, Algorithms: What is the Bottleneck:

Why does Arvind disagree with the commonly held notion that more compute will result in an equal and continuous level of model performance improvement?
Will we continue to see players move into the compute layer in the need to internalise the margin? What does that mean for Nvidia?
Why does Arvind not believe that data is the bottleneck? How does Arvind analyse the future of synthetic data? Where is it useful? Where is it not?

2. The Future of Models:

Does Arvind agree that this is the fastest commoditization of a technology he has seen?
How does Arvind analyse the future of the model landscape? Will we see a world of few very large models or a world of many unbundled and verticalised models?
Where does Arvind believe the most value will accrue in the model layer?
Is it possible for smaller companies or university research institutions to even play in the model space given the intense cash needed to fund model development?

3. Education, Healthcare and Misinformation: When AI Goes Wrong:

What are the single biggest dangers that AI poses to society today?
To what extent does Arvind believe misinformation through generative AI is going to be a massive problem in democracies and misinformation?
How does Arvind analyse AI impacting the future of education? What does he believe everyone gets wrong about AI and education?
Does Arvind agree that AI will be able to put a doctor in everyone's pocket? Where does he believe this theory is weak and falls down?

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The Twenty Minute VC (20VC): Venture Capital | Startup Funding | The Pitch

20VC: AI Scaling Myths: More Compute is not the Answer | The Core Bottlenecks in AI Today: Data, Algorithms and Compute | The Future of Models: Open vs Closed, Small vs Large with Arvind Narayanan, Professor of Computer Science @ Princeton

Episode guests