LIMITLESS - AI DEBATE: Runaway Superintelligence or Normal Technology? | Daniel Kokotajlo vs Arvind
Two visions for the future of AI clash in this debate between Daniel Kokotajlo and Arvind Narayanan.
Is AI a revolutionary new species destined for runaway superintelligence, or just another step in humanity’s technological evolution—like electricity or the internet?
Daniel, a former OpenAI researcher and author of AI 2027, argues for a fast-approaching intelligence explosion. Arvind, a Princeton professor and co-author of AI Snake Oil, contends that AI is powerful but ultimately controllable and slow to reshape society. Moderated by Ryan and David, this conversation dives into the crux of capability vs. power, economic transformation, and the future of democratic agency in an AI-driven world.
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TIMESTAMPS
0:00 Intro
4:44 Is AI Normal Tech?
30:20 Capability & Power
44:52 Manageable or Existential?
52:36 AGI Milestone
1:00:31 AI by 2030
1:12:07 Making Sense
1:16:08 Closing & Disclaimers
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RESOURCES
Daniel Kokotajlo
https://x.com/dkokotajlo
Arvind Narayanan
https://x.com/random_walker
Two Paths For AI - Daniel
https://www.newyorker.com/culture/open-questions/two-paths-for-ai
AI as Normal Technology - Arvind
https://knightcolumbia.org/content/ai-as-normal-technology
AI 2027 - Daniel
https://ai-2027.com/
AI Snake Oil - Arvind
https://www.aisnakeoil.com/
https://www.amazon.com/Snake-Oil-Artificial-Intelligence-Difference/dp/069124913X
Pause AI
https://pauseai.info/pdoom
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https://www.bankless.com/disclosures
Meta on Trial + Is A.I. a ‘Normal’ Technology? + HatGPT
This week Meta is on trial, in a landmark case over whether it illegally snuffed out competition when it acquired Instagram and WhatsApp. We discuss some of the most surprising revelations from old email messages made public as evidence in the case, and explain why we think the F.T.C.’s argument has gotten weaker in the years since the lawsuit was filed. Then we hear from Princeton computer scientist Arvind Narayanan on why he believes it will take decades, not years, for A.I. to transform society in the ways the big A.I. labs predict. And finally, what do dolphins, Katy Perry and A1 steak sauce have in common? They’re all important characters in our latest round of HatGPT.
Tickets to Hard Fork live are on sale now! See us June 24 at SFJAZZ.
Guest:
Arvind Narayanan, director of the Center for Information Technology at Princeton and co-author of “AI Snake Oil: What Artificial Intelligence Can Do, What it Can’t, and How to Tell the Difference.”
Additional Reading:
What if Mark Zuckerberg Had Not Bought Instagram and WhatsApp?
AI as Normal Technology
One Giant Stunt for Womankind
We want to hear from you. Email us at hardfork@nytimes.com. Find “Hard Fork” on YouTube and TikTok.
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AI Agents: Substance or Snake Oil with Arvind Narayanan - #704
Today, we're joined by Arvind Narayanan, professor of Computer Science at Princeton University to discuss his recent works, AI Agents That Matter and AI Snake Oil. In “AI Agents That Matter”, we explore the range of agentic behaviors, the challenges in benchmarking agents, and the ‘capability and reliability gap’, which creates risks when deploying AI agents in real-world applications. We also discuss the importance of verifiers as a technique for safeguarding agent behavior. We then dig into the AI Snake Oil book, which uncovers examples of problematic and overhyped claims in AI. Arvind shares various use cases of failed applications of AI, outlines a taxonomy of AI risks, and shares his insights on AI’s catastrophic risks. Additionally, we also touched on different approaches to LLM-based reasoning, his views on tech policy and regulation, and his work on CORE-Bench, a benchmark designed to measure AI agents' accuracy in computational reproducibility tasks.
The complete show notes for this episode can be found at https://twimlai.com/go/704.
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
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?
