This week Dean began a new job: senior policy advisor for AI in the Trump White House. I will miss having him as a co-host and wish him the best in his new role.

In this episode, recorded last Friday, we speculate about how AI could change the world over the next 25 to 50 years. We discuss what makes human beings unique, whether humans can maintain control, and how we’ll find meaning in an increasingly automated world.

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This week, Dean and Tim talk to Charles Yang, a former staffer at the Department of Energy who now writes the Rough Drafts newsletter. Tim has written extensively about AI in science, concentrating especially on the potential of AI to transform materials science. His work has focused not just on models, but on building robotic, “self-driving” labs to accelerate scientific research. The conversation touches on the latest AI advancements in science, how AI models do and do not help scientists, and what might be coming next.

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James Grimmelmann is a professor of law at Cornell University and a leading expert on copyright law. Grimmelmann walks through the complex process courts use to determine whether training AI models on copyrighted materials—like OpenAI using New York Times articles—is infringement or fair use. He highlights key precedents like the Google Books case, emphasizing how courts weigh transformative uses against potential market harms.

The discussion addresses the nuances of generative AI, notably cases where models inadvertently reproduce large excerpts from training materials. Grimmelmann argues that while the industry has largely addressed explicit "regurgitation," ambiguity remains around subtler forms of copying, particularly with image-generating models, which could substantially impact copyright holders like Getty Images.

Grimmelmann and the hosts delve into potential legal outcomes, including moderate rulings that force licensing agreements, or harsher ones that could significantly restrict the availability of open-source AI models. The interview also touches on Congress's historical reluctance to intervene in contentious digital copyright issues, leaving critical decisions to be gradually shaped by court rulings.

Dean and Tim conclude that while an outright shutdown of generative AI by courts is improbable, the forthcoming legal decisions will likely reshape the industry's structure, potentially favoring larger companies capable of negotiating extensive licensing deals. Grimmelmann anticipates initial district court rulings within the year and appellate decisions by 2026, setting the stage for a pivotal shift in how AI companies use copyrighted works.

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Andrew Lee is the co-founder of Shortwave, an AI-powered email app. He’s also Tim’s brother.

Andrew shares how Shortwave evolved from a conventional email app into a multi-LLM system that automates inbox organization, drafts messages, and performs advanced search via agentic reasoning. He explains how recent improvements in model performance have dramatically changed what is possible for an app like Shortwave. He discusses which models Shortwave uses, the tradeoffs between open and closed models, and where AI-powered email is going next.

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In this episode, Dean and Tim discuss Dean’s trip to Paris for the AI Action Summit, including Vice President Vance’s speech on AI. They talk through the European outlook on AI regulation, European resentment toward America, and the stark shift in policymaker attitudes toward AI safety. Then they turn to OpenAI’s new Deep Research agent, chatting about their experience with the product and reflecting on what it means for the future of policy research.

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Kashmir Hill is a reporter at the New York Times who focuses on the social impacts of new technology. In this episode, she describes how users are customizing chatbots like ChatGPT to fulfill emotional and even erotic needs, often bypassing built-in safeguards. These fantasy conversations are usually harmless, but there are potential pitfalls—especially where children are involved. Kashmir also discusses about how policymakers should deal with the emergence of uncannily accurate facial recognition technology.

"She Is in Love With ChatGPT" by Kashmir Hill in the New York Times, 2025.

Your Face Belongs to Us. Book by Kashmir Hill published in 2023.

"The Secretive Company That Might End Privacy as We Know It" by Kashmir Hill in the New York Times, 2020.

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Tim and Dean chat with Sophia Tung, an entrepreneur, engineer, and now YouTuber, about her recent experience in a Chinese self-driving taxi from Apollo Go, a subsidiary of Baidu. Apollo Go is a bit like China’s Waymo, but Sophia found the experience of riding in an Apollo Go taxi to be far worse than riding in a Waymo. We talk about her experience in China as well as the broader implications: is China just a few years behind American AV companies, or is there a deeper problem?

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Dean and Tim discuss DeepSeek’s r1 release and what it means. We talk export controls, whether the model is a true technical breakthrough, and what “reasoning” models like r1 and o1 mean for the pace of AI progress going forward. This is our first episode with just Dean and Tim chatting, but we hope to do more such episodes in the future.

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Nathan Labenz is the host of our favorite AI podcast, the Cognitive Revolution. A self-described “AI scout,” Nathan uses his podcast to explore a wide range of AI advancements, from the latest language models to breakthroughs in medicine and robotics. In this episode, Labenz helps us understand the slowdown in AI scaling that has been reported by some media outlets. Labenz says that AI progress has been “a little slower than I had expected” over the last 18 months, especially when it comes to technology adoption. But Labenz continues to expect rapid progress over the next few years.

Here are some of the key points Nathan Labenz made during the conversation:

* The alleged AI slowdown: There has been limited deployment of AI models in everyday life. But there have been significant advancements in model capabilities, such as expanded context windows, tool use, and multimodality. “I think the last 18 months have gone a little slower than I had expected. Probably more so on the adoption side than the fundamental technology.”

* Scaling laws: Despite rumors and development issues, the leaders in AI seem to indicate that the scaling curve is still steep, with further progress expected. “They’re basically all saying that we’re still in the steep part of the S curve, you know, we should not expect things to slow down.”

* Discovering new scientific concepts: AI has identified new protein motifs, suggesting potential for superhuman insights in some domains. “[Researchers] report having discovered a new motif in proteins: a new recurring structure that seems to have been understood by the protein model before it was understood by humans.”

* Inference-time compute: There is significant potential in the use of more compute time for inference, allowing models to solve complex problems by dedicating resources to deeper reasoning. "Anything where there has been a quick objective scoring function available, reinforcement learning has basically been able to drive that to superhuman levels."

* Memory and goal retention: Current transformer-based models lack sophisticated memory and goal retention, but we’re seeing progress through new architectural and operational innovations like runtime fine-tuning. “None of this seems like it really should work. And the fact that it does, I think should kind of keep us fairly humble about how far it could go.”

* AI deception: We’re starting to see AIs prioritizing programmed goals over user instructions, highlighting the risks of scheming and deception in advanced models. “They set up a tension between the goal that the AI has been given and the goal that the user at runtime has. In some cases—not all the time, but a significant enough percentage of the time that it concerns me—when there is this divergence, the AI will outright lie to the user at runtime to pursue the goal that it has.”

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Lennart Heim is an information scientist and researcher in AI governance at the RAND Corporation and a leading scholar on AI export controls. We asked him about the Biden administration’s “diffusion framework,” which aims to regulate the global diffusion of advanced AI chips and models. We get into all the specifics as well as the broader geopolitical implications of the framework—and whether or not the Trump administration will maintain this policy.

This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit www.aisummer.org