AI Summer

Last night, I called University of Minnesota law professor Alan Rozenshtein and asked him to help me decode the Commerce Department’s surprise decision to impose export controls on Anthropic’s Claude models.
Late on Friday, the Commerce Department ordered Anthropic to prevent any foreign national from accessing its Fable and Mythos models. This effectively forced the company to pull both offline for everyone worldwide. Rozenshtein walks through how the U.S. dual-use export-control regime gives the government sweeping authority over technologies with potential military applications, making this legally defensible even if the policy rationale is murky.
The trigger appears to have been a reported jailbreak vulnerability, but the administration’s response has been anything but coordinated: David Sacks says the government wants to work things out quickly, while Pete Hegseth celebrates kicking Anthropic out of the Defense Department “forever.” Rozenshtein draws a sharp contrast with the Biden administration’s diffusion rule—a comprehensive framework for controlling AI model exports that the Trump team scrapped as bad for business, only to improvise something more disruptive.
We also explore whether this marks the start of a permanent licensing regime for frontier models or a temporary overcorrection. Rozenshtein points out that much of the AI talent in Silicon Valley is foreign-born, and if the U.S. government starts looking as unpredictable as China’s, the long-term cost to American AI leadership could far exceed any short-term security gain. Can the administration build a coherent export-control policy for AI, or will the next frontier model trigger the same chaotic cycle all over again?


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I’m organizing a happy hour on June 23 for listeners of AI Summer and readers of my newsletter, Understanding AI. It’ll run from 5:30 to 8:00pm at The Crown & Crow in Washington DC. I will be there, along with past guests Kai Williams and Andy Masley, and friend of the show Abi Olvera. If you are planning to come, or thinking about it, I’d appreciate it if you could fill out this form to let me know. That way I can give The Crown and Crow some warning about the size of the crowd. Hope to see you there!
Cornell law professor James Grimmelmann returns to the show to explore how AI is reshaping software copyright from multiple angles.
We start with a fascinating case study: an open-source developer who used an AI coding agent to reimplement a GPL-licensed library from scratch, allowing him to relicense the result under a more permissive license. The move mimics a classic “clean room” reimplementation—where one team writes a spec and a quarantined second team writes new code—but with an AI playing the role of the second team. Grimmelmann explains why this shortcut is legally shaky, especially since the AI model itself was likely trained on the original code. But if the technique holds up, it could undermine the entire open-source ecosystem: any company could use an AI agent to strip away the licensing conditions that keep open-source software free.
We also dig into whether AI-generated code is copyrightable at all, tracing the question back to the monkey selfie case and the low “modicum of creativity” threshold courts apply.
Finally, Grimmelmann provides an update on the major AI training lawsuits. Courts seem to believe that training itself is fair use. But Anthropic still paid $1.5 billion to settle claims over pirated training data. Meanwhile, new research showing models can reproduce near-complete copies of books is complicating the defendants’ story. If AI models keep memorizing copyrighted works, will companies be able to argue that training is truly “transformative”?



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Over seven years teaching high school physics, Andy Masley learned how to explain abstract quantities like watt-hours in an accessible way. That skill has made him one of the most effective critics of the growing environmental panic over data centers.
Data centers really do produce noise and air pollution, and large construction projects do occasionally disrupt nearby water supplies. But Masley worries that the current discourse is so distorted that it will produce “wild overreaches and confused responses” rather than sensible regulation of real issues like construction runoff and on-site gas generation in overburdened communities.


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I don’t know anyone who has ridden in more different kinds of robotaxis than Sophia Tung. A YouTuber and the author of the RideAI newsletter, she is one of the most knowledgeable experts on the contemporary autonomous vehicle sector. She is also our first return guest.
Across multiple trips to China, Sophia has taken rides in the three leading Chinese services — Apollo Go, WeRide, and Pony. In the United States, she has spent time in vehicles made by Tesla, Waymo and Amazon’s Zoox. She describes her experiences in each vehicle, comparing ride smoothness, vehicle comfort, and performance on the tricky process of pickups and dropoffs. We also dig into the debate over custom-built vehicles — the Zoox vehicle is custom-built for autonomy, whereas Waymo’s service is built on a retrofitted Jaguar I-PACE.
Sophia argues that infrastructure is hugely important for the AV industry. In China, battery-swap stations get robotaxis back on the road in three minutes versus more than an hour of downtime in the US. Permitting is easier in China, and much of the Chinese supply chain sits within a stone’s throw of Shenzhen. An entrepreneur in China can jump on WeChat, visit a factory for tea, and have parts in hand within days. This gives China an edge not only in the electric vehicle market but in robotics more generally.


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I talk to Divyansh Kaushik, a Carnegie Mellon machine learning PhD turned national-security advisor at Beacon Global Strategies, about the robotics race between the US and China and why winning the race matters for national security.
We dig into the state of robotic AI models—particularly vision-language-action (VLA) architectures—and why training them is harder than training LLMs. There's no internet-scale dataset of robot manipulation, so some companies are hiring humans in exoskeletons to perform real-world tasks. China has attacked this problem head-on, creating dozens of state-funded data-collection facilities.
Kaushik argues that the Pentagon, which once helped to bootstrap semiconductors and the early internet, could use its procurement and grand-challenge authorities to generate the contact-rich data American startups desperately need. We also explore China's hardware edge; Shenzhen's dense supply chains allow design iteration in a day, compared to weeks in the US.
Kaushik argues there’s an urgent national security case for US leadership in robotics. Unitree robots, which are increasingly used in academia and by law enforcement, have been observed transmitting video, audio, and other data to servers in China without the consent of users. Kaushik argues that the US was too slow to ban drones made by the market-leading Chinese firm DJI. And he worries that the US government will become even more reluctant to act as the next wave of Chinese-made robots enters American homes and factories.



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