AI Summer

Timothy B. Lee
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Timothy B. Lee interviews leading experts about the future of AI technology and policy. www.aisummer.org

  1. Alan Rozenshtein on Friday's shocking shutdown of Claude Fable 5

    2日前

    Alan Rozenshtein on Friday's shocking shutdown of Claude Fable 5

    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? 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

    56分
  2. James Grimmelmann explains how AI is changing copyright

    6日前

    James Grimmelmann explains how AI is changing copyright

    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”? 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

    54分
  3. Andy Masley on the data center backlash

    6月2日

    Andy Masley on the data center backlash

    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. 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

    58分
  4. Sophia Tung on the state of autonomous vehicles

    5月25日

    Sophia Tung on the state of autonomous vehicles

    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. 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

    54分
  5. Divyansh Kaushik on the robotics race between China and the US

    5月13日

    Divyansh Kaushik on the robotics race between China and the US

    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. 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

    1時間6分
  6. Alex Imas explains why AI (probably) won't put everyone out of work

    4月29日

    Alex Imas explains why AI (probably) won't put everyone out of work

    Alex Imas is an economist at the University of Chicago Booth School who argues that the most important thing about an AI-saturated economy won’t be what machines can produce—it’ll be what humans still want from each other. Imas’s central claim, laid out in his essay “What Will Be Scarce,” is that when AI can replicate every cognitive and physical task, demand for human provenance becomes the economy’s binding constraint. He backs this up with experimental evidence: in controlled settings, people’s willingness to pay for an identical good roughly doubles when it’s scarce and human-made, even when the hedonics are exactly the same. We talk through how this plays out in practice—Starbucks pulling back automation because customers missed the barista experience, the historical pattern of agriculture and manufacturing shrinking as shares of GDP while services absorb displaced income, and the debate with economist Phil Trammell over whether new AI-created goods could crowd out the relational sector entirely. The conversation turns darker when we discuss the transition to a post-AI world. Imas draws parallels to the Industrial Revolution, warning there were “huge losers” whose suffering gets swept under the rug. He favors David Autor’s proposal for a “universal basic capital” over simple UBI, but acknowledges a deep cultural problem: the relational jobs that survive are likely to disproportionately be care roles traditionally held by women, while the jobs most vulnerable to automation skew male. Can retraining programs—which have a poor track record—really bridge that gap? Or are we headed for a gendered economic rupture? 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

    1時間3分
  7. Sayash Kapoor on Claude Mythos as normal technology

    4月13日

    Sayash Kapoor on Claude Mythos as normal technology

    Last week Anthropic stunned the AI world by announcing Claude Mythos Preview—and then refusing to release it. Princeton’s Sayash Kapoor, co-author of the newsletter AI as Normal Technology, joins Tim and Kai Williams to make sense of the moment. Kapoor argues that Mythos’ vulnerability-finding prowess, including unearthing a 27-year-old OpenBSD bug, fits a familiar pattern: fuzzing tools triggered similar alarm decades ago but ultimately strengthened defenders more than attackers. Kapoor’s “normal technology” thesis holds that AI’s impact is shaped less by capability jumps than by downstream adoption—how industries, legal systems, and institutions absorb the technology. The conversation turns to whether alignment or control is the more promising safety strategy. Kapoor contends that the Mythos system card’s examples of the model bypassing access controls reveal shortcomings in control mechanisms, not alignment failures, and calls for ecosystem-level hardening—formal verification, sandboxing, network security—rather than relying on any single model behaving well. Kapoor then shares his latest research finding that AI agent reliability is improving four to ten times more slowly than average-case accuracy, and that current frontier models—including GPT-5.2—haven’t cleared even “one nine” of reliability. On Sierra’s TauBench, agents confidently book wrong flights and refund thousands of dollars in error, with Gemini 2.5 claiming 100% confidence even when it fails. If each additional nine of reliability is harder than the last, does that mean the real timeline for autonomous AI isn’t set by when models get smart enough, but by when the surrounding infrastructure catches up? 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

    58分
  8. Nat Purser explains how progressives are thinking about AI

    4月3日

    Nat Purser explains how progressives are thinking about AI

    Tim talks to Nat Purser, a tech policy advocate at Public Knowledge and a veteran of Democratic campaigns, about how policymakers on the left side of the political spectrum view AI. Purser describes a Democratic landscape split between those who see AI as a real but threatening force and those who dismiss it as another crypto-style bubble. She traces how Sen. Bernie Sanders broke from the pack by treating AI as genuinely transformative—meeting with AI safety figures like Eliezer Yudkowsky and Nate Soares, proposing a federal data center moratorium with Rep. Alexandria Ocasio-Cortez, and openly saying he uses Claude himself. Purser contrasts this with the dismissive attitude she sometimes encounters among progressive elites. She also details the fractures within labor: Hollywood actors and writers see AI as an existential threat to creativity, while construction unions welcome data center jobs. On the legislative front, she recounts how a bipartisan coalition crushed Ted Cruz’s ten-year preemption of state AI laws in a 99–1 vote, and argues that narrowly scoped preemption paired with federal standards is the only defensible approach. Purser predicts the "stochastic parrots" camp — those who dismiss AI as mere corporate hype — will lose influence as AI capabilities grow. But it’s too early to say whether Democratic leaders, including the next Democratic presidential nominee, will embrace Sanders’s apocalyptic framing or take a more conventional approach focused on issues like privacy and nondiscrimination. 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

    1時間18分
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番組について

Timothy B. Lee interviews leading experts about the future of AI technology and policy. www.aisummer.org

情報

  • クリエイター
    Timothy B. Lee
  • 配信期間
    25年 - 26年
  • エピソード
    29
  • 制限指定
    不適切な内容を含まない
  • Copyright
    © Timothy B. Lee
  • 番組のWebサイト
    AI Summer

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