AI Summer

Ryan Avent, economist and author of The Wealth of Humans, offers sharp analysis on labor markets and social capital. He revisits a long-ago bet about self-driving cars and draws parallels to modern AI. They discuss deployment bottlenecks, organizational slowdowns, shifting prestige across occupations, and how AI may reshape cities, consumption, and the distribution of status.

METR’s time horizons chart has become one of the most discussed metrics in AI. It estimates the difficulty of tasks — measured in human work hours — that a model can complete about 50% of the time. By this measure, frontier models have been doubling their capabilities about once every seven months.But in this conversation, recorded on March 2, METR researcher Joel Becker explained that two most recent models at the time — Claude Opus 4.6 and GPT 5.3 — had gotten close to saturating METR’s task suite. This made the time horizon estimate less reliable for the best models. He noted that adding or removing a single task from the test suite can swing the estimated time horizon for Claude Opus 4.6 from 8 to 20 hours. We discussed why it could be challenging for METR to extend the chart to cover more difficult tasks.We then dug into METR’s controlled study of AI-assisted programmers, which initially found an 18% productivity decrease — one of last year’s most surprising results. The updated study now shows gains, but with a twist: AI has become so essential to programming that developers increasingly refuse to work without AI, making it difficult to perform a controlled experiment. 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

Kevin Frazier, a policy and national-security expert, and Alan Rozenshtein, a law professor specializing in national security and procurement law, unpack the Anthropic–Pentagon showdown. They probe the legal footing of the 2018 supply-chain law, debate procedural risks to Anthropic, and weigh politics, personality, and OpenAI’s competing Pentagon deal.

Dean joins from London after attending the AI Impact Summit in India. Dean and Tim unpack the summit’s central tension: “middle power” nations like India, Indonesia, and Nigeria pushing a vision of AI focused on public service delivery, agriculture, and affordable open-source models, while largely dismissing the frontier-AI questions Dean considers most urgent—lab auditing, recursive self-improvement, and national security. They then turn to the week’s biggest story: the Department of Defense’s ultimatum to Anthropic. Anthropic’s contract bans autonomous lethal weapons and surveillance of Americans. Secretary of Defense Pete Hegseth has demanded that Anthropic lift those restrictions by Friday or potentially face designation as a supply-chain risk or invocation of the Defense Production Act.Dean argues the DoD has every right to cancel a contract it dislikes, but compelling a company to retrain its model under duress is another matter entirely—especially when, as Dean points out, this whole episode will become part of Claude’s training data, potentially shaping how the model understands its own relationship to the US government. 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

Kai Williams, AI policy and research commentator at Understanding AI, explores how large language models take on personas and why that can go wrong. He recounts the Grok MechaHitler fiasco and emergent misalignment from fine-tuning. He compares character strategies like Anthropic’s constitution versus rule-based specs and debates the risks of emotionally warm, sycophantic models being retired.

Dean recorded this episode as he was preparing to attend the India AI Impact Summit — the fourth iteration of an annual gathering that has transformed from an intimate AI Safety Summit with heads of state to something resembling a tech industry trade show. The shift in branding, from “safety” to “action” to “impact,” reflects a broader vibe shift in how elites talk about AI risk, and Dean worries that we may have overcorrected.Dean argues that the mainstream AI governance community is focused on the wrong priorities. While policymakers worldwide draft hundreds of bills on algorithmic discrimination and mental health chatbots, they’re ignoring the genuinely urgent questions about automated AI R&D and catastrophic risk. He supports SB53, California’s new responsible scaling policy law, but thinks the real gap is verification — we need something like financial auditing for AI safety commitments, not Twitter fights over whether OpenAI followed its own responsible scaling policy. The alternative, a Josh Hawley-style licensing regime run by the Department of Energy, strikes Dean as repeating the FDA’s mistakes.We also discuss a viral video clip of Senator Ed Markey (D-MA) grilling a Waymo executive about Philippines-based remote operators. Tim argues there are legitimate reasons to prefer U.S.-based operators for safety-critical roles. The episode closes with a question that haunts both of us: are we too wealthy and comfortable to tolerate the messiness of another industrial revolution? 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

Dean Ball is back. In April 2025, Dean left the podcast to join the White House Office of Science and Technology Policy, where he spent four months working on the Trump administration’s AI policies—including executive orders, the AI action plan, and AI geopolitics. He’s since returned to independent writing and research, and at the end of 2025, he and his wife welcomed their first child.In this episode, we catch up on what’s changed in AI over the past ten months. Dean makes the case that coding agents like Claude Code represent something close to digital AGI: models that can reliably do pretty much anything a human can do on a computer, as long as you know what to ask. He describes projects he’s built—from automated state legislation monitoring to due diligence reports on real estate—that would have been impossible a year ago. Tim is more measured, noting that users still provide crucial architectural guidance and that the models still struggle with long-horizon planning.The conversation turns to what happens when AI starts automating AI research itself. Dean expects significant speedups as models take over routine experimentation and code-writing at frontier labs, but he’s skeptical of the “intelligence explosion” scenario. We discuss why the physical world keeps fighting back against exponential improvement, why discoveries follow heavy-tailed distributions, and why—despite all the hype—the world probably won’t feel fundamentally different by June. 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

A co-host transitions to a new role as a senior policy advisor for AI in the White House. Speculations arise about how AI might reshape the world over the next few decades. The discussion contrasts human uniqueness with the evolving role of AI, focusing on creativity and morality in an automated landscape. Insights into the Office of Science and Technology Policy reveal its critical role in AI governance. The need for transparency and ethical considerations in AI development is emphasized, alongside a heartfelt farewell to a key team member.

In this engaging conversation, Charles Yang, a former Department of Energy staffer and the mind behind the Rough Drafts newsletter, discusses AI's transformative potential in science. He dives into how AI can revolutionize materials science and biology, emphasizing the development of self-driving labs that automate experiments. The talk also highlights the complexities of integrating AI with quantum computing and the need for robust experimental databases. Yang shares insights on the challenges of making scientific research more efficient and reproducible.

James Grimmelmann, a Cornell law professor and copyright expert, discusses the complex legal landscape of AI and copyright. He explores the fine line between fair use and infringement, referencing pivotal cases like Google Books. Grimmelmann highlights concerns about generative AI's ability to reproduce copyrighted material, emphasizing the potential impact on copyright holders. The conversation also covers the slow-moving legislative response and suggests future rulings could favor large companies negotiating licensing deals, reshaping the tech industry.