AI Summer

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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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?


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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?



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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.


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Author Ryan Avent joins Tim to revisit a bet they made 16 years ago—and to ask whether the lessons of self-driving cars apply to modern AI.
Back in 2010, Avent wagered that his newborn daughter would never need a driver’s license thanks to self-driving cars. Tim bet she would and ultimately won $500. But he was right for the wrong reasons. Tim assumed regulation would be a major obstacle to progress in self-driving technology, but logistical challenges and a long tail of edge cases have done more to hamper Waymo’s growth.
The parallel to LLMs is striking: ChatGPT’s early demos convinced many people that we were close to human-level intelligence, just as Google’s early autonomous vehicle demos convinced people we were close to human-level driving. But deployment of LLMs is bottlenecked by everything from data center buildouts to the glacial pace at which large organizations reorganize around new tools.
Avent, who wrote The Wealth of Humans in 2016 and has a new book on social capital arriving in April, argues that AI’s deepest impact won’t be unemployment but a wholesale reshuffling of status. White-collar professionals may face the same loss of prestige that blue-collar workers experienced a generation ago. Tim pushes back with an optimistic take: if the college wage premium compresses, the long-run equilibrium might actually be more egalitarian, echoing the mid-20th-century economy some people remember fondly. But we only got to that economy after two world wars and decades of organizing by the labor movement. Could today’s transition be equally turbulent?



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