Dwarkesh Patel
Deeply researched interviews.
AI Progress Is Being Bought With Data, Not Sample Efficiency
Dwarkesh Patel argues that recent AI progress is driven less by clear gains in sample efficiency than by an immense expansion of training data, including synthetic rollouts and highly specific human expert examples. In his account, frontier models can display broad professional competence because labs keep pushing more tasks into the training distribution, not because the systems learn new domains the way humans do. Patel says that data-heavy approach may still be commercially powerful when capabilities can be amortized across billions of uses, but it leaves unresolved whether current systems can solve their own sample-efficiency problem.
Geography Keeps Russia and China Trapped in Continental Power Politics
Military historian Sarah Paine argues that Russia and China’s strategic behavior is rooted less in ideology than in geography. In this lecture, she contrasts continental powers, which seek security through territory, buffers and mass armies, with maritime powers such as Britain and the United States, which can use the sea as a shield and build wealth through trade, alliances and rules for the commons. Her case is that Russia and China may want the benefits of maritime power, but their borders, neighbors and constrained sea access keep pulling them back toward the older logic of land empire.
Relational Work and Capital Ownership May Decide Who Gains From AGI
Economists Alex Imas and Phil Trammell argue that the central question after AGI is not simply which jobs machines can do, but what remains scarce once machine-made goods become cheap and varied. In a conversation with Dwarkesh Patel, they frame labor’s future around demand for human involvement, capital-produced variety, and whether people or future agents satiate on machine-made goods. They also argue that redistribution will depend less on generic transfers than on whether households and countries can hold claims on the assets that capture AI surplus.
AlphaGo Shows How Search Can Turn RL Into Supervised Learning
Eric Jang rebuilds AlphaGo as a way to examine why its combination of search, value learning and self-play still matters for modern AI. His central claim is that AlphaGo’s Monte Carlo Tree Search turns each move into a better supervised-learning target, avoiding the long-horizon credit-assignment problem that makes much reinforcement learning for language models inefficient. Jang also argues that current LLM research assistants can already help execute and optimize experiments, but still struggle with the harder judgment of choosing which research paths are worth pursuing.
Ancient DNA Shows Natural Selection Accelerated During the Bronze Age
David Reich argues that recent human evolution was not dormant after the rise of agriculture but unusually active, especially in and around the Bronze Age. In a discussion of new ancient-DNA work with Ali Akbari, Reich says a large West Eurasian dataset shows widespread directional selection over the past 10,000 to 18,000 years after controlling for migration, drift and admixture. The strongest signals involve immune and metabolic traits, but Reich also reports substantial movement in polygenic scores linked today to cognition, education, pigmentation and body fat, while cautioning that those modern predictors are difficult to interpret in ancient societies.