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- 2 weeks agonarrative1h 38m7-min read
Adam Brown – Einstein's happiest thought: General Relativity from scratch
General relativity, Einstein’s revolutionary theory of gravity, redefines gravity not as a force but as the curvature of space-time caused by mass and energy. Despite being the product of a single mind, it has extraordinary predictive power across scales—from falling apples to black holes and the expanding universe.
- 4 weeks agointerview1h 33m6-min read
Grant Sanderson – AI and the future of math
AI is making rapid progress in mathematics, not because it has achieved general intelligence, but because math is both verifiable and grindable, allowing models to improve through massive parallelized training. However, true breakthroughs—like creating new fields or definitions—remain elusive and may require rethinking how AI generates ideas, not just how smart it is.
- Jun 26, 2026solo19 min4-min read
The next big breakthrough will be AIs learning on the job
The podcast argues that while current AI progress relies heavily on reinforcement learning in verifiable, replayable environments (RLVR), true general intelligence requires solving two deeper challenges: sample efficiency during training and continual learning from real-world deployment. Without the ability to learn from sparse, unstructured, real-world interactions and distill those learnings back into model weights, AIs will remain limited despite massive scaling. The speaker emphasizes that real-world deployment already exposes models to invaluable tacit knowledge, yet today’s architectures fail to retain or generalize from it—making the shift from static training to dynamic, experience-driven learning essential for AGI.
- Jun 19, 2026solo11 min3-min read
The data black hole at the center of AI
Sample efficiency, or the amount of data required to achieve competence, is a critical but underappreciated dimension of intelligence, and current AI systems remain vastly less sample-efficient than humans despite their impressive capabilities. The primary driver of AI progress is not algorithmic sophistication but the massive scale of data and compute.
- Jun 16, 2026narrative2h 8m7-min read
Ada Palmer – Machiavelli is the most misunderstood thinker of all time
Machiavelli’s The Prince is not a cynical manual for self-serving power, but a desperate, patriotic plea to stabilize a fractured Italy, written by a man who sacrificed everything to serve his country. The episode reframes Machiavelli as a selfless intellectual, not the villain his name has become.
- Jun 4, 2026interview1h 16m7-min read
Alex Imas and Phil Trammell – What remains scarce after AGI?
The conversation explores the economic implications of advanced AI and automation, focusing on what will remain scarce in a future of abundance, how labor and capital shares might evolve, and the challenges of redistribution. The guests argue that predictions are inherently uncertain, emphasizing the need for better data and models rather than relying on intuition.
- May 22, 2026interview1h 20m6-min read
Reiner Pope – Chip design from the bottom up
AI chips are optimized to maximize compute efficiency by minimizing data movement, with systolic arrays enabling massive matrix multiplication efficiency through spatial computation and local storage of weights. The clock cycle synchronizes all chip operations, and design trade-offs center on balancing compute, communication, and area.
- May 15, 2026interview2h 37m8-min read
Eric Jang – Building AlphaGo from scratch
AlphaGo's breakthrough lies in using neural networks to make intractable search problems computationally feasible, demonstrating that deep learning can effectively amortize complex reasoning. This insight has profound implications for AI's ability to solve problems previously thought to be beyond reach.
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