Sep 25, 2026

Raise the ceiling: how to scale intent, quality, and artistry with Al | Katie Dill (Stripe)

Katie Dill from Stripe discusses how the rapid advancements in AI risk leading to "zombie UI" – generic, uncared-for digital products. She draws parallels to post-WWII architecture, highlighting the dangers of building without intentionality or a clear point of view. Dill then offers four key recommendations for developers and designers to leverage AI not just for speed, but to create original, soulful, and user-centric products: defining a strong point of view, encoding quality standards directly into AI tools, embracing meticulous editing beyond the first AI-generated draft, and actively using AI to foster new forms of creativity and unique interfaces.

Sep 01, 2026

Can AI Learn Mathematical Intuition?

Daniel Litt, a mathematician, discusses AI's impact on his field, highlighting its strengths in computation and applying known techniques, while noting its weaknesses in intuition and theory-building. He emphasizes that the goal of math is understanding, not just proofs, and warns that current academic incentives might be distorted by AI-generated papers. Litt raises broader concerns about preserving human intellectual development as AI advances.

Oct 31, 2025

Marc Andreessen and Ben Horowitz on the State of AI

A discussion with Marc Andreessen and Ben Horowitz on the true nature of AI creativity, the limitations of intelligence in leadership, why the current AI boom is not a bubble, and the coming platform shifts and geopolitical race in robotics.

Sep 04, 2025

The Day AI Solves My Puzzles Is The Day I Worry (Prof. Cristopher Moore)

Professor Cristopher Moore of the Santa Fe Institute discusses the surprising effectiveness of AI, arguing it stems from the rich, non-random structure of the real world. He explores the limits of current models, the nature of intelligence as creative problem-solving and abstraction, the importance of grounding and shared reality, and the profound implications of computational irreducibility and the need for algorithmic transparency in high-stakes applications.