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AI-Native Discovery Engines @xuster For centuries, scientific discovery has run on the same loop: hypothesize, experiment, interpret, repeat. It works, but it's slow. Frontier models have now reached PhD-level performance on scientific reasoning, and the frontier is shifting from copilots to systems that can run closed discovery loops on their own.






The deadline to apply for the YC Summer 2026 batch is tomorrow, May 4th, at 8pm PDT! Apply at ycombinator.com/apply.

The deadline to apply for the YC Summer 2026 batch is tomorrow, May 4th, at 8pm PDT! Apply at ycombinator.com/apply.



The deadline to apply for the YC Summer 2026 batch is this Monday, May 4th at 8pm PDT. All you need is an idea: ycombinator.com/apply



I sat down with @ulrikstighansen and @EricEncord, the co-founders of Encord, after their $60M Series C led by Wellington Management. From a wild bet to their incredible success with Physical AI, their story is wild! A seed fund once rejected them because they didn't believe in AI and invested in an Icelandic dating app instead 😅 Here are a few things from the conversation that stuck with me.

Demis Hassabis (@demishassabis) has had one of the most extraordinary careers in tech. He started as a chess prodigy and video game designer at 17 before getting a PhD in neuroscience and going on to found DeepMind. His lab cracked Go, solved protein structure prediction with AlphaFold, and then gave it away free to every scientist on earth. That work won him the 2024 Nobel Prize in Chemistry. Today he leads @GoogleDeepMind, pushing toward the same goal he set as a teenager: AGI. On this special live episode of How to Build the Future, he sat down with YC's @garrytan to talk about what still needs to happen to get us to AGI, his advice for founders on how to stay ahead of the curve, and what the next big scientific breakthroughs might be. 01:48 — What’s Missing Before We Get To AGI? 03:36 — Why Memory Is Still Unsolved 06:14 — How AlphaGo Shaped Gemini 08:06 — Why Smaller Models Are Getting So Powerful 10:46 — The 1000x Engineer 12:40 — Continual Learning and the Future of Agents 13:32 — Why AI Still Fails at Basic Reasoning 15:33 — Are Agents Overhyped or Just Getting Started? 18:31 — Can AI Become Truly Creative? 20:26 — Open Models, Gemma, and Local AI 22:26 — Why Gemini Was Built Multimodal 24:08 — What Happens When Inference Gets Cheap? 25:24 — From AlphaFold to the Virtual Cells 28:24 — AI as the Ultimate Tool for Science 30:43 — Advice for Founders 33:30 — The AlphaFold Breakthrough Pattern 35:20 — Can AI Make Real Scientific Discoveries? 37:59 — What to Build Before AGI Arrives