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Mirendil aims to cut AI research teams from 200 people to one and accelerate science, including Alzheimer’s research.

Mirendil cofounders Behnam Neyshabur and Harsh Mehta explain why they left Anthropic to build AI systems that can advance AI research and, in turn, speed up science. Their goal is to remove the AI bottleneck for scientific teams: work that might require 200 frontier-AI specialists could eventually take 10, two, or one. They say stronger models need less oversight, and Mirendil has made technical progress with perhaps 10 times fewer people and resources than frontier labs. The founders argue that existing AI companies’ business models can discourage sharing tools that let customers build their own models, so Mirendil wants businesses to own AI tailored to their data, workflows, and infrastructure. They discuss safety guardrails, coordinating people and agents, and the challenge of scaling systems without losing productivity. Their preferred outcome is scientific progress on difficult problems such as Alzheimer’s disease—not simply automating jobs—and they stress that more pre-training alone will not solve such challenges.

Chapters

  1. 0:00Intro: AI Research with About 10 Times Fewer People
  2. 1:10Meet Mirendil: Founded to Make Self-Accelerating AI Available
  3. 3:11The Shortest Path to Accelerating Science: AI That Improves Research and Engineering
  4. 6:14What Self-Accelerating AI Means: From AlphaGo to One-Person AI Teams
  5. 12:50From Anthropic to Mirendil: Five Years from Math Models to AI Engineering
  6. 17:54Safety and Access: Targeted Guardrails for AI Research and Bioweapon Risks
  7. 22:02Giving Businesses Their Own AI: PyTorch, JAX, and Company-Controlled Infrastructure
  8. 26:34Scaling Agents and Humans: A 20-Person Team Building Systems That Improve Themselves
  9. 31:09Scaling Systems of Agents and Humans: Better Coordination to Reach Breakthroughs Faster
  10. 35:53Where Does It All End? Directing AI Toward Scientific Progress and Alzheimer’s

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