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Back to Beam: The Great American Open Model with ReflectionAI Co-Founder and CEO Misha Laskin, with Misha Laskin.

How Many Researchers Does a Frontier AI Lab Need?

Misha Laskin estimates a steady state around an order of 100 researchers for a large effort like the one he is running. Asked whether the same effort that needs 100 people today needs 20 next year, he agrees, then points to expanding ambition that leaves the team understaffed rather than overstaffed. A staff-to-compute ratio still applies: 3,000 researchers would not be very helpful, and a couple of hundred would.

A co-host sketches an extreme case: give every extra unit of compute to the handful of people who contribute the most ideas, in physics, biology, or any field, and the researcher count stays static or shrinks. The argument has merit. Projects never needed an extraordinary number of people. AlphaGo was maybe an order of 10, and a large effort like his is maybe an order of 100. Whether the number collapses back to 10 is hard to say. The estimate is a steady state around an order of 100.

A lot of work remains at that scale, and taking the results into real problems creates jobs in applied research.

Transcript

Beam: The Great American Open Model with ReflectionAI Co-Founder and CEO Misha Laskin ·

No Priors: Artificial Intelligence | Technology | Startups

More from this episode

  1. What Was Hardest About Building Reflection AI?Misha Laskin says the hardest part of building Reflection was everything.
  2. Is Catching the AI Frontier Getting More Expensive?Misha Laskin says catching up to the frontier can be done a lot more capital efficiently, and the price still climbs every year.
  3. How Was the Open Model Beam Trained?Misha Laskin says Beam was trained as a 500-billion-parameter model, with 23 billion of those parameters active. Pre-training ran on 6,000 GB300s.
  4. What Sets a Foundation for Intelligence?Misha Laskin says code and agentic work set a foundation for intelligence, and the intelligence is jagged.
  5. Have Open Models Overtaken Closed Models?Misha Laskin says the mix of tokens on model gateways has flipped from mostly closed to mostly open, and he thinks the shift will only accelerate.
  6. When Do Enterprises Switch to Open Models?Misha Laskin says enterprises consider open models after they ramp up significant workloads on closed models.
  7. What Do Open Models Do to AI Margins?Misha Laskin says open models compress margins across the whole AI stack.
  8. How Do Chinese Open Models Help Companies in the West?Misha Laskin says companies in the West have built more durable businesses because a great open-model ecosystem came from China, and he calls that a gift.
  9. Why Is Openness Safer for AI?Misha Laskin says openness is safer for AI because openness is already the safety default in software, and a few hundred researchers inside closed labs cannot…
  10. Can a PhD Take a Week With AI?Misha Laskin says a PhD can now take a week. Finding the right question and executing the answer to write a doctorate took a few years.
  11. How Does Beam Choose Its Next Training Bets?Misha Laskin says Beam chooses its next training bets by exhausting the high-impact methods the team already knows work, then expanding into riskier ideas.

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