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

When Do Enterprises Switch to Open Models?

Misha Laskin says enterprises consider open models after they ramp up significant workloads on closed models. He has not seen a straight sprint from renting models to owning them. A company has to pass through the rental stage first. The bill has to become a big enough cost driver before leaders ask how to cut costs and keep work valuable enough to continue.

Spending $100 million or more a year on closed models is not uncommon, and that is when a company starts looking for a better way to do the work. A compute shortage is also bringing machines back on site. An enterprise buying from a favorite hyperscaler very often cannot get enough compute, and good rates are hard to get. A company spending that much might go to Dell, set up bare metal, and serve models inside the enterprise. He treats the role as enabling enterprises to build successful solutions: go in, assess the biggest sources of value, and help where enterprises need more support than teams that already build their own. An enterprise might already have an agent that spans hundreds of millions of customers on closed models and want to scale it out. The scaling factor is very high. Offering inference alone has not worked.

The closed model ecosystem is competitive, and the open model ecosystem looks increasingly competitive too. Large markets tend to be.

Grokipedia·Dell

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. What Do Open Models Do to AI Margins?Misha Laskin says open models compress margins across the whole AI stack.
  7. 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.
  8. 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…
  9. 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.
  10. 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.
  11. 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.

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