“When something is not the core strategy of the company, you tend to not pay as much attention to it. And you tend to open source, you know, maybe like a lesser version of the thing that you're actually kind of monetizing. And that's kind of what we've seen from open source launches from Google… similar when OpenAI launched GPT-OSS.”

Prof G Markets · Money & Markets · November 2026

Context

Ed Elson asked whether Anthropic and OpenAI will eventually have to go open. Laskin says they will do fine, and that closed labs tend to release weaker open models than the ones they sell.

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Transcript

Prof G Markets Around 48:01 into the episode

Misha Laskin

…You have to ensure that the world builds on top of your stuff for your national security. And while they kind of seem at odds, both things can be true, that it's great that Chinese open models are out there, but it's also something that is a matter of national security for this country.

Ed Elson

…If that's the direction we're headed, and it seems like maybe we are, especially if China's just deciding to do that, why would you not use cheaper open weight models? I mean, does that not mean that Anthropic and OpenAI are going to have to get with the program? Like, what are they going to do?

Misha Laskin

…of the company, you tend to not pay as much attention to it. And you tend to open source, you know, maybe like a lesser version of the thing that you're actually kind of monetizing. And that's kind of what we've seen from open source launches from, you know, Google has done a lot for the open source community with the GEMA launches, but it's a very different model class than the Gemini models, similar when OpenAI launched GPT-OSS. So, right, it's very different from the class of actual internal models they were sitting on. So I don't see that strategy necessarily changing for them because I think that they have so much opportunity ahead of them just chipping away at their kind of current business that I don't think they might. But I think they're also going to do just fine.

Ed Elson

Reflection has been in the news recently. Reportedly, NVIDIA was in talks to acquire your company or at least deepen the relationship, deepen their investment. NVIDIA is already one of Reflection's largest shareholders. If you can speak to that, that would be great. But more generally, what's next…

Misha Laskin

The goal for the company is to continue building great open models, deploy them into customer segments, into enterprise, public sector sovereign, and build a generational company around this. And we hope that we're not kind of the only ones, that we hope to see an ecosystem of open model builders…

Ed Elson

For anyone listening to this who is thinking about entrepreneurship, thinking about AI, you've built an incredibly valuable company, $25 billion as the most recent round. You've worked at Google. I mean, you have a lot of expertise in this area. Any advice for any younger people listening to this…

Speaker names come from our own diarization. The position is estimated from where the line sits in the episode.

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“Reinforcement learning as a problem was solved actually faster than we anticipated. We thought it would take years. It took less than a year, both within external labs, but even within our own experiments.” — Misha Laskin, Prof G Markets

“Compute is scarce because the amount of intelligence you can pack sort of per watt, let's say, keeps growing. So these models get smarter faster than our ability to stand up compute. And when you're in the intelligence market, that's kind of like the market of artificial labor.” — Misha Laskin, Prof G Markets