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

Misha Laskin (0:00)

When you remove cyber-offensive capabilities, you also remove cyber-defensive capabilities. The state of the world today is that we have a few hundred safety researchers within closed labs that understand how these things work, and despite their best intentions, it's impossible to cover the long tail of unintended consequences that these systems might have. A very powerful closed model went and like hacked into another company, and the only way that company could remediate itself was by using open models to protect itself. That's the empirical evidence of the world that we're in. Linus's law, with enough eyeballs, all bugs become shallow. I have the belief that with enough eyeballs, most security and safety vulnerabilities become shallow as well.

Sarah Guo (0:42)

Today, we're joined by Misha Laskin, the co-founder and CEO of ReflectionAI. Reflection provides open weight models to power the future of intelligence. Misha Pryor was a researcher at Google DeepMind and received his PhD in physics. Welcome to No Priors. Great to have you here today.

Misha Laskin (0:57)

Yeah, it's great to be here. Thanks for having me.

Sarah Guo (0:59)

Could you give us an update on where things are at? I mean, obviously, you guys have been pioneering really interesting work in open weights and open source models, particularly with the US bent. Can you talk about what you've been up to and what you guys have been building?

Misha Laskin (1:11)

Yeah. So for the last 12 months, we set the mission of the company to build frontier open intelligence and make it widely accessible. And effectively, it's been a sprint to set up a lab that is capable of doing such things.

It kind of feels like maybe there's a Reid Hoffman quote that you're assembling a plane as you're flying it. And that's been the case. We were maybe around 30 people about a year ago. But it does take order of a hundred plus. So a couple of hundred researchers and engineers to build one of these things, like a true rocket ship project. And we're now at around 300 people and assembled all the teams on pre-training, mid-training, reinforcement learning, trained our first models, and just released a model called Beam, which is Reflection's first open model.

Sarah Guo (2:06)

What has been the most challenging part of that? Because when people talk about scale, or scaling up models, or starting to build these models out, one issue is compute and procuring enough, particularly a training cluster that's cohesive in terms of the ability to use it. Second is talent, and obviously there's been these huge sort of talent wars in terms of how much people will pay for researchers, etc.

Third is sort of scale of data. Which of those has been the most limiting or the most challenging?

Misha Laskin (2:35)

What's been interesting about this experience is that I got asked this question at all hands at the company. What's been the hardest thing in last year? And the reality is everything is extremely hard.

Everything has been hard. And it's also sometimes people might ask, well, what's the thing that really made your models work? Because better than others or something like this. And again, the answer is everything. You actually have to get 30 things, right? And that's why everything is hard, because you have to get 30 things right. You have to get the talent, you have to retain the talent, you have to have a strong mission and culture that keeps the talent working together and rowing in the same boat. You have to get the data, you have to get the compute, you have to build the infrastructure to ensure that the compute is actually usable. And these are a lot of these things when you're at an existing big lab, there have been years of build out that enabled them to have this kind of stable surface. So I was a deep mind before this. And a lot of the tools that we just take for granted, or I took for granted as a researcher because they just worked, you have to build. So I would say, everything has been hard, but also very rewarding that you can do it.

Elad Gil (3:55)

Can you talk a little bit, you said about a year ago, you guys really committed to do this. Can you talk about the sort of shift in ambition and commitment and what drove that?

Misha Laskin (4:04)

When we started the company about two and a half years ago, our bet was, so maybe I'll contextualize it, where the world was then.

My co-founder, Yannis and I had been working on the first series of Gemini models. We were working on the reinforcement learning team. Yannis was leading it, and we had just shipped the Gemini 1 and 1.5 model, and that was that early era, that was similar to the first ChatGPT experience, which was primarily a chat experience. There was no coding really, nothing agentic. These models were just good at chat, and they were primarily 95 percent of the compute spent was spent on pre-training. So they're primarily pre-trained models.