Prof G Markets
00:02:20 · Misha Laskin
[00:02:20] S4: The reason I got interested in physics, it was, I'd moved to The United States as a kid. We moved to a rural part of Washington State, and had a lot of time in my hands, and basically developed an interest in physics at the time. It might be, maybe part of it was that this area was one place where the sites for the Manhattan Project, it was the Hanford site where the plutonium was enriched, so it kind of had a rich history of physics in it. But what I really liked about physics was that it was a fundamental science had based on simple principles that could explain the majority of things that we observe in the world, and was at the root of the technologies that we are using every day. So, I kind of saw physics as the fundamental science of the twentieth century. The first computer program was brought forth by physicists. It was the ENIAC program at the Institute of Advanced Study. This was during the World War II era, when computers were first kind of being used for calculating ballistic missiles coordinates. Then things like the transistors were invented by theoretical physicists at Bell Labs. Systems like GPS only work because of special relativity. You have to account for the speed of light in order to have accurate GPS. So it kind of seemed to me that the fundamental science that was at the root of all the technologies we had at our fingertips was physics. And my switch into AI happened when I noticed that something similar was happening in AI, where it went from a field that had a lot of, I would say, diversity, but there was no real simple way to scale intelligence, really, until deep neural networks were discovered and scaled starting twenty twenty twelve with the ImageNet moment. But when I saw AlphaGo, which was a system that played the game of Go, which is out of the traditional board games like chess and so forth, Go is considered the most complicated one, because it has the highest, what's called branching factor, meaning that the amount of moves, the amount of different kinds of moves that every particular board position or configuration can afford is just very high. So it's a very difficult, intellectually challenging game for people to play, and a neural network was trained to play it at a superhuman level. And in fact, that system had never stopped learning.
00:04:59 · Misha Laskin, Ed Elson
You could just pour more compute into it, and it would have just gotten better. And what I saw happening there was that a science that I thought was as fundamental as physics was being discovered in real time that would power the majority of technological advances coming into the twenty first century, and it kind of seemed like some big discoveries are made in AI that make it kind of the physics of our time. It's kind of the physics of the twenty first century, and that's kind of what got me into AI as a field in around the mid-twenty tens. [00:05:37] S2: So you're working at Google, and then my understanding is that you start Reflection in 2024. Why did you start this company? What did you think was happening in AI or what wasn't happening in AI that you could contribute to?
00:05:52 · Misha Laskin
[00:05:52] S4: The world when we... It's funny, it's 2024, this is only a bit more than two years ago, but it seems a lot longer on AI timelines, and the field looked very different then. So I'll kind of rewind of it. What did the field look like in early twenty twenty four? GPT-four had shipped, and ChatGPT had taken off. The first Gemini models were shipped, Gemini one and one point five. These are models that my co founder Janus and I contributed to, especially on the reinforcement learning side. But at the time, intelligence was really only good at chat. The notion of coding, agents, all this stuff, reasoning, none of that existed, and it didn't work. These models were just good as chatbots. They were not really good at any sort of cognitive work that needed to be done autonomously. And our bet at the time was actually twofold. We saw the beginnings of an open source ecosystem, So some open models had started coming out that are pretty good, like the models from Meta and others. And so we thought that those models would give us a good base to build on top of, instead of having to sink in our own large R and D money into pre training these models. But then the big bet that we made as a company initially was that we thought that the paradigm of reinforcement learning, the same paradigm that created AlphaGo, my co founder was one of the key contributors to AlphaGo. So the same paradigm of teaching AI systems to learn through trial and error could be scaled on top of language models, and create systems that were not just good at chat, but good at cognitive agentic work, meaning... And we actually went in with coding as the entry point, that models that are good at coding, that can build software, that can do other agentic tasks on a computer, legal, finance, and so forth. And we thought that with building on top of open models, scaling up reinforcement learning would allow us to unlock this kind of capability in these systems. Now, what happened very quickly, actually, in the first year of the company, is that we were able to get proof of concept of these systems of reinforcement learning working at small scale on small models, but at the same time, the field had actually discovered how to scale up reinforcement learning. So OpenAI's first model started coming out, the O series of models that had introduced reasoning into the public market.
00:08:22 · Misha Laskin
And so what happened was that 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, and even within our own experiments. And then we saw another thing happening, which is that the Western open models started significantly lagging behind China. Chinese open models started coming out. They were very strong. I think we all kind of remember the DeepSeek V3 moment that happened roughly around the same time as the reasoning models were coming out. And then a whole ecosystem of Chinese models came out that was very strong. And about a year ago, a bit over a year ago, we made the decision that the most impactful thing we can do as a company is to take on the open intelligence mission, because we saw a couple things coming that, again, we thought would take a longer time to play out, but then played out a lot faster. One was that we saw the geopolitical tensions coming, that it cannot... Reason open models are so important is because are kind of the thing that the world... Open source is the thing that the world adopts broadly. When you think about what is the most widely adopted operating system, it's Linux for computers, and Android for mobile. These are open source operating systems. The internet protocols are open, and these were originated in America, and adopted by the rest of the world. So open technology is the thing that becomes kind of foundational thing that everyone adopts. And with it, it brings in the infrastructure, and kind of software of whatever nation is originating, the open source software. So, we saw this geopolitical tension coming, where if it's only one country that's producing open models, then the rest of the world kind of become dependent on those models, and on the infrastructure of that country. And it's extremely important for America, as a country, to be competitive, and not just competitive, but have the best open source software and models when it comes to AI, and ensure that our allies, the world, and domestically, we all build on our own open intelligence. So we saw that coming.
00:10:38 · Ed Elson
[00:10:38] S2: Can I just interrupt for one moment? What actually is, what does open source mean? What what does it mean to be an open model? What what... When when you you describe Linux, which is open source, what is the difference between open source versus closed source?
00:10:53 · Misha Laskin
[00:10:53] S4: The high level distinction. Closed, you cannot own, you can only rent. Open, you can own entirely. Right? So it's kind of like the difference between the rental and the ownership real estate market. When you are renting an apartment, you don't actually own it. Right? You are subject to the whims of your landlord, which could mean price hikes, it could mean an eviction, it could mean all sorts of things, but it's very convenient, right? You just pay rent, and someone else manages your property for you. You don't really have to worry about fixing the pipe. When the pipes are leaking, or something like that, you call, you ask your landlord, and they figure it out. On the other hand, in real estate, there's the ownership market, which is you buy a house, but then you also inherit all the problems that come with ownership. And for various reasons, it makes sense to buy homes for financial reasons, for control reasons, and so forth. And you can think about open source in software and AI as really the same thing. You are either renting software or AI from someone, or you're owning it yourself. And so, when we think about open source, in AI, really think about it, we think about it as open models. The model, right? You're either renting it from someone, or you can put it... You know, I can give it to you on a flash drive. You can own it, and you can run it on your own infrastructure and your computers. That is the fundamental difference.