Prof G Markets
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.
00:12:21 · Ed Elson
[00:12:21] S2: My understanding, which I don't think fully aligns with the analogy, is that an element of open source and the open models is that they're free. It it... It's... I mean, when you when you buy a home and you own a home, you gotta pay money to buy the home. But with open source models, my understanding is anyone can use it, anyone can build on it, customize it, and that is a sort of a crucial element. Do I have that wrong?
00:12:48 · Misha Laskin
[00:12:48] S4: No. I think you're right. But it's kind of like... What would be the analogy here? The model may be free, even though some models choose to license. So you can have an open model that someone can own that has a license attached to it. Right? And that's... You know, Meta's latest model, Muse, has a license attached to it, commercial license if you're a distributor. Kimi k three has a license attached to it if you're a distributor. So you can have open where there's a license attached to it that can be monetized. By the even Linux, it has a license attached to it where if you make certain modifications to it, those have to be open as well. It's not like... There's a whole ecosystem of licenses around open source software and open models, And so it doesn't necessarily mean that open means free. Now, licenses on open models today are free, and even our model, Beam that we released, it's an Apache two license, meaning that it is free. But just because the model is free, doesn't mean that everything around it is free. If you are a customer or an enterprise, and you want to run open models, well, you need to get the compute from somewhere, so you need to pay for the compute. You need to get the software for managing that compute, and for serving the models. Just the technology for serving the models is complex and hard to do, so you have to pay for that. It typically is maybe embedded in some kind of agentic workflow, or customization workflow, and so that typically, you partner with someone, you pay for that. So you can kind of think about the open model as the engine, but the engine is useless without a car, right? So, to have the car around it. And so, while the model itself could be free, because the license is permissive, the model in its own right is not that useful. I can give you our open model on a flash drive, but in order to make it useful, you need to have all the stuff around it.
00:14:58 · Ed Elson
[00:14:58] S2: Would it make sense then to say that for the open models, and one thing also what would be helpful as a clarification on the difference between open weight and open source. Open weight is the new terminology that we are hearing in the AI world, and it's not totally clear what the difference is. But would it be correct to say that for the open models, you're giving away the training for free, but you're not giving away the inference for free? You're saying, here's your model. Here's the underlying model kind of as you say, the engine. That's free. You can have it and you can mess around with that however you want. But if you wanna run that model and if you actually wanna build a business around it, yes, you're gonna have to pay someone to run the model, probably us. Is that the right way to frame this?