Prof G Markets
00:15:49 · Misha Laskin
[00:15:49] S4: That's correct. So the difference between open source and open weight, it's really, I think open source is sort of the high level category around open source software. Open weight is kind of a specific instantiation of it, because in previous iterations of software, were no such thing as weights, Weights are a specific thing to AI, and in particular to deep learning, so it's more of a specific instantiation, and there are some people who would argue... I mean, there's kind of open source purists, I would say, who would argue that it's not open source if you don't open source the data and training recipes and so forth. And our position is that we want... Like, mission of the company is to build open intelligence and make it widely accessible. And so our goal is to open source as much as possible that makes these models easy to consume, and widely accessible. So, we launched Beam, we launched it with 40 different partners, we integrated into a number of different open source fine tuning frameworks, We released our own open source agentic harness called MIRR. We're also about to release a tech report that is sort of open sourcing the science effectively, right? Kind of writing up in some detail how it is that these systems were built, because we think it's important for an ecosystem of researchers to understand how these things are built. This is kind of a fundamental tenet of science that you want scientists to build on your work, and the only way to do that is for them to understand what it is that you did. But it doesn't mean that, for example, in order to get good models, you have to buy a lot of proprietary data, and in the contracts, you're not allowed to release that data to anyone, right? Because that's some people's business, right, selling data. So open source doesn't mean open sourcing absolutely everything, but we try to open source as much as possible to make these models both useful to the scientific community, the research community, the developer community. But when it comes to then adoption within an enterprise or public sector context, then indeed the business is charging for the systems around the model that make it useful, and these systems are effectively serving inference to these enterprises, packaging up agents for those enterprises, fine tuning services for these enterprises, and so forth.
00:18:28 · Ed Elson, S0
[00:18:28] S2: We'll be right back. [00:18:39] S0: You've been asked to run the company's social channels, but company content plays by its own set of rules. It needs a logo in the first three seconds, but it can't feel like an ad. It needs subtitles, except when it doesn't. It needs to run at two times speed and deliver a complex message simply, of course, within nine by 16. Thankfully, your company has Canva, so you can easily edit your video and every version of it. What will you Canva today?
00:19:14 · Ed Elson
[00:19:14] S2: We're back with Misha Laskin. The reason that I I think it's so important for us to understand the difference here and for anyone listening why I'm so interested in in hearing from you is that this difference seems to be upending the entire AI ecosystem as we know it. Because as you pointed out, we had the deep seek moment and part of that was here as a Chinese model that is very powerful, but also part of it was here as an open model that is very powerful. And since then we have seen token prices come way down. We've seen OpenAI reduce its prices by up to 80%. We've seen that the AI names that we are so familiar with, specifically OpenAI and Anthropic whose models are closed, they have they have been forced to basically reprice everything because all of these open models are bringing the costs down. And that's why I'm interested in this idea of it being free because it seems as though that it's not that the entire thing is free, but this one element of it is free, which is resulting in significantly lower prices. I guess just one final question on the difference between open source versus closed. Does open source and open weight... Does part of it also mean that anyone can go in and understand the underlying architecture of the model and go in and change it on their own time? Whereas something like a model from from Anthropic and OpenAI, you can't see the architecture behind it, and you can't know how to build off of it. Is that a correct distinction or not?
00:21:00 · Misha Laskin
[00:21:00] S4: That's correct. So, in practice, it's not even that we don't know the architecture of a closed model. You don't even know the size. We sort of know nothing about it, and that's in a sense, it's kind of like, again, when we think about the car analogy, it's sort of like if it's all packaged up, and you can't peek under the hood, like the hood is sealed effectively, so you can't peek under the hood, you can't tinker with it, and you can't really understand it, customize it, and so forth. With an open model, you can, right? You can see the architecture, you actually can plug it into either your favorite open source fine tuning framework, load it up on your GPUs, and actually customize it for your needs. So, you can take it apart, you can understand it, and fundamentally what that means is that you move from a world where there are maybe, across the closed labs, maybe 5,000 researchers who understand these systems and are working on them. A minority of them are safety researchers. So let's say out of the 5,000, maybe 500 are working on the safety of these models, which is a very small community. And this is such foundational technology that is so powerful that it's impossible for such a small community of researchers to really understand everything that there is to understand about these models, and how to make them safe, how to align them, and so forth. With an open model ecosystem, you're inviting a much larger group of researchers, engineers, developers, right? Anyone who takes an interest can take a look and contribute to these things. And that is fundamentally how science is done, right? Science, it's not alchemy, science is fundamentally open. That's the way cybersecurity works, like the whole field of penetration testing is based on the notion that you have access to software that you can actually see and understand. And actually the most hardened software in the world, the safest software, tends to be open source software because it's been... There's this saying that with enough eyeballs, all bugs become shallow. And that's why open source software is so ubiquitous and robust because it's had hundreds of thousands of developer eyeballs on it, looking at various sorts of vulnerabilities. And while they might not patch all of them, open source software is definitely more... Tends to be much more hardened and robust than closed software.
00:23:44 · Ed Elson
[00:23:44] S2: The thing that that it's quite different from though is the traditional business of intellectual property, where if you create something that is extremely valuable, usually, the number one priority of the business is to put patents on it and make sure that it is extremely closed such that they can build gates around it and then monetize that thing. And so that's why the the open model business, there... For me, I... I'm fascinated by how this business is going to work. Because I look at a company like Anthropic and OpenAI, who are, you know, number one and number two in in the world of LLMs, but they are still losing billions of dollars a year. And, you know, for them, I think a big question around their business model is like, how can they raise prices? How can they keep prices up? And the open model ecosystem is is bringing prices down. But I guess for a company like yours and something I wonder about with DeepSeek, with Qwen, with all of the other Chinese open models is how is that business model actually going to work? Because my understanding is that to build a model and to train it, that's the most expensive part of the whole thing, and that's the part that is technically being given away. So how will the business model work over the long term?
00:25:06 · Misha Laskin
[00:25:06] S4: For most foundational technologies that we're aware of, and even in the software technology era, they started off as closed, but if the technology is important and foundational enough, it was followed by an open version of it that then took up the majority of consumption. And this... Right? Operating systems started closed. Operating systems started closed, and Linux came after. So right? Close could be MS DOS and Windows, Microsoft's closed operating system built a great business around it, open source version of it, Linux came after. And now over 90% of the world's servers, I think the number is even higher, over 95%, run on some variant of an open source operating system, most often Linux. The same thing is true for Android. The first really mobile OS came from Apple, was closed, it was followed by an open source version, Android, that now the majority of the world's phones actually run on Android. The Internet is an open protocol. Start off closed, right, was built basically within government and universities. So this is actually... What's happening here is a continuation of a very natural thing, and I think that the way we probably need to reposition our thinking about AI is that the models are a commodity like electricity or water. When we think about electricity and water, we don't think about, oh, how does my energy company sustain its business? It needs to be making 90 percent margin. Right? That's not... We think about energy and water as commodities, that there is some margin that these companies sustain that's actually low, but it's a massive service that's provided as fundamental infrastructure to the entire country, an entire world. So I think what's happening in terms of margin compression on the model side is a healthy thing, because intelligence should be viewed as foundational fundamentals, electricity, water, it's commodity. Now, the inference margins, even on open models, are actually quite high. And the reason for that is because compute is scarce. Now the reason is compute is scarce, which is GPUs effectively, but CPUs as well. Compute is scarce because the amount of intelligence you can pack sort of per watt, let's say, keeps growing. Right? So the amount... These models get smarter faster than our ability to stand up compute.