Prof G Markets
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?
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?