Prof G Markets
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.
00:27:50 · Misha Laskin
And when you're in the intelligence market, that's kind of like the market of artificial labor. And it's a very kind of positive sub market in the sense that how much artificial labor would I throw at really difficult problems like drug discovery or cyber defense? You'd probably throw as much as possible at it in order to get the best cyber defensive systems, in order to discover new drugs. Right? And what I think will continue happening is that compute for the foreseeable future... I mean, I don't know how long this... You know, how long that lasts, but for the foreseeable future, intelligence is gonna outpace our ability to stand up compute, because compute is you're standing up stuff in the physical world. And so the amount of intelligence you'll get per watt is gonna keep increasing. And so, you wanna drive intelligence, the cost of intelligence down as much as possible, so you can use it more. There's this classic Jevons paradox kind of thing. So, all this to say that intelligence should not be seen like a premium product, like an Apple iPhone or something. Intelligence is more like electricity and water, and it should be a commodity. The whole system around intelligence is expensive. They'll have some margin, but the margins today on inference are actually very healthy for both closed and open model inference.
00:29:26 · Ed Elson
[00:29:26] S2: On the inference though, because... And so I think the utility point is is is really interesting. And I think Sam Altman has said that he he thinks that there's a world where that might happen with with OpenAI. It doesn't seem to be what... The the direction that they're actually headed in terms of what they're pursuing as a business. But the idea of AI as a utility is really interesting and agreed on inference margins are are very healthy. But I do think that we're also, when we say that, leaving out the... This question of the training, which seems to be a very important and significant expense. But on utilities, generally speaking, are in some way subsidized by federal, state, local governments in order to make it accessible forever in electricity, water, etcetera. Do you see that as the world or the or the the AI economy where, you know, perhaps because training is very expensive, that there will be some level of subsidization similar to what we see with utilities?