Prof G Markets
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?
00:30:34 · Misha Laskin
[00:30:34] S4: Perhaps in some future. I think right now the market is kind of too early to tell, right? Because I guess what I'm describing is kind of the desirable end state for AI, is that it's a utility, and Internet. The Internet is effectively a utility. It's a commodity that is affordable to most people, and there are a lot of projects in terms of how do you get Wi Fi to work in rural areas as well. So it's unclear to me whether AI goes down the path of electricity, or it goes down the path of the internet. But nonetheless, whether one is government subsidized or not, both are sort of seen as large scale commodity services, and a lot of very successful high margin companies are built on top of them. A lot of companies with high margins are built on top of of the Internet, even though, you know, access to an Internet in its in its own right is fairly affordable.
00:31:46 · Ed Elson
[00:31:46] S2: So the the Chinese open models are so interesting because deep sea comes along, and... I mean, we all remember what happened in the market. Suddenly, everyone panics, and and you see all of the the AI... US AI stocks going down. But it seems like the... What happened... What is happening in China, which is why you have emerged as this strategically important company for The US, and correct me if I'm wrong on this, but it seems as though China is using vast amounts of government subsidies to create these models, which they then distribute to the world for free, thus changing the entire complexion of the AI model market, bringing down prices significantly. But ultimately, from my understanding, the government... The the CCP seems to be paying for it, at least when it comes to deep seek. I mean, it it seems like that is that is what is happening here. But is that wrong? I mean, to what extent is is is Chinese government involved in funding and promoting these open models?