Prof G Markets
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?
00:32:58 · Misha Laskin, Ed Elson
[00:32:58] S4: I probably don't know, you know, much more than, you know, you do on the subject of the CCP's involvement, but I would agree with you that it's very hard to explain the Chinese open model ecosystem without deep support from the government in some way. Right? It's just hard to explain. These companies were around for actually some time. It took a few years for them to get to the point where they actually started charging meaningful money for their products. And even so, you're right, there's a large investment that needs to be put into it. I think there's a big difference between China, the business models that these companies have in China versus Western labs and distributors. In China, it kind of... I mean, from the outside looking in, it looks in a sense like a captive market in the sense that while there are open models, you know, in China, I don't really see too many distributors of those open models in China outside of the companies that are building them. Right? Like, DeepSeek has their API, and Kimi has their API, and, you know, GLM or Zhipu ZAI have have their API. And it seems to me, again, from the outside looking in, that they are actually... Their business models are not dissimilar from closed models in the West, but in China. Right? But in domestic market in China. [00:34:26] S2: To clarify, you're saying that they operate as closed models within China, but outside of China, they're open? Is that what you mean?