Tribe Capital Chairman and Co-Founder and Termina.ai Co-CEO Arjun Sethi discusses the AI landscape. He speaks with Bloomberg's Sonali Basak.
Joining us with more on Everything AI is longtime tech entrepreneur Arjunseeti. He is the chairman and co founder of VC firm Tribe Capital. He is also the co CEO of Termina AI. Thanks for joining us, especially because your firm has invested in Xai, so you have a very close view on how these lms have been developing and the competition among them. Very curious about what you think about this latest open ai funding round and the corporate interest around it.
Yeah, thanks for having me. I think the right way to think about it is the corporate interest, especially from a company like Nvidia, is more about if you take a look at their portfolio differcification of where the revenue comes from. The fastest growth in the future is going to come from companies like OpenAI and the types of developers that they're going to support moving forward.
So go ahead, please good.
So what that means is that Nvidia is not just a hardware and a company. It's a hardware and software company is attracting developers and they have to partner with other developers and other companies similar to Opening I. So Opening I being the biggest, it's a foregone conclusion that they have to partner with them in some capacity similar to Apple.
So given that you have invested in XAI, draw me a map a little bit on where different companies are going to play in this ecosystem.
So right now we're talking about llms, we're talking about foundational models, but we're not talking about is similar to the past, where you've built the foundation, you've built the infrastructure, and the next layer of application companies are being built, which is essentially what are the next trillion dollar companies that are out there? So opening I being one foundational, XAI could be another one that's foundational, Andthropic is another one that's foundational, but they're supporting the next level of developers. You take a look at what companies are doing today similar to what a lot of venture firms are trying to figure out, which is how do you leverage AI to be more efficient or how do I say cost or increase revenue? Today with the aspect of what you see with open AI is that every single company, every single company in our portfolio is leveraging these prompts and leveraging AI in some way, and so what is in net effect is that they're being twenty five to fifty percent more efficient. It's not ten x yet. We're working our way there as these companies get larger and they build better developer tools, but that's an increased efficiency and that has a high amount of impact. So any corporate interest that's coming into open ai is looking at that not just for themselves, but they're looking at that for the future of their business, keeping the business that they have with their developer community, and making sure that more of these developers are going to continue to succeed on their platforms because that's what's going to continue to drive their business. So for Nvidia, it's a lock in because they have software plus hardware, and that is the defect of monopoly today.
Argene, can you talk to me a little bit about valuation here? You have a report out from your company about what you call a weather report about the supply dynam and dynamics in price markets. When we talk about open ai, we're talking about evaluation that could be over one hundred billion dollars. But really there are only a handful of companies in the world with that kind of valuation underpinning them. So what else are you seeing in the private markets right now?
So you know the way to start to think about private markets is worldwide. So this take China, India, US, and then Latin America as a whole. US and China sort of dominated valuations for a while, and a lot of capital has been flowing into anything that's software and tech enabled. The next phase of that was anything that software tech enabled and the label of machine learning and AI, and so that's where a lot of the capital is going. So far, most of the investments in the private world that's been going into AI has been coming from corporates. It hasn't been coming from venture capital. Venture capital traditionally is invested in anything that's vertically integrated or vertical application, which basically means like how do you leverage AI into a fintech, how do you leverage AI into healthcare, etc. Etc. Those are where most of the investments are going in. So valuations for those types of opportunities in the beginning are really high. Then midway through the cycle it becomes low again, and then what you'll see is something that's much more intrinsic versus options value related.
You know, when you think about the global view here, there's a lot of concerns in the AI world about that competition towards AI spending towards AI development between the US and China. What do the dollars say, Well, most.
Of the dollars today are going towards development in the United States. That's actually very very clear. That said, what you have to look at in terms of what investments had happened outside of the United States, especially in China, was essentially for image recognition, anything that was related to cybersecurity or surveillance. And so that's how they had spent majority of their capital, let's call over the last five years, and we spent that plus more over here that's been around AI, AGI machine learning. So we're much further ahead for now. And you can see that in the types of companies that are being built, types of products that are being built for every single vertical. So I always go back to cybersecurity, healthcare, financial services. You're going to see the first input there. You could call it GDP per capital growth and then cost. So those are the bifurcations between the investments that are really being made and then all the hyperscalers that are out there. They are going to benefit from all of this because you need more compute, you need more space, you need more training. You need more inference, which ends up being in net benefit for all the companies that are part of that stack.
Speaking of hyperscalers, I'm glad you brought this up. Of course, we had Nvidea earnings this week. The initial reaction was disappointment. But something that struck me on the heels of Unvideo earnings is, even when you saw a day of earnings where the stock was down right after, you saw every other company in the Philadelphia Semi Conductor Index immediately have the reaction in the opposite direction, there is still love for that AI boom. Is this argent because there is such a lack of investment opportunity. You mentioned a little earlier that a lot of this benure investment is actually coming from corporations. So what does that mean in terms of exit opportunities? Are there fewer in the future than meets the eye?
Yeah, so there's multiple questions there. I'll take a step back. So you look at Nvidia, and the way in which you should think about it from our framework is that they've have clear product market fit for hardware that they've built, and they have clear product market for the software that enables their hardware. A lot of the other companies that compete with them today don't have that, and so they're racing to be able to lock in developer interests. So all of them, they're racing to be able to try to commoditize that part of the stat stack that hasn't happened yet. And I think that's a key point. So when you think about year over year growth of what Nvidia looks like, if you think about year over year growth for the other companies that are on top of their stack, you know, namely open Ai, Xai Andthropic, et cetera. All of these companies have to rely on that, and then you're going to look at the next part of the stack, which is what do all the developers do, how do they train, and what are the products that they're going to use. Beyond just open source, they still have to work off of hardware. So I think of this very similar to when Apple came out. Everyone had made a bet that you know, Apple's devices are too expensive, it's going to be commoditized and all of the other people are going to come in and compete with them, and then you have software developers moving over to Android. You have roughly about a fifty to fifty market. You don't have that today. You don't have a clear competitor in hardware, and you don't have a clear competitor in software. So the question you have to ask is that what point does that start diverging in terms of overall demand for processing and demand for inference and demand for training. And today it hasn't stopped. I do believe it will will sort of now level out at some point, but we don't know if that's six months from now or two years from now or ten years from now.
Rgine, we have to leave it there. That is Termina AI co CEO, Rgine Sethie. We appreciate having you today. Have a great long weekend.