Speaker 1: Joining us with more on Everything AI is longtime tech
Speaker 1: entrepreneur Arjunseeti. He is the chairman and co founder of
Speaker 1: VC firm Tribe Capital. He is also the co CEO
Speaker 1: of Termina AI. Thanks for joining us, especially because your
Speaker 1: firm has invested in Xai, so you have a very
Speaker 1: close view on how these lms have been developing and
Speaker 1: the competition among them. Very curious about what you think
Speaker 1: about this latest open ai funding round and the corporate
Speaker 1: interest around it.
Speaker 2: Yeah, thanks for having me. I think the right way
Speaker 2: to think about it is the corporate interest, especially from
Speaker 2: a company like Nvidia, is more about if you take
Speaker 2: a look at their portfolio differcification of where the revenue
Speaker 2: comes from. The fastest growth in the future is going
Speaker 2: to come from companies like OpenAI and the types of
Speaker 2: developers that they're going to support moving forward.
Speaker 1: So go ahead, please good.
Speaker 2: So what that means is that Nvidia is not just
Speaker 2: a hardware and a company. It's a hardware and software
Speaker 2: company is attracting developers and they have to partner with
Speaker 2: other developers and other companies similar to Opening I. So
Speaker 2: Opening I being the biggest, it's a foregone conclusion that
Speaker 2: they have to partner with them in some capacity similar
Speaker 2: to Apple.
Speaker 1: So given that you have invested in XAI, draw me
Speaker 1: a map a little bit on where different companies are
Speaker 1: going to play in this ecosystem.
Speaker 2: So right now we're talking about llms, we're talking about
Speaker 2: foundational models, but we're not talking about is similar to
Speaker 2: the past, where you've built the foundation, you've built the infrastructure,
Speaker 2: and the next layer of application companies are being built,
Speaker 2: which is essentially what are the next trillion dollar companies
Speaker 2: that are out there? So opening I being one foundational,
Speaker 2: XAI could be another one that's foundational, Andthropic is another
Speaker 2: one that's foundational, but they're supporting the next level of developers.
Speaker 2: You take a look at what companies are doing today
Speaker 2: similar to what a lot of venture firms are trying
Speaker 2: to figure out, which is how do you leverage AI
Speaker 2: to be more efficient or how do I say cost
Speaker 2: or increase revenue? Today with the aspect of what you
Speaker 2: see with open AI is that every single company, every
Speaker 2: single company in our portfolio is leveraging these prompts and
Speaker 2: leveraging AI in some way, and so what is in
Speaker 2: net effect is that they're being twenty five to fifty
Speaker 2: percent more efficient. It's not ten x yet. We're working
Speaker 2: our way there as these companies get larger and they
Speaker 2: build better developer tools, but that's an increased efficiency and
Speaker 2: that has a high amount of impact. So any corporate
Speaker 2: interest that's coming into open ai is looking at that
Speaker 2: not just for themselves, but they're looking at that for
Speaker 2: the future of their business, keeping the business that they
Speaker 2: have with their developer community, and making sure that more
Speaker 2: of these developers are going to continue to succeed on
Speaker 2: their platforms because that's what's going to continue to drive
Speaker 2: their business. So for Nvidia, it's a lock in because
Speaker 2: they have software plus hardware, and that is the defect
Speaker 2: of monopoly today.
Speaker 1: Argene, can you talk to me a little bit about
Speaker 1: valuation here? You have a report out from your company
Speaker 1: about what you call a weather report about the supply
Speaker 1: dynam and dynamics in price markets. When we talk about
Speaker 1: open ai, we're talking about evaluation that could be over
Speaker 1: one hundred billion dollars. But really there are only a
Speaker 1: handful of companies in the world with that kind of
Speaker 1: valuation underpinning them. So what else are you seeing in
Speaker 1: the private markets right now?
Speaker 2: So you know the way to start to think about
Speaker 2: private markets is worldwide. So this take China, India, US,
Speaker 2: and then Latin America as a whole. US and China
Speaker 2: sort of dominated valuations for a while, and a lot
Speaker 2: of capital has been flowing into anything that's software and
Speaker 2: tech enabled. The next phase of that was anything that
Speaker 2: software tech enabled and the label of machine learning and AI,
Speaker 2: and so that's where a lot of the capital is going.
Speaker 2: So far, most of the investments in the private world
Speaker 2: that's been going into AI has been coming from corporates.
Speaker 2: It hasn't been coming from venture capital. Venture capital traditionally
Speaker 2: is invested in anything that's vertically integrated or vertical application,
Speaker 2: which basically means like how do you leverage AI into
Speaker 2: a fintech, how do you leverage AI into healthcare, etc. Etc.
Speaker 2: Those are where most of the investments are going in.
Speaker 2: So valuations for those types of opportunities in the beginning
Speaker 2: are really high. Then midway through the cycle it becomes
Speaker 2: low again, and then what you'll see is something that's
Speaker 2: much more intrinsic versus options value related.
Speaker 1: You know, when you think about the global view here,
Speaker 1: there's a lot of concerns in the AI world about
Speaker 1: that competition towards AI spending towards AI development between the
Speaker 1: US and China. What do the dollars say, Well, most.
Speaker 2: Of the dollars today are going towards development in the
Speaker 2: United States. That's actually very very clear. That said, what
Speaker 2: you have to look at in terms of what investments
Speaker 2: had happened outside of the United States, especially in China,
Speaker 2: was essentially for image recognition, anything that was related to
Speaker 2: cybersecurity or surveillance. And so that's how they had spent
Speaker 2: majority of their capital, let's call over the last five years,
Speaker 2: and we spent that plus more over here that's been
Speaker 2: around AI, AGI machine learning. So we're much further ahead
Speaker 2: for now. And you can see that in the types
Speaker 2: of companies that are being built, types of products that
Speaker 2: are being built for every single vertical. So I always
Speaker 2: go back to cybersecurity, healthcare, financial services. You're going to
Speaker 2: see the first input there. You could call it GDP
Speaker 2: per capital growth and then cost. So those are the
Speaker 2: bifurcations between the investments that are really being made and
Speaker 2: then all the hyperscalers that are out there. They are
Speaker 2: going to benefit from all of this because you need
Speaker 2: more compute, you need more space, you need more training.
Speaker 2: You need more inference, which ends up being in net
Speaker 2: benefit for all the companies that are part of that stack.
Speaker 1: Speaking of hyperscalers, I'm glad you brought this up. Of course,
Speaker 1: we had Nvidea earnings this week. The initial reaction was disappointment.
Speaker 1: But something that struck me on the heels of Unvideo
Speaker 1: earnings is, even when you saw a day of earnings
Speaker 1: where the stock was down right after, you saw every
Speaker 1: other company in the Philadelphia Semi Conductor Index immediately have
Speaker 1: the reaction in the opposite direction, there is still love
Speaker 1: for that AI boom. Is this argent because there is
Speaker 1: such a lack of investment opportunity. You mentioned a little
Speaker 1: earlier that a lot of this benure investment is actually
Speaker 1: coming from corporations. So what does that mean in terms
Speaker 1: of exit opportunities? Are there fewer in the future than
Speaker 1: meets the eye?
Speaker 2: Yeah, so there's multiple questions there. I'll take a step back.
Speaker 2: So you look at Nvidia, and the way in which
Speaker 2: you should think about it from our framework is that
Speaker 2: they've have clear product market fit for hardware that they've built,
Speaker 2: and they have clear product market for the software that
Speaker 2: enables their hardware. A lot of the other companies that
Speaker 2: compete with them today don't have that, and so they're
Speaker 2: racing to be able to lock in developer interests. So
Speaker 2: all of them, they're racing to be able to try
Speaker 2: to commoditize that part of the stat stack that hasn't
Speaker 2: happened yet. And I think that's a key point. So
Speaker 2: when you think about year over year growth of what
Speaker 2: Nvidia looks like, if you think about year over year
Speaker 2: growth for the other companies that are on top of
Speaker 2: their stack, you know, namely open Ai, Xai Andthropic, et cetera.
Speaker 2: All of these companies have to rely on that, and
Speaker 2: then you're going to look at the next part of
Speaker 2: the stack, which is what do all the developers do,
Speaker 2: how do they train, and what are the products that
Speaker 2: they're going to use. Beyond just open source, they still
Speaker 2: have to work off of hardware. So I think of
Speaker 2: this very similar to when Apple came out. Everyone had
Speaker 2: made a bet that you know, Apple's devices are too expensive,
Speaker 2: it's going to be commoditized and all of the other
Speaker 2: people are going to come in and compete with them,
Speaker 2: and then you have software developers moving over to Android.
Speaker 2: You have roughly about a fifty to fifty market. You
Speaker 2: don't have that today. You don't have a clear competitor
Speaker 2: in hardware, and you don't have a clear competitor in software.
Speaker 2: So the question you have to ask is that what
Speaker 2: point does that start diverging in terms of overall demand
Speaker 2: for processing and demand for inference and demand for training.
Speaker 2: And today it hasn't stopped. I do believe it will
Speaker 2: will sort of now level out at some point, but
Speaker 2: we don't know if that's six months from now or
Speaker 2: two years from now or ten years from now.
Speaker 1: Rgine, we have to leave it there. That is Termina
Speaker 1: AI co CEO, Rgine Sethie. We appreciate having you today.
Speaker 1: Have a great long weekend.