No Priors: Artificial Intelligence | Technology | Startups
00:34:31 · Sarah Guo
[00:34:31] S1: I think a lot of investors, you know, at least for me the last few years since I started a firm, it's been very educational thinking about the different capital sources for more capital intensive companies. I did a lot of software before. So your need to have smart friends with a very different stance and balance sheet was less if you're like, Ah, I need $150,000,000 before this thing gets to, you know, some critical mass. So you've lived that for a very long time, and then you have the unique experience of working with the government as a large stakeholder. How do you think this sort of industrial policy, it's led to huge successes like TSMC, right? Most important companies in the world. It's also been a bit frowned upon in American business culture for a long time. Like, how do you think that should change now, or where is it relevant?
00:35:25 · Lip-Bu Tan
[00:35:25] S0: It's a good question. So I think, clearly, for capital intensive business and infrastructure play, you need to access the capital. And then in some way, I think for our early day venture capital investment, now starting become very capital intensive. And some of the venture firm willing to put $1,000,000,000 into some company is very unheard of in the VC business. Now it's happening. And so in some way, you just have to be I like this kind of bell curve. Either you go in very early, and then because you're starting to do the series A, it's over 1,000,000,000 valuations. And so you had to go in pre money, pre seed to go into that kind of $2,030,000,000,000 valuation. It's very rare right now. So you just have to do that and pick the right one. And then the other part is able to find capital to scale, and that's why some of this mutual fund, they also like to move into the pre market early stage to join me to investing. I delight them because they are very less sensitive of whether I had to own 20% of the There's not too many 20% to give, so you have to find the right investor to come in. And then in term of the capital intensive, like AI in a factory and also the foundry, and then you really need to tap either government funding or some sovereign fund, and also some very big capital. There are some big fund that are doing that, and they're really The fund they've organized is basically support the infrastructure. And we like to tap into some of them and then to make sure that they can scale our operation. So I think in overall, government sovereign fund become very important. And also as a public company, I also purposely want to focus on some of the investor that are more long term growth oriented and so that they can help me to grow the business. And then rather than short term asking capital location, you know, where do you gonna, you know, buy back your shares? Those are good question. But meanwhile, I also had to build the business. Yeah. And so I think it's kind of that balance is important.
00:37:35 · Sarah Guo
[00:37:35] S1: Do you think there is something that investors, like, most misunderstand about Intel at this moment?
00:37:41 · Lip-Bu Tan
[00:37:41] S0: Quite a few things. First of all, think back to this crawl, run, and walk, last four months I crawled, but people are starting to recognize that potential of it. And so the other part is very important. We need to really get the best product out, either PC client, we still have a market share, but we really need to really build more perform better performance. So that's why I'm quietly building up the CPU architect, GPU architect, and the software architect so that we can leapfrog. Just like I look at Intel, I want to be a multiple of startup culture so that we move fast and we can leapfrog using better technology. And then the other part is beside the product, there are some new energy coming in, like in agentic AI, the physical AI. There's a lot of area that we can invest. Market is huge. That's on the product side. And the foundry side, we are very distant from TSMC and then in term of their performance. So there's we have to be humble looking at building the building block that I mentioned earlier, the IP, the yield, the defect density, and the cycle time to make it more efficient and more reliable. Is a trust business. People want to trust you before they give you the wafer to count on you. So those are the things will take longer time. But I think by 2030, 2032, 3132, I think I was starting to surface up. People may not understand how big potential I can be in terms of product, you know, on a PC client. That's our bread and butter. And we move up to the edge and move into the physical AI and agentic AI. And because right now, in the past, you basically provide the server, provide the PC for human. Now you're starting to have another different dimension. It's millions of agent. They need to come access the compute. They access into the software stack. So I think that part, I think we have a chance to really play. The game is not over yet. We can play on the injected AI and also the physical AI. So that's kind of where I'm going. And the AI is just the beginning. You have the training that Jensen owned in the edge and also in terms of agentic AI with agents and also physical AI, I think is the jumble. Everybody have a chance. So I think that's part that I want to go for it. And so I think hopefully the investor will know, even though in fourteen months, we make six time return to the shareholder, it's just a beginning.
00:40:20 · Lip-Bu Tan, Sarah Guo
We still have a lot of room to go. [00:40:23] S1: There's venture returns from here. [00:40:24] S0: Yeah, so I always look for 10x. Being a venture at heart, you want to look for 10x. And at Cadence, when I stepped down as a CEO, I think we make about close to 76 times, and starting from interim CEO, $2.42. And then when I retire as executive chairman, about 85 time return to the shareholder. So it's hard to do that at Intel because the base is bigger. So I kind of said, okay, let's do it at 10 x. You know? And then five year, ten years, if we can do 10x, think it's a good return being a venture capital at heart. That's kind of my goal. [00:41:04] S1: So there's Godspeed on this very, very large mission from this huge base already. There's an embedded belief in what you described about where the workload is, right? Where I think some would say like, we're just gonna build bigger and bigger data centers, and a gigawatt is the beginning. But the centralization and the efficiency from running even the inference compute in a centralized way is the dominant way versus thinking about the edge, thinking about the client. Do you think that there's like an equilibrium state that you believe in of where the compute is, or is it just we will find out from the workload? How do you think about that?
00:41:04 · Sarah Guo, Lip-Bu Tan
[00:41:04] S1: So there's Godspeed on this very, very large mission from this huge base already. There's an embedded belief in what you described about where the workload is, right? Where I think some would say like, we're just gonna build bigger and bigger data centers, and a gigawatt is the beginning. But the centralization and the efficiency from running even the inference compute in a centralized way is the dominant way versus thinking about the edge, thinking about the client. Do you think that there's like an equilibrium state that you believe in of where the compute is, or is it just we will find out from the workload? How do you think about that? [00:41:46] S0: Yeah. I think, you know, it's a very good question. You know, right now, there's a massive buildup in terms of the AI. I think it's the right thing to do. I don't see there's anything to slow it down because the workload is increasing a lot. And then I think the question mark is [00:42:03] S1: how- And we are supply constrained.