No Priors: Artificial Intelligence | Technology | Startups
00:18:16 · Elad Gil, Lip-Bu Tan
[00:18:16] S2: I mean, it's very exciting. And one of the reasons I'm asking about it as well is, to your point, there's always some things that you can vent around, but there are also physical limits where once you hit seven angstroms or whatever the limitation is, you start to run into [00:18:27] S0: New material. Yeah. You need to [00:18:28] S2: find new materials or find other workarounds. Yes. And then the interesting question is, and we've been talking about this for a long time. I remember twenty years ago, people were talking about how we'd eventually hit a point where we ran out of space on this, is do you run into some sort of asymptote that actually normalizes performance across different foundries or not? Yeah, good question. In terms of Moore's law, you say double and then [00:18:50] S0: the power and the cost. And then you can double the performance, but you cannot double down on the cost area. So those are the things you have to give way unless you find some new way of material, [00:19:05] S2: new way of design,
00:19:05 · Elad Gil, Lip-Bu Tan
[00:19:05] S2: new way of design, [00:19:07] S0: that become material science, I starting to hire more people in the material science. So that is kind of innovation in our area. How can we do that? And I still remember eighteen years ago, and I I still investing in semiconductor. And, actually, most of the VC firm, some of them are very nice tier one venture firm, a good friend of mine. And I initially the partners meeting, the whole partners in the room. Then after I talking about semiconductor, half make excuse to run out the room. Then eventually, the other half, they said, available, do you have any software service? So then they even left with only two sympathetically listen to me. So it's kind of the history have changed. And now semiconductor, if you look at it, Jensen is a 5,300,000,000,000 market cap company, and then Broadcom and TSMC is 2,000,000,000,000 market cap company, and Lisa Su, my good friend at AMD, is almost $800,000,000,000 and I'm close to $600,000,000,000 So in some ways, kind of semiconductor become hot again, and it become essential because fifteen years, twenty years ago, when I invest in semiconductor, no VC want to join me except some of the big corporations like Samsung, ARM and SoftBank and others, and investing with me. And then now I'm starting to see a lot of VC like to come investing in semi, so I'm very happy. Given
00:20:33 · Sarah Guo
[00:20:33] S1: the enormous interest in investing in this area that used to be considered too hard. Right? Yes. What do you think I mean, you've been a venture investor with Walden for a very long time as well as an operator. You know, the general fears I'm just gonna list a bunch of them. The general fears have been it's very capital intensive, and you should tell me what I'm missing. It's very unpredictable in terms of, you know, shipping a design that works missing tape out. And you need to understand the workload very well. I think there's another, which is just like it's very high risk for the customer to switch. Right? Think, you know, we've been involved in companies together where, you know, there's a design win, and then there's still the question of like scaling order volume. And then there's a cyclicality, right, of, you know, you build hard manufacturing capacity and demand may change or not in any given year. What is your view on how a bunch of, you know, what makes it hard as an industry and then the secular demand growth from a bunch of different areas, right? So you have the recognition of how important a more diverse supply chain is. And then you have this like explosive demand growth on the AI side. How do you you're still an investor, and then you're making the biggest bet ever. Like, go be CEO. How do you, like, think about these different risks and advise others about where to invest in this supply chain? I realize that's a very large question, but just given your history with it, I think there's a lot of like YOLO action of like, there's a memory shortage. Buy memory stocks as well as just an unwillingness to take on things that have a ten year timeline, like material science.
00:22:20 · Lip-Bu Tan, Sarah Guo
[00:22:20] S0: Good. You have quite a broad range of questions. Let me try to explain that. So first of all, I think the venture capital startup is in my blood, and I really enjoy it. And so I think this is not time to brag about it. And so there's some good exit. I still have 159 IPO, 126 ms and A, and that includes semiconductor. Just break down to semiconductor, I invest over the years February is in US. So what I usually look at some macro [00:22:57] S1: Just to be clear, that's incredible.
00:23:00 · Lip-Bu Tan
[00:23:00] S0: Thank you. Thank you. I just enjoy building it. But more important, I will look at this, first of all, on the investment side, I always look at where is the bottleneck? What are you trying to solve? For example, I invest in a company called Cradle Semiconductor, Australia Lab. Is this interconnect become the bottleneck? So I decided to back and also back Celestial AI in the optical side. And then because speed become more important in the interconnect, in the cluster, so I think optical become very important. Look at Jensen. He invest in almost every company is photonic related. And then the other part I'm looking at is, okay, what are the solution that need? Like, for example, we talk about design and then the complexity and also the cost. Can you find some using AI machine learning to drive a better design and better solution. So a couple of new startup actually go into the EDA related area to drive performance improvement. I think it's a gold mine to do that. And then the other part, you look at the new material. We talk about this indium phosphide. That's why I invested in INFI, and then Val bought it. And then you invest into some of the new material, the gallium nitride and the silicon carbide, and then some of the companies starting to being acquired, include one of them doing power management, and ADI is a sport called mPower. And so again, this IVR, that's a very, very good area in power management become bottleneck now in term of converting from 40 volt down to one volt. And then those in term of that conversion, you lost a lot of power. And how you do it through the power improvement? So I think power, thermal, those become the bottleneck. So I think I always look at from what is the problem we try to solve? Is it real? Is customer crying for it? And then I starting to invest. The next thing is look at it's very important from day one, you'd have to target the first customer. And usually, I like the customer is hyperscale. They have the scale. If they like what you have, they're willing to pay millions of dollars next few years, and even giving some warrant is worth it because you have a big one customer you can scale. So I always look at some of the formula, how do you do that, and then where do you get the talent? And then, yeah, you know, sometimes it's very important to find the talent.
00:25:22 · Lip-Bu Tan, Sarah Guo
That's why I'm really interested in US and then Silicon Valley and then some Austin, and then the other part is Israel. A lot of talent. So I backed quite a few, quite a significant amount of my investment in Israel. And then because they have very disruptive, innovative entrepreneur, They work really hard. Even in this war time, they still have conference call. And sometimes they say, okay, there's a warning. I have to go to underground. And then the internet may not be good. Maybe we just use voice. In some ways, it's kind of fun. They're kind of resilient entrepreneurship I really enjoy. So I think all in all, I felt that there's a lot of opportunity, and especially in the AI. And right now, beside the agentic AI, now you're looking at physical AI. It makes a mixed big frontier. And then you had to really look at a full stack. That's why I'm still involved with a lot of this frontier model that we very familiar and some of the investment I've made because I really like open source frontier, you know, technology for physical AI. I think that's a gold mine. [00:26:27] S1: You mentioned the opportunity to make certain parts of the design and test of chips faster, cheaper, more creative with AI. Given your Cadence experience, like, do you think is most fertile? Is there anything you think is already working?