00:26:55 · Jaimin Rangwalla, Molly O'Shea
It doesn't have to be like, you know, a very complex programming task, but just the footprint of each individual in terms of their semiconductor content, their power content, right? It doesn't just apply to semiconductors, applies to how much power each person consumes, is all going to increase significantly. [00:27:12] S1: Because of those behavioral changes, how is that affecting the architecture of AI?
00:27:17 · Jaimin Rangwalla
[00:27:17] S0: I think that we're now moving to a world where the chatbots, you know, we joke around internally, the chatbots have amnesia, right? Because every time you might give it some files, you give it some medical records, it does a great job of going through and analyzing it. You go back the next day and you give it the same record. You ask a question about those records, it doesn't remember. I don't if you ever watched that movie, fifty First Dates, Bob Sandler, right? Where basically he has to re explain every day what the situation is. It's very similar to that. But what's changing is now you're getting persistent memory with all of the the next evolution of, accelerators. And so I think that you're seeing memory content is increasing. The agent is not gonna have amnesia anymore. The agent is gonna be able to remember. And now because the agents are launching agents and they're doing very complex tasks but also simple tasks, they need offload from CPUs too. Historically, CPUs have done something called serial processing, which is like you just give it a set of instructions and it does one by one by one by one critical instructions, but one in row. And I think that as an agent has some element, which is like, make a reservation, that might just be very serial tasks. It might have something which is like, okay, program something that's super complex and might require parallel tasks. And so I think that that is just creating a massive need for more CPUs and more memory, and we're seeing that. We're actually just at the beginning of that. I think agents is really what is driving that.
00:28:38 · Molly O'Shea
[00:28:38] S1: There was a really interesting slide on that where you're showing the shift of GPUs to CPUs, and it was like a four to one conversion. So how did you kind of model that out, or how are you estimating it?
00:28:49 · Jaimin Rangwalla
[00:28:49] S0: Yeah, so for some amount of the last, the greater part of the last three or four years, it was very much a one CPU to even eight GPUs. That was kind of the, and it had gone up to 16 GPUs, which was all of the math and all of the computation is happening at the GPUs, and then they just kick it out to the CPU when it's time to execute the final step. But now the ratio has been actually moving to one CPU to four GPUs, and so it's improved kind of by 2x already. And we think it actually has a chance to flip the opposite direction, which is one GPU to four CPUs. And some people, very aggressive, say that it could go to one GPU to eight CPUs, right? And so I think that it's just more a function of how many of those tasks are being completed and how many of those new tasks are being done that don't require the parallel compute, but will require more just serial task completion. And I think that, again, it's just another way that I think about it is there's 7,000,000,000 people or so, plus or minus. Everyone has their technology needs. But if every person has, you know, a thousand, right, the 7,000,000,000 multiplies by a thousand. Right? And so just think about how many more people effectively are on the earth than there are today. I think that all of those people are going to require serial processing, parallel processing, memory, like all of the things that every human would have. Think that it's just another way to think about just almost population expansion. If we expanded the population by a thousand, and this is a digital population that we're expanding, just all of the resources that are required for that digital population.
00:30:21 · Molly O'Shea, Jaimin Rangwalla
[00:30:21] S1: Who are the main players within this stack? [00:30:24] S0: On the CPU side, it's Intel, AMD, and ARM. Amazon has done a great job. They actually have one of the most efficient CPUs, but they've always kind of kept it internally. They bought a private company that helped them do this, and it has helped, I think, drive AWS success, especially as we move to CPUs. I think it really helps be a tailwind to their business. But Intel for years has been almost like the forgotten child in semiconductors. They had a lot of missteps along the way, whether it was a technology issue, product issue, They've had a lot of leadership changes, but they have an amazing CEO now. Sometimes we try to keep things simple, which is you have a four to one going to one to four, which is mathematically a 16x improvement in market size. And then you have Elon Musk giving a stamp of approval to Intel and increasing the pace of innovation. And so I think that to me, it's just some of the best thesis are the simple thesis, and you don't want to overthink it. And I think that just the combination of that for a stock that, yes, it's had a big move this year, but if you think about every semiconductor company over the last four years in this heyday of AI, they're all up five, seven, 10x. It's still a laggard over any period beyond twelve months. So to me, there's still a lot of catch up for that company. [00:31:40] S1: Is that really exciting for you? Because you started your career in the semiconductor space, and now you're like, I mean, that was gaming back then, and now
00:31:40 · Molly O'Shea, Jaimin Rangwalla
[00:31:40] S1: Is that really exciting for you? Because you started your career in the semiconductor space, and now you're like, I mean, that was gaming back then, and now [00:31:48] S0: I will tell you the first seven or eight years of my career, all I did was work on it with shorts because semiconductors are viewed as the deep cyclical business. You overbuild at the top, you burn a tremendous amount of cash flow, very fragmented. Just companies go up. They make one product. They sell it to Apple. In a year, Apple says, Okay, we're going to now double source you or triple source you, and the companies go down 90%. Now, I spent almost all of my time working on shorts. We never really owned semiconductor stocks. We owned internet stocks, software stocks, other types of stocks. So to me, it's almost just like, wow, my career has come full circle. I have the chance to make decisions at the portfolio level at a time where the sector that I started twenty years ago is now the most profitable sector, generates the most cash flow. Like, I never would have thought I would tell you that Samsung and Hynix together generate more cash flow than every hyperscaler. Like, it's crazy. Like, it is crazy, but it is exactly what is warranted in this time and why you have to always be changing how you think. If I had just stuck with the view from fifteen years ago that semiconductors are shorts, it would have been the most costly mistake that we would have ever made because those are the stocks that are dominating the market today.