"The Cognitive Revolution"
00:24:48 · Debo Olaosebikan
[00:24:48] S2: So then I looked at the existing chips, mainstream ones, you know, the the GPUs or the TPUs, and I say, where's the energy going? And I I I think many people know this, but a lot of people don't know that for inference especially, the bulk of that energy is actually running across the street to fetch the data from the library, you know, before you before you process it and do your fancy matrix multiplications. So if I wanna improve that system for inference by an order of magnitude at the physics level, it's obvious that the first thing I need to go do is address the memory bottleneck. And the one way you can do that is to bring that memory closer to the processor. And the other thing you want to do is increase the amount of memory that you can store within a certain footprint. Both of these things point us to going three d. And then within the chip, how do you stack devices on top of this transistor to pack more bits per millimeter square? Within the chip, use new materials. We use a class of materials called ferroelectric materials. Ferroelectrics have actually been around for hundred plus years. But the specific type of material, the specific, you know, recipe of the cake, if you will, this is this is this is, you know, proprietary to to Kepler. And you basically use that ferroelectric to make a capacitor, a type of... A capacitor like thing. It's really holding a polarization for for people who are, you know, more technical. But roughly speaking, you can say that this capacitor is storing a one or a zero. And if I replaced six transistors with one transistor, one capacitor sitting on top of that transistor, and then eventually put multiple capacitors on top of each other, I can avoid the the rat race of, like, trying to shrink transistors within a chip. So we have technology, roughly speaking, that's saying, how do you go three d inside a chip? How do you go three d between chips? And how do you use those technologies to increase memory capacity, but also bring memory and compute much closer together while avoiding, you know, conventional, like, you know, issues with with thermals and and and and things like this.
00:27:02 · Nathan Labenz
[00:27:02] S0: Prakash asked about manufacturing. Do new materials need a new process? And what does it buy you not to depend on extreme ultraviolet lithography, the EUV tools everyone is short of?
00:27:15 · Debo Olaosebikan
[00:27:15] S2: Great question. So I think, this is a huge thing. This is like, you know, always say it's similar to how the world thought it didn't have enough taxi drivers, like San Francisco thought it didn't have enough taxi drivers. And then we realized, oh, we have so many taxi drivers if you just build Uber. So, you know, not to undermine the fab shortage or chip capacity sort of manufacturing shortage, but the focus right now is on sort of EUV heavy fabs. They're expensive. They're complex, more complex, you know, processes. And we're saying that we can take existing older node fabs, and we can use them to make chips that are actually superior to the advanced node chips. That doesn't mean that we can't use EUV fabs. EUV... We just we just invent an unorthogonal axis to shrinking transistors. So we also benefit when the transistors shrink, no problem. But we don't require it to make chips that are more advanced. So that's a huge advantage because the cost of these fabs are lower. The lithography you need is, you know, much, much less expensive. And some of these fabs have a lot more capacity in them. Right? For example, if I could take an older node LogicFab and if I could use that to create more advanced SRAM or even more ambitiously, I could use that older node LogicFab to create, HBM class memories, this is a this is a huge huge thing. This is a huge unlock. And even if I needed to build more fabs... So let's say I make use of whatever fab capacity I have and then I need to build more fabs. You know, my my fab per dollar or dollar per fab, whatever ratio you want, is superior to to the person who has to set up an EUV line. So this is all advantage. In terms of the process itself, the key thing to watch for is, are you introducing any brand new tools, any custom tools, any custom equipment that's not standard equipment that already exists in the fab? And, the answer is no for the most part. So the process flow in utilizing those equipment and, you know, some of the materials, sure, that may be new, but we're not bringing in any new, any non sort of standard equipment or causing some equipment maker to build, new equipment for us. And so the reason we've worked hard on this for the past seven years.
00:30:01 · Debo Olaosebikan, Nathan Labenz
So what we've been doing all those years is turning that flow on standard equipment into something that is manufacturable, building test chips inside of proper fabs that basically prove that, hey, okay, once you nail the process flow within these standard tools, you can just stand up these lines within the fab and you're good to go. So once those process flows are sort of finalized and honed, it's very easy to replicate this and scale this up inside of a manufacturing process. So it's really it's really all upside once you have the stuff working. Part four, [00:30:44] S0: act one. Samvit Ramadurgam co founded Forge, the marketplace for pre IPO stock. His new company, Union, is building an agent that brokers deals across markets, introductions, asset sales, private shares. Act one, he says, is coordinating the world economy. I asked what the surprises will be as these systems develop.
00:31:06 · Samvit Ramadurgam
[00:31:06] S3: I think that over the next five years, I think, you know, you guys have probably really bought into this, the world is gonna get very interesting, and there's gonna be a lot of surprises that come from the capabilities of AI. I think we're seeing this right now with mathematics. Like, it's completely incredible. Like, that... Like, math has been revolutionized so quickly, and it's just the beginning of it. And so, like, that's about to happen to many domains. And and so I think you could expect, like, what happened in math and coding in the last year to happen to as as domains that are less and less trivially verifiable with software. The further we get from verifiability, the longer it may take. But this... It's gonna get there because the feedback loops, are gonna be there just like with with longer feedback loops. And so what does that mean for us? Well, I think that one very big surprise that will hit the world is that at some point in the next few years, people will wake up and then realize that huge number of the world's markets are being coordinated by agents. Thousands of markets from, like, you know you know, dairy in, like, a a a country all the way to real estate in in China to, like, the the the pre IPO market in The US. Like, all these markets are gonna be coordinated better with agents, like, period. Because what it is... The way it's being coordinated right now is that there are human... Gonna be humans who are really good at what humans are really good at, building relationships, having taste and judgment, and and advising their friends and trust. Humans will have a place, and they'll be, like... I think they'll to actually... The best ones will actually, I think, do better. I think by and large, most of the work is data coordination, connecting the dots, logistics, operations, orchestration, all of which software and agent's gonna be way better at. So, yeah, we'll have, like, a a period where the same way that software and math is really getting taken over, one of the surprises is that a lot of this deal making that makes the economy function is actually something that agents are quite amenable to that we're focused on.
00:33:03 · Nathan Labenz
[00:33:03] S0: I asked whether he saw reason to worry about disempowerment. What he describes sounds empowering, but some would hear it as the agents running the world.