"The Cognitive Revolution"
00:21:06 · Nathan Labenz, Anastasios Angelopoulos
[00:21:06] S0: What can you tell us about the new Gemini four Carbon? I think this has literally, come out and appeared on the Arena leaderboard while we've been live. [00:21:17] S1: And, you know, where it really shines, it shines on steerability. It's able to land user corrections, really effectively. It it shines on task completion. It shines on user preference. Users love working with this model. It's great at three d design. It's great at general agentic work with documents. Congrats to Google. This is just like, what a big comeback. I think that, leadership must be really happy.
00:21:45 · Nathan Labenz
[00:21:45] S0: Hey. We'll continue our interview in a moment after a word from our sponsors. Today's episode is brought to you by Anthropic, makers of Claude. To show off Claude's latest capabilities, I asked Claude to take lead in making this ad. Given a short creative brief, Claude, using Opus 5.5, studied my past Claude ads, wrote this script, directed a clone of my voice, and designed and coded the animation you're watching. My job? Give feedback and decide. Claude is the AI for problem solvers. It's the collaborator that understands your entire workflow and thinks with you, not for you. Whether you're debugging code at midnight, building a financial model, or strategizing your next business move, Claude extends your thinking to tackle the problems that matter. And with Claude Code Routines, it keeps working in the cloud even after you close your laptop. For problems worth solving, get started with Claude at claude.aitcr. That's claude.aitcr. And check out Claude Pro, which includes access to all of the features mentioned in today's episode. Claude.aitcr. This episode of the Cognitive Revolution is brought to you by OutSystems, the leading agentic systems platform. OutSystems is helping their customers build, modernize, and operate enterprise systems starting from any coding tool in a governed agentic engineering model. OutSystems, you can coordinate and govern your entire agentic workforce within your enterprise ecosystem, accelerate impact with industry proven agentic solutions that are prebuilt, governed, and customizable to your business. It's time to innovate at the speed of AI without compromising quality or control, which is why thousands of enterprises worldwide trust OutSystems for their mission critical apps. Teams any size can use OutSystems to build, deploy, and manage AI apps and agents quickly and cost effectively without compromising reliability and security. With OutSystems, you can accelerate ideas from concept to completion. It's the leading agentic systems platform that is unified, agile, and enterprise proven, allowing you to build your agentic future with AI solutions deeply integrated into your architecture. OutSystems, all your agentic future. Learn more at outsystems.com/tcr. That's outsystems.com/tcr. Part three, an orthogonal axis.
00:24:28 · Nathan Labenz
Debo Olaosebikan is co founder and CEO of Kepler Computing, which came out of stealth in September with a new kind of memory for AI chips, a ferroelectric capacitor stacked on top of each transistor made in older fabs. He started with where the energy goes.
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.