Google I/O 2026, Karpathy Joins Anthropic, and Cerebras’ $95B IPO | EP #256

If you said five years ago, hey, Google's gonna six X its cat bags and the stock will go up. Nobody in the right mind would have said that's even possible. Quadrillions, billions, hundreds of billions, trillions. It gets numbing after a while. There was a lot of conversation that Google was cooked. Google was not gonna make it that their revenue engine was being massively disrupted. And here they are, you know, sort of disrupting the disruptors. All of these numbers, I think, were inevitable. Andre, Caparthly joins Anthropic, he was the co-founder of OpenAI. He left in 2017 to run full self-driving for Elon. I know start and you initiative focused on using Claude to accelerate Claude's own pre-training research. Cerberus record IPO closes up 68% market cap, $95 billion. Andrew Feldman, the CEO of Cerberus. It's like a lifetime achievement, like a Nobel Prize or an Olympic gold medal, where you carry it for the rest of your life. All right, I see Andrew Feldman has entered the room. Andrew, a pleasure to have you here. Thank you for having me on your show. Appreciate it. Now that's the moonshot, ladies and gentlemen. Everybody, welcome to another episode of moonshot. Today, we have our extraordinary group of moonshot mates, DB2, our emperor of AI investing. Selim, professor of all things, exponential organizations, and our very own artificial superintelligence, our moonshot mate, AWG. I'm Peter D. Manas, your host, gentlemen, a pleasure to have you here. And Peter, you're a great guy. I'm going to be back. And so to be back. So we say, happy birthday to you. Happy birthday to you. Happy birthday. Everybody's going to be signing off the tide right here. Right? We are not singers. Stick to the verbal. Well, one of us was. Well, my first career was in the New York City Opera Company. Really? You just seriously. I'm pressing all of us. Well, thank you, gentlemen, for your-- Do you want to hear-- I would like to hear an RES sometime. All right, maybe I'll do an outro. OK. For sure. So we have such a fun surprise birthday party for you. Oh my god. We did. It was crazy. I have never been so surprised in my life. And honestly, you, Selim, are the most giving individuals. So Selim was visiting with Lily and his son Milan. We had a birthday dinner for him Saturday night. But his birthday was Sunday. And he walks me along the beach to a surprise birthday party where there are 50 people. I walk in this room and I've never been more surprised in my life. I mean, I literally dropped to my knees in the level of surprise. All right. But it was your birthday, Selim. And you were-- It was a perfect decoy. It was a great decoy. It was awesome. Oh my god. You're very special. Very special. On Dave, you got a great message over us. Great. That was wonderful. Thank you all for that. So welcome to Moonshatz. Our job here is to get you pumped about the future. No politics, no doomers, just the science and technology driving us along the singularity. Today, we have a special episode, our annual recap of Google's mega event called Google I/O. We'll cover news with Andre Kaparthi joining the topic in Elon's defeat in the trial against OpenAI. And finally, we'll be joined by Andrew Feldman, the CEO of Cerrobras, after an epic IPO. So Dave, it was a blast to be with you at Google I/O. Here's an image of us along with Tyler Donahue. What do you think of it? Yeah, it's great to be where it all started. The vibe on AI is global. But within this epicenter where everything began, it's off the charts. And the first hour, just the amount of stuff to talk about in one hour compared to a year ago, compared to two years ago, I mean, we're taking it all for granted, but it's just crazy. Just the number of new Google brands of new AI products is pretty baffling, so we'll go through it all. We've got it all beautifully cut up today, so you can analyze every piece of it. And Alex, you were watching online, were you? I was. I was watching in real time, dissecting it all for my newsletter, The Intermost Loop. I thought there were some high points, some low points, some mid points, if I may say, an eager to dive in. And some probably some no points. And of course, where's Waldo today, Seleme? Your probability function on planet Earth. So where are you today, Seleme? I just landed in Brazil. And I've got a bunch of meetings and presentations here. So I flew from LA with you, Peter, directly here. Of course, of course you are. I'm late at not be said that you're not the traveling dude. Peter, I'm at the Seribriz headquarters today, too. The vibe here, third biggest tech IPO in history have the vibe here is just, I couldn't resist the opportunity to feel it right after an IPO accompany. It's a once in a lifetime kind of thing for most people. So the vibe is just epic. Yeah, a record IPO, until the next record IPO. Until the next record IPO. I know it's going to be 10x this year. So you got to savor it while you have the record. It's insane. Let's jump into all things Google I/O. I want to kick it off with the opening summary by Sundar quite the year. Let's listen to Sundar and then we'll continue on and dive in. Two years, we were processing 9.7 trillion tokens a month across the surface is a huge number. Last year at I/O, that grew to about 480 trillion tokens. And fast forward to today, that number has jumped seven times to 3.2 quadrillion tokens per month. Over 8.5 million of you are now building new apps and experiences with our models monthly. And our model APIs are now processing around 19 billion tokens per minute. We are, of course, also seeing incredible demand across our products. We now have 13 products with over a billion users each. Five of those have more than 3 billion users. AIO of you now has over 2.5 billion monthly users. And AI mode has been a revelation or biggest upgrade to search ever. People love it. In just a year, it's already surpassed 1 billion monthly users. Last year at I/O, the Gemini app had 400 million monthly active users. Today, we have surpassed 900 million more than doubling in a year. And today, more than 50 billion images have been generated with our nano-brenner models. In 2022, we were spending $31 billion annually in CAPEX. This year, we expect that number to be about six times that, approximately $180 to $190 billion. Instead, we can now seamlessly distribute training across multiple sites, scaling across more than 1 million TPUs globally. This gives us the ability to create the largest training cluster in the world. Both chips are more energy efficient, delivering up to two times better performance per watt. Wow. Quadrillions, billion, hundreds of billions, trillions. It does, but you've got to step back. And six xing your CAPEX. If you said five years ago, hey, Google's going to six x its CAPEX and the stock will go up. And there it is. I mean, those TPUs are-- I mean, that's the Linchpin. And if there are two x to power efficiency, but they're in the hunt now, competing vertically from the transistor all the way through the user experience. And no one else can say that. So yeah, just a lot. I'm just-- Look at the array of logos that have over a billion users. I mean, you have to go back, rewind the video, go back and look at that again. It's just a-- It's the list of Google things now. Yeah. That's a relentless. The Gemini app-- So here having 900 million users is pretty incredible, because that's pretty close to ChatGPT. I found that very striking. It is. Alex, what's your take on these numbers here? A couple of thoughts. You see, well, inevitable in some sense. I'm reminded about 20 years ago. I had a conversation with Larry Page during his interregnum when he wasn't CEO. And I was reminded he was asking me for advice on how to get Google interested in spending $100 million to work on AI. If you can believe that. It's unconscionable by today's standards that Google wasn't interested 20 years ago in AI. And now here they are. It's become the central focus of the company, Full Stack, from chips and data centers all the way through applications. But here we go. It's been widely remarked that if Google hadn't leaned in at every layer of the stack to trying to own, or at least lead in AI, they would have been toast. The original model of Google based on search and ads would have been cooked. So what do you do? You lean into it. Gemini, 900 million plus users. I think that's perhaps just slightly less remarkable than it might seem, given that Gemini is basically being swapped for assistant. Assistant already had quite a bit of traction. But nonetheless, nice to see that Gemini usage is taking off. It's nice, I think, on balance to have something other than a doipoli between open AI and Anthropic, which I think if Google doesn't aggressively lean into consumer and enterprise Gemini adoption, I think that's the default outcome. And now, you know, and I do as well, because, again, I've known Larry since 2003 2004, AI was always his focus. It was he wanted to build an AI company. That's right. Very beginning. And not a lot of difficulty for a while. I mean, it wasn't obvious to everyone else within Google for the first part of its life that AI was where this was all going. And of course, Eric, the adult supervision in the room came in and built the revenue engine. And of course, Google's only able to do what it can do today because of the massive revenue. The thing that people don't realize is that part of Larry's vision early on was also BCI. He wanted to connect the brain to AI. And space stations. Peter, do you remember that whiteboard that Google used to maintain with their long-term tech tree? Sure. And they were going to have a Google space station and BCIs and all of these things. That's all happening now. Finally, like three decades later, the Google whiteboard vision is finally playing out. No. And the whiteboard actually had the tethered satellite on it. They're going to make an elevator into space. A space elevator. Space elevator. Yeah. And they needed a carbon nanotube wire for things to go up on. They manufactured about a meter of it. I was worth money. I was packing it. That's a 20,000 mile cable. We're finally catching up with the whiteboard. I was with Jack Hittery at Sandbox, a queue yesterday, talking about what the large quantitative models, the LQMs, are going to be able to do. And one of his objectives are new materials at the tensile strength to give you a space elevator. So all of, again, what did you say, Alex, with the speedrun, every science fiction movie ever made in the decade? Over the next 10 years, like every sci-fi trope, everywhere, all at once, over the next 10 years, that's the singularity. I just a big shout out and congrats to Sundar and Sergei and Josh and the team there. I mean, hitting their numbers, again, you have to remember, I'm sure, I'm sure, I'm sure, remember that a year and a half ago, there was a lot of conversation that Google was cooked, Google was not going to make it, that their revenue engine was being massively disrupted. I mean, this is an AI-native operating system company because they're now constantly continuous sensing execution adaptation and they've kind of hit in or loop there. This was the company that birthed the transformer. This was the company that my friend, John Smart, I think, perhaps insufficiently famously pointed out that if you looked at the number of words or tokens in an average Google query over a period of 15 years, and this is before everything hits its inflection point in 2017 or so with the transformer. But if you looked at the number of words per Google query, it was following an exponential curve that was inevitably going to end up in people having full conversations with AI. So Google had the exponential trajectory of user interaction. They had the transformer. They had the compute. It was just a matter of putting all of the institutional pieces together and it seems like they're finally coming together. Yeah, amazing. Moving us along after that epic intro by Sundar, the company, Google is launching an entirely new family of AI models called Gemini Omni. It's capable of generating video clips from prompts that include a variety of inputs, including text, photos, videos, and audio. Google says Omni will be the create anything from any input product. So let's take a look at the video here being introduced by Demis. And Demis was a rock star on stage. It was so much fun to see him there. I'm excited to announce Gemini Omni. Models like Vio, Nana Banana and Genie are able to create extremely realistic videos, images and interactive simulations. It's a step change in simulating things like kinetic energy and gravity. DemiNize world knowledge and reasoning really shine in Omni. It can translate complex ideas into highly accurate videos. So for example, you can give it a simple prompt like make a claymation explainer of protein folding and get this. Proteins start as chains of amino acids. They fold into patterns like the alpha helix and flat sections called beta sheets forming a perfect three-dimensional shape. Gemini gives you a more natural way to edit video with conversational language. What's really cool is you can give it your own videos, for example, they're selfie and change reality in a really fun way. I hope people are watching this on YouTube because the video clips are extraordinary. Dave, you were going to say? Yeah, the crowd reaction, actually. Demis had by far the best visuals and the crowd reaction. First of all, the crowd is hugely into medicine and science as the use case that everybody cares about. Demis is the spokesperson for that. But then his visuals on the video stuff were the best too. You should go watch the original YouTube recording and see the full video of him morphing himself through different places and outfits and everything in real time. It's incredible. I think in the worry about AI that's going on globally, we lose the fantasy and the cool factor that you could look forward to this your whole life. Now you can suddenly play with it. I really encourage everybody to just get in there and build some stuff and play with it. Put yourself in a movie as a character, change the backgrounds. Once you've experienced that, you really get a sense of the amazing things that are possible. Starting now and for the next few years, just new things every week. Alex, reality is cooked, isn't it? I think reality is getting enhanced for sure, but I also want to applaud Demis and Google DeepMind for being the only arguably remaining American frontier lab to still be chasing multi-modality. Open AI and the UK. Right. Well, they're really American. I mean, they may have a lot of personnel in the UK, but it's an American frontier lab. They're the only frontier lab still chasing multi-modality. So open AI, cut Sora and arguably deemphasized video and Thropic has never arguably been chasing multi-modality. They've been squarely focused on Cogen. That just leaves Google with the only credible frontier American video model since video is arguably the hardest modality combined with consumer demand. And then you have all of the Chinese frontier labs. China is taking video as a modality far more seriously. My sense from some of GDM's earliest announcements with multi-modality is they have a grand vision of modality scaling, even though video presents as the most consumer-friendly, the most impressive demo. Actually at the back end, they're probably treating biological sequences like DNA or protein sequences as another modality. They probably have dozens of other modalities that they're trying to fold into this omnimodal model looking for modality scaling in a way that the other American frontier labs just aren't. So it's a bet. It's at this point almost an idiosyncratic bet that they're going to get to some form of superintelligence that's distinguishable because it handles all these different modalities, text, audio, video, maybe biological sequence data, maybe other crazier modalities, all in some meta-uniform way that the other labs aren't achieving. But it's a bet, nonetheless. Interesting. I can't imagine a better kind of technology for education and teaching people. I so wish this was existing when I was doing organic chemistry and studying medicine. I mean, this should clear away so much of the craft of trying to figure out how to present things and how different ways of showing things, biological models, et cetera. I mean, this could all become real-time. A full 3D. It's incredible to see what's going to come from this very exciting. The real-time part of it is huge too because in a call like this or a podcast like this, you can create real-time graphics and visuals to fit the dialogue just purely with your voice. They can do it at Google. We can't do it because we don't have the token speed to keep up. So we have to wait a minute. So if you said something really cool right now, Slim, hey, Brazil, let me tell you about data center explosions in Brazil, the graphic that backs that up would take a minute to come back. And so you can't do it in real time, but they can. And it's just purely who has access to the compute. Yeah. It's incredible. It's going to be coming where AI is going to be just creating a soundtrack and a visual track for your life. Yeah. Always present whatever you want. Amazing. Let's dive into their new Gemini 3.5 Flash model. So they just launched Gemini 3.5 Flash. It's the new default for Gemini app and AI search mode. And as you're going about to hear compared to 3.1 Pro, it's better across all the benchmarks. And importantly, Google says this new model is significantly faster in a league of its own in terms of intelligence versus output speed. It's better handling agentic tasks, offering improved agentic coding, richer and more interactive graphics. Let's take a look. And today I'm excited to introduce Gemini 3.5 Flash. Our first in a series of models. When compared to 3.1 Pro, Flash is better across the board, almost all benchmarks. It's made huge progress in coding and look at that extraordinary jump in GDP valve, a benchmark that captures many real world economically valuable tasks. Second, 3.5 Flash is a very capable model at the front here and comparable to the best models, but much, much faster. Which is why when you look at the intelligence versus output speed, it's in a whole league of its own in the top right quadrant. When looking at output tokens per second. It's four times faster than other frontier models, and it's incredible the light to use. - Alex, impressive, what do you think? - Well, remember when I opened saying there were highlights, low lights, and then to use the colloquialism mid. I would call Gemini 3.5 flash solidly mid. If you look at its capabilities, and others have pointed this out as well, just from a raw capability standpoint, not talking about throughput or cost, it doesn't compare favorably with, say, GPT 5.5 high, or X high, or pro. On the other hand, this is a flash series model, so it's not pro yet. Sundar sort of infamously at this point has said, Gemini 3.5 pro, that's coming out in another month, there were growing in the audience at the time. So this isn't intended to be top of range. I think the strategy, if I were to play a Kremlinologist here, the strategy is, I think Google is sort of solidly tier 1.5 at this point in the race to raw frontier capabilities. 3.5 flash represents perhaps pushing the optimal frontier in terms of throughput versus performance, that optimal frontier, but I think it's also very telling that Sundar is highlighting throughput versus performance instead of number of tokens on the X axis, input tokens versus performance, or rather output tokens on the X axis versus performance on the Y axis, or some other metric, he picked the most flattering possible metric. And if you actually, if you look, everyone picks flattering metrics, but some metrics, some flattering metrics, are also more sort of truthful than others in some global sense. And if you look at the metrics that Google's been highlighting, A, their note, it's very telling 3.5 flash is being compared primarily with 3.1 pro, less with frontier other models. But secondly, the areas, the benchmarks where it's really excelling are benchmarks where tool use, in particular, really aggressive tool use is needed. So if I had to squint at this, I would say, the emphasis in Google wasn't necessarily beating with the frontier with 3.5 flash. It was probably, I would say some combination of throughput maxing and tool use maxing, not pushing the boundaries of the frontier, but solid release nonetheless. It's nice that Google's still in the game. And Dave, I'm imagining you, we've talked about the labs pulling their punches. I imagine that releasing, there'll be, the next version of GPT will come out, and then, of course, pro will come out right after that. Well, the scuttle butt here in Silicon Valley is that it's a two-horse race between open AI and anthropic for the best AI in the world. And the talent is flooding into those two buildings in SF. And nothing in that demo or in the vibe on campus that Google contradicts that. So here, you pointed out earlier that Google, or Alex pointed out, that Google is the one remaining horse in the race to the consumer. And this is a very, very fast model that gives the consumer a much better experience. But the other labs have already pivoted to the enterprise and said, look, we're giving up on that. We're going totally after these massive enterprise budgets. And they, if you try and build something sophisticated with AI, you want the smartest AI that solves the problem. And you're not going to back off to a faster model that's not quite as intelligent, you just can't. And so they're going full-bore after self-improvement at the other labs. The other difference from a year ago is Google's unstoppable war chest, 180 billion in CapEx per year and rising. But the other guys in the interim raised, open AI raised 120 billion in cash. And they'll burn that pretty quickly. And anthropic is on a similar trajectory now. So the war chests are actually not as different as they were a year ago. So yeah, I think that's the only disappointing thing in the whole show is the best of the best Gemini is not up there with mythos as far as we know. Now, I will say that Google doesn't-- you know, they do soft sell. They don't announce what's coming in four months and promote and trumpet it because they don't need to. And so if something really, really big is cooking and coming soon, they didn't roll it out, but they don't need to roll it out. The wait until it's proven. I love the naming nomenclature here. Of course, we've got GPD models, 5.4, 5.5, 5.6. And Gemini jumps from 3.1 to 3.5. It's fascinating. So Lee, what do you make of this? I thought one thing this clear is you're seeing this kind of bifurcation now between premium cognition and ultra cheap, but very fast cognition. And I think that's going to continue. I think Alex makes a great point about the group, but this will allow a lot of throughput, right? And there's this continuous march for marginal intelligence cost trends towards zero. Yeah. Here was the next segment that I pulled out. And again, I wanted just a shout out to Gianluca who clipped all these beautifully and provided them in record time for us for the show. Here for-- Yeah, a conversation about synth ID and content credentialing. Really important, especially as we start to encroach on reality, how do you know if something is or is not AI-generated? It's going to become more and more important ever before. Let's take a look here at Sundar talking about synth ID. Since launch, synth ID has now watermarked over 100 billion images and videos along with 60,000 years of audio assets. We are now going a step further and adding content credentials verification across products. This will show you if the origin of the content was AI or a camera, and if it's been edited with generative AI tools. In this example, Gemini can tell this photo was captured with the pixel camera and then edited with Google Photos. Of course, this only works at scale if more partners decide to watermark their own AI-generated content. NVIDIA signed on to synth ID last year. And today, I'm thrilled to announce that OpenAI, Kakao and Levin Labs are adopting synth ID too. I love the fact that we're getting to standards and everybody's picking the best. Alex, how important is this? You know, the irony is so many people were hand-ranging for the past few years that we won't know what's real and what isn't. And my response was always, we're going to get cryptographic, eventually cryptographic chains of custody from reality capture to what is ultimately presented to the user in the same sense that when you use a browser, you can maybe see a little lock icon to indicate end-to-end SSL encryption. We're going to get the same thing for reality. And I've used synth ID, which, by the way, was also just adopted by OpenAI, was created by Google, now also adopted by OpenAI in the same breath. I think it's sort of ironic that we're going to get, it seems, end-to-end authentication of realness, proof of reality, if you will, not coming from the camera end, not coming from the reality capture end, coming from the synthetic end, not coming from all vendors that want to claim credit in some sense that they were the ones who generated the reality. And then the cameras, the camera makers, and all of the recording device manufacturers, they're going to be downstream and the ones who adopt the same protocol. But either way, we're getting our end-to-end proof of reality one way or another. I think there's a much broader story. I'd love to lame to get your thoughts on the bigger, bigger societal implications, because a big topic at Stanford last night, with Eric Brenjolson and his entire team there, the rate that AI is innovating can't be kept up with by Congress. And there's going to be no regulation of any value coming out of Washington. So the industry is starting to self-regulate. And that's the only AI can keep up with AI. And so we may look back on this moment as one of the first moves by the self-regulation community where, OK, now we're going to start water-marking images. Hey, everybody in the community, please adopt our standard for water-marking images. And then there'll be something else a week later, something else a week later, something else a week later. And that'll become the way that we govern ourselves in the future, much more so than any law coming out of Washington, purely because the pace can't keep up. So you think that's basically this is the first move in that direction? I think that's exactly right. When you have intelligence becoming abundant, then the scarcity goes to a stress and scarcity creates value. So we may end up at a point where authenticity is more valuable than creativity. And that line between something being created and knowing how it was created, et cetera, is now merging because of the systems that are being created now. And I think, Dave, the point you make is really, really important. Once you have that trust layer, right, now you can scale. I go back to Jerry McCulloch, you my community member who said, "Scarcer be equals abundance minus trust." And so if you can solve for trust, you solve for abundance. And one of the biggest challenges today, I thought this was really a big deal, because we're moving from the information age to the verification age. And trust is becoming infrastructure. And I think that's a very powerful, valuable pillar for the world going forward. - Yeah, well, I think if you-- - Playing arithmetic, does that mean that abundance? equal scarcity plus trust? [LAUGHTER] It does. It does. Scarcity plus trust gets you to abundance. How about that? How about that? All right. Thank you for-- Thank you for-- That's like grade 5 math. You would like to do that. [LAUGHTER] All right, so-- But because the cost of generating content is collapsing, right, then the value shifts from signal filtering authenticity. We saw this with photography where the big problem in photography is how do I take the best photograph? Because each dollar-- each click costs you a dollar. And you had a bunch of business models crop up around the selling expense of cameras or offering courses in photography and publishing books on composition. Then we moved to digital photography, the cost of creating a photograph went to zero. And now the big problem everybody has in photography is I have six copies of my photographs on seven different online services, and you can't find anything. And the value then comes in that filtering system. And-- So, Liam, we're moving into this intentional world that we design. And everyone's like, what will happen next? What will happen next? What will happen? Whatever we design is what's going to happen next. But Dario and Demis are two probably dominant architects in the future of how we live. And it's just great to hear both guys go back and forth. But if you do a raw word count from Dario, Dario Amadezi, of Anthropic, and you go back, you know, the words are all about transformer architecture, speeds, intelligence, benchmarks. And then they transitioned to UBI ethics. And now they're talking about the way the world should be governed going forward and writing papers on it. And so if you just track the word count, it's also on this exponential change rate, same with Demis. Demis has to be a little more cautious because technically he's an employee of Google, even though he acts very independently. But those are the two guys just very much determining the future of all humanity right now. And so you know, this is a-- The watermarking is just like move one act one of the whole future way we live. And look at what's happening, right? Our trust in legacy institutions is collapsing. And at the same time, AI is building up the capability in the infrastructure and the foundation for delivering trust. So hopefully if that happens elegantly, we'll have an elegant shift from scarcity to abundance rather than a messy one. Everybody, you may not know this, but I've done an incredible research team. And every week, myself, my research team study the meta trends that are impacting the world. Topics like computation, sensors, networks, AI, robotics, 3D printing, synthetic biology. And these meta trend reports I've put out once a week, enabling you to see the future 10 years ahead of anybody else. If you'd like to get access to the meta trends newsletter every week, go to dmandis.com/metatrends. That's dmandis.com/metatrends. The next product that they dove into in a central part of Google's plans is anti-gravity. They released anti-gravity 2.0, a standalone desktop app built to orchestrate multiple agents to execute tasks in parallel. Let's take a listen and then Alex, I'm coming to you for your evaluation. Mittere, Hyteere, NoTeere, let's go on. - At the core is anti-gravity 2.0, a new standalone desktop application that delivers fully on that original glimpse of a truly agent optimized experience. The new anti-gravity is unabashedly agent first, focusing on the core agent conversations, agent-produced artifacts, and multi-agent orchestration. Like I said, unabashedly agent first. As Sundar mentioned, this is the exact experience teams here at Google have been using to drive massive value. Let's take this live and actually show this operating system in action. Try running Doom right now. It just doesn't work. Turns out that the OS is currently missing some necessary video and keyboard drivers. So let's just try and fix it in the new anti-gravity. I have a prompt prepared. I'm going to paste it in. Anti-gravity ended up doing a whole host of research, ended up writing over 100 lines of code, and then finally built the operating system. Let's take a peek and see if it works. Amazing. [APPLAUSE] - So first off, Alex, what is anti-gravity 2.0? And what are you thinking about it? - Yeah. So let's remember where anti-gravity came from. Do you remember Google's hack acquisition of Windsurf during that debacle? So anti-gravity is basically Windsurf rebranded from the Windsurf team that was hack-wired by Google. And then anti-gravity 1.0 versus 2.0, I view you were asking earlier, Peter, is this high, mid, low. This is sort of mid in my mind. If you look at what cursor has been doing by contrast, cursor was much more aggressively leaning from their old interface, which was sort of a reskinned visual studio code centric editor from their 1.0 oriented user interface to their more recent interface, which is agent first. I view this almost as like a copycat fast follow or slow follow or somewhere in between from the Windsurf team within GDM, basically following the same metaphor of saying, no, we're no longer about direct code editing access. Now the primary metaphor is orchestrating fleets of code agents that are doing all of the hard work. So I would say Google's almost hamstringing themselves a little bit by announcing this now as part of I/O versus say in a more timely fast follow or even lead when cursor was doing this months ago. I've used anti-gravity quite a bit, certainly, was using it even more when Google first announced it after the Windsurf hack position. And I would say not super impressive. It was very buggy. I think 2.0. I haven't had a chance to use 2.0 yet, but hopefully it's a good deal stronger. But really, I don't know anyone who's doing their primary development work with anti-gravity at this point. The development is happening either with cloud code or with codex or maybe with cursor. Anti-gravity don't know anyone who's using it. You know, everybody's trying to read it. Except every engineer at Google. Leave for all, except for Google. Maybe. So Dave, you were trying to get on-- Except actually, even that's not true, though. So I mean, this has been probably reported that within Google deep-mind, they're all desperate to get cloud code access for everything. And we talked about that a couple of times ago. Dave, you were playing with this actually during the Google I/O yesterday. Yeah, Tyler and I both installed it in real time as they were rolling it out, which maybe not the smartest move in the world, because there's 16,000 people behind us in the crowd, probably all trying to do the same thing. So that was not a great first experience, but I don't blame Google for that. But this morning, it worked fine and installed great. And completely agree with Alex's assessment. It looks almost identical to the new cursor agent first windows. I mean, like, I almost can't tell where I am. I'm a cursor or I'm an anti-gravity. What they did do, which is a little more extreme than cursor, is it completely replaces anti-gravity 1.0. You can't even see the code anymore. And you have to go and launch the old thing if you want to actually edit code. So cursor didn't go quite that far, but it's really obvious where the puck is going. If you want to build things in the future, you're not even going to look at code. You're going to describe what you want. And you're going to debug at this much higher level of evals and functional comparisons. And I don't like where that button is move it. And so I think in the future, nobody's going to want to go back to autocomplete code editor view. And so they left ahead and said, we're just going to eliminate that entirely. And if you really want to hack, we'll give you a way to get back to it. But we're going where the puck is going and not where it was. But yeah, it really is exactly catch up like Alex said. Everybody's got the same codex and its clawed code and anti-gravity. And they're just going to be leapfrogging each other. I'm just curious if there's going to be some new sort of breakout approach to this that's going to materialize. Alex, do you think there's anything in the future? Well, code is clearly going away as a human endeavor. It's all being abstracted away by code agents that handle all code. And humans are maybe in the near term future trustworthy enough to be even be allowed to write their own code. So I think that's one obvious arrow of time in this space. I think recursive self improvement is another arrow of time. So not even old generation models are trustworthy enough. Maybe older models are trustworthy enough to rewrite themselves and generate newer, better models. But code's going away. I think that's the obvious trend here. Well, I think they also Peter Dancer, your question on the next paradigm. We've only had this paradigm for a couple months. So let's sell it for a little. But no, clearly the next paradigm is exactly the Star Trek holodeck, which Alex has been saying for a while. So right now, you're talking to it. It's building things for you. It's incredible. But it's not natively graphical and visual. And you're not moving things with your hands. So if you say, I want to move that button. I want to change this. I want to connect this to my email. You're not actually seeing the button move in real time. It's regenerating. And then you see a new rendering. And in the future, it'll be a real time graphical experience that's interacting in your comfortable physical space, kind of native human environment. And that next iteration is certainly within this calendar year. All right. Next up is Gemini Spark. In Google's take on OpenClaw, I think is the most out of it. Obviously you say it's a new always on AI agent that can write emails, create study guides, keep an eye out for financial fees that you're being charged. It's Google's, we have open at home moment. It's powered by Gemini 3.5 Flash. And it offers you a 24/7 operation. Let's take a listen to the conversation about Gemini Spark. - Introducing Gemini Spark. Taking action on your behalf and under your direction. It runs on dedicated virtual machines on Google Cloud. And it's 24/7. - A task right off the bat. This is a pretty straightforward example, but it's so useful. Help me draft an email to the team, compile everything about our recent Gemini live launches and wins from the last week. And what's amazing here is Spark will go through, step by step, look at all these steps, all the time it saves you going through. And again, work across the various skills and apps that you have. And what's really amazing is it'll break it down and also be able to generate files for you. So the first one here, this is a live RSVP tracker, writing Google Sheets. You can see that it shows who's confirmed and who hasn't. What's amazing about this is it'll actually update because it's connected to Gmail. So when L Thompson wrote eight RSVPs, it'll update, which is pretty amazing. - I mean, one of the things I find fascinating is it's the integration across all of the Google products is very powerful, right? There's a point at which there's such a cost for not being inside the Google ecosystem that everybody defaults to it. - Isn't that this is most ironic thing I've ever heard? Because people who are younger don't remember that Google only exists because the FTC stopped Microsoft from killing it. So Microsoft had just killed Netscape, taken total control of the browser and integrated it with the operating system and made it impossible to do anything on the internet unless you went through Microsoft. And that triggered the FTC, my Kershland came in, the whole lawsuit stopped Microsoft cold in its tracks and they paid a $1 fine, I don't know if I remember that. (laughs) I'm not hilarious how it come, but they had to unbundle and that opened the door for Google to come into existence. Microsoft pseudo competed with Bing, but they were prevented from competing aggressively and tying it back to the operating system. So here we are all these years later and Google is coming out with this series of kind of exact copy of Kershland, exact copy of, exact copy of, exact copy of, but it's perfectly integrated with these other, you saw on the other slide, what a dozen Google products that have over a billion users, a billion users out of this world population is a massive install base. So if you want open claw, yeah, you can be over there, but if you want open claw equivalent that works natively with Google Docs, Gmail, everything else, Android, everything else, if you're a Google Pixel camera, then you have to use this. And so it's exactly history replaying itself as so ironic, 'cause they were so anti-Microsoft back then, you know, the whole don't be evil, you know, motto was a direct attack on Microsoft, implying they were the big guys that were evil. So here we are, you know, years later, and I'm not saying Google's evil in any way, I'm saying they're tying as their competitive advantage in the exact same way that Microsoft used to. >> Yeah. Alex, what do you think about this compared to open claw? >> I think it's a lazy copycat product. I think it's obviously Google trying to take advantage of the resources that they have. So note, it's hosted in a GCP VM, not necessarily pushed all the way to the edge, although they have aspirations, site to Gemini and Android, halo for that. But if you're Google and you see open claw and you see Jensen out there saying open claw is the next big chat GPT, really, what's the smallest, what's the minimum viable response that you could take? It's, okay, we're going to host GCP VMs with Gemini Flash that integrate together all of our products that run headlessly. That's the sort of minimum viable strategic response. What I would have liked to see from Google DeepMind here was the maximum response. Show us the art of the possible. Show us what a next generation open claw or Hermes competitor actually looks like. Create the benchmarks. Show us next generation capabilities and they didn't deliver that here. Well, Alex, I mean, just to be fair, they're delivering on a lot. It's not just one piece, right? It's a lot that is being deployed on Google I/O Day. But having said that, I still love Skippy, which is an open claw on top of my Mac Studios. I love it because it's got a personality versus being sort of a generic agent that's ever present. I don't know if you can do that with Gemini Spark, but I think the personality side of these are critically important. So many thoughts for you. Two thoughts. One is I agree with Alex. They really could have gone for a little bit more bite here. But on the other hand, when you can make agents generally available to the average Google user, the hundreds of millions more people training up agents. And I think that's generally just good. Open Closel has lost room to be experimental, power user oriented, very opinionated, doing the weird things like the natchet camera stuff. But I think this is a very solid entry into that world to give people a taste of what an agent world could look like. But it is boring. It's safe. It's playing it safe. Like is it a safe entry? Yes, it's a safe entry. Will a lot of people maybe use this to clean up their Gmail inbox? Yeah, probably, but it's not pushing the frontier, which is really what I would have loved to have seen here. Yeah, I think that's your recurring theme on a lot of these, Alex. Is that true? I think, I mean, look, as accelerationist, yes, I'd love to see frontier labs pushing the frontier. And to the extent that this is an avatar of Google DeepMind and not just Google corporate, I would love to see more frontier coming out of the frontier. Dave, you want to close this out? Well, two quicks. Sorry. Two quicks. Sorry. Two quicks. Like there's something very powerful happening here because this is giving everybody an operating system for their lives because of the deep integration with all the other Google stuff. So I think the next productivity jump is going to come from persistence. And this will trade a massive enable across the board. And it's going to go back to the earlier comment. I just want to double down on that, which is a train a lot of people on how to build agents and run agents. And I think that's going to then enable another class of things to come from the frontier in life. Yeah, so there's no doubt that this is all fast follower, exactly the way you're characterizing it. On the other hand, Peter, you love your Skippy. I love my agents that I set up too. But when you talk to somebody on the street and you say, hey, if you set up an open clock or a Hermes, overwhelmingly across the world, people say, no, I haven't done that. And that install and onboarding experience is just too much friction. So I wouldn't underestimate the power of default behavior. Now, over half the world uses Google. And if Google says, OK, Gemini Spark is going to be one click away from a Google search. It's completely integrated. Massive fraction of the world is just going to click the button. And then their first experience with a personalized agent will be via that click. And so I don't think that anything can slow down Google because of their massive distribution advantage. And so they don't have to push the outer boundary. They can afford to be fast follower. And I am equally disappointed, Alex. I'm not saying it. But from a strategy point of view, they don't need those risks. They just need to be as good one day later and integrated with Chrome, integrated with Google search, integrated with Android. And they will win. I think Google's magic potion is making it user friendly, making it easy, making it intuitive. And I think they're going to deliver with Gemini Spark on that particular promise. All right, here's another part of Google's resurrection and dominance. It's a gentick powering of AI search mode, AI everywhere. And remember, the conversation we had search is dead. Well, search is not dead. It's just been reinvented. Let's take a listen. I'm excited to announce we're launching a brand new intelligent search box. Before, the search box was a contained space. But now it's totally reimagined with AI. It expands with your curiosity. And as you ask, search helps you formulate your questions with AI-powered suggestions. This goes beyond autocomplete. It offers nuances that you might not have even thought to add. Now we're taking an exciting step for this vision. We will be able to create and manage multiple AI agents for your many tasks, right in search. Now, let's say you're apartment hunting, you can do a total brain dump of what you're looking for with all your criteria, like location, and natural light and availability. And your agent will continuously scan the entire web across sites, social, and forums. Persistence search here, right? So this is your agent, whenever you've asked a question, it is going to persistently be looking for the latest and greatest. Yes, this new apartment just became available. This product just got cheaper. Your wife loves this topic. And here's a new product delivered to her. The other side though is that autocomplete function. I will just add a little bit of the function. wonder where it's going to take us, right? You're going in asking or thinking about asking one question. And then of course, Google can sort of drift you into asking a different question. You didn't intend to actually ask a lot of interesting perturbations here. Oh my god, yeah. We'll think about a vacation plan where like, you know, I really think I should go to Barbados and it autocomplees to Bermuda. And you're rerouted to a different hotel. The revenue power of that is astounding. You know, that's, you know, opening high rolled out their first ads and a lot of the companies I know have adopted it. But it's very hamphasted, you know, it's it's like, here's some ads on the side. They're obviously ads. But the Google version of it has to preserve $200 billion of existing 90% margin revenue. So they haven't quite figured out how they're going to surf that. But their power of the user decision making is like nothing we've ever seen before. So I'm sure the fun works. So AdWords is now going to it's going to now gently sort of drift you towards a different question that you weren't there asking. And it's absolutely. That would be amazing. It's been, I mean, remember Google instant as well, which also offered relatively fast suggestions. I don't think Google ended up directly monetizing that. But Google does to Dave's point have a long history of steering users toward more profitable queries. So I think that's that's probably quite likely what I probably underline here is the shape of the rectangle changed after decades. How big a deal is that after so many armchair commentators saying that Google was about to be disrupted by Chad G.P.T. with web search turns out Google is able to self disrupt and able to change the shape of that multi decade old rectangle. And it changed the shape in the direction of building AI modalities AI search natively into their search experience, which I think many people were scared wasn't going to happen. They did it. You know guys, my very first venture investment ever was TripAdvisor back when it was first starting. And the big quandary at TripAdvisor was how are we going to have completely unbiased accurate reviews and still get paid by the hotels? We need to make money somehow. How's this going to work? And it turned out that just by sorting the list, you know, the human default behavior is so dominant that they'll go over a well laziness. You mean lazy. Yeah, you know, but lazyness, but we're buried in decisions now. We have so many things coming at us from so many different directions that we have to be lazy. Like only Alex could actually study every single pathway and make an optimal choice. Everyone else, you just have to fall into the default buckets once in a while. And so, you know, 80% of people will click on one of first two or three hotels. So you can have perfectly accurate reviews and just resort the list. So the ones that are paying you're at the top and then you have your cake and eat it too. So I think that default behavior will hugely benefit Google because they will steer the users, but they don't have to be super overt. And, you know, they're not going to misguide you into some fraudulent product. They're allergic to that like crazy. But people will still follow the default suggestions from Gemini. And then Google will collect the revenue from whoever is willing to pay. Celine? Just I just love the fact that they have the courage to risk disrupting, disrupting their own business. I think that's it's such a hard thing organizationally to do. And I give them full props for going after it. Yeah. And for everybody, you know, everybody listening to this, you know, our goal here is to give you an overview of what Google has just done. It's so dominant in the planet. It does steer a lot of humanities sort of abilities. So I hope folks aren't enjoying the summary. Please dive into these, you know, your mindset of curiosity is your single greatest tool. So go and play with these things. You know, when you finish listening to this podcast, go and jump on to Google and play with the new AI search or its capabilities. One more note, Peter, if I may just on Google's self-disruption via search. I think there's this misconception out there that the main obstacle to Google's self-disrupting their search with so-called modern AI was somehow on the business side or the business risk or the advertising side. I think actually the main obstacle was more technical that Google engineers for a couple of years there were concerned that there wasn't a cost-effective way or a time-effective way to squeeze generative models into the very narrow and latency-sensitive and cost-sensitive parameters of just powering a search that the models were too expensive and too slow to yield search results that would be competitive. And this is, I suspect, one of the reasons why you see going back to Gemini 3.5 Flash, emphasizing throughput so much is reflecting Google's own internal dog-fooding needs of having ultra-high throughput models that they could use to power search and some of their own internal applications that maybe open AI and anthropic aren't feeling that demand function as much. Makes sense. Next subject is Google is launching a universal cart that users can add products to from YouTube, from search, from Gemini, from Gmail. Google says this intelligent shopping cart works across a multitude of different merchants from Nike and Target to Walmart to Shopify. So you could literally add a product when you're searching on a Nike site or a Target site and then have it monitored and bought at the same time. Let's take a look. Again, this is part of Google's incredible revenue engine. Okay. >> I am excited to announce the universal cart, a truly intelligent shopping cart. It works across merchants and across services. You'll be able to add things to your cart when you're browsing search, chatting with Gemini, watching YouTube, or even reading your Gmail. The moment you add a product, your cart goes to work for you in the background. It finds these, looks at price drops, gives you insights on the price history, and alerts you when something comes back and stock. >> So reinventing the shopping experience. I've got some comments to hear from you guys first. >> Elephant in the room. Yes. >> Elephant is Amazon. I look at every announcement relating to shopping, quote unquote, from Google through the lens of how are they going to compete with Amazon for retail e-shopping and whether it's trying to commodify, create virtual storefronts for individual retail vendors, whether it's crawling, third party e-commerce websites and assembling virtual pages. And now universal carts. This is all through the lens of how they're going to compete with Amazon. So I think the elephant in the room here is Amazon even going to contemplate going anywhere near, complying or adopting Google standards. My guess is not. >> Well, the follow on here, we're going to be seeing in a few moments the reinvention of Google Glass where you've got imaging capability. And we're going to see probably the next invention of shopping where shopping is always on wherever you're looking and you see something and your AI agent realizes, oh, I'm focusing on Alex's beautiful orchid in the background, which is, I mean, is that orchid real? Alex, I just need to ask. >> I thought Peter, you said reality was cooked, so you tell me. >> Okay. >> Literally when I look at something, my AI agent will say, oh, you're focusing on that. Do you want to purchase it? Or as you're walking through the day, right? Instead of shopping becoming sort of a something you do for an instant of time, it's a continuous function and universal cart is aggregating all the things and then probably at the end of the day saying, hey, do you want to purchase that? Just say yes. We're going to be seeing this is an early step, but not the full instantiation of reinventing shopping. Selim, you're going to say. >> Yeah, so today we go from human to website to shopping cart to check out, right? And tomorrow we're going to go from intent to agent to transaction. And I think there's every CMO in the world in going forward is going to be asking, how do I convince 100 million agents to choose my product? They're going to have to market to the agents, right? And so I think this is powerful. I mean, look, Google helped create a trillion dollar company by helping people do search. The somebody is going to create a trillion dollar company by helping agents buy. >> But are you ever going to market to a agent? >> I mean, my agent knows what I want, knows my genetics, knows my taste from everything else. It may just be buying stuff for me all the time that could be returnable, sort of surprise and delight. Something shows up on the front doorstep. Oh, I thought you'd like this. Here it is. If you don't want, I'll have it picked up and returned. >> And you're not going to do that. >> The disruption to e-commerce is not the better shopping. It's getting really shopping. >> Yes. >> But does this mean experience where, hey, you may, your shoes look a bit dirty from the camera? I looked at, from your doorway camera. I'm sipping you new shoes. You know, it would be that kind of thing. >> I think it's just, it's amazingly shocking the degree to which the big guys don't care anymore about consumer shopping. Because Amazon was nothing but consumer shopping originally. And you know, built their entire empire on consumer shopping and then added AWS. AWS is so much bigger at Amazon now than the entire Amazon we know, you know, all of the shopping. And so Google was already competing with that side of Amazon with, you know, GCP versus AWS. And so there are already in this battle royale over your over compute and data centers and enterprise use and everything. And so Google had already tried to compete in retail with frugal. Remember frugal? >> Of course. >> Yeah, frugal. >> Yeah, frugal. >> That's a great question. right before shopping. You get another rebrand. - Yeah, so I think that this is another attempt to take Amazon head on the shopping side, but I think AI is a big game changer. And so, but it doesn't matter too much whether Amazon defends its turf or whether Google encroaches and wins at the end of the day, the battle on the cloud and the back end is so much bigger and it's already raging. So this is kind of cool. I think it's just the next stage of this trillion dollars retail battle that will rage on for a while. - All right, let's jump into conversation about Gemini app and notebook LM. Here's Josh Woodward who heads Gemini. I think we're getting a Josh on the pod here. We'll talk to him about what he's up to. Let's take a listen. - More than 900 million users are coming to the Gemini app every month. Just on its own, notebook LM has now been used to create more than 1.5 billion notebooks, podcasts, live decks and more. It's now available in more than 230 countries in over 70 languages. It now opens up immediately and in line. And soon you'll be able to pick a regional dialect that resonates with you. - You've got a right, good mix of different accents about like this one from Liverpool. - Gemini Omni is coming right into the Gemini app. Let's look at this place out in the real world. I want you to meet Sasha. She's working on a new song and she wants to create a quick video teaser. So she shares the raw video. She adds some reference visuals to it. Let's take a look at what it looks like. (upbeat music) - The third update today is about how agents are coming to Gemini. One of our newest out of the box agents called the Daily Brief. It's a personalized digest that's designed to be your first stop every morning. Here's how it works. You can see here that it's synthesizing information from across my inbox. And with this travel info, I can just take the next step right in line. - So this is an integration story. This is Google integrating across all of its capabilities and making it so magical that you can't afford not to be in the Google ecosystem. Dave, what do you think about this? - Yeah, it's amazing. When you got one guy on stage demonstrating here, we can build an entire operating system in real time. Let's go consuming a trillion tokens, right? Now building, and nobody gets it, right? Nobody can relate to building an operating system. Then Josh gets up there and says, here's a real human musician. And here's her trying to portray herself to her fan base. The crowd goes crazy. And it just shows you the human aspect of this is so dominant. Even in Google, even within the empire, the human aspect of it is so dominant in people's minds. And it doesn't come through on the video cast. When 20,000 people see Josh present something and they go, wow! Oh my God, I can totally, and the vibe is contagious across the whole crowd. And it just is hard to capture that in a video clip. But yeah, it's just remarkable. It's gonna unleash so much creativity and it's such an exciting time. And I wish that was the only vibe that everyone could just capture and then hold and bottle that in. But this was just a great moment. Alex, you're take, why is Google still branding notebook, LMS notebook, LMS? It should have been folded directly into Gemini or maybe a real workspace or something else. Why does this still have an independent brand? I don't understand it. Well, look at all the other, it's like spark and flash and anti-gravity and like all this is really fragmented all over the map, like divisional kind of branding. Google has this reputation for better, for worse, of launching lots of products and having a culture where product managers get promotions for launching but not maintaining products. I'd really love under the spirit of more wood behind fewer arrows to see all of this functionality just unified in a way that gets sustained. Yeah, especially because because AI is such a unifying force, you can put one voice and interface on top of all this mess. And like, and Google's branding originally was so good. You know, all of the search engine. It was so good. Yeah. And speaking directly to colorful and humanistic and friendly and all of that. So it's directly speaking directly to Josh and the Google team, please just unify all of these offerings and maintain them. Don't keep launching 10 different products and product names that we'll forget about a few months from now. Just please unify all of them and maintain them. You know, the idea of a daily brief, I love it. I skip it, gives me a daily brief. This is a beautiful integration here. But being able to know what you're doing, when you're doing it, what your intention is and giving you updates all the time on your flight. You know, there's a new flight that's available. Now it's five times cheaper or whatever the case might be. And the weather is going to be hotter than expected. So make sure to pack different. That level of overlay intelligence is going to be magical. It's what people do. But at the same time, remember Google Reader, Google's RSS Reader, that Google abandoned, despite having a rabid user base myself included. I know Google really wants to own the newsfeed. That much is obvious. But please just maintain it. OK. So two thoughts. One is, I've always thought about notebook LM to per Alex's earlier comments. That's this weird thing sitting out there because it incorporates presentation, learning, and interaction that kind of does-- you know, what's the difference between doing this and the Gemini app and doing it in Spark? I mean, people are going to create a lot of confusion around this. The point that Alex made, I just want to double down on, which is when, in any big company, we had to say, "Yah, you as well, you're worried for getting something out there." But then you've got a strategic project manager looking across resource allocation by 10 different projects. And so you get a peanut butter problem where you're very thin across all the different projects you don't iterate very well. One of the few companies that iterates very well is Apple. They will relentlessly iterate on their products. And most other companies-- I know some of them-- And then-- And then-- Yeah. And they know a very-- there's a whole thing written by Brad Grelian, it's called the peanut butter manifesto, when I was a Yahoo! It mirrors this little challenge of how do you navigate this and then limited things? Because they're not run as a start-- as each individual team doing startups with KPIs of their own and targets, et cetera. They're run across in hierarchical structures, in many cases. And you suffer a lot from that. All right. Google's new product of called Audio Glasses, where we heard earlier about their partnership with X-Real. Here's a partnership with Samsung and a couple of different glass manufacturers. Let's take a look and how is this going to impact our lives, two videos to show them we'll discuss them. The next big milestone for Android XR is intelligent iWear. Today, I'm excited to announce that our first audio glasses will arrive this fall. They are designed to give you all day help with Gemini that is spoken into your ear privately, rather than shown on a display. And these glasses let you stay hands-free and heads up, the things like listening to music, taking photos, making calls, or tapping into your phone out, all without reaching for your pocket. All right, video number two from Samsung. As Samsung, our vision is to enrich people's lives and help shape how we live tomorrow. In close partnership with Google, we're introducing intelligent iWear that imposes you to connect to the world with confidence. Built with Samsung's precise engineering and craftmanship were merging form, function, and helpful intelligence to create something you'll want to wear. In iWear, every millimeter counts. Today, we're thrilled to share a first look at the upcoming styles co-created with our iWear partners, or be Parker and gentle monster. - All right, the elephant in the room here, it's got forward-looking cameras, but you're not seeing words or images on the screen. You're being spoken to by your AI. Interestingly enough, I think that being present in life has just been cooked as well. I mean, imagine you're walking around and you're just having, you're not talking to your wife, you're girlfriend, you're kids, you're just having the agent whispering to you all the time. So, Lee, what do you make of this? - So a bunch of things. I was really disappointed in that there's no visual on the screen. I mean, doing the audio is, might be in the Alex's words, very men. But we're moving to that point where human computer and interaction becomes continuous and becomes an ambient layer that's just ongoing. And I think that is the bigger story here because that will kind of just continue to play out as we merge with technology. Already we pick up our phones 80,000 times a day. This just continues in a very kind of unnoticeable way. The form factor is very workable. - Google should have owned smart glasses. Instead, meta is running away with this space. Apple is also playing catch-up. Where was Google? Google had, I was one of the earliest users of Google glass. Remember that? - Yes, the glass. - Did you get punched? (laughing) - I did not, fortunately, but they had a battery life of like five minutes. and they self-bricked through operating system updates. It was, I think, even Google would recognize it was prematurely released. Google could have kept iterating to this earlier point about doubling down. Google could have kept iterating on smart glasses from the Google Glass era, and they didn't. They basically abandoned Google Glasses to enterprise and then abandons them completely. And now this is, I think, represent a complete reset except without all of the conveniences that Google Glass had. And meanwhile, meta was iterating away, spending billions of dollars short, but iterating away at smart glasses. And now meta has the lead in the space, not Apple and not Google. So I would love to see a very competitive smart glass market between meta, Google and Apple. But I really would like to see Google XR in particular, Android XR stepping it up a notch and shipping much more quickly right now, meta is running away with the space. Dave, what do you think, pal? Well, I mean, this is where society is going to have a huge rift because the punching in the face was a very real thing. Last time, Google went down this path. And they are trying to own the consumer and be a consumer-friendly brand. But if they roll out a product where half of society is walking around recording everything all day long, and the other half is offended by that, then that's going to be a major, major problem. And they're stuck. They want to own this space. They have the technology to do it. People are going to want to talk to their Skippy, their agent all day long. They're not going to want to lose touch with it. So this is a great way to stay in touch with your kind of agentic world that's working for you behind the scenes. So I'm very eager to use it. I'm also not super eager to get punched in the face. There were three commencement addresses this week, including Eric Schmidt's in Arizona, whereas as soon as the commencement speaker said AI, the whole crowd went, boo. And so if you're not aware that that's what's going on, and it's very easy to live in your echo chamber, especially here in Silicon Valley or in Cambridge, but you've got to walk around Mississippi or walk around Nebraska to really understand how big a deal this is going to be. So I think the glasses are going to be a huge forcing function in this inevitable-- - Real important. - Audio glasses. Hello, OxyMoron. - Well, audiovisual. OxyMoron, now they go together very well. - But the audio feedback layer, I think it was a-- they want to provide a product that actually works consistently, and I think probably the imaging up on AR glasses is still kind of weak. The version of the metaglasses I've tried is okay, but it's still a far way off from being able to turn on an ambient AR layer that's convincing and compelling. The brightness isn't very good. I'll look at it in the right-- But audio, having your AI being able to say, oh, okay, I know you were shopping for that. You like that one, and I'll order it for you right now, or importantly, I don't need to-- when I see Alex approaching me and I've forgotten his name, and he's coming at me, and the glasses can say, oh, that's Alex, weasener, gross, he's got an IQ of 100,000, that should be enough. - I'll never forget your name, Peter, for the record. - Thank you, I appreciate that. Nor I, he or her, is my friend. But I think the audio interface is a smart move to make it clean and compelling and consistent, and something that you can interface with on a regular basis. I am concerned about this issue of, you guys all have this, right? You're with your family or with friends, and something pops up on your phone, and you focus your attention to your phone. The loss of presence in life can be really costful. - Yeah, really can, and also, I think the cameras are always on, they're very low battery consumption. So you can run the cameras continuously, and it's just seeing everything you see, and then talking to you about what you're looking at. So that's Alex, that's the lane. But if you really want to display, it can talk to your phone, and you can look on your phone to see anything it wants to tell you there. So it's all integrated through Bluetooth anyway. So, but I think the consumer would rather have the longer battery life and not have to worry about it, dying every hour, like Alex was saying. So this is a good, this is a good temporary stepping stone, and like you said, Peter, putting the display in front of your eyes, if you think being not present in the moment is bad with this in your ear, imagine when it's flashing between you and your wife, flashing images on your, this is a good, it's a good product design. - Yeah, it's still working great. - I know that Alex, we're gonna lose you in a moment, but I wanted to have the last two segments with you still. Let us jump into Demis' presentation on Gemini for Science. - I'm excited to announce Gemini for Science, which brings together a powerful AI tools to help accelerate research. Gemini can already assist in solving complex problems, but our new labs prototypes streamline daily scientific tasks, whether it's staying on top of newly published papers, transforming research goals into usable code or generating new hypotheses. Another powerful tool for science is simulation. AI simulations are going to be critical for understanding and predicting dynamic systems that are simply too complex to model directly today. Our state of the art, whether next models, can predict hurricane paths faster and more accurately than traditional systems at isomorphic labs with modeling molecular interactions to massively accelerate the development of new medicines supported by leading industry partners. We're now in pre-cleanable stage with multiple projects, including potential treatments for immune disorders and cancer. When we look back at this time, I think we will realize that we were standing in the foothills of the singularity. - Standing in the foothills of the singularity. I love that line. It's awesome. - I wonder where Demis got that line. That's such a nice line. - I'm glad you said that before Alex. - It's a nice line. Thanks, Alex. - Alex, what's your take on this? - I think it's wonderful. I'm broadly supportive of what Demis, Sir Demis, excuse me, is doing for deep-mind in science. I think it represents deep-mind at its best when it's challenging what Demis calls root node problems like fusion or like protein folding. I think it's wonderful. I don't think Google as a business has deeply vested interest in monetizing this. I think this is more for the public benefit from Google's perspective, but I have portfolio companies, companies that I've founded that work very closely with Google on issues and technologies relating to this. And I'm broadly super supportive of deep-mind pushing these out to the public. I think it's very important. And they've made so many interesting, I would call them innovations relating to meta-science. How do you produce more science at the algorithmic level and hope to see much more from them in the future on this front? - Amazing. Alex, and thank you for joining us on this segment. I know you need to jump. Love you as always. Thank you for your brilliance. Appreciate you, pal. - Thank you, Peter. - Thanks, Alex. - Thanks. - Very welcome to the Health section of Moonshots. Brought to you by Fountain Life. You know, AI is impacting every aspect of our lives, how we teach our kids, how we do our business. But one of the most important things that AI can deliver to us is health. And one of the things I think about when shooting for 120 is am I going to have the cognitive health to be able to think clearly and keep my wits about me for the next 50 years? I'm joined here today by Dr. Don Musalim, the chief medical officer of Fountain Life and a member of my Fountain Life medical team, Don, a pleasure. So Don, talk to me about brain health. - Brain health, you know, you're right. This is the number one concern people coming in to Fountain Life have is will I remember the name of my child and the face of my loved one? 45% of dementia cases are entirely preventable with lifestyle. And what was really intriguing to me, Peter, is that a quarter of our members had advanced brain age. But over 13 months of us really helping them live healthier lifestyles, eating healthier, moving their body regularly and optimizing sleep. People overlook that so often, but that sleep optimization is critical for our brain health. What we showed is that we were able to improve the brain age in 46% of those individuals. That's a powerful number. - That's amazing. You know, one of the things I love about Fountain is we're constantly searching the world for the most advanced therapeutics and bringing them to our members. So for me, and all of you, I hope that you appreciate the fact that you can become the CEO of your own health. You can make sure that you've got the cognitive clarity for the next 50 years. Come and check it out, FountainLife.com/Peter to learn more and become the CEO of your health. Now back to the episode. - All right, one more story from Google I/O. This one is very personal. We just announced the $2 million build with Gemini X Prize. Let's take a listen and then I'll provide some detail about it. - It had the ultimate platform to make it impact. We are officially launching the build with Gemini X Prize Hackathon. This global hackathon is going to offer up $2 million in prizes for builders who create apps that solve actual real world challenges. And the premise is simple. Pick a problem worth solving. Build with Gemini and let's all try to positively impact the lives of a billion people. To build at that scale, you're going to need some serious power. - So a call out to all hackers, all builders out there. You know, we've launched two X prizes in the last couple of months. The Future Vision X Prize is asking people to create a film trailer and a film treatment for the movie you'd love to see that shows a hopeful compelter. abundant vision of the future. That ex-price is meant to help shape people's view of the future and actually to help shape agents view of the future so they are positive and supportive and aligned with humans. This is one you know we've talked about on this pod all the time that the future is one of entrepreneurship instead of getting a degree to go get a job instead find a passion find a problem and build on it so I want to say thank you to Google for for funding this they put up three million dollars to for the prize purse a million for operations and again if you want to compete go to GeminiExprice.com you're going to look for a problem that impacts a hundred thousand people or more and then you've got basically 90 days to build your product using AI and again very famously you're going to describe what you want the product to do the market how you want to market it in English the agent is going to build your website your your your your interfaces and the team that's able to build it market it and scale revenue the most in 90 days wins this exprise teaching people to fish instead of giving them fish that's the goal here Salim any thoughts very exciting it reminds me of the there was a problem as a medical problem called lazy i and there's no cure for it and then this a team built an app which had a gamification system that solved greatly lazy i so i think there's all sorts of things as we take obscure problems that it and the reason a number of people and really it go go after it very very excited we see the incredible outcome when you put open innovation cd ecosystems like this throughout the history expires so couldn't be proud of yeah super super pumped and you know full disclosure both Salim and Dave are on my board at exprise foundation Dave you know i think i'm excited i think this is the highest calling for exprise yet you know being involved with exprise i've been incredibly proud of it for a decade now but now with google behind it also open AI has a a hundred billion dollar charity now and you need to to put that money to work as well and so you know all these mega funded companies are suddenly very very interested in turning AI toward good which is not an easy problem and exprise is really really good at taking very hard problems and making them actionable and that's so unleash that capital in ways that actually benefit you may look at the oil cleanup exprise and the impact that is that that's had and kicking off of all the space activity that we have via exprise and so this is just the next chapter but it's the biggest chapter by far love it and a reminder everybody watching and listening if you want to be there at our moonshot gathering on September 25th go to moonshots.com we're going to be awarding both the Gemini exprise as well as a future vision exprise on that day we're going to the five finalists the five creators with the five top film trailers and the five finalists with the Gemini acts Gemini exprise who've created the most revenue they'll be there pitching and if you're in the room you're going to be helping to vote on who the winner is so again September 25th go to moonshots.com to join the moonshot mates and be there with us at the moonshot gathering all right I see Andrew Feldman has entered the room Andrew a pleasure to have you here thank you for having me on your show appreciate it of course by wave introduction Andrew is the co-founder and CEO of Ceribras a pioneering wafer scale computing dedicated to accelerating AI training and inference Ceribras just raised 5.5 billion I should say you just raised 5.5 billion the biggest US IPO since Uber in 2019 you know up 68% and market cap of 95 billion quite the coming out party Andrew. It felt good. Well you had to work for it too. It's super exciting for the team we were able to to bring a portion of the organization and their families and to share it I mean my parents were there and and my wife and and a stab daughter and it we made it into a family of that and it was really something special amazing generational wealth for everybody in your organization extraordinary and we'll come to that story in just a moment a little bit more in AI news before we turn to Ceribras and chips a big story Andrew Kaparthi joins Anthropic Andre is an extraordinary individual you know he's work in he's joined the pre training team at Anthropic and they'll start a new initiative focused on using Claude to accelerate Claude's own pre training research you know his announcement you said the next few years at the frontier of LLM will be especially formative and that's where he wants to be Kaparthi was you know as a stellar resume in AI he was the co-founder of OpenAI he left in 2017 to run full self-driving for Elon Tesla he returned to OpenAI in 2023 and 24 and then he founded Eureka Labs let's take a quick listen to Andre on a podcast called no priors this is a conversation he had before the announcement so listen up when he's doing a shout out to call out to which frontier lab wants to hire me and they're working on what's coming down the line and I think if you're outside of that frontier lab your judgment fundamental will start to drift because you're not part of the you know what's coming down the line right and so I feel like my judgment will inevitably start to drift as well and I won't actually have an understanding of how these systems actually work under the hood that's no big system I won't have a good understanding of how to develop and etc and so I do think that in that sense I agree and something I'm nervous about I think it's worth basically based being in touch with what's actually happening and actually being in front of your lab and if some of the frontier labs would have me come for you know some amount of time and they really good work for them and then maybe coming out looking for a job this is the best idea then I think that's maybe a good setup because I kind of feel like it kind of you know maybe there's like one way to actually be connected to what's actually happening but also not feel like you're necessarily fully controlled by yeah by those entities so I think honestly in my mind like no one can probably get to do extremely good work at at away but also I think his most impactful work can very well be outside of opening eye no that's a call to be an independent researcher that was Andre on five cups of coffee he's quote that he put out on on X I've joined and thropic I think the next few years at the frontier of LLMs will be especially formative I am very excited to join the team here and remain deeply passionate about education all right Dave you're take Andrew who's calling to you that was that was the moment he was so we're just I need your compute please call me nice he does everybody does actually so uh Andre came out with that auto research repo a couple months ago and I installed it it's been auto researching on my cloud for a while now but it desperately wants much much more compute and it doesn't need mega models you know it can run on lean highly focused models but I think he finally realized that every other open AI co-founder has either raised billions and billions of dollars or is that a foundation lab and he's the only guy who doesn't have access to the big machine and it's just he can't just sit there posting on get anymore he has to be part of one of these big machines because we're on the cusp of the singularity and you can't miss it by being outside of the big game. So lean and he thoughts I thought his point was uh well taken that he where he said that if you're not in the foundation in the labs you're missing out things are moving past you uh and so he's trying to be at the cutting-edge knowledge I just I think this it's also notable he would have had his choice as to where to go and I think it's very interesting that he joined on Thrapa. Really interesting and it's interesting that this was announced on the same day as Google I/O a little bit of marketing strategy perhaps. Mm-hmm. Andre what do you take about what you take on this? Look uh he is sort of one of the most important and prolific thinkers in the space right and uh not not just I think as reflected in what he's built but in reflected in sort of how he's taught the community and I think that's interesting I think his point that uh there are small number frontier labs that are sufficiently far ahead that if you're not with them you're not on the frontier probably applies to hardware too right that if if you're not building hardware for engaged with uh at a fundamental level one of the the three labs the three most important labs Google and Thrapa open AI uh you you are not seeing what they're thinking and just like your ideas will drift if you're a model maker your hardware will drift from what they what they need as well and I I thought that that was really sort of I applied that to to our domain um and I I think uh I think he has uh just an an extraordinary track record for useful stuff he's built extraordinary yeah and he's also just a super super ethical guy and known for it so he's a talented tractor you know a lot of people here in the valley want to work for the most ethical organization that they can that they can navigate to and he's got that aura just all over as you as do you Andrew actually has to syriprous where we are right now well speaking about ethical organizations let's jump into the conversation let's jump into the conversation about opening a high versus uh versus Elon okay all right how's that for a transition guys yeah yeah to work for that one uh I did I took advantage of uh of Dave's uh Dave's point so the jury rules against Elon Musk in the open AI lawsuit so federal jury unanimously reject did Elon's lawsuit in just two hours of deliberation? Wow, in jurors ruled that Musk waited too long to sue outside the statute limitations for his claims. Elon's legal team, of course, is going to appeal. I don't know what to say other than, I would love this story to sort of not cloud the entire AI data center conversations that we're having. Well, very curious, Andrew, you know, open AI. You have a very close relationship with open AI. Did you have a lot of skin in the game in this outcome? It's only going to be good news for Saribras, but was it relevant? I think this was a giant distraction and, and, and what will, what will, billionaires in pissing matches interest me not at all. I think, um, these are two of, of the most important thinkers of our generation. I think, uh, what Elon has built is breathtaking. Um, I, I've met him. I didn't know together. He's a polymath. He's brilliant thinker. Um, what Sam has built, sort of one of the fast growing companies in the history of capitalism, but some of his ideas also in the invention of the safe, why combinator, these were enormously meaningful. Yes, the structure of Silicon Valley. I, I think everybody loses when they battle. I, I think, um, you know, I, I want to Elon building cool shit. I want Sam building cool shit. And I, I don't want to waste time or read about sort of disagreements. I just want, want sort of these guys doing what they're the best in the world at, which is building stuff. Well, well said, Andrew, well said. Um, I'm just really angry. I got to this point. Surely they could have looked at it and gone the statute of limitations have expired. You just don't even bother. I mean, why does this is very upsetting? How much angst and, and bullshit time to, to address when it's spent on this? And they could have been building. How much more could they've done? Really good point. I mean, the, the ruling they made was obvious from the beginning if they were ruling based on the, on the timing. But in this is the reason Elon's going to, you know, appeal because he says, that's not the point. I looked into this. I looked into this and the appeal will very, very likely fail because it's a factual, based, um, decision. And it, and the course rarely overturn those. Well, I think to Andrew's point though, you know, Dennis said earlier in this podcast that we're standing in the foothills of the singularity, like an appeal is a slow long. It's going to be a relevant in the timeline that really matters, I think. All right. Let's, let's jump into part of the innermost loop chips. And here we are. Andrew, Serbis record IPO closes up 68% market cap, 95 billion dollars. All right. And Dave, you're in the Serbis offices this morning, aren't you? I am. I love the energy. I, you know, it's such a rare. I think all of America is driven by this moment. You know, people building toward this event and Andrew's journey was longer and harder than, than a lot. And so you actually get there, you know, it's so hard. I, I, because I pushed that button, no, about, you know, 1% of the size of Serbis, but I still push that same button on the NASDAQ. And I didn't realize till that picture has been hanging on my wall for years now. And it, it's like a lifetime achievement, like kind of like a Nobel Prize or an Olympic gold medal where you carry it for the rest of your life. And so, so few people get to experience that. So I couldn't resist the opportunity. It was only a few miles away. Anyway, I couldn't resist the opportunity to come here and just feel the aura of, it's still settling in here clearly. Andrew, is that the way it feels? Yeah. And you're always welcome to come on by and, and, uh, enjoy the mojo that we've got going here. I mean, it's, uh, we, we try and create an environment where, uh, exceptional people can do extraordinary work. Andrew, you and I met at the city bank event, uh, I don't know, six months ago. I was there giving the dinner keynote on longevity. And, uh, I wish I had only had a chance to go in your friends and family round me before you had this epic, epic release. If you don't mind tell, tell all of our listeners and viewers here, a little bit of the back story, the founding story behind Cerberus. What was the moment where you said this needs to exist? And, uh, you took a very different route than Nvidia and other chip manufacturers. We did. We, the founders had all worked together at, at, at my previous startup and, uh, AMD bought that in 2012. And by 2015, we, we'd wandered off a little bit and we started meeting and we, uh, we saw AI on the horizon. And what we knew was that this new workload would eat through extraordinary amounts of compute. And we, we made two really big bets. We made a bet that said, um, like graphics produced the GPU and like mobile compute, support of the development of the ARM processor, that this technology, this work would be big enough to require dedicated silicon. And the second bet we made was that, uh, the right strategy wasn't to build a derivative with the GPU that you needed to start with a clean sheet of paper and you needed to do something fundamentally different. And these were enormously contrary in bets at the time and both proved to be dead right. And from that foundation, we, we continued the, the innovative thinking and we said, what, what, what AI is going to need is memory bandwidth. That's the sort of speed with which you can move data from memory to compute. And the, the way to, uh, to innovate on that dimension is to use a different type of memory than everybody else uses, right? We have two types of memory. We have memory that, uh, can store a lot that's slow. We call that DRAM or HBM. And we have memory that, uh, is fast, but can't store very much for square millimeter. And so what we hypothesized was that if we could build a chip, the size of a dinner plate, a chip sort of, 58 times larger than any chip ever built before, we could stuff it to the gills with SRAM. Therefore, thereby overcoming its weakness and not being able to store very much for square millimeter and benefit from a strength. And that proved to be a very difficult problem to tackle. But when we got it proved to be right, we are somewhere between 15 and 20 times faster than the GPU on any inference problem. And so, uh, the, the challenge along the way was that nobody had ever built a chip this big, not once in the 75 year history of the compute industry. Yeah, that kept on slicing them thinner and thinner and smaller and smaller. That's right. And that, that even sort of those on the, the, the sort of Mount Rushmore of our industry, people like Jean Amdall had failed, crashed and burned. And interestingly, even after we solve this problem, we had people come and visit our labs and then try and build it. And they also failed. And so what it took was years of perseverance and innovation and all the credit goes to the engineering team, Gary and Sean and Michael and JP and the team we had, we failed for years. And in August of 2019, we announced we'd solved this problem that had been unsolved forever. And we thought everybody would, would, would rush to our door and the world didn't care one bit. What was utterly indifferent. And over the neck. I mean, in the first generation, I think we saw 12, 12 systems. And in the second generation, we sold 300, 350. And in the third generation, we're selling many, many, many, many thousands. And so what happened was we solved this problem and we're way ahead of the market. And it wasn't till 2024, late 24, early 25 that the models got fast enough. And the models got smart enough that people wanted to use inference everywhere. And that that's what happened and so there we were with the, the fastest inference machines on earth. By orders of magnitude. And suddenly, people wanted to use AI and the way we use AI is with inference. And we were just crushed with demand. And then in December of 2025, we signed a deal with open AI north of $20 billion over several years. One of the largest deals ever signed in Silicon Valley. In March, we, we, we signed a term sheet with AWS for deployment in their data centers. And business has been, been pretty good since. Well, congratulations. I'm just taking a second and welcome Alex back. Alex. Hey, Alex, good to have you back. Good to be back. Amazing to meet you, Andrew. How are you going, Alex? I wanted to ask you, Andrew, I was talking to Valle van yesterday, your chief product architect, brilliant guy in Toronto. Awesome, awesome friend. But I didn't realize that the company had a whole history as a training side company. You know, inference is now at 80, 90% of the market. And, you know, moving a huge amount of data from SRAM, you know, through the, through the processing massively benefits on the inference side. But did you see that coming when the initial design that were, I don't think a lot of the research people even knew that inference would be so dominant. I think that we got many, many bets wrong. And I think any CEO who looks back over a decade that moved as quickly as ours and says they got it all right is probably not a guy you want as you're at your birthday party. We got an enormous amount wrong. But one of the things we got right was an understanding that we make AI with training and we use it with inference. And if AI is going to be smart and if it's going to be useful, you need to have an inference business. And that bit we saw early and the real problem between 2020 and 2024, 25, was that it wasn't smart enough to be useful. And so everybody was focused on all the labs we're talking about, number of parameters. And now people don't care. The only question is, is there right good code, does it give me good answers, can it do things that I want done? And that's because we've moved into a regime, into a world in which it's useful. And that's how it's measured. And so we did recognize this. We are really good at training. But right now there's such demand for fast inference, such overwhelming demand that we're allocating a lot of our attention to it. I'm curious Andrew, SRAM, you mentioned SRAM earlier. The largest models, really the standard models at this point that offer frontier capabilities in some cases up to 10 trillion parameters. How do you think about SRAM when, correct me if I'm wrong, the wafer scale engine version three, I think has maybe in the tens of gigabytes of SRAM 40, 40, 50, something like that. The largest models are in the trillions of parameters. How do you think about the future of SRAM, given that as you said, you're stuffing it to the gills. Right, I think the following, I think that models that size have to be divided up whether you're using GPUs or or TPUs or or or us. They have to be cut up and they have to be spread over over multiple chips, right? Remember models that size Alex, they have a very large matrix multiply in the attention head. And that doesn't fit on a GPU. You have to cut it up and you have to go tens or model parallel. And you don't have to do that with us. But what you do have to do is spread that over four or six or eight chips. And what you do is you divide the model very carefully and you divide it such that no layer runs over two chips. And so what you're moving, it results from one layer to the next. And you can move it because that's a very small vector. That's a results vector. You can move it over 100 gigahertz or net. And it is slower. That little hop is slower. But the calculations that take up such a big portion of the time are so much faster that you pay a very small penalty for breaking it up into four, eight, sixteen, twenty chips on the order of two percent. Now, other SRAM solutions that are small, like for example, GROC, that Nvidia acquired here. They have to break it up because they have only 800 square millimeters to use. They have to break a big model up over two or three thousand chips. And each of those hops hurts their performance. While we have to do a few hops, they have to do thousands. And so there's no way ever to fit everything on any size, right, amount of memory. But there is a very nice and simple way for us to cleave models, to spread it over multiple chips. And yesterday we announced and posted numbers on Kimi K2, which is a trillion parameter model in the open source community. We were of course, an order of magnitude faster than anybody else. Oh, really? How many chip, how many waitpers on that? I forget. I've been busy. I mentioned. But it was about a thousand tokens per second where a really good gigoo shop like Firework is running at 70. Yeah. Right. And so they're really good shop. And so 15X. That's pretty good. Peter and I were at Google I/O yesterday and they showed a whole rack of TPUs operating together, generating 1400 tokens per second, writing code. And you see that and you're like, I need that. I need that tomorrow. Well, the quick there Dave sometimes is an Nvidia's been masterful at this light of hand is not telling you whether they mean tokens per second per user or aggregate throughput. Right. The GPU is an extraordinarily good machine at generating slow tokens. You can generate an NVL72 at 35 tokens per second, which is painfully slow. Can generate millions of tokens. On the other hand, if you ask it to generate tokens at 200 tokens per second per user, it can support one or two users. That's a $4 million solution working on one user. Right. And so it's really important when you sort of dig into these, they tell you, gross throughput. Is this a lot of customers who are unhappy with their performance? Or is this the, are they able to serve that to individual customers and how many of them? Yeah. Andrew, you said go ahead. Yeah, you were being complimentary of Elon as an extraordinary builder, entrepreneur, you know, sort of falling down earlier. So one of the best in history for sure. I'm curious. He steps up and announces tariff app, produce 50 times the amount of chips on the planet that exists today, outstripping, you know, TSMC and everybody else. What do you think of tariff app? I'm super curious. Look, I think Elon has proven himself on, on multiple dimensions. He's proven himself to, to be a, a visionary. Right. You know, the, the number of people said you're an idiot to try and build cars in free months. Right. I mean, we've got the highest labor rates in, in the, in the country, maybe the, among the highest in the world. We've got a regulatory regime that is unfriendly to business. The number of people who said you shouldn't build a rocket company. The number of people who didn't understand that he was building a rocket company because he wanted to satellite company needed, I mean, he has been ahead of everybody for a very long time. Okay. And so your, your decision side, that's the vision side. He's also been able to execute on some of them, not all. And that, that's what's cool is he is trying to do things that other people can do. Now, this particular problem I know a little bit about and building fab is very hard. And it is hard in a different way than some of the other problems he's attacked. I'm not saying he can't do it. I'm saying it will always take longer than he says. It will cost vastly more money. And that's the, the challenge of building extraordinary things. It is not a five or 10 year project in my humble view. I've been wrong before, but I put this at a 15 or 20 year project. I think it's interesting. It's probably good for the US that we have domestic fabs. But I think that there is a reason why even with the exact same equipment from ASML, right? Samsung and TSMC aren't at the same node. TSMC is ahead and they're extraordinary. And the amount of received wisdom and learning from the fabs they've built over generations cannot be underestimated. But if anybody can do it, Elon can do it. What does that mean for cerebris? Because, you know, obviously, US manufacturing and like you said, you know, a lot about this topic. But US manufacturing of chips is critical. I mean, critical for everything, for national defense, for, you know, the economy, everything. Yeah. And so if it's going to take 15, 20 years, that's just tariff, fab. And you know, the, the TSMC migration to the US is going very slowly, way behind schedule. So what does that mean just in terms of, well, first of all, supply demand, you know, just just the raw ability to get things made. You must do what this every day. These things are hard to build, right? I mean, fabs are pyramids. They are our pyramids. And TSMC is the greatest manufacturing company on earth. And the challenge is these things take five years to build, six years to build, and 50 billion dollars, 40 billion dollars from the people who built the last one, right? And that's true, whether it's TSMC or Samsung or any of the great builders here. These are unbelievably difficult to build. And that's where there, there, there, there have been behind schedule in, in the US. I think they encountered some challenges that were unforeseen. I think we have political challenges in the, that these things take a long enough time that they cross administration boundaries, right? When your projects are more, can't be done in four years and have to cut across multiple administrations, maybe two or three different administrations, right? For a period of time, when you have local ordinances that get in the way of building, as happened with Samsung's fab in, in Texas, they redesigned the fab because of a local fire ordinance that made no sense. These are, are painful problems. that our system hasn't found a way to overcome. And so I think that we have to find sort of a way to do better because I think the reshoring of FAB, and not just the FAB, FAB gets all the glory, but the packaging business. Everybody's important in something we lost entirely when the FAB's left. - Yeah, yeah, actually, good question. By the time you get something ready to put into one of your data centers or a third party data center, how many different manufacturing partners has that wafer been through? - A fair number. - Yeah, fair number. - It goes to me and it goes to someone who ASE, who deposits RDL on the backside, it's diced, it's cleaned, it comes to us for a step. I mean, it is a long process. I think, you know, when we stopped caring about FABs in the '90s and IBM sort of left and global FABs sort of, we didn't do anything to keep them, we lost this collection of surrounding expertise, when a chip comes off a FAB, it's a dead piece of silicon. The package is how you breathe power and life into it. How you get IO into it and how you get power into it. And that's also an enormously challenging technology. It takes material scientists, it takes manufacturing, engineering, process engineers, deposition engineering. And we punted all of that by not caring about this industry and it's all sitting in Taipei and in Korea. The materials are manufactured in Japan, Kiyasera is one of the leaders there. And we gotta get it all back. And we gotta make a decades-long commitment to this industry. - Well, if you said that the Tera FAB is 10 plus years out, if you look five years out, do you think that your, you know, cerebris is able to manufacture on Intel, Samsung, anti-SMC, are there any other choices or? - You know, we've committed our three nanometer design to TSMC, so that will take us out a little bit. We do manufacture some components at Samsung and have a great deal of respect for, for, for Samsung's fab capabilities. We have never used Intel. Lipu is an extraordinary leader and a long time advocate for hardware and Silicon Valley. As you know, Dave, there's a period between about 2007, 2006 and 2015 or '16 where every VC firm was filled with somebody from VMware who didn't know anything about hardware, who thought compute was made by, by flea feces in the cloud. And, right, that there was some sort of thing in the ether that somehow was generating compute and we tried to explain for a long time that the way you make more virtual computers to begin with real compute. - Well, I gotta tell you, you've inspired so many people that are in campuses right now, that are eager to be part of your mission to get that back. - So, the more I'm gonna route as many as I can through this film that you do. - Please do, I mean, guys like Andy Beckishine and Lipu and a few others were continuing to put money into hardware, continuing to peer Le Mans, continued to do it and support us as we, as it was tough going to raise money over that time. - Over that period. - And I think, while I know Lipu, they've got a lot of work to do. And he's done great things so far, but we've got the work to do before we could move to Intel. - Alex, Sir Salim, you have a question? - Yeah, I have a quick one, Andrew, you've gone from raw inventions, solving fundamental big problems to going to now production when you wanna scale these things. What's the, can you say how long it takes to create one of those chips in over time? As you get better and more efficient in the manufacturing process, what do you hope it shrinks to? - Well, I say that the first one took four years and maybe half a billion dollars, somewhere between $405 million. That's why I take it to dinner when I go with my wife. (laughing) Like a ten-year-old with a first dirt bike. I mean, it's coming to bed, it's in the bedroom, it's not outside of the garage, it is being carried around everywhere I go. I got away for it. I think the inventions cut across lithography, chip architecture, packaging. - Cooling. - Cooling, power delivery and cooling. They included sort of compiler inventions, algorithmic inventions. In fact, some of the hardest problems that we encountered were packaging. And we solved them seven or eight years before others encountered them. So the B200 was, or the B200 was 18 months late. And it was late because they had a problem with their COOS. - What's that mean? - COOS is a process step where TSMC uses a 65 nanometer chunk of silicon as a motherboard. And so they put on that, they put on Nvidia's chips in the memory. And instead of putting it on a green board, right, when you a traditional motherboard, they put it on a piece of silicon. And the wires are more efficient in silicon. They can be narrower. And so this was a big invention. But we knew that there would be a problem with the coefficient of thermal expansion. And we knew that because we'd solved that problem in 2018. And so there they were in 2024, 25, struggling with a problem that we'd solved seven years earlier. And that's what happens when you do pioneering work. Is you encounter problems, you have a chance to solve them long before the rest of the industry even encounters them and knows there are problem. And so that is one of the joys. Obviously in everything we do in engineering, there's a trade-off. The downside is there's some low days, right? There are some days you go home and some of these days stack up and we had about 18 months where we're spending 8 million a month and we couldn't solve the problem. And when you have board meetings every six weeks, and you come in and you still can solve it, you can't solve it and you're $100 million in more in the whole and then you're $120 million in the whole, then you're $140 million on the whole and you still can't solve it. This is some low days. And then you have the IPO of the year and it's a high day. - That's right. - I think Peter, you know what? - It was an archetypal story if it happened. - It's the entrepreneurial journey. - Yeah. - I think Selim, one of the things I've learned along the way, this is my fifth startup is that this shit will kill you if you can't modulate the highs and the lows. (laughing) - Yeah, it will. - And for every entrepreneur, every CEO, I tell them first that this is a pressure test on your soul. And second, the number of times you can get kicked in the gut before lunch time and have it still be a good day as a CEO of a startup is amazing. - Would you rather be doing anything else? - No, this is all I know how to do. I'm a professional David in the battle with Goliath. I know I have no interest in doing other things and I have no interest in working with people who are other than those who want to attack the hardest problems. - Yeah. - Amazing. Alex, please. - Speaking of the hardest problems, and it's almost in the name Cerubris, you have I think a four trillion transistor budget with your third generation wafer scale engine. I'd love to talk maybe a little bit about what's at the end of the rainbow, projecting out, say, 10 years when you're on your end generation model. What does the future look like? Does it look like brain uploads running on WSE? WSE eight, what's the killer app? What does this look like in 10 years? - So Alex, I think one of the fun things about being an infrastructure builder is you don't have to have those ideas. No, really, I was with the team and many of them are here in the mid 90s that helped drive down the cost in networking. We built some of the first and fastest Ethernet switches. And we had no idea that what's up would arrive and that it would make possible even for the poorest members of our society to communicate home. And when I grew up in the 70s, the only thing I heard my grandmother say on the phone was put your brother on its expensive. It was $4 a minute from my mother to call Australia, where her mother was. They spoke for six minutes a week. And the only thing I heard my grandmother say was I'd say hello, Bubba, she'd say put your brother on its expensive, right? And we put in a company called Yago, along with many others, with Juniper and others. We put a small brick in the wall that made the cost of IP transport so low that somebody else could invent a technology that made it such that every person can talk to their grandparents and no matter how poor they are anywhere in the world. And that's something that's been done But we didn't know those aren't, that's not the problem I set out to solve, and our company set out to solve. We set out to solve a problem as an infrastructure builder that we build roads. And what you drive over those roads and how far you take them, that's other people's work. What we're trying to do is allow people to do extraordinary things on our infrastructure. And so when I think about what we're enabling, that's work for Sam. That's work for Ili. That's work for others. What we're trying to do is make a compute platform on which their ideas can take flight. And what we know is you need fast calculations. So what I think I heard you say is that you're very deliberately not having opinions as to the future shape of the workloads that will run on your infrastructure and you're primarily at this point deferring to the frontier labs to steer the future architecture of workloads. Today's frontier labs are new frontier labs, right? We are making bets that the world will continue to depend on Sparrow and your algebra is ununderpinning for all these calculations. This episode is brought to you by Blitzy, autonomous software development with infinite code context. Blitzy uses thousands of specialized AI agents that think for hours to understand and to price scale code bases with millions of lines of code. Engineers start every development sprint with the Blitzy platform bringing in their development requirements. The Blitzy platform provides a plan, then generates and pre-compiles code for each task. Blitzy delivers 80% or more of the development work autonomously while providing a guide for the final 20% of human development work required to complete the sprint. Enterprises are achieving a 5X engineering velocity increase when incorporating Blitzy as their pre-IDE development tool, pairing it with their coding co-pilot of choice to bring an AI-nated SDLC into their org. Ready to 5X your engineering velocity? Visit Blitzy.com to schedule a demo and start building with Blitzy today. In the final question, orbital data centers. Fiction, real must-have. Are you going to put your chips up there? I think first we have serious advantage in space. In space some of the most expensive work is the chip-to-chip communication. We've had chips in space for a long time. That's what a satellite is. A satellite looks like a PC motherboard with a big camera stuck on it, a big telescope. If you unpack what's in a small satellites, every computer hobbyist will say, "Holy cow, that looks like a server motherboard with a big telescope stuck to it." Then it's hardened. Communicating in building a cluster is actually much more complicated because you have to do a lot of communication in this work. Creating the data from the land to the cluster is a problem that we've solved a long time ago. They will continue to improve it. Being a big chip and having to move things off, chip less often is a huge advantage. I think this is an exciting domain. I think like many hard problems, the last 10%, don't take 10%, they take 90%. Self-driving is one of those categories. At the last 10%, we've been sitting at for eight or ten years and we're just now getting over the hump of the last 10%. It isn't really 10%. I've got it in the seven to ten year category. It's interesting, Andrew, just to pull on that very briefly, what I would have expected you to say to that would have been something like with the wafer scale engine you had to design around faults. You had to be incredibly fault tolerant at wafer scale. In a space environment with lots of ionizing radiation, you also need to be fault tolerant and that serubus with its experience with fault tolerance at wafer scale is the perfect computing platform for highly ionizing environments. I think we have lots of advantage, Alex, and you put your finger on one of them that you have to try and sort of shield your silicon very differently in space. And you will get more flaws and the single bit errors, their hard errors, their whole collection of errors that you have to contemplate. And our ability to shut down a core and route around it is an enormous advantage in that environment. I think we've got as a community some work to do over the next four or five years before we have sort of the truly hard part of getting them in space or concentrating with software or getting them to communicate. So I've got it sort of out the better part of a decade before we have sort of production in space. I think it's a very sort of a worthwhile project to pursue, but it's out of the ways. All right, Andrew, we close out these segments with an AMA with our incredible subscriber base and would love to have you join us. So we've chosen eight questions from our comments, which we all love and read and we'll be peppering them along. So I'll put them up here. Selim, I'm going to give you first shot. Andrew, you can look at the others and see, get ready for one of them. We have a second page. We'll go to you. So Selim, why don't you pick one of these? I'll go with the first one. If the world becomes compute constrained is the 10 cent lawyer for everyone's thesis, so hold this. Yeah, I become a luxury. Only the rich can afford and this comes from at geo rust one. So this is a fairly, if you've been listening to the podcast, there should be a fairly clear path here, right? Because every major technology starts with scarce and very expensive and then you saw this was computing bandwidth, Danny sequencing, solar energy, they all looked very constrained and very expensive initially. But then the learning curve kicks in infrastructure kicks in Javon paradox kicks and rights law arrives, competition arrives and the cost, perhaps AI computer is going exactly down the same path. You may have some bottlenecks like chips and power generation data centers, but those become investment, honey met pots and capital floods to the tours those bottlenecks, right? But the bigger insight is that AI is not consuming compute. It's helping design chips and optimize infrastructure. What Alex calls the interloop, it's improving, it's compressing the models, et cetera, for that example. And the system becomes recursive. And as you have intelligence building more intelligence, this is why we get so excited by this future, it's going to drive the cost of everything down to the, maybe it's a $2 lawyer for us, maybe it's 50 cents, but over time, it's going to get to 10 cents. And it's down from a thousand hour. Yes. That's another one. Number two, I think the problem with lawyers and accountants is the structure of their business. Wrong business model for the future. It's exactly wrong. It's something hours out. Their business is to stand between ordinary people and obscure knowledge. That's what your accountant does. He's sitting. You don't want to figure out what the tax rules are with related to depreciation on a property you bought or was gifted to you in 2020. Right. Who wants to know that? And so what their business is is sort of the acquisition of obscure knowledge and the application of that knowledge to particular problems. That's exactly what, what do you think Andrew that generalizes though? What are you other than gatekeeper of obscure knowledge regarding the highest. Oh, I agree with that. I don't think we have like what we're, I don't think so. I think Andrew is just like an engineer. And when he's at their best, they are actually, you know, the reason they're called council is when they're giving advice, not on legal matters. When they're giving good business counsel, when it, when common sense is challenging in a confusing environment, those are when they're at their best. I think when you're drafting all the documents you've need for most things, we're already been drafted. Right. We don't need another lawyer reviewing another NDA. We don't need that. Let's give you, let's give you a next shot, which question two, three or four you want to choose. I, I, I think number three is interesting. I think, I think there is a profound misunderstanding about how to, let's, let's read the question. Why can't money by Elon or Zuck, a lead in AI? Can't they just buy the best talent and that's from @novaRift? Great question. No, the answer is no. And I think, you know, why couldn't Intel build a cell phone processor? They had the time, they had the best fabs, at the time, they had the best computer architects, and they destroyed tens of billions of shareholders dollars failing, same with the MD. It turns out in our industry that money and the acquisition of talent isn't enough. What is? There's something else. Well, MTP. What? Mastively transformative purpose. I think that's incredibly important. Intel could have said yes to Apple though. No, no, but the. they could have. But the truth is is what led them to believe that they were in a position to say no to Intel was chasing margins. Intel love was infatuated with its own profits. They had an armed division that they sold off. Intel could have sold. Why? That's the thing we're trying to understand is the innovators dilemma. They were fat and happy and lazy. Maybe, maybe, or maybe that there is something in your DNA that makes big mutations. Well, luck. I'm sitting here saying all day long will take luck over school. But I'll also say that all day long that extremely hardworking people, a tremendous grit end up more lucky. And that both of those true. That is life. It is really hardworking people over long periods of time who who have integrity and ethics, right? They get lucky more often. And that's luck is not equally distributed to those who work hard and those who don't. I got a short. Go ahead. Go ahead. I'm going to quick plunk here. We're launching this service next week. It's shaping luck.com because one of the things is that luck is non-linear. And in a world that's going to exponentially want non-linear outcomes. So if you're interested, go join us at a webinar. What's shaping luck.com is the URL. Okay. Awesome. Free webinar. Come along. But I think Alex, the question isn't, of course they could. Why didn't they? Why did they miss it? Why did AMD miss it? Why did, right? Why did, for example, why did Nvidia fail for decades at everything that wasn't a GPU? They failed to build an ARM processor that worked. I think it was called Snapdragon. I think they failed at Northbridge Southbridge part. And they succeeded beyond anybody's expectations at a GPU. I think that the same question I think holds true for the Yankees. Right? No, no, no. Why doesn't the team with the biggest money when every year in the NFL? Why? I mean, there is something that we have, as in thinking about organizations and talent, that we don't do a very good job at describing that says there is something that is very hard to buy and that has to be made. And that we don't seem to be able to articulate it well. And buying the most talent doesn't seem to be sufficient. It is, you have to have a lot of talent. It's necessary. But it's clearly not sufficient. Alex, let's go to you next for a question when I get through our lightning round. Yes, of course. I have some pretty different answers to some of these other questions. But I think I have to answer question number two, which it looks like might have been a response to a comment that I made in a previous pod episode. So the question is why wouldn't Sam, I think referring to Sam Altman, cut a deal with Bezos and Blue Origin to become the other counterweight to Elon. And this is from Scott Ray Brumfield. So I think the answer is that's probably on the table. If I were Sam, I would be exploring a variety of potential heavy launch partnerships to become a counterweight to Elon and SpaceX AI's Dyson Swarm. And I think heavy launch is going to become is already arguably part of a critical element of the stack now for the future of compute space. But as you know, right? New Glenn is more akin to Falcon 9 and doesn't hold a candle to Starship, which by the way, we'll be making a launch attempt. Probably by the time this is out, good luck to Elon on that launch attempt. Super excited. But Starship is coming in at a factor probably a hundred times cheaper. I'm not sure that I would say matters. Oh, go ahead and enter. Go ahead. No, I think Alex, all your points are right. And I think that you underestimate Sam at your tremendous cost. I think what Sam is sort of done again and again in our industry is see around corners that other people must. He was trying to lock down data center capacity in space last year and the year before when all the other foundation labs didn't see it. Really? It's trying to lock down memory. Oh, yeah, for sure. I didn't know that. His ability to look at an exponential and not be afraid of what it says in two or three years. Well, everybody else is afraid saying, Oh, we're not going to need that much. It is extraordinary. And his reach is extraordinary. And so with 100% certainty, I will tell you that he is exploring deals with every possible way to get access to compute and data center capacity. And I can say that having watched from a distance, I have no inside information, but I've been dazzled by that ability as I think you underestimate that guy. I mean, you would think it would be enough to build the fastest growing company in the history of capitalism to get a lot of respect. Right? You think that might be a sufficient feather in your cap. But I think he will certainly be in conversations to get compute whether it's in space, whether it's under the sea, or whether it's on, you know, using falling water, using geothermal, he will be in those conversations and his team will be there every single day. Alex, one of them is so cool to have another friend who's on the big, big stage. It's great. Alex, that insight perspective is awesome. Alex one point here is again, if you know, Elon's Dyson Swarm is 500,000 satellites to a million satellites, it was like a launch every couple of minutes of a starship. You don't get that when you Glenn. So that vehicle isn't designed for the frequency of launch that we're talking about here. So could, you know, could Sam put up a mini constellation with Bezos? Sure. Could he put something up to really compete with with what Elon's proposed? Not without new launch capability. That capability is unique on the planet if it pans out as expected. Yeah, a few thoughts. There are lots of options. Yes, I agree with the contention that SpaceX is completely dominating mass to orbit. No question about it, including dominating historic mass to orbit. So if I'm, if I'm Sam, I would be exploring probably a multi-channel strategy. A, I'd be exploring a deal with Elon and SpaceX to leverage SpaceX launch for my own Dyson Swarm. I would be exploring alternative launch capabilities. And then if you really believe, again, I think this is the elephant in this space room. If you really believe that we're on this singularity, ask, ask exponential, then the fabs don't need to be on the ground. We can build fabs in space and we may not be addicted to heavy launch five to 10 years from now. And if you're Sam and you're playing the long game, then you're looking for, then you're looking for ways to build fabs on the moon and in Leo that don't need the SpaceX near monopoly. Amazing. Dave, do you want to take us to question for? I enter you. Did you have another? No, I, I, I think, I think, I think building a fab on land is hard enough for me. Yeah, I mean, 40 or 50 billion in five years doing something. Nobody else, I mean, that wanted to other companies in the world have ever successfully done. I mean, I, I, I can't really think hard about building fabs in space. No, I agree. Great segue to question four, which is directly related. China is building massive compute capacity. Could they sell tokens to us? Losers, losers, users at very low prices and disrupt providers? It's, it's the same answer you just gave Andrew. Like, no, China is not building massive compute capacity when you're looking at tokens per second, you know, the driver of AI. They have, that's why they're so desperately want to import from the US. So they're, they're building as quickly as they possibly can, but it's not, you know, five nanometer, four nanometer, you know, three nanometer technology. And so it's all bottlenecked today. It's ML Machines, fab construction. Everything Andrew's been talking about. So if they had the ability, they would love to do that, but they just don't have the compute. Yeah, the, the, the dimension in which they've chosen to invest so far is in power infrastructure. Yep. And at that, they're just playing better than us right now. They have upgraded their grid. They have tremendous power infrastructure. And we've made bad decisions there. We are stuck with a grid that's built in the 50s. It's designed not for what we like it for today. And we have trouble politically at the local, at the municipal, at the state, the federal level doing projects like infrastructure. And so what they have done is a tremendous amount of investment there. They are obviously starved of compute. But they're going to try and build on what they have, which is an absurd amount of power infrastructure. Yeah. I would say when enterprises are going to be doing most of the token purchasing, you're not just buying the token, you're buying trust, you're buying governance, you're buying reliability, your buying compliance, et cetera, et cetera. I would prefer Perhaps just add to this, it's worth noting that in the past week or so, there's been quite a bit of public reporting about how China is operating proxy services that are selling American tokens to Chinese users at incredibly low prices, like TANX, TANX discounts in order to siphon the reasoning phrases for training their own models. And that is quite disruptive and anthropic is pursuing that. Amazing. Andrew, we want to thank you for being on. We close out every episode with user-generated content. This is sort of our outro music. And so let's enjoy this one. It's called We Are As Gods. I guess a comment to my new book. It's from my side. Did you tell your address as a guide? So we have to like bow down to you. I think being a CEO is sufficient. But I don't want the responsibility. Congratulations on the epic IPO. Amazing. Let's listen to We Are As Gods by Musad Zamanee. All right, enjoy. [Music] All right. That was a good one. Very cool. Again, thank you for joining us. Gentlemen, always a pleasure. I think we can cut those conversations going for a couple more hours. Easily. Easily. Yeah. Thank you for having me. Really appreciate it. Be well now. Thank you, Andrew. Later to the end of this episode, which you obviously did, I consider you a moonshot mate. Every week my moonshot mates and I spend a lot of energy and time to really deliver you the news that matters. If you're a subscriber, thank you. If you're not a subscriber yet, please consider subscribing so you get the news as it comes out. I also want to invite you to join me on my weekly newsletter called Metatrends. I have a research team. You may not know this, but we spend the entire week looking at the Metatrends that are impacting your family, your company, your industry, your nation. And I put this into a two minute read every week. If you'd like to get access to the Metatrends newsletter every week, go to dmandis.com/metatrends. Thank you again for joining us today. It's a blast for us to put this together every week. [Music]