Very recently, we’ve seen the creation of Sora 2. We’re seeing in front of our eyes the transition from algorithmic content selection in social media to algorithmic content generation. >> This isn’t about sharing content. The creation of the content is completely up for grabs. >> Meta launches Vibes app for AI generated videos. >> They’re spending a billion dollars on single employees, yet they turn to Mid Journey and Black Forest to to build this out. The most shocking thing about this isn’t how real it is. It isn’t how easy it is to use. It’s the fact that it’s free. That is shocking. OpenAI is bringing ads to Chat GPT. The AI is going to be incredibly good at convincing you to do things whether they’re right or wrong. It’s a very tricky balance. And because they’re spending so much money on the data centers, there’s a huge incentive to get really aggressive with the advertising. >> Making all of the demonetization and democratization occur around the world are the ongoing AI wars. Let’s jump in.
[00:01:04] >> Now that’s a moonshot, ladies and gentlemen. >> Hey everybody, welcome to Moonshots. Another episode of WTF Just Happen in Tech. here with my favorite friends on the planet, Dave Dave Blondon. Good to see you, pal. >> Hey, >> Selma. >> I’m back. >> You are back in a in AWG, you’re back from your top secret mission. >> Thank God. >> Thank God we missed it. >> Can you tell us anything about it? To the extent that you think that we’re on the verge of a sharp takeoff, a hard takeoff, if you will, I was traveling in Europe to see what the world looks like beforehand. >> Yeah. So, you’re up you’re updating your baseline of what the world is before things go hyper exponential. Amazing. >> If if if it isn’t a gentle singularity, I’d like to know what it looks like beforehand. >> Okay, great. You know what I was doing last week? I was running my abundance
[00:02:00] longevity summit. I had 50 of the world’s top scientists, entrepreneurs who are focused on adding decades, maybe doubling our human lifespan. And it was awesome. So I walk away with the greatest confidence in the world that uh uh at least our friends and our subscribers are going to be hearing us talk about this stuff for the next 50 years or some version of ourselves. >> That is that is really a frightening thought. Uh, all right everybody, welcome to uh to Moonshots. And let me begin uh with a moment of thanks. I want to just give a shout out to one of our subscribers, Bill Jacobs 386. I’m going to read uh a note he posted. We do read your notes. We love it. We uh this is we’re here to serve you. And he wrote, “I am continually humbled by the amount of commitment and effort that’s required to put this podcast together weekly. I’m not asking for anything in return. Nothing that is except to listen and
[00:03:00] hopefully learn before it’s too late. The future is now. And I think I’m speaking for most of us here how grateful we are. Thank you. Um appreciate that, Bill. It’s uh that kind of feedback actually makes it fun for us to serve uh serve our subscribers, serve all of you. Uh Dave, you want to say anything to that? >> Well, most most of that thanks goes to the team behind the scenes. There’s a huge amount of news out there that gets scoured down to the bullets that we think really really matter to people and then also to Alex’s agents which are getting bigger by the day. His his AI force is coming up. I mean it’s just it’s incredible how rapidly the the feedback coming from that agent force is is filling the pipeline of possible news and then of course the human factor whittling it down. So it’s a it’s a big machine. >> Yeah. and and we do spend a good 20 plus hours. I was up at 4:30 theory this morning uh going through everything, doing my background research and getting ready because if I’m not ready, I will
[00:04:01] get completely decimated by the brilliance of of these are the three moonshot mates. >> Well, you know, I also I feel like I I work really hard to keep up with everything going on. Then every time the team comes up with a deck, there’s like 30 40% of it are things I hadn’t even heard of. >> Yeah. >> And so it’s it’s great. It’s really healthy for all of us, I think, to to do this. I mean it’s I can palpably feel the singularity coming. Uh you know See, I remember you and I were on stage during the early days of Singularity University and we would like update our slides or the conversation or our stick every like three or four months. >> It was it was we actually worked it out as a faculty we the technologies between nanotech and biotech and neuroscience and robotics and AI and so on the content was changing 20% a quarter on on average. Uh, but like this is like 80% a week right now. So, this is a whole other ball game that we’re in. >> It really is. I look back at the at our our pods from a year ago and it’s like, “Oh my god, that is so ancient history.”
[00:05:02] >> Shelf life dropping radically. >> Yeah. Uh, it is, but it’s becoming more and more fun. Uh, let’s jump in. I’ve labeled this first segment the video and audiog battles. Uh, and let’s begin with this video. Uh, Meta launches vibes app for AI generated videos. All right, let’s check it out. Now I if you’re listening to this not watching this on YouTube uh it’s just music but it’s uh it’s beautiful imagery that Vibes has generated. This is through a partnership with Midjourney and Black Forest Labs. Uh Alex or Dave
[00:06:01] you want anything here? I I I think there are probably two stories here. One is that we’re seeing in front of our eyes the transition from algorithmic content selection in social media to algorithmic content generation. It’s pretty obvious story. The perhaps less obvious story is that the space is moving so quickly that Meta was apparently compelled to partner with third parties for such AI generation rather than using in-house first party models. So I I I I think this is a very quickly moving space and now very competitive as well. >> I I was going to say the exact same thing and and riffing on it. You know, they’re spending a billion dollars on single employees. They have a you know a $600 billion three fiveyear budget yet they turn to mid Journey and Black Forest to to build this out. Well, that’s that’s because the really really smart creative people all want to do startups and they don’t want to join the big companies. So uh it’s really encouraging for the startups because
[00:07:00] this you know the other big labs are doing their own you know Google and and OpenAI are doing their own uh video generation and uh it’s encouraging for the startups that are right in the middle of the crosshairs to say well even here we’re thriving so it’s it’s a good sign. >> Every week my team and I study the top 10 technology meta trends that will transform industries over the decade ahead. I cover trends ranging from humanoid robotics, AGI, and quantum computing to transport, energy, longevity, and more. There’s no fluff, only the most important stuff that matters, that impacts our lives, our companies, and our careers. If you want me to share these meta trends with you, I write a newsletter twice a week, sending it out as a short two-minute read via email. And if you want to discover the most important meta trends 10 years before anyone else, this report’s for you. Readers include founders and CEOs from the world’s most disruptive companies and entrepreneurs building the world’s most disruptive tech. It’s not for you if you don’t want to be informed about what’s coming, why it matters, and how you can benefit from it. To subscribe for free, go to
[00:08:01] dmandis.com/metats to gain access to the trends 10 years before anyone else. All right, now back to this episode. So, this is free. And the other thing that’s interesting is they’re generating a Tik Tok like, you know, swipe the video, swipe the video. We’ve seen X do that as well if you’re watching on videos. And of course, uh it’s not just Meta. We’ve seen VO3, uh Google with their video generation. And very recently, we’ve seen the creation of Sora 2. So Sora 2’s launching viral AI generated videos. and I’m going to share a video I created for myself and talk about how easy it is to to create it. So, let’s check this out. Suiting up for the ride. Helmet secure. Pressure’s good. Visor locked. Let’s make it count. Heading to the rocket. Jumping in. >> Cabin comm is live. You’re looking good. >> Strapped in and ready for launch. Let’s go. One.
[00:09:01] Two. That’s 500 done. >> Double our reach every 12 months. In 10 years, we multiply a thousand fold. What else drives? compounding data set. >> Each new user improves the model and makes the product more valuable. Pulling in the next wave. Pair that with automation. When marginal cost drops towards zero, growth accelerates on its own. >> Thanks for inviting me to the studio, Peter. I’ve been looking forward to sitting down with you on moonshots. >> Likewise. It’s great to have you here. People have been asking for an episode that dives into AI and longevity. >> Happy to help. It’s one of my favorite that was that was fun to make. So, if you were listening here, this is a version of me on the moon, then a version of me pumping 500 lb in the gym. Uh, and then, uh, six or seven of me having a conversation about exponential growth and then sitting down uh, with Sam Waltman for a moonshot conversation. They didn’t get the audio model right and I’ll have to re-record that, but uh, it was it was pretty fun. Uh, gentlemen, thoughts when you want to grade on performance? >> I thought a couple of things. One is uh it’s as you connect this with the previous story, this is like Hollywood, Tik Tok, Spotify all kind of merging
[00:10:01] into one thing and I think Alex’s point was really really important that this isn’t about sharing content. It’s about now the creation of the content is completely up for grabs in a new in a new way. So I think all of that happens at the same time >> and the interface to create it is entirely voice and prompt. There’s no coding and no interface. Like all of our lives since the computer was invented, we’ve been learning incredibly complicated interfaces to everything, you know, from the microwave oven to the laptop to Chrome and Safari, Peter. Uh, and all of that is about to disappear from the earth forever >> and just go to a straight natural language interface. And we’ll see later in the pod, you know, much more important actually software creation. But you know after that comes building creation and highway creation and all that is going to be done through just a a voice it into existence right out of the Star Trek holiday. >> It’s it is godlike right first you know it’s speaking the word and and creating
[00:11:00] reality. It’s going from mind to materialization. It’s extraordinary. >> Uh >> I also think we’re we’re seeing video emerge as a firstass modality for frontier models. So right now most people are interacting with the frontier models via text or images. Video is still this separate channel with a separate distribution mechanism. These are on a collision course. We’re going to see the video form factor and the underlying model architectures probably diffusion transformer-based merge into the more auto reggressive transformer presumably based text and image models. And one could even imagine the ultimate user experience here. Maybe not the ultimate, but an intermediate UX looks something like a magic mirror that does this in real time. Right now, Sora 2 takes a few seconds to to generate with fully realistic audio realistic physics. The the physics if if you ask Sora 2 to
[00:12:00] reproduce some generic say high school or college level physics demos, it’s pretty amazing. Uh so all of this ability to reason physical world models if I ask you to think of a pink elephant you will visualize in your mind’s eye a pink elephant Sora 2 and and similar video models once they’re incorporated into the chain of thought for Frontier model will enable entirely new I think classes of reasoning ability >> yeah it’s got it’s got physics consistency which is extraordinary go ahead I want to talk about how I made those videos again >> I asked it to create a video of a water dropping into a glass of water drop dropping into a glass of water because it’s a common image. It was extraordinary how accurate it was. It was absolutely amazing. >> Yeah, it it has real world physics modeling built in. So, I encourage everybody listening to actually try it out. I mean, when you when Open does this, it’s creating sort of a viral engine uh that is getting people, you
[00:13:00] know, getting them from 800 million users up to a billion. But you need to get an invite code. Once you have the invite code, it’s super simple. On your phone, you download the Sora app from OpenAI. Um, you basically hit a few prompts and it photographs you speaking three words or three numbers. Uh, and then has you look to the right, look up, look down, captures your face, and from there fundamentally it’s a very simple prompt. Uh, and if the individuals like Sam Alman or others make themselves open for other people to use and you can make yourself open for use or not, uh, you can pull people into it and it’s pretty easy and fun. >> Yeah. >> The viral loop, the viral loop now. >> It’s super fun. Try it. You got to try it. It’s super fun. >> The viral loop The viral loop now goes from prompt to publish to explode in no time flat. >> Yeah. >> Right. you used to take weeks at least or now it’s like nothing for >> I I saw a great uh podcast of Bill Gates
[00:14:02] talking about how we in the computer science world slaved away for 20 years just trying to get speech recognition alone to work I don’t know if you remember do you remember Lee Heatherington Peter from MIT >> crazy brilliant guy like right up there almost almost Alex level >> um he spent 20 years in Victor 2’s lab trying to make speech recognition >> remember dragon system do you remember dragon systems Yeah, that was one of the earliest voice recognition systems and or I mean >> it really is unfathomable how fast it’s going and we take this stuff for granted which is insane. >> That’s that’s the point. So so Bill Gates made that exact point because he had you know billions of dollars of R&D to try and make speech recognition work. Uh and now it’s an afterthought in the big neural nets. They do speech and then move to video then move to video generation then they move to complex math and physics all in two years. I mean, it’s just it’s just so easy to take it for granted, but it’s it’s it’s massive amounts of converging
[00:15:00] technologies that are suddenly unleashing new capabilities and so many opportunities to glue together the different components and build an incredible new experience. Yeah, everyone should reread the future is faster than you think. You know, Peter’s one of Peter’s many great bestsellers, but it’s all about the converging technologies. But I think when you wrote that book, there were maybe eight or 10 things to consider. Now there’s like 800. >> Oh my god, it’s we’re just wrapped up arguous book. We are as gods and it is so difficult to like to send it to the publisher. No, no, >> when do you draw the line, right? When do you draw the line? >> Yeah, it’s insane. And by the way, you know, Vibes and Sora too, they’re free. I mean, this extraordinary technology again, the most shocking thing about this isn’t how real it is, isn’t how easy it is to use. It’s the fact that it’s free. That is shocking. Absolutely. Well, let’s continue our journey on uh on on generation. Uh here is a product called Suno5. Uh it’s AI generated studio quality lifelike vocals. Uh you
[00:16:02] can basically create something that’s a full 8 minutes run length. And just because we’re called Moonshots, let’s play a Moonshot thematic piece uh called Moonshots [Music] >> again. [Music] >> All right. a Bondlike thematic moonshots >> audio. >> Can I give us a challenge? >> Yeah, sure. >> For before the next episode, we should all play with this and come up with our own versions of what the theme song should be for the podcast and then we’ll let the viewers pick which ones they like the best. The theme song for the podcast, >> you know, uh Nick and Dana and uh the team are working in that in the background mode. So, we might have just taken the workload off of them, but
[00:17:02] absolutely. All right. That was that that was my bid if you will. I think it’s probably also worth noting again in passing musical touring test passed. We we barely discussed it. Anyone can compose a top 40 song or an opera. And this is the beginning maybe of disposable or casual art. >> Wait, what would have been the test? >> Uh the ability perhaps to to generate an undistinguishable from human Bond type song in this case or top 40 song. Yeah, we just passed that. >> And Alex, I’m sorry I didn’t give you credit for that, but thank you for uh for playing. I mean, one of the most exciting things we get a chance to do is play with the stuff as it’s coming out. Uh and the good news is all of you can play with it, too. So, give us >> So, for eight bucks a month, we now have a personal hand zimmer. >> Like, that’s a minimum and quite a bit more. >> Yeah. Uh making all of the demonetization and
[00:18:01] democratization occur around the world. are the ongoing AI wars. Uh let’s jump in. All right. Anthropic uh announces Sonnet 4.5 claims the best coding agent available. Uh Alex, would you walk us through this? Yeah, it’s really remarkable what a single-minded focus on call it code maxing or codegen maxing is is doing for anthropic with its model. So, in using this model, in in testing it, one of my favorite test cases is to ask the model to singleshot the generation of a cyberpunk firsterson shooter. And >> Claude Sonnet 4.5 does an amazing job. It gets nearly all the way there with minimal handholding. And I have very high confidence that some iteration of Sonnet 4.5 will get all of the way there with visually stunning graphics, music, um, elaborate firsterson controls. I I
[00:19:01] think the the risk that that one can perceive on the horizon is on on the one hand focusing on codegen is perhaps a a very ambitious bet towards recursive self-improvement. If if the code can write itself really well, maybe that’s the critical path to an intelligence explosion. On the other hand, if it turns out that other modalities are important, like video, for example, that we were just seeing or music, then the risk is that single-minded focus on codegen in particular, may not be critical path. And I I I suspect we’ll know the answer in the next six to 12 months. >> Dave, you want to add something? >> Well, shout out to Blitzy. Now the the top benchmark on here uh 82% on Sweetbench, but Blitzy got to 86.8 on that benchmark by combining models. So that’ll go up a little bit now with uh Sonet 4.5 under the covers. >> But just by hitting all the models and iterating a lot, you can actually squeeze in more performance out of these
[00:20:00] benchmarks. And uh you know, this is pretty much maxed out now. Um they’re working on a new benchmark with MIT for for long form coding. So if your if your process is writing code for 8 10 12 hours, how do you benchmark the quality of the output? So uh it’s a really cool new benchmark. We’ll get into benchmarks later in the podcast too because lot of capabilities in the world that didn’t exist a year ago. We have to have some kind of metric for all of them. >> Yeah, I love the way the uh these hyperscalers, these frontier labs are all incrementing uh their software by.5, right? you know, assignment four, 4.5, silk five. We’ve got gro where are we on the Gro? Are we at Grock 4 now? >> That’s right. >> Gro probably also worth dwelling for for just a few seconds on the autonomy length scale. So, sonnet 4.5 maybe at somewhat infamously at this point working for 30 plus hours straight. I I recall in a a past episode we were talking about the characteristic autonomy time of some of the bleeding
[00:21:00] edge frontier models being 7 hours and before 7 hours 1 hour. If if you had just taken meter’s original exponential fit for the amount of time frontier models can work independently and and just extrapolated a mere exponential time, we’d be far below 30 plus hours. So if if lots of reproductions hold true to this 30 plus hour time estimate, that would strongly suggest that in fact we’re on a hyper exponential rather than an exponential in terms of autonomy and really crazy things maybe start to happen in the next year or so if that’s the case. >> And Alex Dario is in particular famous for really focusing on making uh what he would consider safe AI. And one of the final bullets here is that anthropic or sonet 4.5 has reduced its ability to lie and seek power by a factor of 10. So what does that mean? It’s like you know when you ask it to turn off and it doesn’t or if it’s trying to aggregate
[00:22:00] resources or it’s lying to you. U those are not good things. there there is an entire cottage industry at this point of for-profit and not for-profit basically red teaming labs that are fed early access to these frontier models that look for these sorts of traits. I I think it’s an an interesting research level question as to whether power seeking for example is instrumentally convergent as as a goal for super intelligence. instrumentally convergent, meaning that regardless of whatever the long-term goal that’s assigned to the model or whatever it’s prompted to do, whether if above some threshold of intelligence or super intelligence, it more or less is required to power seek. I I’ve published research in that area. In my mind, this is still very much an open question regarding the so-called orthogonality thesis of whether the ultimate goal of of an AI can even be decoupled from uh from its intelligence level. >> It would be super interesting to see how Gemini and XAI uh and OpenAI all all
[00:23:05] rate on lying and power seeking of its models. Do you have any idea? I I see I I see lots of different measures for this. It It’s difficult to to register a uniform assessment across the industry. >> Yeah, >> that’s a fun challenge, though. That could go bad in so many ways, but that would be so fun. Like, let’s put together a benchmark for for how it lies. >> How well it lies. >> Let’s see if we can prompt it into lying as much as possible. >> Well, I could imagine, you know, listen, there’s an allout competition between all these frontier labs. Um, and if the way you get ahead is that your AI is more power seeking than its neighbor, uh, are you optimizing for it or against it? We’ll find out. >> All right. Continuing on, uh, imagine with Claude. So, uh, live app creation demo of son of 4.5 that generates apps
[00:24:01] in real time. Let’s take a quick look at this video and then I’ll ask you to, uh, tell us about it, Alex. Imagine if Claude is still building software, but we’ve cut out the middleman. Instead of writing code that describes this text box, Claude just makes the text box. We’ve given it access to software tools that construct software directly and substantially faster. Claude isn’t writing code in the standard way. It doesn’t have to plan it all out in advance. Instead, it generates new software on the fly. When we click something here, it isn’t running pre-written code. It’s producing the new parts of the interface right there and then. >> Amazing. So, Alex, I saw you were playing with it this morning. It’s we’re we’re living in the future, Peter, where the models are so high throughput apparently that now it’s possible to do just in time code generation uh on every event. You you click within a user interface within imagine and new code is
[00:25:02] generated on the fly. You can ask for new apps to be spun up on demand. They’ll be generated on demand. And I I think it it’s an interesting thought experiment to ask where does this go in extremists when throughputs continue on their exponential or maybe hyperexonential trajectory. And I I I suspect naively where this ends up is every single pixel is going to be generated. Yeah. >> Not just Yeah. Not just like vector art, not just UX, you know, windows icons, menus, pointers, every pixel. >> And I imagine your version of Jarvis, your personal, you know, uh, entourage of agents are spinning up capabilities for you that they think you might need on standby, ready for you to to request access to. >> We could end up with a gray goo type problem on this because you could AI that says No, no. I’m just saying I I I it’s somewhat of a positive thing, but
[00:26:00] it’s going to be surreal because you create an AI that starts generating apps and we’ll get end up with billions of apps flooding the app store. It’s going to cause some interesting uh challenges on the >> but there will be no app store that you know you will not be choosing you’ll be not be choosing an app. >> It’ll be algorithmic obviously. >> It’ll be you know the capabilities you need in the moment to achieve your objective >> will be curving up as you’re >> materialized. Yeah. >> Yeah. the the the term of art is at at this point slop. And I’m a lot less concerned about slop overwhelming civilization than than perhaps some folks. I think there are so many ultra high value transformative problems that that will set AIs on while we’re sleeping. I’m I I’m incredibly not worried that we’re going to drown in slop. >> I agree. I completely agree. Also, I think it’s a good place. See, a lot of business leaders out there aren’t reserving their compute and they’re like, “Well, I I won’t need that much or I’ll I’ll wait and see what happens.” This is a great use case to show you like if if you say, “Look, I want this
[00:27:01] software to exist in real time.” It’s entirely possible, but you have to have a lot of compute dedicated to you in order to make it happen in real time. How quickly can you imagine 400 500 concurrent things that you want it working on very very quickly? So if you have access to that compute, all of that can be created for you in real time and it’s an absolute joy to do. If you don’t have the compute, you’re not going to get it. You know, the demand for this is so mind-blowingly big. Uh and you just got to figure out where am I going to get the compute to do exactly what we just saw. >> Alex, how easy was this to use? What do you have to do to to spin it up? >> Trivial. Uh so all I had to do was go to the Imagine with Claude site. I asked it first to generate a calculator app for me. Create a calculator. It created a functional calculator. But most interestingly, as I was testing the calculator, clicking on each button in in the calculator app, it was generating code in real time. So, this is a a
[00:28:01] transformative way of thinking. We’re we’re accustomed to historically thinking that there’s a software development time and then later an execution time. And this completely blurs that boundary where even at execution time every software event results in new codegen on demand. It it changes the the just in time paradigm. >> So you don’t have as a as a coder you don’t have to think through every possible use of it. Uh this is is building out the use tree as it’s requested. That that’s right and Verer Venji one of my favorite writers used to write in Rainbow’s End another book other than Accelerondo that I would highly recommend write about what would happen when we have too many transistors transistors too cheap to meter as it were and our transistor budgets go through the roof. I I think this ends up being one of these use cases. If we have so much compute just slloshing around, the ability to delay app code generation until user event time. That’s incredible
[00:29:02] and that will certainly mop up lots of compute. >> Yeah, we haven’t heard much from uh at least on our WTF episodes uh from about Claude over the last month. It’s good to see Claude coming out, Anthropa coming out with some great products. It’s quietly winning in the marketplace. >> Yeah. Uh let’s go to OpenAI. Open AAI is introducing Chat GPT pulse. So I love the idea. I haven’t played with it yet. Uh the idea of being, you know, in the morning when I’m using my my chat GPT voice and having a conversation uh with Ember, which is the voice model I’m using there. uh you know I have to think okay what’s a unique idea or concept I just learned about that I want to speak you know let’s talk about the fox3 gene and and how it’s impacting longevity whatever the case might be here’s flipping the model based upon all your conversations you’ve had with chat GPT it’s actually coming up with topics you might want to learn about so it’s
[00:30:00] prompting us and then we’re prompting it back has anybody played with it >> I thought this was a really subtle but important thing where you’re not quering it it’s quering you and I think that starts a new vector of really interesting development. >> Yeah, it feels a bit like a successor to tasks which are also still available from within chat GPT. But I I think I in in my dream world what I would love to see is perhaps in addition to being able to set sort of cronab style periodically scheduled tasks. If if I want compute running on my own behalf while I sleep, I would love the ability to have longunning tasks on hard problems, single tasks that run for days or weeks on end rather than just smaller tasks that run say once per day while I >> give us an example of a multi-day or multi-week task that you would spin up right now. >> I was going to say exactly the same thing. Go for it. I want to hear what comes out of your
[00:31:00] >> I I want to cure every disease. That that’s like a a beautiful well-posed task that is surely going to absorb many billions of dollars of inference time compute. >> Mhm. Okay, that’s great. I want anti-gravity. I want warp drive. I want a lot of things. All right, so all right, let’s move on here. Next up on OpenAI’s docket is OpenAI is bringing ads to Chat GPT. So uh their new uh chief ad officer uh Fijimo has come on and you know what I find interesting is OpenAI is going after massive revenue streams. Dave, do you want to plug in this one? >> Well, the ad revenue is inevitable. That’s, you know, $300 billion for Google. It’s all going to move over to to AI conversations. Uh, and um, yeah, a lot a lot of complexity to figure out there. She has a challenge on her hand trying to figure out how you balance
[00:32:00] like the AI is going to be incredibly good at convincing you to do things whether they’re right or wrong. >> Mhm. >> And there’s a lot of revenue tied to that. And I think Meta did a very good job of balancing the news feed quality with promotions that are blended in. But it’s a it’s a very tricky balance. And because they’re spending so much money on the data centers, there’s a huge incentive to get really aggressive with the advertising. >> Yeah. >> Yeah. And so that, you know, but then there’ll be consumer backlash and everyone will move to some other model. So it’s that’s a really hairy balance. But the AI >> is both the best ally you’ve ever had in buying things, but also if it’s misguided, could walk you down some seriously bad paths. So the trust >> the trust seem to be the trust seem to be for like will you trust insights from an AI that had that has ads baked into it and has an ulterior motive and what do you do then? >> Yeah, for sure. I think the ad model is ultimately going to disappear. I think
[00:33:01] there’s a limited value here, right? Because once we have pendants or glasses and our AIs are able to see where we’re focusing, like if I’m like if my retinal gaze is on that lamp behind uh Alex and I say I love that lamp and I I’m just focusing a lot on it, attention is going to is going to, you know, equate to some level of interest and my AI may be popping up and say, would you like me to buy that for you? Right? So rather than having an ad come, it’s mostly just where am I focusing, listening to my conversations. And then the other thing that’s going to be interesting is if I give my AI a surprise and delight budget, I say, “Hey, you can spend up to 500 bucks a month to surprise me and stuff starts showing up or it knows I’m running out of toothpaste or, you know, my I my t-shirts are run down.” I >> I’ll tell you, Peter, uh the two sentences you said back to back there, I’ll tell you where the conflict is between the two. uh you want your AI to
[00:34:01] surprise and delight you and it absolutely will. >> Most consumer products are 70 80 90 95% margin hugely huge margin where there are two or more absolutely identical products. >> Sure. >> You know two different sets of sunglasses two toothpaste you know like it makes no difference whatsoever. And if the AI says well okay I’ll get Crest instead of Colgate 95% margin went to that company instead of that company. And so there’s a huge amount at stake where the consumer is still happy either way. Where’s that money all land? Right now it all lands at Google and in the future it’s going to land on the AI advisor. So both both things can be in harmony with each other yet there’s a massive amount of money under the covers. So it’s still ad revenue or it’s it’s decisionm you know routing. >> Take it take it a step further Dave because my AI probably knows the exact makeup of the molecules in the toothpaste. that actually happens to know my taste buds better than I do and knows my genetic makeup and it will order a you know a toothpaste that is
[00:35:00] perfect for me uh at that is one half the price and I know that it’s maximized what’s best for me and you know Google’s not getting it you know no one’s getting it the AI is buying it direct >> yeah we’ll see we’ll see because if you you if you look at toilet paper as an example you can buy it for literally 5% of the retail cost. >> Uh, and if you deflate the margin and say, well, the consumer is much happier or they’re only paying 5%. But all the margin got sucked out of the value chain, then the marketing company at the front also isn’t making any money. So, what tends to happen is the opposite of that, that the marketing front end is complicit with the the backend consumer products companies to keep the margins high. Uh, and the consumer just says, “Okay, fine. I’ll just buy that toilet paper.” and then you don’t think about it. >> But do you think my my AI could think about it and could sort of circumn all of those uh all those price gouging?
[00:36:01] >> I think you’re you’re on to something really interesting there, which is packaged ecosystems where you know the number of things you can buy is getting so complex and the number of choices is so complex. You know, for a while there there was an Eddie Bower edition Ford Explorer >> and it was for like I’ve just bought into the Eddie Bower package. you know, I’ll get the car, I’ll get the clothes, I get it’s just like part of the overall thing. And if you read Neil Stevenson diamond age, everybody moves into these culture packages where the AI has figured out all the parts. I think that’s a real thing >> just because complexity of decision-m gets so high over time that you just want to you want to join kind of like AP as a group and you know and you know >> it’s trusted but it’s also a brand affiliation, right? So I think one of the last moes that’s going to exist someplace is going to be brands where I want because I’m showing my wealth or my affiliation. I’m seeing a lot more by the brands I’m using but not on toothpaste. No one goes to my bathroom and says, “Hey, what toothpaste are you using?” All right, let’s move on. But
[00:37:01] just the point here, OpenAI is building revenue streams. And here’s another one. Uh they partnered with Stripe for instant checkout and chat GPT. I think this is brilliant. uh the ability for uh for OpenAI to generate revenue on the sales of products starting with Etsy and soon Shopify. Who wants to weigh in? >> I I’ll weigh in on this one. I I think if you squint, we can see maybe the outlines of what at least near future super intelligence microeconomics look like where you have >> a you have some power law distribution. You have a long tale of consumer subscriptions or consumer ads or consumer affiliate fees for agentic commerce. Then you have a middle chunk where white collar so-called knowledge work gets automated in in part and whole by AI. That’s sort of the the middle chunk of of what turns the wheel. And then the head of the power law is all
[00:38:02] solving all these transformative problems. I I think Sam would say like curing cancer or curing all disease that are worth many trillions of dollars. And I think that the key question of our time or at least of the near future is what’s the what exact power law do these follow? Uh, is is it a fat tail with lots of consumers using Stripe powered instant checkout to to power a very fat tail? Or is it very thin tail where almost all of the the revenues that are flowing to the frontier labs to justify the soon trillions of dollars of capex to build data centers are all being driven by transformative inventions and discoveries and uh the instant checkout, if you will, ends up being rounding error. I I don’t know the answer, but I think this is the defining constant. >> I and I think they’re reaching for near-term revenues that are easy to get right now, but in the long term, it’s going to be the invention of new materials, new biotica, all kinds of things. I mean, it’s interesting the the number here is by the end of 2025, it’s
[00:39:02] projected $142 billion in consumer purchases via chat bots. And I think the one thing that we all have in common is a constraint on time. So if I’m in the middle of researching a product and I’m in the midst of doing comparative analysis on open AI and it pops up and says we have to buy it. Um I mean it’s >> may may be but maybe in the near-term future the scarcity I I think you would say of attention also gets alleviated and we find ourselves in a postcarce attention world. >> Interesting. Uh in which case what we shop around more we have more hours in the day. >> Yeah. But we have so much more to do with those hours that like when you think about the software of through voice that we were just doing and also the suno through voice it’s so compelling and so fun. You’ll eat up every one of those hours and more. >> Yeah. >> Um so I guess those those are harmonious statements. There’s no but I’ll tell you
[00:40:01] one thing. When when Alex says I don’t know what’s going to happen. You know you’re going into crazy times. time timelines are really short and timelines I think are like 2 to 3 years at this point max >> I I thought I thought this was profound because this could be a big threat to Amazon because if I can chat and then basically go straight to the source of where something’s being made. Uh I’ve been using Chachi PT and Gemini to do um comparison shopping for the last few months and I don’t buy anything else without saying hey show me good alternatives of this or this or this and it’s remarkably good at crawling the web and finding all the stuff that I would take take me ages to figure out and now I can do direct commerce with this. That’s huge. >> Yeah. Otherwise you copy paste into Amazon and buy it there probably right. Uh, amazing. And and travel, I mean, and I’m, you know, it’s interesting using a large language model for travel, saying, “I’ve got to be at this location by this time. Uh, which airlines have the highest uptime reliability and get me
[00:41:02] there and what’s the travel time and set up the schedule for me?” And instantly it’s there. And then it should say, “Do you want me to buy the tickets and set up the Uber for you?” And, you know, anyway, I think it’s pretty >> I I would just remind also this is still nibbling at the edges of consumer spending. AI is going to eat the whole economy. So that that starts to look like AI eating real estate expenses, AI eating healthcare, AI eating utilities and food right now buying consumer packaged goods. This is just not to diminish the CPG sector, but this is just nibbling at the edges right now of of disruption. Well, I’ll tell you, Peter, since Jeff Bezos is your is your friend, uh, you know, Lee Bosio, who used to run, he was a single threaded leader for Alexa over at at when he was at Amazon, he used to work for us and Jeff Bezos saw this coming a mile away. uh and that is why he built out this massive investment in fulfillment and
[00:42:00] and you know eBay didn’t >> uh because the interface is going to change for sure and he can rely on the fulfillment side of it to route all that volume through Amazon but he knew this was coming when he invested in Alexa >> and we still haven’t seen Alexa play out fully right Alexa is still very antiquated we haven’t seen Amazon’s AI play yet >> very much it doesn’t hold state no memory there’s There’s a lot there’s a lot to build there. >> Well, you know what they’re doing? So, I I had a call with the chairman of ibanking at uh the largest bank in England. Uh and they’re huge anthropic and AWS fans. >> And I said, why? I said, well, because we want the AI to have client data, account data, payroll data, you know, all this hypersensitive data. And Anthropic is the only company that can support it securely. And we run it all inside AWS’s infra infrastructure. So what they did with warehouses and fulfillment on retail, they’re also doing with digital compute and data center fulfillment in AI. So they’re the
[00:43:01] same playbook just moved over to the AI era. >> It’s interesting and Anthropic tends to be the you know the friendly little brother to Google and others as well. Uh they’re wellliked, well respected. Um we’ll see how how they team up. All right. All right. >> 10 times less lying. And we saw that on the other side >> and power seeking. Okay. I trust my anthropic AI. All right. Here we go. GDP val measures performance of our models on real world tasks. Uh so released uh tests for real world tasks across 44 jobs in nine industries. Uh with GPT5 and Claude Opus uh nearing expert quality 100 times faster and cheaper. Alex, do you want to lead the conversation? Sure. Well, as you know, Peter, I’ve beaten the drum in the past here on the importance of new evals, new benchmarks. This is a very important benchmark. OpenAI has alluded to this
[00:44:00] benchmark in the past, but actually looking at the benchmark which is available open source for folks who want to look at the prompts, this feels like a benchmark for knowledge work. It’s it’s pretty diverse and to the extent that you you look at this chart and other charts that have been made available showing progress on GDP val which covers a number of different industries lots of tasks it appears very thoughtfully put together. If you just extrapolate by the law of straight lines, you extrapolate progress on the ability to perform all these real world tasks, you you find you you predict that in the next 6 to 12 months, we’re talking about substantially all knowledge work across a number of industries being superhuman uh as performed by AI. That’s for some would say I think that’s a very short timeline. We’re talking about EVEL’s literally solving the economy or at least a good chunk of the the knowledge work economy. >> Yeah, we’re it’s here now. I mean, do
[00:45:00] not look for some decade future. This is the next year or two. You know, one of the one of the quotes here, the models completed tasks up to 100 times faster and cheaper than human experts, highlighting both their potential and the need for oversight. See, you were going to say >> two two points. One is I remember, you know, there was such a big shift in car making when you had a robot opening and closing a car door 10,000 times to test the hinges. Quality just went through the roof after that. And now we can have AI doing the same thing for this type of stuff. And what I thought was really powerful about this was this isn’t some kind of toy problem benchmark. This is real world stuff. And now we have um the ability to gauge AI doing real world stuff. And now this becomes very tangible. >> Fantastic. All right, let’s go to yet another conversation here. Uh, this is a video I’m gonna play with Brendan Foody, the CEO of Merkor, uh, who Dave knows
[00:46:02] extremely well. And this is Merkor’s AI productivity index. All right, let’s take a listen. >> Decided to test how well today’s leading AI models can actually do your job. And the results are astounding. Introducing the AI productivity index or Apex, an evaluation that measures how well we’ve automated the most valuable industries in the world. We studied model capabilities in law, medicine, consulting, and finance in partnership with industry experts in each domain. Apex is designed to give an accurate forecast of how AI is going to impact jobs. But this version just scratches the surface of measuring model capabilities. All right, Brendan uh catapulting yourself to the top of the class. How old is Brendan? >> Uh 23, I think now. Yeah. Founded at 19. He’s ahead of Mark Zuckerberg uh in
[00:47:00] terms of company valuation age, race to a billionaire age. So, I don’t know if anyone since Mark has been on that curve. And I tell you, as long as we’re talking about Brendan, uh, a whole bunch of inbound calls, people wanting to buy our Merkor stock from us, like, you know, it’s a$10 billion valuation, right? And like, yeah, but if you look historically at people who’ve reached where Brendan is at that age, every one of them or almost all of them become whatever, you know, Elon Musk, Mark Zuckerberg, Bill Gates, whatever. Uh, so he’s on a trajectory like nobody else. And everybody loves him. you look at him on on screen there. He just he’s he’s the guy everybody’s cheering for. So, it’s pretty cool to see. I think these last two slides, you know, back on the topic of the slides, really really important because uh you know, AI is so general purpose and so capable in so many areas and you know, Alex and I have had all kinds of torture trying to interact with the state house here with other government officials to get them to realize the urgency and the
[00:48:01] implications. It’s so hard. But then when you throw a really good benchmark at it, it makes it much much easier to explain why this is so urgent. So Brendan is taking on all things related to work productivity across all areas. And that that’s a really big ambition, very very worthy ambition for him. >> Alex, this is how economics gets solved. If we want to live in an abundant future where the cost of service labor is driven to zero, step zero is creating benchmarks. So, Apex uh and GDP val I think are beautiful examples. It’s still early days obviously but beautiful examples of benchmarks for call it knowledge work or knowledge work-based services in the economy. I would like to see many many more benchmarks get created including for robotic labor manual labor >> ju just within our portfolios you know we have 28 seedstage companies doing AI just here in the building and if I take any one of them like you know mcato
[00:49:01] doing mechanical design what’s the benchmark for the quality of the design uh prim and vocara doing voice sales and customer service you AI voices what what’s the conversion rate and the customer satisfaction rate on an incrementally smarter AI how do you benchmark that every one of these companies should be inventing a benchmark. Blitzy already is doing it for coding. But you know, whatever you’re doing, if you don’t create the benchmark, then it just turns to to mud. You know, there’s no way for any because it’s like how do you know if it’s a smarter AI? I don’t know. And the challenge the challenge is we saturate them all and we’re comparing them all to human productivity. But we need to have a whole brand new set of of benchmarks that are >> I don’t know are anchored in in what, Alex? >> Well, the good news is we we already know how to benchmark superhuman performance. There are relative ELO based benchmarks that we know how to do. We know how to as a as a civilization, we know how to build systems that are more energetic than humans are, uh that
[00:50:01] are faster than humans are, and we’re still able to measure them even though they’re they’re superhuman along some dimensions. So, we have no trouble measuring superhuman intelligence capabilities. >> Thousand of horsepower. All right. Exactly. Uh Microsoft’s not being let out of the game. So, Microsoft unveils agent mode. uh and think of this as uh the ability for you to have access to it in all of your favorite Microsoft tools. Uh Sel, do you want to jump in or Dave? >> I I tr I’ve been trying to get Microsoft Copilot to work in any kind of AI useful way and have failed miserably for the last few months. I hope this one is a better effort. I I I’m not going to make an enemy out of Microsoft, as powerful as they are, but but I will say that adding AI as a feature to something that already exists, that’s the wrong that’s the wrong attitude. >> Uh I feel like Apple and Microsoft are are the worst offenders of this. It’s it’s not going to work. >> So that’s a great point, right? They’re
[00:51:00] trying to move, you know, maintain their customer base and and scratch their AI itch versus uh AI native, you know, clean sheet startups. Well, and every corporate CEO should should understand the same thing applies. I see so many people that are saying, “Yeah, we’re doing AI. Uh, I added it as a feature in one department and so now I don’t have to think about it anymore. Let me go back and, you know, get back to my country club.” And you’re going to get crushed with that kind of perspective. It’s not a feature. It’s a brand new everything. It’s a complete different field opportunity. There’s a bunch of stuff I was trying to do in Excel and I literally I tried to use an AI mechanism to do it. I just couldn’t do it. Finally, I ended up using Comet uh to do it in the browser and it did it way better and way faster. So, I think this is a this is a huge gap. Uh I don’t know where they’re going to go with this. >> All right. Well, we’ve covered OpenAI and Enthropic. Let’s not leave XAI out
[00:52:00] of the picture here. Uh Elon has uh cut a deal with the government. XI struck a deal with the US uh GSA to let federal agencies use Grock for 42 cents for 18 months. It was either 69 cents or 42 cents. I guess he went with the cheaper option 42 cents. Uh I’ll leave that alone. Uh any particular comments on uh on Grock entering DC? the the price point is 42, which is 420, which is the, you know, the magic number, which is the the $20 million SEC fine that he had. Remember that? It’s all of course it’s all it’s always tongue and cheek with Elon. And I love that, you know, even at that scale, just making it fun and interesting. Kind of like Taylor Swift, there’s always a hidden message. >> And people love that stuff. And, you know, it’s it’s good. It it keeps people engaged. Um but uh what’s going on between corporate America and government America, government America is completely unprecedented. Uh a little
[00:53:01] scary. Um it’s working really really well and it’s it’s helping the country a lot. Uh but uh it’s very odd to be cutting, you know, investing in Intel and then cutting deals to move things in. It’s for the government to be directly involved in corporate America like this has never happened before. Well, it’s it’s looking a little bit like China, right? Where China is picking winners and and forcing partnerships and creating, you know, robot cities, gene engineering cities, AI cities and such. It’s fascinating. But >> but isn’t the government signing deals with every like uh Chachu PT, etc., etc. We saw earlier. So, it sounds like what they’re doing is trying them all and seeing which one is the best over time. >> Well, that would be fine. I mean, that’s like government procurement, but that’s not what’s going on at all. You go into the White House and you’re either genulecting and being the anointed one or you’re not. And it’s it’s not these are not arms length procurement through the Air Force or something like that. These are White House
[00:54:00] >> edict come in and talk. Yes. Yes. >> Yeah. Uh and and we’ll we’ll get to we’ll talk about intel in the section called this is not investment advice which is coming up. All right. Meanwhile, in other AI news, uh here we go. former meta researcher is building a math whiz. I’m going to bring this to you, Alex. Teach us. >> I haven’t seen any indication thus far that math is not going to be solved in the next few months. How’s that for a double negative? >> Few a few months. Okay. >> So again, I >> So So wait, wait, wait, hold on. So, Alex, you’ve said that before and everybody’s asking me, please have Alex explain what it means to solve all math. So, could you could you just >> before we do that, let’s let’s just let’s just uh speak out this particular article. So, this is a this is a woman >> u it’s great to see female CEOs in the AI world or not enough of them. Karina
[00:55:01] Hung, uh she’s the founder of Axiom Math. uh she’s 24 years old and she wants to build the ultimate AI mathematician. Uh she’s raised 64 million at a $300 million valuation. And again, we’re seeing this over and over again. We’re seeing you know starting valuations in the hundreds of millions of dollars. Uh I don’t know if it’s at a you know a preede round or whatever, but intelligent individuals who have got a monomomaniacal focus are getting incred incredible capital backing. Okay, now back to back to you, Alex. What does solve math really mean? >> There are, I think, a few different ways one could operationalize what it means to solve math. One way would be to look at a benchmark like the Frontier Math Tier 4 benchmark, which measures the ability of AI to solve extremely difficult but nonetheless pre-solved problems that would take human researchers several weeks to accomplish. If you just do a a naive logistic
[00:56:02] extrapolation of progress in frontier math tier 4, you find that by the law, again, straight lines as it were, that by the end of this year, by the end of 2025, we’re starting to pass 10 15% of problems in the benchmark that AI can solve. And at that point, I would argue we’re in a situation, we’re in a regime where algorithmically we have clear line of sight to solving any math problem that we might have today. Just pour more compute on. So that that that would also I think point to the second oper operationalization I would have in mind when I speak of solving math. I don’t mean literally every math problem that we can think of today has been solved. What I mean is that the process of mathematics has been solved to the extent that we have a clear line of sight where if you pour millions, billions, maybe trillions of dollars
[00:57:00] into opex in data centers, no new algorithmic advances are needed, we can reasonably forecast that any mathematical problem that’s solvable will be solved with the same algorithms just with a lot more computing. >> Okay. Now take me to the implications of that for the general public. >> It’s tricky. It It’s tricky. Probably I I I would This is in in the the territory of speculation. Um, but I I think one of the more obvious downstream consequences of solving math is that any problem that depends on the difficulty of math or let’s say math being difficult that isn’t protected in a a formal sense by the so-called complexity hierarchy. Mathematicians and computer scientists have this notion of certain problems being provably harder in in some sense than others. Maybe you’ve heard of P versus NP. But I if there’s no formal protection for certain classes of problems being provably harder than
[00:58:01] other classes, I think certain types of tasks that we encounter in the everyday economy, for example, maybe hypothetically certain hash functions that cryptocurrencies depend on or or other everyday economic functions depend on are at risk of volatility. If if suddenly, for example, again, speculatively, not investment advice, if there were a super AI mathematician tomorrow that could say invert the AES cipher suite or invert the hash functions underneath AES, that could be potentially extremely disruptive to to the economy, cause a lot of volatility. I think the point you’re making is if AI cracks advanced math, it just isn’t it’s not just solving equations. It’s creating the scaffolding to solve all these other areas like cryptography, economics, physics, etc. That’s what you’re really saying. >> Yeah. I I mean I I to that point I I would say the way I would frame it
[00:59:02] perhaps is first order consequences, problems that depend on math being hard experience some volatility. second order consequences. I think it’s the ultimate canary for any any domain that requires the ability to do mathematical reasoning. So I would expect in short order a variety of math oriented science and engineering and medicine and and other domains are going to fall in rapid succession. If if this theory of the future ends up being correct, I was alluding a few minutes ago to timelines being short, we may find ourselves in a world 2 to three years from now where we’re just drowning under math, science, engineering being solved in rapid succession. >> Dr. drowning under serial and you know sort of uh uh Cambrian explosion of breakthroughs. >> Exactly. That will also parenthetically be potentially quite difficult for society to metabolize. >> Yeah. the the economic impacts of that are going to be unbelievable. >> This episode is brought to you by Blitzy, autonomous software development
[01:00:01] with infinite code context. Blitzy uses thousands of specialized AI agents that think for hours to understand enterprisecale 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-ompiles 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 preIDE development tool, pairing it with their coding co-pilot of choice to bring an AI native SDLC into their org. Ready to 5x your engineering velocity? Visit blitzy.com to schedule a demo and start building with Blitzy today.
[01:01:01] >> All right. uh speaking about economics. So AI can now pass the hardest level of a CFA exam in minutes. So let’s take a a quick look at this. So CFA is a chartered financial analyst. Uh and it deals with investment management, portfolio management, financial analysis, and ethics in finance, which I find absolutely fascinating. And I looked it up, the CFA level three part of the exam. It’s about portfolio management and wealth planning. So >> I I want to make a comment on this one. Yeah. >> So we’re advising one of the big four accounting firms on how to think about transformation and this one we’ve been predicting this with them to be happening because this requires real world reasoning and the fact that it is doing this is a huge implication. All their finance jobs essentially get rewritten now and recreated. That’s it’s a it’s a it’s a body blow to the the
[01:02:01] accounting world. >> Well, what I find interesting is, you know, leveling the playing field across all investments. You know, do I with access to the specific AI have access to the best investment advice that, you know, Warren Buffett has access to as well? Is this a leveling the playing field across all economics? I think it is. But I I I you know what I’m excited about is, >> you know, we America lost and then Europe too lost almost all of its manufacturing. You know, despite inventing the car, inventing the plane, inventing the microchip, inventing the computer, all the manufacturing of that stuff moved to other countries. >> Yeah. We we gave we gave it up. >> We gave it up. And you’re like, well, but our economy kept growing. What are we all doing? Well, we’re a service economy. We’re doing services. What the hell does that mean? Well, you look under the covers and a huge fraction of very smart people are working in this totally circular nonsensical world where we created a complex law, complex taxes,
[01:03:00] complex accounting and then this other huge group of people need to solve the complex accounting and it produces absolutely nothing useful for humanity in this huge >> IRS code. IRS code I mean I mean for God’s sakes >> holy crap. Yeah. Ronald Reagan was the last guy to say this is insane. We got to get this down by 10x. And ever ever since, you know, everyone bloats it up. You the accounting lobby is the biggest lobby in the country and it is bigger. We finally >> Yeah. Lawyers and accountants. We finally have an opportunity here >> to get rid of it once and for all. Not by eliminating it, but by having the AI automate both sides. >> Yeah. >> And then it just becomes something we don’t have to do anymore. And all that talent can create things that actually benefit humanity. I’m so excited for that. I I would also >> the the relief is so palpable in your voice there. It’s it’s incredible. >> Peter to your question, I would also encourage the thought experiment if if everyone has the best investment advice thanks to super intelligent investment
[01:04:01] advisors. What does the economy look like and what is the rational act? What’s the rational course of action for an investor if everyone has equally super investment advice? Uh, >> it goes to your point, Alex, of buying the index. >> Yeah, >> damn it. He’s right again. >> Fight on that one, but we’ll get to it. >> Uh, I I, you know, I thought I’d bring quantum into the conversation. I know Dave, you and Alex have been working on this. uh a couple years back I started a spa with Sherp and Pishavar and we took D-Wave public uh which is now seeing incredible resurgence. It’s gone from like 69 cents a share up to 30 bucks a share uh and done extremely well. We’ve seen Regetti Computing, Chad Regetti’s been a friend for some time uh D-Wave. So, all of these independent, you know, quantum uh computing companies are are
[01:05:01] getting some real traction. Uh here’s a quote though from uh Julian Kelly that Google Quantum’s AI director. The technology is 5 years out from a real breakthrough. Uh Alex, you’ve been tracking this. What are your thoughts on quantum computing? >> I think it’s early. I I’m reminded that the GPU or call it the accelerated compute market via the avatar of Nvidia had to pivot several times before it took over the economy. It started with PC gaming and then pivoted for a bit to crypto and and now AI and maybe there’s a postAI act. But I I think what is missing right now, at least to my knowledge, is the killer app for quantum accelerated compute. There’s a school of thought out there that maybe we’ll use quantum at inference time to to generate large synthetic data sets of quantum chemistry data. Say that will be used as training data for classical AI. It’s difficult for me to buy that that’s going to be an enormous market. My best
[01:06:00] guess is that to the extent that there will be a killer app for for quantum compute, it’s probably something like AI accelerated generalist training for AI or inference for AI. And at least again, to my knowledge, no one has yet published the the killer app for uh quantum ML. There are lots of proposals out there. Nothing has seemed to to scale yet. You know, this year at the Abundance Summit, I’m going to have Jack Hery back on stage uh speaking about Sandbox AQ. Uh it’s interesting. This is the spinout out of Google X. Eric Schmidt is the chairman of the company and they, you know, they booted up at a $500 million valuation and have had, I think, in excess of hund00 million of revenue. And they’re not a quantum computer-based company. They’re an AI company using the quantum equations uh to provide different products and services. So they’re uh they’re
[01:07:00] basically looking at new uh you know navigation systems that’s able to measure slight pertabbations in the earth’s magnetic fields. When GPS is down, you can still navigate because magnetic fields are not being spoofed like GPS is being spoofed in the Middle East. uh you’re using it for different uh biomedical uh looking at uh your heart uh your heart’s electromagnetic system if you would uh they’re using it for encryption methodologies but it’s a real revenue engine there. You know, one of the things that we should speak to for a moment because we do have a lot of crypto uh listeners as well is everybody’s like, “Oh my god, when is quantum going to break, you know, the the encryption codes that’s going to destroy Bitcoin?” And it’s just important for everybody to know that if in fact we have quantum computation breaking encryption, your keys to your Bitcoin wallet are the last thing to worry about because the same encryption codes being broken are the nuclear codes, the banking system, and
[01:08:00] everything that runs the financial systems around the world. I I would actually t take the position that postquantum crypto is is nearing a state maybe not evenly distributed yet, but at least in theory of of approaching quasi maturity. I if if I lost sleep at night worrying about inversion attacks against widely used crypto systems, it’s not quantum information processing I’d be worried about. It’s it’s AI solving math. I I think that’s that that’s a a far more insidious threat to the to crypto security in in general than quantum. We we know how to do postquantum crypto. But but the same thing then, right? AI solving math uh if it’s breaking encryption, it’s breaking encryption across a multitude of other much more concerning uh financial and uh and defense areas. >> Yes. Well, as a as a practical matter, this is imminent either way. Uh it’s not going to affect nuclear codes or
[01:09:00] Bitcoin. What it will affect though is anything that you’ve encrypted and left around. You know, using AES 256 or 128 that’s already vulnerable within a year, if not if not today. So, it’s all the designs, files, and stuff that you thought you encrypted and you left on a server or left in your desk. All that is going to be wide open. So just so you’re aware >> I think Dave that’s a really important point. It’s the stuff in the past. It’s not really the stuff that’s current or in the future because we’ll become up with quantum encryption capabilities etc. There’s one thing about this uh story that popped out at me that I would just want to flag which is that uh in 2008 we heard a quantum computing expert saying we’re 5 years out from having a real breakout. So this has been a constant pattern for a while. I think with the AI changes this may actually really be the case that we’re 5 years out. It may be much less than that given what the the potential way out to solve a lot of these problems, but just the lean. >> Well, no, I’ve heard this. Yeah, it’s
[01:10:00] great advice. And as a general pattern, when somebody tells you, hey, blah, blah, blah, is going to happen. Invest in it. It’s 5 years out. Nine times out of 10, it’s 20 or 30 years out. >> Fusion has been 5 years out since the 50s. >> Well, it’s been it’s been 50 years out since the 50s. Let’s be real, >> not five years out. >> Well, and the opposite is true, too. when somebody tells you something’s imminent, like this is happening right now, guys, don’t ignore it. It’s it’s very likely that that’s also like you’re almost late to the party. So, I think that’s great advice. >> All right, let’s move on to chips and data centers. A lot happening here. I’ll start with a uh an open letter that Sam Alman put out on abundant intelligence. I’ll just quote from it. says, “With 10 gawatt of compute, AI can cure cancer or provide customized tutoring to every student on Earth. If we’re limited by compute, we’ll have to choose which one to prioritize. No one wants to make the choice. We want to create a factory that produces a gigawatt of new AI infrastructure every week.” So uh this
[01:11:02] is basically Sam saying give us all the compute and capital so we don’t have to choose between education and solving cancer or longevity. Uh it’s an important point uh I don’t know any thoughts on this one Dave. >> Oh yeah lots. I mean, it’s amazing how it’s becoming increasingly clear that Sam is very small compared to to Zuck and uh and Google, I guess. Sundar. >> Small in what way? >> Uh well, I mean, he, you know, he signed a hundred billion and a 300 billion deal. Uh and that made big news, but he doesn’t have anywhere near 100 billion or 300 billion. He has he’s like onetenth that at most. Uh meanwhile, Mark Zuckerberg said, “Yeah, we’re going to put $600 billion into this over the next few years.” But he has it. He had he has the cash and the credit to actually and he will really do it. So, uh you know Sam is up against some serious heavy hitters >> and and Google and Google’s got massive engines. I mean they’ve got so much capital in the bank that they can expend
[01:12:00] here and Elon just you know Elon moves his pinky and capital flows into XAI at whatever he needs. >> Um >> but what I love about that dynamic is that Sam is the one guy driving the vision and driving the agenda. everybody else can afford to just kind of be an afterthink or a soft cell or a and without Sam out there opening everyone’s eyes nobody else like Google would have never even rolled it out I don’t think without without Sam putting the pressure on so here I think he’s exactly right like uh is it on this slide or is it coming up he’s I think it’s coming up yeah I’ll wait for it >> here we go >> so yeah this this article here is openai Oracle soft bank expands stargate with five new AI data centers >> uh so aiming to hit $500 billion or 10 gawatt goal before the end of 2025 and being ahead of schedule. Uh tiling the earth, Dave, continue >> telling the earth. Well, yeah. So Sam is saying, look, we’re going to try and organize around 10 incremental gigawatts per year in perpetuity or or
[01:13:02] accelerating uh and and that will just barely keep up with the use cases and the demand. And so that’s really really cool to hear someone articulate because then you know the the land the governors the you know the the plumbing the yeah all of that stuff can start to get rallied around a long-term view of what it means to to stay ahead in this race. And so I think it’s great to articulate it because you know the numbers are so big no one else will say it. Santa’s the one guy that’ll actually say it. But it’s >> let’s put the let’s put the numbers out there here. So, Stargate’s $500 billion investment dwarfs all the other hyperscalers uh in 2024. Microsoft put in 40 billion uh into uh into AI data centers in 24 planned 80 billion for this year. Amazon with 16 billion, Google Alphabet 29 billion and Meta 23 billion. Uh I I think they’re all going to be massively accelerating, but just to give some numbers for folks to
[01:14:00] compare this to. >> I I do think for what it’s worth, we are tiling already the earth quite literally. But there there’s also a certain sense in which I expect to if you remember President Reagan’s nuclear policy of building up to build down. I I can imagine uh high likelihood scenarios where efficiency advances maybe onlogical shocks perhaps onlogical shocks that result from these data centers make the the naive assumption that we’re going to scale in extremist to Dyson swarms which is of course you know you just project out we’re after we’re done tiling the earth tile the solar system um make that look a little bit silly but I I think in in the short term all systems go at least for the next 5 to 10 years. >> I mean I have to imagine that one of the first areas where AI is going to cause a massive disruption is energy efficiency on and compute efficiencies on these centers.
[01:15:00] >> Yes. And and this is a regime right now where um we were talking about quantum few minutes ago. Maybe there’s photonics as an intermediate substrate before if if at all we migrate to fully quantum systems. There are there’s, as Fineman said, there’s so much room at the bottom. There are so many new low-level in the infrastructable. It there are scenarios where we don’t need to to fully tile the Earth and the data centers solve a whole bunch of low-level physical problems for us, enabling us to to keep this relatively contained. >> And if you remember, if you go back to our Sorry, you’re going to say the >> I was going to say the same thing. You remember Brockman said we want a a GPU per human. >> Yeah. >> And then as soon as you have it, you’ll want more. >> Yeah. >> Uh if you go to our podcast from a couple months ago, uh we had a whole section on the the software breakthroughs, you know, to Alex’s comment of the the opportunity at the
[01:16:00] bottom. Uh minimum 10x, more like 10,000x is the best guess, but somewhere between 10 and 10,000x just software improvement that’s coming. But we’ll use all of it and want more. There’s no doubt in my mind. Uh and then you know there’s hardware on top of that as well. But we did a whole analysis of the the different dimensions and now they’re multiplicative. We should revisit that because we have a lot more a lot more color now. >> Yeah. But I think to Sam Alman’s earlier point about choosing between health and education. Uh there will be a fundamental breakthrough. There just needs to be because we can’t expect that the systems that we had, you know, from a couple years ago are going to perpetuate going forward. So I I think we’ll have the compute to do all these things. Uh this is fascinating. Here’s uh this is a article saying Nvidia discussing new business model chip leasing. OpenAI struck a hundred billion dollar deal to lease not buy Nvidia’s AI chips spread over 5 years. I can just imagine the conversation between Jensen and Sam. Hey, listen Jensen. I I want
[01:17:02] those chips. I just don’t have hundred billion. Well, Sam, what if I just leased them to you over 5 years? Are you good for the payments over 5 years? Cuz I think, you know, our investors love to have like, you know, guaranteed revenues over 5 years. And who takes a depreciation risk, right? Uh Dave, what are your thoughts here? Uh actually a bunch of our MIT best buddies including Kush Bavaria here are starting new companies around this entire area of creating new securities that allow you to finance all this stuff. Um the hyperscalers are just going ballistic. I mean this is so much bigger than all other forms of real estate investment in combined is the aggregation of data centers and chips. Uh so the leasing was inevitable because again you know Sam doesn’t have cash on the barrel head. Meanwhile, Jensen, uh, you know, he’s got the lead right now and he has a $4.5 trillion market cap. One way to lock that in is to use the
[01:18:02] leverage. And this is why Larry Ellison’s the richest guy in the world or was a week or two ago because he used his balance sheet and his his borrowing ability at 4% to finance a lot of bottlenecks that, you know, the the startups can’t afford it and and Sam obviously can’t afford it. Uh, who’s going to fund it? Well, you know, just cuz you’re leasing it, somebody saw us to buy the chip up front. So, Nvidia is saying, “Okay, well, we’ll fund the purchase of our own chips using our massive balance sheet and our massive market cap.” >> This felt this I agree with you. This felt inevitable to me. It was going to happen at some point. >> I I think it’s it’s easy for skeptics to paint this as smacking of financial engineering and some sort of GPU credit bubble. I I think that that I I I think the the GPU credit bubble though the story in addition as folks here already mentioned depreciation. The the other I think story line that’s being missed is right now Nvidia is in a very high
[01:19:00] margin GPU hardware business and there’s an impedance mismatch between selling high margin GPUs and low to negative margin neocloud and cloud businesses. And so leasing is the market I I I perceive the market contorting itself to accommodate that mismatch between high margin GPU hardware low to negative neocloud. >> Well uh Rob Fischer who used to run link studio here went off to build data centers >> and he said he’s signing deals uh two three four a week now. Uh so I think what’s happened is you know the the visionaries started building data centers ahead of the curve knowing the demand would come and everybody’s a little nervous about that. Well the demand at least as far as Rob is concerned the demand is here now like and and you you can see it in all the use cases we demoed earlier in the pod. You know those things didn’t exist six months ago. Now anyone seeing those is going to want to do it immediately whether it’s in a corporate or it’s personal or you know just a theme song for the podcast.
[01:20:00] >> Everyone’s like wow that’s really usable. Where do I get it? Well, it has to run on a data center somewhere. It’s not magic. And so, I think the demand is starting to catch up to the construction and and the demand will get way ahead of the construction. >> I don’t I don’t think we’ve seen anything yet in terms of demand. I mean, everybody is still just barely tickling, you know, chat GPT and not really plugging in. I mean, once we are spinning up agents and we’re uh we’re building new capabilities and transforming our lives, I mean, we’re going to see a thousandx per individual. >> All right. Uh, I call this segment not investment advice. Okay, let’s jump in. >> Create titles, then you don’t even have to say it. >> So, I’m going to continue on our Intel saga. Um, you know, Dave, congratulations on your uh on your options. I finally I finally bought in uh probably, you know, a generation of of Intel options later than you did. But here’s a chart. This is a quote from Chimathi. says, “President Trump got
[01:21:00] Intel to give Team America 10% of itself at $0. He has a better IRR than Buffett.” Well, of course, if you get something for for $0, you have an infinite IRRa. Uh, but here we go. Uh, we see President Trump makes 80% on Intel purchase in 6 weeks. >> Not Not bad. I mean, this was predictable, right? Intel, the US cannot afford to let Intel fail. Yeah, we remember we did a podcast that was exactly concurrent with Lipu being at the White House. >> Yep. >> And so that was August 11th. Uh I think it came out the next day. Uh and we said, “Okay, Lipu will come out of the White House. It’ll either be black smoke or white smoke depending on how that meeting goes. But what you’re looking for is either Lipu to quietly disappear in a good way. Not >> quietly disappear or Donald Trump to reverse course.” Remember he tweeted Lipu must go. He’s completely conflicted. He’s invested in China.
[01:22:00] >> Yeah. >> Or, you know, so Donald will either reverse. It depends on whether Lipu says, “Look, I’m as an American as apple pie and I will build the best fabs in the world right here on our soil.” Or he says something else. Well, so it came out, you know, white smoke and that means Donald is going to make this succeed one way or another. And then, you know, so the rest is kind of the the slides imply that Intel is way up this year, but it was August 11th. You know, that that was the date that it was at its low for the year or near low for the year. So, this has only been Yeah. Well, six weeks like it says, >> it’s it’s crazy. This is sovereign venture capital, right? This is the government uh basically driving investor confidence and and triggering momentum. Uh, and you know, I I’m a libertarian capitalist. I don’t know how to think about this, but I do believe that Intel is a critical asset for America and it needs to be
[01:23:01] partnered up, supported, and along those lines, we’ve got this other piece of news that Intel stock extends its gain, hoping for AMD to go from rival to partner. Uh and the two big deals that are out there for Intel are a partnership with AMD uh and Apple and Nvidia. So uh you know this is again going to Chimath’s terms not mine. Team America here. >> I I think Peter there’s a certain sense in which this was almost predetermined. Uh by by this I mean call it the quasi nationalization of of Intel. I remember conversations I had with Intel engineers 20 plus years ago and they knew as has continued to be the case. Everyone knows Moore’s first law that number of transistors or transistor density doubles every 18 or 24 months depending on which version of the law you like. Not as many folks perhaps pay attention to Moore’s second law which is the cost
[01:24:00] of a fab doubles approximately every four years. So 20 plus years ago, you could imagine just extrapolating Moore’s second law out and realizing at some point new fabs become so expensive that really only sovereign nation states would be in a position to finance it. And this was reasonably well known within the the semi community 20 plus years ago that at some point as Moore’s first law is starting to end and Moore’s second law is starting to become so expensive that only sovereign interests can afford to finance this something like this in some sense I think was bound to happen eventually. >> Bound to happen. Yeah. Exactly right. And I’ll tell you a lot of people don’t talk about this but you know a few years ago uh we outsourced all of our PC board. you know, the the green boards inside of your laptop. Outsourced all of that to China uh for cheap manufacturing for years, for for decades. And lo and behold, there were little spy chips that were, you know, about the size, very small, like a rice grain sized thing
[01:25:00] stuck between the layers >> of the PC boards and that made it into all the US data centers. And so that was grabbing all the passwords and transmitting them back to China. >> Wow. And so the the US government discovered this. It had been in going on for years. And then rather than make a big international incident out of it, they said, “Holy crap, this is this is going to be devastating. We’re going to lose confidence in all financial instruments and everything. We’re going to squaltch this story.” And it kind of disappeared from the news. Uh and they’ve been quietly for a long time trying to clean it all up. Uh, and so now the idea that you would trust your highestend chip manufacturing to be done offshore and repeat that same mistake, non-starter. There there’s just no way that that that’s the right choice because these chips go right into all of our weapons. They go right into the tanks, right into the planes. I mean, the these are like if there’s spyware embedded in the microode, it’s the
[01:26:01] biggest disaster you could possibly imagine. So there’s no way that they were >> I start thinking Dave of what else falls into the we cannot let it fail category and my mind turns to energy. I I think that we’re we’ll talk about that in the next segment here. But the US government needing to prop up uh form consolidation, reduce regulatory and really accelerate our energy economy. So, but I’ll be keeping an eye out for this uh Ashen Brener-like moment of finding a company that is uh I don’t say too big to fail. I would say too too centrally critical to fail. >> You know, >> too scarce to fail. >> Too yes, don’t talk about scarcity. All right, let’s move on here. Uh speaking about scarcity, uh so Jensen goes on record with I think something very important. electrician and plumbers needed in the new working world. So last
[01:27:00] podcast we talked about how universities are failing. The perceived value of a college degree has fallen through the floor. At the same time, the category of workers who are out of jobs the longest are the new college graduates. Uh it’s an insane. So how does higher education continue to uh to charge what they charge uh in this scenario? So here are the numbers. It’s estimated that hundreds of thousands of electricians, plumbers, and carpenters are needed. Uh the US is short 500,000 construction workers in 2025. And rather than coming out of school, you know,$100 $200,000 in debt. Uh why don’t you come out with a job that’s paying $100 to $200,000 and where you need it instantly? >> Yeah. And it’s not just construction, it’s construction automation, too. This is why I can’t wait to go to Abalene to meet with Chase Lockach Miller because you’re like, wait, why would an MIT aereroastro guy be the right guy to be running Stargate in Abolene? Well,
[01:28:00] because he looks at every one of these jobs and he thinks, how can I build a robot for that? How can I automate that? How can I restructure it so it’s modular? And and so I think that’s going to be the other side of this. It’s not just jobs in raw wiring and plumbing. It’s jobs in management and construction automation. So some very very high-end jobs. massive opportunity for employment and and I really wish some more states would recognize that if you want your population in your state to be well off, you got to get the data centers up and running in your state. >> Yeah. So, here’s another stat. Gen Z is choosing trades over college. 16% rise in trade programs since 2023. Uh and construction is the fastest growing industry for new college grads in 2025. Find that absolutely fascinating. >> All right. Uh I added these slides. Uh I’m calling it an exponential reality check. So a couple of days ago, uh one
[01:29:00] of my boys wants to build a computer. So we’re going to build a gaming computer. And we’re going through and researching, you know, the GPUs, the CPUs, the memory, and so forth. And we’re going on ordering them. Turns out, you know, you can order everything you need, every component uh on Amazon. So, I’m on Amazon and I’m buying uh you know, this uh DDR5 RAM kit, 32 GB of RAM for 101 bucks. And the back of my mind, I’m like, I wonder what that would have cost in the uh in the 80s when I was building my first computer. And then we go on and I’m ordering 4 TBTE internal hard drive for $84. four terabytes for 84 bucks. I’m going, “Holy that’s crazy.” Uh, so I hopped on uh on on on GPT on chat GPT and said, “Okay, give me an estimate of what this would have cost in the mid80s.” So, here are the numbers. They’re pretty staggering. So, instead of 100 bucks for 32 GB of RAM, it was
[01:30:02] $150 million back in the 80s. And a 4 TBTE hard drive that did not exist would have cost you about 1.2 26 billion to cobble together. I mean, I just I was just in awe of this. >> If if the if the top speed of a car had increased as the same pace as this these curves, we’d have cars that went faster than the speed of light. >> Yeah. You know what I find incredibly fascinating, you know, is that we finally have an answer to something that’s vexed all of the AI and psychology community for decades, which is, you know, what would it take to create human level thinking outside of a human brain? And it turns out it takes, you know, about 8 to 16 GPUs of capacity and those are about $30,000 each. But you can store the the human brain storage fits on two of these. So it’s about you know 160 bucks of storage to
[01:31:00] to everything that can fit into a human brain and then actually then a lot more. Um so we have massive abundance overabundance of storage. >> Yeah. >> But but you know computer is still you know processing is still you know the human brain is doing really really well on 20 watts. >> So Alex the best I can figure is we’re going to go to like molecular memory that will effectively be free in a couple of decades. >> We can do better than that. We can memory >> but we can do better than molecular memory. >> Okay, >> we can also do better than free but we could do atomic based memory. We could do there are proposals for uh for picometer level memory albeit at faster time scales. Uh we could do phento scale computing and storage. We could go sub femto scale. There are there are the the physics of our universe goes so many orders of magnitude down to to plunk and and even whether plon is physical is is still an open research question. Uh there there we’re not going to run out of degrees of freedom to store uh cat
[01:32:03] images or whatever else it is that we’re trying to use storage for. There’s there’s lots of room at the bottom. I always found it fascinating when I was doing my physics degree that no matter how big you want to go in the universe or how small you have, infinity essentially in either direction. >> I I do think for what it’s worth there there are scenarios where we start to run up against fundamental physics limitations, but we’re we’re still many orders of magnitude away at the moment. >> Not something to worry about tonight on your drive home, folks. >> Wait a few years. I added this as a segment we might want to, you know, have in future episodes as well, which is sort of exponential book recommendations. We’ve been talking about Accelerando. A few of our subscribers and listeners have have reached out about that book. I thought I would take a moment to just chat about it. And then one of my favorite books by one of a dear dear friend who’s on stage with me and Sem often at the Abundant Summit, Rome Nam. Uh he wrote a trilogy called Nexus. So,
[01:33:02] uh, Alex, uh, tell us about Accelerondo a moment. Again, this is sort of if you want some fun reading between episodes of WTF. Here’s a couple of books for you. >> Sure. Love the book corner concept. So, I I would say Accelerando is is my my favorite book ever. It tells the story of a multigenerational family starting before the singularity, passes through the singularity, goes after the singularity. And it is probably in my mind the the single best fiction or non-fiction fiction in this case depiction of what the 21st century is likely to to look like and has so many important concepts ranging from obviously AI, nanotech, space development, first contact that are difficult to synthesize in at least have apparently proven for other authors difficult to synthesize. And I I think it just reading Accelerando, which I
[01:34:02] first encountered in in grad school, has made me such a sci-fi snob that it it’s difficult it it it’s difficult to uh I I judge every other bit of science fiction by by the standard. I I had the opportunity to to create a poster sized version of Accelerando, which is available as a creative common licensed ebook presented to Charlie, which was a real pleasure. But I I would encourage every sci-fi writer out there, hold yourself to the standard of accelerando, both in terms of optimism and in terms of physical realism. It’s there’s always the temptation if you’re a sci-fi author to just take one dimension of the world and extrapolate it narrowly and that ends up creating I think highly unrealistic scenarios. Accelerando does a much better job. >> He does. He only he uh he fails me on his extrapolation on space and and space technologies, but you know, I’m not going to be it’s an amazing book. I’m I’m reading it actually listening to it
[01:35:01] for the second time. Uh it’s got a great audible as well. Uh Nexus by Romesh Nam came out in 2012. It’s 13 years old, but it holds incredibly good, so it reads as fresh today as it did back in 2012. And it’s a story of a guy named Kaden Lane. He’s a young scientist who develops uh something called Nexus. It’s a nanotechnology basically like neuralace that links human brains directly to the cloud and links them to other brains. Uh it gives uh you know birth to a collective consciousness and allows you to run software apps on your brain and it’s also goes deep into bioengineering. It’s a look at where we’re going to get to on the flip side of what Ray Kurszswwell predicts in the mid 2030s as high bandwidth brain computer interface. An amazing book, an amazing trilogy, one of my favorites. I’ve read it three times now. The last time with my 14-year-old son. So, See and Dave, any favorite books for you?
[01:36:00] >> Uh, Foundation series from Asimov is a classic that’s just a must readad for everybody. >> Okay, Dave. Yeah, I I only read what Alex tells me to read and I because you know his recommendations have been 100% 100% perfect so I don’t want to I don’t want to trump his great advice but but I will say that the terminology in the books alone makes it worth the investment. The stories are great too but but if you read the books then you get the terminology then you can keep up with what he’s saying. >> And I think that’s really really important. It’s a great investment to make. >> Uh Alex, would you come up with a uh another recommendation? And I’ll do the same for next time. >> Absolutely. So my my second and third favorite >> hold it hold it for then. Hold it for next time. >> Okay. Sure. >> Okay. All right. Got to keep our subscribers coming back. All right. Let’s jump into energy and robotics. Uh so open AAI is planning 125fold energy capacity increase uh over the next eight years. This is more than India itself is
[01:37:01] putting out 250 gawatts of energy by 2033. Uh where are they today at uh roughly you know heading towards 2 gawatt. Thoughts gentlemen? >> If you do the arithmetic on this if my arithmetic is correct 250 gawatts I obviously this represents a tremendous expansion over where we are now on the one hand. On the other hand, it it only corresponds to approximately a 20th of a percent of the uh the insulation, the inbound insulation on Earth’s surface that could be captured or recovered with solar photovoltaics. So, we’re still even with 250 gawatt for one Frontier lab, we’re still pretty far from Carter level one, let alone Dyson swarms. I I think I I would like to see terowatts, tens, hundreds of terowatts. And we’ll get to solar in just a moment. Uh I found this fascinating. So the US is planning to use emergency powers to save
[01:38:00] more coal coal plants. So the energy department kept a Michigan and Pennsylvania oil and coal plant running past retirement. Reason they want grid reliability and they don’t want to risk the demands. We’ve seen the consumer price index for energy starting to spike uh and definitive need for more energy. So there’s 100 coal plants that are set to retire in 2028. Uh and of course, you know, this White House in particular has been pro- energy of any and all types. Uh let me let me hop into solar and then we can circle back to this conversation if that’s okay with you guys. >> Sure. >> All right. So, uh I found this chart fascinating. So, Ember, which put it out, is an independent energy and climate think tank in the UK. And you can see this is a chart that plots energy from 2000 to 2025 across solar, coal, natural gas, hydro, nuclear, oil, and bioeny.
[01:39:00] And it makes the point that over the last 15 years between 2010 and 2025 uh global solar capacity went from the lowest of 40 gawatt to today the highest at almost 3 terowatts of energy. So uh Seem take us away here. >> Well this is a really important uh piece to point out. We do this in all of our presentations where we point out how hard it is to spot this >> and how badly cognitively our brains are at seeing this curve, right? And we you guys had talked about Chris Wright and his comment that you in 50 years we’ll see solar still below 10%. >> Which which kind of blows my mind. If we can flip the next slide, >> right? I want to give a couple of examples here because this is so so >> so read this one out for those who are listening. >> So this is an exponential graph with Venode Kosla on it and what he did was he went back we saw exponential growth of mobile phones through the decade of
[01:40:01] 2000 to 2010 doubling every two years. Okay, he went back and he had a research analyst go and look at what did all the industry expert analysts say would be the growth of mobile phones and in 20 in 2002 they predicted 16% growth year-on-year. Okay. 2 years later gone up 100%. And the 2004 prediction was not 18 or 20 or 25%. It went down. It went down to 14%. Growth >> predict predicted. >> Why? Because they thought they predicting they thought there would be 14% growth because they thought it would level off. Okay, we just had 100% growth over 2 years. It’s got to level off now. In 2006, they predicted 12% growth. It went up another 100% in reality. And between 2006 to 2008, it went up another 100%. Uh, and they predicted 10% growth. Okay? Then it went up another 100%. I mean, how much more wrong can you be from 10% prediction when the actual reality is 100%. So, this is the mobile phone uh predictions of all the top
[01:41:02] analysts, by the way, Gartners, all these guys. Okay? So, this is kind of critical, but this slide I think is killer. And if you were driving, pull over and park and just look at this for a second. What you see in the black is the actual growth of solar energy over a 15 20 year period. Okay. What you see in the colored lines are the and the curve by the way is a total hockey stick up into the right an exponential of epic levels. Okay. Just going vertical. Um what you see in the colored lines which are all horizontal are the predictions year after year from the top energy experts in the world as to the future of solar. And we see is every time solar goes literally vertical, all the experts go linear. >> They basically basically year after year after year. >> They can’t continue scaling like it’s got to keep it’s got to just level off. It’s >> got to level off, >> right? Year and this goes from like to 201 like 12 to 2017 2018. Now the 2018 graph was even worse. It actually showed it going down. The cost is dropping 50%
[01:42:02] every 18 months. How do you predict that it’s going to go down? I I this kind of drives me nuts because this is not a a math error. This is a cognitive error. And this, by the way, let me just point out again, these are not lay people. These are the top energy experts in the world getting it 180 degrees wrong. Right? Literally, if I made predictions like this year after year, I should literally lose my job if I’m that far different from reality. And this is the problem we have because our governments are listening to these experts. >> It depends who employed them. if it was you know >> it’s re it really is kind of unbelievable that there’s a whole other one about electric cars that I won’t get into. They predicted that we would not have more than a million electric cars by 2040 and we crossed it in 2014 and even then they didn’t update their things. I’m going to give one more here. Uh so what this is a graph of of solar modules dropping uh and then leveling off for a bit and then dropping again like a stone. And in 2003, the leading
[01:43:00] energy expert in the world in solar energy itself, okay, made a comment and he said, “Look, if you add up the cost of the the the silver and the glass and the wiring, the physical component cost of a solar module, you’ll never get below a dollar a watt. That’s that’s the limit. That’s the actual limit.” Now, the market actually believes them for a while and it flattens out for a few years, then it starts dropping. By 2014, it’s 50 cents a watt. Now, it actually goes off the the uh the bottom of the graph. Where we are today would be where my feet are sitting on this chair. When the graph is this big, we’re down to about two cents a watt or one close to a penny a watt. And his comment when he was showing this was okay, getting below a dollar exceeded my expectations. That was his comment after being this. So, it’s really really hard. And and I want to give a final example that we don’t have a slide for just to give be fair to these folks is how hard it is. So over the last 20 years, if you own a car wash in Buenosarius in in Argentina, your your revenues as a car wash owner have
[01:44:00] dropped by 50%. Okay. Now, one of our community members, Santiago Belinkas, who I think Peter you know well, um is lives there and says this makes no sense. The middle class has exploded. We we have a ton more Mercedes and BMWs running around. Argentinians are very proud. They like to keep their cars clean. There should be a doubling or tripling of revenues. Why is there a 50% drop? Is there water restrictions? or there are hyper competition and there legal issues or something. He starts looking into and over a couple of months gets rid of all of the obvious factors. Then he finds the answer which literally turns out to be Moore’s law because our computational ability over that 20 years has increased quite a bit. Our ability to model the weather has gotten a lot better. And over that 20-year period, we’re exactly 50% better at knowing when it’s going to rain. >> And when you know it’s going to rain, you don’t wash your car, right? And the reason this is important is you can be the smartest car wash owner in the world and you will never see that coming. Right? And we we call this in the book the orthogonal effect of innovation
[01:45:00] where a breakthrough in one domain affects you radically and you don’t see it. You can’t see it. Right? And so it’s so critical to keep track not just of the demand side but the supply side side of things. The most the most famous in all these I’ll just and I’ll end my rant here is in the 1980s McKenzie’s advised AT&T on the future of mobile phones and they predicted by the year 2000 there will not be more than a million mobile phones in the world and AT&T left the business said that market doesn’t work. Um by by by the year 2000 we had 100 million mobile phones. So they’re off by 99% in one of those. in one of our executive programs at Singularity. Peter, this guy puts up his hand when I mention this, I co-authored that report, right? I’m like, “Oh my god, what’s he is he going to rebut this?” Whatever. He goes, “No, you’re absolutely right. The reason we got it wrong was when you had these big handsets with these briefcase batteries, there’s we figured there’s no way you’re going to sell more than a million of those.” We we didn’t see was that within a couple years that had shrunk to a clamshell and that you could
[01:46:00] actually sell a ton of. And so that’s the part that people miss. So when you track these, be really really careful of making these outlandish predictions like it’ll never get below this or never get above that. We’ve seen repeatedly >> I don’t know why you want to end that rant. That was the coolest thing ever. >> It’s just we for years we’ve been struggling with this talking to governments and they’re like, “Yeah, this will never happen. That’ll happen. We go we go berserk.” >> Love those slides. >> I love those slides. And you know what else? Uh when when Bill Gross was on the pod, he said, “You know, all the land where pumpro has already been bought. I did a little research and actually not true. Lots of land where pumped hydro makes a ton of sense, but it’s not quite as sunny has not yet been bought if anyone’s listening. >> And because the solar panels are getting so cheap, you can just put more of them there. >> And so heads up, you know, there’s a there’s a theme. If the governor of New Hampshire is listening, please give me a call. Uh but there’s lots of opportunity
[01:47:01] that hasn’t been tapped in real estate. >> I have two more quick energy factoids. >> Okay. >> One, uh I did a little bit of research and I was talking to one of our energy gurus in our ecosystem. It turns out there, if you add up all the dams in the US, there’s 10 gawatt of potential hydroelectric power that’s not been tapped. >> So we could all those dams. That’s a big Sorry, I’m really going off here. But but I remember we were on the pod and we said, “Holy the Hoover Dam right now is operating at about 5 to 10% capacity because it hasn’t rained.” >> Yeah. >> So, we’re like, why the hell are we not doing pumped hydro right here? Just pump the water from the bottom to the top. Tons of sunshine right there. Turns out somebody had already >> thought it put together an entire entire investment thesis around it. But it was exactly the right idea. But but that that theme isn’t over. That that is very hot. I think the point we start this whole conversation is is China’s running away with solar deployment. And I don’t understand why. We don’t see it here in the US. You know, I’m a pilot. I fly at
[01:48:01] a Santa Monic airport. I fly over LA and all I see is naked roofs that could be all be producing electricity. You know, there’s a few solar thermal farms out in the middle of the desert. But there’s so much potential. So so much potential. >> All right. >> Geopolitical. it >> it’s geopolitical because uh China is a pretty much a lock on the supply chain and the panels >> I would be you know I’d be investing in building out solar capacity manufacturing here right so solar >> cities and actually what I would look what I would look to do is say what’s the 10x to 100x breakthrough on photonix or solar past the next level and go after that >> and Alex you know digital super intelligence will give us a new material sciences will give us a new capabilities uh for that. So there will be >> that’s why Alex is standing there not looking worried at all. He’s like what are these guys? >> I I I think there are many ways to to generate useful energy. I think fision
[01:49:01] in the form of SMRs fusion potentially as soon as as we’ve discussed in the past 2028 to 2030. I I think there are so many nonsolar novelish forms of energy that are on the verge of coming online. I’m not losing sleep over geopolitical imbalances over solar photovoltaics. All right, let’s jump into robotics here. Uh this is a a fascinating uh uh tweet turned into an article here. China’s robotic boom is going global. So if you look at the first half of 2025 and the companies or the countries around the world that are purchasing robots from China, Poland is up 1,700%, Mexico 275%, Russia 135%, Vietnam 114%. As a as opposed to South Korea, Germany and USA uh which is you know minus3 to
[01:50:01] US at 58%. The point here is the countries that are you know are blank sheet are not uh don’t have a robotics industry are buying from China. So countries are starting their automation journey and buying from China. So this is uh this is something that the US needs to be looking at. Uh basically China is staking its flag in countries around the world uh by deploying both AI and robotics uh in a very cost-effective fashion. Dodge. >> I wouldn’t be I wouldn’t be surprised given how central robotics in general general purpose robotics more particularly human general purpose or humanoid general purpose robotics even more particularly how central those are to this emerging industrial ecology of batteries and fabs and chips and AI
[01:51:03] compute and probably SMRs and drones that we see an emerging demand function for fully sovereign robotic ecologies. It it seems to the extent Peter you were suggesting earlier you’re looking for for other maybe you don’t want to call them sort of too scarce to to fail resources but robotics I I think is is a plausible candidate for wanting to be sovereign aligned resources in the near-term future. >> Yeah. You know, I had dinner with Rod Rod Brooks, the the founder of iRoot when we were out in California a couple weeks ago. >> Yep. >> And he he reaffirmed what I think we all know that our our whole parts supply chain, component supply chain is garbage compared to what China has because, you know, all those years of manufacturing moving over to China, industrialization moving over to China, they developed a very, very flexible parts and components contract supply chain. So if you need
[01:52:01] something to build your robot, you can call someone and have them make it and it’ll be there in a in a few days. There’s no equivalent in the US. So it’s going to take a while to rebuild that whole supply chain. So what Alex said is exactly right. This is ripe for national involvement to to kickstart it. It’s also not not naturally happening in the venture community. You know, >> it’s really tough for a venture capitalist to plunk down 10 20 million bucks for like a a electric motor winding company or a you know, a gear company. Uh >> they should have a we should have a Manhattan style project for supply chain for robots and drones. >> Uh >> there are various initiatives that that have been discussed in including um famously perhaps the soft. We heard we heard this from we heard this from uh from Bert Borick, CEO of 1X. We heard this from Brett Adcock, from Elon directly. They’ve had to completely build their entire bottomup uh supply chain internally. Every component is
[01:53:01] manufactured inside the company right now, which is which is insane. >> What a waste. But the other thing that’s going to be interesting is there will be a scarcity in robots uh for the foreseeable future until production gets ramped up. So we’re going to start to see governments probably bidding like you know we’ll buy a million robots here in Saudi or the Emirates or Qatar uh in order to get early supplies delivered there. And that may that may bid up the prices in early days too. >> Good for the world. I I would view any emerging robot scarcity as just a facet of compute scarcity. The the most important robots are just going to be GPUs on legs and and the the compute ultimately is I I think the fundamental scarce factor here. >> Uh all right. Uh next item here is a interesting graph uh which asks the question what if everyone in the US drove like Whimo? So here’s the
[01:54:03] extension. If every US vehicle performed as well as Whimo, we’d prevent 33 to 39 deaths annually. So uh pretty pretty profound. Uh it’s >> I found a better I found a better related statistic. >> Please. >> Uh which is it turns out about 50% of all the court cases in the US are car accidents. >> Wow. >> 50%. So you take out a bunch of lawyers also, which you know that’s not bad. That’s a good thing. That’s a good thing. With all due respect to some lawyers, uh reducing the number is uh is definitely function. So he is huge. >> And interesting for Whimo, nearly half of all Whimo impacts, crashes happen under one mile per hour. >> So these are just bumps. They’re not actually crashes. So I saw this I saw this stat and I said, “That’s got to be global, not US.” Cuz that’s that’s about the total number >> of We kill 1. It’s 1.2 million people a
[01:55:03] year die around the world with car accidents globally. >> Around the world. Yeah. Well, that’s why I thought, you know, 40,000 out of 1.2 million is viable, but 40,000 in the US isn’t. But then if you read the fine print in the notes, it’s actually a 90% reduction in fatal crashes. >> It’s it’s huge. >> And 15% of all organ donations come from auto accidents, >> interestingly enough. >> Right. So, I just I live here in Santa Monica and Whimos are all over the place. I just started seeing the Zuks vehicle from Amazon uh going and collecting data, right? It’s a piloted vehicle with all of the uh the LAR and cameras around it going and mapping the streets. It was about a year ago that you saw all the piloted Whimo vehicles mapping the streets. So, we’re going to have Zuks, we’re going to have Whimo, we’re going to see uh Cyber Cab or whatever Elon calls it uh very very soon. >> Meanwhile, we have people Meanwhile, we have people attacking the Whimos. Yeah, >> Brad Templeton used to joke because we
[01:56:00] don’t want to be killed by robots, we’d much rather be killed by drunk people, which is what’s happening today. >> I I I suspect for at least most Americans, their first encounter with a generalist robot is going to be by encountering either by driving in or or seeing a Whimo or FSD based car zuks or equivalent. And this is just the beginning of of a longer journey. It’s it’s we start with these uh these generalist robots on the roads and they’ll be in our homes before we know it. >> And guys, just a quick announcement. Uh Dra, the CEO of Uber uh will be joining us on stage at the Abundance Summit. >> And uh yeah, super cool. And so Uber is partnered in part with Whimo. Uh we’ll be offering Whimo as part of your Uber app. and they’re also working with Joby for uh for flying cars. So, uh super fun. We’ll be talking about all of those things and and where Uber is going in the future. >> Flying cars is my big um hope for
[01:57:02] technology in the near future. >> Yeah. Tired of driving. >> Airport transfers are just horrible. >> Oh, it’s it is it is awful. All right, we’re going to wrap up with health and biotech. I think one of the most important subjects uh at least in my life is how do we double our human lifespan? How do we avoid all of the travesty of chronic disease? Uh first article comes in from a friend Joe Leetsz Lacroy. Uh Joe’s company. He’s the CEO of Retrobiosciences. It’s one of Sam’s uh companies. Sam is founded with $180 million of backing back in 2021. Uh their mission is to add 10 healthy years on human lifespan. They’re one of the teams competing for our $101 million X-P prize health span. Uh and See and Dave, since you’re on the board of X-Prize, I mean pretty amazing. that competition, just for everybody if you haven’t heard of it, we I raised $157 million for a
[01:58:02] global competition to add up to 20 healthy years on people’s lives in particular uh in immune cognition and muscle. And we now have over 730 teams that have entered that competition, which is uh which is pretty amazing if you ask me. So >> that’s got to be a record, right? >> It is. That’s incredible. >> Yeah. Well, actually for Elon’s $100 million carbon prize, we had 1300 teams. But >> I would I would have to say this is as hard or harder because you have to run effectively a clinical trial and prove on a human population that your therapy didn’t just do cognition, didn’t just do muscle or immune, it did all of them. So anyway, uh I love the fact that Retro is going after this uh their uh their product is entering human trials uh next year with a hope of uh in Australia in
[01:59:01] late 2025 and and they’re going to be hopefully getting something on the market next couple years. This is called RTR242. It’s an experimental Alzheimer’s pill designed to restart the brain’s natural recycling process of toxic proteins. This is your glimpmphatic system. When you’re in deep sleep, your glimpmphatic system is clearing your brain of those toxic proteins. So, uh, one of the biggest things I had, uh, uh, I had Mett Oz speaking at the platinum event at their abundance longevity summit as well and his biggest concern for the future is ner degenerative disease and also one other disease called loneliness. We should talk about that sometime. Um, I want to end with this article. I find this fascinating. This is out of China. And one of the things about longevity in biotech is if it works in China, it’ll work in Chicago. If it works in Boston, it’ll work in Batswana. We all have the same biology. So this rocketed around
[02:00:00] the world is in use uh this past weekend. So Chinese scientists have genetically engineered a gene called FOX3 uh that is a critical stress resistant transcription factor. and they’ve been able as they modify this to reduce aging by 3 to 5 years. Uh and for me, this is a is a huge huge deal. So, uh in 61 different tissues, end of the day, uh we’re going to start to see longevity uh becoming more and more real. And everyone listening, I want to let you know that the next 50 years that you’re alive and hearing us on this podcast, it’s going to be awesome. Just don’t get hit by a bus in the next couple years. >> Yeah, exactly. Don’t die from something stupid. Uh in in the interim, >> Peter, there was a comment I heard a few years ago, a couple of years ago, and I wanted to just ratify where we are with that. Somebody on one of the abundance
[02:01:00] stages said that we have a the labs, m labs today that are living to the equivalent of 300 years old already. Is that And are we are we really there? >> No, we’re not there yet. you know the average mouse is living uh on the order of 20 to 24 months. We’ve seen extension of 30 to 40%. Uh there are I just I was just over at at Harvard with spent the day and the weekend with David Sinclair and then the day at the whis institute the visit with George Church and those experiments where they hope to double the mouse’s lifespan are going on right now. Uh we’ve also seen uh the first epigenetic reprogramming trials are going on in humans starting in January. So uh Life Bioscience is one of David Sinclair’s companies is uh is going into humans. It’s been very successful in animal models including uh non-human primates. >> Uh after this longevity trip, when’s
[02:02:01] your best prediction of when we break through the uh aging barrier life escape velocity? So I asked the smartest people on the trip that I know uh and uh their belief is there is no upper limit to how long we can live. Just let’s begin with that. Uh and the belief is that the breakthroughs required to understand why we age, how to slow it, stop it, reverse it is going to fall at the knees of digital super intelligence. And you know this is we heard Daario talk about this doubling the human lifespan in 5 to 10 years and you know it’s interesting uh the we had a bunch of scientists from MIT and Harvard principally uh at the summit and uh they fell into two groups those that amongst themselves were consistent saying uh we’re going to see this doubling we’re going to see the significant lifespan and health span extension and those saying nope not
[02:03:02] going to happen. Uh it just extremely on the other side, >> right? >> Wow. >> Uh and so it’s interesting because I I define expert as someone who can tell you exactly how it can’t be done. >> Yes. >> Yeah. >> And for for what it’s worth, See, I I’ve asked this question of all of the best frontier models of the day, when do we get longevity escape velocity? And their consensus is 2030. >> Yeah. which ironically is the same time when Bitcoin hits a million dollars according to all the frontier models >> which is which is exactly what Ry predicted 2030. >> It’s like damn it was right. >> Damn the man. >> He may be proof that time travel is real. >> Yeah. That and that and Elon. Yes. Exactly. >> So everybody you got to hang on. Stay in good health. Sleep, diet, exercise, mindset. Don’t die from something stupid. You got uh hold on for the next five t years. There are therapies coming. Um uh and
[02:04:01] they’re significant therapies. I did a podcast with David Sinclair on moonshots. If you haven’t listened to it, please do. >> It’s an amazing podcast that one. It’s has to it’s a must listen. >> Let me give kudos to the uh to the moonshot uh community here. One moment. You know, when I did that podcast with David, he came on and he was really miffed. the Harvard White House uh you know debate and uh and butt headbutting had cancelled all his his funding. $4 million of funding got cancelled and he was on the verge of letting his entire research team go, all of his researchers. And I was just pissed and I said, “Let’s turn this around.” And on the podcast almost off the cuff, we announced this thing called uh Friends of Sinclair Lab where folks would contribute $50,000. I I was the first to to offer to contribute as was David himself. And since then, we have gotten
[02:05:00] over $4 million of donations from the people listening uh to this podcast. >> Wow. >> Which is insane. So, we completely replaced uh the government funding. >> I’m looking to buy a Ferrari if >> anybody wants to donate to that. >> No, but this is decentralized science. >> It’s citizen-driven bottomup science. It’s so awesome. And the challenge is that when you’re funded by government and re and have peer review, you’re stuck in incrementalism. >> Yeah. >> Anything dramatically different, you know, they don’t want to get it funded. >> Yeah. It’s great. >> Dave, what’s your week look like for you, buddy? Uh well it’s Friday so um yeah you know we have uh a lot of our best and brightest that are coming through the lab are getting funding right now. A lot of them are getting west coast term sheets at like two or three times higher than the east coast. So there’s quite a bit of migration west
[02:06:02] going on. Um one of our coolest companies that we we signed the term sheet in Mark Zuckerberg’s old dorm room. Uh, and you know there’s a poster of the social network movie signed by Mark Zuckerberg on the wall. So we signed the term sheet right in front of the poster. Then that got all around Harvard. So 20 people joined the company for no salary because they’re so hot. Anyway, they’re smoking hot now. It’s called biography. They’re moving to the west coast. So I got I got a whole bunch of open seats here in the lab. So I’m really excited to spend time on campus backfilling, you know, trying we’re going to try and get 16 more teams in. And you know, January is coming fast. You know, MIT has January off. Yes, I >> that’s the perfect time I AP >> perfect time to to boot up a company some >> if you’re if you’re at MIT or Harvard or Nor Eastern and you’re hearing this podcast. First of all, Dave’s a rockstar. Uh if you’ve got a couple of best friends and you want to start an AI company, uh where do they go, Dave? >> Uh go to the Link Ventures website. Uh
[02:07:01] or just email Dan Oliviveri or Kush Bavaria. Their names are on the website and it’s just K. Bavaria or Dolivari at Link Ventures and you got to have uh at least three people that are bonafide best friends and we’ll check we’ll we’ll we’ll poke around and ask your other friends are you really best friends? Uh but we only only bring in teams that are super tightnet. >> Uh it keeps it all really really fun. >> Salem, how about you? What’s uh what’s the week ahead look like? Um, we’re doing a whole bunch of planning with our ecosystem to think about how we leapfrog everything we’ve done in the past and go 10x faster, better, cheaper with all the offerings that we have. Uh, we have our next uh exo 10x shift workshop on October 15th. Uh, it’s 100 bucks. People, those are all selling out. Those are great. and we cover the model and say show people how to take their organization literally 10 to 100x now um through that two-hour workshop. Um and I’ve got a a little bit of travel but
[02:08:01] not too much before the madness towards the end of the month. Visionering is coming up which I’m super excited about. >> Yeah, for sure. And Alex, welcome back from your secret mission and uh excited to work on our project together uh which we’ll unveil at some point. We’re going to keep it secret for the time being. Uh, how about what’s what’s on your agenda? >> Uh, trying to accelerate the singularity or whatever it is. Maybe singularity at this point isn’t even the the right term, but smoothing out and moving whatever we want to call it, the intelligence explosion or if you’re a technological determinist, the what was always going to happen, the inevitable byproduct of building an internet and then compressing the internet and then using that to solve everything else. I I I think timelines are very short at this point. Every week my timelines are getting shorter. Um I usually it’s the case that I’m the accelerationista in the room. Uh not always, but usually. And my timelines are incredibly short at this point. So my my favorite thing
[02:09:00] these days in these podcasts is watching Alex’s faces where we rant about energy or healthcare or something. He’s like super intelligent is going to just solve that. Why are we even talking about this? This great look on his face that shows I mean you’re reading my face, I think, correctly. There is a certain sense of uh like hyper hyperdelationary mentality. Why do anything >> really >> paralysis? It’s it’s like the starship. It’s like the starship who heads out and when they get there they find out you know warp tribe had been invented and it’s a term for it. It’s term for it. It’s called the weight equation and it does cause singularity paralysis for for lack of a better term. And I’m seeing it more and more in every day in conversations I have as as it dawns on more and more subject matter experts that AI is about to transcend their capabilities in call it two to three years if if if the the current extrapolations hold. What happens next? And I spent a lot of time thinking about
[02:10:00] that. >> Amazing. Well, everybody, uh, thank you for joining us. Subscribers, if you haven’t yet, subscribe so we can tell you when the next WTF episode is taking place. Hope you found this super useful. Be optimistic. We’re living into the most extraordinary time ever in human history. A time where we can uplift every man, woman, and child, where each of us is going to be able to take on the grand challenges we desire and really go from success to significance on a global scale. Uh, so so happy to be alive right now. and so happy to be with my moonshot mates. All right, guys. Until we see each other next time. Every week, my team and I study the top 10 technology meta trends that will transform industries over the decade ahead. I cover trends ranging from humanoid robotics, AGI, and quantum computing to transport, energy, longevity, and more. There’s no fluff, only the most important stuff that matters that impacts our lives, our companies, and our careers. If you want me to share these meta trends with you, I write a newsletter twice a week, sending it out
[02:11:01] as a short two-minute read via email. And if you want to discover the most important metat trends 10 years before anyone else, this report’s for you. Readers include founders and CEOs from the world’s most disruptive companies and entrepreneurs building the world’s most disruptive tech. It’s not for you. If you don’t want to be informed about what’s coming, why it matters, and how you can benefit from it. To subscribe for free, go to dmmandis.com/metatrends to gain access to the trends 10 years before anyone else. All right, now back to this episode. [Music]