People who are already in the ecosystem [music] now have a super intelligence at their beck and call. That’s probably the least interesting thing. >> When they’re on the cusp of the singularity, they’ll start softelling it. >> Gemini 3.0, which has in just today climbed all [music] the third party AI rankings. Let’s break down though what this so-called Gemini leap means. >> This will change the game completely for everything everywhere. >> Why is this just not another, you know, little faster, little better capability? We have a way of measuring progress in our civilization. AI is [music] imminently, I think, well positioned now that these benchmarks are saturating to start solving the hardest [music] problems on Earth in math, science, engineering, medicine. All of a sudden, you can build software by talking to the machine. [music] This is like a different world starting today from the day that we lived in yesterday. >> Now, that’s a moonshot, ladies and gentlemen. You know, the the hardest thing for me when I’m going over the slides is what
[00:01:01] to cut out. I mean, it’s all so good, right? Every every one of them could be like an entire hour conversation. The question of how we group it and how we actually make it such that um it’s a fun conversation uh is is so is so challenging. I mean, so much going on. We almost have an episode on robotics, an episode on energy, an episode on AI. >> Yeah. But then if we do that, you know, we’re publishing more than once a week, which is a lot. And sometimes we do. And then if you’ve gone like three weeks without covering one of the fields, it’s like, you know, >> disruptive shock therapy. >> The world is over. [laughter] >> Well, so and people, the audience has a limited amount of time, too. So, we got to try and help them as much as possible. >> Yeah. >> Twice a week, basically. And that’s all you can do. >> I mean, I hope you guys have as much fun as I do on this. >> Oh, yes. >> It It’s awesome scanning all the
[00:02:00] breakthroughs and looking at how fast it’s all moving. It’s really >> And then trying to figure out, okay, what does this really mean? Okay. Besides yet another benchmark or besides yet another, you know, this number is greater than that number. Okay. So, like what does it mean for everybody? Um my >> Well, for me, you know, I get [laughter] I get so buried in the day-to-day, you know, there’s just so much going on. And if if it weren’t for the podcast pulling me out of the weeds, I would miss all kinds of things. And I tell you, I get really frustrated when people don’t know what’s going on and they’re not reacting to it. I’m like, well, the only reason I know what’s going on is because we do the podcast and and that prep time for it is what pulls me up out of the So, I I love this time >> for sure. And it’s like my I told my son, “Hey, you know, Gemini 3 is out and it’s got amazing benchmarks.” And he goes, “Yeah, insert name of model here, insert number here.” Like every week you tell me that. [laughter] It’s like, “Yeah, you’re right.” >> Yeah. >> Uh well, we’re the antidote for that because, uh, you know, as we’re always saying there, people get inured to things so quickly and they
[00:03:00] [clears throat] miss the implications. And that’s true even at MIT where I’ve been for the last three days. And um, but it’s just not true. This is this is like step function life-changing stuff >> week by week. >> Yeah, >> I think we should just jump in cuz like there’s a lot if you guys are ready. All right, so I’m here with DB2 AWG Mr. Exo you call signs and uh and let’s let’s jump. So >> airports. >> They’re all threeletter airport signifiers. Okay. Uh let’s let’s get going here. So welcome to Moonshots, everybody. Uh this is another episode of WTF just happened in tech. The real news uh and for us like the only news and the implications and what does it mean and hopefully we can go deeper into what does it mean for you, your family, your business, your company, your country, all of those things. Uh we’re going to open up with the hyperscalers, uh Google XAI, OpenAI, and the TLDDR for this episode is Google is winning. A lot
[00:04:02] going on in Google first. We just saw the release of Gemini 3 yesterday, which is why we’re recording today. Trying to be right here, right now. All right, let’s jump in. I’m going to share a video from Josh Woodward. Josh is a friend. I had him on the Abundance stage uh a year ago. Uh he now heads Gemini and Google Labs. A brilliant presenter. Uh we’re going to have him on this podcast right in the new year. Excited for that. All right, let’s jump in. Hey everyone, my name is Josh and I lead the Gemini app, Google Labs and AI Studio. And today is the day. Gemini 3 is here and it’s in the app. You can try it right now. It’s our smartest model ever. We have this new feature called agent and you can actually go in now to Gemini, describe a task and it’ll get to work for you. So you can plan a trip, you can research products, all these things acts on your behalf, takes multi-step actions, tool calls, all of it. The other thing I’m really excited about, we’re entering into a new era
[00:05:00] where you can create UI dynamically. The model creates these generative UIs. So you can go in and when you ask a question, Gemini will not just respond with a wall of text. It’ll actually pull in images, different interactive widgets, gives you a much more customized experience based on what you’re looking for. All of this gives you a more helpful response. And so I hope you go out, try both of those features and more today. We look forward to your feedback. All right, one more video here from Gemini, then we’ll discuss it. This is their official introducing Gemini video. And again, congratulations to Josh for taking the lead there and crushing it. Uh, crushing it. We’ll we’ll talk about the benchmarks with, of course, AWG in a little bit, but before then, >> Gemini 3 is the strongest model in the world for multimodality and reasoning. It’s our most intelligent model that helps you bring any idea to life. Google Search. Gemini 3 enables new kinds of generative user interfaces. [music] It codes interactive simulations like this one customuilt for your search.
[00:06:02] In the Gemini app, you could supercharge how you learn, create, plan, take action, analyze [music] complex videos, and more. We’re even introducing a new platform, Google Anti-gravity. [music] It’s our vision of software development at the frontier of model intelligence. [music] It lets you use Gemini 3’s agentic coding capabilities to accelerate how you build. This is just the beginning of our Gemini 3 series. >> Okay. Who wants to dive in first? Dave, you want to want to jump in? What’s this? [laughter] What’s this mean to you? Why is this why is this just not another, you know, little faster, [laughter] little better capability? >> Dave is full kid in the candy store here. This is great. Well, I can’t wait to hear Alex’s take on this, too. It’s at 50% almost in humanity’s last exam. It’s such a step function change in history. And I was over at MIT last night, um, talking to a bunch of undergrads, and I’m trying to
[00:07:00] tell them like, look, you don’t know this, but you know, 40 years ago, we started writing code as a species, and we started with cobalt and, you know, [clears throat] we started with ones and zeros and hexodimals where we started. >> That’s true. We started with assembly. Uh, and I swear to God, if you look at what happens today when you write code versus 40 years ago, it’s identical. It’s like a higher level language. Nothing’s really changed. All of a sudden, you can build software by talking to the machine. It is such a different world starting today and moving forward. And and I I I’m hoping they can then generalize and say, well, it’s coding today, it’s gene sequencing tomorrow, it’s it’s all white collar automation the day after that, then it’s all industrial design of robotics is done by voice. This is this is like a different world starting today from the day that we lived in yesterday. And it’s really hard to get people to fully understand the implications. Uh so I’m
[00:08:00] it’s just such a Well, anyway, we we’ll get into it. All right. I can’t tell you how big this is. >> Alex, what’s Yeah. What’s your takeway, buddy? >> I’ve said in the past here, I think the singularity is probably an optical illusion. When you’re in the midst of it, spacetime feels flat. And every time I hear the question, well, what else is new? The benchmarks are going up and to the right. Doesn’t feel really transformative. That to me is a sign that when you’re in the midst of a singularity, that spaceime feels flat. and breakthroughs that are happening essentially every week or every day feel prosaic. There are so many transformative aspects of Gemini 3. Just walking through those two videos starting from maybe the the least transformative aspects. The Gemini app itself, which is how many people are likely to first encounter Gemini 3, now is integrated with all of the other Google properties. So, there’s been a lot of belly aching over the past year, like why can’t I agentically have Gemini write my Gmail for me or have it
[00:09:00] organize my calendar for me or or interact with YouTube movies? That that’s I I’ve been playing with Gemini agent with the agent mode part of Gemini Gemini 3 and that that’s seamless at this point. It’s literally a single click to get Gemini 3 order your entire Google platformbased existence or Google Workspace based existence. That’s probably the least interesting thing, >> but a powerful a powerful driver for people to switch to Google as an all-in platform, right? I mean, that’s what’s really the situation that they’re they’re striving for. >> Google Google has billions of users across all of its products already. So, I’m I’m not sure at the margin the greatest impact on humanity is getting people to switch to Google. I think it’s more people who are already in the ecosystem now have a super intelligence at their beck and call. And again, that’s like that’s the least interesting thing. Couple of more interesting things in interacting with the model itself. And again, this is not focusing yet on the benchmarks. This is just on interacting with the client. It smells. People in the community refer to
[00:10:01] something sometimes as big model smell. A model that has certain types of capabilities that can’t be arrived at through extended reasoning or through other sort of smaller uh footprint attempts to uh to to extend the capabilities of a model. Gemini 3 has what I I think can be fairly termed big model smell. You you can ask it to do crossmodal or multimodal tasks that are very challenging to do elsewhere. Uh one of my first tasks was I fed it a photo of the MIT campus and I asked it generate a 3D voxal block world type rendering that I can interact with and one shot basically zero shot it produced an interactive 3D rendering of the MIT campus. There’s also uh I don’t want to to let this this point drop anti-gravity uh the the code development environment the integrated development environment that was focused on Gemini 3. My understanding is that the windsurf team
[00:11:00] we’ve talked about windsurf uh in past cursor competitor many of the the core members of the team joined Google deep mind and anti-gravity as a result I was interacting with anti-gravity was a very impressive visual studio codederived experience for code development. So there are so many pieces here and that’s before we get to the truly interesting stuff in my mind which is the benchmarks. >> Yeah. Yeah. You know, one of the things they one one of the things we said a while ago is when they’re on the cusp of the singularity, they’ll start softelling it >> and you noticed, you know, Google put out all these benchmarks that are mind-blowing and the only thing they put out in terms of content is that Josh Woodward clip from a second ago. You know, it’s contra contrast that to contrast that to the open, you know, the GPT5 release, right? >> Um, which [clears throat] was a special hour-long presentation by Sam and so forth. This was, like you said, very soft, a very soft cell. You know, one thing I found fascinating is the speed at which we’re sort of upleveling the
[00:12:00] models, right? Gemini 2 uh was December of last year, 11 months ago, and now we’ve got Gemini 3 coming out. So increasing speed at which we’re deploying. We’re seeing that across the board with the hyperscalers. >> Maybe just to to comment narrowly on on that from my perspective, Gemini 3 is the biggest model release since OpenAI’s 03 in April. All of 7ish months ago, GPT5 to the extent GPT5 may have felt slightly underwhelming, I would argue it’s because almost all of its raw capability jumps actually happened a bit before in the form of 03. And then maybe think of GPT5 as O3, which was actually O2 because O2 was trademarked, so it had to be called 03. GPT5 was actually like 02.1. So I I think we can’t take credit away from OpenAI on the achievement that was 03 and then partially repackaged as GPT5. Every week, my team and I study the top 10 technology meta trends that will transform industries over the
[00:13:00] 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 dmmandis.com/tatrends to gain access to the trends 10 years before anyone else. All right, now back to this episode. This for me is is seeing Google go from reactive assistant right where you’re asking it for something to autonomous agent um and handling you know complex real world
[00:14:00] data and we’re going to see that in the next uh next slide. Let’s go there. So let’s go to Gemini 3 delivers breakthrough profitability in AI run mini economy. Uh this is the vending bench benchmark which I love this uh and Gemini 3 outperforms Grock Claude GP uh chat GPT in long-term business management tasks uh to uh explain to us what this means the the king of benchmarks. Alex, let’s go to you. >> I I I love benchmarks. I love this benchmark in particular. So this is a benchmark vending bench arena that’s maintained by a company named Andon Labs. It’s derivative of another benchmark that they maintain named vending bench 2. The basic premise is AI agents are given simulated $500 to start. They’re put in charge of a simulated vending machine. They’re given tools that they can manage. So they have the ability to send and read emails like real full natural language emails.
[00:15:02] They’re given the ability to search a simulated internet. They have a simulated bank balance. They can send money. They can receive money. They can stock and restock the vending machine. They can set prices, check inventory, collect cash, etc. So, this really is performing the role almost of a middle manager in charge of a vending machine. And if they the simulated agents maintaining the vending machine, if they fail to pay a $2 daily fee for 10 consecutive days, they go bankrupt. And the the goal of the game is to maximize the return on investment for that initial simulated $500. And I I think this is just such a lovely self-contained proxy for AI agents as first class economic actors. If if AIs can do a spectacular job of managing this pretty rich simulated vending machine world, then I I think they’re halfway to autonomously running their own real world businesses and becoming AI entrepreneurs, at which point we get
[00:16:00] zero human startups. >> Wow. It’s amazing, right? We talk about this. Gemini Gemini 3 is delivering almost 3,000% more profit >> um than GPT5 or Claude Sonnet. And uh you’re right, we’ve talked about going after stable coins and uh agents together, spinning up new businesses faster you can possibly. Now, the one thing this doesn’t do is it doesn’t account for the messiness of employees. And this would have to be a nonhuman business that it’s running in order for it to really maximize profitability without dealing with >> Yeah. Go ahead. >> I I I I would actually argue that the email functionality built into the benchmark. So it when it sends and receives emails, there’s a large language model counterparty at the other end writing full natural language emails. So I could imagine a generalization maybe a future version three or four of vending mench that that does take into account say like performance reviews and interacting with
[00:17:01] employees. All of that I I think is not technically that much more difficult >> testing. >> If you can manage [clears throat] vendors if you can manage vendors and suppliers then email communication with employees not that much harder. >> Interesting Dave the internet well I was the internet advertising business is $300 billion a year completely non-human. The whole thing is automated bidding, automated placement. I’d be surprised if the non-human economy is is anything less than a trillion dollars already. Uh so the the parts of the economy where you can just deploy this are going to grow very rapidly now, which I think. But did you notice how Alex has a lot more emotion in his voice right as the AI is getting more sophisticated? So, [laughter] so is that improvements in the algorithm or is that just enthusiasm? Like [clears throat] his true identity is being revealed. [laughter] >> I think he’s And if personhood is granted and I get to be a real person, real boy as it were, then I get to run my own business too, I guess. [laughter] >> Well, on this topic, I completely agree like we need many, many, many more
[00:18:00] benchmarks. And the more real and practical they are and the less technical they are, the more it opens up people’s eyes to what’s possible. And I think we desperately need more benchmarks in the medical area. And Peter, you’re the top guy on the planet in this, but we’re getting so close >> so close >> to being able to cure first extend people’s health span, uh, delay cancer, delay heart disease, and then cure it. And if we do that quickly, I think we can save 30 million lives. You know, there’s 10 million a year. And this is very, very important to me personally, just because of some, you know, friends that I have in this situation. And I swear to God, this step function improvement today puts that right in front of us. And and I I think it’s [clears throat] almost criminal to remap >> Dave. Imagine this in the future instead of AI agents managing vending machines. Uh you’re going to be a part of a population and the agent’s going to manage you. It’s like go outside, take a walk right now, drink another glass of water, right? Go take these.
[00:19:01] >> That’s the promise of the Jarvis thing you keep talking about here. >> Yeah, it it’s coming buddy. So, See, I know you got a pesky uh uh you know, leaf blower outside. I tell you, I keep on saying to Elon, “Would you please make electric leaf blowers? Just make them quieter.” >> That uh Nat Friedman has a $100,000 prize for anyone who can create a silent electric leaf picking up machine. >> Oh, crazy, right? And that should be We’re going to elevate it to an X-P prize and put $10 million behind [laughter] it. >> Let’s Let’s do it. Let’s That’s a great idea. The >> noise. I’ve got a couple of thoughts. One is the entire stack of society can now be AI mediated, right? Which is kind of an incredible thing to be able to say. And the second part of this is we there’s a really important point that Alex made, which is you can now build a company with literally zero employees. We were talking about three employees a few weeks months ago, Peter, and a year ago. Right now it’s down to zero. And this is going to change the game and absolutely will happen. As Dave says, there’s already a trillion dollar or so economy
[00:20:01] out there and this is going to get automated very quickly. >> All right, so keep your eyes on this. I mean, it is uh, you know, as an entrepreneur, I think about this. When can I start spinning up companies? Can I give, you know, uh, $10,000 in stable coins to my AI agents and say, “Go make me some more money.” And now the question is, is that available for everybody? Can anyone and everyone, you know, spin up an agent that is going out there and generating revenue for them? Because if it isn’t, then we’re beginning to have a, you know, a widening, uh, wealth gap. All right, let’s go to, uh, our next story here. Uh, and, uh, this is a story about, uh, a one-shot cyberpunk first shooter that, uh, I think it was you made it, Alex. >> That’s right. I I get I I I see the comments sometimes people I I’ve remarked in the past that one of my favorite evals for a fresh model is to
[00:21:02] ask it to generate a cyberpunk firsterson shooter and some folks in past have suggested as nonsense. So I I thought it might be instructive given the the strength of Gemini 3 to ask it to one-shot the generation of a cyberpunk firsterson shooter. the prompt that I gave it. The only prompt was create a visually stunning cyberpunk FPS that I can play. It should have nice music and rich visuals. And let’s let’s play the video. If you’re watching on YouTube, enjoy this. If not, you know, go to YouTube. >> So, Neon Protocol. [music] I do like the music. Actually, Alex’s I I I immediately copied Alex’s prompt and extended it, and my music came out absolutely [clears throat] nauseating. I said, “Make it make it even faster action and make it a deeper pumping bass.” >> And my version was just nauseating beyond the >> Okay, [laughter] so I mean, listen, I mean, this is I keep on telling my kids
[00:22:00] instead of playing video games, at least design them and build them. And so, uh, this is just making it so much easier. And the prompt is >> for everybody listening, you can you can do this. This is not like something you have to have special access. You can do exactly what Alex did in less than five minutes. So go ahead and try it and then modify it. It’s it’s super fun. Also, you know, Google has a limited amount of compute >> and everybody can do this for free, but after you hammer it for a few hours, it’ll throttle you. >> So take advantage of your first few free hours and have some serious fun and learn a lot. I was with Jack Hery at FII and one of the conversations I had with Jack and I respect this very much. He says instead of waking up in the morning and consuming like just you know scrolling through everything get up in the morning and create something build something. Uh so uh and you can go on go on Alex >> and and and to that point it’s never been easier. That was probably 140 characters or or fewer. If if you can post on X or post a short social media
[00:23:02] message, you can create a game on on demand, which means that I I think we should expect to see billions of games created in the next year because it’s now so easy. It’s the most competent oneshotting I’ve ever seen. >> Gaming slop. >> Just the just to echo the conversation from last week with 140 characters and flying cars. It’ll be amazing when the inner loop gets to a point where you can just use 140 characters to say, “Build me a flying car.” Correct. Yeah, it goes and does it. >> You can do that right now. You can with 140 characters create a simulated flying car with Gemini 3. >> Yeah. You know, there there are 6 million people in America whose full-time job is influencer. >> And [clears throat] that was enabled by the camera phone. You like prior to that, you needed a production crew and heavy cameras. Like, you couldn’t be an influencer. All of a sudden, because there’s a a 4K camera on every iPhone and there’s great editing, 6 million people shift to influencer as a career. This is at least as big a shift. You know, if you if you say video games are generic right now, let me make something
[00:24:00] custom to my community, custom to people, you can actually create it. Even if you couldn’t code yesterday, today you can create something just using your thoughts and your voice. And so it opens up career opportunities. >> Let’s take a listen to this. This is the next article here. Is is Gemini Live a more natural voice? >> Is there any fish on this menu? >> Yes, there’s a sea bass. >> Yeah, I love sea bass. Can you help me order that in Spanish? >> Of course. Try meust laavore. >> How’s this? Meusteria la lubina porfavore. >> That sounds great. >> Yeah. So, uh, you know, I think they made a nice move forward here. I used to love my my GPT5 voice. Uh, I use Ember when I’m talking to it and Gemini was felt, you know, stilted and not natural. So, they really did a great job moving us forward. Uh so super excited about that. You know, interesting on the translation side. You know, we talked in one of the previous pods about Dualingo
[00:25:00] being disrupted. Well, over the year now, it’s down almost 50% um in the last year. So, a lot of challenges there. They’re going to have to reinvent their business model, which I’m sure they will. Uh Dave, what are your thoughts on >> to remember? Yeah, I’d like you to remember what Peter just said, you know, for later in the pod because I had the exact same experience where the OpenAI version of the voice was much more engaging. I can talk to it while I’m driving. It it’s great. And then the the Google version was stilted and robotic and just no fun. So now Google has leaprogged and it’s it’s actually better. But they did it under competitive pressure from Open AI. And I think you’re going to see that theme throughout everything that we see on this pod that open AI hopefully will will catch up and leaprog again. But that’s the only reason Google moves is because of that pressure. Otherwise things just stall. >> I mean Dave, we had that we had that conversation uh and you noted it in our chat. you know, a lot of the AI capability, a large amount of the large
[00:26:00] language models uh were developed in Google, but until OpenAI released them onto the open web, Google was holding back. It was the responsible thing to do. Don’t don’t allow it to code itself. Don’t put on open web. That was the basic thesis of the last decade. And when OpenAI moved, Google had no other option but to move as well. >> It’s just it’s just big company you know? But I and I get it because I’ve run companies with hundreds or thousands of employees. >> It’s hard to make your company move. But then you get competitive pressure from a little nimble company >> and it’s much easier as a CEO to say, “Guys, get your asses in gear. There’s a threat here.” And it’s it’s kind of the dynamic that makes America and and the global economy move forward at all. But all this technology, like you said, Peter, was invented originally, the transformer algorithm was invented inside Google and it was just sitting there. Like literally not not coming out the door at all. And we could go through all the reasons. We’ve talked about them before, but sorry, Alex, you were going
[00:27:01] to say >> I I I would perhaps go even further and argue that many of these underlying capabilities are not just available, but they’re available in the underlying data distribution that these models are being trained from. and that exposing for example different accents is probably more of an unhobbling as they would say than anything else. It’s not so much that capabilities are being added as is restrictions being removed and Frontier models in particular when when we see like live audio type engagement are moving from what they’ve been in the recent past which is audio to text to text to audio just directly audio to audio which enables much much richer audio interactions including accents. >> Yeah. I mean, and where we’re going here [clears throat] with the next generation of AR glasses everyone’s developing. Um, and basically plugging into your your auditory and visual input. It’s simultaneous translation. Uh, it is, uh, it is going to change how we communicate
[00:28:00] with people around the world in an extraordinary fashion. This was a fun one. Um, again, continuing on the Google theme, uh, the TLDDR, they really have gone and won hands down. I know Dave, you and I are looking at uh at the prediction markets that uh you know, Google has literally skyrocketed to be the contender that’s going to be is the winner by the end of the year. And I think they uh uh they got that mantle. Google AI helps users shop, compare, and call stores for the holidays. So, new agentic features can call your nearby nearby stores, check stocks, pricing. Uh Gemini apps add built-in shopping tools. I mean, this is like, “Hey, call 20 stores within 10 miles of me and uh and find out who’s got the cheapest prices and put it on hold or better yet, purchase it for me and have it delivered tomorrow.” Holy cow. Uh, a lot to unpack there. So, I had to check on this one, Peter. It was all of 7 years ago that Google launched Duplex, their AI store calling functionality at at IO. 7 years
[00:29:01] ago, 2018, the the year after attention is all you need. It’s been seven years for this to to make it into some fully realized format, but I I think this is finally the the beginning of AI starting to autonomously index the physical world. If you can have AI call stores autonomously, you can send AI powered robots out into the physical world to index everything that’s going on as well. I >> I’m curious what the what the consumer behavior is going to be like, right? Is it going to be just become actually what I’m really cons interested in is what’s it like on the other end when you’re in the store and you’re getting all of these calls inbound and like at what point is like more than 50% or AI calls and >> well you have AI answer the AI calls obviously >> I mean is it going to be that you have to identify yourself as an AI probably >> that that that is what duplex has historically done it announces itself as an AI assistant. Yeah, actually so far it’s going to be state by state, but so far the AIS are not announcing
[00:30:01] themselves and about half we do a lot of this inside our lab here. So about half the time people are like am I talking to an AI and the other half they have no idea >> and so do you have to answer it if it asks if you ask >> you don’t have to in some most states you don’t have to but >> you know again regulatory consideration is moving so slowly it’s just completely ambiguous but as of right now you don’t have to but it doesn’t hurt to say yeah I’m an AI or even declare it up front it’s not hurting the call performance rates at all so you might as well just say hey I’m an AI but I’m so much more helpful than the guy you were going to talk to >> my buying new business idea then is a little button on your phone. When an AI calls you, you flip it over to your AI cuz I want when I’m calling a store, I want to speak to a human, but um you know the human at the store, what do you think about that product? You know, you know, how good is it? Are people returning it? Uh and that interaction is you know a prohumanto human interaction, but I’m not going to have that tolerance with an AI. >> AI. >> Wait, I want to challenge you on that. >> If you if you if you call a store, why
[00:31:01] do you want to talk to a human? An AI is going to know way more about the inventory, the situation than a human. >> No. Yeah, exactly right, Sel. And and not just that, the AI can pull up images in real time and show you the product and spin it around and stuff. So, it’s not nothing like talking to a human in a store. It’s far far more engaging. [laughter] >> Um, I’ll tell you what else. Uh, the the Voice Run guys here in the lab are doing OpenT doing, you know, restaurant bookings and stuff. >> And you wouldn’t believe the fraction of restaurant bookings that are a non-English-speaking person. >> Uhhuh. or you know or going the other way if you’re traveling internationally. It’s a lifesaver to be able to talk in a different language and do your full booking and then the the AI just translates it. >> Fascinating. >> I do think this is how we get to APIs for everything there. There’s right now the need for an escape valve for surfaces for business interactions that don’t support APIs. With with an AI that can make voice calls and have arbitrary unstructured interaction, we get APIs
[00:32:00] for everything. Yeah, we do. Okay. Uh, one more article on the Gemini front. Uh, Gemini 3 benchmarks. We should probably skip this. I don’t think anybody’s interested in it, but Okay. [laughter] You’re >> You can’t You’re teasing good. >> Alex [laughter] has just sent a drone to your house there. Yeah. Watch your roof >> to take me out. >> I’m going to send my duplex AI to give you a phone call. [laughter] >> All right, Alex, clue us in here. Gemini 3 benchmarks. How good are they? And at the end of the day, what do they really mean? I mean, just to represent people watching this, listening and and watching our our Moonshots program. Okay, Alex, I hear you talking about benchmarks every time, right? We’re going to talk about some more benchmarks in a little bit, but what does it really mean? What does it mean to me? So, >> Sure. So, so I I guess there’s the headline, the numbers are going up and to the right, so who cares? who who cares is we have a way of measuring progress in our civilization and this is
[00:33:01] a precious moment when with raw numbers day by day at this point we can track progress towards solving some of the hardest problems that our civilization faces. humanity’s last exam. Say what you like about it. Some like it, some uh like it less, but it’s an attempt, as are all of these benchmarks, to encapsulate in a measurable quantitative way, progress by AI towards solving hard problems. In humanity’s last exams case, it’s an attempt to measure the ability for AI to solve PhD level problems. In the case of ARC AGI2, it’s an attempt to model human level ability to visually reason. the the so what is these benchmarks are all saturating which means that AI is [clears throat] at this point has the ability to perform PhD level research when we think about the so what for the so-called average person it’s going to be that AI is imminently I think well positioned now that these benchmarks are saturating to start solving the hardest problems on earth in
[00:34:02] math science engineering medicine that’s the so what we we spoke about that last episode last pod uh with Sam Alman speaking about you know science breakthroughs coming on GPT6 that’s his expectation and and here the numbers are impressive right if we if we’re looking at GPT 5.1 um Gemini 3 is basically doubling the ARC AGI2 benchmark um it is effectively [clears throat] doubling claude 4.5 on humanity’s last exam I mean these are sign these are not incremental moves uh they’re significant stepups And critically, it’s not benchmaxing that we’re seeing. There there are some labs that have been accused of of just optimizing their AIs to do well at one or two of the benchmarks and then when you ask them something out of distribution, they fall over. That doesn’t appear to be the case here. It feels like the the team behind Gemini 3 really did a professional job not overoptimizing towards narrow spiky
[00:35:00] intelligence on any of these benchmarks to do well in a press release. This feels like a well-rounded generalist AI model. And given the trajectory towards saturating these benchmarks, I I’d be very surprised if by the end of say next year, we’re not seeing hard research problems succumb to AI models like this one. >> Do you remember two podcasts ago, Alex, we had that paper that came out on how to measure AGI, like defining it in terms of I don’t know if it was 10 or 12 different quadrants. I wonder how Gemini 3 does on that. I I’m sure we’ll know soon enough. Uh but I I would expect it to do generically well on on the spikes where models historically were doing well. The the as I recall one of the spikes where one of those those dimensions where models historically did poorly was on continuous learning with ultra-large context. Off the cuff, I wouldn’t expect Gemini 3 Pro to do amazingly better on ultraong context, but it does really well on retrieval scores. I don’t think it’s shown in this slide, but there are
[00:36:01] other needle in a haststack type benchmarks that attempt to measure how well models are able to retrieve tiny facts of information buried in their context window. Gemini 3 does or 3 Pro does amazingly well at retrieval as well. So I I I think almost everything is going up and to the right at this point. >> Yeah, >> there was one one observation I had and I wanted to check with you guys what you think of this. When you have coherence at this scale, it implies we have systems level thinking inside these models. Is that accurate? >> Could you say a little bit more Selma about what that means? >> Well, because you’ve got essentially, you know, systemic thinking is one of the holy grails of deep deep reasoning, right? Because you can look at the entire patterns of things shifting. And it looks like feels to me like we’re at this level of AI competency, you can get to that kind of systems level thinking. That means you can do world modeling at a really powerful way using uh almost you shift the whole thing into symbolic reasoning almost when you can think in those concepts. So don’t we get to that
[00:37:01] level very quickly? Now I have so many thoughts but the the first thought that immediately jumps out at me is of course these are world models and of course they’re able to symbolically reason. They’re they’re solving math problems and they’re writing source code. I I I would argue that that uh in past you you’ve seen some commentators argue that there’s some sort of nebulous neuros symbolic type advancement that’s waiting to drop. I think that’s utter nonsense. Of course they’re able to reason symbolically that the the tokens are in some discrete space and of course there are systems level thinkers that they’re able to solve PhD level problems across dozens of disciplines. That requires understanding the world as a system. So yes, >> yeah, I I I agree with that and it turns into a philosophical debate and nothing great usually comes out of it. But I will say that that this is a 7 trillion parameter class model and last year all the naysayers were saying, well there’s there’s evidence that things will slow down because last year we’re at a trillion parameters and they were
[00:38:00] clearly wrong. You know, when you went from 1 to 7, we know next year is at least a 10x and up to a 40x step up in raw horsepower. And the naysayers are saying, “Well, things are going to level off unless we crack through some other level of system two-level thinking.” But they’re clearly not leveling off. And I I would challenge the technical audience out there looking at at these benchmarks to you. You’re you’re almost obligated to think about two things if you’re in allincclined. One of them is where on these benchmarks does it become self-improving? read all of Ray Herzwhile and really have an opinion on that because that’s tied heavily to benchmarks 1, four, and six on this slide and to just have an opinion. I have my opinion, but have an opinion about where you need to be on 1, four, and six in order for this thing to improve its own algorithm. That’s a critical point. And then the other one is where do you need to be on the benchmarks to start proposing cures to diseases and being right. And if you work in anywhere in health tech and you
[00:39:01] have no opinion on that topic, you’re doing a disservice that’s bordering on, in my opinion, bordering on negligent homicide because this can save lives if you work on it, if you apply it to whatever you’re doing in health tech. And it’s you’re obligated to get your head out of the sand, look at this podcast, study the numbers, and at least have an opinion. And even if that opinion is no, it’s not going to work. That’s fine. I’m okay with that. But to say I don’t know or I didn’t listen to the pod, that is absolute negligence. >> Can I ask a question to uh to you Dave and and uh Alex? You know, Yan Lun comes out saying we have gone down the LLM rabbit hole. Um and that’s the wrong direction. Uh we’re optimizing on that. We need to go through a different uh evolutionary tree to really get to AGI. What are your thoughts? All the old people say that and all the young people don’t. When that tells you something out of the gate, you know, you’re sorting yourself into an age bucket just by just by saying it. Um,
[00:40:01] and there’s definitely a philosophical divide in there. But the question I would ask isn’t is there another innovation that we need, it’s whether a human will have that innovation or this exact AI scale will have that innovation. I would bet that’s I would bet on the AI. Either way, either way, we have so much more, so much to absorb just from where we are now. Forget everything else that may come along later. >> Yeah, >> I think there are also many p many paths to AGI and I I know and respect Yan’s work. And I I know he favors an approach toward AGI that’s more focused on actions in an embedded space rather than in in terms of [clears throat] auto reggressive models. That may be a perfectly legitimate approach as well. But when I see the scaling laws continue to hold and capabilities continue to go up and to the right without any new paradigms, it it makes me think maybe really we can just continue scaling and don’t need to worry as much about yet another paradigm shift >> and let AI do that. All right, let’s go
[00:41:01] on here. Let’s >> insert my normal rant about AGI here and we can move on. >> Okay. Yeah. So, so noted uh and and approved. This episode is brought to you by Blitzy, autonomous software development with infinite code [music] context. Blitzy uses thousands of specialized AI agents that think for hours to understand enterprise scale code bases with millions of lines of code. Engineers start every development sprint with the Blitzy platform, bringing in their development requirements. The Blitzy platform provides a plan, then generates and pre-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
[00:42:01] native SDLC into their org. Ready to 5x your engineering velocity? Visit blitzy.com to schedule a demo and start building with Blitzy today. [music] >> All right, next story. OpenAI introduces GPT 5.1 for developers. So, uh again, this is a benchmark question. Uh first of all, this was announced before Gemini 3 came out. So, I’m curious AWG whether this is still the case and why again why does this matter? Yeah, I I think the the economics of this, the microeconomics are maybe even more interesting than the technical side. So, we’re we’re starting to see and and and this is somewhat visualized in in the chart you’re showing the beginning of inference time compute start to conform to the economic productivity of queries. So, you know how like in Google search for example, if you search for meotheloma litigation, you’re going to
[00:43:00] see a bunch of very expensive AdWords ads. It’s a very economically valuable query. On the other hand, if you >> search for the lawyers for the lawyers, >> for the lawyers, uh if you search for like an arithmetic query, you’ll see none or or almost no ads because it’s not that economically valuable. We’re starting to see, I think, that same dynamic here emerge here where certain queries require lots of inference time compute. And so what we’re seeing at the routing layer with GPT 5.1 is even more compute being allocated to queries to prompts that really require a lot of compute. And then for the the lighter, easier queries or or prompts, we’re seeing less compute get allocated. And I I think this actually pretty profound. It’s not just a matter of moving around the the deck chairs in some sort of zero sum game. I I think this is actually almost a premonition for what the economics of post super intelligence will look like. I one of the things I I think the most about is who’s going to
[00:44:01] pay at the end of the day for the trillions of dollars of capex in data center buildout? Who’s going to pay for it? Is it going to be the consumer? Will will the consumers on average be spending hundreds of dollars per month on core subscriptions for AI or will it be enterprises that are spending billions of dollars in in some cases for enterprise level tasks and I think what we’re starting to see here is that modally probably it’s going to be the enterprises paying lots of money for the most valuable tasks in in the same way we’re seeing right now in microcosm some of these harder tasks harder prompts get allocated a lot more inference time compute at the expense of easier queries. >> I would totally bet on that direction just because if you’re say Target, you can manage merchandising and get 20% extra margin on something, then it’s worth the extra compute on the back end and we’ll see a lot of that. >> But there are places where consumers will spend hundreds of dollars a month on their iPhone, on their plan because
[00:45:01] it enables them in an extraordinary fashion. But remember that the the money to be made here is on the margin from persuading people to switch their behavior from what they otherwise would have done. If if they were going to spend the money anyway, that money doesn’t go to the AI. It goes to the entire value chain underneath the the phone manufacturer. >> Mhm. All right. >> Well, I can tell you in my experience, you have to operate at the margin at the extreme end of what these are capable of. And I’ve tried to either save money or to get more speed by dumbing it down by a half step and it just isn’t the same. And so it just feels like everybody wants to be at the forefront. And this is the weirdest product that’s ever been launched on humanity in that it’s it’s talking to you as it’s selling to you. >> And so, [clears throat] you know, you start with a subscription, they give you this incredible experience, and then it tells you, well, you want more of that, you need to upgrade. But it’s actually telling you, it’s talking to you about upgrading. No product, you know, no
[00:46:02] cable company, no iPhone has ever done that before. So, it’s a it’s a salesman baked into its own capabilities. And it’s it’s kind of creepy, actually. It’s kind of it’s very weird. [laughter] >> All right, let’s stay on the OpenAI theme. And this is a fascinating story. It’s an important one. OpenAI backed startup aiming to block AI enabled bioweapons. So, this is a startup called Red Queen Bio. Uh, and they received a $15 million investment from OpenAI, which by the way just sounds really small compared to all the hundred billion and trillion dollar investments being made. But Red Queen is using advanced AI plus lab testing to spot vulnerabilities in biological systems. They’re basically saying, “Hey, we want to stop people from using these AI models to create bioweapons.” Super important. Um, who wants to jump in first? >> I I’d love to speak to this one. Maybe if we’re starting with the the literary reference. So, for those not tracking,
[00:47:02] Red Queen in this case is a reference to a scene in Through the Looking Glass where Alice and the Queen are constantly running just to stay in the same place. So, the Red Queen’s race in in general is used as a a metaphor to to cases where a lot of effort is required basically to maintain a standstill. And in this case, I I think the other key concept that I I think is ultimately quite profound out of what Red Queen Bio has announced uh and the reason why they’re taking funding is we we’ve just spent quite a bit of time talking about how as you pour more compute onto these models, the capabilities keep increasing. >> Inevitably, you have to worry about alignment and safety as well. In society, if you you’re growing a city and you double the population, you’re going to approximately want to double the police force or the safety force. Wouldn’t it be wonderful if as the capabilities of AI keep scaling, keep increasing, the safety measures, the
[00:48:00] alignment and other properties that make them safe for humanity if those also benefit from scaling with more compute. So seeing scaling laws and Red Queen Bios announced that they’ve uncovered scaling laws for biological safety measures. I I think this is the way we achieve alignment just like again the scaling law for police forces in [clears throat] a city little bit sublinear relative to population. Same idea here, but but nonetheless close. As capabilities increase, we want to live in a world where we achieve so-called defensive co-scaling, where the the resources and capabilities of safety measures scale close to proportionally with the resources and capabilities of the underlying models. >> Yeah, let me add some data to that. So today or at least last year in 2024 the biocurity biod defense market was $34 billion and it’s expected to double by a decade from now 2034 2035. But here’s
[00:49:01] here’s the quote that really hits me, right? So an extreme bioattack scenario um could have a multi- trillion dollar global loss. And the notion is could you create such a boweapon for a thousand bucks, right? It’s the asymmetric situation where a small amount of money using complex models could do a lot of damage. And so there’s got to be this layer of defense. I mean, it’s critical. When I when I talked to Eric Schmidt, I remember a couple years ago at FI, you know, the number one scenario that is of greatest concern are bioweapons. uh something that can be you take an existing virus, you change its viral payload, uh you make it much more infectious and and release it. You know, See, you and I have had this conversation that one of the most important things is going to be to set up these biosensing capabilities at train stations, airports, bus stations that are filtering the air and looking
[00:50:00] and doing rapid sequencing of everything they come across. You know, the majority of the bioweapons that are concerning are airborne, right? So a person coughs or sneezes and it’s it’s there. Uh and one thing that is in our favor is that these these viruses these bioweapons can only move at the speed of an airplane. That’s the fastest it can go, right? Um and it travels. We saw that in uh with with the release of CO. So if you can detect it at an airport, uh sequence it on the spot, develop an antiviral and then transmit that at the speed of light, not the speed of of uh 600, you know, nautical air miles hour. Uh then you have a chance of battling it. Um and this >> is also exactly why uh why open source AI is dead in America. You know, Meta decided, okay, we’re not open sourcing. So now none of the US labs are open
[00:51:01] sourcing anymore. So the only open source models are coming from China. But uh you know if you’re a US company, you know, usually a terrorist in a basement in some you know jurisdiction somewhere in the world isn’t the sharpest tool in the shed and you’re counting on them not knowing how to build the weapon. But when you give them genius level AI as a as a sidekick, you know, suddenly they’re empowered to build virtually anything in that basement. And that’s the risk. No US company wants to be responsible for that. So they’re trying to cut it off at the query level saying as soon as you ask the AI to help you with create a bioweapon it stops >> and so the open source you know would be a huge leak in that. So the US labs don’t do the open source anymore. The Chinese still do. So >> Alex comment on that. >> How do you deal with that if it’s a model running on my laptop um and somehow it contains enough knowledge to do this and I can query my laptop. No one ever knows the query I’ve made. It’s just resident there. How do we deal with that? >> Yeah, I I I think ultimately it all
[00:52:02] reduces to co-scaling. So if if you imagine having a fully self-contained facility hypothetically in in your basement uh and the the ultimate societal protection will be having lots of sensors and more importantly having lots of AI screening super intelligent AI screening that can spot hidden agents. I I I have th this dictim that I I think is super important on so many different levels in in the software engineering world. Uh there is uh Lenus Tvoltz who who created Linux h has this so-called Torvaltz law that and I’m going to butcher this slightly that with enough eyeballs all bugs become shallow and and I I [clears throat] would propose sort of a generalization to that that with enough super intelligence all hidden agents become shallow. >> To the extent that we have hidden agents in their basement building super weapons I would expect with enough super intelligence defensively coscaled they
[00:53:02] become shallow. So, I made I’ve made the comment if I made the comment before that, you know, that privacy is an illusion. Um, and this is just going to shatter even that illusion because if you want safety, you’re going to want agents, you know, uh, listening and watching everything all the time. See, >> this is an arms race. I think what we’ve seen throughout history when we tried to we thought oh my god email’s going to crash because of of all the scams and then we thought we had fishing we can’t solve for that and we’ve used uh AI consistently in that sense because people forget the bad actors may use AI and they will but the good actors can also use AI and therefore you just have to be one step ahead the question is if that gap gets too big one of the challenges with what you were saying earlier Peter is you may not know what to look for in some [clears throat] of these and That’s the danger point. >> Yeah. An interesting little case study too because uh you know if you rewind the clock before Gmail took over uh you
[00:54:02] know Microsoft had Outlook and Hotmail and Google launched Gmail. And the two promises were very different. Microsoft said we will never read your email. >> And Google said we will read every word of every email that you receive but it’s going to be read by an AI and not by a human. So we won’t let the human eyes look at your email. but we’re going to do all kinds of things based on the information in your email read by the AI and people didn’t care and so everybody moved to Gmail. So you have an interesting case study in the in how this plays out you know just the human behavior. So here I think the equivalent is hey I’m talking to AI about my most personal things in the world and Peter I think you’re right the the AI is going to listen to every single word and if you’re designing a b bot terror weapon or a cyber cyber attack it’s going to flag it and escalate it and if you’re talking about your virtual girlfriend or whatever it’s going to be fine I’m just going to kind of hide that. >> Yeah I >> I remember talking to the head of one of the major intelligence agencies and they
[00:55:00] had a very clever thing. And they said, “Look, when there’s known things like nuclear weapons or whatever, we put eyes on it. We try and watch it. When you have something like this that could be developed in secret, they’ve been actively open up opening up these communities and actually funding the biohacking uh movements because then you can see things earlier. But this is if you can do open-source bioweapon development in a lab in a bunker, that really causes a huge issue. We’re going to have to rethink an approach something to along the lines to what Alex said. You remember when you gave the Ein Rand award to Mike Sailor? I don’t know if you you did the keynote. Mike did this incredible speech, but those people are probably vomiting right now based on how this is evolving. >> Yeah. Well, listen, the the bioweapon I mean, you’re not going to create a novel virus that has zero uh history involved. And there is there are extensive um registries of every virus that’s ever been ever been mapped. And so when at an airport if you identify if you sequence
[00:56:01] something and it’s not on that registry you can then look at it and and LM or the future bio LMS will be able to look at okay this is an infectious agent. This is something that’s able to be airborne or water soluble. You know when you look at the proteins you can you can tell what kind of a of a virus or protein it’s it’s generating. So you’re going to you’re going to be able to learn instantly um when you sequence it. and rapid sequencing is here. Uh but we’re going to need this and I think giving up privacy to a large degree which you’ve talked about sem right when you’re in an airport you basically have given up your privacy right there. >> Yeah. You know you’re being surveiled and you know your rights can be taken away at any time and and the one framing of a rear is that we’re living essentially in a global airport. I think that continues to some extent. I don’t see a way of coming back from that. >> Well good luck although Brad and the EFF folks they say there is a way of doing it. you don’t have to compromise privacy for security. There’s lots of mechanisms for solving this in other ways. That’s
[00:57:01] their complaint that that governments kind of go after the surveillance side just because oh this is great we can surveil people under the excuse of security. Um but many times you don’t have to. >> Yeah, if I might close the the discussion on this. I I I want to make sure we don’t overindex on safety concerns or so-called safety. I I think these are very important concerns, but I also think that AI can be scaled to combat the concerns just like one might naively expect prior to the development of like modern cities that crime would be overwhelming and that humanity would not be able to support itself in urban environments at scale. Turns out that we are able to. I would also, we’re not doing a book corner this episode, encourage everyone to read Verer Vji’s Rainbow’s End, which does a glorious job of depicting what the future of AI enabled biosafety looks like. >> Amazing. Well, uh, I’m, you know, the eternal optimist here, and I’m absolutely clear we’re going to be able to overcome this. Let’s move on to one
[00:58:02] more benchmark here. This is XAI releases Grock 4.1, ranks number one in major leaderboards for reasoning and writing. Uh back to our resident uh leaderboard expert. >> My comment on this one is short. Th this lead in the text arena benchmark lasted approximately 1 week and was over. [laughter] So my my my short comment here is the race for the frontier is so intense that even if Frontier Lab is perhaps even benchmaxing towards a singular benchmark, generalist models seem to be able to push the Frontier at this point on a weekly basis. I can only imagine as timelines progress what this is going to look like when these benchmarks are being toppled on a daily basis. >> Well, I’m sure Grock 4.5 and five is around the corner. Let’s move on to cursor. So, cursor triples its valuation in just a few months from June through November going from roughly 10 billion to roughly $30 billion in 6 months time
[00:59:02] raised 2.3 billion. Uh there’s Michael the CEO of Curser. Uh who wants to jump in here? I mean, this is a hot race between a whole slew of different uh coding tools out there. >> This seems to be in Dave’s wheelhouse. >> Dave, yeah. What do you think? >> Well, I’ll tell you, [laughter] I I I think this team is phenomenal and most of the people around here think that they’ll rise to the occasion and and succeed. But I also think that anti-gravity looks exactly like cursor. [laughter] I mean, like I actually have both open on my laptop side by side and other than a little cosmetic here and there, you don’t even know which one you’re in. >> And so then you look under the covers and it’s like, well, I can access all the models through Cursor and I can only access Gemini 3 through anti-gravity. So there’s a difference right there. But then the bet at Cursor is that that the Anthropic and the other models will be worth having and Gemini 3 doesn’t just run away with it anyway. So, it’s really
[01:00:01] it’s an interesting horse race right now. And I’m I’m not going to make any prediction on it. Um because because you can’t make a prediction on it because their core positioning is incredibly vulnerable, but the team is brilliant. They’re well capitalized. >> Back up for those who don’t know what cursor is or what it does. >> Um fair. >> Let’s let’s do that basic 101 right now. >> Dave or Alex? >> Yeah. So, Cursor, I think everyone around here that I know uses it every day. It’s it’s the you know it’s the best or has been the best coding assistant that uses AI. It’s fully agentic now. So you can you can just type in a prompt. You can talk to it now too. Uh and it’ll just build things for you. And under the covers though they don’t own their own foundation model. It’s going out to either uh you know OpenAI so or or Grock or it has all of them in there. Enthropic is what I usually use cloud 4.5. Um and it organizes everything. It cranks out the product. you know, configures your laptop for you. It’s just, it’s just makes coding trivially simple. Anyone can do it. Uh,
[01:01:02] and it’s pretty universally used. >> It was early to market. >> When I think about the value of uh in this world, where does value aggregate, right? My list is it’s data scaffolding, user experience, and integration and customization, right? And then the models themselves. So, where would you put cursor in those categories? the scaffolding >> anything other than the Yeah, it’s it’s all the above other than the models themselves >> compared to replet we’ve been talk we’ve talked about replet a bunch um and lovable how do they compare it to cursor >> so replet and lovable are much more for for your mom and pop who want to build some like a video game quickly or an invite to a birthday party with moving graphics or whatever you can build something while you’re flying your plane Peter like you [laughter] did super super easy to onboard cursor is more for hardcore engineers that are moving to AI and trying to get 10x more performance out of their engineering.
[01:02:01] [clears throat] >> I would just note for what it’s worth, all of these or almost all of these integrated development environment companies including cursor are rolling out their own firstparty models. It it’s almost inevitable that they want to climb down the stack to own more of their software supply chain. And I I I think the success that we’re seeing from cursor, which is of course very exciting, is a reflection that software engineering is probably the first high productivity labor category that’s being automated by AI. Won’t be the last, but it’s the first big one that we’re seeing. >> All right. Surely surely AIdriven software development is now the default, right? I mean, you couldn’t do it without it now already in a few months >> to the point where I mean, this is crazy, but I I I see companies that are almost treating potential software engineering hires by vintage. Did they get their their degree and their experience prior to a code or not? Uh like >> are they are they spoiled? [laughter] >> Have they been ruined?
[01:03:00] >> Basically, yes. Did did they get their skills? Did did they learn? Did they have lots of experience prior to the the atrophying that comes perhaps with agent coding? >> All right, I’m going to move us forward to another incredible article. Uh this is a new startup funded by Jeff Bezos called Prometheus. Uh Jeff put in $6.2 billion. And by the way, can I just like call out the ability to start a company with $6 billion on your balance sheet uh is got to be just frightening for a number of startups, right? And and has got to be incredibly accelerating. We’ve never seen this kind of, you know, starting with billions, multiple billions of dollars on day zero. So what is project Prometheus? Uh it’s an AI enabled engineering and manufacturing. It’s basically learning real world experience so they can manufacture efficiently um and focus on physical testing and simulations. And I love this other bullet point here. Prometheus has hired nearly a hundred researchers from
[01:04:01] OpenAI, Google Meta, and other labs. They’re just feasting on each other. They’re stealing each other’s, you know, well trained. >> If the going rate is a billion dollars per researcher, then this is really underfunded. >> They’ve got [laughter] they’ve got six researchers on this. But I find that the the two things I found fascinating off the top, we’ll talk about the meat of what Project Prometheus is in a second, but is starting with that much money and that uh they’re basically stealing from each other. Uh Dave, what do you think? >> Well, I mean, it’s funny. I’ve have probably 12 meetings with different uh MIT teams in the last week, uh you know, 30, 40, 50 at a time, and about half of them are computer science. The other half are not. the other the half that are not are saying how do I get involved what do I do what’s my AI role like you know when when micro strategy started Mike Sailor was an aerastro all the rest of the guys were computer science the company took off under Mike’s leadership it didn’t matter what he studied AI is like that there’s
[01:05:00] nothing in the computer science curriculum that teaches you much of anything anyway don’t be intimidated >> yeah and so you what you’re pointing out here on this slide Peter is okay they stole another hundred people okay clearly Clearly, the industry wants hundred,000 more 100,000 more people to come in. Why are you letting this guy get a a billion dollar signing bonus? Why don’t you get into the market, learn this stuff, and [clears throat] be be there for a hundred million, you know? I mean, just get in the game. But, you know, it’s funny cuz uh people get intimidated away from from it because they feel like it’s it’s all geniuses, you know, and and I I’m not I’m going to get crushed. It’s just not true. Just get in the hunt. Get into the game. This is the thing happening in the world now. >> Yeah. >> And there’s usually only one thing driving all change in the world. This is that thing. So just just get into the middle of it. And then Jeff will Jeff the other thing I’ll point out in this is that um there’s a tendency to be intimidated by you know Elon Musk spent six or seven billion dollars building a
[01:06:01] massive data center in record time. How am I going to compete with that? But the foundation models that will do parts creation or robotics simulation or whatever are different enough from a large language model that you can build a great foundation model company in parallel with OpenAI and Grock and Meta and and Gemini. It’s okay. You shouldn’t be intimidated by that either. And that’s I think what Jeff is saying here. Just one final point. Um Jeff bought all the robotics companies, put them into warehouses and just ran away with with warehouse automation which created a whole litany of new startups working for Walmart and Target and everyone else like Symbotic, you know, where Daniellea Roose is on the board does the the robots now for Walmart’s uh warehouses. Here, Jeff is saying, “Okay, Amazon is big enough that I’m actually going to be able to build a multi-billion dollar company within our own universe, our own channel.” >> But that creates opportunity for
[01:07:00] somebody to be outside of the Bezos universe doing it for everybody else. >> And so, so design is wide. >> We’re going to have Jeff Wilkkey on stage at the Abundance Summit this year. Jeff was the CEO of Amazon Worldwide. There were two divisions. One was AWS and one was everything else. and Jeff Wilkkey ran everything else and he’s actually uh you know super excited about this because this is what he’s doing. He’s got a company called Rebuild Manufacturing uh which is working in this area too. So Alex, let’s get into the nitty-gritty here, right? So he’s building Prometheus is building physical AI. It’s world models again like Feay Lee and a little bit like Genie 3. These are world models understanding the laws of physics and chemistry and engineering. So you can actually do real optimization. What are your thoughts here? >> Yeah, I think we’re starting to see the pivot of the capital markets from funding super intelligence to funding that which comes after super intelligence which is as I’ve argued in
[01:08:00] past solving math, science, engineering and medicine. And I I think it’s a 10x 100x larger market opportunity, larger addressable market solving basically everything else after solving super intelligence than solving super intelligence itself. 6.2 billion is a drop in the bucket. I I would expect it’s going to cost many many trillions of dollars in funding to solve all outstanding problems in math, science, engineering, and medicine. There’s been relatively thin reporting on what Prometheus or what project Prometheus is particularly focusing on. I I have taken note it seems to be absorbing a lot of old biology friends of mine. So it’s possible maybe it ends up focusing a little bit more on biology, a little bit less on manufacturing. But I I think this is where the action is after super intelligence. >> Yeah, I have three points I want to make here. One, this is kind of a shift from chat bots to industrial agents, right? So AI for the office is what we’ve had. This is AI for the factory floor where
[01:09:01] there are physical consequences where the systems are able to operate the factories because they understand the physical uh constraints and situations and logistics. The second thing is you know I met Jeff in college. I was the chairman of SDS uh worldwide at one point and uh and Jeff was the president of SDS at Princeton University when I was in MIT. And so space has always been his passion. You know, congrats to Blue Origin for its recent launch and landing. We we talked about that last time. But this kind of a physical AI system is exactly what you need to operate heavy industry in space, right? to build factories in orbit, to build factories on the moon and to have them fully autonomous and capable. And then the final thing I would say is that um this is going to change and we’ve seen companies like Laya and other companies out there that are going to go from
[01:10:02] invention that happened by you know serendipitous human creation uh to invention coming from a computational one and that’s when it gets super interesting and that’s what you know you’ve been talking about my friend Alex. >> That’s right. What with hip beef with this is this felt to me like he’s creating a backbone AI for everything in his world Amazon space logistics etc. This will service all of those >> and Elon will do the same of course. >> Mhm. >> Like electricity it’s going to run through everything. >> Yeah. Yeah. >> Well also you know I it it the foundation model that I built early in my career was five years from the day I started writing the code until it was done. uh I can recreate it now in about 2 months uh which I just did. Um and so if you look forward a year that’ll come down another you know 5 10x. So you can use AI to build the next AI which is essentially what I what I just did. Uh
[01:11:00] the same applies in mechanical design. So if you said wow building an entire AI platform that designs rockets or designs uh you know robots is really hard. Well, it would have been, but now you can use the old the current AI to build that AI >> and it cuts the time down tremendously. So, if you if you just look forward a year to where where the existing AIs will be, you know, that time is actually not intimidating at all. And so, it’s it’s a good reason to get into the game and, you know, build build these parallel AIs that work on very specific problems, whether it’s biotech, whether it’s mechanical design, whether it’s you know, futures trading, whatever it is, >> build [clears throat] it from the old AI to the new AI. So, the last time I asked our subscribers, and by the way, we’re almost at 400,000 subscribers. So, if you haven’t subscribed yet, push us over the top. We’d appreciate it. Um, our march is towards a million. Uh, not that it really matters other than it’ll make my kids really proud of me. So, that’s my goal. Um, so one of the I asked our subscribers to post questions.
[01:12:00] >> You’re on your way to Mr. Beast. >> Yeah. Well, hey. Yeah. In about a thousand years. Um, I asked uh our subscribers to post questions and uh I took the all the comments, put it into chat GPT and asked for it to summarize the most important questions and uh there was a critical question uh that was asked and um I just want to take a second and and read it because I want to have an AMA about it. It said, “What concrete milestones should people expect to see that prove abundance is coming?” In other words, lower costs, new industries, accessible AI tools, and how do we ensure these benefits reach everyone rather than concentrating wealth among a small AI augmented elite. So, I want to play a video that that was posted on X today and then we’re going to talk about this this question. >> But but AI and humanoid robots will actually eliminate poverty. And Tesla won’t be the only one that makes them. I think Tesla will pioneer this, but there will be many other companies that make humanoid robots. But there there is only
[01:13:02] basically one way to make everyone wealthy, and that is AI and robotics. >> All right, so that’s Elon’s thesis. I posted the question here again. Um, and it’s a real concern. You know, are we going to have runaway wealth concentration? Um and honestly if you want me to believe in this future of abundance you keep talking about guys you know what are the concrete milestones in how do we ensure these benefits reach everyone how do I know it’s actually coming >> and yeah let’s jump into this c >> can I throw out a couple of points >> you know this there’s an important framing here where we let’s not talk about the wealth gap right the reason is that the richest people in the world are always going to keep getting richer and the poorest people are going to have The issue is more can you lift the bottom if you cares right you make this point all the time my next book right I mean a thousand years ago the king and the queen on the hilltop uh lived below
[01:14:00] poverty today by the way and there was thousands of surfs that supported them and what we’ve done >> they died of a tooth infection at age 22 >> or they or they were bled by leeches you know as the king and the queen up there and what we’ve done is yes we’re heading towards a world where there are trillionaires living on Mars. But if every man, woman, and child is got access to all the food, water, energy, healthcare, education they could possibly want, we’ve we’ve lifted the bottom of humanity to a point where mothers can believe their children have access to everything they need. That’s the world I want to live in. That’s the world I want to create. >> So, let me speak just to that for a second, right? We forget because we see all this. We see people getting richer, etc., etc., But we have to remember the unbelievable benefits occurring to every level. I’ll give you a concrete example. When the tsunami hit Indonesia in 2004, all the shiptoshore communications were wiped out. And so the government gave cell phones to all the fishermen saying, “Hey, if you’re out fishing, you see
[01:15:00] another tsunami, texted in, etc., etc.” And they found to their surprise that their their uh incomes had increased by 30% over the next 2 months. So they looked into it and all they were doing was texting in to see what the market price was at the of the fish. Should they stay fishing? Should they come in and sell >> or or which port they go to, who’s paying more? >> Yeah. So So now just that little uh hint of what Alex would call the inner loop allows you to increase income pretty radically by having the democratized access and demonetized access to cell phones, smartphones, and now AI. And this will change the game completely for everything everywhere. I’ll touch two areas. One is education. You can now sit a child down with a smartphone and say, “Create a lesson plan for grade seven algebra and they’re going to learn 10 times faster than all the kids stuck in elementary schools in in the the west that are by law have to go to these things, right? The second is healthcare where every single medical condition can now be diagnosed instantly and when you
[01:16:00] get something early, it the cost of treating it drops by like a 100x. So those two are very concrete areas where AI will make a massive difference in two areas that were traditionally inaccessible and hard to get and expensive. >> Let me read the numbers here. So the US average expenditures uh for a family and this is 2023 was $77,000. So the number one cost was housing, uh 30 33% goes to housing, 17% to transport, 13% to food, uh 12% to insurance and pensions, uh 8% to health, you know, 5% to entertainment, and about 2 and a half% education. So let’s knock these down. Housing, right? So, number one, um, now you can live outside of the city where it’s cheaper and be able to telecommute in, right, and reduce your housing cost. There is a future, it’s not here yet, where we’re 3D printing
[01:17:00] houses, reducing the cost. And what we saw on stage a couple years ago, if you remember, Seem, was 3D printing houses being per square meter the cheapest, but also the most beautiful and most luxurious cuz you could get the greatest designers to create a standardized, you know, print file for people to use. Uh, transportation 17%. Well, guess what? An autonomous electric cyber cab is four to five times cheaper than owning a car. It’s going to be cheaper than an Uber X, cheaper than a a bus. So, we’re going to solve that. Food. Um, we’ve got to solve food better. We need, you know, uh, basically vertical farms and stem cell grown meats. >> Let me give you the stat on vertical farms. Yeah, >> we, you know, we’ve been doing horizontal farming since the beginning of time, right? Vertical farming is just crossing over now into economic viability. You can drip feed water to the plants. uh you know what nutrients the plants need because the sensors know it. You get about seven times the yield of horizontal farming by doing things
[01:18:00] vertically because you have the right frequency of light hitting it. You save 99% of fresh water and by the way we use 70% of our fresh water globally to agriculture. So just that the best calculation we’ve seen is if you took 35 skyscrapers in Manhattan turn them into vertical farms that would feed the entire city sustainably. Okay. Just think about that from a logistics, food security, um, pesticides, fertilizer. There’s massive changes coming down the pike. And this is before we apply AI to the whole mix, right? So the the radical changes coming are going to be so huge that the cost of everything should drop to near zero. The amount of energy you need to feed one person is the amount of sunlight hitting one square meter and that would that energy would feed somebody for a year. So all we have to do is get a better loop of figure out how to convert that energy into consumable foods and we’ve got a long way to go. >> Yeah. Uh you know health care 8% of our cost in healthcare. You said it already. We know that an AI physician
[01:19:02] diagnostician is significantly better than any uh and even in the best you know physicians and a autonomous robot eventually will be the best surgeon and the cost of that will be capex and electricity. I mean, it’s hard for people to believe this stuff now, uh, because it’s on the on the bleeding edge, literally, but we’re going to get there. Um, entertainment, 5% of cost. Well, guess what? I mean, YouTube, you know, what else could you want? Education, you mentioned before, AI, YouTube, all these things. So, [snorts] we’re we’re demonetizing and democratizing this stuff. It’s just hard for people to realize it. I think the challenge is we compare ourselves to the Kardashians, right? We compare ourselves to people uh that we see on TV and on the internet all the time versus comparing ourselves to what it was like for our parents or grandparents. >> Yeah, I think that last point is the key
[01:20:00] one because uh you know, we’ve we’ve had dirt cheap food for a long time, but everybody still wants a $14 Starbucks latte, which you don’t need to pay for, but but there it is. and why why do I feel that need? So the the metric I’d be tracking is actually depression rates and you know because I I think AI properly deployed can can hit that much more quickly than it can hit robotic automation that creates new homes for everybody, you know, that are 10 times larger. >> That’s a great point. >> And so I’d be looking at that one as an early indicator that we’re on the right path. And it’s not a no-brainer. You got to really think it through because, you know, you mentioned rent is at the top. You know, 33% of household income gets spent on housing on average. But when you look below the poverty line, I think spend on drugs, alcohol, and gambling pain relief >> is three. Yeah. The opioid addiction alone is a trillion dollar >> uh error, I guess. >> And it’s about five times more collectively than rent.
[01:21:00] >> Go ahead. >> Well, no. So, I’d be attacking that you if you want to come bottom up and say, “Look, we we want to create universal happiness with AI. We’ve never had a tool that could attack it before, right? You can attack manufacturing automation. You can make food cheaper. You can have, you know, harvesters that, you know, mow down half the Midwest to create wheat. But all that does is create more of stuff that’s already abundant. >> Yeah. >> Now, AI is the is the trigger for a massively more thoughtful way to create universal happiness. And I I would start with depression rates and work up from the bottom because you can do that very very quickly, much more quickly than you can we’ve already looked at the robotics. We know it’s going to build a mansion for everybody in the world, but we’re not going to have the robots for about 15 years because we have to scale them up on this exponential curve. So, you know, some people will have them next year, you’ll have yours this year, but we won’t have enough of them to attack the global problem for about 15 years because of this, you know, just the manufacturing, you know, rate. >> Alex, this is all about benchmarks.
[01:22:00] We’ve talked about this. You and I have been working on a paper on this subject. Can you >> can you speak to that? >> I think it’s so simple. I I think what’s upstream of all of these other milestones is the dollar cost per unit of intelligence. And as we’ve discussed previously right now, that’s hyperdelating by something like 40x year-over-year. So to to keep the party going and to make sure that all of these downstream considerations, cost of living, healthcare, housing, etc. that that these all hyperdelate ultimately alongside cost of intelligence. I think it’s largely a regulatory and and social concern. We’ve spoken previously about, for example, the difficulties of getting Whimos in Boston. That’s a regulatory consideration. The the cost of intelligence needed to autonomously drive cars around. That’s making excellent progress. But ultimately, in order to say provide essentially free autonomous ondemand transit to everyone, there’s a regulatory bottleneck. And in order to avoid making or in order to
[01:23:00] ensure that the benefits of intelligence too cheap to meter become evenly distributed, I I think it’s going to require some revision of social coherence and the social safety net to make everyone comfortable with the downstream consequences of intelligence too cheap to meter, including healthcare and housing and energy and utilities too cheap to meter. >> Yeah, >> I I did a calculation. Okay, so if you wanted to have a reasonable life, you could do it for $20 a day in Bali. Okay, housing costs [clears throat] about $10 a day and your meals are literally about $2 a day and then a bit of extra. Okay, so for about 20 bucks a day, you could do it. If you had half an Ethereum, which is about $2,000, you can put it into DeFi trading pools and earn about a percent a day, which is about $20. Okay, so half an Ethereum of capital allows you to live uh crudely, but allows you to live in a very lovely spot in the world for near
[01:24:01] very low cost. And think about just that feedback loop on that because as you double that, if you triple that, if you 10x that, all of a sudden you get into a really great place. You can survive today on a very small. >> My feet are in the sand. My feet are in the sand already. I’m ready. And of course, See, that Ethereum comment was not investment advice, just to let everybody know. But it is interesting that Harvard is double down on Bitcoin. Uh, and now that we’re in the Bitcoin doldrums, it’s nice to see the institutions. I mean, I remember when we went from like, you know, wacky individuals buying crypto, uh, to now institutions and financial institutions and sovereign funds and so forth, but >> countries also >> not investment advice. All right. What an amazing episode. And we’ve actually just gone through half of our half of our stories. But I think to make this consumable because the feedback we’ve gotten from folks is please try and keep the episodes under an hour and a half. So >> we’re listening uh over uh over
[01:25:01] comments. We’re trying we’re trying hard. So we have to we’ll have to spin up another conversation on everything going on in data centers and energy and space and so much. I mean it’s it’s hard during the during the singularity to keep up with everything. the mind-blowing stuff from Gemini 3 was worth covering properly. So they left it. >> You know, just a reminder, last summer, not that long ago, uh Poly Market had said everybody, you know, the top five had an equal shot at being the best AI model by the end of the year. Now it’s 91% Google, but by next summer that’s down to 60%. So it’s sort of like 50/50 that someone else will take a lead by next summer. >> Lead project. Well, that’s what we should hope for because cuz Alex said the key point as usual, 40x is what you should expect next year. 40x people really struggle 40x in anything. So if the cost per intelligence comes down by 40x or the just raw intelligence goes up by 40x next year, you should expect that. Very hard to visualize all that
[01:26:00] all that that means. So, we’ll do everything we can on the podcast to try and make that tangible for people, but really try and digest that, you know, coming out of this Gemini 3 incredible breakthrough. >> Yeah. And and just hats off to Josh Woodward, to Sundar Pachai, uh to Demis Hassabis uh for an extraordinary job uh on uh on Gemini 3. Just so proud of what they’ve been able to create. Um and of course, a lot more a lot more coming. Um, and >> I made I have one announcement. >> Yeah, please. >> Sometime in December, we’re going to do a meeting of life session online. >> So, I’ve had enough clamoring for my community and other people and Peter people to go to abundance that people want to do it. So, stay tuned. We’ll get more details next time around. >> Well, we’ll do it also at the abundance summit uh on on the Wednesday night. Uh this is Sem uh waxing uh poetically and philosophically for about 5 hours straight. Will it’s like a late night
[01:27:00] French salon type discussion alcohol or equivalent mandatory on the metaphysics philosophy and what does it mean to be alive in today’s >> starts at 10 p.m. What time does it end? Dawn. >> It depends on the audience. But the crazy the crazy ones we’ve gone till dawn. >> Oh my god. >> Because we never get a structured conversation on the meaning of life. We never get that. So let’s have that conversation. >> Well, we’ll we’ll do it. You’ll do it. and I’ll I’ll join you for at least until my bedtime at 9:00 and then I’m exiting the building, >> but I’m going to do it online in about a month. So, we’ll >> we’ll do it do it earlier. Um, and last time we talked about the potential for a moonshot gathering, uh, we’ve had 500 of you email us. So, if we get to a thousand, if you’re interested in a moonshot gathering next fall, uh you can send an email to moonshotsdamandis.com. Uh and let us know you’re interested in having these conversations and gathering with other Moonshot listeners. And once again, we put our call out for outro music. Uh and this is a piece by John uh
[01:28:00] Novotney. Um and it’s called Moonshots Metal Version. But here’s the here’s the key. Uh, you need to see this. This is not just music. This is uh a fun video. See, you look so sexy, Dave. And and I love your ponytail, Alex. AWG’s got a ponytail in this and he’s rocking it. All right. Uh, on our outro, uh, let’s go ahead and, uh, and watch and listen to this. This is heavy metal moonshot music. >> Oh my god, I haven’t seen this. veins. >> Oh my god. [music] >> Oh, that’s a good one. Very gentle.
[01:29:02] [music] >> Oh, not to miss. Oh my god. Freaking this is amazing. [groaning and screaming] >> Cool. Bottling the lightning. [laughter] >> I got to get the ponytail on. Peter and Salem, your look in that video was really good. You should just do that. >> I I I love See with the with the with the sunglass move. Dave, you on the guitar and AWG uh you on the keyboards and the ponytail was you, buddy. You got to got to grow that ponytail >> apparently. So, >> go some lesson. >> Well, thank you, John, for that. That was amazing. >> Yes. Uh DB2, AWG, and Mr. Exo. Uh, have
[01:30:00] a fantastic week. Uh, I love I love doing this and thank you to all of our listeners. >> Great episode. All right, take care. >> Take care, guys. Every week, my team and I study the top 10 technology metat 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. [music] 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
[01:31:00] before anyone else. All right, now back to this episode. [music]