#208 Alexandr Wang - CEO, Scale AI

Alex Wang is the CEO and co-founder of Scale AI, a leading data platform accelerating the development of artificial intelligence applications. Founded in 2016, Scale AI provides high-quality training data for AI models, serving clients like OpenAI, Microsoft, and the U. S. Department of Defense. A former software engineering prodigy, Wang dropped out of MIT to build Scale AI, which is now valued at over $13 billion. Recognized on Forbes’ 30 Under 30 and TIME’s 100 Most Influential People in AI, Wang is a prominent voice in shaping the future of AI innovation and deployment. He advocates for responsible AI development and policies to ensure ethical and secure AI advancements. Shawn Ryan Show Sponsors: ⁠https://www.roka.com⁠ - USE CODE SRS ⁠https://www.americanfinancing.net/srs⁠ NMLS 182334, nmlsconsumeraccess.org ⁠https://www.tryarmra.com/srs⁠ ⁠https://www.betterhelp.com/srs⁠ This episode is sponsored by better help. Give online therapy a try at ⁠betterhelp.com/srs⁠ and get on your way to being your best self. ⁠https://www.shawnlikesgold.com⁠ ⁠https://www.lumen.me/srs⁠ ⁠https://www.patriotmobile.com/srs⁠ ⁠https://www.rocketmoney.com/srs⁠ ⁠https://www.shopify.com/srs⁠ ⁠https://trueclassic.com/srs⁠ Upgrade your wardrobe and save on @trueclassic at ⁠trueclassic.com/srs⁠! #trueclassicpod Alex Wang Links: Website - https://scale.com Scale AI X - https://x.com/scale_ai Alex X - https://x.com/alexandr_wang LI - https://www.linkedin.com/company/scaleai Learn more about your ad choices. Visit podcastchoices.com/adchoices

Speaker names marked ? are inferred from speaking time and the show’s known hosts — they are not voice-verified.

• 0:00Speaker 00

  Now, more than ever, we need someone on our side. But Rob Whitman? Whitman's votes drove up costs, cut Medicaid, and risked 350,000 Virginians' health care to pay for tax breaks for himself and his donors. He's not on our side. Shannon Taylor will fight corruption, cut taxes, and bring down costs. This November, vote for Shannon Taylor. She's for us. Paid for by HMP, the HouseMajorityPact.com. Not authorized by any candidate or candidate's committee.

• 0:29Speaker 01

  Mr. President? My company tore me of the explosion on our rig.

• 0:33Speaker 02

  On October 2nd.

• 0:34Speaker 01

  Causing massive water displacement and triggering a tsunami. They all heap the blame on me.

• 0:38Speaker 02

  Tom Cruise is.

• 0:40Speaker 03

  Digger got us into this mess, and Digger's gonna dig us up.

• 0:43Speaker 02

  From the director of Birdman and the Revenant.

• 0:46Speaker 04

  What would happen if I stopped drilling? You stall, you freeze to death, that's what.

• 0:50Speaker 02

  With great power comes great deniability.

• 0:53Speaker 04

  I'm not the first then.

• 0:54Speaker 02

  Digger. See the trailer online. Only in theaters October 2nd. Rated R. Under 17. Not a minute without parent.

• 1:04Shawn Ryan?

  Alex Wang, welcome to the show, man.

• 1:07Speaker 06

  Yeah, thanks for having me. I'm excited.

• 1:09Shawn Ryan?

  So am I. Like I was telling you at breakfast, I don't, I don't know a whole lot about tech, but ever since Joe came on, I've been trying to wrap my head around it all, and it's just fascinating subject. I love talking about this subject now, so thank you for coming.

• 1:24Speaker 06

  Well, it's becoming so critical to national security and all the stuff that you're very passionate about. So, I mean, I think, I think fundamentally, tech is like, we gotta get it right. Otherwise, stuff gets really dangerous.

• 1:36Shawn Ryan?

  Yeah, yeah, scares the shit out of me. In fact, we were just having a conversation downstairs about, about you having kids.

• 1:44Speaker 06

  Yeah.

• 1:44Shawn Ryan?

  And you're waiting. And Neuralink came up, and I had to, I had to, I had to pause the conversation. Dude, I'm like... I'm worried about Neuralink, but it sounds like you're pretty gung ho about it.

• 1:57Speaker 06

  So, yeah, a few things. So, yeah, I mean, what I mentioned is basically I want to wait to have kids until we figure out how Neuralink or other, it's called brain-computer interfaces, so other ways for brains to interlink with a computer until they start working. Because, so there's a few reasons for this. First is, in your first, like, seven years of life, your brain is more neuroplastic than at any other point in your life, like by an order of magnitude. So, there have been examples where, you know, for example, if somebody, if a kid is born, like you have a newborn that has, let's say they have cataracts in their eyes, so they can't see through

• 2:43Speaker 06

  the cataracts, and then they live their first seven years of their life with those cataracts, and then you have them removed when they're like eight or nine, then even with those removed, they're not going to learn how to see, because it's so important in those first seven years of your development that you're able to, you're able to see that your brain can like learn how to read the signals coming off of your eyes, and if you, if that's not, if you don't have that until you're like eight or nine, then you won't learn how to see. So, because it's so important that your neuroplasticity is so high in that early stage of life, I think when we get Neuralink and we get these other technologies,

• 3:24Speaker 06

  kids who are born with them are going to learn how to use them in like crazy, crazy ways. Like it'll be actually like a part of their brain in a way that it'll never be true for an adult who gets like a Neuralink or whatever hooked into their, hooked into their brain. So that's why to wait. Now Neuralink as a, as a concept, or like hooking your brain up to a computer. I kind of take a pragmatic view on this, which is, you know, my day job, I work on AI. I believe a lot in AI. I think AI is gonna continue becoming smarter and smarter, more and more capable, more and more powerful. AI is gonna, is gonna continue being able to do more and more and more and more.

• 4:07Speaker 06

  We're gonna have robots, we're gonna have other forms for that AI to take over time. And so, and humans, we're only evolving at a certain rate. Like, humans are, you know, we are, humans will get smarter over time, it's just on the time scale of like millions of years, because natural selection and evolution is really slow.

• 4:26Shawn Ryan?

  I don't know, are we getting smarter?

• 4:29Speaker 06

  I don't know, recently, but uh.

• 4:31Shawn Ryan?

  Little setback.

• 4:32Speaker 06

  Yeah, a little, a little blip. So, if you play this forward, right, like you're gonna have AIs that are going to continue getting smarter, continue improving, like, they're gonna keep improving really quickly. And, you know, biology is going to improve only so fast. And so, what, what we need at some point is the ability to tap into AI ourselves. Like, we're gonna need to bring biological life alongside all of the silicon-based or artificial intelligence. And we're gonna wanna be able to tap into that for, for our own sake, for humanity's sake.

• 5:10Speaker 06

  And so, eventually, I think we're gonna need some interlink or hookup between our brains directly to AI and the internet and all these things. And it's, it is potentially dangerous and it's potentially, you know, to your point, terrifying and scary. But, we just are gonna have to do it. Like, AI is gonna go like this, humans are gonna improve at a much slower rate, and we're gonna need to hook into that capability.

• 5:36Shawn Ryan?

  I mean, what, you know that I've already expressed fear in this, and, and so, I'm, I'm curious, without sharing my own fears, I'm just curious, like, what, what, in your mind, what could go wrong?

• 5:48Speaker 06

  I mean, the, there's like, the obvious thing is that some corporation hacks your brain, well, it's a corporation hacks your brain, which, even that's pretty bad, but that'll be like, what, they'll like, they'll like, send ads directly to your brain, or they'll like, make it so that you wanna buy their products or whatnot. But then even worse, obviously, a, you know, foreign actor, a terrorist, an adversary, a state actor, you know, hacks into your brain and, and takes your memories, or takes, you know, like, manipulates you, or all these things. I mean, that is. That's obviously pretty bad.

• 6:22Shawn Ryan?

  Yeah.

• 6:23Speaker 06

  Um, and I think that's, I do, like, it's definitely a huge risk. I mean, for sure, if you have a direct link into someone's brain, um, and you have the ability to like, read their memories, control their thoughts, read their thoughts, like, um, you know, that's pretty bad. Uh, I've, I've talked to a lot of scientists in this space and a lot of people working on this stuff, including the folks at Neuralink. And, um, you know,

• 6:55Speaker 06

  mind reading and mind control are like, those are the, that is where the technology will go over time, right? And so, um, it is like, it's something that we have to, you know, like any advanced technology, we have to not fuck that up. Um, but it's going to be pretty critical if we want, if we want humans to remain relevant as AI keeps getting better.

• 7:21Shawn Ryan?

  I mean, I interviewed, um, Andrew Huberman. Do you know who that is?

• 7:25Speaker 06

  Yeah, yeah, yeah.

• 7:25Shawn Ryan?

  And, and, um, and talked to Ben Car- Dr. Ben Carson about it too, as a kind of a follow-on discussion. But what Huberman was telling me is that, because this whole thing is, it sounds like it's, it's, I don't know a whole lot about Neuralink, but from what I've gathered, it's, it's going to help the blind see. And it sounds like it helps with some, um, connectivity in your joints and bones and stuff for people that are paralyzed. But something that Huberman brought up is that I was like, well, if, if it is going to help the blind see, then could they project a total false reality into your head? Meaning,

• 8:08Shawn Ryan?

  you're seeing who knows what, shit in the skies everywhere. Sounds like they could recreate an entire false reality. He said, yes, they will have that ability, but not only will they have that ability, they can, they can manipulate every one of your senses, touch, smell, taste, uh,

• 8:25Shawn Ryan?

  Insert emotions into your into your brain, fear, whatever it is. And I was like, holy shit, like they they could manipulate your entire reality into a false reality. I mean, you think that's and then I asked Dr. Ben Carson about it and he said, you know, who's a world renowned neurosurgeon, he said, yes, absolutely. They he goes, or, you know, they could use it for good. But he goes, which he kind of put it on me, he's like, well, what do you think would happen? And and like, would it be used for good eventually or would it be used for evil? And I mean, what are your what are your thoughts on that? You think that's a real possibility?

• 9:04Speaker 06

  I mean, yeah, so first of all, like, we don't understand the brain too much today, but eventually we will like we're like, science is going to solve this problem, right? And everything you just mentioned is ultimately going to be on the table, you know? Manipulating your emotions, manipulating your senses, the senses thing is already happening, where I think in monkeys, they've shown that like, they can, you know, they don't know what it's like from the monkey's perspective, but they're able to project like on a grid of a monkey and get them to like, like click on the right button really reliably.

• 9:41Shawn Ryan?

  Wow.

• 9:42Speaker 06

  So they they somehow they hook into basically the neural circuits that are doing the visual processing, visual like image processing in the brain. And they're able to project like things into their into their vision, such that the monkey will like always click the button that you want it to collect to to click. And then, you know, you give it, you know, a treat or something.

• 10:02Shawn Ryan?

  Damn.

• 10:03Speaker 06

  And so, yeah, manipulating vision, manipulating your senses, manipulating your emotions.

• 10:10Speaker 06

  Uh, this is, this will be longer term, but like leveraging your memories, manipulating your memories, manipulating like, uh, those are that stuff is on the table. The other stuff that is, I think, more exciting is like being able to hook into AI. And like, all of a sudden, I have encyclopedic knowledge about everything. And just like, you know, in chat GPT or other AI systems do, I can like think at superhuman speeds. I can, um, all of a sudden, I can like, uh, I have like way more information I can process like I can like understand everything that's going on in the world than process that instantaneously. Like I think there's

• 10:49Speaker 06

  there's an element here where it'll where legitimately turn us superhuman um from a just cognitive standpoint. But then to your point, like the the flip side of that is the um is the risk the other way, which is that you're gonna have uh it's a huge attack vector um.

• 11:09Shawn Ryan?

  Yeah, I mean in

• 11:12Shawn Ryan?

  like I said, I'm not I'm not super tech, but your company Scale AI, you basically correct me if I'm wrong. Scale AI Scale AI is basically the the database that the AI uses to come up with its answers and and answer your prompts and all of that, correct?

• 11:29Speaker 06

  Yeah, so we do. We do a few things. So we help large companies and governments uh deploy safe and secure advanced AI systems. Um, we help with basically every step of the process, but the first thing that we were known for, and we've done very well, is exactly what you're saying, which is creating large scale data sets and creating data foundry is what we call it, but creating the large scale data production that goes into fueling every single one of the major AI models. And you know, if you ask questions in ChatGPT, you know, that question uh, you know, it's able to answer a lot of those questions well because of data that we're able to provide it. And as AI gets more and more advanced, you know, we're continually fueling

• 12:11Speaker 06

  more advanced scientific, advanced information and data into those models. And then we also work with, you know, the largest you know, enterprises and and governments like the DOD and and other agencies in the US to deploy and build full AI systems leveraging their own data. And our strategy as a company has been, you know, how do we focus on? We have, how do we focus on a small number of customers who, where we can have like a really big impact. So we're with the number one bank, we're with the number one pharma company, the number one healthcare system, the number one telco, the number one country, America.

• 12:49Speaker 06

  And, and we work with all of them to like, how can you no kidding, take how you are operating today and take sort of the workflows that you're doing today, or the operations that you have today, and use AI to fundamentally transform them. So if you're like the largest healthcare system in the world, how do you, and you have to, you know, provide care to all of these patients, you know, millions of patients, how do you do so in the most effective manner? How do you do it logistically better? How do you improve your diagnoses? How do you improve the, the overall health outcomes of all of your patients? Like, that's a problem that we help solve with them. Or for the DOD, you know, there's so much that

• 13:29Speaker 06

  we can do to operate more efficiently and, and ultimately in a more automated way. I mean, you'll know this, I think, better than anyone. And so how do you, how do you start implementing those systems with AI?

• 13:40Shawn Ryan?

  Mm hmm. We'll delve way more in the weeds than that later in the interview. Kind of where I was going with this was if, so if originally it was, it was feeding the AI, you're given the data center, you're given the data to the AI to come up with the answers and, and, and answer the prompts. And so where I was going is if, if you have Neuralink in your head and it's accessing your data centers, how easy would it be to just feed bullshit into the data center that then feeds all, everybody that has a Neuralink in their

• 14:21Shawn Ryan?

  head? So it, it could be, I mean, it could be anything. I mean, here, here's an example. I'm a Christian. A lot of people think that AI is going to manipulate the Bible and change a lot of things. And so how easy would it be to just feed that into the AI data center? And then? That's that's the new whatever you feed it that becomes the new truth because that's what everybody's accessing is that specific data.

• 14:49Speaker 06

  Yeah, I mean, I think A, yes, for sure, that's a huge risk. And this is one of the reasons why I think it's really important that US or other democratic countries lead on AI versus the CCP like the Chinese Communist Party or other or Russia or other autocratic countries because the potential to utilize even AI today, by the way, you can use it to propagandize to a dramatic degree. But yeah, once you get towards, you know, you have Neuralink or other brain-computer interfaces that are that can directly, you know, insert thoughts into into people's brains. I mean, it's it's extreme power that has never existed

• 15:32Speaker 06

  before. And so who governs that power? Who governs that technology? Who makes sure that, you know, it's used for the right purposes? Those are like, some of the most important societal questions that we'll have to deal with.

• 15:46Shawn Ryan?

  Man, I mean, where do you even where do you even start with that? Who do you trust to control your fucking mind?

• 15:56Speaker 06

  Yeah, I mean, I think, well, it's interesting. I think the one thing that I think has been, I think a lot of people kind of understand it now. And we were talking a little bit about this at breakfast is like, even the degree to which even just general media today kind of controls your mind or controls the like opinions you have or the beliefs you have and, you know, you know, we were talking about like, you know, does does the media prop up certain military forces to make them seem far more fearsome than they actually are? And like, you know, there's like some low grade, you can kind of view like some low grade forms of like, you know,

• 16:39Shawn Ryan?

  Propaganda.

• 16:39Speaker 06

  Propaganda, manipulation, all that stuff is like happening like, let's say, like, on a scale of one to 10 at the one or two level today. And then once you have Neuralink or other devices, it's going to be like a nine or a 10. And and I think it's really hard. I mean, I think I don't think. Any country is prepared to govern technology as powerful as a technology that we're going to be developing over the next few decades.

• 17:08Shawn Ryan?

  Yeah.

• 17:08Speaker 06

  AI, I don't know if we're prepared.

• 17:12Speaker 06

  Brain commuter interfaces, I don't know if we're prepared. Large scale robotics, I don't know if we're prepared. Like, these are technologies that are just so much more powerful than anything that has come before. Sometimes people will say like, you know, AI is the new mobile. It'll be as big as mobile phones. And it's just, no, it's gonna be like a thousand times bigger and more important and like more impactful. And it's not clear that we did the best job regulating mobile phones even. So there's, it's gonna be, it's gonna be really important that we get it right.

• 17:45Shawn Ryan?

  Yeah, I mean,

• 17:49Shawn Ryan?

  everybody that gets what I mean, you could, you could basically instantaneously have your, have an entire army, an entire nation that's linked into your thoughts, your way of thinking, and manipulate that entire population to do, who the hell knows what? Hopefully something for good, but you know how things wind, how things generally wind up going. But you're gung ho about this stuff. Would you put it in?

• 18:14Speaker 06

  I would, I would put it in, but I would, I would be, you know, I, there's a few things that need to happen before I'd, I'd be willing to put it in. First, I would need to really feel good about the cyber offense defense posture. Like, I need to have really good confidence that I would be able to defend from any attacks, like any sort of cyber attacks into, you know, my brain interface.

• 18:43Speaker 06

  And that's like, that's one big bar. And then I would need to feel pretty confident, I would need to feel confident that there were,

• 18:53Speaker 06

  that it wouldn't deeply alter my consciousness in any major way. Like, and that I think you would see from data of other people who like use it and, you, you know, you'd kind of get a sense just from like other people adopting it.

• 19:07Speaker 06

  Those would be the two things I would need to like feel really, really confident about.

• 19:11Shawn Ryan?

  It's a big thing.

• 19:12Speaker 06

  Yeah.

• 19:13Shawn Ryan?

  Big thing.

• 19:13Speaker 06

  Well, the last thing, you know, and then we should talk about other stuff, but the last thing about this is,

• 19:20Speaker 06

  uh, you know, one of the things that, you know, people, there's a lot of talk right now about how humans will live forever. Right. Or like, can humans live forever? How do you not die? And a lot of that's, you know, a lot of that's focused on keeping our human bodies healthy and keeping our, you know, how do you like, how do you take care of yourself? How do you take care of your human body? How do we cure diseases such that like humans can live to hundreds and hundreds of years? But I think what's the, the actual end game is that we figure out how to upload our consciousnesses from our, from our meat brains into a computer.

• 20:00Speaker 06

  And I kind of think about Neuralink or other like other bridges between your brain and, and computers as like the first step there.

• 20:11Shawn Ryan?

  Well, hold on. What, there's a whole nother rabbit hole we can, so you're saying that we, we, we should be able to upload our consciousness or you want to be able to upload our consciousness into?

• 20:25Shawn Ryan?

  Whatever?

• 20:25Speaker 06

  Yeah. I think, I mean, now we're like, we're on like deep end sci-fi, but, um, but yeah, I mean, I think, I think there will over time be,

• 20:36Speaker 06

  uh, there, so one, I think the technology will exist at some point. We're, we're not close today, right? We're like, we're, we barely have Neuralink, you know, kind of working, right? So we're not close, but the technology will exist to upload your consciousness onto a computer.

• 20:53Shawn Ryan?

  Holy shit.

• 20:54Speaker 06

  And then, okay, let's say, let's say we're sitting here, you know, it's like 50 years from now, this technology exists, um, and you're asking the question, uh, you know,

• 21:08Speaker 06

  Are people going to upload their consciousness? Well, first off, there's a lot of people who who naturally would, like people with terminal illnesses, people near death, you know, people who are like very fringe and, you know, like experimenting with this new technology. There will be a class of people who will just initially do it. And then, and then as that starts to happen and they upload their consciousness, like the, if you have a digital, you have these sort of like digital intelligences. They're, you know, that's true immortality. That's the closest thing you'll get to to true immortality. And so the, I think it's going to become,

• 21:53Speaker 06

  like, once the technology exists, you know, when it exists, it's going to become quite,

• 21:59Speaker 06

  it's probably going to become a very natural path for most humans to go down.

• 22:01Shawn Ryan?

  So what do you, what do you think? What do you think happens if you get your consciousness uploaded in? What would it even be uploaded into, like a cloud or something?

• 22:10Speaker 06

  Yeah, it'd be uploaded to a cloud.

• 22:11Shawn Ryan?

  What, what do you think? Do you think that you can experience life by uploading your consciousness to a cloud?

• 22:19Speaker 06

  Yeah, so, so, yeah, this is, few things. So first, I'm a big believer in robotics. I think we're basically at the start of robotics revolution. And we're in the very early innings of it. But people are starting to make humanoid robots. They're going to get really, really good. People are starting to apply them to manufacturing and industrialization and other contexts. I think the costs are going to come down dramatically. And so eventually, yeah, if you, you would believe that if you uploaded and then you could download or downlink down to a, down to a humanoid robot, then you would

• 23:01Speaker 06

  kind of experience the real world like any other world. Or you would, you could continue in some kind of like simulated universe in a, you could almost like play a video game in the cloud kind of thing, and that could be like the other alternative.

• 23:18Shawn Ryan?

  Wow. What do you, what do you think happens when you die?

• 23:26Speaker 06

  You know, the as AI has gotten so um so Elon always talks about how we're in a we live in a simulation, right? Um, uh, and I remember when I first heard him talk about this, I was like, ah, no, this is like, I don't believe that. I don't believe we're in a simulation. But um, but as AI has gotten better at better at simulating the world, like, I know you've seen these AI video um, generation models like Sora or Vio or some of these models, but you know, they can produce videos that are totally realistic. Um, you would, most people could not tell the difference between AI, well, we're seeing

• 24:11Speaker 06

  this AI generated video and uh, and and real video. And as that's happening, it's making me think more and more that we probably live in a simulation.

• 24:20Shawn Ryan?

  No shit.

• 24:21Speaker 06

  Yeah.

• 24:23Speaker 00

  Now, more than ever, we need someone on our side. But Rob Whitman? Whitman's votes drove up costs, cut Medicaid, and risked 350,000 Virginians' healthcare to pay for tax breaks for himself and his donors. He's not on our side. Shannon Taylor will fight corruption, cut taxes, and bring down costs. This November, vote for Shannon Taylor. She's for us. Paid for by HMP, the House Majority PAC dot com. Not authorized by any candidate or candidate's committee.

• 24:52Speaker 01

  Mr. President, my company formed me of the explosion on our rig. On October 2nd. Causing massive water displacement and triggering a tsunami. They all heap the blame on me. Tom Cruise is.

• 25:03Speaker 03

  Digger got us into this mess. And Digger's gonna dig us up. From the director of Birdman and the Revenant.

• 25:08Speaker 04

  What would happen if I stopped going? You start, you freeze to death, that's what.

• 25:13Speaker 02

  With great power comes great deniability.

• 25:16Speaker 04

  I'm not the first then.

• 25:17Speaker 02

  Digger. See the trailer online. Only in theaters October 2nd. Rated R. Under 17 out of 10 without parent.

• 25:22Shawn Ryan?

  How do you just, this is already fascinating. We haven't even got to the interview yet. How, how, how do you think we're living in a simulation? I mean, I know they, they say they, they cannot disprove it.

• 25:34Speaker 06

  Yeah, you can't like, it's kind of one of these things, there's, there's no way to prove or disprove that, that you live in a simulation. And so it, but it's like, it's like, it's like any, you know, afterlife thought or religious thought, like all these things, they're like fundamentally unprovable. But the reason I think it's the case is, I think in our lifetime, we are going to be able to create simulations of reality that will be hyper realistic. Like, I think we are gonna create the ability to

• 26:10Speaker 06

  simulate different versions of our world with hyper realistic accuracy. Um, and, uh, and that will happen over the next few decades. And if, if we can, like, it's kind of like that Rick and Morty episode, where if we have the ability as an intelligent race to produce, you know, millions of simulated worlds, um, then the likelihood is that we're, you know, we're probably also the simulation of some other, uh, more intelligent or more capable species.

• 26:45Shawn Ryan?

  Where do you think consciousness goes right now when you die?

• 26:48Speaker 06

  Uh.

• 26:50Shawn Ryan?

  What if we are, what if we are the super advanced robotics?

• 26:59Speaker 06

  Yeah, I think, um.

• 27:00Shawn Ryan?

  And your consciousness gets, gets downloaded into another body. Generation.

• 27:06Speaker 06

  Yeah, that's true. That would be, that's, that's something like one way to think about it, which is like, yeah, it's all this big simulation that's running. And as soon as like, you know, you get, you get kind of like downloaded or like taken off or like decommissioned from, you know, one entity, you get like, you know, uploaded to another entity kind of thing. Um, it's kind of that, that's plausible. I think there's another world where like, consciousness is like, is.

• 27:36Speaker 06

  Consciousness may not like be that big a deal, so to speak, like it could be the case that, you know, I definitely as as the models have gotten better and better, as the AI models have gotten better and better, you look at them and, you know, you definitely wonder if at some point you're just going to have models that are properly conscious. And it may just be the fact that like, you know, it's something that can be engineered. And if it's something that can be engineered, then then all bets are off, I think.

• 28:08Shawn Ryan?

  Damn. It's pretty wild to think about.

• 28:11Speaker 06

  Yeah.

• 28:12Shawn Ryan?

  But let's move into the interview. Ready?

• 28:15Speaker 06

  Yeah.

• 28:15Shawn Ryan?

  All right. Everybody starts off with an introduction here. So here we go.

• 28:23Shawn Ryan?

  Alex Wang, founder and CEO of Scale AI, a company that's backbone of the AI revolution, providing the data and infrastructure that powers the AI revolution. Child prodigy who grew up in Los Alamos, New Mexico, surrounded by scientists with parents who were physicists working on military projects. Coding wizard who by age 15 was already solving AI problems at Quora that stumped PhDs. Visionary entrepreneur who dropped out of MIT at 19, turning a Y Combinator startup into a national security powerhouse that's helping the US stay ahead in the global AI race. Youngest self-made billionaire in the world by age 24, built a company valued

• 29:08Shawn Ryan?

  at nearly 25 billion while staying laser focused on solving the biggest bottleneck in AI high-quality data. Unafraid to call the US-China AI competition an AI war, warning that the Chinese startups like DeepSeek are closing the gap faster than most realize. Guided by your mission to build a future where AI drives progress, security, and opportunity. And so. There's a big question right now that everybody's that everybody's thinking about. Is AI the next oil?

• 29:44Speaker 06

  Yeah, I think a few thoughts there.

• 29:50Speaker 06

  In some ways, yes, in some ways, no. So AI is definitely the next.

• 29:58Speaker 06

  Some ways in which it's it is the next oil. AI will fundamentally be the lifeblood of any future economy, any future military, any future government. Like if you play it out, you're like the degree to which a country or economy is able to utilize AI to make its economy more efficient, to automate parts of its economy, um, to do automated research and development, automated R and D, like, you know, push forward in science, um, using AI. All that stuff is going to mean that countries that adopt AI effectively will have like, you know, nearly infinite GDP growth and countries that don't adopt it are gonna get are gonna get left behind.

• 30:42Speaker 06

  Um, so it is, it is sort of the the fuel that will power the future of of every country. And by the way, I think the same is true of of hard power. Like if you look at what the militaries of the future are gonna be like, or or what war looks like in the future. AI is at the at the core of of what that is going to look like. I'm sure we'll get into that.

• 31:07Speaker 06

  And then the ways that it's not like oil is, you know, oil is this finite resource. You know, we we, you know, countries that stumble upon large oil reserves, they they have that large oil reserve. At some point, it's gonna run out, like in Norway, you know, it runs out at some point. And, um, and so it it lends the country power and economic riches for a time period. Um, and then you exhaust it and then you're looking for more oil. Whereas AI is gonna be a technology that will just keep compounding upon itself and will keep, you know,

• 31:46Speaker 06

  the smarter AIs, the more economic power you're gonna get, which means you can build smarter AIs, which means you have more economic power and so on and so forth. And so it's gonna, there's gonna be a flywheel that keeps going on AI, which means that.

• 32:00Speaker 06

  It's not going to be a time-based, a time-limited resource, let's say. It's going to be something that that will just continue racing and accelerating for the entire perpetuity.

• 32:14Shawn Ryan?

  And data is part of that. Data is a big part of that.

• 32:17Speaker 06

  Data is the core part of it. Yeah, so a lot of times, actually, um, uh, I like to compare data to oil versus AI.

• 32:24Shawn Ryan?

  That's actually what I meant. I fucked that up. I meant to say data.

• 32:28Speaker 06

  Yeah, yeah. Well, I mean, I think that's totally true. Like, data, if you think about AI, it boils down to, like, how do you make AI? Well, there's like three pieces. There's, um, the algorithms, like the actual code that goes into the AI systems that, you know, really smart people have to write. Um, I used to, you know, write some of these algorithms, um, back in the day. Uh, then there's the, um, then there's the compute, the computational power, which boils down to large-scale data centers, you know, do you have the power to fuel them? Do you have the chips, um, to go inside them? Like, that's like a large-scale industrial project in question. And then, um, and then data.

• 33:09Speaker 06

  Do you have all of the the lifeblood? Or do you have all the data that feeds into these algorithms that they learn off of? And it's really kind of like the raw material for a lot of this intelligence. And and so that's why I think data is is the closest thing to oil, because it is what what gets fed into these algorithms, fed into the chips to make AI so powerful. Um, and everything we know about AI is that, you know, the better you are at all three of these things, algorithms, computational power, data, the better your AI get. And it's just all about racing ahead on all three of these.

• 33:45Shawn Ryan?

  So when when we see, like, ChatGPT, Grok, these types of things, are they sharing a data center? Or are they all, are they completely separate data centers?

• 33:57Speaker 06

  They all use, they they all have separate data centers. Um, this is actually one of the, one of the major uh, lanes of competition between the companies is who has the ability to secure more power and build bigger data centers. Um, because ultimately,

• 34:19Speaker 06

  As AI gets more and more powerful, the question then becomes, how many AIs can you run? So let's say for a second that we get to, you know, a really powerful AI that can do automated cyber hacking. So it can do like, it can log into any kind of server or log into another, you know, or try to hack some website or try to hack some other, um, try to hack some, some system. Um, then, then the question is just, okay, if I have that, how many of those can I run? Can I run a thousand copies of that? Can I run 10,000 copies of that? Can I run 100 million copies of that?

• 34:58Shawn Ryan?

  Wow.

• 34:58Speaker 06

  And that all just boils down to how many data centers do you have up and running? And that then that boils down to, okay, how much power do you have to fuel those data centers? How many chips do you have to run in those data centers? And how do you keep those online for as long as possible? And what data is constantly fueling those models to keep getting them to become better and better and better? And so this is one of the reasons why one of the major ways that the AI companies compete, you know, between XAI, Elon's company and OpenAI and, um, and Google and Amazon and, and Meta and all these companies, one of the major ways they compete is just who right

• 35:38Speaker 06

  now is securing more power and more real estate for data centers five years from now and six years from now. Um, and so the, the battles five, six years down the line are being fought literally today.

• 35:53Shawn Ryan?

  Wow. Man, that's fascinating stuff. Well, a couple more things before we get into your life story here. Got your gift?

• 36:01Speaker 06

  Oh, man.

• 36:02Shawn Ryan?

  Everybody gets one.

• 36:04Speaker 06

  Love it.

• 36:05Shawn Ryan?

  The John's League Gummy Bears.

• 36:07Speaker 06

  There you go.

• 36:08Shawn Ryan?

  Legal in all 50 states.

• 36:11Shawn Ryan?

  No funny business, just candy made here in the USA.

• 36:14Speaker 06

  Yeah.

• 36:15Shawn Ryan?

  And, um, and then one other thing, got a Patreon account. It's a subscription account. It's turned into quite the community and, um, they've been here with me since the beginning when I was running this thing out of my attic and then we moved here and now we're moving to a new studio and the team's 10 times bigger than what it was, which was just me and my wife. But, um, it's all because of them. And so they're the reason I get to sit here with you today. And, uh, so one of the things I do is I offer them the opportunity to ask every guest a question. This is from Kevin O'Malley.

• 36:49Shawn Ryan?

  With AI now able to essentially replicate so many facets of our reality, do you see a future where all video or photographic evidence presented in trials becomes suspect? Based on the ability for any of it to have been replicated through artificial intelligence tools?

• 37:09Speaker 06

  Yeah, so this goes back to what we're just talking about. I do think AI is gonna enable you to do crazy levels of simulation. And I don't think our courts are ready for it. I think that, like Kevin was saying, AI will be able to generate very convincing video, very convincing images, in a way, like, we're not even really at that point yet. Like, right now, you can still tell when these videos or images are AI-generated. That's gonna keep getting better, and it's gonna be indistinguishable from real video.

• 37:44Shawn Ryan?

  How the hell are we gonna discern what's real and what's AI-generated?

• 37:50Speaker 06

  I think that there's two things. I think first, people are gonna need really good bullshit detectors, like,

• 38:00Speaker 06

  like insanely good. And I think, I think kids today, by the way, already have much better bullshit detectors because they grew up on the internet where there's just so much, there's so much of everything that they already kind of like learned to have better and better bullshit detectors. But, so that's one. And then the second is, I mean, I think there's gonna be, this is an area where, I know there's a lot of push for various forms of policy and regulation, but this is gonna, I mean, it's gonna be a major question like, hey, if there's fabricated video or imagery used in a trial,

• 38:42Speaker 06

  and it's discovered that it was fabricated, like, you know, what are the consequences of that? And I think it's about tuning that such that if you fabricate evidence or you fabricate things, then...

• 38:56Speaker 06

  Then, you know, that's maybe a worst defense, then maybe that's the worst defense of all. Uh, then I think people would, then you deter a lot of usage of those tools then, if, if you set up the incentives in the right way.

• 39:08Shawn Ryan?

  Yeah, I mean, would, you know, first thing that goes to my mind is the US government. I mean, just showing you around the studio and stuff, talking about, hey, this is what the, what the government did to those Blackwater guys I was telling you about, they deleted the evidence. Well, instead of deleting the evidence, they could make new evidence that is a fake gunfight in the Sora Square Baghdad that proves they're guilty and, and then it's the government behind it. You know, we've seen it with Brad Geary, we've seen it with Eddie Gallagher, we've seen it with the Blackwater guys. We've seen it a ton, just, just in my small network circle and

• 39:47Shawn Ryan?

  I could, I mean, you see what's going on with the elections all over Europe, they, they, they pulled Georgescu, calling him a, uh, what was it? I don't know, some, well, under Russian, Russian influence, Marie Le Pen in France, done. I mean, they were talking about pulling somebody in Germany, not too long, maybe about six months ago, and it's, it's just, man, it's fucking crazy, you know, and, and, um, scares the hell out of me, scares the hell out of me, because then they can just frame anybody they want.

• 40:21Speaker 06

  Yeah, I think, uh,

• 40:24Speaker 06

  definitely one of the, one of the outcomes of AI is that institutions that have power today will gain way more power.

• 40:34Shawn Ryan?

  Yeah.

• 40:34Speaker 06

  Um, it will, it's not naturally democratizing, it's a centralizing, um, kind of technology. And so, uh,

• 40:45Speaker 06

  and so, yeah, we need to build mechanisms so we can trust those institutions, otherwise, um, it doesn't end well.

• 40:50Shawn Ryan?

  Yeah. Well, let's get to your story.

• 40:53Speaker 06

  Well, I, I have gifts too, do I do?

• 40:54Shawn Ryan?

  I love gifts.

• 40:55Speaker 06

  Okay, great. Um, so if you think, I mean, we're gonna talk about this, but I grew up in Los Alamos, New Mexico. So my, uh, my parents were both physicists who worked on the national, at the National Lab there. This is the birthplace of the atomic bomb. Um, I don't know if you saw Oppenheimer, but, uh, half of that movie is set in Los Alamos, where I'm from. So we got a Los Alamos hat, uh, Los Alamos National Laboratory hat.

• 41:20Shawn Ryan?

  Dude.

• 41:21Speaker 06

  It's a.

• 41:22Shawn Ryan?

  Very cool.

• 41:23Speaker 06

  We have some Los Alamos coins.

• 41:28Speaker 06

  So.

• 41:28Shawn Ryan?

  Oh, man.

• 41:29Speaker 06

  About the, there's one about the atom bomb, uh, one about the, the Norris Brabber, who's the lab director. Um, and then, uh, and then almost almost coin about the, you know, the father of the atomic bomb. You know? We have a, uh.

• 41:46Speaker 06

  A, like a copy, like a basically a copy of all the, the manual that they, that they gave to the scientists. Uh, they got declassified, um. Uh, from the, from the, uh, actual, uh, from the actual Manhattan Project.

• 42:03Shawn Ryan?

  Wow.

• 42:05Speaker 06

  And.

• 42:05Shawn Ryan?

  This is cool as shit.

• 42:07Speaker 06

  And, uh, this one's just a fun one. It's a, uh, it's a rocket kit for you and kids.

• 42:13Shawn Ryan?

  Oh, man, they're gonna love that.

• 42:14Speaker 06

  Yeah.

• 42:15Shawn Ryan?

  Thank you.

• 42:17Shawn Ryan?

  Dude, thank you. This is gonna look awesome in the studio.

• 42:22Shawn Ryan?

  That's very cool.

• 42:23Speaker 06

  Yeah, it's been kind of surreal. I mean. Everybody calls, uh. AI, the, the next Manhattan Project. And so it's been, uh. It's been funny because that's where I grew up. Uh, it's like, I don't know, feels weird.

• 42:37Shawn Ryan?

  I'll bet it does.

• 42:38Speaker 06

  Yeah.

• 42:39Shawn Ryan?

  I'll bet it does. So what were you into as a kid?

• 42:42Speaker 06

  So. Uh, yeah. So again, both my parents are physicists and my, and my dad's dad was a physicist as well. So I grew up in this like. Pure physics family. Um, so science, technology, physics, math, these were, uh. These were the things I was like, I was like, I was really excited about as a kid. Um, and, uh. I remember like. During, around the dinner table, we would talk about black holes and wormholes and, you know. Alien life and, uh, supernova and, you know, star, faraway galaxies and all that stuff. That stuff was all very captivating to me. I was thinking about kind of like.

• 43:27Speaker 06

  Basically like, you know. Understanding the universe, so for lack of a better term. Um, and then I, I really like math and I realized kind of, you know, in about four, in fourth grade, I entered my very first math competition, which is a thing. Uh, uh, and I, I like, uh, it was in, it was in the whole state of, of New Mexico. Uh, and I scored the best out of any fourth grader in New Mexico. Um, which, uh, and then that like activated this like competitive gene in me. And then I just started like, you know, I got consumed by

• 44:10Speaker 06

  math competitions, science competitions, physics competitions.

• 44:13Shawn Ryan?

  What kind of math are you doing in fourth grade? You, uh, are you doing?

• 44:18Speaker 06

  Yeah, yeah. Fourth, I remember, let's see, my parents taught me algebra in, I wanna say it was second grade, maybe between.

• 44:29Shawn Ryan?

  Are you serious?

• 44:30Speaker 06

  Yeah.

• 44:31Shawn Ryan?

  You mastered algebra in second grade?

• 44:33Speaker 06

  I don't know if I mastered it, but I was, yeah, I was playing around with algebra. They, they taught me the basics of algebra and I would just like spend all the time thinking about it in second grade.

• 44:42Shawn Ryan?

  It's like seven, eight years old, right?

• 44:44Speaker 06

  Yeah, like eight and eight.

• 44:46Shawn Ryan?

  Holy shit.

• 44:48Speaker 06

  Um, and then, and so by the time I was, by the time I was in fourth grade, I could do kind of like, I could do some basic algebra, I could do, um, some basic geometry, stuff like that. And then, uh, let's see, where, where did I do from there? By the time I was in middle school, I was doing calculus. And then, um, and I was it, and then I was doing college level math in middle school as well. So those are the two things I was doing in middle school. And then, um, in high school, I just became obsessed with computers and I just spent all day programming.

• 45:28Speaker 06

  And I realized like, science and math are cool, but um, but with computers and programming, you could actually make stuff. Uh, and that would, that ended up, you know, becoming the, the major obsession.

• 45:41Shawn Ryan?

  Back to the dinner table conversations.

• 45:43Speaker 06

  Yeah.

• 45:43Shawn Ryan?

  I mean, Los Alamos, there's like a lot of conspiracies and all kinds of stuff going on about that place. Remote viewing, all, all this stuff come, seems to stem to Los Alamos. But

• 46:00Shawn Ryan?

  Both the two parents that are physicists at Los Alamos, you guys are talking about black holes and aliens and shit. What do you think? Are we, are there aliens?

• 46:09Speaker 06

  Uh, so there's, there's this famous paradox, the Fermi paradox, which is, you know, what are the odds that we live in this like vast, vast, vast universe? And, uh, and there's like, you know, there's, there's billions, hundreds of billions, trillions of other, of other stars and planets and, um, you know, what are the chances that like none of them have intelligent life? I mean, I think like definitely somewhere else in our universe, there has to be intelligent life.

• 46:40Shawn Ryan?

  You think so?

• 46:41Speaker 06

  For sure. The, the, but the benefit, or I don't know if the benefit, but the, but like part of the issue is if we're really, really, really far apart, like, like millions of light years apart, hundreds of millions of light years apart, there's no way we're ever gonna communicate with each other. Like we're just like super duper far away from each other. Um, so I think that's plausible. And then the, there's the, um, you know, uh, there's the, what's called the dark forest hypothesis. Um, I think this is one of the things I, I actually believe the most in probably. So you have the Fermi paradox that says basically like, hey, what are, what are the odds that there's no intelligent life out there in the universe? Um, there ha, there's probably zero. There has to be

• 47:25Speaker 06

  some intelligent life somewhere else in the universe. And then the question is like, why aren't we seeing any, like, why aren't we seeing any aliens? Why aren't we like coming into contact with them? And so then there's all these like, how do you explain why that is? And there was this, um, there's this hypothesis called the dark forest hypothesis, which originally came out of a sci-fi novel, actually, uh, but is the one that like jives the most with my thoughts, which is, um, the reason you don't run into other intelligent life is, uh, if you play the game theory out.

• 47:58Speaker 06

  If you're an intelligent life, you don't actually wanna be like blaring to every other intelligent life that you exist. Because if you do that, then they're just gonna come and take you out. Like you're basically like, you become like a huge target for other forms of intelligent life. And there's, you know, some intelligent lives out there are gonna be hyper aggressive and are gonna wanna take out, you know, other, other forms of intelligent life. So, the dark forest hypothesis is that, once you become an intelligent life form and you become a multi-planetary species and all that, you realize that you're kind of best off minding your own business and not, you know, sending all these sorts of signals and trying to like

• 48:40Speaker 06

  make contact with other life because it's higher risk to do that than to just kind of like, you know, stay isolated. And so, there is intelligent life out there. There are aliens out there, but everybody's incentive is just to stay isolated.

• 48:53Shawn Ryan?

  Interesting. I don't know. I used to believe in it. Then I interviewed a bunch of guys. I don't know. I don't know. I think all this shit's a big distraction, to be honest with you.

• 49:04Speaker 06

  Yeah, there's definitely, I mean, there's definitely the other portion of this, which is, you know, UFOs are a conspiracy such that, you know, the military can do all sorts of airborne testing and, and it gets discredited because, you know, people say it's UFOs and then, and then nobody believes it, like.

• 49:24Shawn Ryan?

  There's just no, I, I'm, of all the people I've talked to, there's just no hard evidence. And then, and then, and then it's the, well, that's classified. It's like, no, I mean, is it? You're on a podcast tour. But, I don't know. Sometimes I think, you know, this is like all I watch, is the expanding, you know, all this black holes, all the, this is what I fall asleep to at night. And, I don't know, I mean, they, they found what, like Saturn's rings are all water. They think they may have found, you know, there's a possibility of life on some of the moons on Saturn that would, Neptune, I think, was made of, is meant, is it Neptune that's made of water? Like a lot of oceans that are frozen and so there

• 50:09Shawn Ryan?

  may have once been life. Then there's a, they think they found a pyramid on Mars or something. I don't know. I, sometimes I think maybe.

• 50:18Shawn Ryan?

  Maybe it in it any one given in any particular given point in time. There there is only one planet that holds life as we know it at a time. And then maybe when that planet, you know, becomes obsolete, everything goes extinct. Maybe it moves, you know, maybe it was Mars. I don't know, five billion years ago, and that's where life was. And then somehow, you know, shit changed and then it developed on earth. I don't I don't know. That's that's that's where I'm at right now. I go back and forth on this shit all the time.

• 50:55Speaker 06

  Yeah, totally. Well, because because our star has a life cycle, right? And as it goes through that life cycle, different points of our solar system become different temperatures, have different conditions, you know, all that kind of stuff. And so that's plausible theory. I mean, I think I think it's I mean, I think both that and what we're talking about before in terms of like consciousness and the afterlife, these are like some of the some of the great questions because you just, you know, we'll probably never know the answers.

• 51:23Shawn Ryan?

  Yeah, yeah. Well, what were your parents working on at Los Alamos?

• 51:29Speaker 06

  They were.

• 51:30Shawn Ryan?

  Are they still working there?

• 51:33Speaker 06

  Yeah, my mom still is working. Uh, my my dad's not working, but uh, but my mom's still working. And so they were part of uh, of the divisions in Los Alamos National Lab that were that worked on classified work um, that uh, they were they had clearance. Uh, my mom sells clearance um, with the DOE. Uh, and I actually remember like when I grew up, I just assumed they were working on cool physics research because I was like a kid and I I didn't put two and two together. Um, and so I I remember when I grew up, I thought the Los Alamos National Lab like used to be the place where the atomic bomb was built.

• 52:17Speaker 06

  And then uh, decades later is just like this like advanced scientific research area where they're doing research into, you know, all of the, you know, the frontier of human knowledge and it's just this like great scientific research area. And then um, and it wasn't until I I it wasn't until I literally got to college where I was talking to a friend about it and it like dawned on me that. Oh, wait. Los Alamos is probably still mostly weapons research.

• 52:52Speaker 06

  Um, and, uh, and, oh, that's why you would need a clearance to work.

• 52:59Speaker 06

  Stuff in New Mexico. And then, uh, since I left, they actually restarted, um, they restarted, uh, uh, what's called nuclear pit production, but they restarted basically manufacturing the cores of, of nuclear weapons. Um, this is, this must have been like 2018, 2019 in Los Alamos. Um, and then I was like, oh, yeah, no, it's, it's mostly a research facility to, to research new nuclear warheads and new, new, new, new nuclear, new nuclear weapons. Um, uh, and so that had dawned on me until, that didn't dawn on me until I was like all the way in college, but, uh, but yeah. So I, my guess is my parents worked on that, but, uh.

• 53:42Shawn Ryan?

  Probably.

• 53:42Speaker 06

  Yeah.

• 53:43Shawn Ryan?

  Damn, that's crazy. Wow. What else were you into as a kid, other than, other than mathematics?

• 53:52Speaker 06

  I loved math. I loved, I loved coding. I love science. I loved all that stuff. Um, I, uh, I, uh, was really into violin. I was really into, um, I, uh, I would like, I'd practice like, you know, an hour violin a day. Um, a lot of that was because there was sort of like, uh, you know, in some, in some, you know, fields or some areas, there's like, there's just a real beauty to perfection.

• 54:21Shawn Ryan?

  Mm-hmm.

• 54:22Speaker 06

  Um, and I think this is true in like a lot of arts, um, a lot of music, a lot of, a lot of frankly, everything. I mean, I see it even in my current life, in my current day to day job, but, um, but there was just like, hey, if you could, if you practiced enough to get to play a piece perfectly, then it would like, it would be beautiful. And if you, like, along the way, it's like total dog shit until you get to the point of like perfection. Um, there's kind of, there's a lot of beauty to that concept to me, which is like, you know, once you get something totally perfect, it becomes beautiful. Um, that was, that was captivating when I was a, when I was a kid.

• 55:02Shawn Ryan?

  So you were a perfectionist from a young age and you're still a perfectionist today?

• 55:07Speaker 06

  Yeah. I see a lot of beauty in like. You know, now I would say, I don't think, I don't think we have the luxury to be perfectionists. I'm much more pragmatic now, like, you know, like we were talking about, the world is extremely messy, like, like, the, the reality is, you know, stuff is super chaotic, there's a lot of bad shit going on constantly, um, there's a lot of good shit going on constantly, but perfection is not really a, like, plausible objective. We're never gonna get perfection, um, so I'm a lot more pragmatic now, but I do see a lot of beauty in perfection.

• 55:48Shawn Ryan?

  I mean, I'm also a perfectionist, I battle it every fucking day, like, I,

• 55:57Shawn Ryan?

  I'm OCD, I did, but, you know, and I've, I've read about it, I've watched talks about it, it's, and I came to the conclusion, which I hate saying this because I am a perfectionist at heart, you know, that perfectionism can get in the way of success. Did you find that? I mean, it sounds, it sounds weird even, like, asking you the fucking question because you're the youngest billionaire in the world at age 24, and, I mean, 28 years old now, so it sounds weird saying, did perfectionism hold you back, but I'm, did it?

• 56:33Speaker 06

  I think, um, yeah, at some point, I just like, I, I, like, some bit flipped and I realized, like, you gotta just do the 80/20 lots of times, like, you gotta do 20% of the effort, this 80% is good, and you just have to be okay with that, um, and you just have to do that over and over and over again. Um, so at some point, I internalized that, and it's like, it's like anathema to perfectionism, it's like the exact opposite. And so now I think about it as like, hey, there's some things where perfectionism really is the right answer, and there's some things where you just gotta, you just gotta like be okay with imperfection and just, like, speed is the objective versus perfection is the objective, um, so,

• 57:16Speaker 06

  and yeah, I would say now, honestly, I think more things, like, most things are, speed is the objective, not, not perfection. Um, so, yeah, I would say I've kind of had like a whole journey with it.

• 57:27Shawn Ryan?

  What, what was it that flipped you?

• 57:30Speaker 06

  I think what like uh so um there's this thing that um Elon says to uh people at his company when they're in like when they're like a crisis situation um and he says like uh hey like you know let's say you're in a crisis situation and like people are like not figuring out how to deal with it and then he asked like imagine there was a bomb strapped to your body that will go off if you don't come up with a solution to this problem like then what are you gonna do um and then you know most time that people actually like think through that scenario that they like

• 58:11Speaker 06

  focus and they get their act together and like figure out um like like something to do um and I think a lot of times startups are like that like you're like there's so many moments that are so life and death and so high pressure that um you're just in these situations all the time where you're like you have to act and you have to like do something otherwise you're toast and you just have to like figure out what the best plan of action is and the best course of action and just do it um so I think that that the realities of you know having to operate quickly I think just over time remolded my brain

• 58:49Shawn Ryan?

  Interesting and do you have any brothers do you have any siblings

• 58:54Speaker 06

  Yeah I have two brothers two older brothers um they uh they're both I dropped out of college and both my brothers have PhDs so um uh uh but my my oldest brother is an economist um and my my uh my other brother is PhD in neuroscience so they're uh they're smart yeah they're smart guys

• 59:16Shawn Ryan?

  Whole lineage of geniuses huh

• 59:18Speaker 06

  Yeah I think uh my uh yeah I think my parents are are are uh are probably still a little a little myth that none of us became physicists but

• 59:33Shawn Ryan?

  Oh, man. Well, I'm sure I'm sure they're. They gotta be happy with how everything turned out. I mean, wow.

• 59:40Speaker 06

  Yeah, no, I think I my uh my parents are super proud of me.

• 59:43Shawn Ryan?

  So where do you go to where did you go to school? I mean, where do you? Were you homeschooled?

• 59:48Speaker 06

  I went to a Los Alamos uh public high school, Los Alamos Public Middle School. There's there's like the town is ten thousand or so people. Um, now it's more because they do pit, they do manufacturing of these like nuclear cores. So now there's a lot more people there. But when I was growing up, there was like ten to fifteen thousand people, so a pretty small town and um and there's like one public middle school, one public high school, a few elementary schools and uh and yeah, that's the you know, I went to I went to public school. Um I was lucky like I I think those are those are amazing public schools, uh but it's like it is public school like any other public school.

• 1:00:31Speaker 06

  And then I would just get home every day and um and effectively like do math and science like every day.

• 1:00:43Shawn Ryan?

  What?

• 1:00:45Shawn Ryan?

  Like, what? How do you go? What it? What is the average second grader? But you said you had learned algebra in second grade. What what is an average? It's been a long time since I've been in second grade. Things may have changed, but I'm pretty sure it's basic addition.

• 1:01:03Speaker 06

  Yeah, I think it's like addition. Maybe you get to your times tables.

• 1:01:06Shawn Ryan?

  Yeah, maybe some multiplication tables?

• 1:01:08Speaker 06

  Yeah, yeah.

• 1:01:09Shawn Ryan?

  I mean, so how do you?

• 1:01:12Shawn Ryan?

  Dude, what's what is that like to go to go from the night before studying algebra? To two plus two is four?

• 1:01:23Speaker 06

  Yeah, I uh.

• 1:01:27Speaker 06

  I re- like, I definitely remember in school, like, I think like a lot of, a lot of kids in general, just sort of like, generally kind of, um, buying out of the whole thing, if that makes sense, like, like, kind of just, um, tuning out and daydreaming and just kind of like ignoring what was happening in classes. Um, uh, that definitely, that definitely started happening. And then I, what, you know, what, what I would actually do or focus on is like, go back and then do math at home.

• 1:02:01Shawn Ryan?

  I mean, you're more, you're more advanced than the teacher.

• 1:02:05Speaker 06

  There were, I remember one time, there was like, uh, there was, uh, the good thing about where, you know, this, the school this I went to is like, the teachers were really, like, also invested in my education. Like, I think they, um, many of my teachers wanted to see me, like, thrive and continue learning and, um, and that was, that was awesome. Like, I could, I can imagine a totally separate school where it's like, the teachers don't care because, you know, um, you know, it's just like, their lives are chaotic, the classroom's chaotic, all that kind of stuff. But, but I was lucky to have teachers who really cared.

• 1:02:43Shawn Ryan?

  Yeah. I mean, seems like it worked out well. I mean, for all the success that you've amassed in 28 years, I mean, you're a very grounded person. And I never really know what I'm gonna get with you guys. At breakfast, I'm, I was super impressed. I'm like, wow, this guy's like, really grounded person and seems like a really good person. So.

• 1:03:07Speaker 06

  Oh, you're too nice.

• 1:03:08Shawn Ryan?

  Kudos to you, man.

• 1:03:09Speaker 06

  Appreciate it, man.

• 1:03:10Shawn Ryan?

  Hey, let's take a quick break. When we come back, we'll get into MIT.

• 1:03:18Speaker 00

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• 1:03:47Speaker 01

  Mr. President, my company told me of the explosion on our rig.

• 1:03:50Speaker 00

  On October 2nd.

• 1:03:51Speaker 01

  Causing massive water displacement and triggering a tsunami. They all heap the blame on me.

• 1:03:56Speaker 00

  Tom Cruise is.

• 1:03:58Speaker 03

  Digger got us into this mess, and Digger's gonna dig us up.

• 1:04:01Speaker 00

  From the director of Birdman and The Revenant.

• 1:04:04Speaker 04

  What would happen if I stopped going? You stall, you freeze in there, that's what.

• 1:04:08Speaker 00

  With great power comes great deniability.

• 1:04:11Speaker 04

  I'm not the first then.

• 1:04:12Speaker 00

  Digger. See the trailer online. Only in theaters October 2nd. Rated R. Under 17. Not a minute without parent.

• 1:04:22Speaker 01

  Mr. President, my company told me of the explosion on our rig.

• 1:04:25Speaker 00

  On October 2nd.

• 1:04:26Speaker 01

  Causing massive water displacement and triggering a tsunami. They all heap the blame on me.

• 1:04:31Speaker 00

  Tom Cruise is.

• 1:04:33Speaker 03

  Digger got us into this mess, and Digger's gonna dig us up.

• 1:04:36Speaker 00

  From the director of Birdman and The Revenant.

• 1:04:39Speaker 04

  What would happen if I stopped going? You stall, you freeze in there, that's what.

• 1:04:43Speaker 00

  With great power comes great deniability.

• 1:04:46Speaker 04

  I'm not the first then.

• 1:04:47Speaker 00

  Digger. See the trailer online. Only in theaters October 2nd. Rated R. Under 17. Not a minute without parent.

• 1:04:59Shawn Ryan?

  All right, Alex, we're back from the break. We're getting ready to move into you going to college. So you started at MIT, correct?

• 1:05:05Speaker 06

  Yep.

• 1:05:06Shawn Ryan?

  How did that go?

• 1:05:07Speaker 06

  Yeah, so, let's see. I was, uh... So, I'll say the first, the few years before that. So I dropped out of high school, actually.

• 1:05:16Shawn Ryan?

  Oh, you dropped out of high school?

• 1:05:17Speaker 06

  Yeah, dropped out of high school.

• 1:05:20Shawn Ryan?

  Why not? Why? Wasn't challenging enough for you?

• 1:05:24Speaker 06

  Uh, I dropped out a year early to, to go work at Quora, at this tech company. I think a lot of people run into Quora, it's like the question answer website. But, uh, but I went to go work at a tech company for a year. And, uh, and then after a year of that, I decided, okay, it's time to go to college. So I went to, I, I went to MIT.

• 1:05:47Shawn Ryan?

  Yeah, fifteen years stumping PhDs.

• 1:05:51Speaker 06

  It was maybe not quite that, maybe not quite that early, but, uh, but yeah, like by sixteen, seventeen. Yeah, was, uh, I was more, I was more competent by that point.

• 1:06:00Shawn Ryan?

  What, what are you stumping these guys on?

• 1:06:03Speaker 06

  So, um, well, at that point, that was like early, early AI. It wasn't even called AI yet. It was called machine learning. That was like the more popular term. And it was about training different algorithms that would, you know, uh, re-rank content. It was just like all the like, um, all the algorithms for like these social media style, um, style things. And it's like, okay, what algorithm creates the most engagement or what algorithm like gets people, you know, the most hooked on, on these feeds. That's what I, that's what I was working on back then.

• 1:06:38Shawn Ryan?

  Gotcha.

• 1:06:40Speaker 06

  And so, uh, so I went, so I, I worked, I worked for a bit and then I went to MIT and, um,

• 1:06:48Shawn Ryan?

  What are you, what are you, sorry to interrupt. Couple more questions. What is it like for you to be sixteen, seventeen years old stumping PhDs? I mean, is that, is that just like normal life for you?

• 1:07:07Shawn Ryan?

  I mean, you, you know what I mean? Like, does it, does it set in like, holy shit, I'm really fucking smart, you know, and or?

• 1:07:16Speaker 06

  I think um, I think something that I internalized pretty early on was that um, was that focus was really, really critical. And so I didn't think necessarily, I mean, like, I think a lot of people are really smart. And I don't know if necessarily I'm like way smarter fundamentally than a lot of these other people. But I was like hyper focused on math as a kid. And then hyper focused on ph- physics and then in high school is hyper focused on programming. And then um, and so if you, if you're like hyper focused and you're just like,

• 1:07:57Speaker 06

  you like really invest the time and the effort, you can make really, really fast progress.

• 1:08:03Shawn Ryan?

  Mm hmm.

• 1:08:03Speaker 06

  So one of the things that I always like, I, I've believed in for a long time is that if you, if you overdo things, like you like really like invest lots of time, lots of effort. You go the extra mile, you go the extra 10 miles and you're like constantly overdoing things. Then you will improve faster than anybody else by many times. Um, and a lot of other people, maybe they're just not going the extra mile or maybe they're just not as focused. Or you know, they're like meandering a bit more. And so that's really like, I, I definitely like, for me, I think a lot of, a lot of what I attribute

• 1:08:47Speaker 06

  um, being able to accomplish so much to is really about focus and, and overdoing it, going the extra mile. Um, that's, that's what I think boils down to.

• 1:08:58Shawn Ryan?

  What did your parents think when you dropped out of school?

• 1:09:01Speaker 06

  Um, you know, they, my parents, I think still probably really want me to get a PhD and, and do scientific research. So um, uh, they.

• 1:09:14Speaker 06

  I think they view, and I respect this belief, uh, you know, I think they view the pursuit of science, the pursuit of knowledge as above all else.

• 1:09:26Speaker 06

  And, and so I would always tell them, hey, I'm just, you know, this is like a little detour, but ultimately I'm going to come back and, you know, finish my degree and finish my, you know, get a PhD and, you know, I'll be on the straight and narrow.

• 1:09:40Speaker 06

  Uh, so that's what I always, what I always tell them, but, uh, and then at some point it just didn't be, it wasn't believable. So I just stopped telling them that. Why'd you decide to go to school? I went to school because, um, uh, well, there were two things. One was like genuinely, I wanted to learn a lot about AI very quickly. And I knew I could kind of do that while working maybe, but, um, the best thing to do really would be to like go to school, like invest all my time into it and, and, uh, and try to learn, learn very, very quickly. Um, and then the second thing was like, you know, almost anyone you'll, not anyone,

• 1:10:25Speaker 06

  but like many, many people, if you ask them like, what were the best years of your life? Like a lot of people will say their college years. And so I was like, shit, I can't, I'm not going to sacrifice the college years. Um, so, so, uh, yeah, I went to school. I like, I decided to just go really, really deep into AI. I took all of the AI courses I could, um, uh, while I was at MIT. I was only there for a year, but I, I started out, I remember I took a, um, I wanted to take the, the sort of like hardest machine learning course, the first semester I got there. And the, my freshman advisor, the person who was like, I get all my courses approved with was the professor of that course.

• 1:11:10Speaker 06

  Uh, this was like happened to be the case. And, uh, I like signed up for her course and then she, she said like, You're a freshman, you're you're not gonna, you know, this is gonna be, this is gonna be too much for you. And I was like, ah, just give me a chance, like, you know, I I just wanna try it, like, I'm really passionate about the topic. And he's like, okay, well, we'll let you uh, we'll let you go till the first, you know, for the first few weeks and see how you do. And so then I get in and then uh, I remember I was like, I felt uh, I felt like the stakes were really high. Cause like I wanted to like prove that I could do this. And so the first test rolls around. And uh, I think by like sheer luck, it just happened to mostly be about things that like,

• 1:11:52Speaker 06

  like there were a lot of things in the course I didn't understand, but happening about stuff that I did understand in the course pretty well. And I got like one of the top marks in that course and there were like hundreds of people in this class. Um, and so then after that point, the professor, let me do whatever I wanted. And then uh, and so then I did all of these, I I was, I went really deep into AI and all the, and all the AI coursework at MIT. Um, and then this was the year when uh, DeepMind, the this like AI company out of London, came out with AlphaGo, which was the first AI that beat um, the best Go players in the world. Which was viewed at that point as like, probably the hardest strategy game, or the hardest sort of like,

• 1:12:35Speaker 06

  um, yeah, the hardest strategy game for AIs to beat. And that was a big deal. And then I started tinkering with AI on my own. So I built, I wanted to build like a camera inside my fridge that would tell me when my roommates were stealing my food.

• 1:12:52Speaker 06

  And uh, and and so I started tinkering with it and then I pretty quickly realized, um, uh, kind of what we're just, what we're talking about earlier, that data was gonna be like, that everything was gonna be blocked on data. Like if we, no matter what you wanted AI to do, that was, that was going to rely on data to make the AI do those things. Um, and so I, and I looked around, I was like, nobody's working on this problem, you know? You have plenty of guys working on building great algorithms, you have plenty of people working on building the chips and the computational capacity and and um, and all that.

• 1:13:30Speaker 06

  Nobody working on data. So I was, you know, I was impatient. You know, I was nineteen years old. I was kind of impatient. I was like, well, if nobody's gonna do it, I might as well do it. Dropped out, started the company, and was off to the races.

• 1:13:43Shawn Ryan?

  Damn. So did you, uh, perfect the refrigerator AI to tell you if your roommates are stealing your food?

• 1:13:50Speaker 06

  I, uh, that was part of the problem. I was like, I was, I, I was trying to build it and then I realized I didn't have anywhere near enough data. So we're always like fire incorrectly and always have false positives, false negatives, et cetera. And then, um, and I realized like, uh, it then that was like the lightbulb moment. It's like, oh shit, if I really want to make this, I need like, like, like a million times more data than I have now. And that's going to be true for like every AI thing that anyone ever wants to build. Um, and so that was kind of the, the genesis of the, the idea, really.

• 1:14:25Shawn Ryan?

  So you left MIT?

• 1:14:27Speaker 06

  Left MIT. I remember I moved, I, I flew straight from Boston to San Francisco, um, to start the company, uh, and, um, basically immediately went from like,

• 1:14:42Shawn Ryan?

  At nineteen years old?

• 1:14:43Speaker 06

  Nineteen years old. Yeah, I immediately left and then I started coding, um, uh, in San Francisco. And I was part of this, uh, this like accelerator, like I was part of this program called Y Combinator. And, um, and it's kind of like the Hunger Games for startups. Um, so there's like, there's like, it starts out, there's a hundred startups at the start of the summer. And you're all like grinding away, you're all working, you're all trying to like, show milestones and show progress. And then it culminates at the end of, at the end of the, uh, of, of Y Combinator, at the end of it all, there's a demo day where everybody presents their companies, presents their progress and tries to get investment.

• 1:15:27Speaker 06

  And, uh, and it, so it literally, quite literally is the Hunger Games. It's like you go through this whole thing at the end. If you get investment, you get money, you've won. If you didn't, um, you've lost. Uh, and, uh, and so that was like, that was, that was the beginning of the company. We ended up getting good investment.

• 1:15:44Shawn Ryan?

  Well, what, what did you do?

• 1:15:46Speaker 06

  Uh, well, at that time, we were, we were, it was, it was around data for AI. So it was all around like, uh, how do we fuel data for, for, um, what people want to build with AI? But at that time, it was like so early that like the use cases were pretty stupid. Like, we were helping one company try to detect, like, it was like a T-shirt company. They made like custom T-shirt designs and we're trying to help them detect when people, um, were like, use a T-shirt design that was like, that was like, um, like, uh, that was like unfit for to print, like, you know, had like gore or, or like, um, you know,

• 1:16:28Speaker 06

  you, all sorts of like illegal stuff, like if basically like identifying illegal T-shirt designs. Kind of like stupid now they said. And then we're helping another company. Um, it was like a furniture marketplace. We're helping them like improve their search algorithm with AI. And then maybe a few months, maybe three months in, we started working with autonomous vehicle companies and self-driving companies. Um, and then that, and that ended up being like the real, the real meat behind our effort for the first three, four years. So we worked with, you know, General Motors and Toyota and, um, Waymo and, you know, all of the major automakers. Wow.

• 1:17:11Speaker 06

  And helping them build self-driving cars. How many people were you competing against? I mean, um, I think in anything you do in startup land, like you have like tens of competitors, um, you know, and, and there were, there were definitely tens of competitors at that time. Um, and, uh, and so it was like, you know, these are competitive spaces, but, um, where, uh,

• 1:17:36Speaker 06

  As we described, I don't mind competition for math competition days. And so, and so we, we were just like really focused on the problem, really focused on how do you, what are the best possible data sets for these self-driving cars? A lot of that had to do with, it's called sensor fusion. So, you know, there's so many different kinds of sensors. And how do you combine all these different sensors to get, you know, one output? So like if multiple sensors sense a person, how do you like collect all that together to say, that's one person right there and that's one car right there. And that's one, you know, bicycle over there. Um, so that was kind of our specialty as a company. And then, um, then we're kind of off the races.

• 1:18:21Speaker 06

  Just on that, we, we grew the company to like a hundred or so people.

• 1:18:26Shawn Ryan?

  Let's go back just a little bit.

• 1:18:27Speaker 06

  Okay.

• 1:18:28Shawn Ryan?

  So you, you go to San Francisco by yourself as a 19-year-old kid who had just dropped out of MIT. How do you?

• 1:18:38Shawn Ryan?

  But you, you're immature at that point. And so how do you develop leadership skills? And I mean, how do you have, how do you have the know-how? And make the connections to build a company as a 19-year-old kid?

• 1:18:56Speaker 06

  Yeah, you, so, um, let's see what happened. So basically, early on, like, um, it's about who you get investment from. And so if you get.

• 1:19:08Shawn Ryan?

  So it was just you with the competition? There was no team?

• 1:19:11Speaker 06

  No team, no team. And then, um, and so I, and I was coding every day. And then I got, um, we got Y Combinator to invest in us. And then we got, um, this, this investment firm called Excel, which was, we were one of the early investors into Facebook to invest. And so we got, um, some, some good investors. And then they helped me build the team, like find people to hire. I also hired, you know, what actually happened is I mostly hired people I knew from school.

• 1:19:46Shawn Ryan?

  Really?

• 1:19:47Speaker 06

  Yeah. So like.

• 1:19:49Shawn Ryan?

  Cause you could trust them?

• 1:19:51Speaker 06

  I think more that they could trust me because I think if it like at the time if I went to like a a twenty five year old engineer in San Francisco and I was like hey we should we should work together I had no credibility like um I remember I waste I like I get coffee with these people and I would say like yeah this is what we're working on it's super cool you should join us and then they would all just be like okay um cool I guess I'm gonna go back to my job now

• 1:20:21Speaker 06

  um so early on I had no credibility except for with people I went to uh college with uh who we were just like friends and we liked each other and so I managed to recruit a bunch of them over um

• 1:20:33Shawn Ryan?

  They dropped out too?

• 1:20:35Speaker 06

  Some of them dropped out some of them just happened to you know were like seniors or whatever finished school and then joined um it was like a mix it was a mix um and uh and that was like the early nucleus of the team the early sort of like cohort of the team and then um and then we started picking up momentum because we're starting to work with large automotive companies we're starting to work with you know these very futuristic autonomous driving companies and then as momentum started to pick up like you know we were able to grow and build out the team over time

• 1:21:10Shawn Ryan?

  I mean so so where did you get your business sense or did you hire somebody to run all of that you were you were the mastermind behind everything?

• 1:21:17Speaker 06

  Um I uh maybe about a year in I hired somebody literally with the title head of business um but uh but until then I was just kinda like I was just trying to like learn it all

• 1:21:33Shawn Ryan?

  How did you get the product out there?

• 1:21:35Speaker 06

  Uh I uh I just coded it all up and then there are like I like put it out on one of these there's all these like websites where you can launch startups and I put it on the one we put it on one of those websites and uh it went like micro viral you know um like viral among like people who were on Twitter to look for new startup ideas um and then it was kind of that was like the early seed that just that ended up uh enabling everything to grow but it was like I mean

• 1:22:14Speaker 06

  At the time, it was, I mean, it was it was tough going, you know, you you're like, like, I would just like, we I would just spend all my time coding, then every once in a while, I would like, post something to the internet to the internet and just like, and then I would beg all my friends, I was like, I would say like, please go upload this, please go like this, like, please, like, you know, give me some ounce of traction and uh, yeah, those were the early days.

• 1:22:40Shawn Ryan?

  Damn, was it Scale AI at the beginning?

• 1:22:43Speaker 06

  Yeah, Scale AI, actually, it was called, it was uh, it was Scale API at first, and then, because that was just like, that website was available, and then it became Scale AI like, year and a half later. Um, but uh, yeah, so so the whole, the whole, I mean, early startups are so gnarly, um, it's, I mean, it's really crazy, if you look at like, all these big companies and you're like, you know, think about what they were like in the early days, they're all, they're all pretty, pretty uh, pretty rough and tumble, um, but but the coolest thing, like, we, because we started working with all these automotive companies and working on self-driving, um,

• 1:23:24Speaker 06

  uh, it quickly became hyper-interesting, um, because, you know, this was like, one of the great scientific and and and engineering challenges of the time, um, and uh, and we ultimately ended up being successful, like Waymo, one of our customers, is now launched and driving, um, large-scale robotaxis services in, you know, San Francisco, LA, Phoenix, they're launching in more cities, like,

• 1:23:54Shawn Ryan?

  Wow.

• 1:23:54Speaker 06

  Um, it's pretty amazing.

• 1:23:56Shawn Ryan?

  Wow, damn, and the company grew how fast?

• 1:24:01Speaker 06

  So, let's see, I think the numbers are something like,

• 1:24:05Shawn Ryan?

  Five years, you are the youngest, five years from when you started it, you become the youngest billionaire in the world.

• 1:24:13Speaker 06

  Yeah, that's crazy to think about. That did not feel obvious. The first year, it was like, it was like,

• 1:24:20Speaker 06

  for the first ye- first twelve months, it was like,

• 1:24:25Speaker 06

  One to three people, like it was like, it was like almost nobody. It was like me and like one or two other people working on it for the first year. That's it for the first one year. And then after the second year, we go from that like one to three people and we start hiring more people. We get to maybe like like fifteen or so people. And then that third year, we went from fifteen or so people to

• 1:24:59Speaker 06

  like maybe a hundred. And then we were kind of off the, then it was like a hundred and then we, and then we were like two hundred and then five hundred and then we kept growing and now we're up to like eleven hundred people. But the first, it was like really slow going at first and

• 1:25:18Speaker 06

  yeah, and we, we, we, uh, we focused on first it was autonomous driving. And then, and then starting, um, starting about three years in, we started focusing on defense um, and working with the D O D.

• 1:25:37Shawn Ryan?

  What are you guys doing in defense?

• 1:25:39Speaker 06

  So, uh, we do, we do a few things. So one of the, the first things we did was help the D O D with its, its own data problem to help them be able to train A I systems. So, um, you know, one of the first things that we worked on was like, you know, they wanted to, the D O D wanted to do image recognition on satellite imagery, SAR imagery, you know, other like all forms of overhead imagery. But they had this huge data problem, you know, just like me with the fridge. Um, they had the same problem, like how, you know, they need to be able to have data that lets them detect things in all this imagery.

• 1:26:22Speaker 06

  And so we, the first thing we did was fuel, um, the data sets and data capabilities for the D O D. That was true for the first few years and then, um, more recently, we've been working with them to do large scale fielding of A I capabilities.

• 1:26:38Shawn Ryan?

  What, what, what, what kind of stuff is D O D looking for in imagery?

• 1:26:43Speaker 06

  Uh, so.

• 1:26:45Shawn Ryan?

  I mean, let me, let me, let me also, so basically the way I understand this is.

• 1:26:52Shawn Ryan?

  You don't need a human to detect something maybe like a nuclear reactor. Is that, is it, am I on the right track here?

• 1:26:59Speaker 06

  Yeah. Or a missile silo, or, yeah.

• 1:27:03Shawn Ryan?

  And so AI is detecting all these, which drastically reduces human error, human manpower, all that kind of stuff. It's more accurate.

• 1:27:11Speaker 06

  Yeah, and it's, and I mean, mostly it's, it's scalable, like. I mean, the, we, we, the number of satellites in space has like exploded, so we have so much more sensing. Um, today, like way more imagery, way more sensing today than it's even like feasible for humans to, to work their way through.

• 1:27:30Shawn Ryan?

  Wow.

• 1:27:31Speaker 06

  Um, so that was, yeah, that was like the first problem.

• 1:27:34Shawn Ryan?

  How do you fuel it?

• 1:27:36Speaker 06

  You, well, you have to build, so there's, there's two parts. So first, you have to build effectively like a data foundry. You have to build a, a mechanism by which you're able to generate lots and lots of data to fuel these algorithms. Um, a lot of it synthetically, so using the algorithms themselves to generate the data. But then a lot of it, you still need humans to validate and verify. So one of the things we did actually for this whole project is, um, we created a facility in St. Louis, Missouri. Next to, uh, NGA, the National Geospatial Intelligence Agency.

• 1:28:14Speaker 06

  And we produced a center for AI data processing where we, uh, hired up, uh, imagery analysts to be able to validate the outputs coming out of the AI systems to ensure that, um, we were getting the correct, you know, we're getting accurate and high integrity data to feed back into the AI systems.

• 1:28:36Shawn Ryan?

  Wow. Wow. Damn. Where do we go from here?

• 1:28:42Speaker 06

  Yeah, so then, so we were doing, so we were doing, um, lots of stuff around imagery and computer vision. And then, um, uh, and then we started working with the DOD on, you know, more ambitious and larger scale AI projects. So one of the things we're working with them now is this program called Thunderforge, which is using AI for, um, military planning and operational planning. So, um, more broadly, so, so the basic idea here is can you use AI to?

• 1:29:15Speaker 06

  Effectively like automate major parts of the military planning process so that you're able to plan within hours versus taking many days.

• 1:29:23Shawn Ryan?

  This sounds like Palantir.

• 1:29:25Speaker 06

  It's, um, yeah, they target different parts of the problem and we target different parts of the problem. And ultimately we work together pretty well. But the, this is part of a broader concept that we have around a gen, what we call agentic warfare. So the use of AI and AI agents in warfare. And the basic idea is can you go from these current processes where humans are the loop to humans being on the loop? And so can you go from, you know, situations where, you know, these workflows have to go from a person has to do a bunch of work, then pass the next person, they have to do a bunch of work, pass the next person to the AI agents are just doing a lot of that work and humans are just checking and verifying along the way.

• 1:30:09Speaker 06

  Um, and it's, it's a big change. So going from, you know, you know, if you compare both set, set up side by side, here you have individuals, humans with decades of single domain experience who are doing each step, step of this process. And then if you have the AI agents doing it, ideally you have AI agents who have, you know, thousands of years of, of knowledge, all domain knowledge and are, are, you know, a thousand times faster at under, at doing the actual tasks. And so it's all about taking, and this exists at many, many different levels. So, you know, there's, you can think about this for the sensing and intel portion that we're talking about before.

• 1:30:52Speaker 06

  So, you know, can you accelerate the intelligence gathering, the, the process by which we take all of the sensor data and turn that into insight? You can think about it for the operational planning process, like how can you accelerate that, uh, that entire flow? You can think about it in terms of, um, you know, on the tactical side, how do you accelerate tactical decision making? Um, so it bleeds into every sort of like level warfare, every, or every component. But at its core, how do you use AI agents to be faster, more adaptive? And have humans just check their work?

• 1:31:28Shawn Ryan?

  So when you're talking about it helps with mission planning, especially in a tactical environment, because that's where I come from. I mean, what? What is, it could be any example, but can you give me an example of how it speeds up the mission planning process in a tactical environment?

• 1:31:46Speaker 06

  Yeah, so, so let's say that, so this thing that we have, by the way, you know, we're working on it with Indo Paycom and UCOM right now, and we'll deploy them more broadly. But let's say that there's a, what's a good, what's a good example? Let's say there's some kind of alert that pops up, like there's something that we didn't expect that we need to figure out how we're going to respond to.

• 1:32:13Shawn Ryan?

  Like, what kind of an alert?

• 1:32:15Speaker 06

  So, I mean, let's say there was like, you know, there's like, I mean, you can imagine at different levels, but let's say there's like a ship that popped up that we didn't expect.

• 1:32:24Shawn Ryan?

  Okay.

• 1:32:25Speaker 06

  As a simple example. So then, that alert flows into a bunch of AI systems that are going to, the first step is sensing, so where, like, let's look through all of our sensing capabilities, and let's like, go reanalyze all of the data that we have, and figure out, how much do we know about that ship? Right? So, now, a person would, like, an analyst would go through and like, do all this, you know, all the PED and all the stuff to be able to undergo this work, but ideally you have AI agents that are just going, they can look through all the historical sensor data, they can figure out, oh, actually, there's like, kind of a thing that showed up on this radar, and there's kind of a thing that showed up on this satellite imagery, and we can kind of like, sketch together this like, you know, the trajectory of this, of this ship.

• 1:33:09Speaker 06

  Okay, so you go through that process, you try to understand what's going on, and then you, and then you go through and, and figure out, okay, what are the, what are the possible courses of actions? So, once you have situational awareness, then what are the courses of actions against this particular scenario? And you can have an AI agent honestly just propose courses of actions, like, hey, in this scenario, given this ship is, is coming here, you know, we could fire at it, we could just wait to see what happens, we could reposition, so that we're, you know, we're able to, to, you know, handle the threat better, you know, all sorts of, we could, we could reposition some satellites, so we have greater sensing, you know, there's all sorts of different courses of actions that we could take.

• 1:33:55Speaker 06

  And then, uh, once the AI produces those course of actions, it'll run each of those different course of actions through a simulator. So it'll then run.

• 1:34:07Shawn Ryan?

  It war games at real time.

• 1:34:09Speaker 06

  Exactly. It'll war game at real time. And so then it'll run it through a simulator and say, okay, what's going to happen if we fire at it? Like, you know, this is what we know about red forces. This is what we know about blue forces right now. Um, if we fire at it, this is like, you know, this is the war game of how that plays out. If we, um, just increase our sensing, like, these are the things that, that the red forces could do to fuck us up. And like, that's the risk that we take on. And, um, and then the benefit is because all of this is automatic, you can run it, these war games and these simulations a million times. So it's not just like one, you know, military planners, just like trying to like war game and plan it out, like, you know, in human time. It's like you could run a million simulations because

• 1:34:54Speaker 06

  you don't have perfect information. You don't have perfect knowledge. So you need to kind of figure out based on the uncertainties of the situation, what are all the potential outcomes that, that pop out of that?

• 1:35:04Shawn Ryan?

  Wow.

• 1:35:05Speaker 06

  And then, so you run like a million different simulations of each of these different courses of action. And then you can give a commander, direct, like you just give them this whole, like, brief and presentation, which is basically, these are the courses of actions we considered. This is the, this, these are the likely outcomes in those courses of action. We can show you the simulated, like, outcome in each one of these scenarios. So we can like, show you what it would look like in every one of those scenarios if it happened, like representative, um, simulations. And then the commander makes a call.

• 1:35:41Shawn Ryan?

  Wow. So it's, this is what it is. This is what it's doing. These are the possible courses of action. These are the consequences of each action. This is the percentage.

• 1:35:52Speaker 06

  Yeah, exactly.

• 1:35:53Shawn Ryan?

  And it spits that out in what, a matter of seconds?

• 1:35:56Speaker 06

  Then now it takes a, you know, probably takes, even now, it probably takes a few hours because, you know, these models are a lot slower than they will be in the future. But, yeah, I mean, compare that to, I mean, depending on the situation, like, that could take, you know, that could take days for humans to do today. Like it's, and, and it's not from lack of will or effort or, or capability. It's just, it's a really complicated situation. If a ship pops up out of nowhere, like. There's a lot of stuff you have to consider. Um, and so, uh, that's really the, the, the step changer is just like a, uh, like dramatically accelerating situational awareness, dramatically accelerating, like, eh, an understanding of what the different course of actions are, what could happen, what are the consequences, um, and surfacing that to commander.

• 1:36:42Speaker 06

  Does it make a recommendation? Um, this is kind of an interesting thing. We,

• 1:36:49Speaker 06

  we go back and forth if we want to make a recommendation because ultimately like, we don't want, um, to just be like, you know, we don't want to let commanders kind of like sleepwalk, if that makes sense. We want them to like, you know, our military commanders are the best humans in the world, like considering all of the potential consequences of these different courses of action and also considering, you know, um, uh, i- in, and ultimately making a call based on those potential consequences. So I think we want to ensure that

• 1:37:25Speaker 06

  commanders are still exercising their judgment in these decisions versus just, you know, making it easier for them to just say, oh, go with what the AI says.

• 1:37:36Speaker 06

  Interesting. Wow.

• 1:37:39Speaker 06

  But this, but then, okay, think about what happens next. So, um, and this is where stuff gets really freaky. So let's say that, um, obviously in a world where just the blue force, just the United States has this capability. That's great. You know, we're gonna, we're gonna be running circles around everyone else. Um, but then what happens if the red force, you know, China, Russia, whomever, also has the capability? Then you're in this situation where

• 1:38:08Speaker 06

  I've war gamed out the whole situation. You know, they've instantaneously wargamed out the whole situation. And then it's like, then, then it, I think, I honestly think, so then it's like, we know, and you know, like, blue forces, red forces, we both know that we both have like, you know, this perfectly wargame scenarios, which avenue do you pick? And then it becomes this really complicated, almost like psychological, you know, kind of, kind of situation, where it's like, then it like, all comes down to how good our intel is. So, how good is our intel about that commander? How good is our intel about what their collection capabilities are? How good is our intel about, you know, what they likely know about us?

• 1:38:51Speaker 06

  And vice versa. Um, and it gets pretty...

• 1:38:55Shawn Ryan?

  So this is actually, let's just,

• 1:39:02Shawn Ryan?

  so let's say China, Russia, our enemies have this capability, we have this capability, then it, then it kind of becomes...

• 1:39:16Shawn Ryan?

  It's like the same process that we deal with now. Who has the better intel, right? It's just developing, and, and, and you're going to a course of action quicker. And the enemy's doing the exact same thing quicker. So it's, essentially, it's the exact same thing that we're doing now,

• 1:39:36Shawn Ryan?

  but faster. And so if we develop it first, then we achieve basically global domination. Am I correct here?

• 1:39:47Speaker 06

  Yeah, I thi- and I think timing really matters here, because if we get this capability,

• 1:39:55Speaker 06

  and this will go for, I mean, there's like, there's way more, there's, there's way more AI we'll be able to do, but let's say we get this capability, you know, a year ahead of adversaries.

• 1:40:06Speaker 06

  Then you're then like, we're just gonna be able to respond so much faster. The analogy I often use is like, imagine we were playing chess, but for every one move you take, I can take 10 moves. Like, I'm just gonna win.

• 1:40:18Shawn Ryan?

  Uh huh.

• 1:40:19Speaker 06

  Uh, and that's what, that's the asymmetric advantage that that comes out of this, of this capability. Um, and then once it, but then once it equalizes, then, then it's like this very, you know, it's like, to your point, it becomes like adversarial intel-based, you know, capability-based kind of conflict.

• 1:40:40Shawn Ryan?

  How?

• 1:40:43Shawn Ryan?

  How do we?

• 1:40:49Shawn Ryan?

  I mean, how do we combat our adversaries from having this type of intel, from having this type of AI system?

• 1:40:58Speaker 06

  So, I think the, then, I mean, China has demonstrated with DeepSeek, uh, and, you know, models have come out since then, they're gonna be very competitive on AI. And in, uh, I think in 2024, so last year, there were something like 80 contracts between, uh, large language model AI companies in China and the People Liberation Army, the PLA.

• 1:41:32Speaker 06

  That number is not 80 in the United States. Like, the United States is like way, way less than 80. So, they're very clearly accelerating the integration of AI into their, um, national security and into their military apparatus very quickly. I don't think, at this point realistically, we can stop them from having this, this capability they described. So then you go to the next layer down. So, uh, Intel. So, um, well, the next layer down, the next two things that you look at is, okay, how does AI impact Intel? And how does AI, how can we, what is the adversarial AI dynamic?

• 1:42:15Speaker 06

  Like, can we use our AIs to sabotage their AIs? Can they use their AIs to sabotage, sabotage ours? Um, and it's like AI on AI warfare, effectively. Um, then when you look at that scenario, okay, so the, so let's dig into that.

• 1:42:32Speaker 06

  The first level analysis here is kind of what we were talking about before, which is that probably just boils down to how many copies of these AI systems do I have running versus how many copies do you have running? So it turns to a numbers game. If I have 10,000 AI copies running and you only have 100 AI copies running, then I'm gonna run circles, I'm still gonna run circles around you. And that boils down to who has so. So let's say, um, so let's say you have, you have 100 AIs, I have, I have 10,000 AIs. I will, I will take half of my AIs, I will take 5,000 of my AIs and just focus them on hacking your AIs.

• 1:43:16Speaker 06

  So I'm gonna, they're all gonna be looking for vulnerabilities in your, um, in your, uh, in your information, architecture, in your data centers, I'm gonna look for, I'm gonna, you know, I'm just like purely focused on cyber hacking of your 100 AIs. And then my other 5,000 copies are gonna do the military planning process for myself.

• 1:43:40Speaker 06

  Then, then look, think about the adversary. I have this choice. I have 100 AIs. If I have them all focus on doing, um, the military planning process, I'm gonna get hacked. Cause I'm not doing any cyber defense. And then even if I have all of them focused on cyber defense, even those numbers are bad. It's like 100 AIs versus 5,000 AIs from you. And so I probably still get hacked. So the numbers end up mattering a lot.

• 1:44:08Speaker 06

  If, even if they had, even if the other adversary, let's say it's only a 2X advantage. I have 10,000 copies running and the adversary is 5,000 copies running. I can do the same thing. 5,000 of my copies are just focused on hacking your AI. So that your AI is incapacitated or has incorrect information or, um, or is poisoned in some way, like basically is incapable incapacity for some reason. And the other half of my AIs are focused on the military playing process. Again, the adversary is screwed, because to properly deal with a cyber attack, I need probably all 5,000 copies to be focused on cyber defense.

• 1:44:44Speaker 06

  And then I have no capacity left to do the military planning.

• 1:44:47Shawn Ryan?

  Wow.

• 1:44:48Speaker 06

  So it really turns into this like, uh, very like, just in the same way that you would, you would command your forces, um, today, like all of your, your, you know, your various, uh, your forces across all domains to like, try to pincer out, maneuver the enemy, you'll do the same kind of planning for your like AI army, so to speak, or your AI.

• 1:45:12Shawn Ryan?

  Allocation of assets.

• 1:45:13Speaker 06

  Yeah, your allocation of assets, exactly. And a lot of it will be, okay, how many am I dedicating towards, um, uh, hacking and sabotaging the opponent? How many am I dedicating towards my own military planning and war gaming process? Um, the other thing is how many you allocate towards, um, uh, towards, you know, the, the other key component here is drones. And, uh, how many are allocating towards doing the like, very tactical mission level autonomy to accomplish, you know, mission level objectives? Um, but it'll be, it'll be like, I think it really boils down to ultimately who has more resources. And then

• 1:45:56Speaker 06

  what are those resources? That's going to be about large scale data centers. So who has bigger data centers and more power to run all these AI agents?

• 1:46:10Shawn Ryan?

  And who, who makes the determination of how many AIs we're going to put in tactical environment? How many AIs are going to go after cybersecurity, trying to hack into the other AIs? Is that a human or is that another layer of AI that, that, that spits out exactly what you just said? This is what we, this is our situation. Here's the courses of action.

• 1:46:36Shawn Ryan?

  Here's the, here's the consequences of what happened. So is it just AI after AI after AI that's doing all of this? All these simulations?

• 1:46:45Speaker 06

  Yeah, that, yeah, no, you're exactly right. Then I, yeah, exactly. You have another AI that's planning out and mapping out. You know, how should I allocate my AI resources? To properly deal with the adversary, given what I know about the adversary? And then, so then what are the ways in which? You know, what are, so then what are the key dimensions that would give you an edge versus your adversary? Well, it's if. A, your AI is different somehow. So it actually is like. Hard for your adversary to know exactly how you're, how you would act. Like basically strategic surprise in some form, in the form of like a different thinking process or a different sort of like. Way of reasoning of the AI systems.

• 1:47:29Speaker 06

  Um, and the other one is like, uh, ambiguity of how many, how many, what your resources actually are. Like if somehow I can make the adversary think that. I have way fewer resources than I actually do, or way more resources than I actually do. That'll be a critical element of, of, um. Yeah, a strategic surprise in those kinds of situations as well.

• 1:47:53Shawn Ryan?

  Wow. Would an AI be able to be able to, would, would, would AI? Be able to?

• 1:48:02Shawn Ryan?

  Alert?

• 1:48:04Shawn Ryan?

  If, if it, will it know it's been hacked?

• 1:48:07Speaker 06

  So, yeah, this is, this is a great question that, you know, um.

• 1:48:16Speaker 06

  Right now, probably yes. Uh, but. The. You, it's definitely possible in the future that you will be able to. Effectively hack into a system or somehow poison an AI system. And, uh, have that activity be relatively untraceable. Because you would basically, um. You would you would hack into that AI system. So there's two ways you would do it. One is you poison the data that goes into that AI. So I'm not hacking into the AI itself. I'm just poisoning all the data that's feeding into that AI.

• 1:48:58Speaker 06

  Such that at any moment in the future, I like I can activate that AI. And basically hack it without any sort of active intrusion. But I can just do it because I've poisoned. I've like poisoned the AI that the data that goes into the AI such that if I like, you know.

• 1:49:18Shawn Ryan?

  It alters the decision making process.

• 1:49:21Speaker 06

  Yeah, exactly.

• 1:49:22Shawn Ryan?

  But the but the the end decision maker, which would be a human, would not realize that.

• 1:49:28Speaker 06

  Yeah, exactly.

• 1:49:29Shawn Ryan?

  Okay.

• 1:49:29Speaker 06

  So so data poisoning is gonna is but this is what's so terrifying about deep sea. One of the reasons why deep sea is really scary is.

• 1:49:39Speaker 06

  You know, China chose to open source the model, right? So there's a lot of corporates, large scale corporates in the United States. That have chosen to use deep sea. Because they're like, oh, it's a good model and it's a good AI and it's free. Why not use it?

• 1:49:57Speaker 06

  But deep sea itself as a model could already be compromised, could already be poisoned in some way such that, you know. There are characteristics or behavior or ways to activate deep sea that. This the CCP and the PLA know about that that we don't. So so that's why deep sea is scary and why so so the first area is just data poisoning. So basically, can you poison the data that we're using to train the AIs such that? To your point, I've altered the behavior of your AIs in a way that you don't know about. And that's going to affect that's going to have cascading effects across your whole military operation. That's one.

• 1:50:41Speaker 06

  And then the second one is.

• 1:50:46Speaker 06

  Is basically, you know.

• 1:50:50Speaker 06

  If if you're able to do the whole operation quickly enough, you basically hack in and you

• 1:50:59Speaker 06

  it's kind of as we were talking about before, you would like destroy the traces, you destroyed any sort of trace that like you had hacked in and you have an agent that like hacked in, like removed that trace and the evidence of you hacking in

• 1:51:12Speaker 06

  before anybody, before it was alerted or notified. That's maybe a bit more extreme, but definitely the data poisoning stuff is is more concerning in the near term.

• 1:51:20Shawn Ryan?

  Damn. So how would you? How would you defeat it? I mean, it's so if if it were to be hacked and you knew it was hacked, then AI becomes completely irrelevant, correct?

• 1:51:33Speaker 06

  Well, the issue is we're still going to rely on it for lots of things. So.

• 1:51:37Shawn Ryan?

  It would it would have to come down to the human mind again. And you would have to, you would have to, let's say it's a ship, you would have to

• 1:51:50Shawn Ryan?

  know everything that you've done in the history so that it doesn't detect what tactic you're going to use and do something just something that's never been seen before in order to confuse the adversary's AI, correct?

• 1:52:07Speaker 06

  Yeah.

• 1:52:07Shawn Ryan?

  So you have to make a drastic change that you don't know if it's actually going to work so that the AI doesn't detect, oh shit, we've seen this before, this is what it's about to do.

• 1:52:17Speaker 06

  Yeah. Yeah. So so to your point, yes, strategic surprise becomes the name of the game very quickly. And and how do you create an operation such that you maximize the amount of strategic surprise against an adversarial AI? That's one. And then honestly, the second thing that's that's really critical is a lot of this will just plain up boil down to like straight up boil down to how many copies you have running and how large your data centers are and how much industrial capacity you have to run these AIs both centrally and at the edge in all the war in all the theaters and all the the in every

• 1:53:02Speaker 06

  in every environment.

• 1:53:05Shawn Ryan?

  How fast will it learn new technology? So let's just take, for example, Sironic, they're making autonomous surface warfare vehicles or Palmer Lucky, you know, he's doing the autonomous submarines and.

• 1:53:20Shawn Ryan?

  And so when, when, what am I trying to say here? So let's say we're at war with China. China has all the data, all the history back from whatever. World War II on different capabilities that we have. And what happens when a new, when something new is introduced onto the battle space? Like Sironix Autonomous Vehicles or Epirus or, uh, or Palmer's Rockets or his submarines? How, how would the, how would the AI get the data set?

• 1:53:59Shawn Ryan?

  To make a decision or, or not make decisions, but come up with what you're talking about? Courses of actions, consequences, what it's about to do, you know? Probability of what's going to happen? How, how fast will it be able to learn?

• 1:54:15Shawn Ryan?

  When something new is introduced onto the battle space?

• 1:54:17Speaker 06

  Yeah. This is, this is a great question. In general, so the, so like the first time it sees a totally new, let's say a USV or a UUV or whatever it might be that, that it's never seen before.

• 1:54:34Speaker 06

  It won't be, you know, it won't be able to predict what's going to happen like, cause you know, it won't know how fast it's going to go. It won't know what, what, you know, what, um, uh, what munitions it has. It won't know what its range is. It won't, it won't know all the key, uh, the key facts. Unless, by the way, they have really good intel and they already know all those things because they've hacked us. But let's assume they don't know. So the first few conflicts, it's not really going to be able to, to figure out what's happening. And that, that's a, that's a key component of strategic surprises. Always having new platforms that won't be sort of simulatable, let's say by enemy wargaming tech.

• 1:55:16Speaker 06

  So that's, that's definitely part of it. But at a certain point, it's going to know what the hardware are capable of and it's going to be able to run the simulations to, to understand how that changes the calculus. Uh, because ultimately, right? What's going to happen is? And some of this stuff like, you know, this is, this is like, you know, some of this stuff is dissonant because obviously if you look at what happens today in the military, it looks nothing like this. But let's play the, play the tape forward and like, see what happens in the future. Ultimately, you're going to run large scale simulations and it's going to figure out, hey, this new, you know, uh,

• 1:56:00Speaker 06

  uh, unmanned surface vehicle has this much range, it can go this quickly, it can maneuver in this way, it has this kinds of munitions. Um, it has this kind of connectivity. Uh, it is vulnerable to these kinds of, you know, EW attacks, whatever they may be. Um, it can be jammed in these ways and those will all just be parameters for the simulation, um, to run. So I think.

• 1:56:24Shawn Ryan?

  But initially you would have no recommendations.

• 1:56:27Speaker 06

  Initially you'd have strategic surprise.

• 1:56:29Shawn Ryan?

  So OPSEC, when it comes to weapons capabilities is still just paramount. And it will, I mean, will it always come back to the human mind?

• 1:56:40Speaker 06

  Uh, yeah, I believe so. I believe that, you know, we have this concept that we talk about a lot, which is human sovereignty. So, um, AI systems are going to get way better, but how do we ensure that humans remain sovereign? How do we main ensure that humans maintain real control over what matters? So maintain control over our political systems, maintain control over our militaries, maintain control over our economic systems, you know, uh, our major industries, all that kind of stuff. And so, um, and I believe it's pretty paramount in the military. You're not, you are not going to want to take, certainly just as like a, as like a simplistic thing, we're not going to

• 1:57:24Speaker 06

  give AI the capabilities to unilaterally fire nuclear weapons.

• 1:57:27Shawn Ryan?

  Mm-hmm.

• 1:57:28Speaker 06

  Like, we're never going to do that. Um, and so ultimately, so much of what is going to become really critical is the aggregation of information, simulations, wargaming, um, planning to humans to ultimately make. The proper decisions. And by the way, so much of this will will start bleeding into the diplomatic, like diplomacy, diplomatic decisions that need to be made. It'll bleed into like

• 1:58:01Speaker 06

  into economic warfare, like it'll bleed into.

• 1:58:04Shawn Ryan?

  I mean, this this goes all the way into.

• 1:58:08Shawn Ryan?

  I could see this going all the way into relationship building with with in between nations. Should we, you know, what are the what are the outcomes if we become allies with? Russia?

• 1:58:23Speaker 06

  Yeah.

• 1:58:23Shawn Ryan?

  You know, what what are the courses of action? What are the consequences? I mean, is it? Does it? So it bleeds into everything, politics, allies, adversaries, warfare, economics, all of it.

• 1:58:40Speaker 06

  Yeah, totally. Because if you ultimately boil it down, what is the capability? The capability is sensing and situational awareness. So I'm I'm going to know I'm going to be able to go through troves and troves of data. OSINT, other forms of

• 1:58:56Speaker 06

  like open source intel, different kinds of of various intel feeds that I have and know what is the current status, what's going on, what is the what is the current situation? It'll be able to aggregate all that data in to provide a a comprehensive view as to what those behaviors are. And it'll give you the ability to predict. And it'll give you the ability to effectively play forward. You know, every potential action you could take, what would happen in those scenarios with some probabilistic view, some some probabilities. And then, yeah, you're going to use that for every major decision, like the the military and the government should use this for every major decision we make. We should do it for

• 1:59:41Speaker 06

  trade policies. We should do it for diplomatic relations. We should do it for

• 1:59:48Speaker 06

  we should do it off, you know, we're looking outwards, but honestly, we should also do it for like internal policies, like, you know, what are our healthcare policies? What are our, you know,

• 1:59:59Speaker 06

  all that kind of stuff, too. But

• 2:00:02Speaker 06

  so it will, this capability of sort of

• 2:00:06Speaker 06

  effectively all domain sensing plus planning is going to be paramount.

• 2:00:13Shawn Ryan?

  Do you

• 2:00:16Shawn Ryan?

  and I have so many questions.

• 2:00:19Shawn Ryan?

  Do you see a world where

• 2:00:22Shawn Ryan?

  AI becomes so powerful throughout the world that it becomes obsolete?

• 2:00:31Shawn Ryan?

  And we're right back to where we we are. We were, I don't know, 10 years ago, 20 years ago, where it's all human decision making.

• 2:00:43Speaker 06

  Well.

• 2:00:43Shawn Ryan?

  Will it outdo itself?

• 2:00:46Speaker 06

  Um. A few thoughts here. I think so.

• 2:00:53Speaker 06

  One of the things, so I think the first stage of what's gonna happen is like kind of what I'm saying, like, human is the loop to human on the loop. Like, we're gonna, right now, humans do a lot of just like, like, brute force, manpower, work in all sorts of different places, you know, in the economy and in warfare, etc. Um, that'll, that's, that's like the first level of, of, of major automation that's gonna, that's gonna take place. So then it's like about, you know, your um, the strategic decision making

• 2:01:29Speaker 06

  and your ability to, um, and your ability to make high judgment decisions, um, they consider long term, short term, medium term, all that kind of stuff. Um. At a certain point of, of, well,

• 2:01:45Speaker 06

  as certain, as AI continues to improve and improve and improve and improve, um, it will operate at a pace that is very, very difficult for humans to keep up with. Um, and in, you know, this will start happening in R and D first, in research and development, like, AI will be able to start doing lots of scientific research, lots of R and D into new weapon systems, lots of R and D into new mil-, you know, military platforms, etc. Um, much faster than, than humans are, you know, would be able to do. And then humans will just check over their work and, and decide. And so it's gonna sort of race faster and faster and faster.

• 2:02:24Speaker 06

  And um, and so then what happens, I think it'll, what it'll do is it'll create dramatically more weight on the few decisions that humans make. So any decision that like all the way to the extreme, right, is um, you know, the president or or, you know, whomever making decisions about, do I let my AI collaborate with another country's AI? Like, that'll be like a decision of just like dramatic consequence, uh, much higher consequence than like similar decisions today. So I think it, almost your point, it like, it will, as it accelerates, will end up at

• 2:03:06Speaker 06

  a place where you're right, it all boils down to human decision making, but those decisions will carry like a thousand times more consequence.

• 2:03:16Shawn Ryan?

  Mm hmm, mm hmm. How do you decide who you're gonna work with? I mean, it's an international company.

• 2:03:24Speaker 06

  Yeah. Um, so we've had.

• 2:03:27Shawn Ryan?

  Who all are you working with?

• 2:03:29Speaker 06

  Uh, well, so first thing is, we're pretty, we're pretty picky about who we work with. Ultimately, just because we have, um, we only have so many resources and building these systems and building these data sets like is pretty involved, um, as, as kind of we've, we've discussed. So, you know, our aim generally is how do you work with the best in every industry? You know, how do, how do you work with, you know, like I was mentioning, the number one bank, the number one pharma, number one telco, number one military, et cetera. Um, the only addition to this that I would say we viewed as, as, as important is how are we, um, as you, as we play the tape for and everything we're just discussing.

• 2:04:16Speaker 06

  It's really important that as much of the world runs on an American AI stack versus a CCP AI stack. That becomes really, really important. And it matters not only for ideology and, you know, kind of what we were talking about before, like propaganda and control and all that kind of stuff. But it also really matters just for like, you know, at a pure operational level, like we're going to want to be able to have as extended of AI capabilities as possible.

• 2:04:54Shawn Ryan?

  So.

• 2:04:58Shawn Ryan?

  Okay, so the way I understand this is.

• 2:05:05Shawn Ryan?

  You're working with X country, would you say, would you say country X?

• 2:05:13Shawn Ryan?

  You give country X the AI model to utilize for whatever they're doing. Let's just say warfare. We own, but they have to tap into a US-based data center. Am I correct here? And so as long as we control the data center that's feeding that AI model, we essentially own it. And that, and country X just has to trust that

• 2:05:43Shawn Ryan?

  Scale AI has their best interest.

• 2:05:45Speaker 06

  Yeah, it's like next level.

• 2:05:47Shawn Ryan?

  And if they change, if they change, let's say country X now forms an alliance with China, they decide they don't want to be a part of America, then we just yank the AI or the, not the AI, the, the, the data that feeds that AI or manipulate that data to where it's essentially been hacked. Am I correct? And that's how we keep ourselves safe.

• 2:06:16Speaker 06

  Uh, yes, and then with the addition, like, I think of the way that at least we think about it today, and I think a lot of people think about it today is like.

• 2:06:26Speaker 06

  It's okay for the data center to be located elsewhere, located in the country, um, as long as it's US owned and operated.

• 2:06:35Speaker 06

  Because then we still have control and, you know, any sort of scenario that happens. And, um, the only other thing I would say is we're much more focused initially on just, um, low stakes uses of AI. So can you use AI to help, uh, the education, uh, industry in one of these countries? Or can you use it to help the healthcare industry? Or can you use it to aid in, um, in like, you know, permitting processes? Or, you know, I think low stakes use cases matter a lot more initially. Um, but I really do think like, uh, you know, we have this concept of geopolitical swing states. There are, there are a number of countries right now in the world where,

• 2:07:16Speaker 06

  whether they side with the US or China over time, is going to have immense consequences for certainly what a, what a potential conflict scenario looks like, but also even what like the long-term Cold War scenario looks like. Like what happens, um, over time in this, as, you know, our countries are interacting. So, um, so I view, I view AI as like one of these key elements of diplomacy and long-term, sort of, uh, like, long-term, um, strategic impact in the, in the international war game.

• 2:07:58Shawn Ryan?

  How would AI be implemented into our government? I mean, I can't remember exactly what you said. Implemented to run, you know, our political sphere. What does that look like?

• 2:08:11Speaker 06

  Yeah, so, um,

• 2:08:13Shawn Ryan?

  Because so much of that is people's values and, and what people believe in and stand for. And, you know, I mean, it, like today, for example, I mean, country is probably more polarized than it's ever been. And so how do you, how do you get an AI model to run government? When it is this polarized and there's so many different ideologies and

• 2:08:41Shawn Ryan?

  part of the country is way over here, the other part's way over here. How, how would an AI model?

• 2:08:49Shawn Ryan?

  Run that.

• 2:08:50Speaker 06

  Yeah, so um the we have this concept of kind of like agentic warfare, agentic government. So can you just like the same thing? Can you take these very inefficient processes in government and start replacing those with AI related functions so that you're you're just you're just improving efficiency and improving outcomes?

• 2:09:13Shawn Ryan?

  Give me give me a specific example.

• 2:09:16Speaker 06

  Yeah, so what like one super simple one. Right now, I think the the average time it takes for a veteran to see a doctor in the VA is something like 22 days.

• 2:09:27Shawn Ryan?

  Uh huh.

• 2:09:28Speaker 06

  Um it's way too long. And part of that is because of a host of antiquated processes and workflows and, you know, um just in general, that system's not working. I think we can all look at that and say, that's not a that's not a functional system. And so um can you use AI to, you know, AI agents to automate some parts of that process, automatically get whatever approvals need to be gotten, get whatever information needs to be gotten, such that that that 22 days becomes a day or two or something like that? That, I think, is like a no brainer, just pure win for government efficiency overall. Another one that uh other ones that are like big are like, you know, permitting processes.

• 2:10:11Speaker 06

  So if I want to build a new data center somewhere, or even I just want to like remodel my home, the, you know, permitting processes, depending where you are, could take, could literally take years uh for all of that to go down. And part of that is like, there's so many different approvals that need to happen, there's so many, like, there's all these like different workflows and things that need to like happen. What if instead we just codified what are the rules of the system? And an AI agent just go automatically go through that permitting process so that you could get that permit or or get the permit denied within like a day, right? So and just that times a million, like, like the uh like um like one of the things from from Doge that they found, right, is that, you know, the uh the retirements are

• 2:10:57Speaker 06

  stored in the mine, Iron Mountain Mine, uh literal like.

• 2:11:02Speaker 06

  Iron mine or like the paper copies of the retirements for all the federal employees. Like, can we just take that, which is two generations behind in terms of tackling? It's like literally pen and paper and then use AI to go from two generations behind to two generations forward. Like, can we just automate as much of those processes as possible? So, um, so I see it as just like, you know, all over the place. There's so much low hanging fruit in terms of just making current government services and government processes way more efficient. I think that I I haven't met anybody who doesn't think this is the case.

• 2:11:41Shawn Ryan?

  Okay.

• 2:11:41Speaker 06

  So that's just, that's just all the level one stuff. Um, I think, uh, the, yeah, that's just all the level one stuff. I'm improving how our government operates.

• 2:11:53Shawn Ryan?

  Would it, would it eventually replace politicians?

• 2:11:59Speaker 06

  That's a good question. I think ultimately, like, we, um, so

• 2:12:08Speaker 06

  first off, just like, um, uh, taking a step back, it's definitely the case that policy, the speed of policymaking and the speed of legislation and the speed at which the government reacts to new technologies, like, that's gonna have to speed up. Um, you know, we, uh, I've spent a lot of time in DC trying to make sure that, you know, as a country, we get the right kind of AI legislation and the right kind of AI regulation to ensure that this all goes well for us.

• 2:12:40Speaker 06

  It's been years of trying to get that done, you know? We still haven't really figured that out as a country. What is, what is the right AI regulatory framework? Like, that's still, it's still undecided.

• 2:12:51Shawn Ryan?

  I mean, how do you even describe this stuff to the dinosaurs that are still sitting in DC? I mean, we've got people stroking out on camera. We've got people not literally dying in office. I mean, we got people up there that probably can't even figure out how to open a fucking email. And then you come in 28 years old, built scale AI.

• 2:13:16Shawn Ryan?

  I mean, I just, just going all the way back to when, you know, Zuckerberg's sitting there, you know, talking to Congress, it's, it's, I mean, and I don't agree with everything he did and whatever, it doesn't matter, but I look at that and I'm like, you guys have been sitting in DC, probably don't even know how to open your own email.

• 2:13:41Shawn Ryan?

  And you're talking to a tech genius who's trying to dub this down and make you understand. I mean, I get one day with you, you know what I mean? And to try to wrap my head around this, and they have 50 million other things they're dealing with. They're not up to speed on tech. I mean, how, how do you even begin to?

• 2:14:03Speaker 06

  Yeah.

• 2:14:04Shawn Ryan?

  Tap in?

• 2:14:05Speaker 06

  I mean, I think a lot of it, I think, um, the first thing, and I think this is like, a lot of people in the know understand this, like, a lot of the minute decisions really end up being made by staffers. Right? And, um, and I think, like, generally speaking, like staffer, you have to be extremely competent as a staffer, um, no matter what, like, there's just, it's a very chaotic job, there's a lot that's, there's a lot that's going on, and they have to make very fast decisions. Um, uh, the other thing is, I think, I think analogies are, are pretty helpful. Like, I think, you know, everybody alive today has seen the pace of technology progress just

• 2:14:47Speaker 06

  increase and increase and increase and increase. Like, I think that, you know, you'd be hard pressed to find anyone who doesn't believe that AI will be this world-changing technology. Now, exactly how it'll change the world, I think that's where it gets fuzzier, but, um, but it will be world-changing technology. Um, but, but the issue is like, I mean, the, the political system just doesn't respond very quickly. Right? And, and that's, that's gonna, that's gonna be very harmful. I mean, we need to be able to respond very quickly to these new technologies. Um, and so.

• 2:15:26Speaker 06

  Uh, and I think they'll become more and more obvious. Like, I think, I think as AI and other technologies accelerate, it'll be very obvious that like the world will just change so quickly. And frankly, I think voters are gonna demand faster action.

• 2:15:41Shawn Ryan?

  Mm-hmm.

• 2:15:41Speaker 06

  Um, and I, so I think, I think our government is set up to, um, to accelerate, but, um, but that's, that's what needs to happen.

• 2:15:52Shawn Ryan?

  How do we power all this? I mean, that's, that's a big discussion, you know, and everybody is seems so apprehensive to go nuclear.

• 2:16:04Shawn Ryan?

  The grid is extremely outdated. I mean, we just saw the light flickers here about, I don't know, thirty minutes ago. Power outages happening all the time. There was just a big one, all of Spain.

• 2:16:18Speaker 06

  All Spain.

• 2:16:18Shawn Ryan?

  Uh, who, Portugal, Italy, I mean,

• 2:16:24Shawn Ryan?

  it's happening all the time in the US, power outages.

• 2:16:28Shawn Ryan?

  How are we gonna be able to power all this stuff? What would you like to see happen?

• 2:16:33Speaker 06

  Yeah, I mean, first of all, if you look at, if you take a graph of Chinese total, China's total power capacity over the past twenty years versus US total power capacity for the past twenty years, the China graph is like straight up into the right, they're just adding crazy amounts of power. They've doubled it in the last decade, I think.

• 2:16:56Shawn Ryan?

  Doubled it?

• 2:16:57Speaker 06

  Doubled their power capacity in the last decade. Um, and, uh, the United States is basically flat. Um, it's grown like a little bit. Uh, and so we're like, that, that's what's happening right now. Right now, China's doubling every decade or so. Um, US is, is, is basically flat. And we're looking at, you know, the, for, to just power the data centers that that today AI companies know they wanna build, we're gonna need something like a doubling of our energy capacity. And that needs to happen very, very quickly, like almost, you know, that's happen almost immediately. Um, and

• 2:17:41Speaker 06

  so you have to believe that our graph is gonna go from totally flat to vertical, faster vertical than, than China's energy, um, uh, growth. And

• 2:17:52Speaker 06

  China in the meantime is just is growing is is growing perfectly quickly. They'll accelerate. They'll add more power to their grid. Like, I think it's very hard to imagine realistic scenarios where without drastic action, the United States is able to grow its energy capacity faster than China.

• 2:18:12Shawn Ryan?

  Now, where are we on the so if China's going straight up and we're flatlined, I mean, does that mean, are you saying that China has surpassed our power capabilities? Or are we still above them even though they're on the rise?

• 2:18:25Speaker 06

  Uh, they're they're definitely above us because they have a bigger population and they have way more industrials. Uh, so they have, uh, I'll double check. I they definitely have more power total than us. Uh, more power generation capabilities and, um, and by the way, like, it's actually not rocket science why that is is if you look at if you then break that down to sources of that power in China, it's because coal is like eighty percent of that.

• 2:18:52Shawn Ryan?

  Yeah, it's like they're all they're all coal.

• 2:18:54Speaker 06

  Yeah, it's just tons of coal. And then we've actually like, if you look in the US, renewables have grown a lot, but a lot of it, the reason the overall number is flat is because we're using renewables to replace coal, natural gas, like fossil fuels. Um, and so when you net it out in the US, we're flat and then in China, it's it's straight up. So that's the first thing like we need. We need drastic action. You know, the administration has the National Energy Dominance Council. Um, we've sat down with them a few times like that. We we got to we have to take drastic action to enable us to to at least start matching their speed of adding energy to the grid and ideally surpass it. That's like, that's the first thing.

• 2:19:36Speaker 06

  The second thing like you're talking about is our grid is extremely antiquated and that's a major strategic risk. Um, you know, I don't know what the what the cause or the source of the the outage across Spain was. But, you know, some people think it was a foreign actor, um, or some kind of some kind of cyber attack of some sort.

• 2:20:00Speaker 06

  I guarantee you the US energy grid is extremely susceptible to large scale cyber attacks. It would be, you know, and the way, you know, the sophistication of these cyber attacks sometimes is like so stupid. It's like if you find the right like, uh, like power plant login terminal to go into. Sometimes people don't change the username and password from the default, which is username and password. And so you can just find like some power station in like Wyoming that still has the username and password is username and password. You log in and you can shut down the entire power in the entire region.

• 2:20:42Speaker 06

  So the like, the, so, so our grid, just because of how antiquated, how decentralized it is, every, all of that is hyper, hyper susceptible to, uh, to cyber attacks, hyper susceptible to foreign action, foreign activity. And, um, that matters now. Like right now, if you take the energy grid in a major city, people will die. So it's like, it's bad now, but then let's go back to what we're just talking about with AI. Like, let's say we have large scale AI on AI warfare with China. They just take out the power grid, take out our data centers and the power fueling those data centers. And then we're sitting ducks.

• 2:21:21Shawn Ryan?

  I mean, not only that, but it's my understanding that China actually produces and manufactures a lot of the major components that go into our grid, like the transformers. If we don't even, to my understanding, we don't even check those for malware, Trojan horses, shit like that. In fact, DOE actually did an inspection on one and never, it never even released the results of what they found, which probably means they found some shit. And, I mean, I just, I don't know.

• 2:22:00Speaker 06

  How we combat that? I mean, just like the, like, what, what is, where did that happen elsewhere? Like, look at, um, Salt Typhoon. Like, uh, this was a recent hack that was declassified, which is that Chinese malware and cyber activity, like, basically had fully infiltrated our, um, major telecom providers. I think AT&T was like entirely, uh, like, um, entirely compromised by this hack called Salt Typhoon, um, from the CCP. And, uh, and that's, they did that so that they could read all the messages, like, all the SMS, all the audio, um, they were able to, to capture as part of that, as part of an intel gathering operation.

• 2:22:45Speaker 06

  Um, but if they're able to hack into our telco, they've, sure as hell, you know, they're clearly capable of hacking into our energy grid, clearly have capable of hacking to any other, any of our other critical infrastructure. And, um, and it just goes back to what we're talking about, like, the energy grid, A, if we can't produce enough power, we're hosed. And B, if the adversaries can take out our power at will, we're hosed. Mm-hmm. Um, and so we have this major, major vulnerability as a country on just like the cyber posture of our energy grid. I think it's like, I think it's one of the, the biggest, like, very obvious, like, flat-out,

• 2:23:28Speaker 06

  um, uh, like, clear vulnerabilities of our overall, of our entire country. Um, A, just like, you create civil unrest, you can, like, take, you know, imagine you took Houston's power grid out, people would die, and, uh, you cause, like, all sorts of chaos. But then, if you, but then you take out these data centers, you take out, um, military bases, you take out radar systems, um, you take out, you know, you name it. You can take out almost any piece of homeland infrastructure, and that goes, create huge strategic openings for adversaries.

• 2:24:06Shawn Ryan?

  I mean, what you have to run in these circles. I mean, you're building massive data centers, correct? And so when you go to DC and you're advocating, hey, we need more power and you're just I didn't. What's the association you met with?

• 2:24:22Speaker 06

  Uh, the National Energy Dominance Council.

• 2:24:24Shawn Ryan?

  What do they say?

• 2:24:26Speaker 06

  They totally agree. I mean, they know we have to build more power. And then it's about so then you get to the next layer of detail. It's like, okay, how can we, how do we accelerate nuclear? How do we accelerate the permitting process? Um, what are existing power generation capabilities that we turned off that we can turn back on? Um, like you go through all the natural things to do, like it's, I mean, I think, I think we know what to do. The question is if we can get out of our own way. And if, and then if our grid is so antiquated that even that vulnerability, like, kind of means that we can be taken out anytime.

• 2:25:00Shawn Ryan?

  I mean, I may have made an assumption. Are you, are you building data centers?

• 2:25:04Speaker 06

  We, we ourselves are not building data centers.

• 2:25:06Shawn Ryan?

  Feeding the data.

• 2:25:07Speaker 06

  We partner with companies that, yeah, that are building, you know, the largest data centers in the world.

• 2:25:11Shawn Ryan?

  Okay. And so I've, I've also heard rumors that these major data centers are starting to just create their own power source. Is that, is there any validity to that?

• 2:25:21Speaker 06

  Yeah. So a lot of designs these days involve, can you just create a SMRs, a small, um, uh, like a, like a nuclear reactor per data center? Can you basically like have a nuclear reactor co-located with the data center, um, to, uh, to power that, that data center's capacity? Um, which I think is a good idea. The issue is like, I mean, China's going to be way ahead of us on that. The largest nuclear power plant in the world is in China. So, um, you know, we're, yeah, obviously we need to lean into nuclear. That needs to happen. Obviously, we need to, to lean into all power generation sources. We need kind of an all the above approach to power

• 2:26:02Speaker 06

  generation. Um, uh, but even that doesn't get us to a posture where you're confidently exceeding China. You're just kind of catching up to where they are. And so, um, I mean, this is a huge, a huge issue.

• 2:26:16Shawn Ryan?

  Yeah. Let's take a quick break. When we come back, I want to, I want to dive more into China's capabilities and, and our capabilities.

• 2:26:27Speaker 00

  Now, more than ever, we need someone on our side. But Rob Whitman? Whitman's votes drove up costs, cut Medicaid, and risked 350,000 Virginians' health care to pay for tax breaks for himself and his donors. He's not on our side. Shannon Taylor will fight corruption, cut taxes, and bring down costs. This November, vote for Shannon Taylor. She's for us. Paid for by HMP, the HouseMajorityPac.com. Not authorized by any candidate or candidate's committee.

• 2:27:02Speaker 00

  Now, more than ever, we need someone on our side. But Rob Whitman? Whitman's votes drove up costs, cut Medicaid, and risked 350,000 Virginians' health care to pay for tax breaks for himself and his donors. He's not on our side. Shannon Taylor will fight corruption, cut taxes, and bring down costs. This November, vote for Shannon Taylor. She's for us. Paid for by HMP, the HouseMajorityPac.com. Not authorized by any candidate or candidate's committee.

• 2:27:38Shawn Ryan?

  All right, Alex, we're back from the break. We're getting ready to discuss some of our capabilities versus China's capabilities. And, you know, we just got done kind of talking about power. Is China leading the US in any other realms when it comes to the AI race? I mean, Xi Jinping has even, has said himself, you know, the winner of the AI race will achieve global domination.

• 2:28:05Speaker 06

  Yeah, I think, well, the first thing, almost as you're mentioning to understand is, China has been operating against an AI master plan since 2018. They, the CCP put out a broad whole-of-government, you know, civil-military fusion plan to win on AI. Like you're mentioning, Xi Jinping himself has been, has spoken about how AI is going to define the future winners of this global competition. In military, from a military standpoint, they say explicitly, hey, we believe that AI...

• 2:28:43Speaker 06

  Is a leapfrog technology, which means even though our military is worse than America's military today, if we over invest in AI, we we have a more AI-enabled military than theirs, we can leapfrog them. So they've been super invested. Right now, I think the best way to kind of paint the current situation is they are way ahead on power and power generation. They're behind on chips, but catching up on chips. They are ahead of us on data. China has had, again, since 2018, a large-scale operation to dominate on data.

• 2:29:26Speaker 06

  And today, in 2023, I think, there were over 2 million people in China who are working as, working inside data factories, basically as data lablers or annotators, basically creating data to fuel into the, into AI systems. I think that number in the US by comparison is something like 100,000. So they're outspending us 12 to 1 on data. They have over seven cities, full cities in China, that are dedicated data hubs that are basically powering, you know, this, this like broad approach to data dominance.

• 2:30:07Speaker 06

  And then on algorithms, I think they have, they are on par with us because of large-scale espionage. So, and this is, I think, one of these open secrets in the tech industry that Chinese intelligence basically steals all of the IP and technological secrets from, from the United States. There are a bunch of very concerning reports here. So one is there was a Google engineer who took the designs and, and all the IP of how Google designed their AI chips and just took those and, and moved to China and then started a company.

• 2:30:53Speaker 06

  On top, using those, using those designs. The way he got those designs, by the way, is this guy Leon, Leon Ding, I think. The way he stole the data out of, out of Google's corporate cloud, by the way, was that he, it was so stupid. He just took all the code. He copy pasted it into Apple Notes, into like the Notes app. And then exported to a PDF and printed it. And just walked, walked out with it. That's it? That's it.

• 2:31:26Speaker 06

  So that was, this was later discovered, you know, we felt, we found out this happened, but for months, we had, you know, we had no idea that they'd stolen all this critical IP. Stanford University, this just came out last week, Stanford University is, is entirely infiltrated by CCP operatives. Few crazy facts. So first, it is by law in China, any Chinese citizen must comply with Chinese, with CCP intelligence gathering operations. So if you're a Chinese citizen, you're living in the United States and the intelligence agencies in China reach out to you, you have to comply with them. And so you have to give them what you're seeing, what you're, what you're finding, et cetera.

• 2:32:10Speaker 06

  So that, and there's tons of Chinese nationals, Chinese citizens in, across all the major elite universities, across all the major tech companies, across all the major AI labs, like they're everywhere. The second thing that's crazy is, you know, about a sixth of Chinese students. So, so student, like Chinese citizens who are students in America are on scholarships sponsored by the CCP itself. And for those on these scholarships, they have to report back to a handler, basically. What are the things they find? What are the things they, they're learning? Otherwise, their scholarships get revoked.

• 2:32:49Speaker 06

  So we have, there's, there's an incredibly large scale intelligence operation running in the, against the US tech industry, which is just collecting all the information and secrets and technological secrets from our greatest research institutions, our universities, our lab, AI labs, our tech companies at, at massive scale.

• 2:33:13Speaker 06

  And honestly, I think this is a very underrated element of how China caught up so quickly. So, um, you know, DeepSeek came out of nowhere. Everyone was so surprised at how capable their model was and how they learned all these tricks. You know, how much of that is because they came up with all of them on their own? Or they managed to have a like exquisite, high-end espionage operation to steal all of our trade secrets from the United States and then re-implement them back in China?

• 2:33:42Shawn Ryan?

  What does our espionage look like?

• 2:33:45Speaker 06

  Well, there was a, uh, I think nowhere close to as good. I mean, I think, um, so one thing that, uh, that, that the CCP did for DeepSeek, the DeepSeek Lab, is, um, after DeepSeek blew up and, uh, and, um, the CEO of DeepSeek met with the Chinese premier, uh, they then locked up all the researchers into a, um, uh, inside, I shouldn't say locked up, but they like huddled all the researchers together and they took all their passports. So none of the AI researchers who work at DeepSeek are, are able to leave the country at all. And they can't, they don't come into contact with any foreigners.

• 2:34:27Speaker 06

  So they basically locked down the entire, you know, research effort. Um, so that it, you know, that makes it very, very hard to, to conduct any sort of espionage into the, into that operation.

• 2:34:39Speaker 06

  And then there was that report, uh, this is all in the news, but like, you know, a decade ago, 15 years ago, um, all of, or many of the CIA operatives, US CIA operatives in China, um, were all killed because they were sort of, um, compromised because one of the communication channels they were using was compromised by Chinese intelligence and, you know, the CCP was able to, to effectively like round a lot of them up and kill them. So our comparable, their espionage in us is like extremely deep, you know, huge risk. There's incredible amounts of, of, you know, we're deeply, deeply penetrated by, uh, by Chinese intel.

• 2:35:23Speaker 06

  Um, and comparatively, as far as I know, we have like, you know, much less capability. And I think they've designed it such that it's very hard to infiltrate their AI efforts.

• 2:35:36Speaker 06

  Geez. So that's other, so they're, they're, you know, they're, they're ahead of us on data. They're, they're able to catch up through espionage on algorithms pretty easily.

• 2:35:48Speaker 06

  Uh, they're ahead of us on power. So what are we ahead of? Well, right now we're ahead in chips. Um, and that's kind of our saving grace is that, uh, the Nvidia chips and the entire stack there are the pride of the world and, you know, we're the most advanced on these chips. Chinese chips are also catching up. Um, there's like a bunch of recent reports that Huawei chips are, are getting to be, they're basically like one generation behind the Nvidia chips. So they're close. They're close. Um, so all of this is, uh, is pretty concerning. There was another, um, report that came out of CSIS recently that there was a, um, a Chinese effort called, it's like the next generation,

• 2:36:31Speaker 06

  uh, brain understanding project or something where they're basically trying to use AI to fully, um, understand human, the human personality effectively and human psycho- psychological behaviors. Um, I imagine that's ultimately for effectively like information warfare. Uh, as we were talking about at breakfast, like, I mean, China has large scale information operations, large scale information warfare and has been, has been doing that for decades and, you know, literally decades, um, going back all the way to like in-person operations in Hong Kong. Like they're so sophisticated at all that and AI is going to enable them to just move much faster as well.

• 2:37:16Speaker 06

  How do we combat that? Uh, well, I mean, I think we need our own information operations efforts. Like I think that's pretty critical.

• 2:37:26Speaker 06

  That's, that's specifically on that thread. And then I think we, we need to, uh, we need to acknowledge that at the end of the day, um, you know, we are a more innovative country, but we have to dramatically. You know, get our shit together if we want to win long term in AI. Mm hmm. We need to, um, we need to onshore ship manufacture chip manufacturing. Like, we need to be manufacturing huge numbers of chips. We can't be dependent on Taiwan to manufacture our high end chips. Are we doing that yet at any capacity? Uh, extremely small capacity. Like, there are, there are a few fabs in Arizona that can produce some chips. Um, but the vast majority of the volume still comes out of Taiwan.

• 2:38:10Speaker 06

  Uh, we need to tighten up security in our, in our AI companies dramatically. Like, we need to, we need to have proper counter intel on, you know, what is the espionage risk in, within these companies? Um, you solve the power problem that we talked about. Um, we need to have, uh, we need to be investing into, um, you know, the cyber threats, like investing into large scale cyber defense. Um, we need to invest into data. Uh, we need our own programs around data dominance to ensure that, you know, China doesn't just run away with, uh, with higher quality and greater AI data sets than us. So you can go through each of the elements and build like the proper plan for the United States to win.

• 2:38:55Speaker 06

  Um, but, uh, Have we started any of that? Um, I mean, I think some things are underway, but, uh,

• 2:39:05Speaker 06

  no, I mean, not enough, nowhere close to enough for, for, to, to be sure that the US will win. Definitely not. And they also have a fundamental advantage, you know? Uh, one of the things that, that people say a lot now is like, oh, like what we need in the United States is an AI Manhattan project where we like, you know, we collect all the brilliant minds together. We collect our resources and we have one large, um, effort in, in the United States. Well, it turns out like, it's actually really hard to pull that off in the United States, but China can pull that off super easily. China can just say, hey, all the best AI people, you now work in one company. You, we're gonna pool together all of your resources. You are, you all are gonna, we're gonna put you right next to the largest nuclear power plant

• 2:39:50Speaker 06

  in the, in the world. Like, we're gonna build the largest data center in the world here. All the chips that China has are gonna go towards building this, this like large scale AI project. And they just have the ability to collect all of their resources together and throw it at, at winning on the AI race. Um, whereas in the United States, we have All these companies um and you know the United States government. As of yet, like it's not going to force all these companies to combine and merge like that's that's like such an. That today would be such an overreach of government power um but because of that, we're going to have like, you know.

• 2:40:30Speaker 06

  Five fragmented AI efforts and maybe in aggregate will have way more chips and in aggregate will have more power and in aggregate will have. You know more great researchers, but we're not going to be able to focus those efforts, whereas China is easily going to be able to focus all their efforts.

• 2:40:45Shawn Ryan?

  Wow, you had mentioned something downstairs about uh nuclear weapons.

• 2:40:50Speaker 06

  Yeah.

• 2:40:52Speaker 06

  Yeah, so this is. This is where stuff gets um stuff gets really weird for for um national security, which is. Uh you.

• 2:41:05Speaker 06

  You could you could clearly imagine scenarios where. Advanced, very advanced cyber AI um invalidates nuclear deterrence. What do I mean by this? Right now, um you know, nobody fires nukes because we have. MAD, we have mutually assured destruction, and if I do a first strike against another country, they're going to be able to, while that. Nuke is in the air, do a second strike and um will both, you know, there'll be destruction on both sides. It'll it'll be really bad. So because of this second strike capability. Um luckily, we have a proper, you know, we have real deterrence. Well, what if instead?

• 2:41:49Speaker 06

  Um, let's say, uh, let's say I'm, you know, the United States and I have. The most advanced AI cyber hacking capabilities in the world, so I can build AI agents that uh hack into. Um that can hack into any other country, can like turn off their energy grid, can disable their weapon systems, can disable everything. So what do I do instead? I launch the first strike and I meet and or like first I send in my. My cyber AI agent capabilities, I send my cyber AI.

• 2:42:24Speaker 06

  Uh, you know, force effectively to disable all the weapon systems of uh of the of the enemy country. And because it's uh my I have like such so much AI capacity, I can take out all of your I can like disable all of your weapon systems. And then I send my first strike. And then you don't have a second strike capability. So if that happens, basically, the combination of AI and nuclear be you know, you you cannot deter AI plus nuclear with just nuclear. So then it forces this um that's what will force forces like proliferation of AI capabilities. And so even small countries are going to need to invest in lots of AI capabilities because

• 2:43:07Speaker 06

  their nuclear weapons are no longer a sufficient deterrent. Geez. What about bioweapons? Yeah, I this is this is the the um element that is uh really underrated right now. So Covid leaked out of a virology lab in Wuhan and basically shut the world down for two years. And that's like that's like the the level one, you know, bio risk kind of stuff like this was relatively uh a relatively uh, you know, innocuous, let's say, um pathogen.

• 2:43:50Speaker 06

  Uh, but it still killed, you know, probably at least 10 million people globally and it was still, you know, shut the whole world down for two years. Um, well, recent models, new models, the new AI models are able to uh outperform 95% of MIT virologists. So the newest models from OpenAI and Google are smarter than literally 95% of virologists at MIT, uh, based on a recent study by um the Center for AI Safety. So um, so now you now whether it's right now or whether it's in a few years, um, it will be feasible to use AI-based capabilities to help you design.

• 2:44:38Speaker 06

  Powerful pathogens. And what's more than that, you're going to be able to design in certain characteristics of these pathogens. You know, you'll be able to tune the virality, tune the lethality of them. You know, there's also due to recent advancements in synthetic biology, you now can create viruses that specifically target certain segments of DNA. So I could create a bioweapon that just targeted, you know, any individual with a certain segment of DNA, which means I can target basically like any population or any group or any subsegment of the population in the world, which is

• 2:45:20Speaker 06

  which is really, really bad. And so the the ability, so first, even without AI, like biology, synthetic biology is making so much progress and that there's just like all sorts of inherent risk of

• 2:45:37Speaker 06

  like all sorts of inherent risk of bioweaponry or, you know, leaks of of pathogens and and viruses and whatnot. And then with AI, all of a sudden, you, this is, you know, not, not literally today's models, but a few models, a few generations down, you're going to be able to use these AI systems to design or build, you know, next generation pathogens. So that's, that's an entire, I mean, for good reason, biological warfare is not, you know, one of the is not, you know, there are international treaties such that we don't engage in biological warfare. But if you imagine these scenarios where

• 2:46:21Speaker 06

  countries, you know, nuclear deterrence doesn't work, they don't have the resources to get to use to utilize, to have large scale AI data centers. You know, it can, you know, I'm worried that countries will, will turn to biological weaponry, bioweapons as their deterrence mechanism, which is highly destabilizing for, you know, the world. Wow. That's some scary shit. The flip side is there is new technology that can

• 2:46:53Speaker 06

  That can also prevent this stuff. So there's um, there's this research coming out of this lab in Seattle, David Baker's lab, this guy who just won a Nobel Prize on biological noses, uh, which are digital noses, sorry. Um, which is basically you have these devices that can detect can detect proteins or chemicals or pathogens in the air automatically. And so I think what this will like, you know, the real sort of like offense, defense of of bio and bio weaponry will end up looking like we're just gonna have large scale deployment of digital noses effectively

• 2:47:35Speaker 06

  that in every space, on every like shipping container, on every plane, um, you know, they're just constantly sensing for all existing known pathogens, any new pathogens um that might exist and are constantly just like, you know, containing effect or like detecting and ultimately containing.

• 2:47:55Shawn Ryan?

  Interesting. So it's sniffing real time for all of that shit.

• 2:48:00Speaker 06

  Yeah, exactly.

• 2:48:01Shawn Ryan?

  I mean, also on the flip side, I mean, I guess if AI is developing

• 2:48:09Shawn Ryan?

  a new bioweapon, COVID comes out again, COVID two, we'll just call it. Then our AI should also be able to figure out the the

• 2:48:23Speaker 06

  Vaccines or

• 2:48:24Shawn Ryan?

  Be vaccine, the antidote to it, correct?

• 2:48:26Speaker 06

  Yeah, totally. So there will be, there will be an offense, defense um element to just as, just as in as we're walking through like AI applied to command and control, there's an offense, defense element. AI applied to cyber, there's an offense, defense element. AI applied to bio and bio weaponry, there will be an offense, defense element. So all these, thankfully, there's like, you know, the hope is that

• 2:48:54Speaker 06

  We end up in a, in a, in a global wor- you know, the, the, the world agrees that basically we're not going to go down any of these paths, like, because there's mutual deterrence and we just, you know, it's not worth it for anybody in the world to destabilize, you know, and risk humanity like that. Uh, that's, that's basically where we need to land.

• 2:49:14Shawn Ryan?

  Wow. How concerned are you about China, Taiwan? I mean, we were talking about this a little bit of breakfast and I, I, I can't believe they have not made a move yet. I mean, I thought for sure it would happen towards the end of the last administration, but with their chip production capabilities, I mean, how, how concerned are you about China taking Taiwan?

• 2:49:40Speaker 06

  I think if it's gonna happen, it's gonna happen this decade and it's probably gonna happen this administration. And, um.

• 2:49:48Shawn Ryan?

  Why do you say that?

• 2:49:50Speaker 06

  Uh, I mean, China, at a, at a macro sense, they have huge demographic issues. Those are, I mean, there's not like, that's just like of the force of gravity in their country. They have this huge aging population. They made the wrong bet, you know, many decades ago to have a one-child policy. Um, and so they are gonna have this like huge aging population. Um, over, then that, that plays, that plays out really like quite soon, like over the next, like a decade from now, it's gonna be, um, over time, they're gonna look more and more like Japan in that way, where they have this like large aging population and it'll paralyze a lot of ability to make any sort of aggressive moves. So, partly when it comes to military, industrial capacity, et cetera.

• 2:50:35Speaker 06

  Um, so that's like one force of gravity, uh, that they have to contend with. And then, um, and so I think, I think they're, they're, they're gonna wanna move faster sooner rather than later. And then they've, I mean, they've had such an insane military buildup um, over the course of the past few decades. Um, you know, I don't think it's, and I think, you know, we're, we're currently in a situation where China has far more industrial capacity, far more manufacturing capacity than we do in the United States. And so, um,

• 2:51:13Speaker 06

  That is, you know, that's a window for them.

• 2:51:17Shawn Ryan?

  So do you think they'll do, that they're pressed to do it because of the aging population?

• 2:51:23Speaker 06

  I think a lot of factors. I think, I think Xi is aging, right? This will be an important component of his legacy, as he would view it, I think. They have the aging population, which will minimize their political attitude over time, naturally. And then they have, I mean, they are, they're in this insane window where they have just incredible industrial manufacturing capabilities compared to anywhere else in the world. You know, in 2023, China deployed more industrial robots than the rest of the world combined.

• 2:52:03Speaker 06

  That's like, I mean, we were talking a little bit about like automated factories and automated industrials. Like, they're raising that faster than any other country in the world. And so, so I think that like, you can look at all these dimensions and this window.

• 2:52:20Speaker 06

  You know, there's, if they're, if they're gonna do it, they're gonna do it soon.

• 2:52:24Shawn Ryan?

  Yeah, yeah. I mean, what, what percentage of the chips that we use come from Taiwan?

• 2:52:31Speaker 06

  I mean, 95% of the high-end chips are manufactured in Taiwan.

• 2:52:37Shawn Ryan?

  And so what happens if, if China takes Taiwan?

• 2:52:42Speaker 06

  So, yeah, we're gaming it out. So we're talking a little bit about this. So, let's say China blockades or, or invades Taiwan. Then, then there's a, so these fabs are incredibly, incredibly valuable. Because as we're just describing, if you believe in the pace of AI progress and AI technology, then everything boils down to how much power you got, how many chips you've got. And if they own 95% of the world's. Chip manufacturing capability, I mean, they're gonna run away with it. So then you look at that and you say, will the Taiwanese people bomb the TSMC data centers? And or will the US bomb the TSMC data centers?

• 2:53:27Speaker 06

  And or will some other country bomb the data centers? Or sorry, not the data, the fabs, the TSMC chip fabs.

• 2:53:35Speaker 06

  I think, my personal belief, I don't think the Taiwanese do it. Because even if they get blockaded or invaded, that those fabs are still a huge component of Taiwan's survivability and Taiwan's relevance as a, as a, as an entity. Even if they get blockaded or invaded by Taiwan. So I don't think they do it. China definitely doesn't do it because they are invading partially to get, you know, to gain those capabilities.

• 2:54:07Speaker 06

  And so then, does the US bomb them? If the US bombs them, that's probably World War III. I, it's hard to imagine that not just resulting in massive escalation. And so you're looking at it and there's kind of no good options.

• 2:54:26Speaker 06

  So I think it's, I mean, everyone's very focused on it, obviously, but it is, it is like a real powder keg of a

• 2:54:37Shawn Ryan?

  Damn.

• 2:54:39Speaker 06

  Of a region.

• 2:54:41Shawn Ryan?

  How do you think this all ends?

• 2:54:47Shawn Ryan?

  We had a little discussion about this at breakfast.

• 2:54:49Speaker 06

  Yeah, yeah. I mean, I think, I think if, so let's assume that in the next handful of years, next like three, four years, there's an invasion or blockade of Taiwan. And, you know, I think it's, I think, given how important AI is, it's hard for the US to not take any sort of action in that scenario. And then, you know, almost all the actions you would see escalating into a major, major conflict. So.

• 2:55:33Speaker 06

  Best case scenario is we deter the invasion or blockade altogether. And, um, and I think, you know,

• 2:55:45Speaker 06

  I think it certainly is in everyone's interest to not get into a large scale world war that's hugely destructive and, and kill lots of people. So I think like fundamentally, we should be able to deter that conflict. Um, but that's, uh, that's why all this matters so much. We need to make sure our AI capabilities as a country are the best in the world. We need to make sure that our military AI capabilities are the best in the world. We need to make sure that, you know, um, uh, there's clear, uh, economic deterrence of this kind of scenario. Like we need to, um, we need to be investing in, in every way to deter this conflict such that, um, you know, where this really will break down is if the Chinese, if the CCP calculus,

• 2:56:28Speaker 06

  uh, uh, you know, diverges from our own, if their calculus becomes, oh no, this is going to work out, you know, we, we can take this and then, you know, we're strong enough such that it'll work out for us. And then our calculus is the opposite. That's where, that's where the world war scenario happens. So, um, so it's possible to deter. And I think we have to, you know, there's a lot of things we have to do to make sure that we deter that conflict. And that should be, I mean, certainly, I think it already is like 80% of the focus of the entire DoD. So.

• 2:57:03Shawn Ryan?

  I mean, it's, it's just, we can deter, but I mean, when you're talking about an aging population, I mean, they're getting desperate. And it sounds like in order for them to legitimately win, they have to acquire those chip fabs, correct? And so.

• 2:57:24Shawn Ryan?

  They already have 250 times the shipbuilding capacity, they have way more people, they have more power than we do. I mean, military recruitment in the US, you know, was at an all-time low. I don't know what it is today, but, I mean, even if it, so, I guess what I'm saying is, we can only, you can only deter a desperate entity for so long before they throw a hail mary play, right? Would you agree with that?

• 2:57:57Speaker 06

  Yeah, and then it just depends on their level.

• 2:57:59Shawn Ryan?

  They would have to dedicate an entire military to surround Taiwan to effectively do that, in my opinion.

• 2:58:09Speaker 06

  Yeah, I mean, I think that the, if, if they assess, if the CCP and the PLA assess that Taiwan is all they're at, like, they, they will focus their entire military capacity on seizing Taiwan, then that becomes a really, that becomes a really tricky calculus.

• 2:58:30Shawn Ryan?

  I mean, why wouldn't they? If, if, if, if Xi believes that the winner of the AI race achieves global domination, he's getting older. You just talked about how important his legacy is to him, which I'm sure you're right. I don't know how you deter that. And then they win the AI race.

• 2:58:53Speaker 06

  Yeah, the only thing that we can do, I think this is a long shot, but I think it's important, is if, um, if ultimately, we actually end up collaborating on AI. And I know that sounds kind of crazy, but, um, but if we're able, as a country, to demonstrate...

• 2:59:17Speaker 06

  Just we're so far ahead and there's like, you know, the one, one key element of, of how the whole AI thing plays out is this idea of AI self-improvement or intelligence recursion, sometimes people call it, but basically, um, once AIs get sufficiently good, then you can start utilizing the AIs to help you build the next AI. As sci-fi as that sounds, you utilize your current generation AI to build the next generation AI faster and faster and faster and faster. And so at some point, um, your AI capabilities enable you, like, you know, there's some form of like, you know, just exponential takeoff. They just,

• 3:00:00Speaker 06

  they just, you know, your AI capabilities get good really, really quickly. And if somebody's even three to six months behind you, then they're, they're, they're never going to catch up to you because you're running the self-improvement loop faster than anybody else. And so this is a, this is a key idea. I mean, it's, I think it's, um, it's a little bit theoretical right now. Like it's not clear whether or not this intelligence recursion is going to be how it plays out, but, but a lot of people in AI believe it. Um, and I probably, I probably believe it too, that, that we will be able to use AIs to help us continue training the next AIs and improve things more quickly. And if you believe that, then

• 3:00:43Speaker 06

  if we're, let's say three, three to six months ahead of China, um, and we maintain that advantage and we take off faster, then they're going to be way behind. And then ultimately, we're going to be in a great position to say, hey, actually like we're way ahead and we should just, you know, you guys should quit your efforts. Um, we'll give you AI for all of your economic and, uh, and, um, humanitarian uses throughout your society. Uh, and we agree, we're not going to battle on military AI.

• 3:01:24Shawn Ryan?

  What would it take to take the chip building capabilities that Taiwan has and implement that here in the US to protect it?

• 3:01:35Speaker 06

  So, yeah, um, so the first thing is, uh, there's been hundreds of billions of dollars invested just into like the build out of those fabs and the, the, we call foundries, but the buildup of these, of these large scale chip factories effectively and the, um, and all the high-end equipment and tooling inside of them. Hundreds of billions of dollars of investment. So first off, there needs to be hundreds of billions of dollars of investment in the US. That's not the hard part. The second part that's, that's really the hard part is, um, all, it's basically a large

• 3:02:17Speaker 06

  scale factory operated by highly, highly skilled, um, uh, uh, workers who are very experienced in those processes and the whole thing operates like a, you know, like clockwork. Um, and unless you can get those people to the US, you know, you're going to have to like rebuild all that know-how and all that technical capability. And that's what takes a really long time. And that's one of the things, you know.

• 3:02:48Shawn Ryan?

  Why do you think we haven't done that? Why do you think we have not incentivized these brilliant minds to come here and do it for us?

• 3:02:57Speaker 06

  So TSMC, the Taiwan Semiconductor, um, the company that, you know, builds these fabs, they have stood up a few fabs in Arizona. Um, but they cited issues like first there were issues around permitting and gain of power and they dealt with some EPA issues and then, um, and then they just have issues where the like, uh, you know, the technicians working in Arizona don't, aren't as skilled or don't work as hard as those working in Taiwan. Um, so they've built a few fabs in the United States.

• 3:03:34Shawn Ryan?

  They've tried to do it, but our, our red tape and our, our power is not what it needs to be to be able to do this.

• 3:03:44Speaker 06

  Red tape, power, workforce. And then there's another key thing, which is

• 3:03:51Speaker 06

  If you look at it from Taiwan Semiconductor, from TSMC's perspective, they're not all that incentivized to stand up all these capabilities in the United States.

• 3:04:01Shawn Ryan?

  Uh huh.

• 3:04:01Speaker 06

  Like, if, as soon as they start standing up all these capabilities in the United States, the United States is not incentivized to defend Taiwan.

• 3:04:11Shawn Ryan?

  Yeah.

• 3:04:11Speaker 06

  And it's a Taiwanese company. Um, so, and it's a critical part of their survival strategy. Um, so, so that's, that's really where the rubber hits the road is. Are they actually incentivized to do a large-scale build-out of, of chip manufacturing capacity in the United States? Um, I think the answer is like, no.

• 3:04:34Shawn Ryan?

  Makes sense. I mean, there would have to be some type of a, some type of a deal struck where they fall under our wing.

• 3:04:42Speaker 06

  Yeah, I mean, you could imagine some kind of deal with, with China, um, between the US and China. It'd have to be like a diplomatic deal at the highest levels, which is something along the lines of, you know,

• 3:04:56Speaker 06

  hey, you guys can have Taiwan, but we need large-scale fabs in, you know, we need large-scale chip manufacturing in the United States, or something like that. And like, you know, maybe there's worlds where that kind of deal could get, could get drawn up. I don't know. Um, uh, but that would, I mean, that would also mean that the United States would just have to say, hey, all we care about actually at this point is, is chip manufacturing. And that we don't care actually about the Taiwanese people and the, the country and all that stuff.

• 3:05:29Shawn Ryan?

  Man, man, and are they working with China at all?

• 3:05:36Speaker 06

  Um.

• 3:05:36Shawn Ryan?

  Capacity?

• 3:05:38Speaker 06

  The TSMC?

• 3:05:38Shawn Ryan?

  Yeah.

• 3:05:39Speaker 06

  So they're, um, I think they're technically not supposed to, but a lot of the, the, um, Huawei, one of the leading companies in China, has been able to get, um,

• 3:05:56Speaker 06

  Tons of chips from, uh, tons of dies, it's called, but basically tons of chips or chip, um, uh, uh, prerequisites from Taiwan. And they usually do it through like, they like start some cutout company that doesn't seem associated with them in like Singapore and then that Singaporean company buys a bunch of, um, uh, or Malaysia and that, or the Singapore and Malaysian companies buy a bunch of chips from TSMC and then they mail it back or something. But there's clearly been, uh, there's been a lot of TSMC high-end, um, outputs that have gone to, to the Chinese companies. Wow. Wow.

• 3:06:40Speaker 06

  Scary shit, man. It gets, I mean, I think this is where, um, you have to believe, like, right now, if you look at the, you know, just as we were right now, like, if you look at the situation and all of the, um, all of the dynamics at play right now, it's, it's like, it's a powder keg. It's like very, very, very volatile, um, highly problematic in many ways. And this is where, I mean, you just ultimately have to believe that there's, there's got to be some effort towards diplomatic solutions. Yeah.

• 3:07:24Speaker 06

  Because it is definitely true, like, war will be really bad for both sides. Yeah. Yeah. How do we coordinate with China with the AI? Yeah, so, um. What does that look like? So, yeah, right now, right now, we're definitely, um, US and China, um, we're definitely in, in all-out race dynamic. And, you know, we're gonna race, and I think this is correct, we're gonna race to build the best AI systems, they're gonna race to build the best AI systems. Um, uh, and we're both all in on this approach and, um, we're both all in on, on racing towards building the most advanced AI capabilities, the largest data centers, largest capacity, et cetera, et cetera.

• 3:08:09Speaker 06

  Um, the, uh-

• 3:08:14Speaker 06

  And this is, if you recall, kind of how nuclear was. Like, you know, in nuclear war, as well as application of nuclear towards

• 3:08:26Speaker 06

  towards a power production, it was kind of, you know, all systems go, like, everyone racing towards building, capacity building capability. And then Chernobyl and Three Mile Island happen. And it creates large-scale consternation around the technology and the risks of those technologies. And there were a bunch of international treaties and there was a large international response towards coordinating on nuclear technology. Now, all said and done, if you really, you know, if you look at nuclear, like,

• 3:09:05Speaker 06

  that set our country back, set many countries back, you know, many generations in terms of power generation, but what it took was effectively these, like, small-scale disasters to take place that effectively were the forcing function for international cooperation.

• 3:09:26Speaker 06

  You can imagine a scenario with AI where, because of all the things that we've been talking about,

• 3:09:36Speaker 06

  there's some scenario where maybe some terrorist group or some non-state actor or some, you know, North Korea or whomever, somebody decides to use it for, in a particularly adversarial or, you know, inhumane way and create, and that disaster has some large-scale fallout. So it create, you know, you take out the, you take out power in, like, one of the largest cities in the world and tons of people die, or you take out, or there's some pathogen that gets released and, like, tens of millions of people die, or, you know, one of these things happens that causes the international community and everyone in the world to realize,

• 3:10:21Speaker 06

  oh, shoot, we have to be coordinating on this. And, you know, we should be collaborating for AI to improve our societies and improve our economies and improve the lives of our people, but we shouldn't, you know, we need to, we need to coordinate on its use towards.

• 3:10:39Speaker 06

  For lack of a better term, scary things like bio or cyber warfare or, you know, the list goes on.

• 3:10:45Speaker 06

  So, long story short, I think the path really is some kind of, you know, we, sometimes we talk about like an AI oil spill or some kind of, of incident that really causes the international community to realize like, hey, we, we have to, we have to start coordinating on this.

• 3:11:04Shawn Ryan?

  I mean, it's such, you say China's all out, you know, gone all in on the race day ice and the US has gone all out on the race day, but we're kneecapping ourselves. I mean, you just mentioned the red tape, the EPA permitting and the power. And we're not producing more power. We're flatlined. We've established that.

• 3:11:29Shawn Ryan?

  As far as I know, we're not getting rid of the red tape, you know, to, to, to jet launch this. And it just seems like

• 3:11:42Shawn Ryan?

  we're cutting ourselves off at the knees here.

• 3:11:46Speaker 06

  Right, right now, I mean, we have a lot of work to do, for sure. We have to, we have to build strategies to win, to have energy dominance, to have data dominance. To, on the algorithms, I think we'll be okay. They're gonna espionage, but I think we'll be okay on algorithms. We need to ensure we have chip dominance long-term. We need to make sure all this lends itself to military dominance. I totally agree with you. I mean, we need to,

• 3:12:13Speaker 06

  we need to today ensure that we have the proper strategies in place so that we stay ahead on all these areas. The worst case scenario for the United States is the following, which is CCP.

• 3:12:31Speaker 06

  Uh, does a large scale Manhattan style project inside their country. Um, realizes they can start because of all the factors that we've talked about. They they realize they can start overtaking the US on AI. That lends itself to extreme hyper military advantage. And they use that to take over the world. That's like, that's like worst case scenario for the US. If US and AI, AI, US and China AI capabilities are even just roughly on par. I think you have deterrence. I don't think either country will take the risk. I think if US is way ahead of China. I think you maintain US leadership and that's a pretty safe world.

• 3:13:14Speaker 06

  So that the the worst case scenario is they get ahead of us.

• 3:13:18Shawn Ryan?

  Are there any other players other than the US and China involved in this? Who else do we need to be watching out for?

• 3:13:26Speaker 06

  So, yeah, right now, definitely US and China. Um, the, a lot of other countries will matter. Um, but not all of them have enough ingredients to really properly be AI superpowers. So, uh, but other countries are gonna, they have, they have key ingredients. So, um, to to name a few. A, uh, everything we've talked about with with cyber warfare and information warfare, information operations. Russia has very advanced operations in in those areas. Um, and that could end up mattering a lot if they ally with with CCP.

• 3:14:11Speaker 06

  Um, there's a lot of ways they can team up and and have, uh, and that could be pretty bad. Um, there's, uh,

• 3:14:21Speaker 06

  You know, the countries in the Middle East will be very important because they have incredible amounts of capital and they have lots of energy. And so that's, these are, you know, they're critical players in how all this plays out. India matters a lot. India has a lot of high-end technical talent. I don't know if, I think right now, but I don't know if between India and China, which has more high-end technical talent, but there's a lot in India for sure. Massive population, also starting to industrialize in a real way. And right next to, right next to China. So India will matter a lot.

• 3:15:05Speaker 06

  And then, you know, there's a lot of, there's a lot of technical talent in Europe as well. I think it's unclear exactly how this plays out with the European capabilities. I mean, they have to, it seems like there's some efforts now for Europe to try to build up large-scale power, build up large data centers, you know, make a play. I think yet to be seen how effective those efforts are going to be. But you can clearly see some scenarios where if they make a hard, a hard turn and go all in, they could be relevant as well.

• 3:15:44Shawn Ryan?

  Is there a world where AI takes out of mind of its own?

• 3:15:49Speaker 06

  So, you know, obviously you can hypothetically paint the scenario where like, you know, you have super intelligence or you have really powerful AI and then, you know, realizes at some point that humans are kind of annoying and takes us all out. But, but I think, I think it's a very, like, that's so preventable as an outcome because...

• 3:16:17Speaker 06

  First of all, all the things we just talked about are like the very real things that happened long before you have, you know, this hyper advanced AI that takes everyone out. That's first, that's first thing. So we have lots of things we have to get right before then. And then second is, um, you know, for AI to actually, um, be capable of, you know, having mind of its own and taking all humans out, like, we'd have to give it just incredible amounts of control. Like, it would have to just basically be running everything and we're just sort of like along for the ride.

• 3:16:55Shawn Ryan?

  Uh huh.

• 3:16:56Speaker 06

  And that's a choice. We have this choice of whether or not to like, give all of our control to AI systems. And as I was talking about before, like, human sovereignty. I, my belief is, we should not cede control of our most critical systems. Like, we should, we should design all the systems such that human decision making, human control is really, really important. Human oversight is really important. Um, this is one of the things that I actually think is, is, um, one of the things that we're working on as a company. It's honestly one of, like, as I think about like, long-term missions, one of the most important things is creating human sovereignty. So first is, how do we make sure all the data that goes into these AI models,

• 3:17:40Speaker 06

  um, increases human sovereignty such that the models are going to do what we tell them are aligned with humans and aligned with, um, uh, our objectives. And two is that we create oversight. So as AI starts doing more and more actions, doing more planning, you know, taking out, you know, carrying out more things in the, in the world, in the economy, in military, et cetera, that humans are watching and supervising every one of those actions. So that's, that's how we maintain control. And that's how we prevent, you know, the Terminator scenarios or the, you know, AI takes us out kind of scenarios.

• 3:18:22Shawn Ryan?

  Interesting. Well, Alex, wrapping up the interview here, but man, what a fascinating discussion. Thank you. Thank you for being here. One last question. If you had three guests you'd like to see on the show, who would it be?

• 3:18:38Speaker 06

  Oh, that's a good question. Um.

• 3:18:42Speaker 06

  Who would I like to see?

• 3:18:46Speaker 06

  Well, I really like what you've been doing recently, which is getting more tech folks on the on the pod. So.

• 3:18:55Speaker 06

  So I go in that direction. I mean, I think Elon would be great to see on the show. I think. I think. We were talking about this, Zach would be would be cool to see on the show. I think. Sam Altman would be cool to see on the show. So definitely like. More people in tech outside of that.

• 3:19:18Speaker 06

  I think. And we're talking about some of this like.

• 3:19:25Speaker 06

  International. Leadership, like international, like leaders of other countries.

• 3:19:30Shawn Ryan?

  Yeah.

• 3:19:30Speaker 06

  Super important because. We talk about all these scenarios, like international cooperation is going to matter so much.

• 3:19:38Shawn Ryan?

  Right on. We'll we'll reach out to them and.

• 3:19:43Shawn Ryan?

  Yeah, as far as world leaders is concerned, we're on it. But well, Alex, thanks again for coming, man. Fascinating discussion. I'm just. Super happy to see all the success that you've amassed throughout your 28 years. It's it is.

• 3:20:01Shawn Ryan?

  I love saying it. So thank you for being here. I know you're a busy guy.

• 3:20:05Speaker 06

  Yeah, thanks for having me. It was fun.