AI in Warfare, OpenClaw & The Stargate Mega-Campus | This Week in AI E3

15:52Chase Lochmiller:You know, it's kind of like, you know, Anastasios is kind of the king of benchmarks over here. But, you know, it does feel like one of these like moving benchmarks that like, as it gets better, it gets more impressive, we sort of like move the goalposts a bit. You know, I kind of had this, you know, personal definition, like having come from more of a, you know, math background, I felt like if, if, to me, one of the big tests is going to be when, you know, there's a set of very difficult math problems. called the Millennium Problems that are hosted by the Clay Mathematics Institute. And to me, like the breakthrough is gonna be, you know, there's a million dollar bounty.

16:31Chase Lochmiller:There's one of them has been solved of the seven. The guy actually never even collected the bounty. But to me, the definition is like when AI is able to solve one of those problems, that's like -

16:46Naveen Rao:Wait, but Anastasios, this is a problem that humans have not solved.

16:50Chase Lochmiller:Correct.

16:51Naveen Rao:So artificial intelligence.

16:53Chase Lochmiller:So maybe that's like, maybe that's super intelligence. Maybe that's exceeding. Sure, sure, sure, sure.

16:57Naveen Rao:So ground us here, Anastasios, because I think you're making the point, Chase, is that we are, we're like in the red zone. We're within 20 yards of the end zone. And we're like, you know what? Let's push it another 10 yards. It's 120 yard touchdown. Anastasios, how do you look at it? Define it for us here.

17:15Anastasios Angelopoulos:Yeah. I mean, so it's very funny. I had actually a similar bet with a friend of mine who's a professor at Stanford Stats that we made the bet just it was a few months ago. So, you know, I think things are moving in my favor that in 10 years, two of these problems will have been solved autonomously by AI. The reason why it's important that these problems, you know, to sort of chase this point, are unsolved by humans is that once they've been solved and the information's out there, they're no longer good benchmarks because they overfit. And so I do think that there's a component of this that's when we create benchmarks for AGI or what have you, or just performance in math and coding and instruction following and production settings, we need to make sure that the data is constantly fresh to avoid overfitting.

18:04Naveen Rao:What do you define it as then? Did you have like a personal definition you like? And then, Yavin, I'll go to you because I know you have a lot of thoughts here too.

18:11Anastasios Angelopoulos:Yeah, I honestly don't think about the definition of AGI much because I think that basically all these debates reduce down to what's your definition? And so to me, it's much more about how do we ensure that when we deploy the system, we can do so reliably and responsibly that we're helping people, not hurting people, and use it as a tool. And I just, you know, the AGI aspect of things is not really part of my daily life in terms of my thinking. However, what is, is that I think that a large fraction of human labor is going to be commoditized by AI. And that's going to have very wide-reaching economic impacts and probably require a new economic system to deal with it.

18:56Naveen Rao:All right, Naveem, we just hit on all seven possible issues on the docket. Let me get Naveem involved in here because this is kind of what happens when you have people who are in it every day. Yeah. These things are turning into a flywheel in our minds. You have open claw driving Jevin's paradox. You know, then you have Goodhart's law in action. It's all kind of happening at once. So just to ground us again, AGI and then what's happening inside of enterprises and what you think of these open source models, take it where you want to go.

19:30Anastasios Angelopoulos:Well, I think there's a few things here. I was talking about the definition. I mean, Chase is talking about one specific one where I would say it's kind of what we think is what smart people are. It's solving these kinds of like mathematical problems. That's kind of intrinsically difficult for humans. And there's only a small subset of humans that can actually do these things. What I actually think is even more interesting is that we haven't solved problems that every human and actually many animals can solve still. So this is why this definition keeps sliding around because, you know, it's a little like porn.

20:06Anastasios Angelopoulos:You know it when you see it kind of thing. Many times we build a model and it solves some benchmark, as Anastasia was talking about. And maybe it even solves some really hard problem. But then it falls flat on some really stupid thing that everybody can do. So then you're kind of like, is that really AGI, right? So I think we're in this place where we're creating a new kind of intelligence that solves a certain set of problems. Now, every computer has existed for a purpose. And, you know, there's a lot of economic value, regardless of whether we call it AGI or not. But I'm a motor control neuroscientist, okay?

20:38Anastasios Angelopoulos:I studied how brains, you know, can actually solve physical problems in the world. I think physical problems in some ways are very hard. They're very high dimensional and there's so many sources of error and noise that come into play. And these models do not do well in those domains.

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22:58Naveen Rao:Chase is working on scaling this, finding new energy sources. Obviously, Jensen's working on next level chips and he bought Grock. And at the same time, the models are getting smaller. And then Apple Silicon, which Steve Jobs conceived of in 2008, executed on in 2018 when the first, I guess, M1 and some of these things started to come out. You know, what an amazing pressing thing. You know, obviously, Steve Jobs, he figures these things out early. And now we have people saying, you know what, I'll just run some stuff local and we're going to have this crazy world where things are working on multiple platforms, Chase.

23:38Chase Lochmiller:Humans, we sort of have this like system one, system two, like thought process. Like you have sort of a, you know, really fast system that, you know, is very intuitive. And then you have like your system two, like higher dimensional reasoning system. And I think, you know, in because, you know, you made the comment that like models are getting smaller, they're simultaneously getting smaller and bigger, right? We have things that are bigger, more capable models that are these higher level reasoning models. And then we also have things that we're able to get better accuracy, better output, sharper intelligence with smaller distilled models.

24:16Chase Lochmiller:And I think what's important for that is like when you think about efficiency of how intelligence not only gets created but actually gets distributed to the world, it's really important to think about like where does the compute actually happen? And that's part of the point you were making around like, wow, these things – this new version of Quinn can run on an iPhone in airplane mode and wow, holy shit, that's pretty cool. Excuse me. Wow, that's pretty cool. It's a podcast.

24:46Anastasios Angelopoulos:Actually, I'll sort of challenge a little bit here. So I think the bigger models, what we found over time, now we're still learning how intelligence works. And these bigger models are very good at retrieving lots of different kinds of information. So pre-training and adding more parameters basically gave us more access to information. Now I think we're moving this realm, like you said, bigger models are reasoning models. I actually think we're moving to a realm where potentially we can separate out the reasoning capability, at least partially, from the retrieval part of the system. And when you do that, you actually might be able to build something that has the reasoning part that's small, and then you give it access to data in different ways.

25:25Anastasios Angelopoulos:That's kind of a paradigm, I think, is a little bit more like biology, frankly. Like our brains don't necessarily remember tons and tons of really specific information, but we have mental models that we can reason through new data presented to us. Interesting. Potentially where we can make it much smaller, right? I actually have a question, Chase, about the world of data centers in the future. Because there is this, you know, as we're talking about this kind of divide between like this on device, whatever, blah, blah, blah. Then there's maybe going to be massive training runs. Where do you see the data centers that you're building fitting into like that world?

26:02Chase Lochmiller:My vision for the future of like compute and data center infrastructure is sort of this bimodal distribution. where you end up with these mega campuses, like what we're doing in Abilene, Texas. We have another campus we announced in Wyoming. We have a couple of other gigawatt scale campuses that we're building that are really about enabling, training the next breakthrough foundational model. These are hundreds of thousands, soon to be millions of GPUs interconnected on the same high-performance backend network and all acting as one cohesive giant computer, right? And what that's useful for is kind of, again, those next breakthrough models training.

26:47Chase Lochmiller:And then also a lot of this test time compute scaling, a lot of these reasoning models that are given a very complex task that need to be thought through, that need to think more deeply about sort of an answer. We also have a product called Crusoe Spark. And Crusoe Spark is a fully self-contained modular AI factory. We have a 500kW unit. We have a one megawatt unit that is, frankly, manufactured in a factory owned by Crusoe. And those can be rapidly deployed anywhere where we can access power. It can be on site in a parking lot for an autonomous factory. It can be at a location where we actually have -

27:32Naveen Rao:Oh, that's wild looking. So it's basically like a trailer or like a mobile home. Exactly.

27:38Chase Lochmiller:It's a modular building. And that you can actually see we connected two together there. so that they're on sort of a shared fabric. That's what that connection there is.

27:53Anastasios Angelopoulos:Is it just like Armada a little bit?

27:56Chase Lochmiller:Yeah, I guess similar. I think they're more focused on military applications and those things being deployed in the field. But that use case, that picture you just showed, that was actually a fully off-grid solar plus batteries that we were able to power those modular AI data centers. And, you know, I sort of view this as like we're going to have swarms of these things. Right. And, you know, and I think one of the power issues that you have is that, you know, it's it's hard to go on the grid and be able to get a gigawatt of power. Right. So going to a utility and say, hey, I need a gigawatt at a single location.

28:34Chase Lochmiller:They're going to be like, okay, how's 2037 sound? And, you know, but if you go to a utility and you say, hey, I need 20 megawatts, they'll be like, hey, there's actually like 100 locations where 20 megawatts of power would actually be useful to us because of transmission constraints, because of, you know, the way they're load balancing the grid. And if you can have like a fleet of these small modular data centers that get deployed in those regions, it actually unlocks a lot of underutilized power. So that's one great use case. And so then just that's not every. Yeah, not not every. Instead of chasing just so people understand.

29:12Naveen Rao:Instead of like the power chasing the data center, the data center is autonomously driving around to find the power.

29:23Chase Lochmiller:Oh, no, it's not driving around. No, I know. I'm being slightly facetious here, but the data center will go find a way.

29:30Naveen Rao:It goes and sniffs out. Oh, here's some power. Let me put this by this dam or, hey, you found oil in this pocket.

29:37Chase Lochmiller:you know yeah totally um and you know it's it's really intelligence everywhere and especially for inference scaling and inference workloads you don't need a million cluster gpu cluster to to run an inference workload it's you're very happy with a rack or you know a couple of gpus sometimes there's you know multi-node inference workloads naveen knows about these very uh deeply but uh you know and and and you know one of the one of the use cases i love about like the when i think about the future is like as humanoid robotics and robotics scale, you know, I think about these factories that are going to be in the future.

30:13Chase Lochmiller:And, you know, the reindustrialization of the United States, a lot of those factories, they actually want to move a lot of the reasoning computing off device because it limits the battery life to power the electrical mechanical systems. And you'll have kind of like a system one smart kind of like it can move it around, But then it's offloading a lot of the spatial reasoning to a locally run data center that's operating a factory of humanoid robots that are building some widget. And I think that's a pretty cool thing to think about.

30:48Anastasios Angelopoulos:It's interesting. I'm actually giving a talk tomorrow. And what you just described, there's a slide on it. It's kind of funny. Yes. When we're successful in greatly reducing the power of inference, it'll actually result in way more smaller data centers. You're just going to have them everywhere. You're basically going to fill every niche and you don't have to transmit power. You don't have all that loss. I'm sure you know the numbers off the top of your head, but transmitting power is hugely lossy. It's a very expensive operation.

31:17Chase Lochmiller:So it's also like massive lead times because you need these huge right-aways to like build like seven, you know, ultra high voltage like transmission lines. Like you're building giant power lines. You need everybody's approval to make that happen. That's like, you know, not an easy task.

31:33Naveen Rao:So Anastasios, if we were to think about this with the optimists, right? This is something Elon's been talking about a lot is like, hey, what's the battery power going to be here? And then what's the GPU in it? Because now you have to serve two masters. The thing has to be able to move. That requires energy. And it needs to be able to think. That requires energy. So now we're thinking, hey, if we're going to do a bimodal breakdown, maybe it's got some ability to operate in the world with a model physically attached to it. But in some use cases, you may have it on Mars and it needs to have the whole kit and caboodle and be offline and be in airplane mode.

32:13Naveen Rao:How do you think about how these models might become bimodal and live in multiple places for inference, for training the model, and then local because you want privacy? And now you've got this, you know, breaking it up by what does the actual physical nature of the self-driving car or robot need?

32:33Anastasios Angelopoulos:Well, I think the system one, system two analogy is probably the right one. And the sense that, you know, there's just to reiterate what was previously said, there's a well-known sort of cognitive system that humans use when thinking, which is that when they need to make quick decisions that are not so high stakes and maybe easier, you just sort of immediately react to that. That's system one thinking. And then system two thinking is that more deeper reasoning. And of course, it's somewhat asynchronous. It can happen over hours, it can happen over days, weeks. And so you might be able to potentially offload that to a different system that's, you know, a different brain or data center somewhere else that's thinking about it.

33:21Anastasios Angelopoulos:But, you know, with the speed that things are moving, for systems like an Optus, my bet would be that in the, you know, medium term, almost all of that is going to be handled by a very small model that's just running on the robot. If you look at, for example, like Quen with these recent like small model releases, the rate of improvement and the rate of compression is so high. Like QN 3.527B, the like medium scale model. It has performance on par with the 10X larger last generation model, the QN 235B, at least on our leaderboard. Same performance, approximately. 10X smaller model, few months difference in release date.

34:20Naveen Rao:Extrapolate that out. Where are we going to be at the end of the year, end of next year, in terms of running these models?

34:27Anastasios Angelopoulos:Well, I mean, I'm not sure how much extrapolation I even need to do. It's moving faster than we can even predict. Like these small QN models that were released yesterday, these 4B, 9B models, at least, you know, they haven't been evaluated by an external entity. So we don't yet know what the real performance is. But based on the benchmarks that they self-reported, it again happened that those models are on par with and maybe even slightly better than the 27B version from the previous generation. So it's moving so fast that within the year, we could have, you know, models that are a billion parameters or hundreds of millions of parameters and have the same performance as a model that last year might have been hundreds of billions of parameters.

35:15Anastasios Angelopoulos:it's kind of interesting because just talking through this it just became apparent that this kind of worked in inverse to biology in a way like biology sort of started small figured out some basic principles and those principles scaled so the efficiency came first uh what we've done is actually the inverse we've we brute forced our way through it throwing every everything we possibly could and now we're understanding oh actually i didn't need to do all that there's a lot of things i can i could keep chipping away at and i can actually go smaller and smaller and smaller. So, I mean, just to kind of put it in perspective, like biology through this process of kind of the bottoms up, uh, like a squirrel runs on 10 milliwatts of energy.

35:53Anastasios Angelopoulos:Your cell phone runs on about one watt, 10 milliwatts. And it can do things at precision levels that we cannot do in a megawatt. Like I can't make an, a robot jump between branches in the wind and hit the branch perfectly a thousand times out of a thousand right now. I can't do it, but I get biology's had billions of years, I guess, to get this right.

36:14Naveen Rao:And we've done this all in 50 years of modern compute, I guess.

36:20Chase Lochmiller:A lot of this, Jason, it kind of reminds me, you opened up talking about your team using OpenClaw and running up this massive bill and saying, this is going to be our whole payroll. It's like, just spent on OpenClaw. And I think there's an important thing to remind yourself of during this moment is that there are so many exponentials happening, whether it's like models getting exponentially better, you know, Anastasios just talked about, you know, a model one 10th, the size getting similar performance only a few months later, you know, compute is getting exponentially better, you know, through, you know, just sort of the classical trend that, you know, we're, we're, we're going down with GPUs as well as hopefully step function breakthroughs and things like what Naveen's working on you know, power systems are getting more, more efficient.

37:04Chase Lochmiller:So you just always have to remind yourself that this is the worst these things are ever going to be. It's the most expensive intelligence ever going to be. It's only going one direction and that's down. And, you know, it's the cost is only going down and the performance is only going up.

37:20Naveen Rao:Okay. So that leads us to the obvious next question. Jevon's paradox. Open clause seems to have opened up collectively in 30 days. We're sitting here like literally 30 days into this kind of expanding and actually hitting pop culture. I don't know if any of you have had a family member say like, hey, tell me about OpenClaw. Can I set this up for my company? I'm assuming I see not across the board. My wife's like, how do I get OpenClaw? And I'm like, okay, here we go. So I hooked up OpenClaw to Instacart. And I now have my OpenClaw going, my OpenClaw going into OpenCart saying, hey, from H Mart, which is like the Asian Korean market and looking at the last six orders, figuring out what we ordered the most, and then preparing a basket.

38:12Naveen Rao:And then my wife approves the basket. Then the week after that, we're going to go for just order a basket and add three items you think we'd enjoy. And we'll give you feedback on it. So like this could change everything. Like this is hours of work a week is like feeding a family. Putting that aside, it seems to have cracked open the floodgates in token usage. So then we need to ask the question and pair that, this incredible open source project opening up, I don't know, maybe 5 % of the workforce is into this right now in America, maybe less, maybe it's 2%. But let's pick a number. 5 % of people are really into this.

38:51Naveen Rao:It's going to max out what's available. It's already giving challenges to Claude and they're having to time things out and block your usage of it if you're on a max plan, push it towards the API, et cetera. And then you dovetail that with employment. And I'm putting it conservatively at making our employees 10 to 20%, offloading 10 % conservatively a month of their work right now. Now, I don't know when it hits diminishing returns and flattens out, maybe it's 50, 60 % of their chores, but it does feel like then what we saw from Jack last week at Block, formerly Square, laying off some percentage of the employee base.

39:33Naveen Rao:Amazon saying they're going to lay off, continuing laying off and not hire. So how do we think about, it was 40%, I think, of the workforce Jack did. Obviously, he was probably a little bit bloated post-COVID. But how do we think about the tension here and employment in knowledge work?

39:51Anastasios Angelopoulos:The Block one I have some insight on, I was on the board of Square. Okay. The Block right as COVID was hitting. I think there was definitely a bit of a overhiring it's like woohoo now we can move everything to slack and you know all the electronic media and I can just hire like mad and and I think that's what happened I'm sure there is automation but I but I think what I see over and over again is that it's not that people don't want great engineers that are building I think this becomes an excuse to kind of cut the fat that accumulated for whatever structural reason and what I'm seeing more of is not that, hey, I want to see fewer people doing the same amount of work.

40:30Anastasios Angelopoulos:It's that I want to see the same amount of people doing more work. I want to see more output. I want to see more software releases. I want to see more features. I want to see, because my customer base is going to expect more, you know, it's not that you're going to say, oh, I'm just going to, I'm happy with my shitty, you know, two-year release cycle of enterprise software. It's like, no, I want you to fix the bug now, right? I want you to update this thing now.

40:55Naveen Rao:Okay, Anastasius, what do you think? Are you a doomer at this point? And do you flip-flop like I do from, oh my God, there's so many problems to solve. Yeah, just we'll create more startups or create more. And then sometimes I'm like, I wonder what young people are going to do because this is the first 10 or 15 years of your career is doing this grunt work. Where do you sit on this? Is it making you stay up at night?

41:19Anastasios Angelopoulos:Well, maybe like many founders, I tend to be optimistic about everything. So I think the future is very bright. It's filled with plenty that we're going to solve problems that we never thought that we would be able to solve in our lifetimes. They're going to be solved by AI, that it's going to make us so much more productive, free up our time to do things that we really care about doing. At the same time, we have to be honest with ourselves that the majority of economically valuable work in this country and in the world is done by training people to do repeatable tasks.

42:05Naveen Rao:Okay.

42:06Anastasios Angelopoulos:That's the foundation of work today. That is the foundation of work today. Training people to do repeatable tasks, whether that's working in a factory, whether that's delivering food, whether that's writing software. and a lot of that repeatable work is no longer going to we're no longer going to consider that work moving forward and so the question is then what remains maybe there's some use for humans in the coming decades for doing work that isn't so repeatable maybe requires some more creativity or context that's difficult for the models to understand. But I do think that we are going to enter a crisis of labor where, you know, the so-called, where basically labor does not equal value anymore.

42:58Anastasios Angelopoulos:And that, you know, something else will be potentially valuable that humans will be able to do. And a lot of the, what we now call labor will be done in, you know, Crusoe's data centers or done on, you know, Naveen's chips. Um, and that humans will play a different role in society.

43:16Naveen Rao:Chase, do you, uh, where do you fall on it now? And do you honestly worry about it a bit? And does it keep you up at night or you just think human creativity is always going to figure out a way and we're going to move towards a Star Trek world where work and passion are optional and there's so much abundance. Nobody would worry about something like food, energy, water, school, et cetera. It's all free anyway.

43:38Chase Lochmiller:I sort of am definitely the optimist in how I view the future. And to me, AI and the harnessing of this digitally native intelligence and labor is going to be the greatest catalyst for energy abundance, for abundance of intelligence, and for driving economic growth. And if you look at it just through the lens of economics, like, you know, I think what's fascinating is like for the first time in history, we're able to sort of manufacture intelligence, right? That's like the, that is the breakthrough that we're sort of seeing. And, you know, the units of intelligence that we're seeing are these tokens, right?

44:17Chase Lochmiller:These output tokens of querying, you know, large language models or, you know, these big AI models. I think Naveen was actually the first person that gave me this analogy that, you know, LLM is a bit like a statistical database where you're sort of, you know, it's just doing a next word prediction in terms of like, you know, you send a set of input prompts and then it sort of gives, you know, these output tokens. But I think what's fascinating when you look at what's happening with OpenClaw, with CloudCode, with a lot of these agentic platforms that are actually wrapping those models and actually utilizing how tokens are generated to actually create a useful task, actually producing digital labor, not just digital intelligence.

45:03Chase Lochmiller:It's like you give it a task and it goes and accomplishes that task. That is another step function breakthrough. And when you look at like, you know, I think the primary KPI that we sort of look at in terms of like economic prosperity, it's GDP. And when you, you know, going back to your introduction to macroeconomics class, you know, the growth in GDP is really the sum of three things. It's the sum of change in capital, change in labor, and change in technology. And when you look at this massive acceleration we're seeing, obviously it's a huge delta in technology and the efficiencies we're able to gain.

45:40Chase Lochmiller:But for the first time in history, we're actually able to massively accelerate that delta L, the change in labor, with this new digital labor force that's creating massive growth in what we're able to do and what we're able to accomplish. So, you know, I think we're going to see GDP growth that we've never seen before because you've been able to like sort of like amplify it through digital agentic labor.

46:06Naveen Rao:Yeah, I was talking to my team and I said, you know, if we ever hire people, I think we'd be hiring people for the company as like a luxury almost. Like we just want to have more people around because it would be fun to have a new person hanging out with us. And then I was thinking.

46:23Chase Lochmiller:just interviews based on vibes you know that's yeah just like you know i just we'd like to have

46:28Naveen Rao:another member join the grateful dead and yeah they won't go play bongos we would like to have a sax player in you know dire straits you know went from you know four or five people and then mark knoff was like what if we had two pianos what if we had like a saxophone in here and some of the great dire straits songs had a saxophone like it just he just added it um it wasn't like an economic decision was an artistic one. And I was trying to film the metaphor, and I'm a foodie. And I was just thinking, can you imagine taking somebody from 200 years ago in an agricultural environment, and then taking them on a food tour of, you know, like a modern supermarket, Erewhon, HEB, the what's the place in Barcelona, the market, you know, or Skigi Fish Market, You just take them to these markets and show them what's available.

47:19Naveen Rao:And they would be like, why? And then take them to a bunch of Michelin star restaurants and force them to go through a 20-course meal, one little 50-calorie, 150-calorie morsel at a time. They would be like, what are you doing? This is such wasted effort. You don't need this many types of potato chips or cereal. Now take us as a society, and you could probably do that with entertainment choices. You take somebody from 1950 and having watched TV that was on for four hours a day, and the rest was just an American flag, you know, testing signal, and put them in front of the internet and YouTube and just watch their brain explode.

48:00Naveen Rao:Like, why does this exist? So if we start to think about that for what's about to happen, Naveen, I mean, what could society look like? Like in a, and I guess then for the people who are on the bottom in these up and coming jobs and knowledge workers coming out of college, I think there has to be some message for them in terms of what would you advise them? What do we advise, Anastasios, of, you know, the Berkeley student coming in now, you're going to be 400K in debt or 250K in debt. Here's what you do when you graduate. So, so maybe answer both questions, you know, the, the abundance one, you know, and the, oh my God one.

48:35Anastasios Angelopoulos:Well, it's interesting. So, you know, you got to really kind of click into what economic value is. I think you kind of went there. It's sort of like, oh, it's vibes or I want another guy in the band. Well, that is it's a human construct, right? Like these things are driven by things that we want. We're moving away from a world where we buy things maybe that we have to have. Like our subsistence will be covered, right? We will have food. We will have plenty of it. We will have electricity. We'll have shelter. Those things are going to be covered. But we still want to we move resources around based upon what we want.

49:04Anastasios Angelopoulos:and that becomes as long as humans are still in control of those resources if that changes things are get pretty funky but but for now let's assume that humans are in control of the spending i actually think we end up in this world where we don't we actually start to reject some of these digital artifacts like i actually had someone it was really funny like um i had a slide deck put together and you know when you put your slide decks together you draw some figures you sometimes are a little misaligned on the lines and you get a little like overlap they're like that's like an organic slide deck.

49:35Anastasios Angelopoulos:Like you made that. I was like, damn right. I made that. That was bespoke. It's handmade. It's acoustic. It's exactly right. So we actually, it's artisan. It's like, it's handcrafted, you know? And I think we actually call back to that because when I did that, the guy who looks at it's like, oh, I've done that before. And it kind of relates to an experience, the human thing. And that's why we like imperfection. And so I think that drives the And we can't forget that. Economically valuable work is not just productivity of churning out more shit, right? It's about what we want to buy. And I think you've got to think about the supply of it and the demand of it.

50:12Anastasios Angelopoulos:And honestly, that's why I'm super skeptical of all these Doomer scenarios. I don't think we go to this place where everyone sits there like a zombie. I'm going to be like, screw that. I want to buy something that I can relate to. That's where I'm going to spend my money. So anyway.

50:29Chase Lochmiller:I think, Jason, I loved your analogy because transporting someone from 200 years ago and allowing them to see the industrialization of food, of just supply chains, and the abundance and prosperity, and honestly, the volume of people that that's able to support these days. When you look at like the percentage of humanity that used to be involved in agriculture versus today, it's like – I don't know the exact numbers, but it's some very, very high percentage to some very, very low percentage. And the amount of leverage we're able to get on human time. I think that's what we're seeing right now with this industrialization of intelligence and industrialization of labor with this digital workforce that's going to be created and this booming that's going to unfold.

51:21Chase Lochmiller:Um, when, when, when you, when you asked about like how you would advise kids coming out of college or, you know, even parents raising kids, um, I think is an important thing. You know, I'm a father, I've, you know, young kids. And when I sort of think about, yeah, Naveen's got both, both ends of the spectrum, uh, both young and older. But, uh, I, I think, uh, what's important, you know, when I think about, you know, my own kids, I think that the things that I'm optimizing for are one, like a high sense of curiosity, like being able to like take a thread and really pull on that and ask the next question and really just like get deeply curious about something.

52:01Chase Lochmiller:And, and, and number two is actually a very high sense of agency, which like sort of goes in line with that, you know, sense of curiosity because in the future, right? Like my kids are going to have access to the workforce equivalent of millions of people worth of labor, just like at their fingertips, like on their phone. And, you know, if you have incredibly high agency and curiosity to solve a problem that, you know, speaks to some other human, you're going to be able to just will that into existence. And you're going to be able to, you know, get more leverage than you've ever had in the history of humanity.

52:38Chase Lochmiller:And, you know, I think that's going to be a very unique and cool and abundant future that, you know, my kids are, you know, going to be able to live in.

52:45Naveen Rao:It's a great pairing. Curiosity, agency. I'm going with, I have a 16-year-old, two 10-year-olds. I'm going with, and I think we'll just go around the horn here, Anastasius, I want to get your advice to students. And I don't know if you have kids. You have kids?

52:58Anastasios Angelopoulos:No.

52:58Naveen Rao:No kids yet. Okay. So just, you have students, you have an unlimited, you could give advice to in the university. I'm going with radical self-reliance and then the ability to communicate and socialize, you know, with other humans and the ability to learn new skills, like problem solve, learn new skills. Because what I'm seeing is the people in my own organization, I have a lot of young people, the three or four who took to Open Claw first, in 30 days less 20 days they became literally five times more valuable than the people who didn't wow anastasius at that point i said i'm calling you know a red a red notice here like i'm calling a red uh what do they call it a code red i'm calling code red this sunday the five people know how to do this oliver lucas train everybody else 15 people showed up and now we have the whole organization.

53:55So Anastasius, what do you think is the best advice for young people who might be

53:59Naveen Rao:scared right now or people who are the Uber driver or work in a factory or a paralegal? What should they do? What should they do?

54:09Anastasios Angelopoulos:It's a great question. The economic consequences of this technology are going to be large. I think all of us can agree with that. And I think it would be, it's basically naive to ignore it. And if I were somebody in any of those industries, I'd be thinking, how can I leverage it? Let me become the expert so that I'm at the bleeding edge of this technology so that I can help shape how it's used so that I can be helping the economy modernize. Because if you don't, I think that it's likely people will be left in the dust.

54:43Naveen Rao:So if you are the Uber driver going and figuring out how self-driving cars are trained, going and figuring out how these new depots are going to work, how the new shepherds of 10 cars are going to work.

54:54Anastasios Angelopoulos:Yeah. How is this going to change? Let's say I'm a DoorDash dasher. What would be going through my head is, okay, it's clear that the self-driving part is going to be solved, right? There's going to be self-driving cars that can deliver XYZ. So what's the utility of what? So basically like, what's the next evolution of that market? Is it going to be that you need somebody to coordinate with the store? Is there a sort of a last few steps problem of who's going to actually deliver the food to the door? Where is it that humans are going to be fitting into that process? And how can I sort of accelerate and take part in that next phase of the system?

55:38Naveen Rao:This is such a great insight. We literally invested in a company called AutoLane, Their premise, the target parking lot is becoming like an airport. You have Waymo's dropping people off. You have Zipline coming and picking up packages from drones. So their air traffic control is their pitch. And the idea is to have people. You're going to need people to do that last mile, as you pointed out. OK, here's a mall, the Dominion here in Austin. and there's 150 stores. Somebody has to coordinate those 150 stores and me sending my Tesla, my personal Tesla to go pick something up after it drops me off at work.

56:22Naveen Rao:There's gonna be something there or going working in a cloud kitchen and creating the niche food that doesn't exist yet in this suburb that has no food options because it's in a food desert. Should people learn to code? This is, I think, the big question. And we had the stock market. I mean, God, the news cycle is so crazy, which is why I started this podcast. Two weeks ago, the entire SaaS space had a SaaSpocalypse. Everybody thought this is the end of days. Salesforce is over. HubSpot's over. IBM is over. All the stocks got hit. And we have this big question, Naveen, should you learn to code?

57:01Naveen Rao:And one of the great breakout moments I think anybody who uses OpenClaw has is when OpenClaw says, oh, I don't have, it said to me, I don't have a CRM. or I don't have the ability to, I can't find a service to download YouTube videos for you. Shall I make one? And the shall I make you a CRM since you don't have one on this computer or shall I make you a video tool that rips TikTok and Twitter and YouTube videos instead of using this Russian website with spam spyware on it? That's like a mind-blowing experience. Should people learn to code? and is everybody now a developer?

57:43Anastasios Angelopoulos:Well, I think both of those things can be true. Everyone's a developer in a sense, but I actually think the ones who still understand the machine are gonna have an advantage. So to me, learning to code is not a tradesman skill. It's a structured thinking process, right? Like the vast majority of coding as a professional engineer actually goes in before you physically write the code. Like generally you think about all the interfaces and you structure it. You write a lot of boxes on a whiteboard. And then when you go and code it, it's like, just go and execute it. So that part, we can kind of just lead to the machines.

58:17Anastasios Angelopoulos:But I think understanding how to structure a problem and think through it is still important. But you kind of brought up, like, what do you do with your kids? I have a 19-year-old, an 18-year-old. My 19-year-old's studying math. And I'm like, just learn to think. Learn to break down a problem. And agency, like agency combined with learning to think will still be valuable. You'll still have to, we'll still need to do that. And like all the things you're talking about, Anastasios, is, okay, last mile problem. Well, you got to think about how does the whole system work? If you don't understand how to break down the problem and understand how the system works, you can't supply your agency to fix something.

58:51Anastasios Angelopoulos:So to me, the answer is actually resoundingly yes, learn to code. But for the purpose of not being a tradesman in building software anymore, but more just to understand how machines work, how processes work, how to break problems down. So I think I'm still pretty bullish on people understanding how computers work. Chase, learn to code or not?

59:12Chase Lochmiller:I think the volume of people that need to learn to code is going to be smaller. And I think things like these coding schools that we're teaching folks how to use React and Ruby on Rails and these things that spinning up a website, that's not useful coding. They've already been abstracted away.

59:32Naveen Rao:They've already been abstracted away.

59:33Chase Lochmiller:But to Naveen's point, like learning, you know, low level system architectures and how software interfaces with hardware and how memory allocation works and how, you know, understanding the actual core engineering principles that go into operating the machine, I think are going to be important because you're going to need to be able to think through how to solve problems with, you know, computers as a vehicle to solve those problems. and it will also help drive the innovations that are gonna lead to the breakthrough computer architectures that are gonna be the next generation. I mean, I think a lot about like what Naveen's working on now, it's like an inspiration to me.

1:00:11Chase Lochmiller:It's like, you know, just rewrite everything from first principles. Why is, you know, like why is a computer design the way it is? Why is the transistor design the way it is? Like, do we have to do it that way? Is that the best way to do it? Is in the age of, you know, AI? I don't know. Um, and, but just reevaluating a lot of those, uh, core primitives, I think is a, uh, important exercise and those that actually understand machines and software and, you know, what's actually happening in the underlying hardware, I think is actually a very critical thing to learn for a small subset of people.

1:00:45Anastasios Angelopoulos:I think embedded in your question is actually kind of two questions. It's more like, how do you, should you learn to code if you want to be in the sort of elite group who's kind of leading these things and also what should everybody do i think those are two separate answers actually to lock's point uh guys to chase his point here yeah and and one other

1:01:05Chase Lochmiller:thing i'll add is that you know should you learn to code like to me a lot of like cloud code is a new way of coding you are coding right but you're doing it through a different vehicle in the same way that like should you learn how to write assembly like probably not like should you you know, like learn how to write C that's like a, maybe a useful skill for a foundational knowledge piece, but like, uh, you know, languages have extracted to higher, like, uh,

1:01:32Naveen Rao:it's the paradigm shift and higher. It reminds me of a big debate that happened in the nineties when digital film cameras came out, they started to make digital films center of the world, a film, uh, with Peter Sarsgaard that I have a small part in, um, Bennett Miller did one called the cruise a documentary it was all because this vx 1000 sony digital camera came out and you could edit on your macbook and there was a big controversy at sundance like well what do we do with these digital films because anybody can make a film now and they were like yeah that's the whole point of sundance anybody can make a film we used to have people submit them on eight millimeter 16 millimeter but you you had this paradigm shift should i learn how to cut film and splice it and edit in a film room and tape the film together?

1:02:16Naveen Rao:Or is this new paradigm that feels in some ways analogous to this moment in coding? Yeah.

1:02:21Anastasios Angelopoulos:I think Chase had, you know, was saying exactly the right thing, which is that, and you're sort of supporting it, which is that the history of computing has involved layered abstraction, starting from the bare metal assembly to higher and higher level languages. and the relevant question when we ask, should people learn to code is what do you mean by code? Because if you're asking the question, should people learn to think? The answer to that is probably yes. But is thinking the same thing as coding and communicating the same thing as coding? It might be. You know, as Karpathy said, the next programming language is natural language.

1:03:09Chase Lochmiller:No, that's spot on. Like Python felt that way a little bit when people first started using Python, it's like, oh, am I coding? I'm just sort of like writing out in English what I want to happen. But like, you know, clod code and cursor and these things are sort of like a further layer of abstraction away from that. And sure, it gets compiled and, you know, put into all these scripts and compiled into bytecode and all this stuff. But it is, that's spot on. You know, natural language is the -

1:03:33Naveen Rao:Yeah, or trying to draw a circle on the screen of the computer using BASIC on your IBM PC versus like just drawing a circle with your Microsoft Paint or Photoshop tool. Let's end with something a little spicy here. There's been a big debate over the last couple of days. Obviously, we're sitting here right after this military action with Iran. Put aside the politics of that. But more importantly, AI's role in things like war is important. Things like surveillance. And you've obviously had Claude take stances, OpenAI flip-flop their stances. A lot of politics in here. There's a lot of nuance to it.

1:04:19But I guess the two things that Dario brought up that are super relevant, should a technologist, and is this technology ready to be deployed on the front lines to build murder bots?

1:04:34Naveen Rao:Should we be building that? And then should technologists, because this technology is powerful in a way that other things have not been powerful. Probably nuclear power would be the next best analogy. Should scientists, physicists, you know, be making nuclear bombs? Feels actually eerily similar. And then obviously the police state. Nobody wants to live in a police state. That's a 90%, 95 % issue here in America. and this technology would be uniquely, give people the unique ability to do that. Where do you guys stand when you read these stories and you think about it? Because this feels like a moment in time, Chase.

1:05:16Naveen Rao:I'll start with you and just go right on the line.

1:05:18Chase Lochmiller:The question specifically is like, should we use AI in warfare?

1:05:24Naveen Rao:Well, I think maybe the role in the tool builder, building this, the person who is manifesting this technology in the world and then giving it to an agency like the Department of War, you know, just like the, you know, physicists giving, you know, the atom bomb to, you know, a president or the Department of Defense back then. Yeah. How do you, what's your general take on what we've seen in the debate last week? I'll let you take it where you want.

1:05:55Chase Lochmiller:I mean, look, I think my perspective is that, you know, with any, you know, technological shift that's happened over all of human history, you know, that those new technologies are used by people to gain power, influence, control over other people. And, you know, you've seen this, you know, with the development, you know, from like World War I, which was sort of like the first war fought with like Gatling guns, right? You know, like, you know, versus these muskets that you would like, you know, know, manually load and, you know, couldn't, couldn't really do that much damage with, like you had these like automated, you know, industrial weapons that, you know, caused tremendous, you know, damage and, um, you know, were able to, they were mass killing machines and, you know, the, the, the, the country or the superpower that had, you know, the, the best control over those things ultimately kind of like was one, right.

1:06:54So, and, and I, I, I guess, you know, and then world war two with, you know, the advent

1:07:00Chase Lochmiller:of the nuclear bomb. I think like, you know, there's, you know, still an ongoing debate as to whether the Japanese would have surrendered if we wouldn't have, you know, you know, dropped, you know, little man and fat boy or fat boy and fat man and little boy. Yeah. You know, and I think, you know, sort of independent of what I think should happen or anything else, I think any any global conflict is going to be at the foundation of it. It's going to be fought with information. It's going to be fought with AI and AI is going to be embedded in weapon systems. And like, so I don't know that it's my job to opine on the, should this be happening or not?

1:07:44Chase Lochmiller:I just think more it's like, to me, it's an inevitability that, you know, AI is going to be used by the US. It's going to be used by China in any sort of, you know, it's going to be used to the extent you know whatever they have access to it's going to be used by you know iran israel like anybody who can get access to it they're gonna use that to try to get a leg up so i i don't i don't know that i have a perspective on whether it should be happening i just know that it will happen and you know what do you think yeah so when i was at intel actually um if you remember

1:08:15Anastasios Angelopoulos:the uighur muslim targeting in china yeah that was running on my on my hardware my group's hardware So we developed a responsibility framework and how to think about this a bit. And basically, it came down to how close to the end application you really are as a technologist. So if I was building, if I were building the software that was running in the camera that's specifically looking for this ethnicity, I would say I bear a lot of responsibility. And I have a choice to make as a technologist whether I want to build that or not. When I build a chip, I don't because I'm building kind of a basic capability.

1:08:49Anastasios Angelopoulos:And I would actually argue AI falls into this category. And so moralistic questions I don't think should be so far out. Personally, as a country, I think we should use every advantage we have. As Chase was saying, like, that's how wars are won. And it's not my responsibility as a non-elected person to make that moralistic judgment. I can do it if I'm building the end technology, and then I can choose whether I put my time and effort into that. But as someone who's built a generally applicable piece of technology, I think anyone should be able to use that for how it can work. Now, what I would have done in this scenario if I were a Dario, like my dad actually asked me this question, and I said I would have moved the conversation to technology.

1:09:36Anastasios Angelopoulos:I wouldn't have done it on moral grounds or ideological grounds. I would have said, look, I am not okay with you using this for targeting for weapons or whatever, because the technology, it hits the limitations of the technology. So I would agree to a contract to say like, we will help you assess, is this a use case that this technology can actually do or not? Is it in bounds of what the technology is possible to accomplish or not? And that's the framework.

1:10:02Naveen Rao:And if they make that choice, it's basically like taking a certain airplane that you built beyond the spec. And you're like, you can't take it to 50 ,000 feet. It's not rated for that. If you do, the pilots die and the fuselage breaks up. It's not meant for that speed or height. It's out of parameter.

1:10:21Anastasios Angelopoulos:But if you want to transport rubber dog shit within spec, that's on you. You can do this.

1:10:28Naveen Rao:Anastasios, you must have been thinking about this. You're thoughtful. Greeks always are.

1:10:34Anastasios Angelopoulos:There you go. So let's think this through here.

1:10:39Naveen Rao:You know, is this, hey, we're making batteries and you're putting batteries in a drone and batteries can be put in a Walkman and, you know, in your EV. We're just battery makers. Use it as you will. Or is this more analogous to, hey, we're giving you the atom bomb and this is a very unique new technology. Yeah. For the love of God, we don't want you to use it to build murder bots that you cannot control because this isn't ready. You can see it if you use, you know, chat GPT and you've had it hallucinate. You understand it makes mistakes and then apologize and says, oh yeah, thanks for catching that.

1:11:13Naveen Rao:I made a mistake, which doesn't work if the murder bot turns around like on the movie Robocop, pretty prescient, and murders the executives at the company. So take me through your thinking on this. Where do you sit?

1:11:26Anastasios Angelopoulos:In my view, a lot of this question depends on how you view the responsibilities of a corporation in the context of a nation. Okay, corporation and nation, got it. How you believe decisions should be made in the context of a company within a country. Countries go to war. It's a fact of life, as we've said. And when you go to war, there's a lot of information that the country has that the corporation doesn't have. they just need to be if we're going to be part if you believe the corporation is part of the same is is a part of the country and is supportive of that country part of what that means is that the country is responsible for going to war and dealing with that and the country therefore has certain abilities to force you to conscript you to war they can take all of this and tell us to go to the military if they want that's the right of the country and the country has yes and they have and the country has rights to do to other things of that nature and because they have information that we don't have and they have the right to set the strategy and to some level if we want if we want the country to be successful there's a level of disagree and commit that's sort of enshrined into law by virtue of the capability of the country to you know seize your company and and you know just like they've they've done previously and make you you know You're no longer making cars.

1:12:53Naveen Rao:Defense Production Act.

1:12:54Anastasios Angelopoulos:Yeah. To make you do things in favor of the war. So the country has those tools at its disposal, and it should have those tools at this disposal. Otherwise, it cannot be expected to deliver on the outcome that we've all told the country to do, which is to be successful in governing in war and so on. And then we have the political system in order to elect people that we think are going to be good leaders on this front.

1:13:21Naveen Rao:So you don't object in any way for them making their feelings. No, I can object. Yes.

1:13:27Anastasios Angelopoulos:I can object.

1:13:28Naveen Rao:That's critically important. You can object and still be a patriot. And still be a patriot. You can give warnings to Naveen's point and say, hey, just so you know, this may or may not work.

1:13:39Anastasios Angelopoulos:Yeah, I can object, but it's ultimately a nation with laws and we set a direction and we have to kind of unify together to go, you know, it is one country at the end of the day there can only be one choice yeah um and i think that companies can of course express their opinions and so on and so forth and so should individuals and that's a patriotic thing to do um so that's and that's a great part of it chase you can

1:14:09Naveen Rao:speak your mind and then ultimately if we were ever in a situation where god forbid the country was under attack again, hey, this is the rules and the operating system of the corporation known as USA.

1:14:22Chase Lochmiller:I really liked some of those points you made. And it is sort of fascinating evolution to think about how the public sector and private sector sort of work together. Because in many ways, you have these inherently like natively international, natively digital private corporations that are building technology and sure they're headquartered maybe in the United States, but like they serve a global population. And then, you know, you have this private or you, sorry, you have this, this, this nation that, you know, has other geopolitical goals or aspirations. And it's just a very different paradigm from like, you know, the development of the atom bomb, you know, that was a, you know, government sponsored government mandated, you know, technology and weapons development program, right?

1:15:14Chase Lochmiller:And when you think about like this, this, like, you know, Claude was like funded by independent venture capitalists, private capital resources to serve a global population. What is their, you know, obligation to service the, you know, needs or desires of the United States, a single country and sort of their global set? I don't, I don't know. It is complicated. And I don't think I have the answers, But I just know that AI is going to be part of whatever global geopolitical conflicts unfold. Yep.

1:15:47Naveen Rao:All right. This has been an amazing episode of This Week in AI. Wow. What a strong panel. Chase, Crusoe, Naveen, Unconventional AI, Anastasios from Arena.ai. Gentlemen, thank you so much. We will have you back. If you would like to subscribe to the program, we're at ThisWeekinAI.ai, and you'll see all the links there to subscribe. brand new podcast. So tell your friends, like, rate, subscribe, all those great things. And we'll see you next time. Bye-bye.