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The Network State Podcast - Episode #15: Jeremy Howard
Overview In this episode of The Network State Podcast, Balaji Srinivasan interviews Jeremy Howard, the founder of Fast.ai, where they discuss the implications of artificial intelligence (AI) on education, the democratization of AI knowledge, and the importance of nurturing untapped talent around the globe.
Key Participants
- Balaji Srinivasan: Host of the podcast and an advocate for decentralized technologies and the concept of network states.
- Jeremy Howard: Founder of Fast.ai, which offers online deep learning courses, and Answer.ai. Former CEO of Kaggle.
Episode Highlights
Introduction
- Jeremy Howard shares his background, highlighting his role as the founder of Fast.ai and his previous experiences with Kaggle and various startups.
- He emphasizes the mission of Fast.ai: to make deep learning more accessible and prevent the centralization of AI knowledge and power.
The Motivation Behind Fast.ai
- Fast.ai started as a response to the limited accessibility of AI techniques, which were confined to a few elite labs and institutions.
- Jeremy and his wife aimed to democratize AI education, making it available to those with fewer resources.
Competition and Innovation
- Jeremy discusses competing in a global AI competition (DawnBench) and how Fast.ai's innovative strategies allowed them to outperform larger organizations like Google and Intel.
- Utilized downsized images for training to save resources, showcasing an example of ingenious problem-solving.
The Importance of Decentralized Education
- The conversation shifts to the need for decentralized education, particularly in AI.
- Jeremy highlights how Fast.ai has successfully reached learners in developing countries, including instances of students from Nigeria and the Ivory Coast seeking alternative learning methods due to limited internet access.
The Concept of ‘Dark Talent’
- Jeremy introduces the idea of "dark talent," individuals from underserved regions possessing immense potential but lacking access to quality education.
- Fast.ai aims to empower these individuals through accessible online learning and support networks.
Future Educational Innovations
- Balaji mentions his initiative, Network School, which aims to create a global community for tech founders and AI creators.
- They discuss how Network School can potentially align with Fast.ai's objectives to nurture talent from diverse backgrounds.
The Role of AI in Education
- Jeremy elaborates on how AI can transform education through personalized learning experiences that adapt to individual students' needs.
- Emphasizes the importance of fostering curiosity and creativity in learners rather than merely teaching compliance.
The Future of Work and Talent
- The conversation touches on the changing landscape of work due to AI and the need for continuous education and skills development.
- Jeremy advocates for a learning environment that encourages experimentation and iterative problem-solving.
Counteracting Power Concentration
- The discussion turns to socio-economic dynamics, focusing on the potential for concentration of power in the hands of few and the importance of counteracting this through education and innovation.
- Balaji and Jeremy discuss the implications of AI and technology on societal structures and the need for equitable opportunities.
Conclusion
- The episode concludes with both Jeremy and Balaji reflecting on their shared vision of a future where education is accessible, empowering individuals globally to reach their potential.
Key Takeaways
- Democratization of Knowledge: The importance of making AI education accessible to all, especially in developing countries.
- Innovative Learning Methods: The effectiveness of iterative learning processes and the role of AI in personalizing education.
- Empowerment of 'Dark Talent': The potential of untapped talent in underserved regions and the responsibility of educational platforms to support them.
- Social and Economic Dynamics: The need to address the concentration of power and wealth to create a more equitable society.
Resources
- Explore Jeremy Howard's work and courses at [Fast.ai](https://www.fast.ai)
- For more information on Network School, visit [ns.com](https://ns.com).
This episode provides valuable insights into the future of education and the transformative potential of AI, emphasizing the need for accessibility and innovation in learning.
Written by AI. May contain mistakes. Listen to the episode to check what was said.
Transcript
Automatic transcript. May contain errors.0:00Jeremy, welcome to Network State Podcast. and we've been friends or friendly online, I think, for a while. You are the founder of Fast.ai, which is this incredible course that's online. We both taught large online courses, so we kind of have talked about that. You're the founder of Answer.ai. Before that, I think you were at Kaggle, right? And you're Australian. You have an interest in biomedicine. And I think we're also into, peace and trade broadly, internationalism and so on. Give me the spiel. Did I nail everything? Give me Jeremy on Jeremy. Yeah, no, pretty much. I mean, I'll say maybe Fast AI, most people know us for the course because that's how most people interact with us, but that was only one quarter of it.
0:44So Fast AI was all about trying to avoid a kind of massive centralization of power and inequality due to what my wife and I saw in 2012 is likely to be a rapid growth of AI. And so we wanted to - So similar to OpenAI's mission. In theory, except we actually were Open, yeah. At least that was their initial mission. Yeah, so we basically decided to get AI into the hands of as many people as possible, including people with few resources. And so we did a lot of research to figure out how to make AI more accessible, because at that time, only five labs in the world. And yeah, the techniques to actually use AI in practice were not published they were kind of like little recipes yeah yeah so my wife rachel actually asked um earlier when he was presenting in like 2012 or something about some of his work and it's like okay so how did you actually do that bit what weights did you use how you know what fine treating myth to use he's like oh we don't we don't publish any of that that's our bag of tricks so we were like okay this is not okay like this is this technology is going to change the world and it requires a bag of tricks that you have to go to Stanford to learn.
1:56You know, so we figured out all the tricks and built a lot more tricks of our own. And then, you know, everybody then tried to make it all about money. So then Google eventually started creating TPUs and stuff instead of saying like, oh, you can't. I remember Jeff Dean saying there's no point trying to do stuff with AI unless you're at Google because only we have compute. Yeah. and we beat them in a global competition to train image net i mean a kaggle uh no no at um that fast ai oh really i didn't actually know that yeah yeah there was a global competition called dawn bench and we competed against intel they had like a cluster of thousands dawn bench d-a-w-n bench d-e-n-c-h by the way i love i i'm friendly with jeff dean i think he's amazing and so yeah yeah so so that that's actually pretty impressive i mean i'm sure he was impressed that you're able to do so much oh yeah no he was he was great about it you know they they published a post they published a paper and they credited us and those don't have feelings you know uh but we just wanted we just we wanted to say like no you don't have to be a rich google person to you know how is that successful actually maybe you can talk about that because uh like that's a little surprising to me because you know obviously deep seek has brought costs down recently but back then was it did you Obviously, Google had massive amounts of clean data and huge compute resources and so on.
3:20How could the student projects be competitive with Google during Dawnbench? Because these big labs suffer from being over-resourced. In fact, not as bad now, but particularly around that time and the next few years at Google, you were explicitly rewarded for using more compute. where else we were like hey we don't have much money like we uh we made no revenue we had no grants it was just my wife and i put our own money into fast ai excuse me how are they rewarded for you so they were basically if you could use more tpus that's like a good tick on your performance no really yeah wow okay so you know we came along and said hey like so for example um it's because they wanted people to use the tpus since they were and they wanted to like show off how big their their rig was oh look at our big rig and these people using our big rig to do these big things so for example in dawn bench it was an image recognition competition be as fast as you can to train a model and the images were 224 by 224 pixels and we thought like okay well 90 percent of the time, the first 90 % of training, we're going to train on 64 by 64 pixel downsized versions.
4:40It makes perfect sense. They look the same. The last 10 % will use bigger ones. That 4x or 16x delta was enough. Nobody else thought of that. This is one of the many tricks we used. And why would anybody at an OpenAI or Google try and do that? Because it's like oh, now we're not using our amazing TPUs. Well, it's interesting because, you know, that's actually, I'm actually going to put out a little comic on this, actually, on that, which is, you know that meme about a secret third thing? People will say, oh, you're not an X or a Y, but a secret third thing. And they'll say it sarcastically, like, oh, you must be a Democrat or Republican.
5:20You're not a secret third thing, right? But actually, if you think about like an image, zero or one, one pixel is not enough to describe the complexity of an image. You need not just a secret third thing, but a secret fourth and fifth and thousandth and millionth and so on, right? Pixels. But, you know, there is a minimum necessary complexity, right? And it's interesting because obviously if you go all the way down to like a, you know, if you have the number of pixels all the way down to just one, you're not going to get enough, right? So it's an empirical question going from 256 to 64. It still works.
5:53I don't know, maybe going to 32, it still works. Maybe going to a favicon, it even kind of still works. I don't know if you did that, if you scale it. Absolutely, we did. And I first just did it visually. You know, I just downscaled it and I looked and I was like, can I still see what that is? Right. And if I couldn't see it, then I thought the computer probably won't be able to do as well. What was it? Was it like, was it 16? Was it 32? It was kind of your... 64. Yeah. Okay. Yeah, at 32, you... You have to squint. Yeah, you can kind of see it's maybe a dog, but you can't see what kind of dog it is.
6:25I see. Interesting. Okay. So, okay, I want to... Actually, I love this. So, first of all, I want to actually show you something. We'll jump around or whatever. I want to show you something that we have done that I think is a complement to Fast AI. And this also... So I taught a MOOC in 2013 called Startup Engineering. I'm a big fan of it, yeah. Okay, great. Great. So I did that with Vijay Pandey. My colleague is here. I'm a big fan of Vijay as well. Great. So he's now at the BioFund. We've invested a lot of bio stuff together. So we have that overlap as well. We're interested in buying it. You know, you and Steve Huffman created those two fantastic courses.
6:59I don't know if you ever looked at Steve's. I don't know Steve Huffman's course. What's his course? Yeah. So a similar thing. They were both like kind of end-to-end, like how to make stuff. Oh, okay. Got it. That's the Reddit founder. Yeah. He's my friend also. I didn't actually know he'd be a course. Yeah. So, and neither of them are really available anymore. And the, you know, I free web development course by Steve Huffman. Interesting. We need a, we need a, we need a modern one. All right. Okay. So how about this? Maybe I'll do a refresher and we can, we'll send it to the fast AI people, we'll put it online and something like that.
7:28I think that I do think a 2025 version. So actually, you know, let me tell you what I'm planning to do next on this. Well, so the reason I taught that course very similar, I think in some ways to your, you know, kind of, the kind of thing is I know there's a lot of talent on the internet, right. And actually really around the world. And you know how the kinds of the dark matter and the Hubble telescope, and you can find the dark matter around the globe, or not the globe, in the universe, right? Like gravitational lensing. Yeah, exactly. That's right. And so you need a special telescope to see that, right?
8:01So by analogy, just a fun analogy, the mobile telescope, like the phones that billions of people now have, allow us to find, if the Hubble telescope allows us to find the dark matter, the mobile telescope, so to speak, allows us to find the dark talent around the world. Basically people who really have nothing other than their phone and their hunger to learn. And we can offer them a course, and that's like a skyhook and a bootstrap. That's what Fast.ai was about as well. We really reached out to parts of India and Africa and stuff that had nothing. So we had a guy from the Ivory Coast who was asking, is there some way to get this on CDs?
8:44because we don't have internet here. And yeah, turned out like one of our biggest markets was in Lagos. That's amazing. So actually I have a fair number of folks in Nigeria. Basically anywhere there's Anglophones around the world, in India, Nigeria, in the Philippines, right? There's actually all these Anglophones, meaning just English. I do want to translate into other languages and so on, but I think that's like the V1, right? Yeah, absolutely. Yeah. No, I mean, it was just like, there's all this talent around the world and it drives me crazy that it's not being used. They're like picking coffee beans or whatever.
9:22And as you say, so many of them were saying, I'm training a neural, particularly when Colab, Google came along, they're like, I'm training a neural net on my phone through Colab. Can you help me do this or that? And I'm just like, oh, this is great. So there was a young woman from Bangladesh, one of our first courses, who contacted me. And she was like, Jeremy, you probably don't even know who I am, but I'm in Bangladesh and I'm a teenager. And she was like, I want to know if what I'm doing is okay because I feel shame. And she said, I don't know anybody else in my province that does anything with AI.
10:05I don't know any other girls that use computers. Everybody thinks I'm weird. I want you to know, I want to know if you think it's okay for me through AI. Oh, she just needed the social encouragement. And I, and I, and I wrote back and I said, not only is it okay, but like, you know, you're going to put your province on the map, you know? And you know what? Like a couple of years later, she wrote to me from Google in Silicon Valley. She said, thanks to you. I'm now a Google scholar. They flew me over to San Francisco. What I like to do is I like to find these, folks, mention them, train them, stand them up.
10:42And now they're leaders in their own communities. It's a, you know, quote, teach a man, teach a man to fish or teach a man to recognize an image of a fish, you know, so to speak. Right. Um, actually, you know, you can use that. That's a good one liner, you know, cause you open with the, um, you open with the bird thing from, uh, from XKCD. So teach a man to recognize an image of fish or woman, you know, right. You know, the fish specifically you need to know is the tench. Tench? The tench. Anybody who's understands computer vision knows about the tench. Yeah. Because tench is the first image net category so anybody who's ever worked for the image net right yeah so teach a man to recognize a tench yes yeah that's good that's right actually that's like replaced uh lena yes exactly yes that's right okay so let's see um now why don't you give me the germy life story so like before so i know fast ai i know kaggle i know answer ai i know the covid and and you know masks what what's uh like what's uh what's the before kaggle so yeah So Anthony and I kind of got Kaggle started in Melbourne, in Australia, and then we flew out here.
11:48He had this crazy idea that venture capitalists in America would put money into our little startup, and I thought it was crazy. I thought there was no way, but he was right, and I was wrong. I was like, okay, I'll come. I'll give it a go. But, you know. Does Kaggle have some, is it an Australian, is just sort of just like a funny word made up word just a made up word okay yeah like google kaggle yeah yeah and uh and yeah we uh we spoke to some of your old colleagues we spoke to mark mark andresen um and it was interesting at that time uh andresen horowitz hadn't done anything in machine learning and in the end they were very good about it they passed on our round and they said look we don't know anything about machine learning maybe it's going to be a big deal but we don't have anybody here that can judge that or not but you know so we ended up with like an old clothesler and other folks uh put the money in but before that i had two startups in that i ran out of australia one was called fast mail which became a very popular global email company and then the other was called optimal decisions which if you're in insurance you would definitely know and if you are not you definitely wouldn't it basically trans changed how insurance companies price away from using just actuarial methods to using optimization based methods.
13:07Like convicts optimization or something like that? Yeah, yeah. Just, you know, pretty classic optimization. But the key thing was to model elasticity and competitor price, not just risk. Because if all you do is model risk, all you can do is cost plus pricing, which, as you know, is economically very suboptimal. so we made insurance companies a lot more profitable which I have no pride over in hindsight I don't know why I spent years of my life working on that but yeah originally I don't know like coming out of school I was a bit lost to be honest because like I was interested in stuff that nobody else was interested in so I was interested in like spreadsheets and databases and PCs.
13:52This is a bit over 30 years ago. I didn't know any other adults or kids that were interested in any of those things. In Australia? Yeah. Okay. And there weren't any university courses you could go to that were about data. So I ended up doing philosophy, but I actually ended up not going to any classes because I happened to get a job at McKinsey and Company where they really appreciated this odd set of skills I had. So tell me about, so McKinsey is actually interesting to me because there's the, let me give the negative and the positive view of McKinsey. So the negative view of McKinsey is, oh, you know, you're hiring overpriced consultants to tell you to fire people and blah, blah, blah, blah, blah, right?
14:41And the positive view is it's something that takes young people and gives them lots of different kinds of business experience and, you know, lets them actually see the actual numbers of lots of businesses and actually trains people to make, of course, good slide decks and good presentations, but really to communicate well and understand the gears and nuts and bolts of businesses. And actually, when I've hired former McKinsey and Bain and so on people, they've actually done fairly well. They're very good, non-technical athletes, like power users or what have you, right? I don't know. Give me your thoughts on that.
15:13Well, I mean, you know, I was in this unusual situation. Sorry to be negative. I didn't mean, I was just like the pro and con. No, I love, like, please challenge me. Okay, go ahead. If I say something worth challenging, challenge me. Because otherwise it's boring for everybody listening too. And boring for me. Look, I started there when I was 19. Oh, really? Wow. That's interesting. Yeah. So I was years younger than everybody else. And for me, it was eye-opening and it was great because suddenly there were people who cared. about what I did. And you're right, they're generally non-technical people, which is one of the reasons why as a 19-year-old I could be really successful there.
15:55Did you feel you leveled up when you were there? Yes and no. It's funny you say it's this kind of polarizing thing. It was polarizing in my life too, right? Because at one level I felt like, okay, I need to learn business because I didn't know any of that stuff and I wanted to create my own companies. Yeah. You're very commercial for a professor. Yeah. Professor type. Yeah. Yeah. Well, I mean, I never went into, I've never been a professional academic in my life. Right. But you've got, you've got the, I think we both have that disposition. Yeah, sure. No, absolutely. And so I was trying to learn business and by being at McKinsey, I did learn a lot about how business worked, but also in a lot of ways, it's a very conservative organization because I was telling my colleagues at the time, hey, this new internet thing, I think it's going to be big, you know?
16:44And they'd be like, I don't know, Jeremy, this computer stuff, this is pretty nerdy. It's like, what's it for? They're like, I don't know exactly. But I feel like, it's like very early 90s, I feel like it's going to impact business. And they're just like, no, look, let me explain how business works. You know, business is about relationships and strategy and capital and, you know, and in the end, like they were wrong, you know, and I didn't have the trust in myself. At the time you didn't know whether you were wrong or they were wrong. I was sure I was wrong. Right. And I just kept trying to figure out why I'm so wrong.
17:19I felt really upset with myself for being stupid that they, everybody else can see it. It's so obvious that they're just like, look, Jeremy, let me try to explain it. I just couldn't get it. so i wish i had you know i stayed in consulting for 10 years oh really wow i should have done it just two because that's enough and like what i really learned there was a sales like it's really great for learning sales well what did you like uh i don't know what are the top three five things you learned in mckinsey like sales okay yeah so i was and at at carney so i went from there to Waiti Kani. What I learned was like, okay, it's all about change and influence, right?
18:03So it's not just sales, but it's a kind of sales. It's like you're trying to sell an idea or you're trying to sell a piece of work, whatever. So we were very careful about mapping out the organization, you know? So it's like, okay, we want to sell this piece of work next, or we want to help our client sell this idea. Okay. Who's everybody in the organization who's in any way a stakeholder? who could have an opinion, who could cause this to succeed, who could cause this to fail. Like, okay, who do we know who knows that person? And like extremely kind of careful and optimized process of creating change through human management, human connections.
18:46We brought professional actors in to like play the role of different types of clients and we would then interact with them and then, you know, then talk about what the results were. It was just way more intense human optimization than I'd ever conceived of. I'd always thought of that human side as being like, oh, some people are charismatic, you know, or, oh, some people are just good at convincing people. It's like, no, they're skills. There's a science. There's a logic. it's like a different kind of logic to programming a computer but if you want to get an organization to do a thing you have to know how to map it out in some ways it felt it's a graph traversal in some ways it felt cold and kind of calculating and horrible to be like oh this human being I'm not seeing that as a human being I'm seeing them as like this cog and this machine and I'm going to use this process but it totally worked Yes.
19:52You know, and so it made me, after a while, I changed my view of it. I was like, you know what? Like getting organizations to do things is important. It is absolutely important. And so if that involves treating people as machine parts sometimes, because humans are very predictable. Yes. You know, and so if you learn how to manage different types of humans and different types of situations. And like, you know, so like you get the one person to be your kind of inside mole who's like super. Right, you're a champion or whatever. And they've recognized that they can use you to advance their career.
20:32And then you talk to them specifically about how they can advance their career. And then they tell you who's going to get in the way. And then you get three more people. And then you use that to put pressure on the fifth person who is well known to, you know, be somebody who likes following rather than leading. And, you know, you structure it out. it all play out and at the end it's like okay it happened you know it's funny like the way you know do you know mark cranny at a6nz i don't know if you know him he's a very different personality than you but he also he's like a gruff mormon a few words but he's like a sales genius actually right and very similar like the way i think about it that kind of reconciles all of it is it's a nested set of like win-win relationships all the way up to the organization level right But the best kind of sales is when you are genuinely selling them something that will improve their business or their product or something in some way.
21:24And then it will also improve at a nested level the career of this person who approves it and so on and so forth. It's almost like a venture investment all the way through. And that is actually what I think is the reason that that will work is that's the most consistent kind of thing where even if you're flipping them to do it, they will like it in the medium to long run. Yeah, and if you're trying to have a dent on the world, you know, and you've got good ideas and develop good things, but you're unable to influence anybody to buy it or use it, then you're not going to have a dent on the world.
21:54Like that's actually, you know, it's funny. I mean, there's a lot of great things about your course, but one of the best is the domain name. Fast.ai, right? Like I learn AI fast. Amazing. Okay, that's what I want, right? So that's like an example of sort of an inbuilt marketing kind of thing, which is great, right? And I'm sure there was some thought into that because lots of people could have named it. Oh, yeah, we did a lot of marketing stuff there. We also, as far as I know, we were the first company in the world to do A-B tests on our homepage. Oh, is that right? Interesting. I think we were also the first to have all the free email accounts.
22:26A little footer would be added to every email message, marketing, the service. We did a lot of little things like that, little viral things that today are everywhere. Yes. So, okay, great. Actually, I want to show you something, which is, so we took, so let me describe problem and then solution and get your thoughts, right? So you and I have both taught large online courses, right? And the typical thing that happens with a large online course is people, it's a little bit like signing up for like a workout, right? People aspirationally want to do it. And then, um, they want to have done it. They want to have done it.
23:06Yes, exactly. That's right. And then they want to be the kind of person that would have done that. That's right. And there's something good out of that. Right. But, uh, what happens is they sign up for, and the problem is allocating the time, or then if they have the time, the energy or the discouragement or what have you, there've been various mechanisms and so on to try to solve that, address that, right. There's like cohort based learning and, you know, and so on. And those things work to an extent cohorts are great yes so that that can work um but let me show you something that we did uh which we call um a learn-a-thon when should you use a random forest what is the confusion matrix don't know what about collaborative filtering don't know
24:03When should you use a random forest? Tabular data. And if you have a lot of noisy features. What is the confusion matrix? Like a table of actual answers against the predicted answers. And then comparing how often it gets it right. And then when and how much it gets it wrong. What is collaborative filtering? Recommendation algorithm. By clustering people or items or things by similarity. so basically we're going to do a version you know updated version of that but basically so the fastest so essentially literally we took because what is it's like about 10 hours 11 hours of videos right yeah so over two days we said okay you really want to do fast AI okay sign up come here 9 a.m on uh on Saturday morning and nine to nine Saturday nine to nine Sunday they watch every single video start to finish no phones yeah right yeah and then when it was time to go and type things in, you know, laptops out, do that.
24:59Absolutely. And it drives me crazy because so many people tell me like, Oh, Jeremy, I, I started your course. I meant to finish, you know, I've tried three times. I haven't managed to finish. I always think like, look, yeah, you could just put aside one weekend and just binge it, you know, get it done. Yes, exactly. And I want to, did I show you the fellowship video? Okay. Hold on. Take a look at this. Okay. Global meritocracy is finally here because we're awarding$100 ,000 in funding for the new network school fellowship. And anyone from anywhere can apply. And they might well ask how? Well, you see, we've set up shop on an island right off the coast of Singapore in the new special economic zone.
25:39And it has an enlightened immigration policy. That means it's the perfect place to assemble a global community of tech founders and AI creators. And that's what we've done. We've co-working, fitness classes, yoga, fast Wi-Fi, office pods, a state-of-the-art gym, healthy snacks, Starlink, a makerspace, a content studio, guest lectures from the most successful founders and investors in the world, nomad visas, and help with everything else you might need. And we have funding too, if you're good. So go and apply for the Networks Tool Fellowship now at ns.com. The only connection you need is an internet connection.
26:15That's very inspiring. I want to come. Great. Also, Malaysia is awesome. So go to Malaysia. That's right. So basically the combination of Singapore, Malaysia, and the new Singapore Johor special economic zone. You know, it was one of the things where there was theory and then somebody had to put that into practice, right? So the theory is like Singapore has a lot of capital, but it doesn't have a lot of land. Malaysia is actually improving a lot. Yeah. But it doesn't have - I mean, Malaysia's got a good education system. It's a strong country. Yeah, very underrated. and it's improving a lot. And you can basically live a pretty good life there, I think.
26:51Super good. And it's right next door, right? Yeah. So Malaysia has land. It has less capital. You can literally drive there. You literally drive there. I literally drive back and forth all the time, right? In fact, we're just like 30 minutes from Singapore, basically. It's literally, you know, just go over the bridge, pop. You can see Singapore directly from it, right? So, and we'll probably have a ferry or something back and forth that'll give down to like 15 minutes. Oh, that'd be nice. Yeah. So I want like these autonomous boat kind of things, right? Yeah, why not? Yeah. Yeah. So knock on wood, let's get that, right?
27:17So this is something, what you're seeing in that video is something I've wanted to do for more than 10 years, right? And you just have to build all the, you know, overnight thing, 10 years in the making. So certainly anybody who's like doing fast AI, who's taking the deep learning courses, we're looking for the kinds of people who have completed your course and we can fund them and help them build things. And in particular, the thing about, so let me explain kind of the motivation behind what we're doing with Network school right so a it's very hard obviously now to get student visas skilled worker visas into the u.s it's i mean even like people who are tourist visas like they're getting strip surged or crazy things happen you saw there's actually some australian or what have you like some terrible thing happened to them or that right and i think every almost every country now has some story examples of people citizens of their country that have been screwed around tourist visas student visas skilled worker visas like the and in southeast asia these countries are now competing for that talent with their digital visas with their startup visas exactly they're so smart this is exactly that's right and this is the thing i was like i want australia to get on that boat too you know we've had this global talent visa in australia which is pretty good um so yeah i have but everybody needs to do this you know the the country's offering digital nomad visas right so So there's this weird thing where the U.S.
28:35is taking itself out of the global economy just as everybody else is diving in. Exactly. And all of America's big value creators are tech. That's right. Exactly. And they're globally mobile because there's no Silicon in the Silicon Valley. No. We're not like mining. So our team, Answer.ai, is fully distributed. Oh, awesome. So we've got folks in Turkey, Japan, Australia, Ireland. If you ever want to co-locate them, we can host them in every school for a week or a month or something like this. One of the things we want to do is co-location for remote teams. That's a nice idea. Because we've got together for the first time ever in person here in Singapore.
29:14Oh, great. And we're all like, oh, it's so nice to spend a week together. Eric Rees and I at Answer.ai, we did something a bit unusual. We decided to only have one policy. and our only policy at answer ai is to only have one policy okay i don't know what is that policy the policy is to only have one policy oh it's very meta is this like one of those recursive kind of things go ahead i'm done we only have one policy and it's to only have one policy so you can't have no policies because that's a policy okay okay so we have no policies other than the policy that we're only going to have one policy.
29:56I see. Okay. Got it. And why? Well, policies, they're like ideologies. They're like, they're these fixed things which say like, oh, you can turn your brain off now because, because we've decided X, you know, in this situation, this is how you're meant to behave. Like I am equally skeptical of ideologies and policies and all of these cognitive shortcuts that basically say like, oh, I believe in this thing because that's what my ideology says. Yes. So let me give an analogy or a way of thinking about this that I have from the Network State book, which is like programming paradigms. You can have imperative programming, functional programming, declarative programming, and so on and so forth.
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30:40And for certain problem domains, certain style, it just makes it very easy and concise to solve that problem domain right but then you also want like a multi-paradigm language like like python or something like with haskell you know you can just do everything as f of g of h of x and you can actually get far with that but it's sometimes nice to do things in an imperative style or what have you right and um and so that's how i think about political paradigms right like um another analogy is you know i'm not a big ufc guy but like ultimate fighting championship is some people are using grappling, some boxing, some Muay Thai, and it's situational as to do I solve this with a kick or a punch, right?
31:22Do I solve this as functional or imperative? And I think like Lee Kuan Yew was someone who was like that, where he understood many different political schools of thought. And then he just like applied the right technique that was sort of self-consistent in that school of thought for that situation right and so that's like the beyond ideology thing which is you're aware of a lot of these different things and you situationally figure out which one is appropriate and you use that because uh and you you know you're constantly curious and interested and you know what you care about is doing a good job you know rather than being consistent with other members of your tribe Most humans are mainly interested in being consistent with other members of their tribe.
32:09That's right. That's the number one driving force. And the thing about that is there's a meta-rationality to that, I think. It's kind of like evolutionary game theory, right? So you can imagine you have two populations of people who are conformists and dissidents, so to speak, right? And the dissidents are constantly exploring, and they're taking high risk, and sometimes they fall off a cliff, and sometimes they have reward, and then the tribe follows them, right? And the conformists are just, you know, they're like, this is risk capital, and this is just, you know, stay home money or what have you, so to speak, right?
32:42So you can make an argument for a portfolio strategy as to why you want a small number of dissidents who are sometimes wrong when they're wrong, or contrarians, or whatever you want to call it, entrepreneurs, right? And then most people should actually, like, go with the tribe so they don't run off a cliff, but they could actually find, you know, a better pasture or something over here. That's one way of thinking about the respective balance. Go ahead. Yeah, I mean, I'm kind of curious about this because like globally, somehow every jurisdiction has settled on the same education system and the education system teaches children to be conformist.
33:17Yes. If you, you know, the test tests whether you can feedback the things you were taught in the way that you were taught them, you will get rewarded if you do what you're told. and like i'm kind of curious about how much of this thing we see in the world is because every single child basically in the western world at least has learned this same have you heard the concept of the prussian educational system yeah okay do you know what preceded that no okay so there's this great book uh we can put it on screen called uh called the craft apprentice okay and one of my macro kind of theories of the world is that history is running in reverse.
33:59And I can show you a bunch of graphs on that or what have you, but literally like a U curve where in many ways, our future is more like our past, like more like, let's say the 1850s. And then eventually the 1750s than the 1950s. Like there's a lot of U curves, which have their minimum or maximum in 1950. And I can, I can show you some graphs on that. And so one premise of that is like prior to the Prussian educational system, which was, which is what we currently know, K through 12 and so on, that was all set up. It was inspired by Bismarck after German unification to have all the children get basically the same software in their heads.
34:32It's like, you know how with Windows you have like the default install that comes off the factory, and then you have like, you know, Windows Premium Ultimate, maybe for college graduates. And then you have the service packs from, you know, mainstream media. That's how I kind of think about, right, right. And there's a reason for that, because then everybody kind of has the same references. They salute the flag and, you know, they've just got the same basic install and they can interoperate, right? There's a rationale for that. It's how you, it's a softer part of constructing a nation. In fact, arguably that's even as important as quote the hardware part, right?
35:02Which is like the physical territory and the people and so on. But before that, there was a different system which was all based on apprenticeship. And they would start working from an early age and they would just learn practical skills very, very early on. Or they'd be like Jebediah and Abigail would have 12 kids and they'd all be working on the farm and they'd be like mini industrial robots, so to speak. picking fruit or something like that, you know, mending fences very, very early on. So the entire concept of extended adolescence wasn't there. The concept of being on your parents' health insurance till 26 or whatever, it wasn't there.
35:34And now the reason that that stuff got introduced in part is because I think in the late 1800s with the advent of like industrialization and factories, these kids were no longer under the supervision of their parents or of people the parents knew. they were under the supervision of factory owners who would push them too hard, right? Like these were like the child labor factories, you know, and so on and so forth. And that was a disalignment between like the interest of the factory owner and the interest of the kids. That's when the child labor laws were passed and so on. I mean, that took a long time.
36:07It took a long time. It's like, what was it, like 60, 70 years, Britain was the first in the world to introduce child labor laws, but still took much longer than it should have. That's right. This old Dickensian kind of era or what have you, right? So now there was a good to that at first, but then that's what actually led to the modern era of adolescence. And I'm having fun as a kid for a long period of time. And now we have this extremely extended adolescence and training period where some people are like students as doctors all the way up into their 30s before they start their career and they're almost middle-aged.
36:41And I think that the corrective to that is because everything good, you can always overdo it. right and so you can go from quote you know like being opposing to child labor to not allowing people to even work until their their 30s as a doctor for example right so i think the opposite of that the thesis antithesis synthesis is when the kid is at home and they're under the supervision of their parent but they're able to start earning online by doing development software development and so on even 10 12 years ago i had a bunch of kids some of my best students at stanford 10 12 of years ago were kids who had actually earned their first dollar doing online programming in their teens, right?
37:24And it's not even so much about the amount of money. It is that the market is a grader. This is how I think. Have you seen the grade inflation graphs? Yeah. So like, you put that on screen, but basically kind of crazy. Everybody gets a 4.0, basically. Students are the customers. So they're basically buying a job. And so how do you deal with that? And my answer is the market is a greater, right? So now you have kids, they're doing software. They can't hurt themselves like in a factory. They're under supervision because they're working remote at home, but they're also like apprenticing, right? I think with network school, we also want to make that happen where now they're in a friendly environment along a bunch of other adults.
38:01They can run around and roam and so on. And then they can level up. They can be next to an electrical engineer, next to a mechanical engineer as they're building robots and stuff like that, and just help them with small things, right? And they start to see what the, like what adults are doing and it's not just being you know sitting at a desk the whole day right so let me pause there that's kind of how i'm thinking about part of the future education maybe you have some thoughts have a lot of thoughts yeah so i mean i i know a lot of kids who are in that kind of interesting group who are basically ready to go to university when they're like 11 or 12 and adults all try to stop them oh interesting it's like we don't for some reason the vast majority of adults i deal don't want children to learn when they're ready to learn.
38:47They have to learn at the speed which they're expected to learn. They want a speed limit. Yeah. Yes. And they assume any kid that's keen to learn more, it must be the parent's fault that they're pushing them. Kids are not allowed to have curiosity and drive and passion. But actually not every kid learns everything at the same speed. Yeah, so I'm very interested in how do we help this start talent at the much younger age. Not because I want to make them more productive or whatever, but just because I know so many of these kids are deeply unhappy when they're artificially held back. And I want to help them all have the opportunity to have that excitement of feeling like they're achieving their potential, that they're just really happy with the things they're building.
39:39So I've got a kid, you know, she's nine and she's, we let her basically have whatever opportunities she wants, you know, and she chooses her curriculum and she chooses what she does. And she's happy for us to provide her some guidance as well, you know, but we don't force her to do anything. and yeah she's got this great cohort of friends all around the world now who learn in this way and are all doing it at their own speed obviously with AI there's a lot of opportunities to help more and more of these kinds of kids develop as they're ready you know and get a much more customized, personalized dynamic education experience one that's not focused on conformity or authority you know sometimes my daughter comes back she like she does lots and lots of extracurricular things you know one of them is trampolining she comes back from trampolining sometimes she'd be like oh i got a gold star for good behavior isn't that great no so like i don't know i'm not sure i want you to have great behavior you know um why do you think that's so important to have great behavior?
41:02Well, well, of course it depends. Obviously like a layer of dissidence and so on, on top of a fundamentally pro-social attitude is good. But if people are like antisocial and they're littering or they're, you know, yelling in the street, that's. No, exactly. It's, it's, it's not necessarily, you know, being the best behaved kid in the class and getting the gold star that week is not necessarily the great thing. And it's not something, not something i want her to be proud of right right you know um yeah she's incredibly pro-social she's incredibly kind she's incredibly generous but that doesn't mean she has to do everything she's told as soon as she's told to do it that's right and this is it's funny you say this because basically particularly for a girl like like yeah like like girls are particularly taught to to like fit in and do what they're told and i don't want her to be somebody in society who just fits in and does what she's told.
41:52I think this concept of like the balance and so on, where it's like, you know, as you said, they're pro-social and they're kind, but they also don't obey every single command. And so, so. Yeah, I tend to focus on empathy with my daughter, which maybe ends up in a similar place. Yeah. Just like, particularly for younger kids, empathy doesn't necessarily come as easily. So I have to kind of say like, okay, you thought that was funny. now can you try to imagine what that person's situation was do you think they would have found it funny you know if you were them in that situation has anything similar happened to you before and eventually some just say oh wow did i just do that thing to them that another person did to me that made me sad like oh wow i feel so sad i didn't want to make upset that person it's funny because um you know sometimes you can get to like just like with religions you can often and get to a similar behavior pattern by different kinds of religion.
42:47So I had a recent tweet, a little bit viral on actually that exact topic of empathy. And essentially what I said is, because I was talking to conservatives and I was saying, look, empathy is actually a useful concept even for a completely cold-blooded capitalist, right? Why? Because you have to understand the other guy's point of view and their win-win, right? And a lot of the, like, especially in today's America, they've gotten themselves in the mental state where they think everybody's exploiting them, everybody's ripping them off, right? And that like Australia is an enemy and Canada is an enemy and Vietnam is an enemy and whatever, right?
43:28And it's like, you know, lots of people are just neutral, right? They're just business partners or they're just like living their lives and you don't have to like fight and you can't fight the entire world. And you also have to have some understanding of, okay what's their win and how can we get to a win-win often a win-win is more profitable for both parties involved and so on and so forth right so you can and actually altruism is programmed into us like this is something we've discovered like evolutionarily been programmed into all of us to not be altruistic is to fight against your basic instincts and that's really dangerous because Because when you fight against things that evolution has programmed you to do, you're creating a new unstable equilibrium.
44:14Yes. So why has that happened? Well, presumably there were plenty of groups that had no altruism in their villages. You know, just genetically they didn't have that as part of their DNA. They couldn't cooperate and they died out. They died out. And so we as a species, you know, we're not perfect, right? But you don't want to underestimate the power of what we're born with. Altruism is not weakness. Altruism is strength. These are the people that survived. And if you want to fight against that, then you're fighting against a basic survival instinct. Also, it's nigh on impossible to design and organize such a complex system.
44:57they arise over a very long period of time to create these marvelously stable equilibria, you know. And this is what kind of terrifies me at the moment, is there are so many opportunities to destabilize that equilibria right now, you know, with technology and the connectivity we have. And historically, each time you get a previously stable equilibrium is damaged, sometimes ending up with, you know, hundreds of years of societal misery. And so I always just like, I'm definitely very keen to see change and growth, but I want people to understand the power of where we're at and know how hard it was to get there and to know enough history to know that, you know, creating, you know, destabilizing an equilibrium creates a power vacuum.
45:55and there are certain people who are extremely motivated and good at taking advantage of power vacuums and they're the people you definitely don't want in power. I don't know, like somehow Singapore did an amazing job, like the one country in the world that like, I think they just got lucky with Lee Kuan Yew. Do you know what I mean? They ended up with a guy who's kind of incorruptible. He doesn't have a huge chip on his shoulder. He just cares about outcomes. But most places around the world in that situation end up with, you know, basically a, you know, deeply insecure, chip on their shoulder, power hungry person.
46:33You know, it's funny about Lee Kuan Yew, which I think is very underappreciated, is he like he could argue his case in English. I think this is the most underappreciated aspect of Lee Kuan Yew. Because he would argue his case in English, he could argue on the global stage. Right. Other people understood at least his point of view. He could make it cogently. He could do it in short form. He could do it in long form. Soundbites and then long speeches, extemporaneously or in policy papers. And he made sure that Singapore won the argument. And if you win the argument, then you often don't have to fight, right?
47:11Because there's like that swing vote in the middle who's like, you know what, he has a point here. We should do it his way and so on and so forth, right? And I feel that, for example, there's other folks in East Asia who delivered comparable economic results to LKY, right? For example, in South Korea or in Taiwan or what have you. But they couldn't make their argument in English, right? That's a really exceptional aspect of LKY. They could speak in Korean, they could speak in Chinese, but they couldn't make their case on a global stage, right? And I think that's very underrated. And it's something I think about a lot because, so let me actually slightly counter argue with you on the power vacuum thing, which is there, right?
47:52I think that we are about to enter a period where the future is China versus the internet. Should I elaborate on what I mean by that? China versus the internet? China versus the internet. Sure, go ahead. So the 20th century was sort of a symmetric thing, you know, almost like basketball, like the final four plays. And it then ends up as the US versus the USSR. Everybody slugs it out, right? Sean McMeekin has this book called Stalin's War, where he kind of makes a point that World War I and World War II can be seen almost as like a 30 years war, like an extended bar brawl with people like smashing chairs over each other's heads all around the world, right?
48:28And then it kind of lands up as the U.S. versus USSR, right? With Japan and Germany eliminated and other powers too, U.S., UK, France, blah, blah, right? I think this century is going to be different where it's not a symmetric thing, but asymmetric. Like China and the internet are, I think, the balancing things. And China's obvious. I think the internet is non-obvious. What do you mean by China's obvious? China, if you take the, quote, American empire, I think China inherits the manufacturing and the money and the military. Or not all the money, but the manufacturing, the military, and really the might of it globally, like a lot of the alliances and so on.
49:06The world is, after this tariff thing, re-centralizing around China. Totally. Right, quickly. It'll be interesting to see how eminent that is, but it's something very deep happening there. Yeah, so I think what's going to happen— And it's not just economically, also culturally. You know, America's cultural power has been enormous. It has been. That's right. And now in Australia, I'm seeing people being like, oh, America's kind of cringe now. It's cringe now. That's right. But I think that the other air, the less visible but as important air, is the internet, which it has the people, the values, and the language.
49:42And the reason I say that is the only thing that has economic scale comparable to China is actually the internet. Why am I into crypto? I'm into crypto because everybody in the internet is equal, meaning you're peer-to-peer. You can send packets back and forth. You have the same property rights. You have the same contract law, right? You have the same monetary policy. And so whatever you were born into, you can opt in to a system of law that is superior to the one that you were born into. And it's like emigrating to at least half of what a government is, right? It's not the land. It's not the physical territory yet.
50:17I'll come to that. But it's at least the property rights, and you have to have some sacrifice. You have to buy some of the coin or whatever. You start interacting with this. Now you have like a system of law that's often superior to the one that you inherited, whether it was in Nigeria or it was in, you know, Lebanon or something like that. These places have destroyed currencies. They don't guard property rights. Now you can finally save because, you know, the blockchain protects your savings, right? So I think that the internet has half of what we want, which is it has a system of government with all these blockchains, multiple systems of government.
50:51And I actually compare it. One of the ways I think about it is, with early America, it didn't actually think of itself as America at first. They were British colonists. They were like the Virginia colony, Massachusetts colony. And they had a land and they had a people, but they didn't have a government because the government was in London. And it took a while for them to develop a sense of national consciousness and realize, oh, that's actually not our government. our government is here, right? So then they had land, people had government, they became America, right? I think the internet is evolving in the opposite way.
51:19It has the people and actually has the government in the form of the blockchain, but it doesn't yet have land. I think that's the next step. And hopefully it won't be versus. Unfortunately, Xi Jinping has moved into a power vacuum in China. Prior to that, actually, China was much more of a democracy than people realized. Talk about this. Well, I think a lot of people don't understand how the political situation in China worked. So there was a lot of voting. But unlike most Western democracies, the voting was entirely within the party. Yep. Now, people might think, oh, that's not very big. It's actually 100 million people.
52:01And then you go to the flip area. And it's not like, so I spent a lot of time in China and with a lot of really great people in China, young people. and the vast majority of the best of the people, most of what they wanted to do was to get into the party. So not commenting on whether this is good or bad, but it ends up with a democracy of the hardest working, most intellectually capable people. Can I make a provocative comment? So there's a book called The Party Decides. The point of that book was the American Uniparty decides who's actually running on the Democrat and Republican side. For many years, people have said a choice, not an echo or whatever, right?
52:44And so there's a similarity to that where there were quote, smoke-filled rooms where the candidate was determined. And certainly with a recent Democrat primary, it was something where basically the party determined who was running and so on and so forth. Then there was a whole disaster with the whole Biden comma thing. So there's more similarity to the American system for many years, where there was essentially a uniparty that decided who the candidates were than some would argue. And now I'd say, in a sense, we've had true democracy burst forth, but that's what people conceptualize as democracy.
53:14Let me pause there. Yeah, so that's another whole kind of worms I'll leave aside for a moment, which is that actually, yeah, there's actually a lot more conspiracies in the world than people realize. There's a lot of smoke-filled rooms. I've been in plenty of them. Yeah, it's funny. But the thing I just wanted to mention is the thing that was missing in what you said is the key power for me, the key issue for me, which is the presence of positive feedback loops. Now, when I say positive feedback loop, I don't mean good feedback loop. I mean a feedback loop which goes back and causes more of itself.
53:45So power and wealth. Like viral reproduction. Something like that. But like power and wealth are naturally positive feedback loops. Getting more power puts you in a position to be able to get more power. Getting more wealth puts you in a position to get more wealth. and then you've got the cross-correlation, getting more power helps you get more wealth, getting more wealth helps you get more power. I talked earlier about the importance of a stable equilibrium. How can you get a stable equilibrium in a situation where somebody getting ahead can let them get more ahead, right? There's a huge tension here, right?
54:22And this is where democracy and capitalism and the market economy come into a huge problem, right which is if if you allow those positive feedback loops to happen then you end up with people who have incredible riches and incredible power because they're on the right side of that feedback loop you know yes okay and so it's a natural it's a natural disequilibrium and it's not compatible with actual market forces or with democracy because you're now in a situation where you can like you can buy the media you know you can or nowadays like the social networks or whatever you can you know tip all the odds in your favor and that is not again that's not a resilient state to be in so somehow many societies in the world have managed to create sophisticated complex equilibria that have avoided this for decades, you know, but it's not the natural state of things.
55:31The natural state of things is for there to be, you know, one incredibly wealthy and powerful person that, you know, is there because of the power of positive feedback loops. Okay. So let me, let me disagree with that in two ways. And then I mean, counter argument, counter argument. The first is there's a saying like shirt sleeves to shirt sleeves in three generations, right? Which is to say that like this guy, he starts a factory, his son inherits it, and his disparate grandson puts a fortune up his nose and, you know, does drugs and, you know, basically spends on the whole thing, right? And this is like the resource curse concept, where when people get too wealthy or too powerful, they just get extremely lazy.
56:11They forget cause and effect, especially if they're two or three generations out. They don't even know what hard work resulted in that fortune in the first place. And they just blow the whole thing up. And that's actually what's happening with the U.S. right now. Like in many ways, I think the people who are currently running the U.S. government are not founders, they're heirs. They've inherited the system that like better people set up decades and decades ago. They don't even understand how it works. It's like a factory they've inherited and they don't understand how it produces widgets or how it maintains global order, global peace.
56:42And they just think I'm big and powerful and they don't understand why it I think that's true, but it doesn't matter. Because what the data shows is that over multiple hundreds of years periods, the wealthy families stay, the wealthy families. And at like the highest levels of power, you know, like if you look at the history of the, you know, English royal family or whatever, or Chinese emperors, like they stay there for hundreds of years. and they create feudal systems underneath themselves which are critical for establishing loyalty and all that. That's the more natural state of things that things fall into unless you can maintain that equilibrium.
57:29Okay, so I want to counter again, Sep, from an argument that I think is interesting, at least maybe you'll disagree. So if you have an heir, or let's say you have a Genghis Khan, right? They have two, like they have a child, they've got half their DNA. Then another child, they've got a fourth. Then another child, they've got an eighth, right? And most of the time, people don't have an exponentially increasing number of children. So that means that that fortune, for example, would, or whatever it is, it's very hard to pass a fortune down many generations, number one. And number two is that person almost doesn't even exist anymore because their genes are being split up, diluted.
58:05Like, does the person even, in what sense is somebody who's only one-sixteenth part of the same family, right? I think you're dramatically, though, overemphasizing the importance of genes over context. Well, but if they're four generations down, how is it, they've got a bunch of descendants, right? The vast majority of their descendants must, like, what does it even mean to say a family across four or five generations? That family doesn't, like, arguably... That's not how power is transferred, right? So power is transferred by picking an heir. Yes. And then they have an heir, and they have an heir.
58:42And then as soon as there's, like, a lack of a clear heir, then you get 100 years of war, and then somebody wins, and now they have another, you know, heir, heir, heir. Like, that's the thing. they generate a system of hierarchical loyalty, and they do. Like, you can see historically that people do maintain it. But I'd made two points. First is most of their heirs are not inheriting that fortune. So the majority of the family or the descendants or whatever are not, right, because it would be divided. And the second is even this fourth or fifth generation guy is now like 132nd Genghis Khan or what have you.
59:24And so they may just not have the zeal or the energy of the original Genghis, right? Say lose, and then there's a new guy who takes over, right? So basically what I'm saying is it's almost like there's a huge tax, like a 50 % tax every generation. That makes it very hard to keep concentrating the same stuff in the same, because the same people don't even exist three or four. Even if there's some inbreeding. But you see, you've got the premise wrong, right? Your premise is that what better there is the genes. And what I'm saying is, no, Balaji, what matters is the power of the positive feedback loop.
59:58Power begets power. It doesn't matter if I'm five generations away from Genghis Khan. What matters is I'm the king of England or I am the king of France. You know, like you saw what happened in China, hundreds of years of terrible emperors, opium addicts, destroying the country, they still maintain the power, right? And the country went from like during the Tang Dynasty, the vast majority of GDP in the world was in China. Cultural center was in China. Scientific center was in China. And then through power concentration, the civilization died. We don't want that to happen to us. So let me agree with you on that.
1:00:48and I do think that there needs to be alternatives and so on and so forth. I'll just make one other point, which is if that person is only 132nd or 164th Genghis Khan, then there were 31 or 63 other people or families that rose. So the mobility is actually there. If it's a sufficiently exogamous society, then all these folks did rise to become rulers because their bloodlines actually did get up there. So essentially what I'm agreeing with you is the title got passed down, but the family doesn't even exist beyond five, six, four, whatever number of generations. The family just gets diluted out.
1:01:30Does that make any sense? Yeah, but that's what I'm saying. It doesn't matter. What matters is that the positive feedback loop created a power and wealth concentration that was maintained for hundreds of years, and most people in the country suffered. And that's a thing that we want to avoid, and it's incredibly difficult to avoid because that's the natural state of things because of positive feedback loops. I guess maybe, and this is an empirical question, and we can look at different trajectories, but I think it is difficult to maintain that power and wealth concentration without zeal. And that zeal, if it's not there, people get fat and happy a few generations out.
1:02:13Like, we've seen that. I mean, maybe we're just thinking of different kinds of examples, right? And, for example, in tech, it's almost entirely, quote, new money, right? And what I find is that people who've inherited fortunes are just lethargic. They don't have that energy. So we are seeing this internet disruption, this dark talent that's hungrier. I would always invest in that. I would always back that because it's hungrier and it wants it. So I'm almost seeing anti-compounding. And I agree with all that, but I'm trying to get you to think about the end state. Okay, go, go, go. I agree with everything you're saying, right?
1:02:53But what I'm trying to say is, okay, consider the positive feedback loop here, right? With AI now, you've got the ability to create more power, you know, and more wealth, and we're more connected. Like we could literally end up with a global dictator, and we could literally end up with a permanent underclass representing 99.99 % of the world. So let's talk about how we prevent that, right? Because this is something I do think about, right? So my view is, and you may or may just agree with this or not, is that people got more left than they expected. Now they're getting more right than they expected, more MAGA, and then they're going to get more China than they expected.
1:03:37Like basically, I think what's going to happen is China's rolling up a lot of alliances, like the EU is doing deals with China, all its historical rivals in Southeast Asia are now just all folding in. So the whole global economy is recentralizing around China, and America has not just become isolationist, they've isolated itself from the world. And the most punishing, they've sort of self-imposed the most punishing sanctions of all time on themselves. Like a rogue state, North Korea, Iran, would face this kind of embargo, but it was like self-imposed because they think it's going to make them strong.
1:04:08It's really kind of crazy stuff, magamauism or whatever, right? So as a consequence, I think a lot of power get centralized in China. And along with that, interestingly, you're seeing a huge, this kind of cultural isolationism happening in America. Yes. Also like quite difficult to undo, potentially. Extremely difficult, because they don't understand. They're going to end up like Japan pre the Maiji restoration, you know. They thought they're powerful, they thought they're strong, but actually they separate themselves and society and become weak. That's a good outcome. I actually, I think it's quite like.
1:04:39That's a good outcome. Yeah. Fair enough. I think, I mean, because that's actually something where they give up the empire, but they're just like, you know, a country or what have you. Stay isolationist, except they've got nuclear weapons. Well, that's the problem. The thing is, I think, you know, there's a lot of people who will say like, actually both on the left and the right will say, we need to, you know, a republic, not an empire, or we need to shut down, you know. And the problem is that, first of all, maybe you'll agree with these things. I'll give a view and then maybe shoot at it, right?
1:05:08I think the first thing, at least that I start with, is American empire is real. and it was spectacular in the sense of arguably for all its faults, one of the greatest of all time. Absolutely. It did have capitalism, democracy, world peace in many ways. Then lost its way, especially recently. And now you've got a very common kind of thing where the folks on the left think, oh, the U.S. is bombing lots of countries. It should stop doing that. Folks on the right think the U.S. is being exploited by all these foreigners abroad. It's being cheated. We've de-industrialized. We need to stop all that, bring all those jobs.
1:05:40Okay, fine. So this group thinks the U.S. is harming the world. This group thinks the world is harming the U.S. Both of them think they want to shut down the empire, bring the troops home, you know, and so on. Okay. I remember also like during that heyday of the 50s, you know, the American top marginal tax rate was like 80%. 90%. Yeah. Yeah. They're working very hard to avoid this positive feedback loop I mentioned, you know, redistributing the wealthiest. So Mike, Mike, Mike, okay. So on that point, just to talk about that, At that time, though, power was completely centralized in the U.S. government.
1:06:13So you almost have a toothpaste tube squeezing where if you avoid centralization on one axis, you often get it in another kind of thing. That's fair. Because the people who want power will find ways to get it. Yeah, you can have total centralization of government power, or you can have totalization of corporate power, or maybe military power. Or you can have checks and balances. And where I think the world is going to go is a billion person Chinese super state. And then eventually like a thousand million person network states. Like, and then I think India is going to be in the middle. I think there's other countries are going to be in the middle and so on and so forth.
1:06:50But that's, that's where I think things go by like 2040 or so. Right. And, and so hopefully that gives, I'm not saying they're all a million person network states. Some might be bigger, some might be smaller, but I, but I do think that we'll have a lot of choice of jurisdictions. I mean, that would be nice. That's at least the hope. But. Yeah, go ahead. I just got to say, keep thinking about the positive feedback loop problem, because I think it still has it, you know? It feels, you know, rosy to the level of being like, well, that seems not in line with how power dynamics work. I guess my biggest argument against that is arbitrage, because it's very difficult to get, or let me give a game theoretic argument, right?
1:07:33which is going back to your sales example. If you have two people, you have four possible outcomes in a win-lose thing. You can have win-win, win-lose, lose-win, lose-lose. If you have three people, you have two to the third. So eight possible outcomes. Win-win-win, win-win-lose. And you have K people, you have two to the K possible outcomes where N of them can win and N minus K can lose and so on for any value of N and K. So this is how I think about managing a startup. With a startup, if you have 100 people, what you don't want is political behavior where some subset of them loses and the other subset wins.
1:08:11You want to have a single thing which aligns everybody, and that's like equity and that's like the exit. So they all know if I work together, we all get the maximum payoff when it's all win, win, win, win across the board. However, there's limits to how large you can make that. You might make that 100 people. You might make that 1 ,000. and you might make it even a million people, like cryptocurrencies of getting it to tens or hundreds of millions of people, right? But I don't think you can get to everybody. And the reason you can't get to everybody is at some point there is an incentive to break away, to disalign.
1:08:43It's what I call network defect, right? And so that is the counterweight to kind of, I think what you're saying about infinite compounding. It's actually really hard. If you're allowed to. Go ahead. If you're allowed to. Like, I mean, like, yeah, it's like, oh, you know, the people in Wessex could have left or whatever. It's like, no, they're in a feudal state and they would have got killed and there's violence. And like if you add AI in the mix, then you can have like absolute global surveillance and power and total control. Right. So now, okay. It's fine. In theory, you could go and do something else.
1:09:12In practice, if you even talk about it, you get shot in the face. Yeah. So the practical way where I do agree with you is the Chinese drone armada, right? Because they can manufacture huge numbers of robots and those robots are, they're no longer like human beings who can defect, right? Because they can't defect, all these concepts I've been talking about with the game, the principal agent problem goes away and it's just one guy pushing a button and it's like a machine that just enacts their action around the world, right? That is definitely something which changes these dynamics. That is actually something where you could have centralization of power for a long time.
1:09:52And that is actually something we should think of as the most important thing to build counterweights to going three, five, 10. So I think your network states idea can hit that too. So Fast.ai, you've got this practical deep learning for coders, part one, part two. We've done a new course called How to Solve It with Code. And we built a whole new platform for it. We basically beta tested it. We opened up signups for 24 hours, kind of reasonably quietly. A thousand people signed up within 24 hours. So then we closed it. We did that. and the reactions we got were amazing. Like we've had hundreds of people come back and say, this changed my life.
1:10:34I've got a new job. What's your role? Well, it's not open to everybody, but it's solver.fast.ai. So we're trying to figure out how to now make the most of this because we've created something clearly extraordinary. Basically, the fundamental idea, I don't know how familiar you are with the Polio book, but it's basically like... It's a bag of tricks for solving math problems. Yeah, but it's more than a bag of tricks. It's actually a fundamental idea, which is to do things iteratively, step by step. And when you apply that idea to coding, and then you bring AI into the mix as well, we've kind of come up with this way of solving problems with code and AI, where you're constantly in control of the AI.
1:11:18You never get into that situation where the AI is kind of controlling you. Yeah, so like I say, this was from months ago. We haven't let anybody use it for months because we've been running it and testing it. Yeah, so it's a bit of a long story, but basically it's a whole different way of thinking about problem solving, which is the exact opposite of the whole vibe coding kind of style. It's like let's think step by step for humans. Yeah, let's think step by step for human plus AI together. The AI sees all of your thinking. You see the AI is thinking. you write code the ai write code you're constantly focused on learning and iteratively improving you know vibe coding it's just like one shot thing where you don't learn anything you get up more more technical debt so it's actually it's interesting like my co-founder eric reese has this lean startup approach which it turns out is really similar to the polya approach again it's like highly iterative learning based so we're hoping that through this solve it course that we're going to eventually build something like your startup engineering oh great but but using this solve it approach and with with the help of ai to allow and then to create like a thousand new startups from that course and then work with investors to give each of them you know a start financially and maybe hopefully build the next generation of founders amazing and i think um you know would be good i want to actually talk about the network school fellowship with your fast ai folks because i think a lot of them could benefit from applying or what have you so okay awesome thank you very much jeremy thank you sir okay
From the publisher
Jeremy Howard is the creator of fast.ai, perhaps the best way to get a running start in modern deep learning. We talk about AI research, decentralizing education, and funding the world's dark talent. If you're interested in these ideas, especially regarding AI-first education, come to Network School. You can apply online at https://ns.com.
