A (2:26)

I didn't actually know that.

B (2:27)

Yeah, yeah, there was a global Competition called Dawn Bench and we competed against Intel. They had like a classic Dawn Bench. D A W N Bench E, N.

A (2:37)

C H by the. I love. I, I'm friendly with Jeff Dean. I think he's amazing. 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.

B (2:45)

Oh yeah, no, he was, he was great about it. You know, they, they, they published a paper and they accredited us and there's no hard feelings, you know, but we just wanted to say like, no, you don't have to be a rich Google person to, you know, how is that happening? Successful.

A (3:02)

Actually maybe you can talk about that because 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 like. Obviously Google had massive amounts of clean data and huge compute resources and so on. Why could, how could the student projects be competitive with Google during Dawn Bench?

B (3:25)

Because these big labs suffer from being over resourced. In fact, not as bad now, but particularly around that time in the next few years at Google you were explicitly rewarded for using more computer. Where else? We were like, hey, we don't have much money, we made no revenue, we had no grants. It was just my wife and I put our own money into Fast AI.

A (3:52)

Can you explain that to me? How are they rewarded for using.

B (3:54)

So they were basically, if you could use more TPUs, that's like a good tick on your performance.

A (4:00)

No. Really?

B (4:01)

Yeah.

A (4:01)

Wow. Okay.

B (4:02)

So we came along and said, hey, so for example is because they wanted.

A (4:09)

People to use the TPUs since they were.

B (4:10)

Yeah. And they wanted to like show off how big their, their rig was and like 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% of the time, the first 90% of training, we're going to train on 64 by 64 pixel downsized versions. Yeah, makes perfect sense. They look the same. You know, the last 10%, we use.

A (4:44)

Bigger ones that 4x or 16x delta.

B (4:47)

Nobody else thought of that. You know, this is one of the many tricks we used. And why would anybody at like an OpenAI or Google try and do that? Because it's like, oh, well now we're not using our amazing GPUs.

A (5:00)

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 is 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. You're not a secret third thing. Right. But actually, if you think about, like an image, 0 or 1, 1 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. 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. I 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.

B (5:59)

Absolutely, we did. And that. 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?

A (6:08)

Right.

B (6:08)

And if I couldn't see it, then I thought computer probably won't be able to do as well.

A (6:11)

What was it? Was it like, was it 16? Was it 32?

B (6:14)

Was kind of 64.

A (6:15)

64, yeah. Okay.

B (6:16)

Yeah. At 32, you squint. Yeah. You can kind of see it's maybe a dog, but you can't see what kind of dog it is.

A (6:25)

I see. Interesting.

B (6:27)

Yeah.

A (6:27)

Okay. So. Okay, I want to. There's ton, actually. I love. 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 compliment to fast AI. And this also. So I taught a MOOC in 2013 called startup engineering.

B (6:44)

I'm a big fan of it. Yeah.

A (6:45)

Okay, great. So I did that with Vijay Pandey. My colleague is here.

B (6:48)

I'm a big fan of Vijay as well.

A (6:50)

Great. So he's now at the Bio Fund. We've invested a lot of bias stuff together, so we have that overlap as well.

B (6:54)

We're interested in you and Steve Huffman created those two fantastic courses. I don't know if you ever look. I don't know.

A (7:00)

Steve Huffman's course.

B (7:00)

What's his course? Yeah, so similar thing. They were both like kind of end to end, like how to make stuff.

A (7:07)

Oh, okay, got it. And not the Reddit founder. Yeah, he's my friend also. I didn't actually know he had done a course.

B (7:13)

Yeah, so. And neither of them are really available anymore. And they're, you know, I.

A (7:17)

Free web development course by Steve Huffman. Interesting.

B (7:20)

We need a modern one.

A (7:21)

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 will put it online and something like that. I 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, 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 like the, it comes to the dark matter and like the Hubble telescope and you can find the dark matter around the globe or not in the globe, the. In the universe. Right.

B (7:56)

So like gravitational lensing.

A (7:58)

Yeah, exactly, that's right. And so you need like a special telescope to see that. Right. So by analogy, just a fun analogy, the mobile telescope, like the, the phones that billions of people now have allow us to find. If the, 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. Right. Basically, people who really have nothing other than their phone and their hunger to learn.

B (8:24)

Right.

A (8:25)

And we can offer them a course, and that's like a skyhook and a bootstrap.

B (8:28)

That's what Fast AI was about as well. Like, we really reached out to parts of India and Africa and stuff that had nothing. So we had like a guy from the Ivory coast who was like asking like, is there some way to get this on CDs? Because we don't have Internet here. And yeah, turned out like one of our biggest markets was in Lagos.

A (8:50)

It's amazing. So actually I, I have a fair number of folks in, 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 I, I do want to translate into other languages and so on, but I think that's like the V1, right?

B (9:07)

Absolutely. Yeah. No, I mean it. And it, it was just like it. There's all this talent around the world, and it drives me crazy that it's not being used. You know, they're like picking coffee beans or whatever.

A (9:21)

Yeah.

B (9:21)

And as you say, like they've got like so many of them were saying, like, I'm training a neural. Particularly when Colab Google Payload came along. They're like, I'm training a neural net on my phone, you know, through Colab, you know, can you help me do this or that? And I'm just like, oh, this is great. 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. I don't know any other girls that use computers. Everybody thinks I'm weird. I want to know if you think it's okay for me to do AI.

A (10:17)

Oh, she just needed the social encouragement.

B (10:20)

And I wrote back and I said, not only is it okay, but you're going to put your province on the map. And you know what? A couple of years later, she wrote to me from Google in Silicon Valley. Thanks to you, I'm now a Google Scholar. They flew me over to San Francisco.

A (10:37)

What I like to do is I like to find these folks, mentor them, train them, stand them up, and 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.

B (10:48)

Right.

A (10:49)

So to speak. Right. Actually, you know, you can use that. That's a good one liner, you know, because you open with the. You open with the bird thing from, from xkcd. So teach a man to recognize an image of a fish or woman, you know. Right.

B (11:00)

You know, the fish specifically you need to know is the tench. Tench to tench. Anybody who understands computer vision knows about the tench. Yeah. Because Tench is the first ImageNet category. So anybody who's ever worked for ImageNet.

A (11:13)

Right, right.

B (11:13)

Yeah, yeah. So teach a man to recognize a tench.

A (11:16)

Yes.

B (11:17)

Yeah, that's good.

A (11:17)

That's right. Actually, that's like replaced Lena.

B (11:21)

Yes, exactly. Yes, that's right.

A (11:22)

Okay, so let's see now. Why don't you give me the Jeremy life story? So, like before. So I know fast AI, I know Kaggle, I know answer AI. I know the COVID and Masks. What's the answer?

B (11:35)

So before Kaggle. Yeah. So Anthony and I kind of got Kaggle started in Melbourne in Australia and then we flew out here. He 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. It's like, okay, I'll come, I'll give it a go.

A (12:01)

But you know, does Kaggle have some. Is it an Australian is just like a funny word? Made up word.

B (12:09)

Just a made up word.

A (12:10)

Okay, yeah, like Google Kaggle.

B (12:12)

Yeah, yeah. And yeah, we spoke to some of your old colleagues. We spoke to Mark Andreessen and it was interesting at that time Andreessen 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 Bernard Khoestler and other folks put the money in. But before that I had two startups 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 insurance, you would definitely know and if you are not, you definitely wouldn't. It basically changed how insurance companies price away from using just actuarial methods to using optimization based methods like convex optimization.

A (13:08)

Or something like that.

B (13:09)

Yeah, yeah, just 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 make 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. This is a bit over 30 years ago. I didn't know any other adults or kids that were interested in any of.

A (13:59)

Those things in Australia.

B (14:00)

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 & Co. Where they really appreciated this odd set of skills I.

A (14:21)

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. And 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.

B (15:12)

That. Oh, I mean, you know, I was in this unusual.

A (15:14)

Situation. Sorry to be negative. I didn't mean. It's like the pro and.

B (15:16)

Con. No, I love, I love, like, please, like, challenge.

A (15:19)

Me. Okay, go.

B (15:20)

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.

A (15:30)

So. Oh, really? Wow, that's, that's interesting.

B (15:33)

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. Just one of the reasons why as a 19 year old I could be really successful there, you.

A (15:53)

Know. Did you feel you leveled up when you were.

B (15:57)

There? Yeah. 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 it's like 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.

A (16:10)

Companies. Yeah, you're very commercial for a.

B (16:13)

Professor.

A (16:14)

Yeah. Professor.

B (16:15)

Type. Yeah, yeah. Well, I mean, I never went into, I've never been a professional academic in my.

A (16:20)

Life. Right. But you've got, you've got the. I think we both have that.

B (16:23)

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. And they'd be like, I don't know, Jeremy, this computer stuff, this is pretty nerdy. What's it for? They're like, I don't know exactly, but I feel like just 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.

A (17:13)

Time. You didn't know whether you were.

B (17:14)

Wrong or they were wrong. I was sure I was wrong. And I just kept trying to figure out why I'm so wrong. I 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. And I just couldn't get it. So I wish I had. You know, I stayed in Consulting for 10.

A (17:35)

Years. Oh, really?

B (17:36)

Wow. I should have done it just two, because that's enough. And, like, what I really learned there was sales. Like, it's really great for learning.

A (17:47)

Sales. What did you.

B (17:49)

Like? I don't.

A (17:50)

Know. What are the top three, five things you learn in McKinsey? Like.

B (17:53)

Sales. Okay, yeah. So I was. And at Kearney. So I went from there to. At Kearney. What. What I learned was like, okay, it's all about change and influence, right? So 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 changes through human management, human connections. We brought professional actors in to play the role of different types of clients. And we would then interact with them and 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, 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 and how to react. In some ways it.

A (19:34)

Felt. It's like graph.

B (19:36)

Traversal. In some ways, yeah. But 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.

A (19:50)

Worked.

B (19:51)

Yes. And so it made me, after a while, I changed my view of it. I was like, you know what? Getting organizations to do things is.

A (20:01)

Important. It is.

B (20:02)

Important. And so if that involves treating people as machine parts sometimes. Because humans are very.

A (20:13)

Predictable.

B (20:13)

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 your champion or whatever, and they've recognized that they can use you to advance their, their career. And 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 be somebody who likes following rather than leading. And you structure it out, it all play out and at the end it's like, okay, it.

A (20:51)

Happened. It's funny the way. Do you know Mark Craney at a six and Z? 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 a little bit is it's a nested set of like win win relationships all the way up to the organization level. Right. Like 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. Right. And then it will also improve at a nested level the career of this person who approves it. And so, and so it's almost like a, 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.

B (21:39)

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.

A (21:54)

World. Like that's actually. You know what's funny? One of the. 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.

B (22:11)

It. Oh yeah, we did a lot of marketing stuff there. We. We also, as far as I know, we were the first company in the world to do a B tests on our.

A (22:18)

Homepage. Oh, is that right?

B (22:20)

Interesting. I think we were also the first to have all the free email accounts. A little footer would be added to every email message marketing the service. We did a lot of little things like that. Things like that. Little viral things that today are.

A (22:36)

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, for like a workout. Right. People aspirationally want to do it and then they want to have done it. They want to have done it. Yes, exactly. That's.

B (23:07)

Right. Yes. And then they want to be the kind of person that would have done.

A (23:11)

That. That's right. There's something good out of that. Right. But 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 have 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.

B (23:29)

Extent. Cohorts are.

A (23:30)

Great. Yes. So that, that can work. But let me show you something that we did which we call a learn a thon. When should you use a random.

B (23:39)

Forest? No.

A (23:39)

Clue. What is a confusion matrix? What about collaborative.

B (23:44)

Filtering? Don't.

A (23:45)

Know. When could you Use a random forest, tabular data. And if you have a lot of, like, noisy features, what is a confusion matrix? It's like a table of actual answers against, like the predicted answers and then comparing, you know, like, 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, you know, updated version of that, but basically so the fastest. So essentially, literally we took. Because what is it, like about 10 hours, 11 hours of videos.

B (24:36)

Right?

A (24:37)

Yeah. So over two days, we said, okay, you really want to do fast? Okay, sign up. Come here. 9:00am on, on Saturday morning and 9 to 9 Saturday, 9 to 9 Sunday, they watch every single video, start to finish. No.

B (24:52)

Phones. Yeah, right.

A (24:53)

Yeah. And then when it was time to go and type things in, you know, laptops out, do.

B (24:58)

That. Absolutely. 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.

A (25:14)

Done. Yes, exactly. And I want to. Did I show you the fellowship video? Okay, hold on. Take a look at.

B (25:21)

This.

A (25:21)

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. Now, you 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. And 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 set up housing, food, coworking 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@ennis.com the only connection you need is an Internet.

B (26:13)

Connection. That's very inspiring. I want to.

A (26:17)

Come. Great.

B (26:19)

So. So also, Malaysia is awesome. So go to.

A (26:22)

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 is a lot of capital but doesn't have a lot of.

B (26:37)

Land.

A (26:37)

Yeah. Malaysia is actually improving a lot. Yeah, but it.

B (26:41)

Doesn'T. I mean, Malaysia's got a good education system, it's a strong country, very.

A (26:46)

Underrated, and it's improving a lot. And you can basically live a pretty good life.

B (26:50)

There. I. Super.

A (26:51)

Good. And it's right next door.

B (26:52)

Right?

A (26:53)

Yeah. So Malaysia has land, it has.

B (26:54)

Less. You can literally drive.

A (26:56)

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, you can see Singapore directly from. From.

B (27:05)

It.

A (27:05)

Right. So. And we'll have, probably have a ferry or something back and forth. That'll get down to like 15 minutes. Yeah. So I want like these autonomous boat kind of things.

B (27:13)

Right? Yeah, why.

A (27:14)

Not? Yeah. So those. Knock on wood. Let's get, let's get that. Right. So this is something, what you were 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, let me explain kind of the motivation behind what we're doing in network school. Right. So a, it's very hard obviously now to get student visas, skilled worker visas into the U.S. i mean, even like people who are tourist visas, like they're getting strip searched or crazy things happen. You saw there's actually some Australian or what have you, like some terrible thing happened to them or.

B (27:57)

Right. And even I think every, almost every country now has some story, examples of people, citizens of their country that have been screwed.

A (28:04)

Around. Tourist visas, student visas, skilled worker.

B (28:08)

Visas, like the US and in Southeast Asia, these countries are now competing for that talent with their digital visas, with their startup.

A (28:16)

Visas.

B (28:17)

Exactly. It's so.

A (28:18)

Smart. This is exactly. That's right. And this is the.

B (28:21)

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.

A (28:26)

Good.

B (28:27)

So. Yeah, but everybody needs to do.

A (28:29)

This. You know, the, the country's offering digital nomad visas. Right. So there's this weird thing where the US is taking itself out of the global.

B (28:36)

Economy.

A (28:37)

Yeah. Just as everybody.

B (28:38)

Else. As everybody else is diving.

A (28:40)

In.

B (28:40)

Exactly. That's all of America's big value creators are.

A (28:45)

Tech. That's right, exactly. And they're globally mobile because there's no silicon in Silicon Valley. We're not like.

B (28:50)

Mining. So our team Answer AI is fully distributed. So we have folks in, in Turkey, Japan, Australia.

A (29:00)

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. And one of the things we want to do is like colocation for remote.

B (29:09)

Teams. That's a nice idea because like we've got together for the first time ever in person here in.

A (29:13)

Singapore.

B (29:14)

Oh. And we're all like, oh, it's so nice to spend a week together. Eric Rees and I@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.

A (29:31)

Policy. Okay, what is that.

B (29:34)

Policy? The policy is to only have one.

A (29:37)

Policy. Oh, is it very meta? Is this like one of those recursive kind of.

B (29:41)

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.

A (29:49)

Policy. Okay.

B (29:51)

Okay. So we have no policies other than the policy that we're only going to have one.

A (29:55)

Policy. I see. Okay, got.

B (29:57)

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 we've decided X in this situation, this is how you're meant to behave. I am equally skeptical of ideologies and policies and all of these cognitive shortcuts that basically say, oh, I believe in this thing because that's what my ideology.

A (30:25)

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 programming paradigms. You can have imperative programming, functional programming, declarative programming, and so on and so forth. Right. And for certain problem domains, you know, 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, and so that's how I think about political paradigms. Right. Like an Ariana. She 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. Do 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, Andrew.

B (31:50)

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.

A (32:09)

Tribe. That's.

B (32:09)

Right. Number one driving.

A (32:11)

Force. And the thing about that is there's, there's a meta rationality to that. I think it's kind of like, you know, like evolutionary game theory. Right. So like you can imagine you have two populations of people who are conformists and dissidents, so to speak. Right. And the distance are constantly exploring and they're taking high risk and sometimes they fall off a cliff and sometimes they have reward and the tribe follows them. Right. And the conformists are just, you know, they're like, this is, this is risk capital and this is just, you know, stay home money or what have you, so to speak. Right. So 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 go with the tribe so they don't run off a cliff, but they could actually find a better pasture or something over here. That's one way of thinking about the respective balance. Go.

B (33:04)

Ahead. Yeah, I mean, I'm kind of curious about this because globally, somehow every jurisdiction has settled on the same education system. And the education system teaches children to be.

A (33:16)

Conformist.

B (33:17)

Yes. If you, if you, you know, the test tests whether you can feedback the things you are taught in the way that you are 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.

A (33:40)

Same have you heard that comes to the Prussian educational.

B (33:44)

System?

A (33:45)

Yeah. Okay. Do you know what preceded.

B (33:47)

That?

A (33:47)

No. Okay. So there's this great book, we can put it on screen, called the Craft Apprentice. Okay. And one of my macro kind of theories of the world is that history is running in reverse. And 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. It'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.

B (34:44)

About. Right.

A (34:44)

Right. And, and there's a reason for that because then everybody kind of has the same references. They, they salute the flag and you know, they've just got the same basic install and they can interoperate. Right. There's, 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. Which is like the physical territory and the people and so on. But before that, there's 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 is, wasn't there. And now the reason that that stuff got introduced in part is because I think in the, 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 and so forth. And that was a disalignment between like the interest of the factory owner and the kids. That's when the child labor laws were passed and so.

B (36:06)

On. I mean that took a long.

A (36:07)

Time. It took a long.

B (36:08)

Time. It's like what Was it, like 60, 70 years? Britain was the first in the world to introduce child labor laws. But yes, still took much longer than it should.

A (36:16)

Have. That's right. This old Dickensian kind of era or what have you. Right. So then, so now there was a good to that at first, but then that's what actually led to the modern era of adolescence. And you know, 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 before they, you know. And I think that the, the, 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 they're in 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. Ten, 12 years ago were, were kids who had actually earned their first dollar doing online programming in their teens. Right. And it's not even so much about the amount of money. It is that, it's that the market is a greater. This is how I think with. Have you seen the great inflation graphs? Yeah. So like you know, 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, 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. 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, they 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.

B (38:22)

Thoughts? I 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.

A (38:38)

Them. Oh.

B (38:39)

Interesting. It's like we don't. For some reason, the vast majority of adults I deal with don't want children to learn when they're ready to learn. They have to learn at the speed which they're expected to.

A (38:50)

Learn. They want a speed.

B (38:51)

Limit. Yeah. 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 dark 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. So I've got a kid, she's 9, and she's. We let her basically have whatever opportunities she wants, and she chooses her curriculum and she chooses what she does. And she's happy for us to provide her some guidance as well, 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 and get a much more customized, personalized, dynamic education experience, one that's not focused on conformity or authority. Sometimes my daughter comes back, she does lots and lots of extracurricular things. One of them is trampolining. She comes back from trampolining Sometimes she'll be like, oh, I got a gold star for good behavior. Isn't that great? And I would say, like, I don't know, I'm not sure I want you to have great behavior. Why do you think that's so important to have great.

A (40:59)

Behavior? Well, of course it depends. Obviously a layer of dissidents and so on, on top of a fundamentally primary pro social attitude is good. But if people are like antisocial and they're littering or they're, you know, yelling in the street.

B (41:14)

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.

A (41:25)

Of. Right.

B (41:26)

Right. You know, 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.

A (41:36)

It. That's right. And this is, it's funny you.

B (41:38)

Say this because basically, particularly for a girl like, like, 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.

A (41:51)

Told. I think, I 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.

B (42:02)

Command. And so, so yeah, I tend to focus on empathy with my daughter, which maybe ends up in a similar place. 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 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 other person did to me that made me sad? Like, oh, wow, I feel so sad. I didn't want to make upset that.

A (42:39)

Person. It's funny because, you know, sometimes you can get to like, just like with religions, you can often get to a similar behavior pattern by different kinds of religion information. So I had a recent tweet a little bit viral on, on actually that exact topic of empathy. And essentially what I said is because I was, 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, 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's an enemy and Vietnam is an enemy and whatever, right? 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.

B (43:49)

Can. And actually altruism is programmed into us. This is something we've discovered evolutionarily. It's been programmed into all of us. To not be altruistic is to fight against your basic instincts. And that's really dangerous because when you fight against things that evolution has programmed you to do, you're creating a new unstable.

A (44:13)

Equilibrium.

B (44:14)

Yes. So why has that happened? Well, presumably there were plenty of groups that had no altruism in their villages, just genetically. They didn't have that as part of their.

A (44:25)

DNA. They couldn't cooperate and they died.

B (44:27)

Out. They died out. And so we as a species, we're not perfect. But you don't want to underestimate the power of what we're born with when we're born. 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. They arise over a very long period of time to create these marvelously stable equilibria. And this is what kind of terrifies me at the moment, is there are so many opportunities to destabilize that equilibrium right now with technology and the connectivity we have. And historically, each time you get a previously stable equilibrium is damaged, sometimes ending up with hundreds of years of societal misery. And so I always just, 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 destabilizing an equilibrium creates a power vacuum. And there are certain people who are extremely motivated and good at taking advantage of power vacuums. And they're pair the people you definitely don't want in power, you.

A (46:05)

Know?

B (46:05)

Yeah. 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 basically a deeply insecure chip on their shoulder. Power hungry.

A (46:33)

Person. 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, sound bites 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? Because 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, other folks in East Asia who delivered comparable economic results to lky. Right? For example, in, 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 Elk. They could speak in Korean, they could speak in Chinese. But like they couldn't, they couldn't make their case on a global stage. Right? And, and I think that's very underrated and it's something I think about a lot because. So let me, let me actually slightly counter argue with you on the power vacuum thing, which is there, Right. I think that we are about to enter a period where the, the future is China versus the Internet. Should I elaborate on what I mean.

B (48:01)

That? China versus the.

A (48:02)

Internet. China versus the Internet. 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 US versus USSR. Everybody slugs it out, right. Sean McMeeken 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. And then it kind of lands up as the US vs USSR right. With Japan and Germany eliminated and, and, and other powers too. Us, 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, what do I 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, not all the money, but the manufacturing, the military and really the might of it globally, like lot the alliances and so on. The world is after this tariff thing. Recentralizing around.

B (49:09)

China.

A (49:10)

Totally. Right.

B (49:10)

Quickly. It'll be interesting to see how permanent that is. But it's, it's something very deep happening.

A (49:16)

There. Yeah, so, so I think what's.

B (49:18)

Going to happen and it's not just economically, also culturally, you know, America's cultural power has been.

A (49:23)

Enormous. It, it has been. That's.

B (49:25)

Right. And now in Australia I'm seeing people being like, oh, America's kind of cringe.

A (49:30)

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. Okay. And the reason I say that is the only thing that has economic scale comparable to China is actually the Internet. Like so that's that. Why, 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 immigrating to at least half of what a government is. Right. It's not the land, it's not the physical territory yet, I'll come to that. But it's at least the property.

B (50:20)

Rights.

A (50:21)

The. And you have to have some sacrifice, you have to buy some of the coin or whatever. You start to 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 is 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, the, the, 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. And with all these blockchains, multiple systems of government. And I actually compare it. One of the ways I think about it is, you know, with early America. It didn't actually think of itself as America at first. They were British colonists. Right. They were, they're, you know, like the, the Virginia colony, Massachusetts colony. And they had a land and they had a people, but they didn't have a.

B (51:05)

Government.

A (51:06)

Right. Because the government was in London and 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 they had land, people, government. They became America. Right. I think the Internet is evolving in the opposite way. It has the people and actually as a government in the form of the blockchain, but doesn't yet have land. I think that's the next step.

B (51:26)

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.

A (51:39)

Realized. Talk about.

B (51:41)

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.

A (51:55)

The. The.

B (51:56)

Party.

A (51:56)

Party.

B (51:56)

Yep. Now people might think, oh, that's not very.

A (51:58)

Big. It's actually 100 million people. Chase guy's very big. Yeah. And then you go to.

B (52:02)

The. And it's not. And like of my. So I spent 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 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 kind of a democracy of the hardest working, most intellectually capable.

A (52:29)

People. Can I make a provocative comment? So there's a book called the Party Decides. 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. And 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's a whole disaster with the whole Biden Kama thing. So there's more similarity to the American system for many years where there was essentially a unit party that decided like who, who the candidates were then, then some would argue and now I'd say in a sense we've had true democracy burst forth, but that some people conceptualize this democracy. Let me pause.

B (53:14)

There. Yeah, so, yeah, so that's another whole kind of worms I'll leave aside for a moment, which is that actually. Yeah, there's, 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.

A (53:26)

Them. Yeah.

B (53:27)

It'S. 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. So power and wealth, like viral reproduction, something like that. But 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.

A (54:17)

Ahead. The compounding.

B (54:19)

Interest. There's a huge tension here, right? And 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.

A (54:47)

Know? Yes.

B (54:48)

Okay. 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, dip 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. But it's not the natural state of things. The natural State of things is for there to be one incredibly wealthy and powerful person that is there because of the power of positive feedback.

A (55:41)

Loops. Okay, so let me, let me disagree with that in two ways. And then counterargument, Counterargument. 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 dissipate grandson puts a fortune up his nose and does drugs and basically spends it 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. They 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 US right now. Like in, in many ways. I think the people who are currently running the US government are not founders, they're heirs. They've inherited the system that 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. And they just think, I'm big and powerful, and they don't understand why it.

B (56:45)

Exists. 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 of Chinese emperors, like they stay there for hundreds of years, you know, and they create. 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.

A (57:28)

Equilibrium. Okay, so I'm on a counterargi concept from an argument that I think is interesting at least maybe to. To, you know, maybe you'll disagree. So if you have an heir, or you. Let's say you have a, like 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 their 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.

B (57:54)

Would.

A (57:55)

Or. 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. Like does the person even. In what sense is somebody who's only 1 16th part of the same family.

B (58:11)

Right. I think you're dramatically though over emphasizing the importance of genes over.

A (58:19)

Context. So like if they're four generations down, how is it if 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, it's not.

B (58:35)

How power is transferred, right? So power is transferred by picking an air. And then they have an air and they have an air. And 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 air, air, air. Like that's the thing. They generate this system of hierarchical loyalty. And they do. Like you can see historically, sure, people do maintain.

A (59:06)

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 4th or 5th generation guy is now like 1 32nd Genghis Khan or, or what have you. And so they may just not have the, the zeal or the energy of the original Genghis, right? Say lose and then there's a new guy who, who takes over. Right. So basically what I'm saying is it's almost like there's. 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 with there's some.

B (59:48)

Inbreeding. You've got the premise wrong. Your premise is that what mattered there is the genes. And what I'm saying is no biology. What matters is the power of the positive feedback loop. Power gets begets power. It doesn't matter if my 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 saw what happened in China. Hundreds of years of terrible emperors, opium addicts destroying the country. They still maintain the power. And the country went from during the Tang dynasty, the vast majority of GDP in the world was in China. Cultural center was, was in China, Scientific center was in China. And then through power concentration the civilization died. You know.

A (60:43)

Sure.

B (60:44)

So. So we don't want that to.

A (60:45)

Happen. So, so let me agree with you on that. And 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 1 32nd or 164th Genghis Khan, then there were 31 or 63 other people or families that rose. So, so, like the mobility is actually there. If there's, it's. 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 basically what I'm essentially, what I'm not, what I'm agreeing with you is the title got passed down, but the family doesn't even exist beyond 5, 6, 4, whatever number of generations. The family just gets diluted out. Does that make any.

B (61:30)

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.

A (61:56)

Loops. I guess, maybe, and this is an empirical question, 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. Like, 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, right? They don't have that energy. So we are seeing this Internet disruption, right? 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, right? Whereas. So I'm only seeing. Seeing anti. Compounding. I'm almost seeing, I.

B (62:42)

Guess. Yeah. And I agree with all that, but I'm trying to get you to think about the end.

A (62:47)

State. Okay, Go, go.

B (62:48)

Go. Like, I agree with everything you're saying, right? But 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 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.

A (63:19)

World. So let's talk about how we prevent that. Right, Because I, because this is something I do think about, right. So my view is, and you may disagree with this or not, is that we got people got more left than they expected. Now they're getting more right than they expect more MAGA and then they're going to get more China than they expected. Like basically I think what's going to happen is China is 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. It's really kind of crazy stuff, maga, Maoism or whatever. Right. So as a consequence I think a lot of power gets centralized in.

B (64:14)

China and along with that, interestingly you're seeing a huge, this kind of cultural isolationism happening in America which is also like quite difficult to.

A (64:26)

Undo. Potentially extremely difficult because they don't.

B (64:28)

You'Re going to end up like Japan. Pre the my g restoration they thought they're powerful, they thought they're strong, but actually they separate themselves from society and become.

A (64:36)

Weak. That's a good outcome. I actually, I think it's quite.

B (64:38)

Like that's a good outcome. Yeah, fair enough, I think, I.

A (64:42)

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.

B (64:48)

Isolationistic. Except they've got nuclear.

A (64:50)

Weapons. Well, that's, that's a problem. The thing is, I think, you know, there's a lot of people who'll 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 you shoot at it. Right. I 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.

B (65:17)

Time.

A (65:17)

Absolutely. It did have capitalism, democracy, world peace in many ways then lost its way, especially recently and and now you've got a very common kind of thing where the folks on the left think, oh, the US is bombing lots of countries, it should stop doing that. Folks on the right think this is, is, is being exploited by all these foreigners abroad. It's, it's being cheated. We've de. Industrialized. We need to stop all that, bring all those jobs. Okay, fine. So this group thinks the US is harming the world. This group thinks the world is harming the US Both of them think they want to shut down the empire, bring the troops home, you know, and so on.

B (65:50)

Okay. But remember also like during that heyday of the 50s, you know, the American top marginal tax rate was like 80%, like 90%. Yeah. They're working very hard to avoid this positive feedback loop. I mentioned, you know, redistributing the.

A (66:05)

Wealthiest. Okay, so on that point, just to talk about that, at that time though, power was completely centralized in the US Government. Right. So you almost have like a toothpaste tube squeezing where like if you avoid centralization on one axis, you often get it in another kind of.

B (66:21)

Thing. That's fair. Right. So because, because the people who want power will find ways to get.

A (66:27)

It. Yeah. You can have total centralization of government power or you can have totalization of corporate power or maybe military power. And so, 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 that are going to be in the middle and so on and so forth. But that's, that's where I think things go by like 20, 40 or so. Right. And, and so hopefully that gives, I'm not saying they're all a million person networks. Some might be bigger, some might be smaller, but I, but I do think that we'll have a lot of choice of.

B (67:03)

Jurisdictions. I mean that, that would be.

A (67:05)

Nice. That's at least the hope. Yeah, go.

B (67:08)

Ahead. I just got to say, keep thinking about the positive feedback problem because I think it still has it, you know, it feels, you know, rosy to the level of being like. Well, that's, that seems not in line with how power.

A (67:24)

Dynamics. I guess. 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. Which is going back to your sales example, right. 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. Right? If you have three people, you have two to the third. So eight, eight possible. Win, win, win, win, win, lose. Right? And you have k people. You have two to the k possible outcomes where, you know, n of them can win and N minus K can lose and so on for any value of N and K. Okay? So this is how I think about, like, managing a startup, right? With a startup, if you have a hundred people, what you don't want is political behavior where some subset of them loses and the other subset wins. You 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 across the board. Right. However, there's limits to the. To. To how large you can make that, right? You might make that 100 people, might make that a thousand. 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 breakaway, to disalign. It's what I call network defect. Right? And so that is a counterweight to kind of, I think what you're saying about infinite compounding, it's actually very.

B (68:50)

Well, 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.

A (69:01)

And.

B (69:01)

Right. And like, if you add AI in the mix, then you can have, like, absolute global surveillance and power and total.

A (69:07)

Control. Right? So now, okay, so.

B (69:09)

Here'S. It's fine in theory, you could go and do something else. In practice, if you even talk about it, you get shot in the.

A (69:16)

Face. Yeah, right. 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 and power for a long time. And that is actually something we should think of as the most important thing to build counterweights to going 3.

B (69:57)

5. So I think your network states idea can hit that.

A (70:00)

Too. So fast AI, you've got this practical Deep learning for Coders part one, part.

B (70:06)

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, I've got a new.

A (70:35)

Job. What's.

B (70:36)

URL? Well, it's not open to everybody, but it's solveit 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.

A (70:53)

Basically like it's a bag of tricks for solving math.

B (70:55)

Problems. Yeah, but it's more than a bag of tricks, it's actually a fundamental idea which is that 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. You never get into that situation where the AI is kind of controlling you. Yeah. So like I said, 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.

A (71:40)

Of. It's like let's think step by step for.

B (71:42)

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 Greece 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.

A (72:25)

Engineering. Oh.

B (72:26)

Great. But using this solve it approach and 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.

A (72:45)

Founders. Amazing. And I think, 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. Let's try.