Lex Fridman Podcast - #162 – Jim Keller: The Future of Computing, AI, Life, and Consciousness

Episode Date: February 18, 2021

Jim Keller is a legendary microprocessor engineer, previously at AMD, Apple, Tesla, Intel, and now Tenstorrent. Please support this podcast by checking out our sponsors: - Athletic Greens: https://athleticgreens.com/lex and use code LEX to get 1 month of fish oil - Brooklinen: https://brooklinen.com and use code LEX to get $25 off + free shipping - ExpressVPN: https://expressvpn.com/lexpod and use code LexPod to get 3 months free - Belcampo: https://belcampo.com/lex and use code LEX to get 20% off first order EPISODE LINKS: Jim's Twitter: https://twitter.com/jimkxa Jim's Wiki: https://en.wikipedia.org/wiki/Jim_Keller_(engineer) Tenstorrent: https://www.tenstorrent.com/ PODCAST INFO: Podcast website: https://lexfridman.com/podcast Apple Podcasts: https://apple.co/2lwqZIr Spotify: https://spoti.fi/2nEwCF8 RSS: https://lexfridman.com/feed/podcast/ YouTube Full Episodes: https://youtube.com/lexfridman YouTube Clips: https://youtube.com/lexclips SUPPORT & CONNECT: - Check out the sponsors above, it's the best way to support this podcast - Support on Patreon: https://www.patreon.com/lexfridman - Twitter: https://twitter.com/lexfridman - Instagram: https://www.instagram.com/lexfridman - LinkedIn: https://www.linkedin.com/in/lexfridman - Facebook: https://www.facebook.com/LexFridmanPage - Medium: https://medium.com/@lexfridman OUTLINE: Here's the timestamps for the episode. On some podcast players you should be able to click the timestamp to jump to that time. (00:00) - Introduction (07:02) - Good design is both science and engineering (13:03) - Javascript (17:09) - RISC vs CISC (21:09) - What makes a great processor? (22:38) - Intel vs ARM (24:27) - Steve Jobs and Apple (27:05) - Elon Musk and Steve Jobs (32:50) - Father (36:33) - Perfection (42:48) - Modular design (48:22) - Moore's law (55:20) - Hardware for deep learning (1:02:14) - Making neural networks fast at scale (1:09:51) - Andrej Karpathy and Chris Lattner (1:14:05) - How GPUs work (1:18:12) - Tesla Autopilot, NVIDIA, and Mobileye (1:22:52) - Andrej Karpathy and Software 2.0 (1:29:13) - Tesla Dojo (1:31:49) - Neural networks will understand physics better than humans (1:34:02) - Re-engineering the human brain (1:38:56) - Infinite fun and the Culture Series by Iain Banks (1:40:50) - Neuralink (1:46:13) - Dreams (1:50:06) - Ideas (2:00:19) - Aliens (2:05:16) - Jordan Peterson (2:10:13) - Viruses (2:13:22) - WallStreetBets and Robinhood (2:21:25) - Advice for young people (2:23:15) - Human condition (2:25:43) - Fear is a cage (2:30:34) - Love (2:36:57) - Regrets

The following is a conversation with Jim Keller, his second time in the podcast. Jim is a legendary microprocessor architect and is widely seen as one of the greatest engineering minds of the computing age. In a peculiar twist of space time in our simulation, Jim is also a brother-in-law of Jordan Peterson. We talk about this and about computing, artificial intelligence, consciousness, and life. Quick mention of our sponsors.

A flat of greens all in one nutrition drink, Brooklyn and Sheets, ExpressVPN, and Bell Campo Grass-fed meat. Click the sponsor links to get a discount to support this podcast. As a side note, let me say that Jim is someone who on a personal level inspired me to be myself. There were something in his words on and off the mic, or perhaps that he even paid attention to me at all, that almost told me, you're right kid, a kind of pat on the back that can make the difference between a mind that flourishes and a mind that is broken

down by the cynicism of the world. So I guess that's just my brief few words of thank you to Jim and in general gratitude for the people who have given me a chance on this podcast and my work and in life. If you enjoy this thing, subscribe by YouTube, review it on Apple Podcast, follow on Spotify, support our Patreon or connect with me on Spotify, support on Patreon, or connect with me on Twitter, Alexa Friedman. As usual, I'll do a few minutes of ads now and no ads in the middle. I try to make these interesting, but I give you time stamps, so if you skip, please

still check out the sponsors by clicking the links in the description. It is the best way to support this podcast. This show is sponsored by Afflec Greens, the all-in-one daily drink to support better health and peak performance. It replaced the multivitamin for me and went far beyond that with 75 vitamins and minerals. I do intermittent fasting of 16-24 hours every day and always break my fast with Athletic Greens. I'm actually drinking it twice a day now, training for the Goggins challenge. I can't say enough good things about

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Furnold. Follow her on Instagram or wherever else she's active because she happens to be a brilliant chef and just has a scientific view of agriculture and food in general, which I find fascinating and inspiring. Anyway, you can order bell camphor sustainably raised meats to be delivered straight to your door using code Lex at bellcampo.com slash Lex for 20% off the first time customers. That's code Lex at bellcampo.com slash Lex. Trust me the extra bit of cost is worth it. And now here's my conversation with Jim Keller. What's the value and effectiveness of theory versus engineering this dichotomy in building good software or hardware systems?

Well, it's good designs both. I guess that's pretty obvious. By the engineering do you mean, you know, reduction to practice of known methods and then science is the pursuit of discovering things that people don't understand. Or solving unknown problems. Definitions are interesting here, but I was thinking more in theory, constructing models that kind of generalize about how things work. And engineering is actually building stuff, the pragmatic like,

okay, we have these nice models, but how do we actually get things to work? Maybe economics is a nice example. Like economists have all these models of how the economy works and how different policies will have an effect, but then there's the actual, okay, let's call it engineering of like actually deploying the policies. So, computer design is almost all engineering and reduction of practices and all of the policies. So a computer design is almost all engineering

and reduction of practice and all of the methods. Now, because of the complexity of the computers we build, you could think, well, we'll just go write some code and then we'll verify it and we'll put it together and then you find out that the combination of all that stuff is complicated and then you have to be inventive to figure out how to do it. Right, so that's definitely happens a lot. And then

every so often some big idea happens, but it might be one person. And that idea is in what in the space of engineering or in the space of? Well, I'll give you an example. So one limits the computer performance as branch prediction. So and there's a whole bunch of ideas about how good you could predict a branch and people said there's a limit to it's an asphalted curve and somebody came up with a better way to do branch prediction. It's a lot better. And he published a paper on it and every computer in the world now uses it. And it was one idea. So the engineers who build branch prediction hardware

were happy to drop the one kind of training array and put it in another one. So it was a real idea. And branch prediction is one of the key problems underlying all of the lowest low of software it boils down to branch prediction. Boils down the uncertainty. Computers are limited by single you know, single thread computers are limited by two things.

The predictability of the path of the branches and predictability of the locality of data. So we have predictors that now predict both of those pretty well. Yeah. So, memories, you know, a couple hundred cycles away, local cash, this couple cycles away, when you're executing fast, virtually all the data has to be away, local cash, this couple cycles away, when you're executing fast virtually all the data has to be in the local cash. So a simple program says, you know, add one to every element in array,

it's really easy to see what the stream of data will be. But you might have a more complicated program that's, you know, so get an element of this array, look at something, make a decision, go get another element, it's kind of random, and you can think that's really unpredictable. And then you make this big predictor that looks at this kind of pattern and you realize, well, if you get this data and this data, then you probably want that one. And if you get this one and this one and this one, you

probably want that one. And is that theory or is that engineering? Like the paper that was written, was it a asymptotic kind of kind of discussion or is it more like, here's a hack that works well? It's a little bit of both. Like there's information to your unit, I think somewhere. So it's actually trying to prove. But once you know the method, implement it,

it's an engineering problem. Now there's a flip side of this, which is, in a big design team, what percentage of people think there, there, there, there, there a big design team, what percentage of people think they're, they're, they're, they're, they're, their plan or their life's work is engineering versus design, inventing things. So lots of companies will reward you for filing patents. Yes. Some many big companies get stuck because to get promoted, you have to come up with something new. And then what happens is everybody's trying to do some random

new thing, 99% of which doesn't matter. And the basics get neglected. And, or they get to, there's a dichotomy they think, like the cell library and the basic CAD tools, you know, or basic, you know, software validation methods, that's simple stuff, you know, they want to work on the exciting stuff. And then they spend lots of time trying to figure out how to pat and something. And that's mostly useless. But the breakthroughs are on the simple stuff.

No, no, you have to do the simple stuff really well. If you're building a building out of bricks, you want great bricks. So you go to two places to sell bricks. So one guy says, yeah, they're over there, and ugly pile. And the other guy is like lovingly tells you about the 50 kinds of bricks and how hard they are, and how beautiful they are, and how square they are, which one you go buy bricks from, which is going to make a better house. So you're talking about the craftsman, the person who understands bricks, loves bricks, loves the right. That's a good word.

You know, good engineering is great craftsmanship. And when you start thinking engineering is about invention, and set up a system that rewards invention, the craftsmanship gets neglected. Okay, so maybe one perspective is the theory, the science, over emphasizes invention and engineering emphasizes craftsmanship and therefore, like, so if you, it doesn't matter what you do with theory. Well, everybody, like read the tech rags, they're always talking about some breakthrough or intervention, innovation and everybody thinks that's the most important thing. But the number of innovative ideas is actually relatively low. We need them, right? And innovation creates a whole new opportunity. Like when some guy invented the internet, right? Like that was a big thing. The million people that wrote software against that

were mostly doing engineering software writing. So the elaboration of that idea was huge. I don't know if you know Brendan Eich, he wrote JavaScript in 10 days. And that's an interesting story. It makes me wonder, and it was, you know, famously for many years considered to be a pretty crappy programming language. It's still this perhaps.

It's been improving sort of consistently. But the interesting thing about that guy is, you know, he doesn't get any awards. You don't get a Nobel Prize or a field's medal or a crappy piece of, you know, software code that... Well, that is currently the number one programming language in the world that runs now is increasingly running the backhand of the internet. Does he know why everybody uses it? Like that would be an interesting thing. Was it the right thing at the right time? Because like when stuff like JavaScript came out,

like there was a move from writing C-Pro RMS and C++ to, let's call it, what they call manage code frameworks. Where you write simple code, it might be interpreted, it has lots of libraries, productivity is high, you don't have to be an expert. So Java was supposed to solve all the world's problems, it was complicated. Java Script came out after a bunch of other scripting languages, not an expert on it, but was it the right thing at the right time, or was there something clever

because he wasn't the only one. There's a few elements. Maybe if he figured out what it was, then he'd get a prize. Like that destructive figure. Maybe this problem doesn't define this or it just needs a good promoter. Well, I think there's a bunch of blog posts written about it, which is like wrong is right, which is like doing the crappy thing fast, just like hacking together the thing that answers some of the needs and then iterating over time, listening to developers, like listening to people

who actually use the thing. This is something you can do more in software. But the right time, like you have to sense you have to have a good instinct of when is the right time for the right tool and make it super simple and Just get it out there the problem is this is true with hardware This is less true with software is there's back or compatibility that she drags behind you as you know as you try to fix all the mistakes at the past But the timing, there's something about that. It wasn't accidental. You have to give yourself over to the,

you have to have this broad sense of what's needed now, both scientifically and like the community. And just like this, it was obvious that There was no the interesting thing about JavaScript is Everything that ran in the browser at the time like Java and and I think other like scheme other programming languages they were all in a separate external container Mm-hmm And then JavaScript was literally just injected into the web page It was the dumbest possible thing in running in the same thread as everything else.

And like, it was inserted as a comment. So JavaScript code is inserted as a comment in the HTML code. And it was, I mean, there's, it's either genius or super dumb, but it's like, it's no apparatus for like a virtual machine and container. It just executed in the framework of the program that's already running. And it was cool. And then because something about that accessibility,

the ease of its use, resulted in then developers innovating of how to actually use it. I mean, I don't even know what to make of that, but it doesn't seem to echo across different software, like stories of different software. PHP has the same story, really crappy language. It just took over the world. Well, I'm going to have a joke that the random length instructions, variable length instructions,

that's always one, even though they're obviously worse. Like nobody knows why. X-H-E-S-X is arguably the worst architecture. You know, on the planet, it's one of the most private fronts. Well, I mean, isn't that also the story of risk or success? I mean, is that simplicity? There's something about simplicity that us in this evolutionary process is valued. If it's simple, process is valued. If it's simple, it gets it spreads faster. It seems like, or is that not always true? That's not always true. Yeah, it could be simple as good, but too simple as bad. So why did risk win, you think? So far? Did risk win? In the long, archipistry. We don't know. So who's going to win? What's risk? What's this? And who's going to win? What's risk? What's syskin? Who's going to win in that space?

Even these instruction sets. Hey, I saw first going to win, but there'll be little computers that run little programs like normal all over the place. But we're going through another transformation. But you think instruction sets underneath it all will change? Yeah, they evolve slowly. They don't matter very much. They don't matter very much. They don't matter very much, okay. I mean, the limits of performance are, you know, predictability of instructions and data.

I mean, that's the big thing. And then the usability of it is some, you know, quality of design, quality of tools, availability. But right now, X-86 is proprietary with Intel and AMD, but they can change it anyway they want independently. Right? Arm is proprietary to arm and they won't let anybody else change it. So it's like a sole point. And RIS-5 is open source, so anybody can change it, which is super cool. But that also might mean it gets changed in too many random ways that there's no common

subset of it that people can use. Do you like open or do you like close? Like if you were to bet all your money on one or the other risk five or so. No idea. It's case dependent. Well, X86 oddly enough, went until first started developing a day license that like seven people. So it was the open architecture. And then they move faster than others and also bought one or two of them. But there was seven different people making X86 because at the time there was 6502 and Z80s and, you know, 886 and you could argue everybody thought Z80 was the better instruction set. But that was proprietary to one place.

Oh, and the 6800. So there's like five different four or five different micro processors Intel went open Got the market share because people felt like they had multiple sources from it and then over time it narrowed down the two players Why you as a historian Why did Intel win for so long with Why did Intel win for so long with their processors? I mean, they were right. Their process development was great. Also, it's just looking back to JavaScript and Bernanik

is Microsoft and Netscape and all these in-and-out browsers. Microsoft won the browser game because they aggressively stole other people's ideas right after they did it. I don't know if Intel was stealing other people's ideas. They started making ramps, random access memories. At the time when the Japanese manufacturers came up, they were getting out competing on that.

They pivoted to microprocessors and they made the first, you know, integrated microprocessors or programs. It was the 4004 or something. Who was behind that pivot? That's a hell of a pivot. Andy Grove. And he was great. That's a hell of a pivot. And then they led semiconductor industry, like they were just a little company, IPM, all kinds of big companies had boat loads of money and they out-innovated everybody. Out-of-the-innovated, okay. Yeah, so it's not like marketing,

it's not any other stuff. And they're processor designs were pretty good. I think the core two was probably the first one I thought was great, it was a really fast processor the first one I thought was great It was a really fast processor and then how's well was great The what makes a great processor in that oh if you just look at its performance versus everybody else It's you know the size of it that you know usability of it So it's not specific some kind of element that makes it beautiful. It's just like literally just raw performance. Is that how you think about processors? It's just like raw performance?

Of course. It's like a horse race. The fastest one wins. Now, you don't care how. Well, there's the fastest in the environment. Like, you know, for years, you made the fastest one you could and then people started to have power limits So then you made the fastest at the right power point Yeah, and then and then when we started doing multi-processors like If you could scale your processors more than the other guy you could be 10% faster on like a single thread But you have more threads So there's lots of variability and then arm

Really explored like you know, they have the A series and the R series and the M series, like a family of processors for all these different design points from, like, unbelievably small and simple. And so then when you're doing the design, it's sort of like this big palette of CPUs. Like, there are the only ones with a credible, you know, top to bottom palette. And what do you mean a credible top bottom? Well, there's people who make microcontrollers that are small, but they don't have a fast one. There's people make fast processors, but don't have a little a medium one or a small one.

Is that hard to do that full palette? That seems like a, it's a lot of different. So what's the difference? You know, the armed folks and Intel in terms of the way they approach in this problem. Well, Intel, almost all the process of designs were very custom high-end, you know, for the last 15, 20 years. The fastest force possible in one horse. Yeah, and the architecture that they're really good,

but the company itself was fairly insular to what's going on in the industry with CAD tools and stuff. And there's this debate about custom design versus the synthesis. And how do you approach that? I'd say Intel was slow on getting to synthesize processors. ARM came in from the bottom and they generated IP, which went to all kinds of customers. So they had very little say on how the customer implemented their IP. So ARM is super friendly to the synthesis IP environment Resinzel said we're gonna make this great

client-chip server chip with our own CAD tools with our own process with our own, you know other supporting IP and everything only works with our stuff so is that Is arm-winning the mobile platform space in terms of trust? And so in that way you're describing is why they're winning. Well, they had lots of people doing lots of different experiments. So they control the processor architecture and IP, but they let people put in lots of different chips. And there was a lot of variability in what happened there. Whereas Intel,

when they made their mobile, there were 4A in the mobile, they had one team doing one part. Right, so it wasn't 10 experiments. And then their mindset was PC mindset, Microsoft, software mindset, and that brought a whole bunch of things along that the mobile world and embedded world don't do. Do you think it was possible for Intel to pivot hard and win the mobile market? That's a hell of a difficult thing to do, right? For a huge company to just pivot. I mean, so interesting to,

because we'll talk about your current work, it's like, it's clear that PCs were dominating for several decades, like desktop computers and then mobile, it's unclear. It's a leadership question. Like Apple under Steve Jobs, when he came back, they pivoted multiple times. They build iPads and iTunes and phones and tablets

and great Macs, like who knew computers should be made out of aluminum? Nobody knew that. That they're great. It's super fun. Those Steve. Yeah Steve jobs like they pivoted multiple times And uh, you know the old intel they they did that multiple times They made derams and processors and processes and I gotta ask this. What was the like work with Steve jobs. I didn't work with him Did you interact with him twice? I didn't work with him. Did you interact with him?

Twice. I said hi to him twice in the cafeteria. What did you say? Hi. He said, hey fellas. He was friendly. He was wandering around and with somebody, he couldn't find the table because the cafeteria was packed.

And I gave my table. But I worked for my cobert who talked to, like Mike was the unofficial CTO of Apple and a brilliant guy and he worked for Steve for 25 years, maybe more. And he talked to Steve multiple times a day. And he was one of the people who could put up with Steve's, let's say, brilliance and intensity. And Steve really liked him and Steve trusted Mike to translate the shit he thought up into engineering products at work and then Mike ran a group called platform architecture and I was in that group. So many times I'd be sitting with Mike in the phone and rang if you Steve and Mike would hold the phone like this because Steve would be yelling about something or other.

Yeah, and he would translate it. And he would say Steve. And he translated it and then he would say, Steve wants us to do this. So, Well, Steve a good engineer or no? I don't know. He was a great idea guy. Idea person.

He's a really good selector for talent. Yeah. That's supposed to be one of the key elements of leadership, right? And then he was really good first principles guy. Like somebody would say something couldn't be done and he would just think That's obviously wrong right, but you know

Maybe it's hard to do maybe it's expensive to do maybe we need different people, you know There's like a whole bunch of you like if you want to do something hard You know, maybe takes time maybe you have to iterate there's a whole bunch of things yet you could think about but saying it can't be done as stupid How would you compare? So it seems like Elon Musk is more engineering centric, but it's also, I think he considered himself a designer too, he has a design mind. Steve Jobs feels like he's much more idea space, design space versus engineering. Yeah. Just make it happen. The world should be this way, just figure it happen. Like the world should be this way. Just figure it out. But he used computers.

You know, we had computer people talk to them all the time. Like Mike was a really good computer guy. He knew what computers could do. Computer meaning computer hardware. Like we'll offer software, all the pieces. All the things. And then he would, you know, have an idea about what could we do with this next?

That was grounded in reality. It wasn't like he was you know just Finger-painting on the wall and wishing somebody would interpret it like so he had this interesting connection because No, he wasn't a computer architect or designer, but he had an intuition from the computers. We had to what could happen and It's actually a intuition because it seems like he was pissing off a lot of engineers in his intuition about what can and can't be done. Those like the, what is all these stories about like floppy disk and all that kind of stuff like. Yeah. So in Steve, the first round, like he'd go into a lab and look at what's going on and hate it and fire people or assembly in the elevator, what they're doing for Apple and not be happy.

When he came back, my impression was, is he surrounded himself with this relatively small group of people and didn't really interact outside of that as much. And then the joke was, you like I look somebody moving up prototype through the quad with a with a black blanket over it. And that was because it was secret, you know, partly from Steve, because they didn't want Steve to see it until it was ready. Yeah, the dynamic with Johnny Ive and Steve is interesting. It's like you don't want to.

He ruins as many ideas as he generates. Yeah. Yeah. Is it dangerous kind of... ...window walk? If you have a lot of ideas, like... Gordon Bell was famous for ideas, right? And it wasn't that the percentage of good ideas was way higher than anybody else. He had so many ideas and he was also good at talking to people about it and getting

the filters right and you know seeing through stuff. Where Zeylon was like, hey, I want to build rockets. So Steve was hired by T'Racca guys and Zeylon would go to read rocket manuals. So Yon, so better engineer, a sense like or like more like a love and passion for the manuals. Yeah. the manuals and the details the details and the craftsmanship too right? Well I guess you had craftsmanship too but of a different kind. What do you make of the just to stand in for just a little longer? What do you make of like the anger and the passion and all that

the the firing and the mood swings and the madness, the, you know, being emotional and all that that's Steve and I guess Elon too is what is that a bugger feature? It's a feature. So there's a graph which is y-axis productivity, x-axis at zero is chaos, and then it's complete order. So, as you go from the origin, as you improve order, you improve productivity. And at some point productivity peaks, and then it goes back down again. Too much order, nothing can happen. But the question is, how close to the chaos is that? Now, here's the thing.

Once you start moving the direction of order, the force vector to drive you towards order is unstoppable. Oh, it's a slip of course. Every organization will move to the place where their productivity is stimulated by order. So you need a question is, who's the counter force? Like, because it also feels really good. As you get more organized, the productivity goes up.

The organization feels it. They orient towards it, right? They hired more people. They got more guys who couldn't run process. You get bigger, right? And then inevitably, inevitably, the organization gets captured by the bureaucracy that manages all the processes. All right, and then humans really like that.

And so if you just walk into a room and say, guys, love what you're doing. But I need you to have less order. If you don't have some force behind that, nothing will happen. I can't tell you on how many levels that's profound. So that's why I say it's a feature. Now, could you be nicer about it? I don't know. I don't know any good examples of being nicer about it. Well, the funny thing is to get stuff done. You need people who can manage stuff and manage people

because humans are complicated. They need lots of care and feeding. You need to tell them they look nice and they're doing good stuff and pat them on the back. Right? I don't know. Do you tell me, is that, is that needed? If you must need that. I had a friend, he started managing group, and he said, I figured it out. You have to praise them before they do anything. I was waiting until they were done,

and they were always mad at me. Now I tell them what a great job they're doing, while they're doing it. But then you get stuck in that trap, because then when you're not doing something, how do you confront these people? I think a lot of people that had trauma in their childhood would disagree with you, successful people that you need to first do their off stuff and then be nice later. I don't know.

Okay, but engineering companies are full of adults who have all kinds of ranch and childhoods. Most people had okay childhoods. Well, I don't know if... And lots of people only work for praise, which is weird. You mean like everybody. I'm not that interested in this, but, uh, well, you're, you're probably looking for somebody's approval. Mm-hmm. I, even still. Yeah, maybe.

I should think about that. Maybe somebody who's no longer with this kind of thing. Mm-hmm. I don't know. I used to call it my dad and tell him what I was doing. He was very excited about engineering and stuff. You've got his approval? Yeah, a lot.

I was lucky. He decided I was smart and unusual as a kid and that was okay when I was really young. So when I did poorly in school, I was just lucky. I didn't read until I was a third or fourth grade. They didn't care. My parents were like, oh, he'll be fine. So I was fucking, that was cool. Is he still with us?

You miss him? Sure, he had Parkinson's and then cancer. He was left 10 years for tough. And I killed him, killed him a man like that's hard. The mind? Well, it was pretty good. Parkinson's caused a slow dementia and the chemo therapy, I think, accelerated it. But it was like hallucinogenic dementia. So he was clever and funny and interesting and was it was pretty unusual. Do you remember conversations that, of course, from that time, like, what do you have fond memories of the guy? Yeah. Oh, yeah. Anything coming to mind?

A friend told me one time I could draw a computer on the way forward faster than anybody you'd ever met. And I said, you should meet my dad. Like, I was the kid he'd come home and say, I was driving by the bridge. And I was thinking about it. And he pulled out a piece of paper and he'd draw the whole bridge. He was a mechanical engineer. And he would just draw the whole thing. And then he would tell me about it and tell me how he would have changed it. And he had this, you know, idea that he could understand and conceive anything. And I just grew up with that, so that was natural. So, when I interview people, I ask them to draw a picture of something they did

on a whiteboard, and it's really interesting. Like some people draw a little box, and then they'll say, and then this talks to this, and I'll be like, oh, that's this frustrating. And then I had this other guy come in one time, he says, well, I designed a floating point in this chip, but I'd really like to tell you how the whole thing works and then tell you how the floating point works inside of it. You might if I do that he covered two

whiteboards in like 30 minutes and I hired him. Yeah he was great. This craftsman. I mean that's the craftsmanship to that. Yeah but also the mental agility to understand the whole thing. Right. Put the pieces in contacts. You know, you know real view of the balance of how the design worked. Because if you don't understand it properly, when you start to draw, you'll fill up half the way forward with like a little piece of it and, you know, like your ability to lay it out in an understandable way, it takes a lot of understanding. So, and be able to just zoom into the detail and then zoom out and make sure really fast.

And what about the impossible things that your dad believe that you can do anything. That's a weird feature for a craftsman. Yeah. It seems that that echoes in your own behavior. Like that's that's the. Well, it's not that anybody can do anything right now. Right. It's that if you work at it you can get better at it and there might not be a limit

And they did funny things like like he always wanted to pill a piano so at the end of his life started playing a piano When he had Parkinson's and he was terrible But he thought if he really worked out in this life maybe the next life he'd be better at it He might be on to something. Yeah. He enjoyed doing it. Yeah. So that's pretty funny. Do you think the perfect is the enemy of the good and hard-worn software engineering?

It's like we were talking about JavaScript a little bit and the messiness of the 10-day building process. Yeah. So it's creative tension, right? So creative tension is you have two different ideas that you can't do both, right? And but the fact that you wanna do both causes you to go try to solve that problem.

That's the creative part. So if you're building computers, like some people say we have the schedule and anything that doesn't fit in the schedule we can't do. So they throw out the perfect because I have a schedule. I hate that. Then there's other people to say we need to get this perfectly right and no matter what, you know, more people, more money, right? And there's a really clear idea about what you want. Some people are really good at articulating it. So let's call that the perfect. Yeah.

Yeah. All right. But that's also terrible because they never ship anything. They never hit any goals. So now you have now you have your framework. Yes. You can't throw out stuff because you can't get it done today because maybe you get it done tomorrow with the next project.

Right. You can't. So you have to, I work with a guy that I really like working with, but he over filters his ideas. Over filters. He'd start thinking about something, and as soon as he figured out what's wrong with it, he'd throw it out. And then I start thinking about it, you know, you come up with an idea and then you find out what's wrong with it. And then you give it a little time to set because sometimes you figure out how to tweak it or maybe that idea helps some other idea. So idea generation is really funny. So you have to give your idea space, like spaciousness of mind is key, but you also have to execute programs and get shit done. And then it turns out computer engineering is fun because it

takes 100 people to build a computer, 200 to 300, whatever the number is. And people are so variable about temperament and skill sets and stuff. In a big organization, you find that the people who love the perfect ideas and the people that want want to get stuffed on yesterday and people like that come up with ideas and people like the, let's say shoot down ideas and it takes the whole, it takes a large group of people. There's someone good at generating ideas, someone good at filtering ideas and all in that giant mess, you're somehow, I guess the goal is for that giant mess of people to find the perfect path through the tension, the creative tension. Like how do you know when you said there's some people good at articulating what perfect

looks like, what a good design is. Like you're sitting in a room and you have a set of ideas about like how to design a better processor. How do you know this is something special here? This is a good idea. Let's try this. So if you ever brainstormed idea with a couple of people that were really smart,

and you kind of go into it, and you don't quite understand it, and you're working on it. And then you start, you know, talking about it, putting it on the whiteboard, maybe it takes days or weeks, and then your brain starts to kind of synchronize. It's really weird.

And like you start to see what each other's thinking. And it starts to work like you can see work. Like my talent and computer design is I can, I can see how computers work in my head, like really well. And I know other people can do that too. And when you're working with people that can do that, like it is kind of an amazing experience. And then and everyone's boy, you get to that place and then you find the flaw that was just kind of funny because you can you can fool yourself in. But the two of you kind of drifted along that direction.

It was Jesus. Yeah, that happens too. Like you have to, because you know, the nice thing about computers, I always have reduction in practice. Like you come up with your good ideas, and I've noticed some architects who really love ideas, and then they work on them,

and then they put it on the shelf, and they go work on the next idea of put on shelf. They never reduce the practice. So they find out what's good and bad because most every time I've done something really new, by the time it's done, like the good parts are good, but I know all the flaws. Yeah. Would you say your career, just your own experience is your career defined by mostly by flaws or by successes?

Like, again, there's great attention between those. If you haven't tried hard, right, and done something new, right, then you're not going to be facing the challenges when you build it, then you find out all the problems. And but when you look back, you see problems. Okay. Oh, when I look back, what see problems. Oh, okay. Oh, when I look back, what do you remember? I think earlier in my career, like EV5 was the second alpha chip. I was so embarrassed about the mistakes I could barely talk about it. And it was in the Guinness Book of World records, and it was the fastest processor on the planet.

Yeah. So it was, and at some point I realized that was really a bad mental framework to deal with like doing something new. We did a bunch of new things and some worked out great and some were bad. And we learned a lot from it. And then the next one we learned a lot.

That also, EV6 also had some really cool things in it. I think the proportion of good stuff went up, but it had a couple of fatal flaws in it that were painful. And then, yeah. You learned to channel the pain into pride. Not pride, really. Just realization about how the world works. How that kind of idea so works.

Life is suffering. That's the reality. What's not? Well, I know the Buddha said that, and I hope all the people are stuck on it. Now, there's this kind of weird combination of good and bad, light and darkness that you have to tolerate, and deal with. Yeah, there's definitely lots of suffering in the world.

Depends on the perspective, it seems like there's way more darkness, but that makes the light part really nice. What computing hardware or just any kind of even software design are you define beautiful from your own work, from other people's work. We were just talking about the other people's work, we were just talking about the battleground of flaws and mistakes and errors, but things that were just beautifully done. Is there something that pops to mind? Well, when things are beautifully done, usually there's a well set, a set of abstraction layers. So the whole thing works in unison nicely.

Yes. And when I say abstraction layer, that means two different components, when they work together, they work independently. They don't have to know what the other one is doing. So that decoupling. Yeah. So the famous one was the network stack. Like there's a seven layer network stack, you know, data transport and protocol and all the layers. And the innovation was, is when they really wrote, got that right. Because networks before that didn't define those very well. The layers could innovate independently, and occasionally the layer boundary would, you know, the interface would be upgraded. And that, that let, you know, the design space breathe. And you could do something new in layer seven

without having to worry about how layer four worked. And so good design does that. And you see it in processor designs. When we did the Zen design at AMD, we made several components very modular. And, you know, my insistence at the top was, I wanted all the interfaces defined before we wrote the RTL for the pieces. One of the verification leads

had if we do this right, I can test the pieces so well independently when we put it together, we won't find all these interaction bugs because the floating point knows how the cache works. And I was a little skeptical, but he was mostly right. That the modularity of design greatly improved the quality. Is that universally true in general? Would you say about good designs? The modularity is usually... We only talked about this before.

Humans are only so smart. And we're not getting any smarter, right? But the complexity of things is going up. So a beautiful design can't be bigger than the person doing it. It's just their piece of it. The odds of you doing a really beautiful design of something that's way too hard for you is low. If it's way too simple for you, it's not that interesting.

It's like, well, anybody could do that. But when you get the right match of your expertise and mental power to the right design size, that's cool, but that's not big enough to make a meaningful impact in the world. So now you have to have some framework to design the pieces so that the whole thing is big and harmonious. But when you put it together, it's, you know, it's sufficiently, sufficiently interesting to be used. And, you know, so that's like a beautiful design is. Matching the limits of that human cognitive capacity

to the module you can create, and creating a nice interface between those modules. And thereby, do you think there's a limit to the kind of beautiful complex systems we can build with this kind of modular design? It's like, you know, if we build increasingly more complicated, you can think of like the internet. OK, let's scale that.

Well, you can think of like social network, like Twitter as one computinging system and But those are little modules, right? But it's build on it's build on so many components nobody a Twitter even understands Right, so so so if an alien showed up and looked at Twitter He wouldn't just see Twitter as a beautiful simple thing that everybody uses which is really big You would see the networks it runs on the fire optics, the data is transported, the computer, the whole thing is so bloody complicated.

Nobody Twitter understands it. You think that's what the alien would see. So yeah, if an alien showed up and looked at Twitter, or looked at the various different network systems that you can see on Earth, imagine they were really smart and it could comprehend the whole thing. And then they sort of evaluated the human and thought, this is really interesting, no human on this planet comprehends the system they built.

No individual, well, they even see individual humans. That's the, like we humans are very human-centric, entity-centric. And so we think of us as the organ, as the central central organism and the networks as just the connection of organisms, but from a perspective of an an outside perspective, it seems like. Yeah, we're just we're yeah, I get it. We're the answer to the end colony. The end colony. Yeah, or the result of production of the endine, which is like cities and it's, in that sense, he was a pretty impressive,

the modularity that we're able to and how robust we are to noise and mutation, all that kind of stuff. Well, that's cause it's stress tested all the time. Yeah. You know, you build all these cities with buildings and you get earthquakes occasionally. For wars, you know, wars, earthquakes. Viruses, everyone's in a while. Changes in business occasionally and some you know, wars earthquakes.

Liars does everyone's normal. You know changes in business plans for you know, like shipping or something like like as long as there's all stress test it then it keeps adapting to the the situation. So that's a curious phenomenon. Well, let's go. Let's talk about Moore's law a little bit. It's a, at the, brought view of Moore's law was just exponential improvement of computing capability, like open AI, for example, recently published this kind of papers looking at the exponential improvement in the training efficiency of neural networks. For like, imagine that and all that kind of stuff, which has got better on this purely software side,

just figuring out better tricks and algorithms for training neural networks, and that seems to be improving significantly faster than the Moore's Law prediction. So that's in the software space. What do you think if Moore's law continues, or if the general version of Moore's law continues, do you think that comes mostly from the hardware, from the software, some mix of the two, some interesting, totally, so not the reduction of the size of the transistor,

kind of think, but more in the, in the totally interesting kinds of innovations in the hardware space, all that kind of stuff. Well, there's like half a dozen things going on in that graph. So one is, there's initial innovations that had a lot of headroom to be exploited. So, you know, the efficiency of the networks is improved dramatically. And then the decomposability of those, the use, you know, they started running on one computer, then multiple computers, then multiple GPUs, and then arrays of GPUs, and they're up to thousands. And at some point, so it's sort of like they were consumed, they were going from like a single computer application to a thousand computer application. So, that's not really a Moore's Law thing.

That's an independent vector. How many computers can I put on this problem? Because the computers themselves are getting better on a Moore's Law rate. But their ability to go from one to ten to a hundred to a thousand was something. And then, multiplied by the amount of compute it took to resolve like Alex net to ResNet to transform versus it's been quite you know steady improvements. But those are like escars aren't they? That's the exactly kind of escars that are underlying more is law from the very beginning. So what's the biggest, what's the most productive rich source of escurs in the future, do you think? Is it hardware, is it software?

So hardware is going to move along relatively slowly, like, you know, double performance every two years. There's still, like how you call that slow. Yeah, that's the slow version. The snail's pace of Moore's law, maybe we shouldn't, we shouldn't, we should have trained Mark that one. Whereas the scaling by number of computers can go much faster. I'm sure at some point Google had another initial search engine was running out of laptop.

And at some point they really worked on scaling at them and they factored the indexer from this piece and this piece and this piece and they spread the data on more and more things. They did a dozen innovations, but as they scaled up the number of computers on that, it kept breaking, finding new bottlenecks in their software and their schedulers and made them rethink. It seems insane to do a scheduler across a thousand computers, the schedule parts of it, and then send the results to the one computer. But if you want to schedule a million searches, that makes perfect sense.

So there's the scaling by just quantity is probably the richest thing. But then as you scale quantity, like a network that was great on 100 computers, maybe completely the wrong one, you may pick a network that was great on 100 computers, maybe completely the wrong one, you make pick a network that's 10 times slower on 10,000 computers, like per computer. But if you go from 100 to 10,000, it's 100 times. So that's one of the things that happen when we did internet scaling. This efficiency went down, not up. The future of computing is inefficiency not efficiency but scales Inefficient scale. It's it's scaling faster than

inefficiency By two and as long as there's you know dollar value there like scaling costs lots of money Yeah, but Google showed Facebook showed everybody showed that the scale was worth money was that it was and so it was was worth the financial you think Is it possible that like basically the entirety of Earth will be like a computing surface? Like this table will be doing computing, this hedgehog will be doing computing. Like everything, really inefficient, don't computing will be the number.

So fiction books they call it, computer, and we turn everything into computing. Well, most of the elements aren't very good for anything. Like you're not going to make a computer out of iron. Like, you know, silicon and carbon have like nice structures. You know, I will see what you can do with the rest of it. Not I just people talk about well, maybe we can turn the sun into computer, but it's it's hydrogen. And a little bit of helium. So what I mean is more like actually just adding computers to everything. Oh, okay. So you're just converting all the mass of the universe into a computer. No, no, so not using the ironic from the simulation

point of view is like the simulator build mass, the simulates. Yeah, I mean, yeah, so I mean ultimately this is all heading towards a simulation. Yeah. Well, I think I might have told you this story a Tesla They were deciding so they want to measure the current coming out of the battery and they decide between putting a resistor in there and Putting a computer was a sensor in there and the computer was faster than the computer. I worked on in 1982 And we chose the computer because it was cheaper than the resistor. So sure, this hedgehog cost $13 and we can put an AI that's as smart as you and they're for five bucks. It'll have one. So computers will be everywhere. I was hoping it wouldn't be smarter than me because...

Well, everything's going to be smarter than you. But you were saying it's inefficient. I thought it was better't be smarter than me because... Well, everything's gonna be smarter than you. But you were saying it's inefficient. I thought it was better to have a lot of double... Well, well, more is law will slowly compact that stuff. So even the dumb things will be smarter than us. The dumb things are gonna be smart or they're gonna be smart enough to talk to something that's really smart. You know, it's like... Well, just remember, like a big computer jump. You know, it's like an inch by an inch, and you know, 40 microns thick.

It doesn't take very much, very many atoms to make a high power computer. And 10,000 of them can fit in a shoebox. But you know, you have the cooling and power problems, but you know, people are working on that. But they still can't write compelling poetry or music or understand what love is or have a fear of mortality. So we're still winning. Neither can most of humanity. So they can write books about it. So, but speaking about this,

this walk along the path of innovation towards the dumb things being smarter than humans, you are now the CT of 10 store and as of two months ago, they built hardware for deep learning. How do you build scalable and efficient deep learning? This is such a fascinating space. Yeah, yeah. So it's interesting. So up until recently, I thought there was two kinds of computers. There are serial computers that run like C programs and then there's parallel computers. So the way I think about it is, you know, parallel computers have given parallelism. Like GPUs are great because you have a million pixels. And modern GPUs run a program on every pixel. They call it a shader program. Right? So, or like finite element analysis,

you build something, you know, you make this into a little tiny chunk to give each chunk to a computer. So you're given all these chunks of parallelism like that. But most C programs, you write this linear narrative and you have to make a go fast. To make a go fast, you predict all the branches, all the data fetches, and you run that, more parallel, but that's found parallelism. AI is, I'm still trying to decide how fundamental it says. It's a given parallelism problem, but the way people describe the neural networks, and then how they write them in PyTorch it makes graphs Yeah, that might be fundamentally different than the GPU kind of parallelism yet it might be because the when you run the GPU program on all the pixels you're running

Like you know depends you know this group of pixels say it's background blue and it runs a really simple program This pixel is you know some patch of your face. So you have some really interesting shader program to give you the impression of translucency. But the pixels themselves don't talk to each other. There's no graph, right? So you do the image and then you do the next image and you do the next image and you run 8 million pixels, 8 million programs every time and modern GPUs have like 6000 thread engines in them. So, you know, to get 8 million pixels each one runs a program on, you know, 10 or 20 pixels.

And that's how they work, there's no graph. But you think graph might be a totally new way to think about hardware. So, Raj, the good Doryied, I've been having this good conversation about given versus found parallelism. And then the kind of walk, as we got more transistors, like computers way back when did stuff on scale or data. Then we did on vector data, famous vector machines. Now we're making computers that operate on matrices, right?

And then the category we said that it was next with spatial. Like, imagine you have so much data that you want to do the compute on this data. And then when it's done, it says, send the result to this pilot data on some software on that. And it's better to think about it spatially than to move all the data to a central processor and do all the work. So, especially, I mean, moving in the space of data as opposed to moving the data? Yeah, you have a petabyte data space spread across some huge array of computers. And when you do a computation somewhere, you send the result of a computation or maybe a pointer to the next program to some other piece of data and do it.

But I think a better word might be graph, and all the AI neural networks are graphs. Do some computations and a result here, do another computation, do a data transformation, do a merging, do a pooling, do another computation. It is possible to compress and say how we make this thing efficient, this whole process efficient, this whole process efficient, this different. So, first, the fundamental elements in the graphs are things like matrix multiplies, convolution, data manipulations, and data movements.

So, GPUs emulate those things with their little singles, basically running the single threaded program. And then, there's an Nvidia calls it a warp where they group a bunch of programs that are similar together. So for efficiency and instruction use. And then at a higher level, you kind of, you take this graph and you say this part of the graph is the matrix multiplier which runs on these 30-due threads. But the model at the bottom was built for running programs on pixels, not executing graphs. That's emulation. So it's possible to build something that natively runs graphs.

Yes, so it's what Tenstorrent did. So where are we on that? How, like, in the history of that effort, are we in the early days? Yeah, Tenstorrents started by a friend of mine, LeBeesha Bajek, and I was his first investor. So I've been kind of following him and talking to him about it for years and in the fall when I was considering things to do. I decided, you know, we held a conference last year

with a friend to organize it. And we wanted to bring in thinkers and two of the people were Andre Karpathy and Chris Ladner. And Andre gave this talk, it's on YouTube called Software 2.0, which I think is great. Which is, we went from programs, computers, where you write programs to data program computers, like the future of, you know, software is data programs, the networks. And I think that's true. And then Chris has been work, he worked on LLVM, the low level virtual machine, which became the intermediate representation for all compilers.

And now he's working on another project called MLIR, which is mid-level intermediate representation, which is essentially under the graph about how do you represent that kind of computation and then coordinate large numbers of potentially heterogeneous computers. And I would say technically 10st torrents, two pillars of those two ideas, offered 2.0 in mid-level representation,

but it's in service of executing graph programs. The hardware is designed to do that. So it's including the hardware piece. And then the other cool thing is, for a relatively small amount of money, they did a test chip and two production chips. So it's like a super effective team. And unlike some AI startups, where if you don't build the hardware to run the software that they really want to do, then you have to fix it by writing lots more software.

So the hardware naturally does, agents multiply, convolution, the data manipulations, and the data movement between processing elements that you can see in the graph, which I think is all pretty clever. And that's what I'm working on now. So I think it's called the Grace Call Processing Introduce last year.

It's, you know, there's a bunch of measures of performance that we're talking about horses. It seems to outperform 368 trillion operations per second. It seems to outperform in the video, it's test the T-force system. So these are just numbers. What do they actually mean in real world performance? Like what are the metrics for you that you're chasing in your horse race? Like what do you care about? Well, first, so the native language of, you know, people who write AI network programs is PyTorch. Now PyTorch's TensorFlow. There's a couple others. Deep PyTorch is one over TensorFlow. It's just I'm not an expert on that.

I know many people who have switched from TensorFlow to PyTorch. Yeah. And there's technical reasons for it. And I use both, both are still awesome. Both are still awesome. But the deepest love is for PyTorch. And there's technical reasons for it. I use both. Both are still awesome. Both are still awesome. But the deepest love is for PyTorch currently. Yeah. There's more love for that. And that may change. So the first thing is when they write their programs, can the hardware execute it pretty much as it was written. Right. So PyTorch turns into a graph. We have a graph compiler that makes that graph.

Then it fractions the graph down. So if you have big matrix multiply, we turn it in the right size chunks to run on the processing elements. It hooks all the graph up. It lays out all the data. There's a couple of mid-level representations of it that are also simulatable.

So that if you're writing the code, you can see how it's going to go through the machine, which is pretty cool. And then at the bottom, you can see how it's going to go through the machine, which is pretty cool. And then at the bottom, it's scheduled kernels like mass, data manipulation, data movement kernels, which do this stuff. So we don't have to run write a little program to do a matrix multiply because we have a big matrix multiplier. Like there's no SIMD program for that.

But there is scheduling for that, but there is scheduling for that. Right. So that the one of the goals is if you write a piece of PyTorch code that looks pretty reasonable, you should be able to compile it, run it on the hardware without having to tweak it and do all kinds of crazy things to get performance. There's not a lot of intermediate steps. Right. It's running directly as right. Like on a GPU, if you write a large matrix multiplied naively, you'll get five to 10% as a peak performance of the GPU. Right. And then there's a bunch of people

publish papers on this. And I read them about what steps do you have to do? And it goes from pretty reasonable, well, transpose one of the matrices. So you do row order, not column ordered, you know, block it so that you can put a block of the matrix on different SMs, you know groups of threads But some of it gets in the little little details like you have to schedule it's just so so you don't have registered conflicts so the the the the they call them coot in inches I love it to get to the optimal point you either write a pre use a pre to get to the optimal point, you either write a pre-written library, which is a good strategy for some things, or you have to be an expert in microarchitecture to program it. Right, so the optimization step is more complicated with the GPU. So our goal is, if you write PyTorch, that's

good PyTorch, you can do it. Now, there's, as the networks are evolving, they've changed from convolutional to matrix multiply. If people are talking about conditional graphs, they're talking about very large matrices, they're talking about sparsity, they're talking about problems that scale across many, many chips. So the native data item is a packet. So you send the packet to a processor, it gets processed. It does a bunch of work, and then it may send packets to other processors, and they execute in like a data flow graph kind of methodology. We have a big network on chip, and then 16, the next second chip has 16 Ethernet ports to help lots of them together. And it's the same graph compiler

across multiple chips. So that's where the scale comes in. So it's built to scale naturally. Now my experience with scaling is as you scale, you run into lots of interesting problems. So scaling is a mountain of climb. So the hardware is built to do this, and then we're in the process of. Is there a software part to this? What would Ethan and all that?

Well, the protocol at the bottom, we send, it's an ethernet phi, but the protocol basically says send the packet from here to there. It's all point to point. The header bit says which processor to send it to. And we basically take a packet off our own chip network, put an ethernet header on it, send it to the other end, strip the header off and send it to the local saying, it's pretty straightforward. Yeah, human interaction is pretty straightforward too, but we can get a million of us. We're just crazy stuff together.

Yeah, it's gonna be fun. So is that the goal as scale? So like for example, I have been recently doing a bunch of robots at home for my own personal pleasure. Am I going to ever use 10 storene or is this more for? There's all kinds of problems, like there's small inference problems or small training problems or big training problems. What's the big goal?

Is it the big training problems or the small training problems? There's one of the goals is the scale from 100 mW to a megawatt. So like really have some range on the problems in the same kind of AI programs work at all different levels. So, that's cool. The natural, since the natural data item is a packet that we can move around, it's built to scale, but so many people have small problems. Right, right.

But, you know, they're like inside that phone is a small problem to solve. So do you see test or potentially being inside a phone? Well, the power efficiency of local memory, local computation and the way we built it is pretty good. And then there's a lot of efficiency on being able to do conditional graphs and sparsity. I think it's it's for complicated networks that want to go into small factors, it's quite good. But we have to prove that. That's a fun problem.

And that's the early days of the company, right? It's a couple of years, you said. But you think you invested, you think they'll legit and see join. Well, that's also, it's a really interesting place to be. Like AI was exploding, you know? And I looked at some other opportunities like build a faster processor, which people want. But that's more on incremental path

than what's gonna happen in AI in the next 10 years. So this is kind of an exciting place to be part of. And the revolutions will be happening in a very space that's happening. And then lots of people are working on it, but there's lots of technical reasons why some of them aren't going to work out that well. And that's interesting. And there's also the same problem about getting to basics, right?

Like we've talked to customers about exciting features. And at some point, we realized that each of them was realizing they want to hear first about memory bandwidth, local bandwidth, compute intensity, program ability. They want to know the basics, power management, how the network ports work. Where are the basics? Do all the basics work? Because it's easy to say we got this great idea, you know, the crack, GBT3. But the people we talk to want to say, if I buy buy that so we have a piece of express card With our chip on it if you buy the card you plug it in your machine to download the driver How long does it take me to get my network to run?

Right, right. No, that's a real question. It's a very basic question So yeah, is there an answer to that yet or is it's trying to get our goals like an hour? Okay, one can I buy a test or Pretty soon for my for the small test warrant? Uh, pretty soon. For my, for the small case training. Yeah, pretty soon. Month. Good.

I love the idea of you inside a room with the Carpati, under Carpati and Chris Ladner. Very, very interesting, very brilliant people, very out of the box thinkers, but also like first principal thinkers. Well, they both get stuff done. They only get stuff done to get their own projects done. They talk about it clearly. They educate large numbers of people and they've created platforms for other people to go

do their stuff on. Yeah, the clear thinking that's able to be communicated is kind of impressive. It's kind of remarkable, though. Yeah, I'm a fan. Well, let me ask, because I talked to Chris actually a lot these days. He's been one of the, just to give him a shout out, and he's been so supportive as a human being. So everybody's quite different. Like, great engineers are different, but he's been like sensitive to a human being. So, everybody's quite different.

Like, great engineers are different, but he's been like sensitive to the human element in a way that's been fascinating. Like, he was one of the early people on this stupid podcast that I do to say like, don't quit this thing and also talk to whoever the hell you wanna talk to. That kind of from a legit engineer to get like props and be like, you can do this. That was, I mean, that's what a good leader does,

right? So it's just kind of let a little kid do his thing. Like, go, go do it. Let's see, let's see, let's see what turns out. That's a, that's a pretty powerful thing. But what do you, what's your sense about, he used to be, now I think, stepped away from Google, right? He said, sci-fi, I think. What's really impressive to you about the things that Chris has worked on, because we mentioned the optimization that compiled design stuff, the LLVM.

Then there's, he's also a Google work that the TPU stuff. He's obviously worked on Swift so the programming language side Talking about people that work in the entirety of the stack. Yeah What from your time interacting with Chris and knowing the guy what's really impressive to you as just inspires you well Like Ella the end became You know end became the de facto platform for compilers. It's amazing. It was good code quality, good design choices. He hit the right level of abstraction.

There's a little bit of the right time and the right place. Then he built a new programming language called Swift, which after some adoption resistance became very successful. in the right place. And then he built a new programming language called Swift, which after, let's say some adoption resistance became very successful. I don't know that much about his work at Google, although I know that that was a typical, they started TensorFlow stuff and it was new,

they wrote a lot of code and then at some point, it needed to be refactored to be, you know, because it's development slowed down, why PyTorch started a little later and then passed it. So he did a lot of work on that. And then his idea about MLIR, which is what people started to realize is the complexity of the software stack above the low level IR was getting so high that forcing the features of that into the level was putting too much of a burden on it. So, we splitting that in the multiple pieces. And that was one of the inspirations for our first stack where we have several intermediate representations that are all executable. And you can look out and do transformations on them

before you lower the level. So, that was, I think we started before MLA are really got, you know, far enough along to use. But we're interested, man. He's really excited about MLA ads. That's like little baby. So he, and there seems to be some profound ideas on that that are really useful. So, so each one of those things has been as the world of software gets more and more complicated, how do we create the right abstraction levels to simplify it in a way that people can now work independently on different levels of it.

So I would say all three of those projects, all of you, I'm suave to MLIR did that successfully. So I'm interested what he's gonna do next in the same kind of way. Yes. So on either the TPU or maybe the NVIDIA GPU side, how does 10 store and you think or the ideas underlying it doesn't have to be 10 store and just this kind of graph focused, graph centric hardware, deep learning centric hardware, beat, and videos. Do you think it's possible for it to basically overtake and video?

Sure. What's, what's that process look like? What's that, uh, journey look like, you think? Well, GPUs were built around shader programs on millions of pixels, not to run graphs. Yes. So there's a hypothesis that says the way the graphs, you know, our built is going to be really interesting to be inefficient on

computing this. And then the primitives is not a SIMD program. It's matrix multiply convolution. And then the data manipulation are fairly extensive about like how do a fast transpose with a program. I don't know if you've ever written a transpose program. They're ugly and slow, but in hardware, you can do really well. I'm going to give you an example. When GPU accelerators start doing triangles, you have a triangle, which maps on the set of pixels. It's straightforward

to build a hardware engine that will find all those pixels. And it's kind of weird, because you walk along the triangle to get the edge, and then you have to go back down to the next row and walk along, and then you have to decide on the edge, if the line of the triangle is like half on the pixel, what's the pixel color, because it's half of this pixel and half the next one. That's called rasterization. You're saying that could be done in hard work? No, that's an example of that operation as a software program is really bad. I've written

a program that did rasterization. The hardware that does it is actually less code than the software program that does it and it's way faster. Right, so there are certain times when Right. So there are certain times when the abstraction you have, rasterize a triangle, you know, execute a graph, you know, components of a graph, but the right thing to do in the hardware software boundary is for the hardware to naturally do it. So the GPU is really optimized for the rasterization of triangles. Well, no, that's just, well, like in a modern, you know, that's a small piece of modern GPUs,

what they did is that they still restaurant triangles when you're running a game, but for the most part, most of the computation in the area that GPU is running shader programs, but there's single threaded programs on pixels, not graphs. That's, to be honest, let's say I don't actually know the math behind shader, shading and lighting and all that kind of stuff. I don't actually know the math behind shader shading and lighting and all that kind of stuff. I don't know what... They look like little simple floating point programs or complicated ones. You can have 8,000 instructions in a shader program. But I don't have a good intuition why it could be parallelized

so easily. No, it's because you have 8 million pixels in every single. So we have a light, right? Yeah. That comes down. The angle, you know, the amount of light, like, like, say, this is a line of pixels across this table, right? The amount of light on each pixel is subtly different. And each pixel is responsible for figuring out where to find out. Figured out. So that pixel says, I'm this pixel. I know the angle of the light.

I know the occlusion. I know the color I am. Like every single pixel here is a different color. Every single pixel gets a different amount of light Every single pixel has a suddenly different translucency So to make it look realistic the solution was you run a separate program on every pixel See, but I thought there's a reflection from all over the place is it every pixel? Yeah, but there is so so you build a reflection map which also has some pixelated thing

And then when the pixels looking at the reflection map has to calculate what the normal of the surface is, and it does it per pixel. By the way, there's bull loads of hacks on that. You may have a lower resolution, light map, reflection map. There's all these, you know, tax they do. But at the end of the day, it's per pixel computation. And it's so happen that you can map graph-like computation onto the pixel-centric complex. You can do floating-point programs on convolutional matrices. And Nvidia invested for years in CUDA, first for HPC, and then they got lucky with the AI trend. But do you think

they're going to essentially not be able to hardcore pivot out of their... We'll see. That's always interesting. I'll often debate companies hardcore pivot, occasionally. How much do you know about Nvidia, folks? Well, I'm curious as well, who's ultimately... Oh, they've debated several times, but they've also worked really hard on mobile they worked really high on radios You know, you know, they're fundamentally a GPU company Well, they tried to pivot is an interesting little Game and play in autonomous vehicles, right with or a semi-autonomous like playing with Tesla and so on and seeing that's a Dipping at toe into that kind of pivot.

They came out with this platform, which is interesting technically. But it was like a 3,000 watt, 1,000 watt, $3,000 GPU platform. I don't know if it's interesting, technically, it's interesting for a soft-coated. Technically, I don't know if it's the execution, the craftsmanship is there.

I'm not sure, but I didn't get a sense. I think they were repurposing GPUs for an automobiles solution. Right. It's not a real pivot. They didn't build a ground up solution. Like the chips inside Tesla are pretty cheap. Like mobile eyes have been doing this. They're doing the classic work from the simplest thing. They were building 40 millimeter square millimeter chips.

And Nvidia, their solution had 800 millimeter chips and 200 millimeter chips. You know, like, both those are really expensive DRAMs. And you know, it's a really different approach. So, mobile, I fit the, let's say, automotive costs and form factor. And then they added features as it was economically viable. And Nvidia said, take the biggest thing and we're gonna go make it work You know and and that's also influenced like Waymo There's a whole bunch of autonomous startups where they have a 5,000 watt server and a trunk

Right and but that's that's because they think well 5,000 watts and you know $10,000 is okay because it's replacing a driver Elon's approach was that port has to be cheap enough to put it in every single Tesla, whether they turn on autonomous driving or not, which in mobile I was like, we need to fit in the bomb and cost structure that car companies do. So they may sell you a GPS for 1,500 bucks,

but the bomb for that's like $25. PPS for $1,500, but the bomb for that's like $25. Well, and for mobile I, it seems like neural networks were not first class citizens, like the computation. They didn't start out as a... Yeah, it was a CB problem, you know. And then classic CB and found satellites and lines, and they were really good at it. Yeah, and they never, I mean, I don't know what's happening now,

but they never fully pivoted. I mean, it's like, it's the Nvidia thing. Then as opposed to, if you look at the new Tesla work, it's like neural networks from the ground up, right? Yeah. And even Tesla started with a lot of CV stuff and it non-raised basically been eliminated. Move everything into the network. So without, this isn't like confidential stuff, but you sitting on a porch looking over the world, looking at the work that Andre is doing, that Elon's doing with Tesla autopilot, do you like the trajectory of where things are going in the heart? Well, they're making serious progress. I like the videos of people driving the beta stuff.

I get to take into pretty complicated intersections and all that, but it's still an intervention per drive. I mean, I have auto, the current auto palette, my Tesla, I use it every day. Do you have full self-driving beta or no? So you like where this is going? They're making progress. It's taking longer than anybody thought. You know, my wonder was, is hardware you know, hardware three, is it enough computing

off by two, off by five, off by 10, off by 100. And I thought it probably wasn't enough, but they're doing pretty well with it now. And one thing is, the data that gets bigger, the training gets better, and then there's this interesting thing is, you sort of train, the training gets better, and then there's this interesting thing is you sort of train and build an arbitrary size network that solves the problem, and then you refactor the network down to the thing that you can afford to ship. The goal isn't to build a network that fits in the phone. It's to build something that actually works. How do you make that most effective on the hardware you have?

And they seem to be doing that much better than a couple of years ago. Well, the one really important thing is also what they're doing well is how to iterate that quickly, which means like it's not just about one time deployment, one building, it's constantly iterating the network and trying to automate as many steps as possible, right? And that's actually the principles of the software 2.0, like you mentioned with Andre, is it's not just, I mean, I don't know what the actual,

his description of software 2.0 is, if it's just high level philosophical or there's specifics, but the interesting thing about what that actually looks in the real world is, it's that what I think Andre calls the data engine. It's like, it's the iterative improvement of the thing. The Avonual Network that does stuff fails on a bunch of things and learns from it over and over and over.

So you constantly discovering edge cases. That's very much about like data engineering, like figuring out. It's kind of what you were talking about with Tencentoin is you have the data landscape. You have to walk along that data landscape in a way that, uh, that's constantly improving the, uh, the neural network. And that, that feels like that's the central piece itself. And there's two pieces of it. Like, you find the edge cases that don't work, and then you

define something that goes get you data for that. But then the other constraint is whether you have the label or not. Like the amazing thing about like the GPT-3 stuff is it's unsupervised. So there's essentially infinite amount of data. Now there's obviously infinite amount of data available from cars of people are successfully driving. But you know the the current pipelines are mostly running on labeled data, which is human limited. So an app becomes unsupervised. Right. It will create unlimited amount of data, which then will scale. Now the networks that may use that data might be way too big for cars, but then there'll be the transformation from now, if unlimited data, I know exactly what I want.

Now, can I turn that into something that fits in the car? And that process is going to happen all over the place. Every time you get to the place where you have unlimited data, and that's what software 2.0 is about, unlimited data training, networks's what it's all for, 2.0. It's about unlimited data training networks to do stuff without humans writing code to do it. And ultimately, also trying to discover, like you're saying, the self-supervised formulation

of the problem, so the unsupervised formulation of the problem. Like, in driving, there's this really interesting thing, which is you look at a scene that's before you, and you have data about what a successful human driver did in that scene, you know, one second later. It's a little piece of data that you can use just like with GPT-3 as training.

Currently, even though Tesla says they're using that, it's an open question to me, how far can you, can you solve all of the driving with just that self supervised piece of data? And like, I think- That's what Kamayai is doing. That's what Kamayai is doing, but the question is how much data, so what Kamayai doesn't have,

is as good of a data engine, for example, as Tesla does. That's where the, like, the organization of the data, I mean, as far as I know, I haven't talked to George, but they do have the data. The question is how much data is needed? Because we say infinite, very loosely here. It's, and then the other question, which you said, I don't know if you think it's still an open question, is are we on the right order of magnitude for the compute necessary? Is this, is it like what Elon said, this chip that's in there now is enough to do full

self-driving or do any other order of magnitude? I don't think nobody actually knows the answer to that question. I like the confidence that Elon has, but... Yeah, we'll see. There's another funny thing is you don't learn to drive with infinite amounts of data. You learn to drive with an intellectual framework that understands physics and color and horizontal services and laws and roads and all your experience from manipulating your environment, like, like, there's so many factors going to that. So then when you learn to drive,

like driving is a subset of this conceptual framework that you have, right? And so with self-driving cars right now, we're teaching them to drive with driving data. Like, you never teach a human to do that. You teach a human, all kinds of interesting things, like language, like don't do that. Watch out, there's all kinds of stuff going on.

This is where you, I think, the previous time we talked about, where you poetically disagreed with my naive notion about humans. I just think that humans will make this whole driving thing really difficult. Yeah, all right. I said humans don't move that slow.

It's a ballistic problem. The ballistic humans are a ballistic problem, which is like poetry to me. It's very possible that in driving there, indeed, purely a ballistic problem. And I think that's probably the right way to think about it. But I still continue to surprise me with those in damp pedestrians, the cyclists,

other humans and other cars. And yeah, but it's gonna be one of these compensating things. So like when you're driving, you have an intuition about what humans are going to do, but you don't have 360 cameras in radars and you have an intention problem. So you, so the self driving car comes in with no intention problem, 360 cameras, right now,

a bunch of other features. So they'll wipe out a whole class of accidents. And emergency breaking with radar, and especially as it gets AI enhanced, will eliminate collisions. But then you have the other problems of these unexpected things where you know, you think your human intuition is hoping but then cars also have You know a set of hardware features that you're not even close to and the key thing of course is If you wipe out a huge number of kind of accidents, then it might be it just way safer than then a human driver even though Even if humans are still a problem. That's hard to figure out Yeah, that's probably what happens. The times cars will have a small number of accidents humans would have avoided,

but the white, they'll get rid of the bulk of them. What do you think about, like Tesla's dojo efforts, or it can be bigger than Tesla in general, it's kind of like the tense torrent, trying to innovate. Like this is the dichotomy, like should a company try to from scratch build its own, new and network training hardware. Well, first I think it's great. So we need lots of experiments, right? And there's lots of startups working on this and they're pursuing different things.

Now I was there when we started Dojo and it was sort of like, what's the unconstrained computer solution to go do very large training problems? And then there's fun stuff like, you know, we said, well, we have this 10,000 watt board to cool. Well, you go talk to guys at SpaceX and they think 10,000 watts is a really small number, not a big number. And there's brilliant people working on it. I'm curious to see how it'll come out. I couldn't tell you, you know, I know it pivoted a few times since I left. So the cooling, this need to be a big problem. I do like what you know, I said about it, which is like, we don't want to do the thing

unless it's way better than the alternative. Whatever the alternative is. So it has to be way better than like racks of GPUs. Yeah. And the other thing is just like, you know, the Tesla autonomous driving hardware, it was only serving one software stack. And the hardware team and the software team were tightly coupled. Now, if you're building a general purpose AI solution, then you know, there's so many different customers with so many different needs. Now, something Andrei said is, I think this is amazing.

10 years ago, like vision, recommendation, language were completely different disciplines. And he said, the people literally couldn't talk to each other. And three years ago, it was all neural networks, but the very different neural networks. And recently, it's converging on one set of networks. They vary a lot in size, obviously, they vary in data, vary in outputs. But the technology has converged a good bit. Yeah, these transformers behind GBT-3 seems like they could be applied to video, they could be applied to a lot of, and it's like, and they're all really simple.

And it was like, they literally replace letters with pixels. Yeah. It does vision. It's amazing. And it's like, and they're all really. And it was like to literally replace letters with pixels. Yeah. It does vision. It's amazing. So. And then size actually improves the thing. So the bigger it gets, the more compute you throw at it,

the better it gets. And the more data you have, the better it gets. So. So then you start to wonder, well, is that a fundamental thing? Or is this just another step to some fundamental understanding about this kind of computation, which is really interesting? I assume it's don't want to believe that that kind of thing will achieve conceptual understandings you were saying, like you'll figure out physics, but maybe it will. Maybe.

Probably will. Well, it's worse than I do. It'll understand physics in ways that we can't understand. I like your Stephen Wolfram talk where he said, you know, there's three generations of physics. There was physics by reasoning. Well, big things should fall faster than small things, right? That's reasoning. And then there's physics by equations, like, you know, but the number of programs in a world that are solved with the single equations relatively low, almost all programs have, you know, more than a one line of code, maybe a hundred million lines of code. So he said that now we're going to physics by equation, which is his project, which is cool. I might point out there, there was two generations of physics

before reasoning, habit, like all animals, you know, no things fall and you know, birds fly and you know, predators know how to solve a differential equation to cut off an accelerating, curving animal path. And then there was the gods did it. Right? So there's five generations. Now, software 2.0 says programming things is not the last step. Data. So there's going to be a physics,

fast Stevens, Wolfram's comp. That's not explainable. That's not customizable. And actually, there's no reason that I can see while that even that's the limit. Like there's something beyond that. I mean, usually when you have this hierarchy, it's not like, well, if you have this step and this step and this step and the real qualitatively different, and conceptually different, it's not obvious why, you know, six is the right ant number of hierarchy steps and not

seven or eight or... Well, then it's probably impossible for us to comprehend something that's beyond the thing that's not explainable. Yeah, but the thing that you know understands the thing that's not explainable to us, well, conceives the next one. And like, I'm not sure what there's a limit to it. Click your brain heart, sister's head story. Click your brain heart since this had story. If we look at our own brain, which is an interesting illustrative example, in your work with test-thoron and trying to design deep learning architectures, do you think about the brain at all?

Maybe from a hardware designer perspective, if you could change something about the brain, what would you change or do? Funny question. Like how would you do that? So your brain is really weird. Like, you know, your cerebral cortex where we think we do most of our thinking is what, like six or seven neurons thick.

Yeah, like, that's weird. Like all the big networks are way bigger than that. Like way deeper. So that seems odd. And then, when you're thinking, if the input generates a result you can lose, it goes really fast. But if it can't, that generates an output that's interesting, which turns into an input and then your brain until the point where you mull things over for days and how many trips through your brain is that, right? Like it's, you know, 300 milliseconds or something,

you get through seven levels of neurons. I forget the number exactly. But then it does it over and over and over, as it searches. And the brain clearly, it looks like some kind of graph because you have a neuron with connections and it talks to other ones. And it's locally very computationally intense,

but it's also does sparse computations across a pretty big area. There's a lot of messy biological type of things, and it's meaning like, first of all, there's mechanical, chemical, and electrical signals that it's all that's going on. Then there's the asynchronicity of signals, and there's like, there's just a lot of variability. It seems continuous and messy and just the mess of biology. And it's unclear whether that's a good thing or it's a bad thing. Because if it's a good thing, then we need to run the entirety of the evolution. Well, we're going to have to start with basic bacteria to create some.

But imagine we could control, you could build a brain with 10 layers. Would that be better or worse? Or more connections or less connections. Or we don't know to what level our brains are optimized. But if I was changing things, you can only hold seven numbers in your head. Like why not 100 or a million? I know.

Because out of that. And why can't we have a floating point processor that can compute anything we want and see it all properly? Like that would be kind of fun. And why can't we see in four or eight dimensions? Like three Ds kind of a drag. Like all the hard mass transforms are up in multiple dimensions.

So you could imagine a rain architecture that you could enhance with a whole bunch of features that would be really useful for thinking about things. It's possible that the limitations you're describing are actually essential for like the constraints or essential for creating like the depth of intelligence, like that the ability to reason. It's hard to say, because your brain

is clearly a parallel processor. 10 billion neurons talking to each other at a relatively low clock rate, but it produces something that looks like a serial thought process, a serial narrative in your head. That's true. But then there are people famously who are visual thinkers. Like, I think I'm a relatively visual thinker.

I can imagine any object and rotate it in my head and look at it. And there are people who say they don't think that way at all. And recently I read an article about people who say they don't have a voice in their head. They can talk. But when they, you know, it's like, well, what are you thinking? They'll describe something that's visual.

So that's curious. Now if you're saying, if we dedicated more hardware to holding information like 10 numbers or a million numbers, like with that, just distract us from our ability to form those kinds of singular identities. Like it dissipates somehow. But maybe a future humans will have many identities

that have some higher level organization but can actually do lots more things in parallel. Yeah, there's no reason, if we're thinking modularly, there's no reason we can have multiple consciousnesses in one brain. And maybe there's some way to make it faster so that the area of the computation could still have unified feel to it while still having way more ability to do parallel stuff at the same time, could definitely be improved. Could be improved? Yeah.

Well, it's pretty good right now. Actually, people don't give it enough credit. The thing is pretty nice. The fact that the right ends seem to be and give a nice spark of beauty to the whole experience. I don't know. I don't know if it can be improved easily. It could be more beautiful. I don't know how all the ways you can't imagine. No, but that's the whole point. I wouldn't be able to imagine the fact that I can't imagine ways in which it could be more beautiful means.

So you know, Ian Banks, his stories. So the super smart AIs there mostly live in the world of what they call infinite fun, because they can create arbitrary worlds. So they interact and the story has it, they interact in the normal world and they're very smart and they can do all kinds of stuff and you know a given mind can you know talk to a million humans at the same time because we're very slow and for reasons you know artificial the story they're interested in people and doing stuff but they mostly live in this this other land of thinking My

inclination is to think that the ability to create infinite fun will not be so fun. That's sad. Well, there are so many things to do. Imagine being able to make a star, move planets around. Yeah, yeah. But because we can imagine that as wildlife is fun, if we actually were able to do it, it would be a slippery slope where fun would you never have a meaning because we just consistently desensitize ourselves by the infinite amounts

of fun we're having. The sadness, the dark stuff is what makes it fun. I think that could be the Russian. It could be the fun makes it fun and sadness makes it bittersweet. Yeah, that's true. Fun could be the thing that makes it fun. So what do you think about the expansion, not through the biology side, but through the BCI, the brain-computer interfaces? Yeah, you got a chance to check out the neural link stuff. Super interesting. Humans like our thoughts manifest this action. You know, like like as a kid,

you know, like shooting a rifle was super fun driving and many bike doing things. And then computer games, I think for a lot of kids became the thing where they, you know, they can do what they want, they can fly a plane, they can do this, they can do this, right? But you have to have this physical interaction. Now imagine, you know, you could just imagine stuff and it happens, right? Like really richly and interestingly, like we kind of do that when we dream. Like dreams are funny because like if you have some control or awareness in your dreams, like it's very realistic looking or not realistic, it depends on the dream, but you can also manipulate that. And you know, what's possible there is,

is odd in the fact that nobody understands its whole areas, but, do you think it's possible to expand that capability through computing? Sure. Is there some interesting from a hardware designer perspective? Is there, do you think it'll present totally new challenges in the kind of hardware that required that, like, so this hardware isn't standalone computing?

Well, this just networking with the brain. So today, computer games are rendered by GPUs. Right. So, but you've seen the GAN stuff. Right. Where train neural networks render realistic images, but there's no pixels, no triangles, no shaders, no light maps, no nothing. So the future of graphics is probably AI. Right. Yes. Now that AI is heavily trained by lots of real data. Right. So if you have an interface with a AI renderer, right. So if you say render a cat, it won't say, well, how tall is the cat?

And how big it, you know, it'll render a cat. And you might see a little bigger, a little smaller, you know, make it a tabby, shorter hair, you know, like you could tweak it. Like the, the amount of data you'll have to send to interact with a very powerful AI renderer could be low. But the question is, will brain computer interfaces would need to render not onto a screen, but render onto the brain? And directly so there's a bandit.

We'll do it both ways. Our eyes are really good sensors. They could render onto a screen and we could feel like we're participating in it. They're gonna have like the Oculus kind of stuff. It's gonna be so good when a projection to your eyes, you think it's real. You know, there's slowly solving those problems.

Now I suspect when the renderer of that information into your head is also AI mediated, you'll be able to give you the cues that you really want for depth and all kinds of stuff. Your brain is probably faking your visual field, right? Your eyes are twitching around, but you don't notice that. Occasionally they blank, you don't notice that. You know, there's all kinds of things like you think you see over here, but you don't really see there. It's all fabricated. Yeah.

So a peripheral vision is fascinating. So if you have an AI renderer that's trained to understand exactly how you see and the kind of things that enhance the realism of the experience could be super real actually. So I don't know what the limestadder, but obviously if we have a brain interface that goes inside your visual cortex in a better way than your eyes do, which is possible. It's a lot in Iran. Yeah. Maybe that'll be even cooler. Well, the really cool thing is it has to do with the the infinite fun that you're referring to, which is our brains here to be very limited. And like you said, computations. So very plastic. Very plastic. Yeah. Yeah. So it's a it's a

a interesting combination. The interesting open question is the limits of that in your plasticity, like how, how flexible is that thing? Because we don't we haven't really tested it. We know about that experience where they put like a pressure pad on somebody's head and had a visual transducer pressure rise it and somebody slowly learn to see. Yep. It's like it's especially at a young age, if you throw a lot at it, like what can it,

can it complete, so can you like arbitrarily expand it with computing power. So connected to the internet directly somehow. Yeah, there, yes, there's probably else. So the problem with biology and ethics is like, there's a mess there. Like us humans are perhaps unwilling to take risks into directions that are full of uncertainty. No, 90% of the population is unwilling to take risks. The other 10% is rushing into the risks, unated by any infrastructure whatsoever. And that's where all the fun happens in society.

It's been huge transformations in the last couple of thousand years. It's funny, I got in the chance to interact with the Matthew Johnson from John Hopkins. He's doing this large scale study of psychedelics. It's becoming more and more. I've got in the chance to interact with that community of scientists working on psychedelics, but because of that, that opened the door to me to all these, what do they call it, psychonauts, the people who, like you said, the 10% who like, I don't care. I don't know if there's a science behind this, I'm taking this spaceship to, if I'm be the first on Mars, I'll be the, you know, you know, psychedelics, interesting in the sense that in

another dimension, like you said, it's a way to explore the, the limits of the human mind. Like, what is this thing capable of doing? Because you kind of like, when you dream, you detach it, I don't know exactly than your science of it, but you detach your like reality from what know exactly, than your science of it, but you detach your like reality from what your mind, the images your mind is able to conjure up and your mind goes into weird places. And like entities appear somehow Freudian type of like trauma is probably connected in there somehow, but you start to have like these weird vivid worlds that like. So do you actively dream? Do you? Why not?

I feel like six hours of dreams and it's like a really useful time. I know. I haven't, I don't for some reason. I just knock out and I have sometimes like anxiety inducing kind of like very pragmatic like nightmare type of dreams, but nothing fun, nothing, nothing fun, nothing fun. I try, I, I, unfortunately, have mostly have fun in the waking world, which is very limited in the amount of fun you can have. It's not that limited either. Yeah,

that's what we will have to talk. Yeah, and you instructions. Yeah. But there's like a man, no for that. You might want to. I looked it up, I'll ask you on. What, what did you dream? You know, years ago, and I read about, you know, like, you know, a book about how to have, you know,

become aware of your dreams. I worked on it for a while. Like, just this trick about you know Imagine you can see your hands and look out and and I got somewhat good at it like but my mostly when I'm thinking about things are working on problems I I Preped myself before I go to sleep. It's like I I pull into my mind all the things I want to work on or think about and Pull into my mind all the things I want to work on or think about and then That let's say greatly improves the chances that I'll work on that while I'm sleeping and then and then I also

You know basically ask to remember it and I often remember very detailed within the dream. Yeah, or outside the dream well, to bring it up in my dreaming and remember it when I wake up, it's just, it's more of a meditative practice. You say, you know, the prayer yourself to do that. Like if you go to, you know, the sleep, still gnashing your teeth about some random thing that happened that you're not that really interested in, in your dream about it. That's really interesting. Maybe, but you can direct your dreams perhaps somewhat by prepping. Yeah, I'm going to have to try that. It's really interesting. Like the most important, the interesting, not like what what are this guys send an email kind of

like stupid worries stuff, but like fundamental problems you're actually concerned about? Yeah, and prep him. And interesting things you're worried about. Or it's almost your reading or, you know, some great conversation you had or some adventure you want to have. Like there's a lot of space there. And it seems to work that, you know, my percentage of interesting dreams and memories went up.

Is there a, is that the source of, if you were able to deconstruct like where some of your best ideas came from? Is there a process that's at the core of that? Yeah. Like so some people, you know, walk and think, some people like in the shower, the best ideas hit them. If you talk about like Newton, Apple hitting them on the head. Now I, I found out a long time ago, I'm, I, I process things somewhat slowly.

So I can college, I had friends who could study at the last minute, get an A next day. I can't do that at all. So I always front load at all the work. Like I do all the problems early, you know, for finals like the last three days, I wouldn't look at a book because I want, you know, because like a new fact day before finals made screw up, my understanding of what I thought I knew. So my goal was to always get it in and give it time to soak. And I used to, you know, I remember when we were doing like 3D calculus, I would have these amazing dreams of 3D surfaces for normal, you know, I remember when we were doing like 3D calculus, I would have these amazing dreams, 3D surfaces, with normal, you know, calculating the gradient and just like, all

come up. So it was really fun. Like very visual. And if I got cycles of that, that was useful. And the other is, just don't over filter your ideas. Like I like that process of brainstorming where lots of ideas can happen. I like people who have lots of ideas. But then there's a set.

Yeah, let them sit and let it breathe a little bit. And then reduce it to practice. At some point, you really have to does it really work. Is this real or not? But you have to do both. There's greatest tension there. Like how do you be both open and, you know, precise? If you had ideas that you just that sit in your mind for like years before the...

Sure. It's an interesting way to... these generated ideas and just let them sit. Let them sit there for a while. I think I have a few of those ideas. You know, it was so funny. Yeah, I think that's, you know, creativity, this one or something. For the slow thinkers in the in the room, I suppose. As I, some people, like you said, are just like, like the... Yeah, it's really interesting. There's so much diversity in how people think. How fast or slow they are, how well they remember, I'm not super good at remembering facts,

but processes and methods. Like in our engineering, I went to Penn State and almost all our engineering tests were open book. I could remember the page and not the formula. But as soon as I saw the formula, I could remember the page and not the formula. But as soon as I saw the formula, I could remember the whole method if I learned it. Yeah. So it's a funny, where some people could, you know, I had swatched friends like flipping through the book, trying to find the formula, even knowing that they'd done just as much work. And I would just open the book. I was on page 27.

But half I could see the whole thing visually. Yeah. And, you know, and you have to learn that about yourself and figure out what the wouldn't function optimally. I had a friend who was always concerned. He didn't know how he came up with ideas. He had lots of ideas, but he said they just sort of popped up. Like you'd be working on something, you have this idea of like where does it come from.

But you can have more awareness of it. Like, like, like, of it. Like how your brain works as a little murky as you go down from the voice in your head or the obvious visualizations. Like when you visualize something, how does that happen? If I say visualize volcano, it's easy to do it, right? And what does it actually look like when you visualize it? I can visualize to the point where I don't see the very much out of my eyes and I see the colors of the thing of visualizing. Yeah, but there's a shape, there's a texture, there's a color, but there's also conceptual

visualization. What are you actually visualizing when you're visualizing volcano? Just like with peripheral vision, you think you see the whole thing? Yeah, that's a good way to say it. You have this kind of almost peripheral vision of your visualizations. They're like these ghosts. But if you work on it, you can get a pretty high level of detail.

And somehow you can walk along those visualizations that come up in the night, yeah, which is a... But weird. But when you're thinking about solving problems, like you're putting information and you're exercising the stuff you do know, you're sort of teasing the area that you don't understand and don't know but you can almost you know feel You know that process happening, you know, that's that's how I like Like I know sometimes I don't work really hard on something like I get really hot when I'm sleeping and you know

It's like we got the blankets throw all the blankets through on the floor. And you know, every time when it's while I wake up and think, wow, that was great. You know, are you able to reverse engineer what the hell happened there? I was sometimes it's vivid dreams. And sometimes it's just kind of like you say,

like shadow thinking that you sort of have this feeling you're you're going through this stuff, but it's this kind of like you say, like shadow thinking that you sort of have this feeling you're you're going through this stuff But it's it's not that obvious. It's not so amazing that the mind just does all these little Experiments I never you know, I thought I always thought it's like a river that you can't you're just there for the ride But you're right if you prep it. No, it's it's all understandable. The meditation really helps you got to start figuring out you need to learn language if you're on mind. And there's multiple levels of it. But yeah, abstractions again, right? It's somewhat comprehensible and observable and feelable or whatever the right word is.

Yeah, you're not long for the ride. You are the ride. I have to ask you hardware engineer working on your own networks now, what's consciousness, what the hell is that thing? Is that just some little weird quirk of our particular computing device, or is it something fundamental that we really need to crack open for to our to to build like good computers. Do you ever think about consciousness like why it feels like something to be? I know it's it's really weird. So yeah. I mean, everything about it's weird. First is to have a second behind reality. Right. It's a post-hoc narrative about what

happened. You've already done stuff by the time you're conscious of it. And you're conscious, and this generally is a single threaded thing, but we know your brain is 10 billion neurons running some crazy parallel thing. And there's a really big sorting thing going on there. It also seems to be really reflective in the sense that you can create a space in your head. Like we don't really see anything, right? Like photons hit your eyes, it gets turned into signals that go through multiple areas, the neurons, you know, like I'm so curious that, you know, that looks glassy and that looks not glassy. Like, like how the resolution of your vision is so high yet to go through all this processing. Yeah.

We're for most of it, it looks nothing like vision. Like, like there's no theater in your mind. Right. So we, we have a world in our heads. We're literally desicculated behind our sensors, but we can look at it, speculate about it, speculate about alternatives, problem solve, what if, you know, there's so many things going on, and that process is lagging reality. And it's single threaded, even though the underlying thing is like mass-lipped parallel. Yeah, so it's so curious. So imagine you're building an AI computer. If you want to replicate humans,

well, you'd have huge arrays of neural networks and apparently only sixers have indeed, which is hilarious. They don't even remember seven numbers. But I think we can upgrade that a lot, right? And then somewhere in there, you would train the network to create, basically, the world that you live in, right? So like tell stories to itself about the world that is

proceeding. Well, create the great the world, tell stories in the world, and then have many dimensions of, you know, like side jokes to it, like we have an emotional structure, like we have a biological structure, and that seems hierarchical to like, like if you're hungry, dominate your thinking, if you you hungry, dominate your thinking, if you're mad at dominate your thinking, like, and we don't know if that's important to consciousness or not, but it certainly disrupts, you know, intrudes in the consciousness. Like so there's lots of structure to that. And we like to dwell on the past. We like to think about the future. We like to imagine we like to fantasize, right? And the somewhat circular observation of that is the thing we call

consciousness. Now, if you created the computer system, the Dittles, things create worldviews, create the future alternate histories, you know, dwell on past events, you know, accurately or semi accurately. You know, it's, it's, we're conscious. Just bring up like, well, with that feel, look and feel conscious to you. Like you know, it's, it's, it's, we're conscious and just spring up like, like, well, with that feel, look and feel conscious to you. Like you see, just to be what I, I'd start to observe or see. Do you think a thing that looks conscious is conscious? Like, do you, again, this is like an engineering kind of question, I think, because, like, if we want to engineer consciousness, is it okay to engineer something that just looks

conscious? Or is there a difference between that? Well, we have all consciousness because it's a super effective way to manage our affairs. Yeah, right. It's a social element. Yeah. Well, it gives us a planning system. We have a huge amount of stuff.

Like when we're talking, like the reason we can talk really fast is we're modeling each other a really high level of detail. And consciousness is required for that. Well, all those components together manifest consciousness. So if we make intelligence and beings that we wanna interact with,

that we're wondering what they're thinking, looking forward to seeing them. When they interact with them, they're thinking, looking forward to seeing them. When they interact with them, they're interesting, surprising, fascinating. They will probably be feel conscious like we do, and we'll perceive them as conscious. I don't know why not, but don't know. Another fun question on this, because from a computing perspective, we're trying to create something that's human-like or superhuman-like. Let me ask you about aliens.

Aliens. Do you think there's intelligent alien civilizations out there, and do you think their technology, their their Technology they're computing their AI bots There are their chips are of the same nature as ours Yeah, I got no idea. I mean if there's lots of aliens out there. They've been awfully quiet You know there's their speculation about why There seems to be more than enough planets out there. There's a lot. There's intelligence in life on this planet that seems quite different. Dolphins seem plausibly understandable. Octopuses don't seem understandable at all.

If they live longer than a year, maybe they would be running the planet. They seem really smart. And there are neural architectures completely different than ours. Now, who knows how they perceive things. I mean, that's the questions for us intelligent beings We might not be able to perceive other kinds of intelligence if they become sufficiently different than us Yeah, like we live in the current constrained world, you know, it's three-dimensional geometry and the geometry defines a certain amount of physics and you know, there's like how time work seems to work.

There's so many things that seem like a whole bunch of the input parameters to the, you know, another conscious being or the same. Yes. Like if it's biological, biological things seem to be in a relatively narrow temperature range, right? Because, you know, organics don't aren't stable, too cold, or too hot. So, if you specify the list of things that input to that, but, as soon as we make really smart, you know, beings, and they go solve about how to think about a billion numbers

at the same time, and how to think in end dimensions. There's a funny science fiction book where all the society had uploaded it into this matrix. And at some point some of the beings in the matrix thought, I wonder if there's intelligent life out there. So they had to do a whole bunch of work to figure out like how to make a physical thing because their matrix was self-sustaining and they made a little spaceship and they traveled to another planet when they got there. There was like life running around but there was no intelligent life and then they figured out that there was these huge you know organic matrix all over the planet inside there were intelligent beings that uploaded themselves into that matrix. So everywhere, it always at life was soon as it got smart. It upleveled itself

with something way more interesting than 3D geometry and it escaped whatever this is. No, not escaped, but it's involved is better. Yeah. The essence of what we think of as an intelligent being, I tend to like the thought experiment of the organism, like humans aren't the organisms. I like the notion of like Richard Dawkins and memes that ideas themselves are the organisms, like, that are just using our minds to evolve. So, like, we're just like meat receptacles for ideas to breed and multiply and so on. And maybe those are the aliens.

So, Jordan Peterson has lines says, you know, you think you have ideas, but ideas have you. Right? Good line. Which, and then we know about the phenomenon of group think and there are so many things that constrain us. But I think you can examine all that and not be completely owned by the ideas and completely sucked into group think. And part of your responsibility is a human is to escape that kind of phenomena, which isn't, you know, it's one of the creative tension things again. You're constructed by it, but you can still observe it and you can think about it and you can make choices about to some level how constrained you are by it. And, you

know, it's useful to do that. And, but, but they're at the same time, and it could be by doing that, you know, the group in society, your part of becomes collectively even more interesting. So, you know, so the outside observer will think, wow, you know, all these lexes running around with all these really independent ideas have created something even more interesting in the aggregate. So, so I don't know, I'm, those are lenses to look at the situation, but it'll give you some inspiration, but I don't think they're constrained. Right. You know, as a small little quirk of history, it seems like you're related to Jordan

Peterson. Thank you, mentioned. He's going through some rough stuff now. Is there some common you can make about the roughness of the human journey, ups and downs? Well, I became an expert in Benzow withdrawal, which is, you as beans and at some point they interact with GABA circuits You know the reduce anxiety and do a hundred other things like there's actually no known list of everything They do because they interact with so many parts of your body and Then once you're on them you habituate to them and you're you have a dependency It's not like you're a drug dependency. We're trying to get high. It's a it's a metal ball of dependency. And then if you discontinue them,

there's a funny thing called kindling, which is if you stop them and then go, you know, you'll have a horrible withdrawal symptoms. If you go back on them at the same level, you won't be stable. And that unfortunately happened to him. Like because it's so deeply integrated to all the kinds of systems in the body. It literally changes the size and numbers of neurotransmitter sites in your brain. So there's a process called the Ashton Protocol where you taper it down slowly over two years. The people go through that, go through unbelievable hell. And what Jordan went through seemed to be worse because on advice of doctors, you know, we'll stop taking these and take this,

it was the disaster. And he got some, yeah, it was pretty tough. Um, he seems to be doing quite a bit better intellectually. You can see his brain clicking back together. I spent a lot of time with him. I've never seen anybody suffer so much. Well, his brain is also like this powerhouse, right? So I wonder, does a brain that's able to think deeply about the world suffer more through these kinds of withdrawals? Like, I don't know. I've watched videos of people going through withdrawal. They all seem to suffer unbelievably. And you know, my art goes out to everybody. And there's some funny math about this. Some doctors said, as best you can tell, you know, there's the standard recommendations don't take them for more than a month. And then taper over a couple of weeks.

Many doctors prescribe them endlessly, which is against the protocol, but it's common. Right. And then something like 75% of people, when they taper, it's, you know, have to people have difficulty, but 75% get off. Okay. 20% have severe difficulty and 5% have life threatening difficulty. And if you're one of those, it's really bad. And the stories that people have on this is heart breaking and tough. So you put some of the fault that the doctors, they just not know what the hell they're doing? Oh, no. It's hard to say. It's one of those commonly prescribed things.

Like, one doctor said what happens is if you're prescribed them for a reason and then you have a hard time getting off, the protocol basically says you're either crazy or dependent. And you get kind of pushed into a different treatment regime, drug addict or a psychiatric patient. And so like one doctor said, I prescribed for the 10 years thinking I was helping my patients and I realized I wasn't really harming them. And the awareness of that is slowly coming up. The fact that they're casually prescribed, the people is horrible, and it's bloody scary.

And some people are stable on them, but they're on them for life. Like once, you know, it's another one of those drugs that, but Benzo's long range have real impacts on your personality. People talk about the Benzo bubble where you get disassociated from reality and your friends a little bit. It's really terrible. The mind is terrifying. We're talking about how the infinite possibility of fun,

but it's the infinite possibility of suffering too, which is one of the dangers of expansion of the human mind. It's like, I wonder if all the possible experiences that an intelligent computer can have, is it mostly fun or is it mostly suffering? So like if you brute force expand, the set of possibilities, like, are you going to run into some trouble

in terms of like torture and suffering and so on. Maybe our human brain is just protecting us from much more possible pain and suffering. Maybe the space of pain is like much larger than we could possibly imagine. The world's in a balance. You know, all the literature on religion and stuff is, you know, the struggle between court, good and evil is, is balanced for very finely tuned for reasons that are complicated. But that's a, that's the one full, soft, cold conversation. I was speaking of balance that's complicated. I wonder because we're living through one of the more important moments in human history with this particular virus, it seems like pandemics have at least

the ability to kill off most of the human population at their worst. And there's just fascinating, because there's so many viruses in this world. I mean, viruses basically run the world in a sense that they've been around very long time. They're everywhere. They seem to be extremely powerful in their just, in their just tribute kind of way, but at the same time, they're not intelligent and they're not even living. Do you have like high level thoughts about this virus that, like in terms of you being fascinated or terrified or someone between? So I believe in frameworks, right? So like one of them is evolution. So I believe in frameworks, right? So like one of them is evolution.

Like we're evolved creatures, right? Yes. And one of the things about evolution is it's hyper-competitive. And it's not competitive out of a sense of evil. It's competitive in a sense of there's endless variation and variations that work better when. And then over time there's so many levels of that competition. You know, like multisolular life partly exists because of, you know, the competition between,

you know, different kinds of life forms. And we know sex partly exists to scramble our genes so that we have, you know, genetic variation against the invasion of the bacteria and the viruses. And it's endless. Like, I've had some funny statistic, like the density of viruses and bacteria in the ocean is really high. And one third of the bacteria die every day

because the viruses invaded them. Like one third of them. Wow. Like, I don't know if that number is true, but it was like, there's like the amount of competition and what's going on is stunning. And there's a theory as we age, we slowly accumulate back periods and viruses

and as our immune system kind of goes down, that's what slowly kills us. And. It just feels so peaceful from a human perspective when we sit back and are able to have a relaxed conversation and there's wars going on. Right now, you're harrowing how many bacteria and the ones, many of them are parasites on you and some of them are helpful and some of them are modifying your behavior and some of them are.

It's really wild. But this particular manifestation is unusual in the demographic, how it hit, in the political response that it engendered, and the healthcare response that it engendered, and the technology it engendered, it's kind of wild. And the communication on Twitter that it led to all the kind of stuff, I'd ever feel lovely.

But what usually kills life, the big extinctions are caused by meteors and volcanoes. That's the one you're worried about. I was supposed to human-created bombs. And that would be a lot cheaper. Solar flares are another good one. Occasionally, solar flares hit the planet.

So it's nature. Yeah, it's all pretty wild. On another historic moment, this is perhaps outside, but perhaps within your space of frameworks that you think about that just happened, I guess a couple weeks ago, is I don't know if you're paying attention, it's all the game stop and while she bets. So it's really fascinating. There's kind of a theme to this conversation today, because it's like, you know, on that works,

it's cool how there's a large number of people in a distributed way, almost having a kind of fun or able to take on the powerful elites, elite hedge funds, centralized powers, and overpower them. Do you have thoughts on this? I mean, there's whole saga. I don't know enough about finance, but it was like the Elon, you know, Robin Hood guy

when they talked. Yeah, where did you think about that? Well, Robin Hood guy didn't know how the finance system worked. That was clear, right? He was treating like the people who settled the transactions as a black box. And suddenly, somebody called him up and said, hey, black box calling you, your transaction volume means you need to put out $3 billion right now. And he's like, I don't know, $3 billion.

Like I don't even make any money on these trades. Why do I owe $3 billion for your sponsor in the trade? So there was a set of abstractions that, I don't think either, like now we understand it. This happens in chip design. Like you buy wafers from TSMC or Samsung or Intel and they say it works like this and you do your design based on that

and then chip comes back and doesn't work. And then suddenly you start having to open the black boxes and the transistor is really work like they know, what's the real issue? So, so there's a whole set of things that created this opportunity and somebody spotted it. Now, people spot these kinds of opportunities all the time. So it's been flash crashes, there's been, you know, there's always short squeezes. They're fairly regular. Every CEO I know hates the shorts because they're manipulating, they're trying to manipulate

their stock in a way that they make money and you know, deprive value from both this, you know, the company and the investors. So the fact that, you know, some of these stocks were so short, it's hilarious, that this hasn't happened before. I don't know why. I don't actually know why some serious hedge funds didn't do it to other hedge funds. And some of the hedge funds actually made a lot of money on this. So my guess is we know 5% of what really happened and that a lot of the players don't know what happened.

And people probably made the most money art to people that they're talking about. Yeah. That's. Do you think there was something? I mean, this is the cool kind of Elon, you're the same kind of conversationalist, which is like first principles, questions of like, what the hell happened? Just very basic questions of like, was there something shady going on? What, you know, where the parties involved? Is the basic questions that everybody wants to know about? Yeah, so like we're in a very hyper competitive world, right? But transactions like buying, so in stock, is a trust event.

You know, I trust the company representative sells properly, you know, I bought the stock because I think it's going to go up. I trust that the regulations are solid. Now inside of that, there's all kinds of places where you know humans over trust and, you know, this this expose, let's say some weak points in the system. expose, let's say some weak points in the system. I don't know if it's going to get corrected. I don't know if we have close to the real story. My suspicion is we don't. And listen to that guy, he was like a little wide-eyed about him and he did this and then they did that. And I was like, I think you should know more about your business than that. But again, there's many businesses when this layer is really stable. You stop paying attention to it. You pay attention to the stuff that's bugging you or new.

You don't pay attention to the stuff that just seems to work all the time. You just, you know, skies blue every day, California. And I remember once while I was in the rain and I was like, what do we do? Somebody go bring in the lawn furniture, you know, like it's getting wet. You don't know, it's getting wet. Yeah, it does. I was a louferer. I was a louferer.

I was a louferer. I was a louferer. I was a louferer. I was a louferer. I was a louferer. I was a louferer. I was a louferer. I was a louferer. I was a louferer.

I was a louferer. I was a louferer. I was a louferer. I was a loufer. I was a loufer. I was a loufer. I was a loufer. I was a loufer. I was a loufer. about is there's a lot of unexpected things that happen with the scaling. And you have to be, I think the scaling forces you to then return to the fundamentals. Well, it's interesting because when you buy in cell stocks, the scaling is, you know, the stocks don't only move in a certain range, and if you buy a stock, you can only lose that amount

of money. On the short market, you can lose a lot more than you can benefit. Like it has a weird cost, you know, cost function or whatever the right word for that is. So he was trading in a market where he wasn't actually capitalized for the downside. If it got outside a certain range, now whether something of various has happened, I have no idea. But at some point, the financial risk to both him and his customers was way outside of his financial capacity and his understanding how the system worked was clearly weak or he didn't represent himself. I don't know the person.

And I listened to him, Nick. It could have been the surprise question. I was like, and then these guys called and it sounded like he was treating stuff as a black box. Maybe he shouldn't have, but maybe his whole pilot's were somewhere else and it was going on. I don't know. Yeah. I mean, this is, this is one of the qualities of a good leader is under fire. You have to perform. And that means to think clearly and to speak clearly. And he dropped the ball on those things.

And to understand the problem, quickly learn and understand the problem at the basic level, like what the hell happened. And my guess is, you know, at some level it was amateur's trading against expert slash insiders, slash people with special information. All-siders versus insiders. Yeah. And the insiders, you know, experts slash insiders slash people with, you know, special information. outsiders, there's insiders.

Yeah. And the insiders, you know, my guess is the next time this happens, we'll make money on it. The insiders always win. Well, they have more tools and more incentive. I mean, this always happens like the outsiders are doing this for fun. The insiders are doing this 24th. But there's numbers in the outsiders. This is the interesting thing is there's numbers on the insiders are doing this 24-sap. But there's numbers in the outsiders. This is the interesting thing is there's numbers on the insiders too. Different kind of numbers.

Different kind of numbers. But this could be a new era because I don't know, I at least I didn't expect that a bunch of redditors could, you know, there's millions of people that get to you. So the next one will be a surprise. But don't you think the crowd, the people are planning the next attack? We'll see. Good has to be a surprise can't be the same game.

And so the there's like, it could be there's a very large number of games to play. And they can be ads all about it. I don't know. I'm not an expert. Right. That's a good question. How the space of games, how restricted is it? And the system is so complicated, it could be relatively unrestricted. And also, like, you know, during the last couple of financial crashes, you know, what set it off was, you know, sets of derivative events where, you know, the, you know, Nessim Talibs, you know,

saying is they're, they're trying to lower volatility in the short run by creating tail events. And the system's always evolved towards that and then they always crash. The gas curve is the, you know, star low, ramp, plateau, crash. It's 100% effective. In the long run, let me ask you some advice to put on your profound hat. What is a bunch of young folks to listen to this thing for no good reason whatsoever? Undergraduate students, maybe high school students, maybe just young folks, so young

and hard looking for the next steps of taking life. What advice would you give to a young person today about life, maybe career, but also life in general? Get good at some stuff. Well, get to know yourself, right? Get good at something that you're actually interested in. You have to love what you're doing to get good at it. You really got to find that. Don't always tell your time doing stuff that's just boring or bland or numbing. Right? Don't let old people screw you. People get talked into doing all kinds of shit and writing up a huge student of, you know, student deaths and like there's so much crap going on, you know. And the drains your time and drains your point of view. And you know, going on, you know. And the drains your time and drains. Yeah, the earthquake and the you know, thesis that, you know, the older generation

won't let go. Yeah. And they're trapping all the young people. And then there's some truth to that. Yeah. There is. Well, just because you're old doesn't mean you stop thinking, I know that's a really original old people.

I'm an old person. So, but you have to be conscious about it, you can fall into the rots and then do that. I mean, when I hear young people spouting opinions, it sounds like they come from Fox News or CNN, I think they've been captured by group thinking memes and I suppose I think on their own. You know, so if you find yourself repeating what everybody else is saying, you're not going to have a good life. Like, that's not how the world works. And maybe it seems safe, but it puts you at great jeopardy for well-being, boring, or unhappy.

Or how long to take you to find the thing that you have fun with? Oh, I don't know. I've been a fun person since I was pretty little. So everything? I've gone through a couple periods of depression in my life. Or good reason or for the reason this doesn't make any sense. Like something's hard. Like you go through mental transitions in high school. I was really depressed for a year and I think I had my first midlife crisis at 26.

I kind of thought is this all there is. Like I was working first midlife crisis at 26. I kind of thought, is this all there is? Like I was working at a job that I loved. But I was going to work and all my time was consumed. What's the escape out of that depression? What's the answer to is, is this all there is? Well, my friend, I asked him because he was working at S.O. I said, what's your work life balance? Like there's work, friends asked him, because he was working in a cell phone, I said, what's your work life balance? Like, there's, you know, work, friends, family,

personal time. But you balance in the end of that, and he said, work 80% family, 20% and I tried to, I tried to find some time to sleep. Like, there's no personal time, there's no passion at a time. Like, you know, the young people are often passionate about work,

so, and I was sort of like that. But you need to have some space in your life for different things. And that's that creates that makes you resistant to the whole, the deep, the deep dips into depression kind of thing. Yeah, well, you have to get to know yourself too. Meditation helps. Some physical, kind of thing. Yeah, well, you have to get to know yourself too. Meditation helps. Some physical, something physically intense helps. Like the weird places your mind goes kind of thing. Like, and why does it happen? Why do you do what you do? Like triggers, like the things that cause your mind to go to different places kind of thing or like your events, like you're upbringing for better or worse, whether your parents are great people or not.

You, you, you come into, you know, adulthood with all kinds of emotional burdens. Yeah. And you can see some people are so bloody stiff and restrained and they think, you know, the world's fundamentally negative. Like you maybe, you have unexplored territory. Yeah. Or you're afraid of something? Definitely afraid of quite a few things. You tend to go face them.

Like what's the worst thing that can happen? You're going to die, right? Like that's inevitable. You might as well get over that, like a hundred percent that's right. My people are worried about the virus, but you know, the human condition is pretty deadly. There's something about embarrassment that's, I've competed a lot in my life and I think the, if I'm too introspective, the thing I'm most afraid of is being humiliated, I think. Really, and nobody cares about that. Look, you're the only person on the point. Exactly. It cares about you being humiliated. Exactly. So it can really useless thought. It is. It's like, you're

all humiliating something happened in a room full of people. They walk out and they didn't think about it one more second, or maybe somebody told a funny story to somebody else. And then it just fades it throughout. Yeah. Yeah. Now I know it too. I mean, I've been really embarrassed about shit that nobody cared about myself. Yeah. It's a funny thing. So the worst thing ultimately is just, uh, yeah. Yeah, but that's the cage. I mean, you have to get out of it.

Yeah. Like, once you, here's the thing, once you find something like that, you have to be determined to break it. Because otherwise, you'll just, you know, so you accumulate that kind of junk and then you die as a, you know, a mess. So the goal, I guess it's like a cage within a cage. I guess the goal is to die in the biggest possible cage. Well, I believe you'd have no cage. Well, people do get light and I've not a few.

It's great. You found a few. There's a few out there. I don't know. Of course, sir. Either that or they have, you know, it's a great sales pitch. There's like a light in people, write books and do all kinds of stuff.

It's a good way to solve books. I'll give you that. You've never met somebody you just thought, they just kill me. Like this, like mental clarity humor. No, 100%. But I just feel like they're living in a bigger cage. They have their own.

You still think there's a cage. They're still a cage. You secretly suspect there's always a cage. There's no, there's nothing outside the universe. There's nothing outside the cage. You were, you were, you worked in a bunch of companies. You led a lot of amazing teams. I don't, I'm not sure if you've ever been in the early stages of a startup,

but do you have advice for somebody that wants to do a startup or build a company, build a strong team of engineers that are passionate, just want to solve a big problem. Is there more specifically on that point? You have to be really good at stuff. If you're going to lead and build a team, you better be really interested in how people work and think. The people or the solution to the problems, there's two things, right?

One is how people work and the other is the problem. I say, there's quite a few successful startups. There's pretty clearly found, we're still not anything about people. Like the other is. There's quite a few successful startups. There's really clearly founder still knowing about people. Like the idea was so powerful that it prepped them. But I suspect somewhere early, they hired some people who understood people. Because people really need a lot of care

and feeding the collaborate to work together and feel engaged and work hard. Startups are all about out producing other people. Like you're nimble because you don't have any legacy. You don't have a bunch of people who are depressed about life, you know, just showing up. So startups have a lot of advantages that way. You know? Do you like the C-Jobs talked about this idea of A-Players and B-Players?

I don't know if you know this formulation. Yeah, no. Organizations that could take them over by B player leaders, often really underperform their Hairsi players. That said, in big organizations, there's so much work to do. And there's so many people who are happy to do what, like the leadership or the big idea people can consider menial jobs. You need a place for them, but you need an organization that

both values and rewards them but doesn't let them take over the leadership of it. Got it. You need to have an organization that's resistant to that. But in the early days, the notion with Steve was that like one B player in a room of A players will be like destructive to the whole. I've seen that happen. I don't know if it's like always true. Like, you know, you run into people who clearly B players, but they think they're A players,

and so they have a loud voice at the table, and they make lots of demands for that. But there's other people who are like, I know who I am. I just want to work with cool people on cool shit and just tell me what to do and I'll go get it done. So you have to, again, this is like people's skills. What kind of person is it? I've met some really great people I love working with. That weren't the biggest ID people. They're most productive ever, but they show up. They get it done. They create connection and community that people value. It's pretty diverse. I don't think there's a recipe for that. I got to ask you about love. I heard you into this now.

Into this love thing. Yeah. You think this is your solution to your depression? No, I'm just trying to, like you said, the lighten people in occasion trying to sell a book. I'm writing a book about love. You're writing a book about love? No, I'm not. I'm not. I'm not. I'm afraid of it.

You gotta. So you should really write a book about it. You're on your management philosophy. You said it'd be a short book. I'm not. I'm not. Well, that one was all pretty well. What role do you think love, family, friendship, all that kind of human stuff playing a successful

life? You've been exceptionally successful in the space of like running teams, building cool shit in this world, creating some amazing things. What did love get in the way? Did love help? The family get in the way the family help. You want the engineer's answer? Please. So, like, first love is functional, right?

It's functional in a way. So, we habituate ourselves to the environment. And actually Jordan told me, Jordan Peterson told me this line. So, you go through life and you just get used to everything except for the things you love They they they remain new Like this is really useful for you know Like like other people's children and dogs and you know trees You just don't pay that much attention to your own kids your monitor them really closely Like and if they go off a little bit because you love them if you're smart

If you're gonna, if you're going to be a successful parent, you notice it right away. You don't habituate to just things you love. And if you want to be successful at work, if you don't love it, you're not going to put the time in somebody else. That's somebody else that loves it. Because it's new and interesting and that lets you go to the next level. So it's a thing, it's just a function

that generates newness and novelty and surprises, you know, those kinds of things. It's really interesting. But there's people figured out lots of frameworks for this. Like humans seem to go in partnership, go through interest. Like suddenly somebody's interesting and then you're infatuated with them, and then you're in love with them.

And then different people have ideas about parental love or mature love. You go through a cycle of that, which keeps us together, and it's super functional for creating families and creating communities and making you support somebody despite the fact that you don't love them. And it can be really enriching. You know, no, no, in the work-life balance scheme, if all you do is work, you think you may be optimizing your work potential, but if you don't love your work or you don't have optimizing your work potential, but if you don't love your work or you don't have family and friends and things you care about, your brain isn't well balanced.

Like everybody knows the experience of your work, so on something a little week, you went home and took two days off and you came back in. The odds of you working on the thing, you picking up a break where you left off is zero. Your brain refactored it. But being above is great. It's like changes the color of the light in the room. It creates a spaciousness that's different. It helps you think. It makes you strong. Bukowski had this line about love being a fog that dissipates with the first light of reality in the morning.

That's depressing. I think it's the other way around. It lasts. Well, you like you said, it's a function. It's a thing that generates you. It can be the light that actually in live in your world and creates the interest and the power and the strengths and the to go do something. That sounds like there's like physical love, emotional love, intellectual love, spiritual love. Isn't it all the same thing? Nope.

You should differentiate that. Maybe that's your problem. In your book, you should refine that a little bit. It's different chapters. Yeah, there's different chapters. What's that? These are, aren't these just different layers of the same thing, of the stack? No.

Physical. People, some people are addicted to physical love, and they have no idea about emotional or intellectual love. Right. I don't know if they're the same things, I think they're different. That's true, they could be different. I guess the ultimate goal is to be the same. Well, if you want something to be bigger and interesting,

you should find all its components and differentiate them, not clown it together. People do this all the time, they, yeah, and the modularity. Get your abstraction layers right and then you can, you have room to breathe. Well, maybe you can write the forward to my book about love or the afterwards. You really tried. I feel like Lutz has been a lot of proud of this book. Well, you have things in your life that you love. Yeah. Yeah.

So, and then they are, you're right, they're modular. It's, it's quite, and you can have multiple things with the same person or the same thing. And, but, yeah, depending on the moment of the day. Yeah, there's like what Bokoski described is that moment when you go from being in love to having a different kind of love. Yeah. Right. And that's a transition. But when it happens, if you've read that owner's manual and you believed it, you would have said, oh, this happened.

It doesn't mean it's not love. It's a different kind of love. But, but maybe there's something better about that is you grow old. If all you do is regret how you used to be. It's sad. Right? You should have learned a lot of things because like who you can be in your future self is actually more interesting and possibly delightful than you know being a mad kid and love with the the next person like that's super fun when it happens but that's that's you know 5% of the possibility But yeah, that's right that there's a lot more fun to be had in the long lasting stuff. Yeah, or meaning You know if that's anything which is a kind of fun. It's a deeper kind of fun. And it's surprising, you know, that's like like the thing I like is surprises

You know and you just never know what's going to happen. But you have to look carefully, you have to work out, you have to think about it. Yeah, you have to see the surprises when they happen, right? You have to be looking for it. From the branching perspective, you mentioned regrets. Do you have regrets about your own trajectory? Oh, yeah, of course. Yeah, some of it's painful, but you want to hear the painful stuff. I say, like in terms of working with people, when people did say, stuff I didn't like, especially if it was a bit in the various,

I took it personally, and I also felt it was personal about them. But a lot of times, like humans are lot of times, most humans are a mess. And then they act out and they do stuff. And this psychologist, I heard a long time ago, said, you tend to think somebody does something to you. But really what they're doing is they're doing what they're doing while they're in front of you. It's not that much about you. Yeah. Right. And as I got more interested in, you know, when I work with people, I think about

them and probably analyze them and understand them a little bit. And then when they do stuff, I'm way less surprised. And I'm way, you know, and if it's bad, I'm way less hurt. And I react way less. Like I sort of expect everybody's got their shit. Yeah. And it's not about you. It's not about me that much. It's like, you know, you know, you do something and you think you're embarrassed but nobody cares.

Like and somebody's really mad at you. The odds of it being about you. Now they're getting mad the way they're doing that because of some pattern they learned and, you know, and maybe you can help them if you care enough about it. But or you could step, you could see a comment and step out of the way they're doing that because of some pattern they learned. And, you know, and maybe you can help them if you care enough about it, but, or you could, you could see a coming and step out of the way. Like, like, I wish I was way better at that. I'm a bit of a hothead. And so regret that. You said with Steve, that was a feature, not a bug. Yeah. Well, he was using it as the counter for us. So orderliness, that would crush his work. Well, you were doing the same. Yeah. Maybe I don't think I don't think my my vision was big enough.

It was more like I just got pissed off and did stuff. I'm sure that's the yeah, you're telling. I don't know if it had the it didn't have the amazing effect that created the Trillion Thor company was more like I just got pissed off and left and or made enemies that he shouldn't have. Yeah, it's hard. Like I didn't really understand politics until I worked at Apple where Steve was a master player of politics and his staff had to be or they wouldn't survive him and it was definitely part of the culture. And then I've been in companies where they say it's political but it's all fun and games compared to Apple. And it's not that the people at Apple or bad people is just they operate politically at a higher level. It's not like, oh, somebody said something bad about somebody, somebody else,

which is most politics. They had strategies about accomplishing their goals. Sometimes, you know over the dead bodies of their enemies you know with some game of thrones. Yeah, more game of thrones and sophistication and like a big time factor rather than a You know, well, that requires a lot of control over your emotions. I think To to to have a bigger strategy in the way behave. Yeah. And it's effective in the sense that coordinating thousands of people to do really hard things where many of the people in there don't understand themselves much less how they're

participating creates all kinds of drama and problems that, you know, our solution is political and nature. How do you convince people? How do you leverage them? How do you motivate them? How do you get rid of them? There's so many layers of that that are interesting. Even though some of it, let's say, may be tough, it's not evil. Unless you use that skill to evil purposes, which some people obviously do. But it's a skill set that operates. And I wish I'd, you know, I was interested in it, but I, you know, it was sort of like,

I'm an engineer, I do my thing. And, you know, there's times when I could have had a way bigger impact. If I, you know, knew how to, if I paid more attention and knew more about that, about the human layer of the stack. Yeah, that human political power, expression layer of the stack, just complicated. And there's lots to know about it. I mean, people are good at it or just amazing. And when they're good at it and let's say relatively kind and oriented a good direction, you can really feel, you can get lots of stuff done and coordinate things

that you never thought possible. But all people like that also have some pretty hard edges because you know, it's a heavy lift. And I wish I spent more time with that one, I was younger. But maybe I wasn't ready. You know, I was a wide-eyed kid for 30 years. It's a little bit of a kid. I know.

What do you hope your legacy is when there's a book like a H Hikers guy to the galaxy and there's like a one-centren entry bulge in with their from like that guy lived at some point. There's not many, you know, not many people would be remembered. You're one of the sparkling little human creatures that had a big impact in the world. How do you hold, you'll be remembered?

My daughter was trying to get, she added, my Wikipedia page to say that I was a legend in the guru. But they took it out, so she put it back into use 15. I think I think that was probably the best part of my legacy. She got a sister and they were all excited. They were like trying to put it in the references because there's articles in that. I'm telling you that.

So the eyes of your kids here are legend. Well, they're pretty skeptical because I know be better than that. They're like, legend. Well, they're pretty skeptical, because they know be better than that. They're like, dad. So yeah, that's, that's super, that kind of stuff is super fun. In terms of the big legend stuff, I don't care. They don't care.

Legacy on them, I don't really care. He's just an engineer. Yeah, they've been thinking about building a big pyramid. So I did a big with a friend about whether pyramids or craters are cooler. And you realize that there's craters everywhere, but you know, they build a couple of pyramids 5,000 years ago and they remember you for a while. We're still talking about it.

I think that would be cool. Those aren't easy to build. Oh, I know. And they don't actually know how they built them, which is great. There, it's either a GI or aliens could be involved. So I think, I think you're gonna have to figure out quite a few more things than just the basics of civil engineering. So I guess you hope your legacy is pyramids.

That would, that would be cool. And my Wikipedia page, you know, get enough data by my daughter periodically. Like, those two things would pretty much make it. Jim, it's a huge honor talking to you again. I hope we talk many more times in the future. I can't wait to see what you do with TimeStorent. I can't wait to use it. I can't wait for you to revolutionize yet another space in computing. It's a huge honor to talk to you. Thanks for talking to me.

This is fun. Thanks for listening to this conversation with Jim Keller. And thank you to our sponsors. Athletic Greens, all-in-one nutrition drink, Brooklyn and Sheets, express VPN, and Bell Campo Grass-fed meat. Click the sponsor links to get a discount and to support this podcast.

And now, let me leave you with some words from Alan Turing. Those who can imagine anything can create the impossible. Thank you.