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

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 athletic greens all-in-one nutrition drink brooklyn and sheetz expressvpn and belcampo grass-fed meat click the sponsor links to get a discount and 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 was 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 all 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 in my work and in life if you enjoy this thing subscribe on youtube review on apple podcast follow on spotify support on patreon or connect with me on twitter alex friedman and now here's my conversation with jim keller what's the value and effectiveness of theory versus engineering this dichotomy in uh building good software or hardware systems well it's good designs both i guess that's pretty obvious but engineering do you mean you know reduction of 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 like 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 practice is known message now because of the complexity of the computers we built you know you could think you're 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 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 is it in the space well i'll give you an example so one of the limits of computer performance is branch predictions so and there's there's a whole bunch of ideas about how good you could predict the branch and people said there's a limit to it it's an asthmatic curve and somebody came up with a better way to do branch prediction it was 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 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 it was a real idea and branch prediction is is one of the key problems underlying all of sort of the lowest level of software it boils down to branch prediction boils down to uncertainty computers are limited by you know single thread computers limited by two things the predictability of the path of the branches and the predictability of the locality of data so we have predictors that now predict both of those pretty well yeah so memory is you know a couple hundred cycles away local cache is couple cycles away when you're executing fast virtually all the data has to be in the local cache 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 you know says get a 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 in 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 uh well it was asymptotic kind of kind of discussion or is it more like here's a hack that works well um it's a little bit of both like there's information theory in it i think somewhere okay so that's actually trying to prove yeah but once once you know the method implementing it is an engineering problem now there's a flip side of this which is in a big design team what percentage of people think their their their uh their 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 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 they spend lots of time trying to figure out how to patent something and that's mostly useless but the breakthroughs are on the simple stuff no no you no you have to do the simple stuff really well if you're brilliant 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 in an 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 and you know which one you can buy bricks from which is going to make a better house so you're talking about the craftsman the person who understands bricks who loves bricks who loves their varieties 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 in theory well everybody knows 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 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 ike 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 still is 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 medal or uh a crappy piece of you know that software code that is currently the number one programming language in the world that runs now is cons increasingly running the backhand of the internet what does he end up 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's a move from you know writing c programs and c plus plus to let's call what they call managed code frameworks where you write simple code it might be interpreted it has lots of libraries productivity is high and you don't have to be an expert so you know java was supposed to solve all the world's problems it was complicated javascript came out you know after a bunch of other scripting languages i'm not an expert on it but yeah but was it the right thing at the right time or was there something you know clever because he wasn't the only one there's a few elements maybe if he figured out what it was no then he'd get a prize like that destructive theory yeah you know babies probably he hasn't defined this or he this needs a good promoter well i think there was 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 this backward compatibility that just drags behind you as you know as you try to fix all the mistakes of the past but the the timing was good there's something about that it wasn't accidental you have to like give yourself over to the you have to have this like broad sense of what's needed now and both scientifically and like the community and just like this it was obvious that uh 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 and then javascript was literally just injected into the web page it was the dumbest possible thing running in the same thread as everything else and like uh it was inserted as a comment so javascript code is inserted as a comment in the html code and it was i mean it's there's it's either genius or super dumb but it's like right so it has no apparatus for like a virtual machine and container it just executed in the framework the program is already running and it was that's cool and then uh because something about that accessibility the ease of its use uh resulted in then developers innovating on how to actually use it i mean i don't even know what to make of that but it does seem to echo across different software like stories of different software php has the same story really crappy language they just took over the world i always have a joke that the random length instructions that variable length instructions that's always one even though they're obviously worse like nobody knows why x86 is arguably the worst architecture you know on the planet is one of the most popular ones well i mean isn't isn't that also the story of risk versus i mean is that simplicity there's something about simplicity that uh us in this evolutionary process is valued if it's simple it's uh gets it spreads faster it seems like yeah or is that not always true that's always true yeah it could be simple is good but too simple is bad so why did risk win you think so far did risk win in the long arc of history maybe we don't know so who who's going to win what what's risk what's cisco who's going to win in that space in these instruction sets a ice offers going to win but they'll be little computers that run little programs like normal all over the place but but we're we're going through another transformation so 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 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 like right now x86 is proprietary with intel and amd but they can change it any way 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 risk 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 sub subset of it that people can use do you like open or do you like clothes like if you were to bet all your money on one or the other risk five versus no idea it's case dependent well x86 oddly enough when intel first started developing it they licensed 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 were seven different people making x86 because at the time there was 6502 and z80s and you know 8086 and you could argue everybody thought z80 was the better instruction set but that was propriety proprietary to one place oh and the 6800 so there's like five or four or five different microprocessors 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 so why you as a historian uh well why did intel win for so long with the with their processors i mean they were great their process development was great oh so it's just looking back to javascript and brand nike is uh microsoft and netscape and all these uh internet browsers microsoft won the browser game because they aggressively stole other people's ideas like right after they did it you know i i don't know if intel was stealing other people's ideas they started making a just good way they started making rams random access memories and then at the time when the japanese manufacturers came up you know they were getting out competed on that and they pivoted the microprocessors and they made the first you know integrated microprocessor programs uh 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 ibm all kinds of big companies had both loads of money and they out innovated everybody auto innovated okay yeah yeah so it's not like marketing it's not yeah their processor designs were pretty good um i think the you know core 2 was probably the first one i thought was great it was a really fast processor and then haswell was great what makes a great processor in that delay oh if you just look at it's performance versus everybody else it's you know the size of it the 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 [Laughter] just as long as it was well there's the fastest in the environment like right 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 and then and then when we started doing multiprocessors 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 they're the only ones with a credible you know top to bottom palette and what do you mean incredible top to 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 it hard to do that full palette that's that seems like a it's a lot of different so what's the difference in uh the arm folks and intel in terms of the way they're approaching this problem well intel almost all their processor designs were you know very custom high end you know for the last 15 20 years the fastest force possible yeah in one horseshoe yeah and then architecture 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 synthesis and how do you approach that i'd say intel was slow on the 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 how the customer implemented their ip so arm is super friendly to the synthesis ip environment whereas intel said we're going to 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 processors and so in that and what you're describing is why they're winning well they had lots of people doing lots of different experiments so they controlled 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 their foray into 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 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 it's 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 like apple under steve jobs when he came back they pivoted multiple times you know they built ipads and itunes and phones and tablets and great macs like like who knew computers should be made out of aluminum nobody knew that that they're great it's super fun that was steve yeah steve jobs like they pivoted multiple times and uh you know the old intel they they did that multiple times they made drams and processors and processes and i got to ask this what was it like working with steve jobs i didn't work with him did you interact with him twice i said hi to him twice in the cafeteria what did he 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 was packed and i gave my table but i worked for mike colbert 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 and steve really liked him and steve trusted mike to translate the 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 and the phone would ring it'd be 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 translated and then he would say steve wants us to do this so was 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 so that seems to be one of the key elements of leadership right and then he was a really good first principals guy like like somebody 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 know if you want to do something hard you know maybe it takes time maybe you have to iterate there's a whole bunch of things you could think about but saying it can't be done is stupid how would you compare so it seems like elon musk is more engineering centric but it's also i think he considers himself a designer too he has a design mind steve jobs feels like he is much more idea space design space versus engineering yeah just make it happen like the world should be this way just figure it out but but he used computers you know he had computer people talk to him all the time like mike was a really good computer guy he knew what computers could do computer meaning computer hardware like hardware software all of pieces the whole thing 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 a designer but he had an intuition from the computers we had to what could happen and essentially you say intuition because it seems like he was pissing off a lot of engineers in his intuition about what canada can't be done those like the what is all these stories about like floppy disks and all that kind of stuff like yeah so in in steve the first round like he'd go into a lab and look at what's going on and hate it and and uh fire people or or ask somebody in the elevator what they're doing for apple and you know not be happy when he came back my impression was is he surrounded himself with this relatively small group of people yes and didn't really interact outside of that as much and then the joke was you'd see like somebody moving a 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 wanna he ruins as many ideas as he generates yeah yeah it's a dangerous kind of line to walk if you have a lot of ideas like like gordon bell was famous for ideas right and it wasn't that the percentage of good ideas was way higher than anybody else it was he had so many ideas and and he was also good at talking people about it and and getting the filters right and you know seeing through stuff whereas elon was like hey i want to build rockets so steve would hire a bunch of rocket guys and elon would go read rocket manuals so ian is a better engineer a sense like or like more uh like a love and passion for the manuals yeah and the details the details the craftsmanship too right well i guess you had craftsmanship too but of a different kind what do you make of the just the standard 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 um you know being emotional and all that that's steve and i i guess elon too so what is that a is that a bugger feature it's a feature so there's a graph which is uh y-axis productivity yeah x-axis at zero it's chaos yeah and infinity is complete order yeah right so as you go from the you know the origin as you improve order you improve productivity yeah and at some point productivity peaks and then it goes back down again yeah too much order nothing can happen yes but the question is is how close to the chaos is that no no no here's the thing is once you start moving the directional order the force vector to drive you towards order is unstoppable oh this is the same every organization will move to the place where their productivity is stymied by order so you need uh so the question is who's the counter force like because it also feels really good as you get more organized then productivity goes up the organization feels it they orient it towards it right to hire more people they get more guys who can run process you get bigger right and then inevitably inevitably the organization gets captured by the bureaucracy that manages all the processes 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 i can't tell you on how many levels that's profound so so that's why i'd 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 and 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 humans need that i had a friend he started to manage the 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're 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 they're not doing something how do you confront these people i think a lot of people that had trauma in their childhood who disagree with you successful people that you just first do the rough stuff and then be nice later i don't know okay but you know nice engineering companies are full of adults who had all kinds of range of childhoods you know most people had okay childhoods well i don't know if uh 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 you're you're probably looking for somebody's approval um even still yeah maybe i should think about that maybe somebody who's no longer with this kind of thing i don't know i used to call it my dad and tell him what i was doing he was he was very excited about engineering and stuff you got his approval uh yeah a lot i was lucky like he he decided i was smart and unusual as a kid and that was okay when i was really young so when i like did poorly in school i was dyslexic i didn't read until i was third or fourth grade and they didn't care my parents were like oh he'll be fine so that was funny that was cool is he still with us you miss him sure yeah he had parkinson's and then cancer his last 10 years were tough and i killed him killing a man like that's hard the mind well it's pretty good um parkinson's causes slow dementia and uh the chemotherapy 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 uh of course from that time like where do you have fond memories of the guy yeah oh yeah anything come to mind a friend told me one time i could draw a computer on the whiteboard faster than anybody you'd ever met and i said you should meet my dad like when i was a kid he'd come home and say i was driving by this 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 yeah and he would just draw the whole thing and then he would tell me about it and tell me how you would have changed it and he had this you know idea that he could understand and conceive anything and i i just grew up with that so that was natural so if you know like 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 you know and then they'll say and then this talks to this and yeah i'd be like this is frustrating and 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 do you mind if i do that he covered two whiteboards yeah like 30 minutes and i hired him like yeah he was great this is craftsman i mean that's the craftsmanship to that yeah but also the the mental agility to understand the whole thing right put the pieces in contacts like 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 it you'll fill up half the white board 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 zoom into the detail and then zoom out to the zoom really fast what about the impossible thing you see your dad believed that uh you could do anything that's a weird feature for a craftsman yeah it seems that that uh 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 play piano so at the end of his life he started playing the 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 onto something yeah he enjoyed doing it yeah so that's pretty funny do you think the perfect is the enemy of the good in hardware and software engineering it's like we were talking about javascript a little bit and the messiness of the 10-day building process yeah it's you know creative tension right the creative tension is you have two different ideas that you can't do both right right and then but the fact that you want to 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 right so they throw out the perfect because they have a schedule i hate that then there's other people who 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 going to articulate in it right so let's call that the perfect yeah yeah all right but that's also terrible because they never ship anything you never hit any goals so now you have that 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 or 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 figure out what's wrong with it you'd throw it out and then i start thinking about it like 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 know 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 ideas space like spaciousness of mind is key but you also have to execute programs and get done and then it turns out computer engineering is fun because it takes you know 100 people to build a computer 200 to 300 whatever the number is and people are so variable about you know temperament and you know skill sets and stuff that you know in a big organization you find that the people who love the perfect ideas and the people that want to get stuff done yesterday and people like to come up with ideas and people like to let's say shoot down ideas and it takes the whole it takes a large group of people some are good at generating ideas some are good at filtering ideas and then all in that giant mess you somehow i guess the goal is for that giant mess of people to uh find the perfect path through the attention the creative tension but 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 if you're sitting in a in a room and uh you have a set of ideas about like how to design uh a better processor how do you know this is this is something special here this is a good idea let's try this so have you ever brainstormed idea with a couple people that were really smart and you kind of go into it and you 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 start to kind of synchronize it's really weird and like you start to see what each other is thinking and yeah and it starts to work like you can see work like my talent in 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 a an amazing experience and then and every once in a while you get to that place and then you find the flaw which is kind of funny because you you can you can fool yourself in but the two of you kind of drifted along yeah into the direction that was useless yeah that happens too like you have to because you know well the nice thing about computer design there's always reduction in practice like you come up with your good ideas and i know some architects who really love ideas and then they work on them and they put it on the shelf they go work on the next idea and put on the 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 like yeah would you say your career just your own experience is your career defined by mostly by flaws or by successes like if again there's great tension between those if you haven't tried hard yeah 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 with it and but when you look back you see problems okay oh when i look back um what do you think earlier in my career yeah like eb5 was the second alpha chip uh i was so embarrassed about the mistake so i could barely talk about it and it was in the guinness book of worlds 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 uh yeah you learn to channel the pain into like pride not pride really you know just uh realization about how the world works okay or how that kind of ideas that works life is suffering that's the reality what uh no it's not well i know the buddha said that and a couple other people are stuck on it no it's you know there's this kind of weird combination of good and bad you know light and darkness that you have to tolerate and you know 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 uh that makes the light part really nice what uh computing hardware or just any kind of even software design are you uh do you find beautiful from your own work from other people's work that you're just uh we were just talking about the 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 thought out set of abstraction layers so the whole thing works in unison nicely yes and 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 the network stack like there's a seven layer network 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 the design space breathe and you could do something new in layer seven without having to worry about how layer four worked right 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 to find before we wrote the rtl for the pieces one of the verification leads said 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 design greatly improved the quality is that universally true in general would you say about good designs the modularity is like usually talked about this before humans are only so smart like and we're not getting any smarter right but the complexity of things is going up yeah so you know a beautiful design can't be bigger than the person doing it it's just you know their piece of it like the odds of you doing a really beautiful design of something that's way too hard for you is slow right 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 your expertise and you know 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 you know when you put it together it's you know sufficiently sufficiently interesting to to be used and you know so that's like a beautiful design is matching the limits of that human cognitive capacity to uh 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 uh you know if we build increasingly more complicated you can think of like the internet okay let's scale it up you can think of like social network like twitter as one computing system and but those are little modules yeah right but it's built on it's built on so many components nobody at 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 network it runs on the fiber optics the data is transported the computers the whole thing is so bloody complicated nobody twitter understands it and so i think that's what the alienware sees so yeah if an alien showed up and looked at twitter or looked at the various different networked systems that you can see on earth so imagine they were really smart they could comprehend the whole thing and then they sort of you know evaluated the human and thought this is really interesting no human on this planet comprehends the system they built no individual or well would they even see individual humans that's the interest like we humans are very human-centric entity-centric and so we think of us as the organ as the central organism and the networks as just the connection of organisms but from a perspective of an alien from an outside perspective it seems like yeah yeah i get it where the ants and they'd see the ant colony the ant colony yeah or the result the production of the ant colony which is like cities and it's it's uh yeah in that sense humans are pretty impressive the modularity that we're able to and the and how robust we are to noise and mutation all that kind of stuff well that's because it's stress tested all the time yeah you know you build all these cities with buildings and you get earthquakes occasionally and you know some you know wars earthquakes viruses every once in a while you know changes in business plans for you know like shipping or something like like as long as it's all stress tested then it keeps adapting to the the situation so the that's that's a curious phenomena well let's go let's talk about moore's law a little bit uh at the broad view of moore's law was just exponential improvement of uh computing capability uh like openai for example recently published this kind of papers looking at the exponential improvement in the training efficiency of neural networks for like image net and all that kind of stuff we just got better on this is purely software aside just figuring out better tricks and algorithms for training neural networks and that seems to be improving uh significantly faster than the moore's law prediction you know so that's in the software space like 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 uh so not the reduction of the size of the transistor kind of thing but more in the uh in the totally interesting kinds of innovations in the hardware space all that kind of stuff well there's like a half a dozen things going on in that graph so one is there's initial innovations that had a lot of had room to be exploited so you know the efficiency of the networks has improved dramatically and then the decomposability of those and the use go you know they started running on one computer then multiple computers and multiple gpus and then arrays of gpus and they're up to thousands and at some point so 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 like a moore's law rate but their ability to go from one to ten to a hundred to a thousand yeah you know was something and then multiplied by you know the amount of computes it took to resolve like alex net to resnet the transformers it's it's been quite you know steady improvements but those are like s cars aren't they yeah that's the exactly kind of s-curves that are underlying moore's law from the very beginning so so what what's the biggest what's the most uh productive uh rich source of s-curves in in the future do you think is it hardware is it software or is it so hardware is going to move along relatively slowly like you know double performance every two years there are there's still i like how you call that slow yeah that's the slow version the snail's pace of moore's law maybe we should we should we should uh trademark that one whereas the scaling by number of computers you know can go much faster you know i'm sure at some point google had a you know their initial search engine was running on a laptop you know like yeah and at some point they really worked on scaling that and then they factored the indexer from you know this piece and this piece and this piece and they spread the data on more things and you know 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 and made them rethink like it seems insane to do a scheduler across a thousand computers to schedule parts of it and then send the results to one computer but if you want to schedule a million searches that makes perfect sense so so there's the 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 may be completely the wrong one you may pick a network that's 10 times slower on 10 000 computers like per computer but if you go from a hundred to ten thousand that's a hundred times so that's one of the things that happened when we did internet scaling is the efficiency went down not up the future of computing is inefficiency not efficiency but scales in efficient scale it's it's scaling faster than inefficiency bites you 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 where the money was at it was and so it was worth it financially do 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 dumb computing would be fiction books they call it computronium computing 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 we'll we'll see what what you can do with the rest of it 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 to be ironic from the simulation point of view is like the simulator build mass to simulate like yeah i mean yeah so i mean ultimately this is all heading towards the simulation yeah well i i think i might have told you the story a tesla they were deciding so they want to measure the current coming out of the battery and they decide between putting the resistor in there and putting a computer with 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 so sure this hedgehog you know it costs 13 and we can put a you know an ai that's the smartest you in there for five bucks it'll have one you know so computers will be you know he'd 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 to have a lot of doubt well well moore's law will slowly compact that stuff so even the dump things will be smarter than us the dump things are going to be smart or they're going to be smart enough to talk to something that's really smart you know it's like well just remember like a big computer chip yeah 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 yeah and 10 000 of them can fit in the shoe box but you know you have the the cooling and power problems but you know people are working on that but they still can't write uh compelling poetry or music or uh understand what love is or have a fear of mortality so so we're still winning neither can most of humanity so well they can write books about it so uh [Laughter] but but speaking about this uh you know uh this walk along the path of innovation towards uh the dumb things being smarter than humans you are now the cto of uh tens torrent as of two months ago they uh build 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 um 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 you have given parallelism like gpus are great because you have a million pixels and modern gpus run a program on every pixel they call the shader program right so or like finite element analysis you you build something you know you make this into little tiny chunks you give each chunk to a computer so you're giving all these chunks a parallel something like that but most c programs you write this linear narrative and you have to make it go fast to make it go fast you predict all the branches all the data fetches and you run that more in parallel but that's found parallelism ai is i'm still trying to decide how fundamental this is it's a given parallelism problem but the way people describe the neural networks and then how they write them in pi torch it makes graphs yeah that might be fundamentally different than the gpu kind of parallelism yeah 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 that 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 impression of translucency but the pixels themselves don't talk to each other there's no graph right so you 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 6 000 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 that's how they work but there's no graph but you think graph might be a totally new way to think about hardware so raja gadori and i've been having this good conversation about giving versus found parallelism and then the kind of walk cause we got more transistors like you know computers way back when did stuff on scalar data then we did it on vector data famous vector machines now we're making computers that operate on matrices right and then the the category we we said that was next was spatial like imagine you have so much data that you know you want to do the compute on this data and then when it's done it says send the result to this pile of data run some software on that and it's better to 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 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 that computation or maybe a pointer to the next program 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 send the result here do another computation do a data transformation do a merging do a pooling do another computation is it possible to compress and say how we make this thing efficient this whole process efficient that's different so first uh the fundamental elements in the graphs are things like matrix multiplies convolutions data manipulations and data movements so gpus emulate those things with their little singles you know basically running a single threaded program and then there's a you know nvidia calls it a work 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 a matrix multiplier which runs on these 32 threads but the model at the bottom was built for running programs on pixels not executing graphs so it's emulation yes so is it possible to build something that natively runs graphs yes so that's what ten storm did so where are we on that how like in the history of that effort are we in the early days yeah i think so tense torrance started by a friend of mine labisha bajak and i i was his first investor so i've been you know 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 the we we held a conference last year with a friend organized it and and we we wanted to bring in thinkers and two of the people were andre carpassi and chris lattner and andre gave this talk on youtube called software 2.0 which i think is great which is we went from programmed computers where you write programs to data program computers you know like the futures you know of software as data programs the 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 and i would say technically tense torrents you know two pillars are those those those two ideas software 2.0 and 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 yeah 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 teams and 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 matrix multiply convolution the data manipulations and the data movement between processing elements that that you can see in the graph which i think is all pretty clever and that's that's what i'm i'm working on now so uh the i think it's called the grace call processor uh introduced last year it's uh you know there's a bunch of measures of performance we're talking about horses it seems to outperform 368 trillion operations per second seems to outperform nvidia's tesla t4 system so these are just numbers what do they actually mean in real world perform like what are the metrics for you that you're chasing in in your horse race like what do you care about well first so the the native language of you know people who write ai network programs is pie torch now by torch tensorflow there's a couple others the pi torch is one over tensor flows it's just i'm not an expert on that i i know many people have switched from tensorflow to pi torch 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 currently yeah there's more love for that and that 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 pi torch turns into a graph we have a graph compiler that makes that graph then it fractions the graph down so if you have a big matrix multiply we turn it into right-sized chunks that run on the processing elements it hooks all the graph up it lays out all the data there's a couple 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 it schedules kernels like math data manipulation data movement kernels which do this stuff so we don't have to run write a little program to do matrix multiply because we have a big matrix multiplier like there's no cmd program for that but there is scheduling for that right so the 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 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 multiply naively you'll get five to ten percent of the peak performance of the gpu right and then there's a bunch 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 wrote or 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 into little details like you have to schedule it just so so you don't have registered conflicts so the the the they call them cuda ninjas i love it to get to the optimal point you either write a pre use a pre-written library which is a good strategy for some things or you have to be an expert in micro architecture to program it right so the optimization step is way more complicated with the gpa so our our goal is if you write pi torch that's good pi torch you can do it now there's as the networks are evolving you know they've changed from convolutional to matrix multiply people are talking about conditional graphs you're talking about very large matrices they're talking about sparsity you're talking about problems that scale across many many chips so the the native you know data item is a as a packet like so you send a packet to a processor it gets processed it does a bunch of work and then it may send packets to other processors and and they execute like a data flow graph kind of methodology got it we have a big network on chip and then 16 the next second chip has 16 ethernet ports they hook 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 the mountain to climb yeah so the hardware is built to do this and then we're in the process of is there a software part to this with ethernet and all that well the you know the protocol at the bottom you know we send you know it's an ethernet phi but the protocol basically says send a 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 on-chip network put an ethernet header on it send it to the other end to strip the header off and send it to the local thing it's pretty straightforward human human interaction is pretty straightforward too but when you get a million of us we could do some crazy stuff together it could be fun so is that the goal is scale so like for example i've been recently doing a bunch of robots at home for my own personal pleasure uh am i going to ever use 10 story or is this more for there's all kinds of problems like they're small inference problems or small training problems there's big training problems what's the big goal is it the big difference uh training problems or the small training problems well one of the goals is to scale from 100 milliwatts to a to a megawatt you know so like really have some range on the problems and 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 you know small problems right right but but uh you know like inside that phone is a small problem to solve so do you see that storm 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 for complicated networks i want to go in a small factor it's been quite good um but we have to prove that that's a that's a fun problem and that's the early days of the company right it's a couple years you said but you think you invested you think they're legit yeah as you join yeah well that's well it's also it's a really interesting place to be like the ai world is exploding you know and i looked at some other opportunities like build a faster processor which people want yes but that's more on incremental path than what's going to happen in ai in the next 10 years so this is kind of you know an exciting place to be part of the revolutions will be happening in the very space and then lots of people are working on it but there's lots of technical reasons why some of them you know aren't going to work out that well and and you know that's that's interesting and there's also the same problem about getting the basics right like we've talked to customers about exciting features and at some point we realized that each unit was realizing they want to hear first about memory bandwidth local bandwidth compute intensity programmability they want to know the basics power management how the network ports work what are the basics do all the basics work because it's easy to say we got this great idea that you know the crack gbt3 but the the people we talked to want to say if i buy that so we have a pc express card with our chip on it if you buy the card you plug it in your machine you download the driver how long does it take me to get my network to run right right you know that's a real question it's a very basic question so yeah is there an answer to that yet or is it trying to our goal is like an hour okay when can i buy a tesla uh pretty soon for my for the small case training yeah pretty soon months good i love the idea of you inside the room with the carpathi andre kapathi and chris ladner uh very um very interesting very brilliant people very out of the box thinkers but also like first principles thinkers well they both get stuff done they only get stuff done to get their own projects done they 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 the clear thinking that's able to be communicated is kind of impressive it's kind of remarkable to yeah i'm a fan well let me ask because uh i talked to chris actually a lot these days he's been uh one of the cool 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 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 want to 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 they just kind of let a little kid do his thing like go go do it let's see let's see see what turns out that that's a that's a pretty powerful thing but what do you um what's your sense about he used to be he no i think stepped away from google right he said sci-fi i think uh what what's really impressive to you about the things that chris has worked on because it's that we mentioned the optimization the compiled design stuff the llvm uh 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 uh what uh from your time interacting with chris and knowing the guy what's really impressive to you it just inspires you well well like llvm became you know the platform the de facto platform for you know compilers like it's it's amazing and you know it was good code quality good design choices he hit the right level of abstraction there's a little bit of the right time in the right place and then he built a new programming language called swift which you know after you know let's say some adoption resistance became very successful i don't know that much about his work at google although i know that you know that was the typical they started tensorflow stuff and they you know it was new is you know they wrote a lot of code and then at some point it needed to be refactored to be you know because it its 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 a level was was putting too much of a burden on it so he's splitting that into multiple pieces and that was one of the inspirations for our software stack where we have several intermediate representations that are all executable and you can look at them and do transformations on them before you lower the level so that was i think we started before moir really got you know far enough along to use uh but we're interested in that he's really excited about that malaya he's that's that's his like little baby so he you know 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 all three of those projects allovm swift and mlir did that successfully so i'm interested what's what he's going to do next in the same kind of way yes so on either the tpu or maybe the nvidia gpu side how does 10 story you think or the ideas underlying it doesn't have to be testosterone just this kind of graph focused uh graph centric hardware deep learning-centric hardware beat nvidia's do you think it's possible for it to basically overtake nvidia sure what's what's that process look like what's that a 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 are built is going to be really interesting to be inefficient on computing this and then the the primitives is not a cmd program it's matrix multiply convolution and then the data manipulations are fairly extensive about like how do you do a fast transpose with a program i don't know if you've ever written the transpose program they're ugly and slow but in hardware you can do really well like i'll give you an example so when gpu accelerators first started doing triangles like so you have a triangle which maps on the set of pixels so you build it's very easy 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 to 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 because you're saying that could be done in uh in hardware now 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 has actually less code than the software program that does it and it's way faster 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 the right thing to do in the hardware software boundary is for the hardware to naturally do it and 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 rasterize triangles when you're running the game but for the most part most of the computation in the area the gpu is running shader programs but they're single threaded programs on pixels not graphs let's be honest to say i don't actually know the 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 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 when you 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 what figure it out so that pixel says on 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 subtly 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 every picture yeah but there is so so you build a reflection map which also has some pixelated thing and then when the pixel is looking at the reflection map it has to calculate what the normal of the surface is and it does it per pixel by the way there's both loads of hacks on that you're like you may have a lower resolution light map reflection map there's all these you know attacks they do but at the end of the day it's per pixel computation and it so happened that you can map uh graph like computation onto the this pixel essentially you could do floating point programs on convolutions and 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 how often do big companies hardcore pivot occasionally how much do you know about nvidia folks so some yeah well i'm i'm curious as well who's ultimately as a well they've innovated several times but they've also worked really hard on mobile they worked really hard on radios you know you know they're fundamentally a gpu company well they tried to pivot it's an interesting little uh game and play in autonomous vehicles right with or semi-autonomous like playing with tesla and so on and seeing that's a dipping a toe into that kind of pivot they came out with this platform which is interesting technically yeah but it was like a three thousand watt you know you know thousand watt three three thousand dollar you know gpu platform i don't know if it's interesting technically it's interesting philosophically i i technically i don't know if it's the execution that craftsmanship was there i'm not sure but that i didn't get a sense they were repurposing gpus for an automotive solution right it's not a real pivot they didn't they didn't build a ground-up solution right like the like the chips inside tesla are pretty cheap like mobile eye has been doing this they're they're doing the classic work from the simplest thing yeah you know they were building 40 mil square millimeter chips and nvidia their solution had two 800 millimeter chips and two 200 millimeter chips and you know like boatloads are really expensive drams and and you know it's a really different approach the mobilelite fit the let's say automotive cost 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 5000 watt server in their trunk right and but that's that's because they think well 5000 watts and you know 10 000 is okay because it's replacing the driver elon's approach was that port has to be cheap enough to put it in every single tesla whether they turn on it autonomous driving or not which and mobileye was like we need to fit in the bomb and you know cost structure that car companies do so they may sell you a gps for 1500 bucks but the bond for that's like 25 well and uh for mobile eye it seems like neural networks were not first-class citizens like the computation they didn't start out as a yeah it was a cv problem yeah and did classic cv and found stop lights 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 and then as opposed to so if you look at the new tesla work it's like neural networks from the ground up yeah right yeah and even tesla started with a lot of cv stuff in it and andre's basically been eliminating it move it move everything into the network so uh 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 uh do you like the trajectory of where things are going on the floor they're making serious progress i like the videos of people driving the beta stuff like it's taking some pretty complicated intersections and all that but it's it's still an intervention for drive i mean i i have autopilot the current autopilot my my tesla i use it every day do you have full self-driving beta or no no so you you like where this is going we're making progress it's taking longer than anybody thought you know my wonder was is you know hardware three is it enough computing off by two off by five off by ten off by a hundred yeah and and i i thought it probably wasn't enough but they're doing pretty well with it now yeah and one thing is the data set gets bigger 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 right so the the goal isn't to build the network that fits in the phone it's to build something that actually works and then then how do you make that most effective on the hardware you have and they seem to be doing that much better than a couple 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 is constantly entering 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 uh 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 their specifics but the interesting thing about what that actually looks in the real world is it's that uh what i think andre calls the data engine it's like it's the iterative improvement of the thing you have a neural network that uh does stuff fails on a bunch of things and learns from it over and over and over so you're constantly discovering edge cases so it's very much about uh like data engineering like figuring out it's it's kind of what you were talking about with testosterone is you have the data landscape they have to walk along that data landscape in a way that uh that's constantly improving the the the neural network and that that feels like that's the central piece of it yeah itself and there's two pieces of it like you you find edge cases that don't work and then you define something that goes get your data for that but then the other constraint is whether you have to label it or not like the the amazing thing about like the gpt3 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 successfully driving but you know the the current pipelines are mostly running on labeled data which is human limited so when that becomes unsupervised right it it'll create unlimited amount of data which then they'll 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 we have unlimited data i know exactly what i want now can i turn that into something that fits in the car and that pro 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 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 uh you know 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 even though tesla says they're using that it's an open question to me how much how far can you can you sell all of the driving with just that self-supervised piece of data and like i i think that's what comedy is doing that's what common ai is doing but the question is how how much data so what comedy ai 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 uh it's 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 in the right order of magnitude for the compute necessary that is is this is it like what elon said this chip that's in there now is enough to do full self-driving or do we need another order of magnitude i think nobody actually knows the answer to that question i like the confidence that elon has but yeah we'll see and 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 surfaces and laws and roads and you know all your your uh experience from manipulating your environment like look there's so many factors go into 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 you never teach a human to do that you teach a human all kinds of interesting things like language like don't do that you know watch out you know there's all kinds of stuff going on well this is where you i think previous time with we talked about where you poetically uh disagreed with my naive uh notion about humans i just think that humans will will make this whole driving thing really difficult yeah all right like i said humans don't move that slow it's a ballistics problem it's a ballistic human zero ballistics problem which is like poetry to me it's very it's very possible that in driving they're indeed purely a ballistics problem i and i think that's probably the right way to think about it but i still they still continue to surprise me those and damn pedestrians the cyclists other humans and other cars and yeah but it's going to 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 and radars and you have an attention problem so yeah so so the self-driving car comes in with no attention problems 360 cameras right you know a bunch of other features yeah so they'll wipe out a whole class of accidents right and you know you know emergency braking with radar and especially as it gets you know ai enhanced will eliminate collisions right but then you have the other problems of these unexpected things where you know you think your human intuition is helping but then the cars also have you know a set of hardware features that you're not even close to and the key thing of course is uh if you wipe out a huge number of kind of accidents then it might be just way safer than the human driver even though even if humans are still a problem that's hard to figure out yeah that's probably what happens autonomous cars will have a small number of accidents humans would have avoided but they'll wipe they'll get rid of the bulk of them what do you think about uh like tesla's dojo efforts or it can be bigger than tesla in general it's kind of like the tense torrent uh trying to innovate like this is the dichotomy like should a company try to from scratch build its own neural 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 you know 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 yeah and and there's brilliant people working on it i'm curious to see how it'll come out i i couldn't tell you you know i know it pivoted a few times since i left so so the cooling does seem to be a big problem i do like what elon 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 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 you know 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 andre said is i think this is amazing 10 years ago like vision recommendation language were completely different disciplines we 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 varying outputs but the technology has converged a good bit yeah these transformers behind gbt3 it seems like they could be applied to video they could be applied to a lot of yeah and it's like and they're all really 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 the more data you have the better it gets so so so then you start to wonder well is that a fundamental thing or is is this just another step to some fundamental understanding about this kind of computation which is really interesting us humans 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 that it'll understand physics in ways that we can't understand i like to hear stephen will from talk where he said you know there's three generations of physics there was physics by reasoning well big things should false faster than small things right that's reasoning and then there's physics by equations like you know but the number of programs in the world that are solved with the single equations relatively low almost all programs have you know more than one line of code maybe 100 million lines of code so you said that now we're going to physics by equation which is his project which is cool i might point out that there was there was two two generations of physics before reasoning habit like all animals you know know things fall and you know birds fly and you know predators know how to you know solve a differential equation to cut off a accelerating you know curving animal path yep and then there was uh you know the gods did it right so yeah right so you know 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 past stephen's wolfram's com that's not explainable and 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 they're usually like usually when you have this hierarchy it's not like well if you have this step in this step in this step and they're all qualitatively different and conceptually different it's not obvious why you know six is the right hand number of hierarchy steps in not seven or eight or well then it's probably impossible for us to to comprehend something that's beyond the thing that's not explainable yeah because i think but the thing that you know understands the thing that's unexplainable to us we'll conceive the next one and like i'm not sure why there's a limit to it uh your brain hurts that's the sad story if if we look at our own brain which is an interesting uh illustrative example in your work with testor and trying to design deep learning architectures uh do you do you think about the brain at all maybe from a hardware designer perspective if you could uh change something about the brain what would you change or do funny question like how would you so your brain is really weird like you know your cereal 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 you know when you're thinking if it's if 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 to the point where you mold things over for days and how many trips through your brain is that right like it's you know 300 milliseconds or something to 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 is looks like some kind of graph because you have a neuron with you know 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 it's meaning like first of all there's mechanical chemical and electrical signals that's all that's going on then the there's a the asynchronicity of signals and there's like there's just a lot of variability that seems continuous and messy and just a mess of biology and it's unclear whether that's a good thing yeah or it's a bad thing because if if it's a good thing that we need to run the entirety of the evolution well we're going to have to start with basic bacteria to create some imaging we could you could build a brain with 10 layers would that be better or worse or more more connections or less connections or you know we don't know to what level our brains are optimized but if i was changing things like yeah like you know you can only hold like seven numbers in your head yeah like why not 100 or a million never thought of that like and why can't like why can't we have like a floating point processor that can compute anything we want like and see it all properly like that would be kind of fun and why can't we we see in four or eight dimensions like because you know 3d is kind of a drag like all the hard mass transforms are up in multiple dimensions so there's that you know you could imagine a brain architecture that you know you could enhance with a whole bunch of features that would be you know really useful for thinking about things it's possible that the limitations you're describing are actually essential for like the constraints are essential for creating like the depth of intelligence like that the ability to reason you know it's hard to say because like your brain is clearly a parallel processor you know you know 10 billion neurons talking to each other at a relatively low clock rate but it produces something that looks like a serial thought process it's a serial narrative in your head that's true right 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 people who say they don't have a they don't have a voice in their heads they can talk but when they you know it's like well what are you thinking they'll they'll describe something that's visual so that's curious now if if you're saying if we dedicated more hardware to holding information like you know 10 numbers or a million numbers like would that just distract us from our ability to form this kind of singular identity like it dissipates somehow right but but maybe you know 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't have multiple consciousnesses in one brain yeah and maybe there's some way to make it faster so that the you know the the area the computation could could still have a unified feel to it but while still having way more ability to do parallel stuff at the same time could definitely be improved it could be improved okay well it's it's pretty good right now actually people don't give it enough credit the thing is pretty nice the the you know the the fact that the right ends seem to be on give a nice like spark of uh 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 yeah what do you mean what do you mean how all the ways you can't imagine no but that's the whole point i wouldn't be able to i'm at the fact that i can imagine ways in in in which it could be more beautiful means so do you know you know ian banks his stories so the the super smart ais there live mostly live in the world of what they call infinite fun because they can create arbitrary worlds so they interact and you know 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 um will not be so fun that's sad there are so many things to do imagine be able to make a star move planets around yeah yeah but because we can imagine that as wildlife is fun if we can if we actually were able to do it it'd be a slippery slope where fun wouldn't even have a meaning because we just consistently desensitize ourselves by the infinite amounts of fun we're having and the sadness uh the the dark stuff is what makes it fun i think i mean that could be the russian it could be the could be the fun makes it fun and the sadnesses makes it bittersweet yeah that's true fun could be uh 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 it's super interesting like like humans like like our thoughts to manifest as action you know like like as a kid you know like shooting a rifle was super fun driving a mini 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 dream 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 what's possible there is is is odd and the fact that nobody understands it's hilarious but um do you think it's possible to expand that capability through computing sure is there some interesting so from a hardware designer perspective is there do you think you'll present totally new challenges and the kind of hardware that required that like so this hardware isn't standalone computing well this just knows today computer games are rendered by gpus right right so but you've seen the gans stuff yep right where trained 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 aai 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 say well 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 for brain computer interfaces would need to render not onto a screen but render onto the brain and like directly so that there's a bandwidth you could do it both ways i mean our eyes are really good sensors it could render onto a screen and we could feel like we're participating in it you know they're gonna they're gonna have you know 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 they're slowly solving those problems and i suspect when the renderer of that information into your head is also ai mediated you know they'll be able to give you the cues that you know you really want for depth and all kinds of stuff like your your brain is probably faking your your visual field right like 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 yeah it's all fabricated yeah so yeah 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 it could be super real actually so i don't know what the limits that are but obviously if if we have a brain interface that goes in inside your you know visual cortex in a better way than your eyes do which is possible it's a lot neurons yeah um maybe that will 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 seem to be very limited and like you said computational so very plastic very plastic yeah yeah so it's a it's a com interesting combination now the the interesting open question is the limits of that neuroplasticity like how how flexible is that thing because we don't we haven't really tested it we know about that experiments where they they put like a pressure pad on somebody's head and had a visual transducer pressurize it and somebody slowly learned to see yep that's like it's especially at a young age if you throw a lot at it like what what can it uh uh can it completely so can you like arbitrarily expand it with computing power so connected to the internet directly somehow yeah the answer's probably yes so the problem with biology and ethics is like there's a mess there like us humans are perhaps unwilling to take risks in uh into directions that are full of uncertainty so that's like 90 of the population is unwilling to take risks the other 10 is rushing into the risks unaided by any infrastructure whatsoever and you know and that that's where all the fun happens in you know society there's been huge transformations yeah in the last you know a couple thousand years yeah it's funny i mean i got a chance to interact with uh uh this is matthew johnson from johns hopkins he's doing this large-scale study of psychedelics it's it's becoming more and more i've gotten a chance to interact with that community of scientists working on psychedelics but because of that that opened the door to me to all these uh what are they called psychonauts the people who like you said the ten percent who like i don't care i don't know if there's a science behind this i'm taking the spaceship to if i'm being the first on mars i'll be uh the you know you know psychedelic's interesting in the sense that in another dimension uh like you said it's a way to explore the with 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 in 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 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 had like six six hours of dreams and i it's like really useful time i know i do i haven't uh i don't for some reason i just knock out and uh i have sometimes like anxiety inducing kind of like very pragmatic like nightmare type of dreams but not nothing fun nothing nothing fun nothing fun i i try i unfortunately have mostly have fun in uh 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'll have to talk yeah i need instructions uh yeah there's like a manual for that you might wanna i looked it up i'll ask elon what uh what did you dream you know years ago and i 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 there's 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 or working on problems i i i prep 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 then that let's say greatly improves the chances that i'll i'll work on that while i'm sleeping and then and then i also you know basically asked to remember it and i often remember very detailed within the dream yeah or outside the dream well to bring it up in in my dreaming and then remember it when i wake up it's just it's more of a meditative practice you say you know to prepare 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 you'll dream about it that's really interesting maybe but but you can direct your dreams somewhat by prepping you know i'm going to try that it's really interesting like the most important the interesting not like uh what what did this guy send in an email kind of like stupid worry stuff but like fundamental problems you're actually concerned about prepping and interesting things you're worried about or just you're reading or you know some great conversation you had or something some adventure you want to have like there's there's a lot of space there and and it seems to work that you know my percentage of interesting dreams and memories went up is there uh is that the source of uh if you were able to deconstruct like where some of your best ideas came from do 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 no i i found that a long time ago i'm i process things somewhat slowly so like in college i had friends that could study at the last minute get an a next day i can't do that at all so i always front loaded 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 the day before finals may screw up my understanding of what i thought i knew so my my goal was to always get it in and and give it time to soak and i used to you know i remember we were doing like 3d calculus i would have these amazing dreams of 3d surfaces with normal you know calculating the gradient and this is like all come up so it was really fun like very visual and uh and if i got cycles of that that was useful um and the other is 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 and things but that's what's up then there's a yeah let them sit and let it breathe a little bit and then reduce it to practice like at some point you really have to does it really work like you know is this real or not right but you but you have to do both there's creative 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 uh way to is generate ideas and just let them sit let them sit there for a while i think i have a few of those ideas you know that was so funny yeah i think that's you know creativity uh 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 like there's so much diversity in how people think you know how fast or slow they are how well they remember don't like you know 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 whole method if i if i'd learned it yeah you know so it's just a funny or some people could you know i i 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 about half i could see the whole thing visually yeah and you know and you have to learn that about yourself and figure out what to do with the function optimally i had a friend who he 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 having this idea like where does it come from but you can have more awareness of it like like like like how you how your brain works is 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 yes you know if i say you know visualize volcano it's easy to do right and what does it actually look like when you visualize it i can visualize to the point where i don't see very much out of my eyes and i see the colors of the thing i'm visualizing yeah but there's like a there's a shape there's a texture there's a color but there's also conceptual visualization like what are you actually visualizing when you're visualizing volcano just like with peripheral vision you think you see the whole thing yeah yeah that's a good way to say it you know you have this kind of almost peripheral vision of your visualizations they're like these ghosts but if you know if you if you work on it you can get a pretty high level of detail and somehow you can walk along those visualizations to come up with an idea which is uh but weird but when you're thinking about solving problems like you're you're putting information and you're exercising the stuff you do know you're sort of teasing the area that's 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 like i know sometimes when i'm working really hard on something like like i get really hot when i'm sleeping and you know it's like we got the blank throw i wake up all the blankets are on the floor and you know every time it's while i wake up and think wow that was great you know are you able to uh to reverse engineer what the hell happened there oh sometimes it's vivid dreams and sometimes it's this kind of like you say like shadow thinking that you you sort of have this feeling you're you're going through this stuff but it's it's not that obvious isn't that 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 all understandable meditation really helps you you got to start figuring out you need to learn language of your own mind and there's multiple levels of it but the abstractions again right it's somewhat comprehensible and observable and feelable or whatever the right word is no it's you know you're not long for the ride you are the ride i have to ask you hardware engineer working on neural networks now what's consciousness what the hell is that thing is that is that just some little weird quirk of our particular uh computing device or is it something fundamental that we really need to crack open if we're 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 it's really weird so yeah i mean everything about it is weird first it's a half 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 your consciousness 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 create a space in your head right like we don't really see anything right like photons hit your eyes it gets turned into signals it goes through multiple layers 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 you have to go through all this processing yeah where for most of it it looks nothing like vision okay like like there's no theater in your mind right so we we have a world in our heads we're literally just isolated 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 massively parallel so it's so curious so imagine you're building an ai computer if you wanted to replicate humans well you'd have huge arrays of neural networks and apparently only six or seven deep which clarious they only 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 it's perceiving well create this create the world tell stories in the world and then have many dimensions of you know like sideshows to it like we have an emotional structure like we have a biological structure and that seems hierarchical too like like if you're hungry it dominates your thinking if you're mad it dominates your thinking like and we don't know if that's important to consciousness or not but it certainly disrupts you know in truths 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'd like to fantasize right and the somewhat circular observation of that is the thing we call consciousness now if you created a computer system it did all things create world views created future alternate histories you know dwelled on past events you know accurately or semi-accurately you know it's it's consciousness just bring up like natural well would that feel look and feel conscious to you like do you think do you think the thing that looks conscious is conscious like do you uh again this is like an engineering kind of question i think because uh like if we want to engineer consciousness is it okay to engineer something that just looks conscious or is it is there a difference between well we have all consciousness because it's a super effective way to manage our affairs yeah it's right the social development yeah well it gives us the planning system you know 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 right and well all those components together manifest consciousness right so if we make intelligent beings that we want to interact with that we're like you know wondering what they're thinking you know you know looking forward to seeing them you know when they interact with them they they're interesting surprising you know fascinating you know they will probably feel conscious like we do and we'll we'll perceive them as conscious i don't know why not but you never know another fun question on this because in in from a computing perspective we're trying to create something that's human-like or superhuman-like let me ask you about aliens aliens uh do you think there's intelligent alien civilizations out there and do you think their technology their computing their ai bots their uh their chips are of the same nature as ours yeah i got i have no idea i mean if there's lots of aliens out there they've been awfully quiet you know there's there's speculation about why there seems to be more than enough planets out there there's a lot yeah um there's intelligent life on this planet that seems quite different you know like you know dolphins seem like 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 their neural architecture is completely different than ours now who knows how they perceive things i mean that's the question is for us intelligent beings who might not be able to perceive other kinds of intelligence if they become sufficiently different than us so yeah we live in the current constrained world that you know it's three-dimensional geometry and the geometry defines a certain amount of physics and you know you know there's like how time works seems to work like there's so many things that seem like a whole bunch of the input parameters to the you know another conscious being are the same yes like if it's biological biological things seem to be in a relatively narrow temperature range right because you know organic stones aren't stable too cold or too hot you know so so there's if you specified 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 and how to think in n there's a funny science fiction book where the all the society had uploaded into this matrix and at some point some some of the beans 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 where intelligent beings had uploaded themselves into that matrix so everywhere intelligent life was as soon as it got smart it up leveled itself into something way more interesting than 3d geometry and yeah it escaped whatever this is not escaped better yeah the 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 yes so uh jordan peterson has a line says you know you think you have ideas but ideas have you yeah right good line which and and then we know about the phenomena of groupthink and there's 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 groupthink and part of your responsibility as a as a human is to escape that kind of phenomena which isn't you know it's you know it's 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 at the same time and it could be by doing that that you know the the the group and society you're you're part of becomes collectively even more interesting so you know so the outside observer will think wow you know all these lexus running around with all these really independent ideas have created something even more interesting and uh aggregate so so i uh so i don't know i'm those are lenses to look at the situation but i'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 like you mentioned he's going through some rough stuff now is there some comment you can make about the the roughness of the human journey the ups and downs well i i became an expert in benzo withdrawal like which is you took benzodiazepines and at some point they interact with gaba circuits you know to reduce anxiety and do 100 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'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 metabolic 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 it's for all symptoms if you go back on them at the same level you won't be stable and that unfortunately happened to him because it's so deeply integrated into all the kinds of systems in the body it literally changes the size and numbers of neurotransmitter sites in your brain yeah so there's a there's a process called the ashton protocol where you taper it down slowly over two years to people go through that goes through unbelievable hell and what jordan went through seemed to be worse because the 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 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 they all seem to suffer unbelievably and you know my work 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 percent of people when they taper it's you know half the 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 heartbreaking and tough so you put some of the fault that the doctors that just not know what the hell they're doing oh no it's hard to say it's 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 you're a drug drug addict or a psychiatric patient and so like one doctor said you know i prescribed me for 10 years thinking i was helping my patients and i realized i was really harming them and you know the awareness of that is slowly coming up the fact that they're casually prescribed to 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 it's it's it's really terrible the mind is terrifying we were talking about how how the infinite possibility of fun but like it's the infinite possibility of suffering too which is one of the dangers of uh like expansion of the human mind it's like i wonder if all the possible huma experiences that a intelligent computer can have is it mostly fun or is it mostly suffering so like if you if you uh 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 and that the world's in the balance you know all the all the literature on religion and stuff is you know the struggle between good and evil is is balanced versus very finely tuned for reasons that are complicated but that's a that's a long philosophical conversation uh speaking of balance that's complicated i 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 uh kill off most of the human population at their worst and they're just fascinating because there's so many viruses in this world there's so many i mean viruses basically around the world in the sense that uh they've been around very long time they're everywhere they seem to be extremely powerful and they're just in a distributed 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 uh like in terms of you being fascinated about terrified or not somewhere in between so i believe in frameworks right so like one of them is the 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 the sense of there's endless variation in variations that work better when and then over time there's so many levels of that competition you know like multi-cellular life partly exists because of you know the 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 read 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 virus is invading them like one-third of them wow like like i don't know if that number is true but it was like it's 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 bacterias and viruses and as our immune system kind of goes down you know that's what slowly kills us and it just feels so peaceful from a human perspective when we sit back and they're able to have a relaxed conversation uh and there's wars going on out there like right now you're you're harboring how many bacteria and you know 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 you know it's just really it's really wild but you know this particular manifestation is unusual you know in the demographic how it hit and the political you know response that it engendered and you know the health care response it engendered and the technology it's gendered it's kind of wild yeah the communication on twitter that it uh every level all that kind of stuff at every single level yeah but but what usually kills life the big extinctions are caused by meteors and volcanoes that's the one you're worried about as opposed to human-created bombs solar flares are another good one you know occasionally solar flares hit the planet so it's nature oh yes yeah it's all pretty wild on another historic moment this is perhaps outside but perhaps within your uh space of frameworks that you think about that just happened i guess a couple weeks ago is um i don't know if you're paying attention at all it's the the game stop and wall street bets so it's really fascinating there's kind of a theme to this conversation today because it's like you know that works the it's cool how there's a large number of people in a distributed way almost having a kind of fun we're able to take on the powerful elites elite hedge funds centralized powers and overpower them uh do you have thoughts i mean saga i don't know enough about finance but it was like the elon you know robin hood guy when they talked yeah what do you think about that well the robin guy didn't know how the finance system worked that was clear right he was treating like the 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 up three billion dollars right now and he's like i don't have three billion dollars like i don't even make any money on these trades why do i owe three billion dollars while you're sponsoring the trade so so there was a set of abstractions that you know i don't think either like like now he understands it like this happens in chip design like you buy wafers from tsmc or samsung or intel and you know 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 like the transistors really work like they said you know what's the real issue so so the there's a whole set of things that created this opportunity and somebody spotted it now people spot these kinds of opportunities all the times there's been flash crashes there's been you know there's always short squeezes are fairly regular every ceo i know hates the shorts because they're 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 and 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 yes so my my guess is we know five percent of what really happened and that a lot of the players don't know what happened and the people who probably made the most money aren't the people that they're talking about yeah that's do you think there was something uh i mean this is the this is the cool kind of uh elon uh you're the same kind of conversationalist which is like first principles questions of like what the hell happened uh just very basic questions of like was there something shady going on uh what you know who are the parties involved it's 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 and selling stock is a trust event you know i trust the company representing themselves 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 i don't know if it's going to get corrected i don't know if the i don't know if we have close to the real story yeah my suspicion is we don't yeah and listen to that guy he was like a little wide-eyed about and then he did this and then they did that and it was like i think you should know more about that your business than that but again there's many businesses when like 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 the sky's blue every day california and where once a while the continued rains there was like what do we do somebody go bring in the lawn furniture you know like it's getting wet we don't know it's getting wet yeah it doesn't it was blue for like 100 days and now it's you know so but part of the problem here with vlad this the ceo of robin hood is the scaling is that what we've been talking 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 and sell stocks the scaling is you know the stocks only move in a certain range and if you buy a stock you can only lose that amount of money on the short short market you can lose a lot more than you can benefit like it has a 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 the 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 work was clearly weak or or he didn't represent himself i you know i don't know the person when i listened to him nick yeah it could have been the surprise question was like and then these guys called and you know it sounded like he was treating stuff as a black box maybe he shouldn't have but maybe his whole pilot expert somewhere else and it was going on i don't i don't know yep i mean this is uh 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 because and understand the problem quickly learn and understand the problem like at this like basic level like what the hell happened and my guess is you know at some level it was amateurs trading against you know expert slash insiders slash people with you know special information outsiders is 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 24 7. but there's numbers in the outsiders this is the interesting thing well there's numbers on the insiders too like different kind of numbers different kind of numbers but this could be a new era because i don't know at least i didn't expect that uh a bunch of redditors could you know there's uh you know millions of people who can get together the next one won't be a surprise but don't you think the the the crowd the people are planning the next attack we'll see but it has to be a surprise can't be the same game as to the end it could be there's a very large number of games to play and they can be agile about it i don't know i'm not an expert right that's a good question how the space of games how how restricted is it yeah and the system is so complicated it could be relatively unrestricted and also like you know during the last couple financial crashes you know what set it off was you know sets of derivative events where you know the you know nasim talibs you know thing is they're they're they're trying to lower volatility in the short run but creating tail events and systems always evolve towards that and then they always crash like 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 uh there's 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 a young at heart looking for the next steps to 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 waste all your time doing stuff that's just boring or bland or numbing right don't let old people screw you well people get talked into doing all kinds of and wrapping up huge student you know student debts and like there's so much crap going on you know and then it drains your time and drains yeah the eric weinstein you know thesis that you know the older generation will let go yeah and they're trapping all the young people i think there's some truth to that yeah sure just because you're old doesn't mean you stop thinking i know lots of really original yeah old people i'm an old person so um but you have to be conscious about it you can fall into the ruts and then do that you know when i hear young people spouting opinions that sounds like they come from fox news or cnn i think they've been captured by groupthink and memes and supposed to 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 like that's not how the world works it may be it seems safe but it puts you at great jeopardy for well being boring or unhappy or how long did it take you to find the thing that uh you have fun with well 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 for a good reason or for uh the reason that doesn't make any sense yeah like some some things are 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 at a job that i loved and but i was going to work and all my time is consumed what's what's the escape out of that depression what's the answer to is is this all there is well a friend of mine i asked him because he was working his ass off i said what's your work-life balance like like there's you know work friends family personal time are you bouncing in that and he said work 80 family 20 and i try to i try to find some time to sleep like there's no personal time there's no passion at a time because you know young people are often passionate about work so and i was certainly like that but you need to you need to have some space in your life for different things and that's that creates uh that makes you resistant to the whole the the the dip the the deep dips into depression 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 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 uh definitely afraid of quite a few things then you gotta go face them like like what's the worst thing that happened you're going to die right like that's inevitable you might as well get over that like a 100 death rate like people were worried about the virus but you know the human condition is pretty deadly there's something about embarrassment let's see i've competed a lot in my life and i think the if i'm too introspected the thing i'm most afraid of is being like humiliated i think nobody cares about that look you're the only person on the planet zack cares about you being humiliated exactly it's like a really useless thought it is it's like uh you're all humiliating something happened in a room full of people and 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 hates it throughout yeah yeah now i know it too i mean i've been really embarrassed about that nobody cared about myself yeah it's a funny thing so the worst thing ultimately is just uh yeah but that's the cage and then 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 slowly accumulate that kind of junk and then you die as a you know a mess so the goal i guess it's so it's like a cage with a cage i guess the goal is to die in the biggest possible cage well ideally you have no cage people do get enlightened i've got a few it's great you found a few there's a few out there i don't know of course um either that or they have you know it's a great sales pitch there's like enlightened people writing books and doing all kinds of stuff it's a good way to sell a book i'll give you that you've never met somebody you just thought they just killed me like this like like mental clarity humor no 100 but i just feel like they're living in a bigger cage they have their own they don't think there's a cage they're still okay you secretly suspect there's always the case ah there's no there's nothing outside the the unit there's nothing outside the cage [Laughter] you were you worked in a bunch of companies uh you led a lot of amazing teams um i don't i'm not sure if you've ever been like at the early stages of a startup but do you have advice for uh somebody that wants to uh do a startup or build a company like build a strong team of engineers that are passionate just want to uh solve a big problem like is there uh more specifically on that point well 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 actually there's there's quite a few successful startups it's pretty clear the founders don't know anything about people like the idea was so powerful that it propelled them but i suspect somewhere early they they hired some people who understood people because people really need a lot of care and feeding to collaborate and work together and feel engaged and work hard you know like startups are all about out producing other people like you're nimble because you don't have any legacy you don't have you know a bunch of people who are depressed about life you know just showing up you know so startups have a lot of advantages that way you know do you like the the steve jobs talked about this idea of a players and b players i don't know if you uh know this formulation yeah no um organizations that get taken over by pb player leaders often really underperform their rc players that said in big organizations there's so much work to do like and there's so many people who are happy to do what you know like the leadership or the big idea people who can see it consider menial jobs and you know 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 but so so you need to have an organization that's resistant to that but in the early days the the notion with 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 i don't know if it's like always true like you know you you run into people who are clearly b players but they think they're very players and so they have a loud voice at the table and they make lots of demands for that but there's other people are like i know who i am i just want to work with you know cool people and cool and just tell me what to do and i'll go get it done yeah you know so you have to again this is like people skills like what kind of person is it you know i've met some really great people i love working with that weren't the biggest id people the most productive ever but they show up they get it done you know they create connection and community that people value it's it's it's pretty diverse so i don't think there's a recipe for that i gotta ask you about love i heard you're into this now into this love thing yeah is this is you think this is your solution to your depression no i'm just trying to like you said the enlightened people on occasion trying to sell a book i'm writing a book about love you're writing a book about me no i'm not i'm not a friend of mine he's gonna somebody said you should really write a book about your you know your management philosophy he said it'd be a short book [Laughter] well that one was all pretty well uh what role do you think love family friendship all that kind of uh human stuff play in a successful life you've been exceptionally successful in the space of like running teams building cool in this world creating some amazing things what uh did love get in the way did love help the family get in the way to family help friendship you want the engineer's answer please so but first love is functional right it's functional in what 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 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 you monitor them really closely like and if they go off a little bit because you love them if you're smart 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 it's somebody else that loves it like because it's new and interesting and that lets you go to the next level so it's the thing it's just a function that generates newness and novelty and surprises you know those kind of things it's really interesting but and there's people figured out lots of you know frameworks for this you know like like humans seem to go in partnership go through you know interest like somebody suddenly somebody's interesting and then you're infatuated with them and then you're in love with them and then you you know different people have ideas about parental love or mature love like you go through a cycle of that which keeps us together and it's you know super functional for creating families and and creating communities and making you support somebody despite the fact that you don't love them like and and it can be really enriching you know now 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 family and friends and things you care about your brain isn't well balanced like everybody knows the experience of you works on something all 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 right where you left off is zero your brain refactored it but being in blood is great it's like changes the color of the light in the room it creates a spaciousness that's that's different it helps you think it makes you strong buckowski had this line about love being a fog that dissipates with the first light of reality in the morning it's 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 just be the light that actually enlivens your world and creates the interest and the power and the strength and the to go do something well it's like like that sounds like you know there's like physical love emotional of intellectual love spiritually yeah right isn't it all the same thing kind of nope you should differentiate that maybe that's your problem in your book you should you should refine that a little bit different chapters yeah there's different chapters what's that what's these are there aren't these are just different layers of the same thing the stack no physical people people some people are addicted to physical love and they have no idea about emotional or intellectual love i don't know if they're the same thing so i think they're different that's true they could be different it'd be it i guess the ultimate goal is for it to be the same well if you want something to be bigger and interesting you should find all its components and differentiate them not climb 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 yeah or the afterwards and the after you really tried i feel like lex has made a lot of progress in this book but uh well you have things in your life that you love yeah yeah you know so and they are you're right they're modular it's and you can have multiple things with the same person or the same thing and yeah but yeah depending on the moment of the day yeah there's like what bukowski described as that moment you go from being in love to having a different kind of love yeah right and that's the transition but when it happens if you read the 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 as 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 is actually more interesting and possibly delightful than you know being a mad kid in love with the the next person like that's super fun when it happens but that's that's you know five percent of the possibility 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 me 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 gonna happen yeah but you have to look carefully and you have to work at it you have to think about it and you know it's 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 uh 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'd say like in terms of working with people when people did say stuff i didn't like especially if it was a bit nefarious i took it personally and i also felt it was personal about them but a lot of times like humans are you know most humans are a mess right and then they act out and they do stuff and i 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 wait 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 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 at the odds of it being about you yeah no 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 it coming and step out of the way like like i wish i was way better at that i'm i'm a bit of a hothead and and you said with steve that was a feature not a bug yeah well he was using it as the counter force the orderliness that would crush his work well you were doing the same yeah maybe i don't think i don't think my uh my vision was big enough it was more like i just got pissed off and did stuff i'm sure that's just yeah yeah you're telling me i don't know if it had the it didn't have the amazing effect of creating the trillion dollar company it was more like i just got pissed off and left and or made enemies that he shouldn't have been yeah it's hard like i didn't really understand politics until i worked at apple where you know steve was a master player of politics and his staff had to be or they wouldn't survive them 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 you know fun and games compared to apple and it's not that the people apple are bad people it's just they operated politically at a higher level you know it's not like oh somebody said something bad about somebody somebody else which is most politics it's you know they they had strategies about accomplishing their goals sometimes you know over the dead bodies of their enemies you know with some communication 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 uh to do to have a bigger strategy in the way you behave yeah and it's it's 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 yeah creates all kinds of you know drama and problems that you know our solution is political in nature like how do you convince people how do you leverage them how do you motivate them how do you get rid of them how you know like there's there's so many layers of that that are interesting and even though some some of it let's say may be tough it's not evil unless you know you use that skill to evil purposes which some people obviously do but but it's a skill set that operates you know 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 there's times when i could have 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 you know expression layer of the stack which is complicated and there's lots to know about it i mean people are good at it are just amazing and when they're good at it and let's say relatively kind and oriented in a good direction you can really feel it can get lots of stuff done and coordinate things you never thought possible but all people like that also have some pretty hard edges because you know it's it's a heavy lift and i wish i'd spent more time with that when i was younger but 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 when there's a book like a hitchhiker's guide to the galaxy and this is like a one sentence entry ball jim caller from like that guy lived at some point there's not many you know not many people be remembered uh you're one of the sparkling little human creatures that had a big impact on the world how do you hold you'll be remembered my daughter was trying to get uh she added my wikipedia page to say that i was a legend and a guru but they took it out so she put it back in she's 15. i think i think that was probably the best part of my legacy [Laughter] she got her sister they were all excited they were like trying to put it in the references because there's articles in that and they're telling you that so the eyes of your kids your uh legend well they're pretty skeptical because they'll be better than that they're like dad so yeah that's that's stupid that kind of stuff is super fun in terms of the big legends stuff anchor okay legacy i don't really care you're just an engineer no they've been thinking about building a big pyramid so i had a debate with a friend about whether pyramids or craters are cooler and you realize that there's craters everywhere but you know they built a couple of pyramids five thousand years ago in there and they remember you think that would be foreign uh those aren't easy to build oh i know and they don't actually know how they built them which is great it's either uh agi 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 getting updated 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 tennis torrent 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 today this was 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 sheetz expressvpn and bel campbell 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 for listening and hope to see you next time you

Channel: Lex Fridman

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Keywords: agi, ai, ai podcast, artificial intelligence, artificial intelligence podcast, jim keller, lex ai, lex fridman, lex jre, lex mit, lex podcast, mit ai

Id: G4hL5Om4IJ4

Length: 159min 15sec (9555 seconds)

Published: Thu Feb 18 2021

Reddit Comments

[u/LikvidJozsi]

I feel bad for the guy, must be hard living when everything he touches turn to gold. Probably can't even touch his wife because she would immediately turn into the most efficient processor on earth.

[u/[deleted]]

Damn that interviewer was depressing. VERY interesting interview otherwise

[u/chow-zilla]

Before I even clicked on it - lemme guess Lex interviews Jim Keller

[u/SouperFalcon_Maciej]

Why do you make the title sound like he died?

[u/floppydrive]

I can't stop watching this. And I really need to sleep. Thanks!

[u/VeganVagiVore]

WHO

[u/segv]

Previous post with this podcast episode was in /r/sysadmin.

I'd recommend using 1.5-2x playback speed and the timestamps embedded in the video.