title: Ash Fontana: Building Artificial Intelligence
author: North Star Podcast
contenttype: podcast
publication: North Star Podcast
published: 2021-05-07T19:36:57
sourceurl: https://traffic.megaphone.fm/TFTEE1184856498.mp3?updated=1713204240
word_count: 12367
Hello and welcome to the Norstar. I'm your host David Perrell and this is the Norstar podcast. In each episode we explore the intersection between different ideas, cultures, and life philosophies. The guests are diverse but they share profound similarities. They're guided by purpose driven by curiosity and see the world with a unique lens. And in each episode we get to dive into their hard-earned wisdom and apply it to our lives. When I'm not recording podcasts, I write essays on my website, Perrell.com, send a weekly email newsletter called Monday Musings, and run an online writing school called Right of Passage. I hope you enjoy the show. Ashton Tana is three people in one, an entrepreneur, an investor, and an author. As an entrepreneur, he was one of the early employees at an online investing platform called Angelist and from there he became an investor, the managing director at Zeta, which is the first investment fund that was focused on artificial intelligence. And now he is the author of a book called The AI First Company. And this conversation, at least at the beginning, is all about that book. Ashth says that AI First Companies are the only trillion dollar companies and soon they will dominate even more industries, more definitively than ever before. But we don't just talk about the book, that wouldn't be my style, right? We also talk about health, continental philosophy, and ashes obsession with cycling. So please enjoy my conversation with Ashth Fontana. How do you build the systems that learn? That's just a beautiful question. This goes back, actually, your first question, which is, do I need to know anything about AI to get this book or to be interested in AI, or I would say to sort of add to your question, to get started with AI? And I think the answer is no. Because what you start with is a problem. And the problem is sort of framed in a temporal sense. Like I have the problem of trying to understand this thing that mail may not happen in future, or this thing that I know I need to think about in future, like a decision I might need to make in future. And so I know I have this problem because I face this problem a hundred times in my current job. I know I have the problem of trying to figure out what adds to target to what audience to try and figure out when there's going to be a defect under my manufacturing line or whatever, because I've had that problem a hundred times. And so I want to build a system that can help me solve this problem, make this decision, make a prediction around this decision so that I make it better next time. And that's the starting point. And from there, it's all pretty straightforward, depending on the type of problem. But these days, because a lot of the infrastructure is out there now to help you build systems around that. So how do you get started? You think about what you really want to predict and what you really want to solve. And how do you then build the systems that learn to your second question? It's really starting with a good definition. And it's starting with a good experiment. It's like how to design an experiment that makes a little part of that prediction or helps me with a little part of my decision. And let's see how it goes. Let's see how accurate my prediction is. And then let's improve and improve and improve over time. And then you start, once you've really defined it well and run those initial experiments, then we can start talking about building distributed learning systems, which is what people think of as AI. Oh, interesting. That's an interesting definition of artificial intelligence. So within prediction, how do you think about regime changes? So one of the fascinating news stories that I got somewhat into at the beginning of the pandemic was that the airplane price predictions no longer held because we were so far outside of the band of what was statistically significant. And so I guess you could call that like a regime change. So is that some flaw in what artificial intelligence can do or are we beginning to build artificial intelligence systems that can actually respond to that? Yeah, it's very methodology specific. So some systems like a lot of the yield optimization systems that airlines use. And by the way, I love that we're already talking about planes because it's a conversation with David and a couple of minutes in if we can we talk about planes, we're doing well. So I think a lot of those systems that they use or the subsystems of like the price prediction and yield optimization systems that airlines use in particular are based on statistical methods that I guess are very limited in their consideration. So they consider the data they have and they sort of extrapolate forward to oversimplify. Obviously, these systems are very advanced these days. But that's really what they are. They're sort of linear optimization based think it was like pretty simple statistical stuff that extrapolates forward. And so they'll only see within the distribution of what they've considered. So you think of a Gaussian distribution, you think of like these normal curves that we all sort of at least looked at and maybe properly studied in school. That's all that's in their realm of consideration these systems. If you think about different forms of machine learning and artificial intelligence more generally, you can start considering things like out of the realm of previous consideration that could have for example predicted something like this. And so if you think about maybe creating an agent based simulation. So you think about creating a simulation where you've got the number of planes, you've got the people that book see some planes, you've got all these other factors, you've got some constraints. And so you set up like a little environment, like a little game, and you just let it run. That may have got to the point where in the game, it had considered a situation where all of a sudden no one's flying for at all. But that is a very different way to model a problem than using statistics like building games is very different to using statistics and building that game may have been able to let us see a situation like the one we faced. But that's a very different form of intelligent system than what a lot of people use in a production sense. One of the questions that came up to me and you get into this towards the end of the book was about disruption theory. And one of the questions that I've been trying to answer for years was what happens to disruption theory once everybody knows about it. And talk about that because it seems to be something that you've thought about quite a bit here. I love questions like this because it's like, well, what's the end state of everyone knowing something in a zero-sum game is in if we all know the same thing, but someone has to win and someone has to lose who wins and who loses. And the answer is all the bar just goes up. So I think to sort of recap for everyone out there what's disruption theory is the theory. I'm going to totally oversimplify it, but it's a theory that you come into a market just doing one very specific thing like you're not providing all the things your customers need, but you're just providing one element of what they need, but 10 times better faster cheaper. And then because you get in with that little wedge, then your customers are okay with you providing the next thing and the next thing, and eventually you take over all of their needs and the vendor that previously served them is irrelevant to them at that point. So what happens to disruption theory over time? The basis of disruption, the catalyst for the wedge, the initial thing you do just changes. So I think the theory is actually a very good one and we'll stand the test of time, so to speak, but the dimension of disruption changes. And so in the industrial era, the dimension of disruption was if I can run my production line better, if my work is just a little bit faster, if I train them better, then I'm going to be able to do this 10 times faster. Then as we move further on into more like resource-based economies, it became more about scale. So if I can pull more metal out of the ground using the same amount of trucks because I have a richer seam or something like that, then I can provide this thing 10 times cheaper. Now with AI, what it becomes is a question of not scale of data and not speed of calculation necessarily, it's accuracy. And so if I can train a model to be more accurate in the predictions it develops, then I can go in and if it's much more accurate and say to customers, look, I know you've got this system today that's this accurate or doesn't even do any predictions, but mine's a little bit more predictive or a little bit better predictions. And so that's the basis of disruption. They say, okay, we're still going to keep what we have. We're going to still keep out CRM where we keep all our leads for sales because we don't want to throw that out yet. It's got a big database of all our sales leads that we've ever contacted and want to contact. We're going to keep that, but we're going to let you run this little bit on the side where five times a day you can send us a suggestion of who to call. And over time, if those suggestions are good, not completely moved, they'll spend all of their time in your system and they won't even use their CRM anymore because it doesn't matter who they thought they should contact if you're telling them who to contact and it's working. And so that's what happens to disruption theory. I think it's a really good theory. It's the question that just becomes, what's the dimension of disruption? Is it a cheaper resource? Is it a faster processing thing or is it a better prediction? And I think that's what's happening now, the basis of disruption shifting to accuracy and better prediction rather than just calculating stuff a bit faster. Yeah, in the book, what you say is charge customers more for novel AI-based features such as personalization, insight generation, and automation. And the one that's most interesting to me there is insight generation because what an insight is is it's like an emergent property. It's basically you're taking what you have and then you're processing that information differently. You're sort of remixing in some way and then from that remix you end up with something new and novel and I just have no sense for how good computers are at actually doing that. I have a sense of the sci-fi vision of where that could go. But right now, right here, I've no sense for how good computers are actually doing that. Well, I think you can get a sense in daily life. It's not a good sense, but it's a sense how good are the recommendations I get from Amazon about what to buy or from Netflix about what to watch or Spotify for what to listen. And so you can get a sense. They're pretty advanced systems that generate these recommendations. In terms of they generate a lot of recommendations for a lot of people very quickly. Now, they're not that advanced in that their realm of consideration is pretty local. They just looked at what you've listened to, maybe what people you're connected to listen to, what you bought, what you've clicked on, whatever. And so they're not really going to come up with something that you view of as like highly insightful. Like, oh, wow, I was interested in this theme of documentaries and you've recommended something in that theme. They're probably going to recommend a documentary by the same director, something like that, which is not particularly insightful. It's pretty obvious, I guess. But we can, using slightly more advanced techniques, that at the beginning to be able to be deployed at the scale that Amazon and Netflix and whatnot start considering things outside of that really local realm. So for example, they don't just get information from the things you've watched, like who was the director, how long was it, all that sort of stuff. They look at themes. So they look at all the text in the movie you've watched or the text in the podcast you've listened to. And they pick out, they look at it on a bigger scale. So they don't just look at what was said in this first sentence. They look at, well, how many times did this word come up in this one? And then how many times did it come up in another one? Or what was the meaning of this one and a half hour podcast? What was the one sentence summary and doesn't line up in some significant sense to a one sentence summary of something else? That was really hard to do in the past, basically look over huge volumes and try to understand the meaning. We could look at like what was called an engram, like something of a certain number of words long, but it wasn't very long. And so I think you can get a little bit of a sense of how insightful computers can be today, but it's not a very good sense. I think we're going to start seeing more and more of that as some of these newer techniques are able to get to the scale, whether I able to operate in or through the products we use every day. It's funny. So I think of this as sort of if you're going to design an AI intelligence system, there's sort of two categories. There's sort of what you're saying here, the ability to process information. But then there's this other category, which is the ability to create information in a way that can be processed. So if you take a podcast, text is easier to process than audio. And one of the things that I think a lot about is how do you actually design a world that assuming that computers are good, that is easy for computers to process so that then we can have more of the world being driven by computers and we have basically a more intelligent humanity. This is such an interesting sort of thought experiment or design challenge because I can take like a really sort of contemporary example, which is autonomous vehicles. How do you design a community where autonomous vehicles can operate really effectively? Well, you put sensors absolutely everywhere. So they're essentially running on rails. The rails are very small and you can't really see them, but they're running on rails. That's a world in which autonomous vehicles can work. Autonomous vehicles can't work in a current world which wasn't designed for that because there are plot holes in people running around and branches falling off trees and thunder and lightning or whatever. So they can't operate in that world. And you can think about this in lots of different ways. Like how do you design a world where there's a lot of good sensors so that machines can do sense-making from that? And this is really exciting and look if I had the ability to go out today and raise a multi-billion dollar fund, I would and I'd invest it only in news sensors that we can use to help understand the world better. So, for example, imagine if you had an eye that could see on a high-perspectral basis, like had much more of an ability to see different spectra of light or across the light spectrum and you had those eyes everywhere. Imagine if you had smell sensors everywhere, pressure sensors on everything, but they were all like very small, very cheap, had low energy requirements, et cetera, et cetera. If we had more sense gathering, we could do more sense-making and I really like that sort of design challenge you pose there. You can pose it on a very local basis. How could I make my room where I do all my video conferencing better so that it senses how I feel and then communicates that to how someone on the other end receives me or communicates that to them so they can receive me. Like do they know how hot or cold I am, how cold or cold room is, what happened in that room, what time of day it is outside, what's the ambient light, do I have a lot of blue light on my awake, do I have a lot of red light on my falling asleep, and then how do you sort of gather that and communicate that to someone, that's like a hyperlocal example, but you can think of this on a much less local basis, like with roads with the environment, understanding the weather, et cetera, et cetera. Yeah, give me very practical problem in my life. I cook on high heat, a decent amount of my apartment, and the problem is when you cook on high it really hurts the air quality in your apartment, and so I could cough up a couple hundred dollars for an air purifier. It's probably what I'll end up doing, but it'd be really cool if there were sensors on the walls and on the ceilings. Another thing is dust. Some people are really allergic to dust, and I've had in the past done a spring cleaning in my place, and then all of a sudden be like, oh my goodness, I feel so much better, and it's almost like the frog in the boiling water where the frog is just in there, the water gets hotter and hotter, and all of a sudden it's bubbling and the frog dies, because the changes happen so slowly, and without sensors, you don't really realize this stuff. Oh, absolutely. There's so much in our own homes and lives that we are just completely unaware of, dust, mold, et cetera, is one thing, and then those volatile fat particles that have come about because you've cooked above the smoke point, and then they've settled in the curtains or whatever else and flown around. That's another problem as well, and we can solve that problem for you by getting you to cook with macadamia or other part of the way. They're good Australian products, but explain that. Well, because macadamia oil has a very high smoke point, and so as you cook on high heat, it doesn't get volatile at the temperature, which other things get volatile, and therefore the fat particles don't start breaking down, vaporizing, and once they vaporize, they're then in the air, when they settle, and liquefy again, they liquefy in your furniture, on your carpets, whatever, and forget what's happens in your home, think about what's happening in your body. Once you vaporize something, get it to that volatile point, ingest it, then eventually when it settles, that then striates in your arteries, and like hardens them up. So that's why it's good to cook with. If you're going to cook at high heat, cook with oils that have a really high smoke point, like macadamia, so they don't get volatile. Had no idea about that. This is why fried food is so bad, because you are cooking something at a very high temperature, but has a very low smoke point, like vegetable oils, and so they get super volatile, and then once they settle in, yeah, I'm over simplifying it, I can go on and on, but I don't want to bore people to death, but we like the different chain lengths of different triglycerides, but yeah, that's why fried food is terrible for you. Back to AI. I have a dream about databases where, so one of the things that happened in the past 10 years is people, just by being people, became really good photographers. So like if I'm traveling or something going in front of some landmark, I always ask young people to take a photo of me, and my question is, will young people have a similar intuitive understanding of how databases work, or will we continue to sort of go through college, you do your Excel trainings, and then you kind of get into it, because it seems like that's a binding constraint, just how late in life people learn about how to use databases. So I think it's a good question, and I think it would certainly help people understand intelligent systems if they understood how databases work, and how data structures are made, and what data structures are conducive to eventually doing analytics and developing some sense of what reality is represented in that data structure. Sure, but I think the answer to your question is no, and so if I take your sort of comparison to the camera, I don't think young people today understand the physics of camera lenses better than people do the long time ago. In fact, I think they probably understand it less, because in the past with a manual camera, you really had to understand how different lenses interacted with each other as you move them around, and different light conditions based on the film you had, et cetera, et cetera, were probably both being through that. So I think people understand the physics of camera lenses less. What they understand better is what makes the good photo in terms of composition and effects and things at that layer, which is many layers above the physics of lenses and light, and I think what happens or what will happen in the world of the future where we're trying to understand intelligent systems better is people won't understand databases necessarily won't need to, because they'll be thinking many levels about, which is how to interact with systems to effectively train them so they work better for you. What's a productive bit of feedback to give an AI system, a yes, no, or how do you use AI systems? When do you trust them? When do you not trust them? I think that's the level of consideration people will be at, and not how do I reform this data structure so that I end up with a better system 20 steps down the line, where very gracefully moving up the ladder of abstraction in so many different things, because technology is sort of abstracting away all those lower level problems for us. Yeah, I hear you saying that we're conductors in a symphony of intelligent systems where the software is playing the music, not you. Exactly. Just like if you're filming a video these days, you're focused on the movement of the people, and you don't have to focus so much on constantly changing the light and having multiple cameras running at once and doing all those things that are really hard to do and that great cinematographers and camera people and what not do, because software does a lot of that now. It's constantly adjusting levels and whatnot, and you and I right now aren't having to think about sound levels too much, because to some degree they're being adjusted in the background, and so what we're actually doing is we're focused on conducting the conversation, and so I like that, like the conductors, and I think that's how we'll interact with or work with intelligent systems in future. We'll get better at sort of developing the intuitive sense of how to work with them, again, what feedback will make them better, where to trust them, et cetera, rather than constantly tweaking the knobs behind them. You as your position being an investor, what was the last, just like, oh my goodness, I cannot believe what is in front of me technology that you saw, and I'm less interested in the actual technology in terms of this is what the tech did, but in terms of the implications of technology, which is I think what really matters for most people, this is now what you're going to be able to do. It's funny, those moments sort of rare the moments where, as you said, the implications of the technology in Toronto view are so great that you think they'll cause a shift in some significant part of society, and I'm trying to think, I mean, look, there's a lot in the field of quantum computing, but it's sort of for people in the field of quantum computing at this point, it's questions of degree, we're of the sort of shared understanding that it's coming, it's happening, but we're not quite there yet, but we're getting there step by step. From someone coming outside of the field of quantum computing, I think if they came in and saw some of the stuff I've recently seen, they'd go, wow, this is going to change everything, all the ways in which we encrypt data and think that things are secure, we've got a question them again, all of the ways in which we think, okay, you get a computer to do a thing, you wait a second and you come back and it releases the output of that thing, that paradigm totally changes, computers can do so many more things in parallel, quantum computers that is. So I think in the realm of quantum computing, I see so much that will change so many elements of our economy, society, et cetera, et cetera. So that's one thing. I think another thing is related to what you said before or what we were talking about before about sensing. I've seen some, I guess in a simple sense, you call them cameras, but really small cameras lately that can pick up so much more, so can pick up so much more of the spectrum and therefore understand so much more about what's in front of them. And I think the third thing, I'll stop myself after that because I'm lucky to see so many amazing things every day, is AI's that build chips that help AI's run better. So I'll take a step back. If you think about all the different ways in which we build artificial intelligence today, build intelligence systems, all the different models we use, they all run a little bit differently, is in they require different amounts of power, they require different amounts of things to run at the same time or in sequence, some of them collapse all at once, some of them have to have certain things happen in a certain order, they're all a little bit different and so trying to run them all on the same calculator, on the same chip is pretty hard. So that's why we have some different chips now, we have CPUs and people would have heard of GPUs and now Apple has sort of like an integrated thing that's running the conversation we're having now on this laptop I'm using, that's pretty cool, but we're going to have hundreds of different types of intelligent systems of different AI models that we want to run and if we want to run them in the real world we're going to have to do it really cheaply with not much power and so we're going to need to develop different chips. Now the problem is designing a chip is a very difficult thing to do, you have to map out all the different circuits and it's very very difficult work that requires a lot of experience and electrical engineering and then fabricating that chip. So designing a production line that can build it is a very expensive thing to do in the past that's cost at least $10 million to get up production line to build one particular chip. Now something exciting I've seen recently is an AI that can basically take how much power do I want to use, how quickly do I want this thing to run and what are the parameters of this model, how does the model work and go all right this is the optimal chip to run this on and then designs it for you so that you can then just go all right I'm going to go print that chip and so now you've got a whole new computer chip and so we could end up with ZPUs whatever XPUs YPUs whatever all these different types of processing units and hundreds and hundreds and hundreds of them so that's pretty cool. That is really cool and is the extreme cost and economies of scale and then I would assume the benefits of local knowledge of just having a lot of people in a small area know how to design chips is that's why to the best of my knowledge there's big time chip manufacturing in three countries US South Korea and Taiwan I'm at the wrong about that. Yeah well and I put China in that bucket now yes the short answer question is yeah it's really hard to do the economies of scale in doing it and it's not just about the design it's about the process and making it really clean and efficient and all that sort of stuff so yeah that's why there's so much consolidation in that industry and the world I'm thinking about there may still be consolidation on the production side but I think there'll be less consolidation or at least there'll be like a little bit of commoditization it's not really the right word but a little bit of commoditization on the design and development side and that could be really exciting. One of the things with sensors that I find to be pretty interesting is sensors are sort of at least on first glance one side of a two-sided coin which like sensors sort of sending information out and the problem is that that's very closely tied to surveillance and so as just a normal person consumer who knows very little about these ideas one of the things that I would be a huge fan of is how do I send more information out get it anonymized know that it's being used in ways that are helpful at the aggregate scale but then not be tracked at like the super creepy level. That is a problem that I think we need to solve and that is a problem that a few people are working on and that is a problem that I would like to see more people working on which is the sort of tail end of this which is as you said it's one thing to collect all this information it's another thing to make it available to the systems that need to well that will ultimately like get the value from the information from the data I should say not the information by turning the information it's one thing for those systems to be able to do that without sort of exposing any underlying understanding of people's lives people deserve to live lives I think this is my personal view deserve to live lives in private with the level of dignity that they want to live and part of that is being able to do things without things about so yeah I think that would be good if more people are thinking about this and I think it is a big concern yeah I mean look there's certainly some really cool applications where like I just don't worry about it so in my living room or in my kitchen I have a sensor that is a scale and I put my coffee on the scale and once it gets fairly low I automatically get a new shipment of coffee and it's great because I never have to think about ordering coffee it always arrives and I don't really care what people know about your coffee in my life right and eventually I would love to have my kitchen tables be sensors where they can sensor and know how much food I have all the shelves in my fridge or sensors and then you can have really intelligent data on say when something goes bad for example I came back from like a three-day getaway and I was like is this salmon good to eat or is it not good to eat and 50-50 with fish you always say no but I would like more data on what's good what's bad and that just isn't really available and I feel like the kitchen is a great place where a lot of these ideas can really come together in a way that is healthy it's the place where most of my ideas come together at least my better ideas is in the kitchen but yeah I think you're right in saying that the two things there the limitation though sensing and so I've looked at a couple of really exciting companies that have for example developed very small cheap gas sensors that you can put next to around food and whatever else and tell you things like that is it a meeting a noxious gas which means it's bad for you to eat but those companies face challenges in producing these sensors getting them out and then ultimately showing people the value of having that information which is the coffee scale one's a good one if it was just a coffee scale that says you don't have much coffee left that doesn't add anything to your life you can see you don't have much coffee left it's the factor that then communicates to another server through the internet and then that then communicates to another server through the internet that eventually orders you more coffee that arrives on your door with that we're thinking about it the fact that it's saving you time and effort is what makes that sensor valuable and what makes the manufacturer that sense of money and so I think you're right in saying that that's like sort of the limiting reagent of making a lot of these things more effective for us or like really signal value and a lot of these like relatively simple intelligent systems is getting the data in the first place and having the right sensors and then yeah it's another interesting point around like well what am I happy having observed in my home and what am I not and I think this comes up for a lot of people because they have one bit of AI in their home and that's like an Amazon Alexa or Google Home and the problem with that is it's both owned by a big company that has a lot of other data on you and two it's got a very general sort of omnibus sensor in there which is it just detects all sound and then maybe temperature and a few other things as well but there's a lot of things that happen sonically you say a lot you listen to a lot other people say a lot you can't control it and so that's not really a comfortable position to be and to have one of those things that's made by a company that has a lot of data about you and that's getting information from a sensor that can sense a lot of stuff or can gather a lot of information and it's not really comfortable position to have that thing in your home whereas having a sensor that's made by this smaller company that doesn't have any other data about you other than what comes through that sensor and has very specific data it's getting like the weight of something that's totally fine and so the point I'm getting to is I think we will probably if people are actually concerned about their privacy and do actually want to get value out of these systems probably moving to a world where we have more sort of quarantined sensors made by lots of different companies and there's quite a bit of fragmentation and the punchline is I don't think if we actually care about our privacy we get to a world where one company makes all the stuff in our home or one company offers all the AI in our life it's actually lots of little bits of AI that's built on lots of little bits of data from lots of little sensors so how does that square with the very clear scale advantages of AI that you talk so much about I do talk about them in some models do need quite a bit of data to be trained to point and accuracy so a machine needs to see a million images of something to be able to predict the one million and 10th image is that thing to a high degree of accuracy shock in some cases you need that today but I think a lot of problems don't require that much data and a lot of new methods are being developed that also don't require that much data so I think it's just a point in time thing which is that at this point in time we do need a lot of data to train a lot of models that are useful today but in future there are a lot more things that will be useful there are a lot more models we can use to make predictions around them and there are a lot more sensors we can use and these sort of all play in together depending on the problem you're trying to solve you can be quite specific about the data you need and develop a sensor to just get that data what we do today is we try to get information from data that's like not really related to the problem of trying to solve so we try to sort of understand for example what's on a shelf in a supermarket from a photo but if we just had a different sensor like different weight sensors if every product had its own little tag or whatever else we do have these tags they're just really expensive then we wouldn't need any cameras we wouldn't try to like Jerry Riget for using computer vision systems so I think we're just at a moment in time where that's true where there are returns the scale of data but I don't think that will be true forever for all problems it also seems like there's been a lot of visions to basically create conglomerates that have a lot of data that then other companies can tap into because then it helps with a cold start problem where if I'm competing against Google and problem X well I'm at such a data disadvantage that I can't even get started and how are people trying to solve that problem I cover this so much in the book which is all the way to wonderful ways to get data without spending a whole heap of money or by doing it in a very like above forward way and there are lots and lots of ways to do that as you said you can partner with other companies you can say well I've got this data you've got that data they're complimentary I've got data about income you've got data about gender I've got data about weight you've got data about smell and using these two datasets we can both understand more about the person or about the products respectively to those two examples I just get so a lot of people do it through partnerships a lot of people build centers a lot of people build other little side consumer apps to collect data that lots of different ways to get data and again there's just an endless amount of problems to solve in a lot of prediction problems that is and Google's not going to solve all of them Amazon's not going to solve all of them in fact they solve very little of them they solve very very general prediction problems like how do I get from A to B using a map and incorporating traffic info and weather and all that sort of stuff it's a very general problem that I don't solve the problem of well how quickly does this truck need to get from A to B for the salmon in your example to still be good by the time it gets to be that's a very very different problem but it's a problem that you only really need to solve for a couple of logistics companies in North America and so Google's not going to solve that so I really really don't think it's the case that these big companies are going to solve all these problems I think it's just the case that the problems they're solving now do happen to have a scale advantage or do happen to have some sort of scale effect in the background because they're general computer vision problems or something like that sort of switching gears here into just who you are it's really easy to think of the things that you're normally interested in just away from work and the things that you're interested in in work as being totally separate but humans are pretty integrated as individuals and you love continental philosophy and I want to ask why and where is the residue of continental philosophy in how you think about artificial intelligence oh gosh we can go down some very big holes there and they're not rabbit holes they're like full-blown media craters so we can get pretty lost but I like where you started that which is why and I think to be really cynical about myself so it's okay I'm not being cynical about someone else it's just the education system I grew up in there's a whole separate way of understanding the world that in this dichotomy is very eastern that involves holding multiple understandings in your head at the same time involves like much more nonlinear thinking or whatnot I didn't grow up in that world I grew up in a world that is very western which is you're taught to solve problems in like a logical sequence whether it's a physics problem or a mathematical problem or even understanding the course of events in a history class I grew up in that world I think a lot of people did because of the way western industrialized education has proliferated throughout the world and therefore my default reasoning systems are very sequential and my default value systems tell me that like if something is reached rationally and logically it's probably good or right it may not actually give me a better understanding of reality but I think it's good or right and therefore I sort of conflate that would be closer to reality so anyway I think I'm drawn to it because like cynically I just grew up learning that way learning a lot of those or learning using a lot of methods that actually were inspired by like early continental philosophy a lot of logical methods and whatever else now good question in the second part of that which is like where's the residue in how I think about AI and I think tacking under what I just said it's helpful to have a pretty strong background in more logical methods of reasoning when you're trying to understand how AI works today because a lot of the way I work today is based on statistics like sequential calculations that happened very quickly at very large scale but it's still like a whole bunch of sequential rational calculations and so I think it's helped in a very straightforward sense like understand it statistics and mathematics behind it if you're asking how does it help me think about the values around building AI and bringing these products into the world and things like that I think the residue is in understanding whether or not we should build this product on a utilitarian basis it's very much the case that my default position is it would sort of be unethical to not explore the potential of this technology because it offers so much potential improvement to our quality of life whether it's getting us food cheaper, fresher etc or whether it's giving us more time for enjoyment because we've automated away something that we don't have to do anymore or whether it's giving us more enjoyment because we have better video games powered by AI engines I think it's sort of repugnant to not explore the potential of this technology now of course we have to be super careful like any tool it can be used for evil ends but that's the residue I think of a lot of Western continental at least moral philosophy is in having a utilitarian framework for approaching the levels of investment that should or shouldn't be made in various ways to explore AI technology or in AI I feel like to use moral frameworks it seems like AI necessarily sort of perpetuates a world that is really based on utilitarianism which isn't to say that utilitarianism is necessarily bad but I feel like AI kind of reminds me and I say this as an outsider of the way that governments make decisions I think I once read that the value of an American life is something like nine million dollars and they had to quantify that in terms of decision making and I don't know if that's still the same and my point is that the way that for example you deal with your friends the way that you deal with your family is very much not utilitarian at least a lot of the time and so like if I look at the last 100 years of where we've gone ever since sort of John Stuart Mill and stuff it seems like utilitarianism is the sort of framework of a rational world that we end up with so this is a pretty interesting point and I might take it in a direction that you didn't intend but I think is very much linked to what you were saying look most AI models now require you to put weights on things so what's the relative importance of this factor or that factor and that's a very utilitarian thing to do which is like how much utility is in this or that like what are the utils of this or that thing or this or that outcome and so yeah I think the way a lot of AI's work today would probably or do reinforce the preferences of those that design those systems because a lot of those weights at least initially are hard coded there's preferences are hard coded initially now of course over time it learns from the people using those systems and so they will change based on the express preferences of people using their systems so for example over simplify things but the designer the Netflix algorithm could say all happy movies are good and all sad movies are bad but if people want to watch a lot of sad movies then the system will eventually think that sad movies are good and so at least initially it does reinforce preferences of designers now that's for one type of machine learning there are lots of other types of systems that we use to develop recommendations and predictions etc etc so I think that's that might be true today that sort of reinforces this view of the world essentially if you think about it what's a ratio what's rationality it's putting something into a form that is expressed or articulated numerically and so if we want to build systems that can operate on a computational substrate those systems have to be articulated or express to the language that's a numerical language that can be calculated and so we're going to have to rationalize down to that numerical articulation of things now of course this only has to be one part of my life and it is for me it's not really part of my life like I don't use these systems day-to-day in terms of figuring out who I should contact or what music I should listen to with my friends or what recipes I should cook for my friends or whatever I don't use any AIs at all I ignore all of them I ban them all for my life basically because I don't believe that that's how we want to do and to should treat people around us in that sort of way that's reduced to these rational articulations of good bad and otherwise yeah I almost think of that how do you get more air quotes used bookstores in your life things that are sort of wacky and huh never expected to find this one of the fun things of living in Austin is the owner of my local bookstore now buys books for me and she only buys books at auctions and so I'll walk in and oh wow she goes hey I got you this book and it's really fun because honestly most of them aren't that good but every now and then she finds something that sends me in some new space in a way that Spotify discover weekly certainly doesn't do exactly and I think it's a good question like how do you make room for that some people call it so indipity you can call it randomness you can call lots of things none of those things are really yet but you can use words like that to describe it how do you make room for that in a world where your taste is molded by a system that is designed and narrow like how do you broaden how do you maintain open mindedness where so used to having a lot of unpredictability in our lives if you think about someone tending to a farm hundreds of years ago that no way of telling if it was going to be a good or bad season whether the weather was going to be good or bad whether they could even sell what they produced whether it's going to be demand for it or whatever else we have a lot more predictability around those things now because we have predictive systems and that's very good in the farmer's case probably because that predictability allows them to make decisions like all right I know I'm probably going to be able to sell most of my crop this year so I'm going to invest in my house I'll invest in my house and then my family will have somebody better to live and then maybe I can consider having another kid that's like a decision a really significant decision you can make based on having more information about whether or not you can sell your crop this year but having so much predictability around some things is just no fun like it's fun to develop taste around things and it's no fun to have an entirely predictable life so I think we have to understand where we want more predictability and where we want less and like consciously make that decision that's such a beautiful point in the bed of procrusties teleb has an aphorism that he says you are alive in the inverse of the density of cliches in your writing and I think the sort of ashrantana aphorism here is you are alive to the extent that you are surprised by something to the extent that you have broken out of the sloth of predictability exactly have I just Spotify example is one that resonates with me because I don't use Spotify never have because I like developing my own taste in music I like going on an adventure that starts with this song's call who played the flute in this song or who played the bass in this song huh who else did they play with what other bands did they play with huh what period in which were they most productive what albums came out in that period I'm going to listen to that album start to finish and then you listen to that album and you're like wow that's an amazing album that is so exciting and rewarding as a process it's exciting because you didn't expect to find an album that you would like so much it's rewarding because you can see how your effort led to the discovery of that thing and that all starts with letting yourself be open to the unpredictable rather than going no I want to sit down and have a really productive work session and it's going to be productive because Spotify is only going to recommend music for those two hours that I like and so maybe there's some good predictability there that you know that you're going to enjoy that music for two hours and so it's going to motivate you to do the work you're sitting down to do so that's an example where predictability would be good but also where predictability or unpredictability would be good in the music sense yeah I think the way that this manifests itself in the real world in terms of like what we sort of decide to do is like if you were to go out and it's like a new place you can have a night that's a 10 or a night that's a two so you have a high variance night or you could like sit home order in wash Netflix like guaranteed seven or eight I just think over time that gets slowly better and better and better and over time I just think that humans naturally fall towards low variance but I think a life well lived falls towards high variance and so it's almost like a I don't know I want to call it like a cognitive bias but it's not that that you sort of have to sort of shake yourself out of some people refer to this as the explore exploit trade-off do you go let's say you eat out 10 nights a month which is a lot but let's say it's 10 because it's easy and 80% of the time you go to the places that you know you're like you go to the old Greek tavern that's been there for 50 years and you know what you're going to get and it's great and then 20% of the time is in two nights a month you try something completely new and it could be a total failure it could be quite unenjoyable not food you like whatever they haven't got the service game together because then you or whatever but it could be something that makes the stable makes it into your favorites and so is that trade-off and there's an articulation of this in discrete mathematics as it's called the secretary's problem which is not a good name for it which is like well if you have some idea of how many choices you're going to be able to make at what point do you just pick the next best thing as in it's better than all the previous things you've seen such that you're very likely to have picked the best thing so if you take a deck of cards 52 cards at what point in turning over all those cards if your goal is to pick the highest card do you go it's that one and the answer is 1 over e times 52 and so there's an articulation of this explorer exploiting in discrete mathematics people think about this in lots of different ways but I think to your point we have a tendency to again it's not a bias but a tendency to exploit because I think we've just evolved in a world where there were limited opportunities to eat and enjoy ourselves and survive there were limited ways in which we could access the things we need to survive so we try to exploit or we can which is why we love eating stinkers bars because we never know where the next calories can come from so we eat as many as we can when they're in front of us so yeah I think we do have that tendency to exploit we pick the same thing over and over again they are delicious one of the things that always surprised me about you is your relentless pursuit of cycling like when you were living in San Francisco wake up super early and you would race over the gold the gay bridge and go bike the headlands and what inspired or who inspired that pursuit of excellence not oh I want to be in shape the pursuit of excellence that's a great question and it's a really personal one because it's actually something that goes back to that something very Venetian I'm three quarters Venetian one quarter Sicilian and I grew up and the one line that I just always remember growing up is finish the job do it well which is like what my dad used to always say to me don't do half a job don't do things by halves if you say you can do something finish it and do a good job of it and once you've done that you can make the decision about taking on the next job but just finish what you start and do it well and so I think it's that it's just this sort of Venetian work I think of like really focusing on doing a good job and I think this manifests itself in lots of different cultures not just Venetian culture but in many aspects of Japanese culture which is like just getting very very good at one thing and I've just found over time that's where the fun is to fun is in like the last 20% it's really fun to for example start cycling and ride a hundred k's for the first time that is super intimidating when you start cycling and then when you do it you're like oh that's really fun and I'm glad I did that but it's actually nowhere near as fun has the next level which is like hitting a certain wattage number and holding it and going wow I did that or being able to ride up at absolutely gigantic mountain and see the view from the top and do it all off your own steam or come back from a day where you've burnt 10,000 calories and feel completely fine inhaling 10,000 calories that's really really really fun and I got to tell you it's not twice as fun is eating 5,000 calories it's like it's fun so I've just found there's a lot of joy in that last 20% it was inspired by like day constantly telling me like finish the job do it well but it's just been reinforced by my experience of getting to the detail what within cycling is a detail that's fascinating to you that is at the forefront of your consciousness that I'd have no idea about it is the angle of the seat tube and the head tube so there those two bits that are up like the head tube is the bit underneath the handlebars and the seat tube is basically the post just sitting on and just thinking about what angle you're at on the bike and how that affects your center of gravity on different grades of mountain and then how that affects like your back position and then how that affects how you're breathing and how that affects how much your hip angle and how much leverage you have to exercise through your legs really gets to the core of cycling like as a side point the beautiful thing about a bicycle is it allows you to get the most leverage you can possibly get from your body so it is the most efficient way to convert human energy into forward motion the most efficient way gives you the most leverage call it six times I've reached as a starting point and so thinking about those angles I think they're where you get the most leverage on your leverage they're the most fertile variable and the design of a bicycle now there are lots of other elements in the design of a bicycle like the thickness of the tubes and the height of the bottom bracket where the cranks are the turn around and how many gears you have and all that sort of stuff but I think about those angles a lot and modifying them and adjusting them this week I'm making a bike that's point one degree difference from a different bike I was thinking about making but two degrees different like 1.7 degrees different from the bike I have now and I'm really excited to try it on different mountains because I think it's going to be a completely different experience and I'm going to use totally different amount of energy to get up at the same speed so I'm thinking about all of that and as you improved your cycling how much of your assessment is analytical and how much of it is intuitive so this is where I differ and probably why I'm still very slow compared to some of my friends who are very very fast I'm not that analytical about cycling because for me cycling is not just about the exercise I love lots of different forms of exercise I like lifting weights I like walking I like yoga I like mobility exercise yesterday I was doing exercise that was to do with hand-eye coordination I love all of this stuff that's fun and cycling is very good exercise especially of their sort of fat burning variety it's very good at training your metabolic efficiency but that's not what it's about it's about so many other things it's about being in nature it's about socializing it happens at a pace where you can talk and get to know people it's about racing competition it's about mechanics and design it's about culture there's a lot of culture around cycling it's about experiencing other cultures because you can travel long distances on a bike it's like six or seven things in one and because it's so many of those things in one most of the way I've sort of assessed my enjoyment of cycling or whether I should leave the house today and go for ride and not to do with some analysis I've done like well I did this many watts at this many minutes yesterday and today I'm really excited to leave the house because I'm going to aim for this and I'm happy or sad about my session because I did or didn't hit that number I'm not really like that look I try to do those sessions I have had coaches that helped me structure my workouts so that I can be very analytical about them but the answer is no I'm just not like I think about all those other things like how many new places did I discover who did that need what views did I enjoy that's how I think about it that's awesome one of the things that you once said to me is that people tend to do too much high intensity training and not enough mid-intensity training what is it like zone two versus I think zone two is where you want to spend more time is that right yeah look it all depends on your goals but to be sort of controversial for a moment I think this trend to doing three to five high intensity sessions a week is not very healthy and so a high intensity session for example there's something like a full-blown crossfit workout where you're like all on and then a little break and then all on and your heart rate goes really high and all that sort of stuff you release a lot of cortisol or like a really intense 45 minute peloton class or whatnot where your heart rate is getting up to like close to your maximum heart rate like a 80 to 120 percent of your maximum heart rate I think that trend is pretty unhealthy because you release a lot of cortisol cortisol to suppress as your immune system you get sick what you're not doing at that time is you're not training for metabolic efficiency you're not trading your body to use lots of different types of energy like fat and carbohydrates or glycogen to put it simply you're just training it to your sugar and so then you actually want to eat a lot of sugar afterwards or you want to eat low sugar before and after and you're not training your body to use fat and it's good to train your body to use fat because you got a lot of fat sitting in your body you got a hundred thousand calories fat roughly depending on how big you are sitting in your body you only have like one to two thousand calories of glycogen sitting around you burn through that pretty quickly so if you go for a long walk if you go a couple of days or a couple hours without food you've got to be able to burn fat to stay alert and show up or whatever so anyway I think this trends are like just doing this high intensity stuff is pretty unhealthy and can lead to lots of different problems it's very stressful for people it's very stressful people's immune systems and it doesn't lead to a good range of health outcomes like for example not getting diabetes doesn't really help that much with that problem or avoiding that outcome helps a little bit for sure but doesn't help that much with it and I think it reinforces this thing of thinking of exercise as like a tool or like to achieve a goal rather than thinking of movement as part of being a human being and I think spending more of your week moving at all different levels of intensity also is just much better for your mental health as a human being that has evolved to move so I can go on and on about it but that's a trend I'd like to see sort of reverse a little bit people I think that one of the things that I've realized just running an education company is you have a problem where the things that actually facilitate learning are often different from the things that people think facilitate learning which is also different from what's fun and it reminds me of what you're saying here where the most simple kind of makes a lot of sense primal way of thinking about exercise is it was really hard therefore it's good for me and the harder I push myself it's good and a lot of people think about learning like that too it sucked therefore I learned something or I had to memorize something therefore I learned something and I think that you have all these slight issues in the world where what people perceive to be the best can often just be different from what's actually the best exactly and in the learning sense there's some wisdom there and there's an analogy to exercise it's very well proven that having a traumatic experience around something helps you remember that thing very very well and in exercise having a very high intensity session will engender an adaptation they can be really positive however another big part of learning is letting something seep into your subconscious different parts of your brain and that just happens over a really long period of time by like sleeping or walking or just staring into space for a couple hours and that doesn't seem like much and just like in exercise a lot of this fat adaptation stuff I'm talking about and the zone two training just happens on like a really long brisk walk which doesn't seem like exercise to most people or like quite a slow ride slow bike ride and so there's lots of different modalities and intensities of thinking and moving and they all play a role in thinking and moving well or learning and being healthy I want to ask you about just what it was like to be really early at a company that's going to end up being as influential angelist I don't know there's a lot of things that you understand intellectually and then you begin to understand things experientially and I think one of the big ones is to have this felt sense for having been in a garage startup and then have it be something that you read about the New York Times have something that's doing hundreds of million dollars in revenue or something and as you look back at that experience how did it change you so it's a really good question which is like what does it feel like to be there and then how did that feeling change you and what it feels like to be there is really excited and aligned and focused which is like everyone in the room when we were sort of between five and ten people at that company everyone in the room knew why they were there at a very strong sense of mission they knew what they were going to do every day because they were either working with one other person that also knew exactly what they were going to do or they had complete freedom to do whatever they wanted to do and we all just sort of knew that this was going to be big we all just sort of knew that like this is so useful to people because we heard from them every day like wow you changed the trajectory of my company you changed the trajectory of my career we heard from people every day that this was so useful and even though we weren't dealing with many people at the time we didn't have many customers or users or whatnot the level of satisfaction they expressed to us was so great I mean you there are lots of other people out there like them but we're like well this is obviously going to be huge it's just a matter of time and so there was this amazing feeling you're like feeling so aligned with the people around you so aligned with the long-term mission and this just like fair complete like we just knew it was going to happen and so that is just so motivating and great to be around and like it's sort of this weird feeling both being very at peace and steady in terms of knowing what you should do right now but also so excited about like what's going to happen in the sense you couldn't move quickly enough towards that goal but you knew exactly what you had to do today to achieve that goal so they're all expressions of feelings but you asked about feelings that's what it feels like to be at a place like that it's sort of like a sense of the inevitable in the long-term but a very strong sense of the practical and what you need to do today but what became obvious of Angelus was one way you can help people self-actualize is help them start their business and work for themselves because then they don't have someone else getting in the way of their self-actualization then they have someone else actualizing their goal not the individual's goal or the company's goal individual's goal so I solidified my mission which was to help as many people as possible get their company off the ground and start their own thing so that they could then have an environment in which they could self-actualize and express their values and principles and act according to those every day so that's how it changed me it really solidified that mission that's a really beautiful answer well thank you very much this was lovely thank you so much David hey again it's David here one more time before you leave I want to share two things with you first if you enjoyed the podcast please rate the show or leave a review on your favorite podcast app it's a small thing that you can do to give back to the show and it actually makes a huge difference in terms of how many people are able to find it and next I want to tell you about my online writing school called Red of Passage it's made for curious people who want to write more think better and use the internet to spark friendships and by the end of the five week program you'll have a personal website a collection of published articles and a team of people that you can write with in perpetuity if you want to start writing online right of passage is the very best place to begin so that's all for now and thanks for listening I'll see you next time