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The Tim Ferriss Show
#839: Dr. Fei-Fei Li, The Godmother of AI — Asking Audacious Questions, Civilizational Technology, and Finding Your North Star ( #839)
01:18:29
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- [00:00]
A
Hello, boys and girls, ladies and germs, this is Tim Ferriss. Welcome to another episode of the Tim Ferriss show where it's my job to deconstruct world class performers. I interviewed them to tease out the habits, routines, frameworks, et cetera that you can apply to your own lives. My guest today is Dr. Fei Fei Li. She is the inaugural Sequoia professor in the Computer Science department at Stanford University. She's been called the Godmother of AI. She's a founding co director of Stanford's Human Centered AI Institute and and the co founder and CEO of World Labs, a generative AI company focusing on spatial intelligence. She's also the author of the World's Curiosity, Exploration and Discovery at the dawn of AI, her memoir and one of Barack Obama's recommended books on AI and a Financial Times best book of 2023. Her story is incredible. It is one of beating the odds on so many different levels. And let's get straight to it, Dr. Fei Fei Li.
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At this altitude, I can run flat out for a half mile before my hands start shaking. Can I answer your personal question now?
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B
It is in a perfect time.
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A
What if I did the opposite? I'm a cybernetic organism, living tissue over a metal endoskeleton.
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B
Ferris show.
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A
Doctor Lee, it is nice to see you. Thanks for making the time.
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B
Hi Tim. Very nice to be here. Very excited.
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A
And we were chatting a little bit before we started recording about how miraculous and I suppose unfortunate it is that somehow we managed to spend three years on the same campus and didn't bump into each other.
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B
I know. And now I'm wondering which college you were at and which clubs.
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A
Oh yeah, I was Forbes. I was in Forbes College.
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B
No, I was Forbes too.
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A
Okay, this is for people who don't know what the hell we're talking about. There are these residential colleges where students are split up when they come into the school. And Forbes was way out there in the sticks right next to a fast food spot like 711 called Wawa Wawa and next to the commuter train. And then there's something called eating clubs at Princeton, people can look them up, but they're effectively co ed fraternity sororities where you also eat unless you want to make your own meals. And I was in Terrace.
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B
I was not any of that. But for those of you wondering why we didn't meet, we should say we were very studious students who are only in the libraries.
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A
Yeah, we were very studious. I actually made my whatever it was, $6 an hour at guest library Working up in the attic.
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B
Tim, I work in the same library. I don't understand why we did not meet.
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A
Hilarious. Okay, so, well, now we're meeting. Now we're. I mean, we.
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B
Did you change name or something? Maybe we did meet.
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A
I didn't change my name. Here we are, we've reunited. That's wild that we didn't bump into each other. I was also gone for a period of time because I went to Princeton in Beijing and went to the. What was it, Capital? University of Business and Economics after that. So I was gone for a good period of time and then took a year off before graduating with the class of 2000. Still, we had a lot of overlap, but let's hop into the conversation and this is a very perhaps typical way to start, but in your case, I think it's a good place to start, which is just with the basics chronologically. Where did you grow up? And could you describe your upbringing? Because, based on my reading, your parents were pretty atypical for Chinese parents in my experience.
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B
Certainly, you know a lot.
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A
Yeah. Could you speak to that, please?
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B
Yeah. I would say my childhood and leading up to the formative years is a tale of two cities. I grew up in a town in China called Chengdu. I was born in Beijing, but most of my childhood was spent in Chengdu, where it's very famous for panda bears. And at the age of 15, my mom and I joined my dad in a town called Persippity, New Jersey. So I went from a relatively typical middle class Chinese kid to become a new immigrant in a completely different world, of all places, New Jersey, and to learn a new language, to learn a new culture, to embrace a new country. And then from there on, I went to Princeton as a physics major. But I did take some of the classes you took and then went to Caltech as a PhD student to study AI. And the rest is history.
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A
I want to hear about both your parents, but I want to hear a little bit about your dad, because he seems like, based on my reading, a very whimsical sort of creative soul, which is a sharp contrast in some ways to. For instance, I had Beau Xiao on the podcast, Amazing Entrepreneur, and his father was, I suppose, what some folks might think of when they imagine not a tiger mom, but like a tiger dad. So in the case of Bo's upbringing, his father is very strict, but if he, meaning Beau, won a math competition, then he would get extra love and he would be allowed to have certain treats and things like that. Could you just describe your parents a little Bit, Yeah.
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B
So, first of all, clearly you read my book. Thank you for that. It is true. As a child, you don't realize that as I was just going through my own science memoir, I was writing it. The more I wrote about it, the more I realized, oh, my God, I really did not have a typical dad. Dad loved and still loves nature. He's just a curious mite. He finds humor and fun in unserious things. You know, like, he loves bugs, insects. He loves taking me as a kid growing up in the 1980s in China, there isn't much abundance in terms of material resources. So my city, Chengdu, was expanding. So we lived in apartment complexes at the edge of the city, even though my dad and my mom worked in the middle of the city. So on the weekends, my dad and I would just play in the fields where there's still rice fields, there's water buffaloes. I had a puppy and my dad would just really, all my memory is just like finding bugs, really. And then sometimes my dad and I will follow some. I don't know. We took an art class, I took a kid's art class. And we'll go to the mountains, neighboring mountains, to draw. But my entire childhood memory of my dad is just a very unserious parent who had no interest in my grades or what I'm doing in class. Did I achieve anything? Did I bring back any, like, competition, awards? Nothing to do with that. Even when I came to New Jersey with my parents, life became extremely tough. It was immigrant life. We were in a lot of poverty and even that. My memory is that he has so much fun in yard sales. I would just go to yard sales, and those are RV every weekend. It was just, yay, let's go to yard sales and just use that as a treasure hunt, almost. So he's a very curious, childlike mind in that way.
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A
So, Matt, I'm asking about your parents in part because I know you're a parent. And ultimately I'm going to want to ask how you think about parenting. That will come up at some point. But since listeners will certainly be asking themselves this question, and we're not going to get into a geopolitics because there are plenty of people who want to get into that and fight over that, which we're not going to do. But why did your parents leave China? What was the catalyst or what were the reasons behind leaving what you knew or leaving what they knew and coming to a very different foreign country? I mean, you're going from Chengdu, which is a city, to Suburban New Jersey, which is as I think you've described it felt very empty. Right. And then you have the language barriers and the financial barriers. There's so many things. Why the move?
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B
I'll give you two answers. Early teenage Fei Fei would say, I have no idea because my dad left when I was 12, and my mom and I joined him when I was 15. And those years, you're a teenager. Right. Like, there's so many strange things in your head. And all I knew is that, you know, they said, let's go to America. And I had no idea. I really did not know what happened. There was this vague sense of there's opportunities and freedom. Education is very different. And I had a hunch that I was not a typical kid in the sense that I was a girl. And I loved physics. I loved fighter jets, of all things. I can tell you all the fighter jets I love from F117 to F16 to, you know, to all the different things that I loved. So that's all I knew. In hindsight, as a grown up Fei Fei, I appreciated.
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My parents. They're very brave people. Because I don't know this age myself, would just pick up and leave a country I'm familiar with and go to, I don't know, a completely different country that I speak zero language and I have zero connectivity to. And mind you, that's pre Internet, pre AI age. So when you are going to a different country, you might as well go to another planet. You're cut off. Yeah. So I think they're very brave. The grown up Fei Fei realized that they wanted me to have an opportunity that they think will be unprecedented for my education. And it turned out that's kind of true.
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A
Yeah, well, certainly looking at your bio, I mean, it's mind boggling to imagine all of the different sliding door events and different paths you could have taken. So we're going to hop pretty closely along chronologically, but we're going to ultimately get to a lot of the meat and potatoes of the conversation. But I want to touch on maybe some other formative figures. And I would like to hear about your mother as well, because just with the context of your dad, it's like, okay, that seems fascinating and very unusual, particularly if you've spent any time in China, especially during that period of time.
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B
He is very unusual that way.
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A
Yeah, very unusual. So then people might wonder, well, where does the drive come from? Where does the technical focus come from? And I'd love to hear your answer to that and also hear you explain who Bob Sibella was, if I'm pronouncing that correctly.
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B
Yes. Yeah. Yeah. There are two questions, mostly. Is my mom the one who put in the drive and the technical passion and what role did Bob play in my life? So, first one. First of all, my mom has zero technical things. She really has. No. I sometimes still laugh at her. She cannot do math, let's put it this way. So I think the technical passion is just. I was born with it. My dad is more technical, but he's, you know, he loves bugs more than insects, more than equations, for sure. So I think that's, you know, as an educator for so many decades now, myself, and also as a parent, you have to respect the wonders of nature. There is this inner love and fire and passion and curiosity that comes with the package. But my mom is much more disciplined person. She's still not a tiger mom, in a sense. I don't remember my mom ever going after me on grades or she really did not. Both my parents never, ever cared about me bringing any awards home. Maybe I did, maybe I didn't. But I can tell you, in our house, there's zero wall hangings of anything which actually carry to today, even for myself. My own house, my own office have zero of those decorations of achievements or awards. It's just my mom did not care about that, but she did care about me being a focused person. If I want to do something, she doesn't want me to play while doing homework. And that kind of thing would bother her. She would say, just finish your homework. Say by 6pm if you don't finish your homework, you're not allowed to do more homework. You have to deal with the consequences. So she instilled some discipline, but that's about it. She's tougher than my dad. She is very rebellious. She had an unfinished dream herself. She was very academic when she was a kid herself, and cultural revolution really crushed all her dreams. So she became a more rebellious person in that sense that I think I did observe and experience as a daughter. So maybe part of immigration is even part of that. She has this many years later, she would say, I had no plan coming to New Jersey, but I think I'm going to survive. I just believe I'm going to survive, and I'm going to make sure Fei Fei survives. I think that is her strength, her stubbornness and her rebelliousness.
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A
When does Bob enter the picture? And who is Bob?
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B
Bob Cebella was a high school math teacher in Persepoli High School. He was my own math teacher as well as Many, many students. He entered my life. So it's kind of bordering sophomore to junior year in Persepoli High School when I started taking AP calculus. But he quickly became the most influential person in my formative years as a new American immigrant as a teenager because he became my mentor, my friend, and eventually his entire family became my American family and he became my friend. When I was a very lonely ESL English as second language student, I was excelling in math, but I think it's more because I was lonely. And he was very friendly. He treated me more like a friend who talks about books we love, talk about the culture, talks about science fiction, and also listened to me as a very. I wouldn't say confused, but a teenager undergoing a lot of life's turmoil in my unique circumstance. And that unconditional support made me very close to him and his family. And one thing he did to me that I did not appreciate till later is that when Persimmon High School couldn't offer a full Calculus BC class because it just didn't have that, he just sacrificed his lunch hour, his only lunch hour, to teach me Calculus bc. So it was a one to one class. And I'm sure that contributed me, an immigrant kid, getting to Princeton eventually. But later, as I became teacher myself, it's exhausting to teach all day long. And the fact that on top of that, he would use his lunch hours to do that extra class for me is just such a gift that I now appreciate more than I was as a teenager.
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A
Yeah, thank God for the teachers who go the extra mile. It's just incredible. Especially when you get a bit older and you have more context and you can look back and realize I really.
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Think these public teachers in America are the unsung heroes of our society because they are dealing with kids of all backgrounds. They're dealing with the changing times, the kind of stories Bob would share with me in terms of how he went extra miles, not just with me, but with many students. Because Perseverance is a heavily immigrant town. So his students are from all over the world and how he helped them and their family, it's just those are the stories that people don't write about. And that's part of the reason I wrote the book, was to celebrate a teacher like that.
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A
Yeah.
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I have so much I want to cover and I know we're going to run out of time before we run out of topics, so I want to spend more time on Bob and at the same time we're going to want to keep the conversation moving. So we're going to do that and I'll just perhaps hit on a few things and then dig into a number of questions. But certainly at Princeton you, but also your entire family had to survive. So you were involved with operating a dry cleaning shop in New Jersey as one option, right? You ran that for seven years. So through that you gain perspective on it feels like you've gained perspective on many different levels that have then helped inform what you've done professionally so you learn to think about not just people who are protected in ivory tower, but people all the way down across in society. So from every swath of society, your mother also, although she was not technical, she imbued in you this discipline and also seems to have had a very broad appreciation and knowledge of literature and international literature. So now you have this global perspective, presumably at the time in Chinese, and you end up at Princeton. And I know we're going to be hopping around quite a bit, but I'm curious to know how imagenet came about. You can introduce this any way you like. You can tell people what it is and what it became and why it's important and then talk about how it started. Or you can just talk about how it started. But it's such an important chapter.
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B
Yeah. So let me just explain what Imagenet is. Imagenet on the surface was built between 2007 and 2009 when I was assistant professor at Princeton and then I moved to Stanford. So during this transitional time, my student and I built this, at that time the field of AI's largest training and benchmarking data set for computer vision or visual intelligence. The significance Today, after almost 20 years of ImageNet was it was the inflection point of big data. Before imagenet, AI as a field was not working on big data. And because of that and a couple of other reasons which I'll get into, AI was stagnating. The public thinks that was the AI winter, even though as a researcher, young researcher, at that time, it was the most exciting field for me. But I get it that it wasn't showing breakthroughs that the public needs. But ImageNet, together with two other modern computing ingredients, one is called neural network algorithm, the other one is modern chips called GPU, graphic processing unit. These three things converged in a seminal work, milestone work in 2012 called ImageNet Classification, deep convolutional Neural network approach. That was a paper that a group of scientists did to show that the combination of large data by ImageNet, fast parallel computing by GPUs and a neural network algorithm could achieve AI performances in the field of image recognition in a way that's historically unprecedented. And that particular milestone is many people call it the birth of modern AI. And my work, Imagenet was 1/3 of that, if you count the elements. And I think that was the significance. I feel really very lucky and privileged that my own work was pivotal in bringing modern AI to life. But the journey to imagenet was longer than that. The journey to imagenet started in Princeton when I Was an undergrad. You were in the East Asian study department. I was hiding in Jadwin hall, which is our physics department. I loved physics since I was a young kid. I don't know how somehow my dad's love of bugs and insects and nature translated in my head into just the curiosity for the universe. So I loved looking to the stars. I loved the speed of fighter jets. And then the intricate engineering of that eventually translated into the love of the discipline that asks the most audacious question of our civilization, such as, what is the smallest matter? What is the definition of space time? How big is the universe? What is the beginning of the universe? In that early teenagehood love, I loved Einstein. I love his work. And I wanted to go to Princeton for that. But it turned out what physics taught me was not just the math and physics. It was really this passion to ask audacious question. So by the end of my undergrad years, I wanted my own audacious question. I wasn't satisfied with just pursuing somebody else's audacious question. And through reading books and all that, I realized my passion was not the physical matters. It was more about intelligence. I was really, really enamored by the question of what is intelligence and how do we make intelligent machines? So at that time, I swear I did not know it was called AI. I just knew that I wanted to pursue the study of intelligence and intelligent machines. Then I applied to grad school and I went to Caltech. Caltech was my PhD. I started in the turn of the century, 2000. I think I consider that moment I became a budding AI scientist. That was my formal training as a computer scientist in AI. Then my physics training continued in the sense that physics taught me to ask audacious questions and turn them into a North Star. And in scientific terms, that North Star became a hypothesis. And it was very important for me to define my North Star. And my first North Star for the following years to come was solving the problem of visual intelligence is how we can make machines see the world. And it's not just by seeing the RGB colors or the shades of light. It's about making sense of what's seen, which is, you know, I'm looking at you, Tim. I see you. I see a beautiful painting behind you. I see you're sitting on a chair. Like that is seeing. Seeing is making sense of what this world is. So that became my North Star question. And that hypothesis that I had is, I have to solve object recognition. And then that was in my entire PhD was the battle with Object recognition. There were many, many mathematical models we have done, and there are many questions. But me and my field was struggling. We can write papers, no problem, but we did not have a breakthrough. And then, luckily for me, Princeton called me back as a faculty in 2007. It was one of my happiest moments of my life. I feel so validated my alma mater would consider giving me a faculty job. So I happily moved back to Princeton as a faculty this time. And I continue to be a Forbes member, actually. So at Princeton, there was an epiphany is that I realized there was a hypothesis that everybody missed. And that hypothesis was big data.
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A
This is the point that I'm so, so curious about. And I just want to pause for a second. Also, for people who are interested in some of the history of Princeton, it's pretty crazy. They should look up the history of the Princeton Institute for Advanced Study. And I remember taking some of those East Asian Studies classes that you referred to in classrooms where Einstein taught. And it's just the aura, the veneer, you want to believe that you can feel it just permeating the entire campus. And it's fun in that respect. It's very fun. But I'm going to read something from a Wired piece that discussed you at length and as you mentioned, big data before and after in terms of its integration into the type of research that you're describing and as it was written. And please feel free to fact check this or push back on it. But in Wired, they said the problem was a researcher might write one algorithm to identify dogs and another to identify cats. And then you, it says Lee, began to wonder if the problem wasn't the model, but the data. She thought that if a child learns to see by experiencing the visual world, by observing countless objects and scenes in her early years, maybe a computer can learn in a similar way. And I want you to expand on that for sure. And the question for me is, like, why did you see it? Why didn't it happen sooner?
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B
We're all students of history. One thing I actually don't like about the telling of scientific history is there's too much focus on single genius.
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A
Yes, agreed.
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B
We know Newton discovered the modern laws of physics, but yes, he is a genius. Not to take away any of that from Newton, but science is a lineage, and science is actually another nonlinear lineage. For example, why did I see. Why was I inspired by this hypothesis of Big Data? Because many other scientists inspire me. In my book, I talked about this particular lineage of work by Professor Irv Biederman. Who was a psychologist who was not interested in AI, but he was interested in understanding minds. And I was reading his paper and he particularly was talking about the massive number of visual objects that young children was able to learn in early ages. So that piece of work itself is not imagenet. But without reading that piece of work, I would not have formulated my hypothesis. So while I'm proud of what I have done, my book especially wanted to tell the history of AI in a way that so many unsung heroes, so many generations of scientists, so many cross disciplinary ideas pollinate each other. So I was lucky at that time as someone who is passionate about the problem, but also someone who benefited from all these research. So yes, something happened in my brain, but I would really attribute to many things happened across so many people's work throughout their lifetime devotion to science that we got to the point of imagenet.
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A
I'm so glad that you're underscoring this because if you really dig as a. I don't consider myself a scientist, but I love reading about the history of science. There's so many inputs, so many influences, so many interdependencies and the simplicity of the single hero's journey is appealing and it's simplicity, but it's almost never true.
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B
It probably is never true. Even my biggest hero, Einstein, right. Anybody who knows me, anybody who read my book knows how much I revere him and I just love everything he's done. The special relativity equation is a continuation of Lorentz transformation. So even Einstein, he builds upon so many other people's work. So I think it's really important especially. I'm sure we'll talk about it. I'm here calling you in the middle of Silicon Valley and we're in the middle of AI hype and obviously I'm very proud of my field, but I think that when the media or whatever tells the story of AI, it almost always just talk about a few geniuses and it's just not true. It's generations of computer scientists, cognitive scientists and engineers who, who made this field happen.
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A
Yeah, for sure. I mean, everyone knows Watson and Crick for instance, but without Rosalind Franklin and her X ray crystallography, it doesn't happen. It doesn't happen. It just doesn't happen. Point blank, we're going to hop to modern day in a second. But with imagenet, I would love for you to speak to some of the decisions, let's say decisions or moments that were just formative in making that successful. Because for instance, if you're going to try to allow a machine to. And I'm using very simple terms because I'm not technical enough to do otherwise. To learn to identify objects closer to the path that a child would take, you have to label a lot of images, right? So I was reading about how Mechanical Turk came into play, and then there's a competitive aspect that seems to have driven some of the watershed moments. Could you just speak to some of the elements or decisions that made it successful?
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B
A lot of people ask me this question because after imagenet, many, many people have attempted to make data sets, but still only very few are successful. So what made imagenet successful? I think one of the success was timing is that we truly were the first people who see the impact of big data. So that very categorical or qualitative change itself is part of the success. But it's also as you were asking, the hypothesis of big data is not just size. A lot of people actually misunderstand imagenet's significance as well as other data sets significance. Coming with the data set is a scientific hypothesis of what is the question to ask. For example, in visual recognition you can make a data set of discerning rgb and that would not be as impactful of a data set that is organized around objects. We can go down a rabbit hole of why not? Because RGB is easier per se, is because you have to ask the scientific question the right way. So another example is instead of making a data set of objects, why don't you make a data set of cities? That's even more complicated than objects, but then that's dialing too complicated. So every scientific quest you have to have the right hypothesis and asking the right question. So that's one part of the success is we defined visual object categorization as the right hypothesis. That was one rightness. I guess another rightness is that people just think, oh, it's easy, you just collect a lot of data. Well, first of all, it's laborious. But even aside from being laborious, how do you define the quality? You could say, well, if quality is big enough, we don't care about quality. But how do you dial between what is big, what is great, what is good, how do you trade off? That is a deeply scientific question that we have to do a lot of research on. Then another decision that is a set of decision that is really hard is what defines quality in terms of image? Is it every image has higher resolution.
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It'S photorealistic? Is it because it's everyday image that look very cluttered? Is it all product shots that look clean. These are questions that if you're too far away, you wouldn't even think about asking. But as a scientist, as we were formulating the deep question of object recognition, we have to ask this in so many dimensions. And then you mentioned Amazon Mechanical Turkish. That is actually a consequence of desperation.
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Because when we formulated this hypothesis, our conclusion is we need at least tens of millions of high quality images across every possible diverse dimension. Whether it's user photos or is it product shots or is it stock photography. And then we need also high quality labels. Once we made that decision, we realized this has to be human filtered from billions of images. So with that we became very desperate. We're like, how are we going to do that? I did try to hire Princeton undergrads and as you know, Princeton undergrads are very smart, but they have very high.
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A
Opinion of the value of the tool.
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B
Yes. And they're expensive. But even if I had all the money in the world, which we didn't, it would have taken so long. So we were very, very stuck for very, very long. We thought we had other shortcuts, but the truth is human labeling is a gold standard. We want to train machines that are measured against human capabilities. So we cannot shortcut that at that time. So we had to go to what we eventually found out is called crowd engineering, crowdsourcing. And that was a very new technology, was barely a year old or so by Amazon. They created a lot online marketplace for people to do small tasks to earn money when these tasks can be uploaded on the Internet. I remembered when I heard about Amazon Mechanical Turk. I logged into my Amazon account, I checked. The first task I checked out to do just to try was labeling wine bottles or transcribing wine bottle labels. So the task will give you a picture of a wine bottle and you have to say this is 1999 Bordeaux and all that. So people ask, upload these kind of micro tasks and then online workers like someone in their leisure time, like me. If I had leisure time, I would just go sign up and get paid to do that. And we realized that was again out of desperation. That was a massive parallel processing with online global population to do this for us. And that's how we labeled billions of images and distilled it down to 15 million high quality ImageNet images.
- [39:03]
A
It's just so wild when you look at these stories. I just finished a book on Genentech and there were all these little technical inflection points that also allowed things to happen. So if it had been five years earlier or Maybe three years earlier without Mechanical Turk. Boy, it presents a challenge. But also, as you pointed out, I mean, in science, it's one thing to get answers, but you need the input on the front end with a proper hypothesis or a good question. And even with Mechanical Turk, if you're only focused on the mechanics of employing that, you can get yourself into trouble. Because if humans are incentivized to. Let's just say I think this was the example I read about. Identify pandas in photographs. And they're paid for identifying pandas. Well, what's to stop them from identifying a panda in every photo, whether. Whether they exist in the photos or not? So you have to follow the incentives as well. How did you solve for that?
- [40:01]
B
Yeah, I know. This is where my student and I had. I cannot tell you how many hours and hours of conversation we have about controlling the quality. We have to solve for that in multiple steps. We need to first filter out online workers who are serious about doing the work. So for example, we have to have some upfront quizzes so that they understand what a panda is. They read the question and then once they get into they qualify for that. We ask them to label pandas, but there are some images we have pre we know the correct answer. Some are true pandas, some of them are not true pandas. So the labelers don't know. So in a way, we implicitly monitor the quality of the work by knowing where the gold standard answers are. So these are the kind of computational tactics we have to use to ensure the quality of labeling.
- [41:02]
A
Amazing. Yeah, just incredible. All right. And I'll actually just put a recommendation out there for a book, Pattern breakers, by a friend of mine, Mike Maples Jr. He taught me the ropes initially of angel investing, but in terms of identifying inflection points and converging technological, in some cases converging technological trends that for the first time make something possible, which then opens an opportunity for something with the right prepared mind. In your case and those of your collaborators and the people you built upon for something like imagenet, Pattern breakers is a really good read for folks. So let's hop to modern day then for a moment and I would love to ask you, because you've been called the godmother of AI in our alumni magazine, in fact, and elsewhere. But you've had such a, not just technical but historical viewpoint, meaning you've over a broad timeline, you've been able, broad by AI standards, been able to watch the development and forking and perils and promise of this technology. What are people Missing. What do you think is eating up all the oxygen in the room? What are people missing? Whether it's things they should know or things they should be skeptical of or.
- [42:13]
B
Other, otherwise especially I'm here calling you from the heart of Silicon Valley and I think people are missing. The importance of people in AI and there's multiple facades or dimensions to this statement is that AI is absolutely a civilizational technology. I define civilizational technology in the sense that because of the power of this technology, it'll have or already having a profound impact in the economic, social, cultural, political downstream effects of our society. So I just heard this is unverified, but I just heard that 50% of the US GDP growth last year is attributed to AI growth. So apparently this number is 4% for US GDP have grown 4%. If you take away AI, it's only 2%. That's what means so that's civilizational from an economic point of view. It's obviously redefining our culture. Think about, you're talking about the word sucking oxygen out of the room. Everywhere from Hollywood to Wall street to Silicon Valley to political campaign to TikTok to YouTube to Insta Taxis in Japan.
- [43:44]
A
I was just there. And the videos playing on the back of the headset in the taxi, we're all talking about AI. It's everywhere.
- [43:50]
B
It's culturally impactful. Not only impactful, it's shifting our culture, it's going to shift education. Every parent today is wondering, what should their kids study to have a better future. Every grandparent is say, I'm so glad I'm born earlier. I don't have to deal with AI, but still worry about their grandchildren's future. So AI is a civilizational technology. But what I think it's missing right now is that Silicon Valley is very eager to talk about tech and the growth that comes with the tech. Politicians are just eager to talk about whatever gets the boat, I guess. But really, at the end of the day, people at the heart of everything people made AI. People will be using AI, people will be impacted by AI and people should have a say in AI. And no matter how AI advances people's self, dignity as individuals, as community, as society, should not be taken away. And that's what I worry about because I think there's so much more anxiety that because the sense of dignity and sense of agency, sense of being part of the future is slipping in some people and I think we need to change that.
- [45:15]
A
Just a quick thanks to our sponsors and we'll be right back to the show. It has been a wild year for money in the markets, but managing your cash doesn't have to be a guessing game. Wealthfront has a simple solution to help you cut through all the chaos and manage your money with confidence. With the Wealthfront Cash account, your cash can earn 3.5% variable annual percentage yield from partner banks. You get instant fee free withdrawals to eligible accounts 24. 7. Your money is always accessible when you need it and when you're ready to invest. Transferring funds into one of Wealthfront's expert built investing portfolios is easy peasy. For a limited time, Wealthfront is offering my listeners that's you guys, an additional 0.65% boost over the base rate for three months, which means you can get up to 4.15% APY on your first $150,000 in deposits. More than 1 million people trust Wealthfront to help build their wealth. Go to wealthfront.com Tim to receive the boosted APY offer and start earning up to 4.15% variable APY today. This is a paid endorsement of Wealthfront. Wealthfront Brokerage isn't a bank. The base APY is as of November 7, 2025 and subject to change. For more information, see the episode Description hey folks, Tim here. My best selling card game Coyote, which I made with the amazing team at Exploding Kittens, just won the Pop Insider Best geeky game of 2025 and also best Stocking Filler in the Made for mums toy awards 2025. It is a on sale everywhere. It's cheap, it's fast to learn, has 4.8 stars out of five. People are loving it. CoyoteGame.com will take you to all the retailers, but you can find it everywhere. It is a game of thinking fast and laughing faster. Think charades meets hot potato meets a bunch of brain fun. It's good for your head. It's perfect for families with kids age 10 plus or adults who are kids at heart or don't take themselves too seriously. A lot of adults love this game and as I said, it's available everywhere. Amazon, Walmart, Target 8,000 plus retail locations, you name it. So please check it out. I loved making it. People are really enjoying it. It has 300 or 400 million plus social views of gameplay online and try it. Enjoy it this holiday season. Check it out coyotegame.com one more time. That's coyotegame.com or anywhere you buy your games. Now back to the episode.
- [47:45]
I've heard you say that you're an optimist because you're a mother and both optimism and pessimism to an extreme can bias us in ways that are unhelpful or create blind spots. And I'm curious if you try to put your most objective hat on, which is difficult for any human, but if you try to do that, do you think people are too worried, not worried enough, or worrying about the wrong things for people who are not the CEOs and builders and engineers behind AI? Because you're right, of course, I mean, everybody will agree with this, that a lot of people are very worried. And I'm just wondering if it's ill placed because I don't really. If you talk to some of the VCs who are the biggest investors, of course they have this, in my view, beyond all possibilities, techno optimist view of the future where AI solves everything and it's hard to believe there's a free lunch there. And then you have the doomers, the doom and gloom, where suddenly it's Skynet next year and we're all slaves to robots or eliminated, turned into paperclips and reality is probably in between those two. So do you think people are worrying about the right things or have they lost the plot in some way?
- [49:00]
B
First of all, I call myself a pragmatic optimist. I'm not a utopian. So I'm actually the boring kind of. I don't believe in the extreme on both sides. I travel around the world. Just last month I was in Middle East, I was in Europe, I was in uk, I was in Canada, I came back home in America. I think people in America and people in Western Europe are more worried about AI than say people in Middle east. In Asia. We don't have to litigate on why they're more worried, but just to come closer to home, just talk about us.
- [49:47]
I wish I have a megaphone to tell people in the US that you're known to be one of the most innovative people. Our country have innovated so many great things for humanity, for civilization. We have a society that is free and vibrant and we have a political system that we still have so much say in how we want to build our country.
- [50:20]
I do wish that our country has more an optimism and positivity towards the future of using AI than what is being heard now. I think people like me, technologists living in Silicon Valley has a lot of responsibility in the right kind of public communication. So there's a lot of things that was not communicated in the effective way. But I do hope that, that we can instill more sense of hope. And self agency into everybody in our country. Because I think there is so much upside of using AI in the right way. And I want not just people in Silicon Valley or in Manhattan, but I want people in rural communities, in the traditional industries in everywhere 50 states to be able to embrace and benefit from AI.
- [51:29]
A
Why are you building what you're building? What is World Labs? Why decide to do this?
- [51:35]
B
I actually answered this question very often to every member of my team. I built World Labs. There are two levels of this answer. From a technology point of view, World Labs is building the next generation AI, focusing on the spatial intelligence. Because spatial intelligence, just like language intelligence, is fundamental in unlocking incredible capabilities in machines so that it can help humans to create better, to manufacture better, to design better, to build better robots. So spatial intelligence is a linchpin technology. But one level up. Why am I still a technologist? Is because I believe humanity is the only species that builds civilizations. Animals build colonies or herds, but we build civilizations. We build civilizations because we want to be better and better. We want to do good, even though along the way we do a lot of bad things. But there is a desire of having better lives, having better community, having better society, live more healthily, have more prosperity. That desire is where civilization is built upon. And because I believe that humanity can do that, I believe science and technology is the most powerful tool, one of the most powerful tools in building civilizations. And I want to contribute to that. That's why I'm still a scientist and a technologist, and I'm building World Labs for that.
- [53:18]
A
Can you explain to people what spatial intelligence is and what the product is, so to speak, at least as it stands right now that you're building.
- [53:29]
B
Spatial intelligence is a capability that humans have which goes beyond language. Is when you pack a sandwich in a bag, when you take a run or hike in a mountain, when you paint your bedroom, Everything that has to do with seeing and turning that scene into understanding of the 3D world, understanding of the environment, and then in turn, you can interact with it, you can change it, you can enjoy it, you can make things out of it. That whole loop between seeing and doing is supported by the capability of spatial intelligence. Right? The fact that you can pack a sandwich means you know what the bread looks like, you know how to put the knife in between, you know how to put the lettuce leaf on the bread, you know how to like put the sandwich into a ziploc bag. Every part of this is spatial intelligence. And does today's AI have that? It's getting Better. But compared to language intelligence, AI is still very early in that ability to see to reason and also to do in world in both virtual 3D world as well as real 3D world. So that's what World Labs is doing. We are creating a frontier model that can have intelligent capability in the model to create world, to reason around the world and to enable, for example, creators or designers or robots to interact with the world. That's spatial intelligence.
- [55:16]
A
Could you expand on the designers or creatives or robots interacting with the world? So does that mean that you could. And my team has been playing with, with some of the tools, so thank you for that. What does that mean? If you could paint a picture for, let's say a year from now, two years from now, how might someone use this? Or how might a robot use this?
- [55:39]
B
I was just talking to someone a couple of weeks ago and it was really inspiring. Is that high school theaters are very low budget, right? Like, okay, sometimes I go to San Francisco Opera or musicals and the sets that's built for theater are just so beautiful. But it's very hard for high school or middle school to have that budget to do that. Imagine that you can take today's World Labs model, we call it marble, and then you create a set in, I don't know, in medieval French town, and then you put that in the background and use that digital form to help transport the actors and action into that world. And of course, depending on the auxiliary technology, whether you're on a computer or eventually people can use a headset or whatever. You can have that immersive feeling of being in a medieval French town. That would be an amazing creative tool for a lot of creators. That was an example someone and I was talking about it a couple of weeks ago, but we already see creators all over the world. Some of them are VFX creators, some of them are interior design creators, some of them are gaming creators, Some of them are educators who want to build some worlds that transport their students into different experiences. Are already starting to use our model because they find it very powerful at their fingertip to be able to create 3D worlds that they can use to immerse either their characters or themselves into.
- [57:35]
A
And just to process wise, if someone's wondering how this works, let's just say it's a public school teacher. Let's just say who's hoping to inspire and teach their students. Going the extra mile. What does it look like for someone to use this? Are they typing in text describing the world they'd like to create, Uploading Assets or photos, almost like an image board. How does it work if someone's non technical?
- [57:59]
B
Yes. So they don't need to be technical at all. They open our page on desktop or in their phone. But desktop is more fun because it has more features. And then they can type a French medieval town or they can actually go to anywhere. They can use midjourney or nanobanana to create a photo of a French medieval town. Or they can get an actual photo about that and then they upload it. We call it prompt. And then after a few minutes, our model gives you a 3D world that is say a part of the town. It does have a limit in its range. And that that 3D world is generally 3D because you can just use the mouse to drag and turn around and walk around and see that world and then downstream. If you want to use it, you have many ways to use it. You can actually create a movie out of it by like using one of our tools on the website to just put cameras and you can make a particular movie out of it. If you're a game developer.
- [59:08]
A
I was just going to say it sounds a lot like a gaming engine.
- [59:11]
B
Yes. You can put a lot of characters in it if you're a VFX professional. We have a lot of VFX professional. They can actually take this and put it in the workflow of their movie shooting and have real actors shooting movies. We also have psychology researchers using that immersive world, in particular psychiatric studies. We could also use that as the simulation for robotic training, because a lot of robotic training needs a lot of data and then use that for generating a lot of different data.
- [59:47]
A
So is it almost like a flight simulator for robots before they go into the real world?
- [59:52]
B
That's part of the goal. We are still early, so the flight simulator is not complete yet, but that's part of the journey.
- [60:00]
A
You mentioned psychiatric studies. I think that's what you just mentioned. What might that look like?
- [60:06]
B
Yeah. So we actually got this researcher who called us and they're studying people who have psychological disorders like obsessive compulsive disorder, where they're triggered by certain environments and they want to study the trigger and also just study how the treatment. But how do you trigger someone who, let's say is particularly have issue with, let's say a strawberry field? I'm just making it up. I mean, you can take them to a strawberry field, but what about. You want to know if it's strawberry field in the summer or strawberry field at night or is strawberry or it's mainly strawberry. Like, how do you do this? Suddenly, this researcher realized we give them the cheapest possible way of varying all kinds of dimensions, and they can test this out and do their studies.
- [61:02]
A
That's really interesting.
- [61:03]
B
Yeah.
- [61:03]
A
I could see it being applied to. It might be called exposure therapy, but in terms of. Now that you're describing it, I could see how it could be added into, I mean, pretty much everything. Right. I mean, if you think about how humans operate in the real world.
- [61:16]
B
Yes. Yeah. And the boundary between real world and digital world is less and less, thinner and thinner because we live in many screens. We live in the real world. We do things in virtual world. We do things in real world. We'll create machines that can do things in real world and virtual world. So there's a lot we do in digital and physical spaces.
- [61:43]
A
Who are some scientists or researchers who you pay attention to, who are not necessarily kind of the big brand names and marquee lights that are already very public in the world? Is there anybody who stands out where you're like, there's some really tremendous people doing good work?
- [62:01]
B
Well, that's part of the reason I wrote the book, especially in the middle chapters, where I wrote about the journey of doing imagenet that combines cognitive science with computer science. And I actually talk about psychologists and neuroscientists and developmental psychologists. Some of them are still with us, some of them are not. For example, the late Anne Treisman, Irv Biederman. They all passed away in the last few years. But they were giants in cognitive science, whose work has informed computer science and eventually AI. There are still lots of scientists around the world. Many of them are in the US who are thinkers in developmental psychology, in AI. I follow their work. I think that the world of science, just to name some names, right. Liz Belke in Harvard, Alison Gopnik in Berkeley. I love Rodney Brook, who was a former MIT professor in robotics. And there's just a lot of them. I don't mean to just single them out, but you're asking me for names that are not in the news of AI?
- [63:15]
A
Yeah, that's perfect. Thank you. I would also love to get your perspective on what might be. This is a very strong word, but seemingly inevitable in terms of developments in the near intermediate future. And I'll give you an example of what I mean. In 2008, 2009, I became involved with Shopify, the company back when they had like 10 employees. And there were a few things happening around that time. And you could ask questions in the next 10 years or 20 years. Will there be more broadband access or less? More. Okay. Will there be more E commerce or less? There'll be more. Okay. And when you have four or five of those that seem over a long enough time horizon, absolute yeses, it begins to paint a picture of where things are going. Are there any things that in the next handful of years you think are prevalent, perhaps underappreciated as near inevitabilities?
- [64:09]
B
You want me to talk about underappreciated? I mean, I don't know if they're over appreciated, but they're definitely appreciated. The need for power is appreciated. The trend of more AI, not less AI is appreciated. The long term trend of robots coming is appreciated. So these are appreciated. What's underappreciated is spatial intelligence is underappreciated in the sense that everybody's still now talking about large language models. But really world modeling of pixels of 3D worlds is underappreciated because like you were saying, it powers so many things from storytelling to entertainment to experiences to robotic simulation. I think AI and education is underappreciated because what we are going to see is that AI can accelerate the learning for those who want to learn, which will have downstream implication in our school system as well as in just human capital landscape. Like how do we assess qualified workers used to be, which school you graduate from, with which degree that will be changing with AI being at the fingertip of so many people. That's underappreciated. I think AI's impact in our economic structure, including labor market, is underappreciated. The nuance is underappreciated. I think this whole rhetoric of either total utopia post scarcity is hyperbolic or like everybody's job will be gone is hyperbolic. But the messy middle is how from knowledge worker to blue collar to hospitality to all these changes that's happening, it's underappreciated by our policy workers, by our scholars, by just, just overall society.
- [66:21]
A
What are some of the nuances from the job perspective? Maybe this ties into what I promised earlier. I was going to ask you which is what you are telling or will tell at own other ages, your children or recommending, let's just say I don't know how old they are. But if we assume that they just for the sake of discussion of the age where they're trying to decide what they should study, where they should focus things of that nature, how would you think about answering that even provisionally?
- [66:50]
B
I think the ability to learn is even more important because when there was Less tools, fewer tools to learn. It's easier to just follow tracks. You go through elementary school, middle school, high school, college, and then get some training vocationally. And that's kind of a path. And with that is a set of structured credentials from degrees and all that. But AI has really changed it. For example, my startup, when we interview a software engineer, honestly, how much I personally feel the degree they have matters less to us now. It's more about what have you learned, what tools do you use, how, how quickly can you superpower yourself in using these tools? And a lot of these are AI tools. What's your mindset towards using these tools matter more to me at this point in 2025, hiring at World Labs, I would not hire any software engineer who does not embrace AI collaborative software tools. It's not because I believe AI software tools are perfect. It's because I believe that shows first of all the ability of the person to grow with the fast growing toolkits, the open mindedness. And also the end result is if you're able to use these tools, you're able to learn, you can superpower yourself better. That is definitely shifting. So coming back to your question, what do you tell young people tell children? I think the timeless value of learning to learn, the ability to learn is even more important now.
- [68:50]
A
Yeah. It strikes me as we're talking that it's only going to get increasingly easier for the ambitious to act as superpowered autodidacts. We've already seen this with. Certainly YouTube has a nice track record. Now you can either entertain yourself to death and avoid doing things that help with self growth and development, or you can supercharge it. And similarly with AI, you flash forward. We don't even need to flash forward, but it's how does a teacher audit that their students are doing the work they're supposed to be doing on so many levels? It's getting to the point there are some exceptions, but of near impossibility. And students can either avoid all work or they can supercharge their own work, but the output might look very similar, at least for a period of time. So schooling is going to change a lot. It's very, very interesting.
- [69:39]
B
I actually think, Tim, if the school evaluation is structured in a way that whatever AI gives and whatever the student gives is the same, there's something wrong with the structure of the evaluation.
- [69:55]
A
Okay, can you say more about that? That's interesting.
- [69:58]
B
So for example, English essay, this is not me, this is me hearing a story that I so agree with. I'll retell the Story is that as a high school freshman English class teacher, someone told me the story of their kids school. On the first day of school, the teacher actually said to the class, I want to show you how I would score AI So the teacher give an essay topic, show the students this is what the best AI gave me, and I'm going to show you how I think this is good, this is bad, how this is suboptimal, and I'll give it a B minus. Now I will tell you, this is my bar. If you're so lazy that you ask AI to write your essay, this is what you're going to get. You can use AI. That's totally fine. But if you can do the work, learn, think, be the best human creator you can and work on top of that, you can get to a. You can get to A pluses. And that would be, in my opinion, the right way to structure the evaluation is not to pit humans against the AI and then try to police the use or not use of AI Is that to show where the tools, the bar of the tools are and where the bar of the human learner should be.
- [71:24]
A
I'm going to sit with that example and try to think of more examples. It's very interesting. And boy, oh boy, I've been shocked by how quickly the models improve. But yes, that's like as a thought experiment, I'm going to chew on that. I know we only have a few minutes left. Fei. Fei. I wanted to ask you a question. I ask a lot, which is if you could put a quote or a message, something on a billboard, something to get in front of millions, billions of people, just assume they all understand it. Could be an image, could be a question, could be a quote, anything at all. A saying, mantra, doesn't matter. Could be almost anything. What would you, or what might you put on that billboard?
- [72:01]
B
What is your North Star?
- [72:04]
A
What is your North Star? This is of course critically important. And coming back to how you define that or find that for yourself, I mean, you were talking about audacious questions and then that leading to a North Star or hypothesis. Is there another way that you would encourage people on top of that to think about finding their North Star?
- [72:25]
B
I believe that's how that makes us so human and makes us to be so fully alive is that we as a species can live beyond the chasing of just basic needs, right? But dreams, missions and goals and passion and everybody's North Star is different. And that's fine. Not everybody has to have AI as their North Star. But finding that goes to the heart of education again. And I don't mean formal classroom education. It's just the journey of education. And a lot of that is the ability to learn who you are and to learn how to formulate your North Star and how to chase after that.
- [73:13]
A
Last question, did your parents ever explain to you why they named you Fei Fei?
- [73:18]
B
Yes. It's because when my mom was going through labor, my dad was characteristically late to the hospital, and along the way, he caught a bird. He let it go, but he did catch a bird. I don't know. He was just distracted. It was in Beijing, in the city of Beijing. My dad was bicycling to my mom's hospital, and that inspired him to call me Fei. Fei.
- [73:43]
A
Fei Fei.
- [73:44]
B
Fei Fei. Oh, wait, sorry. For those who don't speak Chinese. I forgot you do speak Chinese. But for those who don't speak Chinese, fei means flying. So. Yeah, so be inspired by a bird.
- [73:55]
A
You know, really quick. I'll just say, because it's kind of funny. My first Chinese name that I had was Fei Ting Chung, which is because I was very blunt and honest. So Ting Chung, but Fei Tingchang. But when I was first starting, my tones in China were not polished and people thought I was saying that my name was Feijichang, which is Airport. So I changed. I petitioned my teachers and we changed. Changed my name to something less confusing.
- [74:23]
B
What's your new name?
- [74:30]
A
It's like. It's like. But it's without the. At the bottom. Yeah.
- [74:35]
B
Oh, wow. Fancy name. That's way more sophisticated than mine.
- [74:43]
A
Well, I get to script it with my Chinese teachers, so I have an unfair advantage. Dr. Lee, thank you so much for the time. We will link to the show Notes for Everybody at Tim Blog Podcast. They'll be able to find you easily. And everybody should check out WorldLabs, AI and we'll put every other link, your social and so on in the show links. But thank you for the time. I really appreciate it.
- [75:03]
B
I enjoyed our conversation.
- [75:05]
A
Yeah, likewise.
- [75:06]
B
Bye.
- [75:09]
A
Hey, guys, this is Tim again. Just one more thing before you take off, and that is Five Bullet Friday. Would you enjoy getting a short email from me every Friday that provides a little fun before the weekend? Between 1 and a half and 2 million people subscribe to my free newsletter, my super short newsletter called Five Bullet Friday. Easy to sign up, easy to cancel. It is basically a half page that I send out every Friday to share the coolest things I found or discovered or have started exploring over that week. It's of kind. Kind of like my diary of cool things. It often includes articles I'm reading, books I'm reading, albums, perhaps, gadgets, gizmos, all sorts of tech tricks and so on that get sent to me by my friends, including a lot of podcast guests. And these strange esoteric things end up in my field and then I test them and then I share them with you. So if that sounds fun, again, it's very short. A little tiny bite of good stuff before you head off for the weekend. Something to think about. If you'd like to try it out, just go to Tim Blog Friday, type that into your browser Tim Blog Friday. Drop in your email and you'll get the very next one. Thanks for listening. As many of you know, for the last few years I've been sleeping on a midnight luxe mattress from today's sponsor, Helix Sleep. I also have one in the guest bedroom downstairs and feedback from friends has always, always been fantastic. It's something they comment on without any prompting from me whatsoever. I also recently had a chance to test the Helix Sunset Elite. The Sunset Elite delivers exceptional comfort while putting the right support in the right spots. It is made with five tailored foam layers including a base layer with full perimeter zoned lumbar support right where I need it and middle layers with premium foam and micro coils that create a soft contouring feel feel. Helix offers a 100 night sleep trial, fast free shipping and a 15 year warranty. So check it all out. And now you can get 20% off anything on their website so site wide. So just go to helixsleep.com Tim one more time. Helixsleep.com Tim with Helix, better Sleep starts now. I have been fascinated by the Microsoft microbiome and probiotics as well as prebiotics for decades, but products never quite live up to the hype. Now things are starting to change and that includes this episode's sponsor. Seeds DS01 Daily Symbiotic I've always been very skeptical of most probiotics, but after incorporating two capsules of seeds DSO1 into my morning routine, I have noticed improved digestion and improved overall health. So why is seeds DSO1 so effective? For one, it is a 2 in 1 probiotic and prebiotic formulated with 24 clinically and scientifically studied strains. But if the probiotic strains don't make it to the right place, they are not as effective. So Seed developed a proprietary capsule and capsule delivery system that survives digestion and delivers a precision release of alive and viable probiotics to the colon. And now you can get 20% off your first month month with code 20tim@seed.com Tim.
- [78:15]
Using code 20tim all put together one more time. Seed.com Tim code 20tim.