Pod Insights No Priors Teaching AI to Understand the Physical World, with Dr. Fei-Fei Li of World Labs About this episode In this episode of No Priors, Sarah and Elad are joined by Dr. Fei-Fei Li, AI pioneer, co-director of Stanford’s Human-Centered AI Institute, and founder of World Labs. Fei-Fei shares why she’s building at the intersection of embodiment and intelligence, and what today’s AI systems are still missing. From the early days of ImageNet to her vision for the next generation of robotics, she unpacks the human and technical motivations behind World Labs. They also discuss the challenges of 3D world modeling, her approach to building exceptional teams, and the special qualities that have led her students like Andrej Karpathy to make major breakthroughs.
Show Notes:
0:00 Why and what Dr. Fei-Fei Li is building
3:00 World models at World Labs
6:44 Missing gaps in the AI future
9:16 Robotics and physical intelligence
16:15 Greatest challenges of 3D
19:08 Fei-Fei’s work in PhD in ImageNet
23:05 Special moments in Dr. Li's career
29:33 Building teams
32:05 Human-centered AI
Transcript Speaker names marked ? are inferred from speaking time and the show’s known hosts — they are not voice-verified.
0:05 Sarah Guo? Hi listeners and welcome back to No Priors. Today's guest is Dr. Fei-Fei Li, a pioneer in computer vision and deep learning. She created ImageNet, the groundbreaking dataset that helped spark the deep learning revolution. Fei-Fei is a Stanford professor and the co-director of the Stanford Institute for Human-Centered AI. She's also led AI Google Cloud, advised international policymakers, and recently co-founded World Labs, a company dedicated to developing spatially intelligent AI. Fei-Fei, thank you for joining us today.
0:33 Speaker 01 Well, thanks for inviting me. This is gonna be fun.
0:36 Sarah Guo? So you have made extraordinary contributions to science and policy over the past two decades. I'll start with the biggest question, like, why start a company now?
0:46 Speaker 01 Because in my heart, I wanna build. I see this as such a critical and fun and exciting moment to build some extraordinary technology that everybody can use. And I believe so much in spatial intelligence and the kind of 3D world models that can empower so many people as well as so many use cases. And I think that's just, it's gonna be really exciting, and I can do that with an extraordinarily brilliant group of young technologists.
1:23 Sarah Guo? I wanna come back to, you know, the people you're working with because I know some of your co-founders and was, you know, trying to convince them desperately to start a company a while back, and then they were like, oh, no, we have a bigger mission now with Fei-Fei. What is spatial intelligence? Can you define it for a broader audience?
1:40 Speaker 01 Spatial intelligence, to me, is the ability to to understand, reason, and interact, and generate 3D worlds. Because our world fundamentally, no matter how you say we can project it, fundamentally is 3D. And it's 3D because physically, it's 3D. And digitally, if there is a True three D representation, then we can make a lot of things happen more easily, whether it's designing or creation or navigation or simulation or the experiencing of A R V R. All this to me is part of spatial intelligence.
2:24 Speaker 01 And again, I think it's what really excites me is humans have spatial intelligence. We are, it's part of our core intelligence capabilities. Animals have a spatial intelligence. The entire journey of evolution also is deeply intertwined with the evolution of spatial intelligence. So it's so fundamental. Without spatial intelligence, A I would be incomplete.
2:54 Speaker 02 How does that translate into what you're doing with your company? Or is there anything you can share in terms of what that means relative to what you're building?
2:59 Speaker 01 Yeah, so we're cracking one of the hardest problem in A I, which is actually making world models that are fundamentally three D. Because once you can crack that problem, you can unlock a lot of spatial intelligence problems. So we are the first company we know of that is solving this, the three D generation foundation model problem.
3:28 Sarah Guo? I have many questions, but since you are, you know, describing this first as, you know, the, you know, three D's criticality to just sort of understanding the world. Does that imply you feel that the world models that, you know, World Labs will create or others in academia or in companies will create will someday be like, you know, realistically accurate? Like represent physics and understanding the world that we can do many more things with?
3:59 Speaker 01 Yeah, it should. It should be realistically accurate or plausible. So you can create a fantastical world, but it should be plausible because the geometry and the physics of it need to be plausible. And.
4:15 Speaker 01 And that is fundamental to spatial intelligence.
4:18 Sarah Guo? Does that imply you have a particular um point of view um from a like a neuroscience perspective of like, you know, how fundamental visual you've I mean, you've always been a leader in um uh computer vision, right? But in how important visual intelligence is versus, let's say, like, large language models and textual intelligence?
4:40 Speaker 01 I actually do. I think from a neural and cognitive science point of view, that spatial intelligence is a really hard problem that evolution has to solve for animals. And what's really interesting is, I think animals have solved it to an extent, but not fully solved it. It's one of the hardest problem because um what is the problem animal has to so solve? Animals have to evolve the capability of collecting lights in something which we call eyes mostly. And then with that collection of eyes, it has to reconstruct a 3D world
5:22 Speaker 01 in their mind somehow so that they can navigate and they can do things. And of course, they can interact. For humans, we're the most capable animal in terms of manipulation. We can do a lot of things. And all this is spatial intelligence. To me, that's um that's just rooted in in our intelligence. What is interesting is, it's not a fully solved problem, even in animals. We uh for example, uh for humans, right? Um if I ask you to close your eyes right now and draw out or or or build a 3D model of the environment around you, it's not that easy. We don't have that much capability to generate.
6:10 Speaker 01 Extremely complicated 3D model till we get trained. You know, there are some of us, whether they're architects or designers or just people with a lot of training and a lot of talent. And that's, that's, that's a hard thing to do. And imagine you do it at your fingertips much more easily and allow much more fluid interactivity and editability. That would just be a whole different world for people, no pun intended.
6:43 Speaker 02 Are there other big areas like spatial intelligence that you feel haven't been as developed as it could be from a model perspective or other sort of missing gaps that you think in general, as we think, as we build this sort of AI future, we should focus on over time or people should build out? I was just wondering, in addition to sort of 3D and world generation and are there other big problems like that? Because it feels like there are a few big things that we've solved for over time and other things we're working on.
7:08 Speaker 01 We're sort of solving language. I would say language is solved to a huge extent. And 3D to me is as, you know, critical and difficult as language. So what else that's not solved? I mean, the entire space of emotional intelligence is something that I don't even know how to begin to solve.
7:31 Speaker 02 I know a lot of people who haven't solved it, so.
7:34 Speaker 01 That's when AGI is achieved.
7:37 Sarah Guo? I can tell you the training data for that is not going to come from Silicon Valley people.
7:43 Speaker 01 Don't underestimate Silicon Valley. Yeah, so.
7:48 Sarah Guo? I'll put myself in this bucket, but I think we probably need a broader set of people.
7:52 Speaker 01 Yeah, no, that I agree. But these are the three, three big buckets. To be honest, that's, I don't know. What do you think, Elon, in this era?
8:02 Speaker 02 I think it depends a lot on what you encapsulate in each model. So I agree with your framework in terms of those three. And then. Certain things like, you know, the spatial intelligence, I'm assuming also delves into different types of physics simulation and simulations of the world and that, you know, like those are big areas that I think a lot of people aren't working on that I think are really interesting or important. So, and there's, there's sort of the macro and the micro scale of that. The micro scale eventually becomes material sciences and other very different types of things from what you're talking about, where it's more molecular modeling or, yeah.
8:32 Speaker 01 Right. And also someone goes out the current definition of AI, which I do think they'll be empowered by AI. Of course, there's robotics, but robotics is very much a system integration problem as much as a, you know, even if you look at animals, it's not just the compute in the brain per se, right?
8:52 Speaker 02 Yeah, a lot of these things seem much more distributed in terms of spatial intelligence relative to specific systems that animals have. And in some cases, it's, to your point, not, not as centralized as one would think. So it's, it's very interesting to start thinking in terms of those models of more distributed intelligence across an organism versus a CNS. But yeah, I think, I think it's very interesting stuff.
9:11 Sarah Guo? You've also done work in, in this field, Fei Fei, of, of robotics and like physical intelligence. I think of the data hierarchy for, you know, robotics foundation models and actuation as, you know, people want to, of course, use video, right? Because that is what is available to us. There's a big question on like simulation and how much you can get from that today. Perhaps people are, do not see the future of like the quality and the physics that are going to be available to us. And then there's, you know, close to embodied, like different forms of teleop and then like embodied data collection. Is that the hierarchy you have in your mind? Or do you think people underestimate simulation and world models for the future?
9:52 Speaker 01 Yeah, great question. First of all, I, like you said, I do work in robotics, especially in my lab at Stanford. I have no doubt that humanity will move into an age where we cohabit with robots. And also the world, the world robot is not humanoid per se. Robots taking all kinds of forms and shapes. Actually, a few years ago, my lab wrote a really fun paper about morphological intelligence is where the, the morphology of a, a
10:24 Speaker 01 An agent actually can change by optimizing the tasks they're trying to achieve. So, so we should be a little more imaginative than just human, uh, humanoids. Having said that, had a trained robot, uh, you mentioned this whole data. Some people call it data pyramids or data cakes or whatever. I agree. I think it's gonna be a hybrid of many different forms of data. I also think simulation is underrated. It's, um, actually it's not underrated by a lot of experts and people in the field. If you look at a lot of robotics companies, they are working on simulated, uh, simulation and synthetic data.
11:07 Speaker 01 I also think we have to be, um, also aware that unlike language models or even unlike, um, spatial intelligence foundation models, robotics is a highly multimodal, um, uh, uh, system that I think what is truly underappreciated, in my opinion, is haptics. Is there's so much, especially if we wanna do manipulation, not just navigation. I think haptics data and the ability to really integrate haptics into vision and perception and spatial, uh, data is, is absolutely critical.
11:48 Speaker 02 One thing that you said that I thought was really interesting is, um, how many different, what are the different morphological forms that a robot may adapt or adopt? And, um, there's sort of two kind of arguments people make in terms of the potential future. One argument is that, um, from a supply chain perspective and managing builds and scale of manufacturing, you're gonna have many fewer form factors. And the other argument is that economic value of specialization is very high. And therefore there'll be, you know, thousands and thousands of different form factors as we move to sort of a robot-driven future. Do you have a point of view on sort of where we're likely to land between those two viewpoints?
12:23 Speaker 01 I think we're gonna gradient descending to optimization of productivity and efficiency. My hypothesis is that the requirements of different tasks are so vast that having very few form or, or sticking with one form is energy, energy inefficient.
12:46 Speaker 01 And a lot of tasks can be done and should be done by much more energy efficient form factors. Just an extreme and trivial example. If we put robots underwater, they should not be the shape of humans. They better be in the shape of fish, right? Just think about energy efficiency. And the same with flying. I don't think human form is, our airplanes are becoming more and more robots. And so I, I, I do think there's gonna be diversity.
13:21 Sarah Guo? Robotics is one potential application for the future. You're scientist first, but also, you know, did the Twitter board, involved in startups. What are the near term commercial applications that you can imagine for generating 3D worlds?
13:38 Speaker 01 I believe creativity is a vastly exciting area where humans can be superpowered by, by AI and by spatial intelligence. And here I draw analogy with software engineering. If you look at today's success of L L M's in software engineering, including applications like cursor and windsurf and all that, what you see is, is a lot of collaboration between AI and, and humans. And then the collaboration comes in different levels of skill sets and all that. And I think creativity will be similar is that whether we're talking about designers,
14:22 Speaker 01 3D artists, V F X artists, or even marketing talents and, and, and game developers. There's so much need in, in designing and creating 3D space. And this is fundamentally such a hard problem, even for the trained, skilled people, that having a collaborator will be extremely fun if, if we do it right. And so I see creativity as an area that is really exciting. I also do think that.
15:00 Speaker 01 A lot of what we're waiting for for Metaverse or XR, AR, VR is content creation. I understand hardware itself needs to continue to evolve. But I also think software, we're looking for content creation. And that lends itself so naturally to 3D modeling and 3D or generative spatial models. And that's another interesting area to look into.
15:29 Sarah Guo? Do you have a strong point of view on whether or not world models are like an interesting answer to scalable RL for like more generalizable agents?
15:39 Speaker 01 I actually do think this, this is, like I said, AI is not complete without spatial intelligence. Because the humans interact in 3D worlds. And in the digital world, we need all kinds of interaction. You know, take design as an example. It's a deeply, you know, it has. When we are thinking about design, there's so much we are optimizing for in our mind's eye, whether it's beauty or efficiency or optimization or whatever it is. And that lends itself pretty naturally to RL settings.
16:18 Sarah Guo? What are the biggest challenges in, I guess, trying to go down this path of, you know, designing and training world models? I imagine one is like you worked on images, you worked on video, but we, we have images and we have video and we don't have lots of, you know, 3D worlds like in a format I assume you're building.
16:37 Speaker 01 Yeah, data is absolutely a challenge. You're totally right about that. You know, to create world models, 3D foundation models, we require more and more sophisticated data engineering, data acquisition, data processing and data synthesis. So I am envious of my NLP, LLM colleagues that the data is so abundant on the internet. And we don't necessarily have that luxury. So that's definitely one, one challenge. Another one challenge is that 3D, this is kind of ironic, right? Every one of us use 3D every day.
17:20 Speaker 01 Like in so many settings, basically you open your eye and and and the whole life that you experience is 3D.
17:30 Sarah Guo? Okay.
17:30 Speaker 01 Even when we type on the computer or stare at a screen all the time, yet it's still not as easy a form factor to deliver in the hands of people compared to language. The language is just so easy. And it's also a very active form of, it's not a passive consumption of viewing. Nobody wakes up and say, I'm just gonna sit here and watch 3D, you know? So um, that creates challenges for for for productization and how to do it in the right way.
18:05 Sarah Guo? Were you ever a, like a second life? Player? Or any, anything that?
18:11 Speaker 01 I'm not a gamer, but my kids love Minecraft.
18:15 Sarah Guo? I was gonna ask you if there was like a world that you want to experience or imagine?
18:20 Speaker 01 That's a great question, Sarah. You know, I would love to see worlds. I love seeing worlds I don't see. For example, like zooming in and in and into like microscopic worlds or, you know, um, going to the inside of an engine, you know, knowing how the the the actual engine is is. I know, of course, I know theoretically how it works, but seeing it with my own eyes, experiencing it. Or even, um, you might laugh at this, I wanna be inside a dishwasher and just experience what what that is. All this can be done in a in a virtual way if we manage to create, you know, world models of anything.
19:03 Sarah Guo? Okay, I I I think a lot both, a lot and I both wanna talk a little bit about your past career and maybe some uh insights for anyone doing research or trying to, you know, have an impact within AI. Right before this, I asked Andrei Karpov, what I should ask you? And he said, you know, um, Fei Fei is really magic about ambition and thinking about data. You should ask her about her PhD. Like a- and the creation of that 101 data set with Pietro, um, because it's instructive. So I have to ask you about that.
19:34 Speaker 01 You know, first of all, I have to say it's always really the greatest thing when your student is is um. More well-known and achieving so much more than you can. It makes me so proud, so very proud of Andre. I was, I'm surprised that he remembers my PhD work. So, yes, it's true. It's, well, gosh, it goes back to 2003-ish and the world was just barely scratching the surface of Internet and data was not much of a thing. But doing computer vision, we were, my PhD work was really trying to get object recognition to work.
20:17 Speaker 01 That's the, that's the problem of calling out cats and dogs and microwaves and chairs and all that when you're presented with a picture. And we were beginning to hypothesize that data matters, but we had no idea. There's no scaling law. We had no idea, you know, how far data can go. All we wanted is if we have a machine learning algorithm, whether it's a neural network or base net at that time was very popular or support vector machine, we need some data to train. And there was no data to train. And as a PhD student, you wanna, you know, graduate. And Pietro was like, well, Fei, curate a dataset.
20:58 Speaker 01 And I, and you know, I was thinking, yeah, I do need to curate a dataset because every dataset out there is so tiny. I'm just not convinced. And Pietro and I were just talking, you know, it's 15 different things or 30 different things. And then, God forbid, the PhD advisor set the three-digit number 100. And I was like, you know, that's a lot of work. But I, deep in my heart, I know he's right from a mathematical point of view is pushing the model to generalize. We need enough data at least. So. You know, I did write about this process in my book, The World I See,
21:41 Speaker 01 that I stumbled upon a dictionary somehow, and it really was for my own English study, that the dictionary, I think it's the Webster Dictionary, if I'm not wrong, it just kind of randomly has depiction of, a visual depiction of some words. I don't even know what rule they followed, to be hon- to be honest. Some are flowers, some are bicycles, some are dogs. I was like, okay, this is actually, you can call it a cheat or a tool. I grabbed a hundred and one of those words, and that really made my PhD advisor kind of chuckle, because he's like, ah, yeah, you just wanna do one more than I asked for to, you know, dare me.
22:26 Speaker 01 So that's what I did. And I gotta say that I still remember I downloaded or, you know, tried, you know, from Google, and Google was so new at that point. And the Google image search were so terrible at that point, you know, compared to today. And I had to do so much cleaning. At some point, I got so desperate, I just asked my mom to do clean, the image cleaning, because I wrote a little interface on the computer. She doesn't know computer, but at least she knows click, click. So she helped me to do some of that.
23:01 Speaker 02 Yeah, I mean, you've had one of the most storied careers in AI. And to your point, many of your students have similarly gone on to do really great things across the field, across industry, across, you know, the world. What are two or three moments that you think of when you think back on your career to date? And obviously, there's still a lot of career to come, but I'm just sort of curious. I mean, obviously, there's a lot of things that you did in terms of sort of image and visual recognition-related systems and all sort of. But I'm just sort of curious, like, when you think, think of the last 20 years, what stands out the most, just given everything that you've done?
23:32 Speaker 01 Oh, thank you for asking that question. Of course, image that is one of those, image that is consists of multiple moments from the early struggles and being told I will not get tenure to.
23:46 Speaker 01 To actually realizing Amazon Mechanical Turk comes to rescue to the moment of AlexNet winning and also to a couple of years ago, I was at an event in Toronto with Jeff Hinton. And he said publicly like how that was so defining and he was almost a little bit apologetic that ImageNet was not as recognized as a neural network. So that journey is very validating and for scientists, the validation is not about recognition or awards. It's that you made a difference like that conjecture that no one believed in that hypothesis that
24:30 Speaker 01 no one believed in, we were able to make it happen. So that's one thread.
24:35 Sarah Guo? Just to make sure for any like, you know, people from the business world that are not familiar with it, ImageNet was a large scale, is a large scale data set with millions of labeled images across thousands of categories, not just a hundred and one, right?
24:47 Speaker 01 Fifteen million labeled images.
24:50 Sarah Guo? Fifteen million labeled images. Thank you, Fei Fei. That, you know, led to amazing breakthroughs in deep learning, in particular AlexNet and lots of progress in the field of
25:00 Speaker 02 Yeah, driven a lot of machine vision forward. And I actually remember in 2016 or 2017, I used to show a slide, which was the history of AI or, you know, back then it was CNNs and RNNs and just GANs were, you know, kind of going. And I had ImageNet and AlexNet is like one of the seminal moments of, you know, this very small number of events that really defined AI progress. And obviously now we have transformers as part of that and maybe diffusion models or something, but it was such a big breakthrough.
25:27 Speaker 01 Yeah, thank you. Another moment I'm very proud of was actually Andre and also Justin Johnson and their dissertations. It's where
25:40 Speaker 01 In my opinion, the first time that language and images converged by captioning and writing stories of of the visual world, it was significant for me for two reasons. One is that I literally thought, I kid you not, at the end of my PhD, I thought when, if I can live to one hundred year old, that was the problem we might be able to solve, which is storytelling of pictures. So I entered my my my career, like my first year assistant professor, thinking, okay, I'm gonna do image that to solve object recognition, and then I'm gonna spend the
26:22 Speaker 01 rest of my entire career solving this problem of storytelling. And then by the time Andre and then a little later, Justin Johnson entered my lab, that was around twenty thirteen, twenty fourteen, the beginning of deep learning. And then suddenly, the combination of sequential model at that point is LSTM, it's not transformer models, but LSTM and CNN just had this blasted open, the image captioning work. And Andre and my work were the first together with Googles that was out of the door.
27:06 Speaker 01 And that was really, to me, I I almost had, it was made me so proud, I almost had a crisis, which is like, what am I gonna do for the rest of my seventy years or sixty five years? It's so that was really exciting, how how fast the field has has, you know, evolved.
27:28 Sarah Guo? Can I ask you one more question about this? Just because you have, you know, made this amazing progress, like very efficiently, right? Like you and I have offline talked before about how it you you feel it's really important for there to be, you know, moonshots and creativity in AI research beyond like very large funded corporate labs, let's say. And, you know, you you pointed to several moments that they come from like. Creativity and research in academia. What advice do you have for people about whether or not there's still opportunity for that? Or, you know, it's all just $10 billion training runs from here?
28:07 Speaker 01 My singular advice, and I still say that in my company, in my lab, is be fearless. I think scientists and technologists and entrepreneurs have to be fearless. You know, eventually you have to figure out, do you need $10 billion runs? Or when then you come to Sarah as to how to ask for funding?
28:30 Sarah Guo? Probably a lot, for both, yes.
28:32 Speaker 01 Yeah. Or you have to figure out, you know, I don't know, data. Sometimes fearless is this very interesting position where you're somewhat delusional and crazy, but somewhat just rationally bold. And it kind of is in between because if you're too rational, it's not courageous enough. You're not identifying problems that are big enough. But if you're completely crazy, then I don't know. There's so many things that can go wrong. So be fearless, be courageous.
29:14 Speaker 01 I, to me, that is, you know, even as old as I am, that's how I feel. I started my startup, World Labs, is I want to be fearless and solve this problem of spatial intelligence.
29:28 Speaker 02 As part of problem solving, you've worked with some of the best AI researchers in the world over time and best engineers. How do you think about that in the context of your company? Like, what sorts of people are you trying to hire? Are there open roles currently? And Datali, it's an amazing team. I'm just curious, like, what sorts of folks you want to add and how you're thinking about that over time?
29:46 Speaker 01 Yes, we have open roles and we would love to hire the best engineers as well as product thinkers at this point for our company. So if you're an engineer or AI researcher or product talent out there, passionate about joining the most talented team and solving this problem. Please join us. So who do we hire? First of all, we really do hire in diversity of thinking. And this is where, you know, you call us a AI company, but if you look under the hood, we've got computer graphics experts, we've got computer vision experts, we've got data experts,
30:25 Speaker 01 we've got, you know, generative AI experts, we've got machine learning infra experts, we've got optimization, we've got, so it's actually really important to, to, to hire a diverse group of really talented people because a problem as hard as spatial intelligence is not a homogeneous problem, like it takes talents of all kinds of background to solve it. And, and then I also just, like, I look for fearlessness.
30:57 Speaker 02 How do you do that? Like, how do you identify if somebody has fearlessness in their background or in their thinking processes?
31:03 Speaker 01 It's in their background, you talk to them, you can sense someone is fearless, you know, you can sense what drives them, you know, you can sense the questions they ask if they are, if they start to asking you a lot of things about,
31:23 Speaker 01 I don't know how to get this done. And I mean, of course, you have to ask those questions because you want to get it done. But if, if you sense that it comes from the, the, the, the point of view of being scared of solving that, then that's not fearlessness. But, but those fearless people, they are creative, they're ambitious, they, they, they, they can, they're not afraid of the uncertainty or the unknown. And I really love that.
31:57 Sarah Guo? Well, I think a lot and I, you know, we try to make. Make a business of doing business with fearless people and hopefully those that are technically creative. One last broader question for you because I think an important part of your work has been also thinking how to bring more people into AI, you know, co-directing the Stanford Center for
32:21 Speaker 01 Human centered.
32:21 Sarah Guo? Human centered and artificial intelligence. What is your most, like if you picture, you know, not to use a pun on the book, but if you picture the world like several years out from your last set of predictions, what's your most optimistic view of what human centered AI looks like?
32:39 Speaker 01 Yeah, thanks for asking. In fact, that is another point of my career I feel very proud of is the founding of Human Center AI Institute, HAI, and also the continued movement towards that way of thinking. I think I want to build a world that AI collaborates and superpowers people. I still believe our world, our human world, needs to be human centered, you know, where love, relationship, just prosperity across, you know, all communities, these are really important. Justice and all, these are really important values.
33:18 Speaker 01 And I don't think any piece of machinery, whether it's AI or airplane or biotech, should take those away. But with that, those critical values in mind, having AI to superpower us is really, really important because there's so many unsolved problems. One application area I had worked on is healthcare, for example, at Stanford, right? If you look at healthcare, from drug discovery to cure diseases, to diagnosis that can reach all people in the world, to treatment that can be accessible to all people in the world,
34:03 Speaker 01 to the whole healthcare delivery, how to make aging better, how to take care of chronic diseases, how to deal with mental health, all of this, we do not have an issue of excessive humans or anything. We're lacking help. You know, we are lacking scientific discovery. We're lacking diagnosis. We're lacking precision medicine. We're lacking safer and more effective ways of healthcare delivery and aging help and all that. And I, that's what I believe. I think AI is a tool to help people.
34:37 Sarah Guo? Yeah, I think a lot and I are collectively invested in a series of companies that I hope will be useful here from a bridge to open evidence to latent. But as you said, there's a, there's a huge spectrum of problems.
34:50 Speaker 01 Yes.
34:50 Sarah Guo? Honestly, I've been less optimistic about the adoption of, you know, generally technology and healthcare for the last 15 years, but it does feel like this time it's different. And actually, it's just massively net good here.
35:04 Speaker 02 Yeah, I actually started a digital health company before this, and my hope is finally a lot of the things that people have been talking about for decades will come to fruition. And it seems like AI is a great delivery mechanism for that.
35:15 Speaker 01 Totally, totally.
35:17 Sarah Guo? Well, thank you so much, Fei Fei.
35:18 Speaker 02 It's fantastic.
35:19 Sarah Guo? This has been inspiring and great to hear a little bit more about World Labs as well.
35:24 Speaker 01 Thank you, thank you, La, thank you, Sarah.
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