Top Minds in AI Explain What’s Coming After GPT-4o | EP #130

welcome to a another conversation on AI I don't think we get enough of this conversation going I do want to thank Richard AAS and the FI team for really increasing the conversation this year on AI because I think there is no greater uh topic of import on the financial side on the leadership side education Side Medical side it's transforming everything we have uh three incredible CEOs here um who are representing uh variety of different parts of the AI emergence um I'm going to start by asking each of them to take just one minute introduce themselves and what they're doing and then we're going to jump into where is this going how fast is it going how big is it going to get you know we'll ask the question what is after chat GPT Prem let's begin with yourself awesome thank you you thank you Peter um I'm pre maraju I'm the CEO of stability AI we are one of the leading open- Source image video and 3D um models in in the world and past GPT pictures are worth a thousand words and we're making quite a few of them and in fact 80% of all the images that were generated by AI last year in 2023 were driven by our model stable diffusion amazing Richard hi everyone really excited to be here my name is Richard soer I'm the CEO and founder of u. com y. com it's a productivity engine which is the next Generation after a search and an answer engine so we really make people more productive across a whole host of different kinds of organizations from hedge funds to universities to companies insurance companies and so on Publishers news agencies and uh almost everyone else in between who has sales service marketing research analysis uh and so on I also run a venture fund called aiex Ventures that invests in early stage uh preed seed AI companies and startups been very fortunate that when I was a professor at Stanford I had two students who created this cute company called hugging face invested in that a 5 million valuation worth four and a half billion now so Fun's doing that's that's bragging that's just straight up bragging I wish I could brag like that DrKaiu Lee hi uh I've been working on AI for about 43 years I was two at no at the uh in college when I started Ai and um I think that may have started before my colleagues were born but um I actually worked on machine learning and um and I have a PhD carnegi melon and I have worked at uh Apple Microsoft Google uh some of you may know me as with my books AI superpowers and AI 2041 uh my part-time job is I I run sinovation Ventures which invests globally and then my full-time job is I run 0 one.

it's a uh gener generative AI company uh we build a large language model we're currently ranked as the third company with the highest performance only next to the best models from open and Google um and you can find it online uh we're also building uh consumer and Enterprise Products um we're based in China uh but our products are accessible globally and also we extensively uh do open source as well so incredible and and first of all Kaiu is a legend and one of the greatest leaders globally in this field so very honored to have him on here um Prem I want to start with you um uh you very famously were able to recruit James Cameron onto your board uh and since stability is creating video and is creating sort of the future of Hollywood um I am curious about two things one did uh did Jim get it right with the Terminator um and and secondly uh you know there's been a lot of conversation about the disruption of Hollywood um that we're going to have AIS creating the future of all movies all content and so forth so you said beyond you know GPT models were you know images worth of th000 words talk to us about what this what this future is what is going to happen in sort of the visualization world of of TV and Hollywood love it so did Jim get it right with Terminator uh let's hope not I guess but the um but what a great movie it was and I love when he actually he jokes about it he says I told you guys like you know this is coming and now it absolutely is here um and why did why would someone like him get involved in stability yeah great question so you I I had the great Fortune of of working on Avatar 2 with him when I was the CEO of wether digital before I joined as CEO of stability and that movie took over four years to make and that's because it was fully rendered and I think if you fast forward to 5 to 10 years from now the vast majority of film and television and visual media as we know it today is not going to be render is going to be generated and in fact in Avatar there were certain shots there were certain uh that took 6,000 7,000 hours of compute time to render one single frame thousands of hours that literally can be reduced down to minutes now so I think Jim just wants a whole lot of life back and when you think about like the creative process we all watch films we watch movies we love them from the time we've born to our last memory it's some it's a commodity we never get sick of um we never we never not want to watch it um and so there's this insatiable appetite out there in the world to consume stories and to create stories and I think that we should just accelerate that the problem with the film production process is time and money so what he really wanted to do is rip those things out so we can move from a render to a generated model are we going to see a situation where we're ever going to have ai generating entire movies because it knows my preferences what I love and it's like the perfect movie for me you know personally I kind of hope not um I don't think that actually uh the creative process I think needs to start with a human and I think that human needs to dictate these tools in separate agents to actually make that story and so I'm hoping that you'll probably want to hear stories that other people want to tell you all right well let's take a different direction then sure am I going to see uh Marilyn Monroe and you know all stars of the Past coming back is there a need for human actors if you can generate absolutely lifelike uh actors and actresses perfectly I mean I can't see a situation where they're still around yeah I think that it's actually quite it's faster when you're talking about the Film Production it's actually easier to just shoot plates on an actor just shoot real photography and get their performance I think there's that's the visible layer of of production people gravitate toward it a lot I think that AI will enhance those prod those um performances I think the physicality of a director with a camera and an actor in front of it is a very important part of the creative process and I don't think that that's going to go away too soon and in fact I think about the things that aren't going to change just as much as I think is going to change but I do think after they take one take the director is going to say I got it because they're going to be able to do what you're talking about which is manipulate that performance may ask one more question to you before I move on what is the most dramatic change we're going to see in film and TV 10 years years from now as we see digital super intelligence we like what's what's the craziest vision of what we're going to see in entertainment I think we're going to see on the magnitude of 5 to 10 to 20x more content being created I think we're going to see a variation of time where it's going to be a two-minute like you said you may want to have 20 minutes before you go to bed you want to see a movie that that's you'll have different type of time signatures and I think that you're going to have an explosion of content creation an explosion of number of artists in the world I'm going to come back in 10 years and see if you're right about that okay uh Richard um a lot of your work was instrumental in the early days of bringing neural Nets to natural language processing um so what do you see as the next Frontier Beyond NLP so just explain if you would what NLP is and where is it going next yeah natural language processing NLP used to be a a sub area of AI and it has I think influenced pretty much every other area of AI and uh there lots of different algorithms you could train and 2010 I had this crazy idea to train a single neural network for all of NLP and 2018 we finally really built the first model uh that invented prompt engineering where you can just ask one model all the different questions you have and over time of course you can ask questions not just over text but also over images and so I think next one of the answers to the the panel's main topic of what's after chat gbt is that we have many more multimodal models you'll be able to have conversations over images you have seamless inputs and outputs in not just the modality of text but also programming which is a huge unlock uh visual videos images voice sound but one really interesting modality that not many people have quite realized yet is that of proteins proteins are essentially the basic Lego blocks of all of biology everything in our body is governed by prot proteins and you can create a protein just like you can ask a large language model to write AET for you or a poem for your wife you can ask an llm to create a specific kind of protein it will only bind to SARS Cove 2 or only bind to a specific type of cancer in your brain and what that means is that we will unlock a lot of different aspects in medicine so I'm extremely excited about the future of LMS going into different modalities and we're seeing that with Deep Mind products in you in Alpha proteo and and such so we had a conversation in back but I didn't hear the answer and the question is basically is there an upper limit to intelligence and you know we've talked about and we just did a conclave on digital superintelligence and how fast we're going to get there and what does it mean um as we think about AI becoming more and more intelligent yes I want speak to Elon he said okay 2029 2030 equal to intelligence to the entire human race is it just you know a million times more and then a billion times more and then a trillion times more is there an upper limit to intelligence yeah so really interesting question so just to talk about Alpha fold and Google for a second as you mentioned it like that was really interesting uh to understand how proteins fold because that will help you understand how they are likely to function interact in your body what we did in 2020 is create the first LM that generates a completely new kind of protein and it was 40% different uh to any naturally occurring protein and it actually we synthesized it in the wet lab this was at Salesforce research did scientist there and it was an antibacterial Lio type of protein that is basically has antibacterial properties and just to put that into perspective was really close to covid-19 so make sure you weren't um got to be careful what you say online sometimes um but what was interesting is that multiple startups have now started from this line of research and and I think it's hard for people to Fathom like how much that can change medicine in terms of upper bounds of intelligence it's a really interesting question can it just keep going and going going I think you have to basically look at the different dimensions of intelligence right there's language intelligence visual perception intelligence reasoning knowledge extraction uh and a few others physical manipulation and just I'll show you just one example I don't want to talk talk about this for hours but visual intelligence right there are you know for a long time people have looked at just the electromagnetic uh frequency spectrum of human vision and there you know classifying every object on the planet is actually not that hard and the upper limit is classifying all the objects um on the planet and we're probably going to reach that and we're not too far away from it but that's just human Vision AI could eventually see all the way down to gamma frequencies and see and try to perceive atoms right and there you actually start to hit limits uh of physics like Quantum limits of like what can actually be observable and you can go all the way into like seeing uh massively larger scale things at the universe level and how many uh different sensors do you have in that then you can process all of that information and AI could have billions of uh sensors that go out and then you get into really interesting limits of like the speed of light cone of like so I can talk about for hours it's a really tough subject but in some cases we are astronomically far away from those upper bounds and in some cases we already got pretty close fasc you talk about work productivity as U. C com's objective what does that mean and uh I guess the question is the same is there any limitation on work productivity that we're going to see given the fact that I can command AI agents and robots to just do anything and everything and just and self-improve along the way it seems like we're going to hit sort of an infinite GDP at some point yeah there there's some areas of AI where AI can actually get into a self-training loop if there's a simulation of something that and anything that can be simulated AI can solve everything in that areas for instance chess the game of Go you can perfectly simulate it hence the I can train and play with itself billions and billions of times cre almost infinite amounts of training data and hence solve every problem in that domain what are other domains that we can perfectly simulate is programming if you can programming languages can be run and then you can simulate the outputs obviously in the computer and then the AI can get better and better and eventually get super human uh in terms of programming but where I can't simulate things uh infinitely many times is in like customer service right you can have billions and billions of customers kind of ask about all the different things that uh can go wrong with a product that you're sending and so in those kinds of areas the limits are going to be on data collection can you actually fully digitize a process I often joke like plumbers are probably the safest from AI disruption because no one's even collecting data on how to do plumbing right you like crawl somewhere get different pipes no one's having GoPro and 3D sensors and robotic arms and so on collecting data for that so that will take much much longer um I think in terms of work productivity a lot of us are going to become managers a lot of current employees that are individual contributors are going to have to learn to manage an AI to do the kinds of work that they do and it turns out managing is also a skill not everyone is a good manager from day one you have to really explain to the AI how you do a certain kind of job and what we've seen with for instance uh a really large cyber security company called minecast is we've they've had 200 seat licenses using their product and then we did a workshop with them and actually explained to all the different groups like this is what you can do and someone from marketing can say well I usually get this long product description and then I have to describe it for these different Industries and an email campaign and I have to write three tweets and three LinkedIn messages all this stuff and we're like well just say that to this agent and then the I agent does it for them they're like wow now it's like six to 20 hours of work every other week just got automated by describing this workflow that I used to do manually to a agent and I think that will change pretty much all work and pretty much every industry Kaiu um I can go in a thousand different directions uh here uh first of all uh your Venture fund Innovations which is how many billions of capital a uh we manage about $3 billion about3 billion and you've been one of the most prolific AI investors I've had the pleasure to visit you multiple times in China and thank you for your amazing Hospitality you've now become an entrepreneur um and you're running both uh company in China and a company in the United States uh why did you do that well because this this time is for real right imagine you know this was my dream practice before well this was my dream in when I went to college that AI was nothing no one knew what it was but I felt this was the thing I needed to do and then we went through multiple winters of AI where uh there's disillusionment and I had to do other things and about uh you know seven eight years ago we saw with um you know deep learning it was became clear it would create a lot of value so but at the time I didn't really see it becoming AGI so I was an investor we actually created 12 AI unicorns in sinovation Ventures but this time with generative AI uh the speed at which is growing um is just phenomenal you could help yourself you yeah I felt if I just invested I'd be missing out I I would be in the back seat I want to be in the in the driver's seat by the way everybody I hope you feel the same right I I'm very clear about saying there are two kinds of companies at the end of this decade companies that are fully utilizing Ai and everyone else is out of business and I I fundamentally believe that is it is true um you've written a number of books uh AI superpowers I commend to all of you so since that was published what's the biggest changes in the global AI race and it is an AI arms race going on well it isn't isn't because the companies in China are largely competing against each other for the China market and they're generally not I don't mean Nation to National but it is between companies around the world yeah so you mean Chinese companies what are their characteristics so in my book a superpowers I described uh the American companies are generally speaking more breakthrough Innovative they come up with new things um and then the Chinese companies are better at engineering execution attention to detail doing the grunt work user interfaces user interfaces building apps so um in the case of mobile or deep learning we saw that Americans invented pretty much everything but China created a lot of value arguably more uh given technologies that were largely invented in the US so now we're in this generative AI again invented by Americans and we're in a in a in a unique position where where the technology is disrupting itself very quickly in the US and elsewhere um so it arguably is still the age of Discovery and US ought to win but then the Chinese companies are able to watch the Innovations make some themselves and then do better engineering and deliver Solutions so the company I'm building 01 is doing exactly that we don't claim to have invented everything or even most things we learned a lot from the Giants and silicon valy open Ai and others but we think we build a more solidly faster execute better so an example was I talked about how 01 now has is the third best model modeling company in the world ranking number six in models measured by lmis and UC Berkeley but the most amazing thing I think the thing that shocks my friends in the solic valley is not just our performance but that we train the model with $3 million and GPT 4 was trained by 80 to 100 million and um GPT 5 is rumored to be trained by about a billion dollars so it is not the case we believe in scaling law but when you do excellent detailed engineering it is not the case you have to spend a billion dollars to train a great so this is really important for the audience here because there's a lot of parts of the world that don't have access to you know 100,000 H you know h100 clusters right and the question is oh my God can I really build a business or a product in pick your favorite country with a small number of gpus yeah and I think the constraint on gpus forced you to innovate right can you speak to that I think it's really important we talked about that on our last podcast together yeah I think you know as a company in China first we have limited access to gpus due to the US regulations and secondly the Chinese companies are not valued what American companies are I mean we're F we're we're valued at a fraction of the equivalent American company so when when we have less money and difficulty to get gpus I truly believe that necessity is the mother of in Innovation so when we only have 2,000 gpus well the team has to figure out how to use it I as the CEO have to figure out how to prioritize it and then not only do we have to make training fast we have make inference fast so our inference is designed by figuring out the bottlenecks in the entire process by trying to turn a computational problem to a memory problem by building a multi-layer cache by building a specific inference engine and so on but the bottom line is our inference cost is 10 cents per million tokens and that's uh 130th of what the typical comparable model charges and where's it going where's the 10 cents going yeah it's well the 10 cents would lead to building apps for much lower cost so if you wanted to build a u.