All right, I have the very unenviable task of doing this in 40 minutes when each of you deserve two hours on your own. I'm going to take notes from the conversation this morning and go for each of you, and wish me luck, please. Travis, I'm going to start with you. Founder of Uber, CEO of Cloud Kitchens. We talk about AI a lot and all about the super fancy, big, billion-person transformations. How is AI going to change business right now and transform and expand and change profitability of businesses? You're doing some amazing things with Cloud Kitchens and AI.
Please, I mean those are two different questions. If you just do, when I look generally at what's going on right now, it feels like we're entering an era of bigger is better. That if you are a large company with a strong business model, that the technology that's coming is actually going to give you competitive advantage over folks that are smaller. The accretion of value is going to the bigger players, and there are probably a lot of very simple things that can be done to create more profit and bigger remuneration on those business models, is my guess. So there's just like blocking and tackling, like workflows, customer support, onboarding, sales, all getting automated will make an already strong business model like supercharged. And I think for the biggest companies, create trillions of dollars of value. I mean, we're doing, you know, my company's doing the future of food. It's real estate, software, and robotics for food. And there's lots of fun practical things that are happening. I mean, they're going to be big, but they're also practical. You know, we have a machine that makes food. Like, imagine if you were to go to Chipotle, that front line of people that is making your bowl, we have a machine that does that. So we have restaurants that are operating with no people or one person just overseeing the machine. But if there's only one person in that room, you know, you got to, we now have that person talking to the machine about how the machine is doing as if they were an employee. So yes, so then it's this is simple stuff, but this is where we're going now. Like we have this up and running in restaurants today where you're asking the robot, hey, is the food warm? Am I running out? This kind of thing. But we just said, well, you know, might as well just give it a personality. So now you can ask like, did the Dodgers beat the Yankees today, which they did. And you can have a conversation and it's sort of, we're quickly getting into that Interstellar moment where you're just interacting in a very human way.
I've got to ask this other question just for fun. If you'd remained CEO of Uber or were still CEO, what would you have done differently? What would Uber be doing now?
Oh man, we could take the whole hour on this one. I'll keep that one short. May I'll give you a couple things. Look, we had probably one of the preeminent AI labs when I left Uber, and we saw the seminal paper out of Google, the attention paper. I mean, you guys know how I rolled when I was doing Uber, like we'd probably do some pretty interesting stuff on that front. I mean, sort of in a more practical, on the more practical side, like we were starting to roll out, I mean, of course we're doing the autonomy thing. I'll put that aside, I think Elon's going to take care of that at this point. But we were starting to roll out almost, we looked at transportation as a high-frequency trading business where we would buy trips from drivers and sell it to riders. We were a market maker. And so we were starting to set up quant teams for each city to do the high-frequency trading of the trips so that we could get the lowest cost, reliable ride, more efficient, gain market share, get more profits, all this. It was just kind of a fun, geeky finance thing.
Right. One in 30 seconds, you've disrupted two huge markets. Your one piece of advice for someone here who wants to go disrupt a marketplace today, how do they, what's your piece of advice for them?
You know, look, I've over time I've tried to distill what it means to innovate at speed and at scale into sort of principles or pillars. They basically are cultural values at my company, and so we've distilled it down to truth, trust, and passion. And again, you know, those are high-level sort of ideas, but doing those really well, each of those really well, is sort of how you innovate at speed and at scale. And it's, you know, there's a lot in there, but like think of the childlike playfulness and curiosity with a teenage rebelliousness with an old man's wisdom. That's a beautiful mindset setup.
Thank you. Yeah, thank you, Travis. Jack Hidary, my dear friend, one of our trustees, CEO of Sandbox AQ. You have your chairman sitting right next to you, no pressure, no pressure whatever. So first of all, please explain AQ, but then what are large quantitative models and what role do you envision they'll play in the current evolution of AI?
Well, Peter, first of all, I'm very happy to see that we still have humans on the panel instead of just AIs. So I think it's just another few, I mixed the AI in this, yes, okay, another few years it's just going to be avatars up here. The mantra of AI die is real, is real. It's not just a phrase. It's happening right now. It's happening on several levels, and certainly companies and countries that do not engage will die. But now the question is, okay, let's all say we all agree on that, which AI, what AI, what kind of AI, what mix of AI? And the marketplace now is starting to offer more than just one slice of AI, and that's what's exciting. So we have, of course, large language models. Many people in this room are and on this panel are involved in that and making huge strides. And we're seeing AI now take on reasoning, and I'm sure Eric and others will also address that. But there's a whole another side of AI that has had less focus, and that's quantitative AI. That's AI that's based on equations and data, quantitative data, numerical data. When we think about starting a biopharma industry in a country that never had a biopharma industry, that was really not even possible five or ten years ago. But now we can come to a country like KSA or other countries and say, hey, let's get that going. But it's not large language models that will win the day there. It's models that have been trained on biology, physics, chemistry, electrons, that kind of interaction. So quantitative, large quantitative models, LQMs, as complementary to LLMs, allow us to do that at scale and speed. And we do that on the same GPUs. We have NVIDIA here on the panel that LLMs use, but we tweak them in a very, very different way. We train it on a very, very different kind of data. The data doesn't come from the internet. The data does not come from downloading Wikipedia and Reddit and social media. Instead, we generate the data from the actual equations that govern our world. And this is now a whole new superpower for humans that we just never had before as a species until three years ago. It was not possible to calculate with any kind of accuracy how one molecule that's meant to be, for example, good for Alzheimer's or Parkinson's or brain cancer or pancreatic cancer would fit, would lock in to that receptor. Now, as of just 36 months ago, with the collaboration of many people in this room actually and on this panel, it is now possible. Yeah. So LQMs are really about another key tool in the tool chest, complementing LLMs, sitting alongside the many LLMs out there, but a core tool that LLMs will interact with going forward in the future.
So you mentioned data, and one of the things that we talk about is the importance of data and how the world may be running out of data to train these. So how should leaders think about the proprietary data generation they have and retention as AI models become more commoditized?
Well, Peter, we all talked about Gen AI. Gen AI is a very real and big thing. But now let's talk about Gen Data, generative data. How do you generate data? Well, when you want big data sets that govern our world, of either quantitative finance to look at portfolio optimization, or again, a new material to build a car that would be lightweight, make the car lighter weight so it's more fuel efficient, we can't turn to the internet for that data. That is not where we're going to get it. Instead, we generate the data from these equations. Those become interesting. Heisenberg, Schrodinger, yeah, exactly. That's where quantum comes in. When people hear the word quantum, I know people get scared, sorry, but quantum is not just about quantum computers. It's about today on GPUs from Alphabet, TPU from Alphabet, GPU from NVIDIA and others. There's many others coming out. We're able to run the quantum equations, that is the equations that actually govern our world, at scale on these GPUs and TPUs. This is new. These chips, by the way, were never designed to do this in the first place. And it's the human ingenuity that is quite fascinating that came up with this. You know, you did an incredible job spinning Sandbox AQ, A is for AI, Q is for Quantum, just for you to remember that, out of X at Google. And you have real products today. Can you just very quickly give us a sense of what Sandbox's products are that you're...
Sure. I'll give some real case studies here at FII. We have, I think, Paul Hudson, the CEO of Sanofi, one of the largest pharma companies in the world, and our two companies actually are announcing just today. We were just on CNBC earlier announcing that we're working together to apply this AQ, to apply this LQM, this quantitative AI, to accelerating biomarker development. That is development of diagnostics that will help all of us, and drugs as well. So that's a real-world case study of how we're using it there. We're also working with companies like Dow and others on the chemical side to say, how do we get new catalysts that could bring us new materials. When we think about the hydrocarbon space, think about the output of a refinery. We say, okay, the high-octane fuels, very profitable, jet fuels, kerosene, profitable, naphtha, but how about the bottom of the stack, Peter? That bottom of the stack, petroleum companies don't make much money on. It's sold to smelters and others and burned in the air. And so when we think about what we can do with AI that understands chemistry, we can now take the bottom of the refinery stack up-value it to things like carbon composites. Carbon composites are what make a McLaren car so powerful and yet so light, Aston Martin, Ferrari, and others. But it's not accessible technology to all cars. By working with the hydrocarbon producers, we can say, how do we now make this kind of technology available on a democratized basis? So these are real-world impacts, be it in biopharma, in chemicals, in fuel, in energy. And Elon was talking about energy and energy storage. The battery technology we have today, Peter, is 45 years old, and we need to leapfrog that. And this kind of AI complementing LLMs, this is where it's going to come from.
Let's go to your chairman next. Can we thank Ruth for spinning Sandbox out? Thank you, Ruth. Good job, Ruth. And what a financial success so far.
Absolutely, it's been amazing. I've never seen a company scale revenue as quickly as you have. It's extraordinary.
Dr. Eric Schmidt, former CEO and chairman of Google and Alphabet. There's nobody on the planet that I think holds your stature in this field. Thank you for all that you've done. The path to AGI. We talk about AGI, it's this blurry line of what is AGI. So do you have a definition for it and why is it so exciting? Why is, you know, Sam saying I'll spend $50 billion, whatever it will take. Talk about that, please.
It's worth understanding what will happen in the next five years, please. And the work that these guys highlighted and others on the panel will highlight are going to generate savants, that is specialized assistants that will work with you in whatever you do. An artist savant, a music savant, a physics savant, and so forth. Those savants will work with you to do research, drug discovery, solve problems, can do many, many things. Why five years? Because today we have all the components necessary. We have planning, we have the ability to do forward and backward reasoning, we can do stepwise reasoning, we can go against objective functions that are much more complicated than they used to be before, and we can generate arbitrary code. In the industry, it is believed that somewhere around five years, no one knows exactly, the systems will begin to be able to write their own code. That is, they literally will take their code and make it better. And of course, that's recursive. I thought that is essentially a change in slope. If you're going like this, all of a sudden it goes like that. It's reasonable to expect that within six to eight years from now, so 2030, right after that, maybe 2032, under current growth rate, it will be possible to have a single system that is 80 or 90% of the ability of the expert in every field. So 90% of a physicist, 90% of the best physicist, 90% of the best chemist, 90% of the best artist. When you have such a thing, you have a non-human that is effectively smarter than any human because no human can dominate all of those fields. Maybe Leonardo da Vinci could, but certainly not now. We don't know what happens when such a thing exists, but we know that that race is really important. There are many, many things that this thing, we don't know what to call it except an AGI, could do. For example, it could analyze cyber threats and develop new ones, or it could protect against them. It could come up with new biological solutions, good ones or bad ones. So there's both a national security component and a worry, but also a notion of a huge step change in human efficiency and productivity. I will assert that we as humans are not ready for the arrival of this. We're just not ready for it.
Yeah, I can imagine all future Nobel prizes in math, physics, chemistry, medicine coming from AI systems. And I should say, by the way, that if you go to Formula 1, you enjoy watching the humans drive around the track. Now, it's obvious that automated cars, way more cars, and so forth, could drive faster, but we wouldn't find that an interesting sport. So here we are in the land of golf. Maybe there will be a robotic golfer that will beat all the LIV top golfers, but we won't look at the robot, we'll look at the humans. So we poor humans will take pity on ourselves. Very biased, aren't we, right? That will be how we choose to entertain ourselves. Very briefly, but this takes an hour worth of conversation, what do you consider the most dangerous outcome around AGI or artificial super intelligence?
There's a huge issue around proliferation, and right now we don't fully understand the rate of proliferation of the mid-tier models and open-source models. There's a consensus at the moment that models that cost less than $100 million to train are probably not that dangerous, and ones that cost more than $100 million are more dangerous. I have no idea why we believe that, but that's the number. So what will happen is at some point there will be proliferation of inexpensive tools that can do significant damage. The most obvious one is in biology.
Yeah, thank you. Something you know a lot about. And I do think that we're going to see extension of the human health span because of AI. We live longer, but it'll be more dangerous in some situations. Hopefully we can moderate that. Ruth, a pleasure. Ruth is President and CIO of Alphabet and Google. You met her this morning. Ruth, there's, I want to quote this and then ask you about it. We've all witnessed companies making grand AI for good pronouncements, for sure. But when push comes to shove and financial performance dictates decision-making, often less lofty ideals are what shareholders demand. So how do you think, you know, I think the world of Google. I think Google has transformed the world in extraordinary ways. How do you think of AI for good versus AI for financial gain in the position you're in and Google as a company?
