Ex-Google China President on How China Is Shaping the Future of AI w/ Kai-Fu Lee | EP #134

In this episode, Kai-Fu and Peter discuss 01. AI’s growth, Chinese entrepreneurship, and how open-source AI can impact the world. Recorded on Oct 19th, 2024 Views are my own thoughts; not Financial, Medical, or Legal Advice. Kai-Fu Lee is the Chairman and CEO of Sinovation Ventures, a venture capital firm he founded in 2009 that manages over $2 billion in assets and focuses on fostering the next generation of Chinese high-tech companies. In 2023, Lee launched 01. AI, a startup that built AI applications tailored for China, including Wanzhi, a productivity assistant similar to Microsoft Office 365 Copilot. As a leading figure in artificial intelligence, Lee continues to shape the tech landscape in China, where he recently noted that Chinese AI models are only 6 to 9 months behind their U. S. counterparts. He has authored influential books such as AI Superpowers (2018) and AI 2041 (2021) and was named one of Time Magazine’s 100 most influential people in 2013. Earlier in his career, Lee held prominent positions in tech, including Vice President at Google, President of Google China (2005-2009), and Corporate Vice President at Microsoft (2000- 2005). He also founded and led Microsoft Research Asia from 1998 to 2000. Lee remains a highly respected thought leader in AI and continues to drive innovation in the field. Beago: https://www.beago.ai/ 01. AI: https://www.01.ai/ Kai-Fu’s X: https://x.com/kaifulee Kai-Fu’s LinkedIn: https://www.linkedin.com/in/kaifulee/ Pre-Order my Longevity Guidebook here: https://longevityguidebook.com/ ____________ I only endorse products and services I personally use. To see what they are, please support this podcast by checking out our sponsors: Get started with Fountain Life and become the CEO of your health: https://fountainlife.com/peter/ AI-powered precision diagnosis you NEED for a healthy gut: https://www.viome.com/peter Get 15% off OneSkin with the code PETER at https://www.oneskin.co/ #oneskinpod Get real-time feedback on how diet impacts your health with https://join.levelshealth.com/peter/ _____________ I send weekly emails with the latest insights and trends on today’s and tomorrow’s exponential technologies. Stay ahead of the curve, and sign up now: Blog _____________ Connect With Peter: Twitter Instagram Youtube Moonshots Learn more about your ad choices. Visit megaphone.fm/adchoices

Speaker names marked ? are inferred from speaking time and the show’s known hosts — they are not voice-verified.

• 0:00Speaker 00

  I always remembered when I joined Google, Larry Page came and talked to us and he said, "The ultimate search engine should be one where you ask a question and get a single correct answer."

• 0:11Peter Diamandis?

  You've been at Apple, at Microsoft, President of Google China. I love Google, but they're an engine that has been powered by advertising. How long is that going to last? Do you think that's going to survive?

• 0:23Speaker 00

  I think there needs to be a business model flip at some point, and Google will fail to do that, just as any innovator facing innovator's dilemma.

• 0:34Peter Diamandis?

  In your last book, you said something like, "The US will lead in breakthrough innovations, but China is better in execution." What does that mean?

• 0:41Speaker 00

  The major technology breakthroughs were almost invariably invented by Americans. Now, when it comes to execution, it requires additional capabilities.

• 0:56Peter Diamandis?

  Before we get started, I want to share with you the fact that there are incredible breakthroughs coming on the health span and longevity front. These technologies are enabling us to extend how long we live, how long we're healthy. The truth is, a lot of the approaches are in fact cheap or even free. And I want to share this with you. I just wrote a book called Longevity Guidebook that outlines what I've been doing to reverse my biological age, what I've been doing to increase my health, my strength, my energy, and I want to make this available to my community at cost. So longevityguidebook.com. You can get information or check out the link below. All right, let's jump into this episode. Hey Kaifu, good morning to you.

• 1:37Speaker 00

  Hi Peter, good to be back.

• 1:39Peter Diamandis?

  Yeah, it's great to see you, my friend. We're on flip sides of the planet. I can't wait till we're having this podcast and we're in different parts of the solar system. That'll be, that'll be fun. We need faster than light travel. You know, I, I have the fondest memories of coming and visiting you in China, in your different locations. And I have to say, you know, my takeaway from, I used to come to China every year. You would host a number of the Abundance 360 members I'd bring with me, super gracious. And I remember my takeaways were...

• 2:20Peter Diamandis?

  Were that number one, there was incredible work ethic from Chinese entrepreneurs.

• 2:30Peter Diamandis?

  And I remember you describing it, the work ethic as nine nine six. Is that still, is that still the saying there?

• 2:39Speaker 00

  Yes, yes, definitely.

• 2:41Peter Diamandis?

  Yeah, and a good, a good job was nine AM to nine PM, six days a week. That was a good balance of life.

• 2:49Peter Diamandis?

  And the second thing I remember as a, as a key takeaway was, at least this was, you know, I don't know, decade ago, and it's been some time, but that, you know, in the US entrepreneurs see their, the marketplace as the US, maybe Europe. In, in China, the entrepreneurs saw the marketplace as China and Europe and the US. It was a much more global view. And I am curious if that's still the, the view in, in entrepreneurial world in China today.

• 3:32Peter Diamandis?

  Cause I've heard, you know, and I'm seeing comments where, you know, we're sort of like going into two parallel universes where products develop in China or staying in China and products develop in the US or staying in the US. How do you see that? I'm curious.

• 3:47Speaker 00

  Uh, yeah, I think a lot of the B two B is becoming very much a parallel universe. It's hard to sell B two B, especially given export control and geopolitical issues, especially in the deep tech areas, which you and I care deeply about. B two C areas are much easier. Americans use. Shein and Temu and TikTok. And of course, Chinese use a lot of American products, Mac, Apple, Windows and so on. So that hasn't been as affected. I would also say the in pursuit of scaling law, AGI, Gen AI.

• 4:30Speaker 00

  While the efforts are separate. The collaboration, or at least the sharing of ideas are pretty strong. In paper publishing, open source. Of course, with the notable exception of OpenAI and now Google, who don't publish. But they don't do it for geopolitical reasons. They don't want the competitor to see.

• 4:52Peter Diamandis?

  Yeah, that is fascinating. We'll get we'll get into that because you've taken very much an open source. Focused mindset. And there have been many, many that do. I I have to ask a question. So you've seen you've been at Apple, at Microsoft, President of Google China. You've seen so much and in innovations, I mean.

• 5:18Peter Diamandis?

  You're managing what like three billion in investments, thereabouts. So you've seen it all. I mean. And of course, your two excellent books, which we've we've discussed on my stages before.

• 5:32Peter Diamandis?

  I am curious about something and I'd love your opinion if you're willing. Which is, I love Google. I love Google for many reasons, what they've done, their investments, their mindset of driving breakthroughs. But they're an engine that has been powered by advertising. And they've been able to reinvest that. But what happens now when Gen AI is giving single solutions and the ad powered models? Is. Yeah, how long is that going to last? Do you think that's going to survive? Or is there going to have to be a business model flip for Google?

• 6:12Speaker 00

  I think there needs to be a business model flip at some point. And Google will fail to do that, just as any innovator facing innovator's dilemma. Because Google is critically dependent on the advertising revenue. And to do the flip would require going away, losing all the revenue coming in, going to an at best break even value proposition of a single answer search engine, and then building, rebuilding up the new business model, whether it's subscription or advertising. And that's going to cause a roller coaster ride mostly downwards for the stock price. And that's not something that a publicly listed company can do.

• 6:58Speaker 00

  It's kind of sad to see because Google is clearly in the best position to reinvent.

• 7:03Peter Diamandis?

  It's handcuffs, right? It's handcuffs. Yeah, your quarterly earnings reports are handcuffs. Your, your stock market, your stockholders aren't going to let you sacrifice or take the risks. And, and that's, I mean, that's why a lot of companies that should have jumped to the next generation of technology never made it.

• 7:22Speaker 00

  Right, right. It's, it's such a pity because Google clearly has one of the world's top two AI engines. And by far the world's number one search engine. And now we're talking about merging two areas in which they're the best. Yet they can't win because of this innovator's dilemma. It's unfortunate.

• 7:42Peter Diamandis?

  Yeah. I want to dive in during our conversation into what you're doing now. So for the better part of 30 years, you were one of the lead investors in technology in, in China.

• 7:57Peter Diamandis?

  But you also invested across around the world, but typically in Chinese markets. And you flipped over from being an investor now to being an entrepreneur.

• 8:09Speaker 00

  Right.

• 8:09Peter Diamandis?

  And building O1.ai. What was the causative moment? I mean, because I am curious, I mean, you've seen so many, so many entrepreneurs and so many deals. I mean, just the, you know, what I wrote down here was, you know, you've been investing in NLP tech, enterprise AI, AI-driven financial solutions, autonomous vehicles, autonomous software, a lot. But there was a moment in which you said, okay, I need to go and build a company. Why?

• 8:42Speaker 00

  Yeah, right. By the way, let's call it 01.ai.

• 8:46Peter Diamandis?

  Okay.

• 8:47Speaker 00

  We were flexible before, but now that OpenAI has taken the O1 name, we'll let them have it. We'll just be 01.

• 8:54Peter Diamandis?

  01.ai it is.

• 8:56Speaker 00

  Yeah, yeah. It really means recreating the world with 01 using AI technologies. So, yeah, I was content doing investment in the early days of AI, in the days of deep learning, computer vision, convolutional neural networks. I was super excited because in my 40-year career in AI, I basically saw two AI winters. And even the non-winter days were not that shiny. So finally, I saw, wow, this AI is becoming mature. So I was very excited.

• 9:32Peter Diamandis?

  It's not a fad.

• 9:34Speaker 00

  No, no, right. And I was in the position of being a venture capitalist. So I figured the role I should play is invest because I'm, you know, older, hopefully in some ways wiser and experienced and knows in technology and knows business. So I invested in about 50 AI companies, mostly in China, but some in the US. And they did well. We now have 12 AI unicorns. We'll soon have half a dozen IPOs from just from the AI companies, being the first investor, which is pretty rare. So I kind of got. On the ride and enjoyed watching from the backseat. Uh, the excitement that my entrepreneurs went through. So that was good.

• 10:19Speaker 00

  But then, uh, Gen AI came about. Uh, we all saw, understood Gen AI, but we didn't see how big it would be until OpenAI showed us with ChatGPT. And at that moment, I realized that, uh, I could invest uh in the area, um, in China and elsewhere. I looked at a bunch of Gen AI companies. But then I realized that, uh, to start one that late, to start a Gen AI company after ChatGPT had taken over the world by storm, would really be very, very hard for any entrepreneur because you, you, you have a, you're, you're behind by six or seven years. And if you don't already have a team or product or technologies,

• 11:04Speaker 00

  how could I fund these people? Because uh, China did not have really a lot of Gen AI companies. There was one or two at most. And I just thought, hey, if anyone could do it, maybe I could do it. It would still be a long shot, but given my, uh, years of experience and people network and understanding of the technology and business, let's give it a shot. It may be a long shot, but you know, I feel that, you know, when I'm really, really old, I'm old now, but when I'm really, really old and look back.

• 11:34Peter Diamandis?

  Don't, don't call your, don't call yourself old, my friend. You're still young and vibrant.

• 11:39Speaker 00

  Okay, I, I would, you know, when I'm eighty, I would look, I would not want to look back and say, hey, I just had a cold feet and I decided to invest. And even if I won with a great investor, building China's OpenAI and I were an investor, I would still have regrets because how could I, my, my love of my life not to participate in it this time? And also I saw that, um, if I did it, it really could work. I would have a shot. Others may have a shot too, but I thought I would have a better shot. Because I could pull, pull a great team, um. That have the right ideas.

• 12:23Speaker 00

  And and also I saw the world kind of dividing up into parallel universes and that someone needed to do a G N A I for China, otherwise the Chinese businesses and people will fall way behind and all the work that Deng Xiaoping did to bring China forward could be lost if the world had G N A I but China didn't. So I thought I would do it.

• 12:48Peter Diamandis?

  It's interesting, right, because if, well, here's a question, right, if OpenAI and all the other L L M systems had been equally available in China as they are in other parts of the world, would you still have done it?

• 13:04Speaker 00

  Uh, good chance I might not. I would then have to think about the likelihood of success, right?

• 13:11Peter Diamandis?

  Right.

• 13:12Speaker 00

  That that's the main, becomes the main factor, not as much as to helping the Chinese people in business to have a solution, even if it's not as good as OpenAI. So I it might be fifty fifty in that case. But OpenAI decided not to make it available to China, so that was tough.

• 13:31Peter Diamandis?

  I mean, I I think everyone would agree, every country and every human is going to need to have access to this infrastructure called G N A I. It's going to be your, it's going to be your consultant, your doctor, your educator, your everything. And it would be like, it'd be like denying a country access to oxygen or electricity.

• 13:56Speaker 00

  Yeah, that's why when we founded zero one dot A I, our vision statement was to make A G I beneficial and accessible. And that's very similar to OpenAI's vision, make make G N A I beneficial to humans, but we added accessible. We wanted to stress the point that we want everyone to access it, no matter where they live, whether nationality, their income level, et cetera.

• 14:24Peter Diamandis?

  It it is quite interesting that you've gone the open source road.

• 14:33Peter Diamandis?

  Can you speak to that a little bit? I mean, I just had Ray on the podcast and we're talking about open source and I had the Mozilla Foundation CEO on the podcast talking about open source.

• 14:48Peter Diamandis?

  Why, why aren't all companies going open source? And what was your motivation for open source?

• 14:55Speaker 00

  Right, well, I think a smaller company, a newcomer, really needs the open source community. Because having a hundred people trying to compete with a thousand people at, you know, Google and starting 10 years late is a lose, definite lose proposition. If you don't somehow work with the open source community to help each other make progress. So that's just out of a practical consideration. Secondly, we saw a lot of good work in, in open source. From universities, from Meta, from Microsoft, from Nvidia.

• 15:36Speaker 00

  And we couldn't start our company without these, especially Nvidia's Megatron, Microsoft's DeepSpeed. Without these, it would have taken us much longer to start the company. So, so we said, well, if we're going to take from the open source community, well, we should rightfully give back. Every model we make, except the most frontier model. So we would keep closed source, the very, very best model that we make. Everything else would become open source. And that is a way of giving back. I know some companies open source everything, but we, we do, we can't do that. We do need a business model and some commercial advantage.

• 16:19Speaker 00

  And also, we decided the way we would do open source is through the Apache license. We would not be asking people, you know, to get our approval for commercialization, nor would we put a limit that if you started making too much money or have too many users, we have to sit down, talk commercial terms. We want everyone to have what we have. Just like we took from Nvidia and Microsoft, they're open source. They didn't ask for anything back and we thought we also should not.

• 16:50Peter Diamandis?

  So you're putting everything up on, on Hugging Face?

• 16:54Speaker 00

  Yes.

• 16:54Peter Diamandis?

  For, for people to access?

• 16:56Speaker 00

  Yeah.

• 16:56Peter Diamandis?

  And GitHub?

• 16:57Speaker 00

  Yeah.

• 16:58Peter Diamandis?

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• 19:06Speaker 00

  Yeah, I think, you know, if we look at the global GDP, it's interesting to note that the PC era brought about an uplift of the global GDP. Then it kind of saturated, then mobile brought another, then it kind of saturated. Now, there are many factors to the GDP. I don't claim PC and mobile were the only factors, but they were clearly uh major factors that greatly enhance productivity and change the way we worked. We as humans do more or less the same things for thousands of years. We work, we play, uh, we communicate, we learn. Um, but the way in which we do them change from PC to mobile. And I would say with AI, it would be in some sense a similar change.

• 19:50Speaker 00

  It would be a new platform that rather than infusing a computer on every desktop or allowing anywhere, anytime uh mobile access, we would make super intelligent AI in every app. And we would have apps that are super intelligent, that could do work for us, that could give us answers. And I think that is clear that this is not only the third um platform revolution, third um productivity revolution, but by far the largest one because of how much value uh it adds.

• 20:26Peter Diamandis?

  There was something you've said uh that I that you wrote about in your last book. I thought was fascinating. It's an approximate quote, but you said something like the US will lead in breakthrough innovations, but China is better in execution.

• 20:40Speaker 00

  Yes.

• 20:40Peter Diamandis?

  Fascinated about that, uh, please. Elaborate, what does that, what does that mean for entrepreneurs here in the US, entrepreneurs in China?

• 20:49Speaker 00

  Yeah, I think, you know, we've seen this through mobile revolution and through the early days of deep learning and computer vision, AI revolution, that the, the major technology breakthroughs were almost invariably invented by Americans. And that's because of the great university system, research labs, and the culture that, that encourages and rewards risk-taking and innovation. And, and the amazing early-stage venture community that allows new ideas to be funded. And also the patent system, all of that basically started in the US. And no, no wonder that US is best at discovering new technologies

• 21:34Speaker 00

  in the phases where new ideas were coming out. Now, when it comes to execution, it requires additional capabilities. I think the breakthrough innovation is less important, but more important would be figuring out what to build and being focused on building it and ask no questions and execute and work incredibly hard. In particular, asking really, really smart people to say, well, you're not writing papers. You're writing code to get this out there. And to view success as a success of a product or a business, not a success of a paper or an award.

• 22:18Peter Diamandis?

  So it's, it's the notion that a lot of AI researchers today are more concerned about the sightings they get on their paper versus making something that is generating revenue and users. And I, I see that. It was, it was fascinating, of course, that, you know, OpenAI turned on ChatGPT and made a very successful first-user product. But I've criticized, and many have, Google for not having an app, right? You have to do a lot of steps between something you're doing to get to a Gemini, you know, Gemini search.

• 23:03Peter Diamandis?

  So you think that the Chinese entrepreneurs are better at execution and better at creating something that's a beautiful user interface? Is that the primary?

• 23:12Speaker 00

  Yeah, but that interface is not just the artistic beauty, but rather using all the principles, again, invented in Silicon Valley, the lean startup, zero to one, that is building MVP, doing A/B tests and tweaking. And really, it's the availability of the internet as an instrumentation that allows entrepreneurs to no longer have to be Steve Jobs. You don't have to know what the user's thinking. You just test it and tweak it, and if you work hard around the clock and measure the right things, improve the right things, you will evolve to the right user interface. And that's where hard work becomes the oil, right?

• 23:57Speaker 00

  That makes the engine work in building a good app. It's not just brilliance and insight. And brilliance insight would have favored the American entrepreneur.

• 24:07Peter Diamandis?

  I mean, there's another thing going on with a lot of US AI companies, which are called the race to AGI. And I'd like to show a short video of a statement by Sam Altman. Let's go and pull that up and what you think about this.

• 24:31Speaker 02

  Whether we burn 500 million a year, or 5 billion, or 50 billion a year, I don't care. I genuinely don't. As long as we can, I think, stay on the trajectory where eventually we create way more value for society than that, and as long as we can figure out a way to pay the bills, like, we're making AGI. It's gonna be expensive. It's totally worth it.

• 24:51Peter Diamandis?

  So what's your reaction to that? How do you think about that?

• 24:56Speaker 00

  Well, I I think we all aspire to build AGI. Uh, I know you do. I've wanted I've wanted it for forty plus years that I've been in AI. Um, and we're very lucky to be at the point where scaling law appears to still be working. Meaning that if you throw ten times more computing at the AGI problem, it gets smarter. So it's tempting and logical to want to keep throwing ten times more compute every one and a half years or so. Of course, where this runs into some issues is, you know, is it a good investment? Once you're putting fifty billion dollars into it, uh, are you sure there would not be diminishing returns?

• 25:38Speaker 00

  Um, and also, are you too focused on the breakthrough AGI? And not enough on the application ecosystem?

• 25:46Peter Diamandis?

  Yeah, is it a bunch of researchers geeking out uh versus people building building businesses?

• 25:52Speaker 00

  Yeah.

• 25:52Peter Diamandis?

  You know, uh, there's a there's a another chart here I wanna bring up. And the question is, as I watch the cost of uh of models, uh, in particular, uh inference models and such plummeting in price.

• 26:11Peter Diamandis?

  And the question is, is it a race to the bottom? Uh, is there? I mean, it's fascinating that the single most powerful technology in the world is effectively free. So uh, this is your chart, Kai Fu. Tell me what this means for you.

• 26:26Speaker 00

  Yeah, uh, actually, I wouldn't quite draw that conclusion about effectively free. It's eventually free. So given a particular technology, let's say G P T four in this chart. Um, it started, it was launched in May twenty twenty three at seventy five dollars per million tokens. And today it's at only four dollars and forty cents. And it's using a better version, G P T four O, which is smaller, faster, better, and much cheaper. Roughly coming down ten times a year. And this is a good thing. This is the market leader, reducing price. Um, and it's reflecting the lower costs that they've accomplished. Because GPU costs have come down. It's reflecting better technologies.

• 27:11Speaker 00

  Because you can get better performance with a smaller model and Just as we had Moore's law, um, I think the scaling law is a law because we do seem to see every year and a half or so, it gets better. And also the cost comes down ten X per year. So, so I, I, I would uh see a conclusion of wow, this is going great. Uh, we just can sit around and then um all the things we want that are too expensive will become cheap. Uh, but I would also have a word of caution because we are basically in the stratosphere. Going at turbo speed.

• 27:54Speaker 00

  So one year is a really, really long time. Just think two, one years ago, we had no idea any of this was happening, right? One year ago, we were still complaining. Chat G P T was hallucinating, didn't know anything that's recent and all that's been changed. So this industry is moving in one year. What mobile probably would have taken seven or 10 years to do. So a year is a long time. And I would also argue that even at four dollars and forty cents, G P D four O is way too expensive for applications. Um, for example, let's take a look at, uh, let's say A I search, the example we talked about earlier.

• 28:34Speaker 00

  I think if you took G P D four O and used it to build an A I search. Uh, you would end up basically uh paying um something like ten cents or more uh per search query. And Google only makes one point six cents of revenue per search query. So you'd be on a fast road to bankruptcy due to the cost of G P D four O. And that's not even counting. You have to build a search infrastructure. That's just the L L M cost. It's just way, way, way, way too high. Um, I think more, more precisely, um, it is, um, yeah, around ten cents.

• 29:14Peter Diamandis?

  So to summarize, you find yourself a situation where Chat G P T is not available to China and.

• 29:23Peter Diamandis?

  And the people of China need generative AI. And you look around and say, who better to do this than me? And you've got a huge amount of experience. So you jump in and you create zero one dot AI. So what's the background there? And one of the products you showed me was BGO, which is beautiful and fast and apparently cheap. So let's talk about the history real quick of zero one dot AI. And then let's jump into BGO.

• 29:52Speaker 00

  Yeah, in zero one dot AI, we realized we were way behind OpenAI. We were perhaps seven years behind when we founded it seventeen months ago. It was only seventeen months ago, and I didn't have a team of engineers. I had to use the first four or five months to hire people. But but even with that, basically, the playbook that I took was from my own book, AI Superpowers. We said, we're not going to beat OpenAI at their own game. Can we build things very quickly? Sometimes the most challenging part of building something is proving a an unknown idea to be feasible, which ChatGPT had done, which GPT four and GPT four O and now GPT O one have done.

• 30:38Speaker 00

  And that with the the leaders in research demonstrating that something is feasible. That is all we need to know. Because when someone builds a nuclear bomb or puts a man on the moon, for others to do it is much, much easier because empirically it had been demonstrated. So we were just saying now we just have to be more diligent, read more papers and work harder around the clock and leverage the strength that we have as Chinese entrepreneurs and engineers and just go nine nine six or longer if needed until we get to products that are competitive and and and efficient, right?

• 31:20Speaker 00

  Because we probably can't win on accuracy, but can we make the equally accurate product much cheaper? Cheaper to train, cheaper to inference, cheaper to train because we're poor. We don't have the fifty billion or five billion dollars Sam Altman talked about. And cheap to inference because we want apps to run lightning fast. And and that is what it would take for adoption because you want fast and low cost of inference. And in the last seventeen months, we achieved all that.

• 31:52Peter Diamandis?

  So you just said something that's fascinating, which is access to compute. I mean, I think everybody imagines China has huge, infinite resources, but it hasn't been the case in terms of GPUs. And does that scarcity of resources cause you to think differently? And not be lazy or to be more innovative?

• 32:19Speaker 00

  Yes, one is just the difficulty of acquiring GPUs, given the US restrictions. But also, we only raised a small amount of money. So we couldn't afford, you know, 10,000 GPUs anyway. And basically, everything we've done, we did production runs on only 2,000 GPUs, which is a small fraction of what the US companies are using. Elon Musk just put together 100,000 H100s. And OpenAI, Beagle have even more. It's impressive, but we have basically, you know, less than 2% of their compute. But I'm a deep believer in efficiency, power of engineering,

• 33:05Speaker 00

  small teams working together, vertical integration. And I'm a strong believer that necessity is the mother of innovation. So I have a team. I told them, all we got is 2,000 GPUs. We don't have 100,000. I don't need you to, you know, invent the GPT-5. I want you to take a look at GPT-4, GPT-40, and can we match that in 12 months, in five months? And in the process of making it, all we have is 2,000 GPUs. You don't have a lot of compute. And when you make it, by the way, if you train it very efficiently, can we also have an inference that's very efficient,

• 33:50Speaker 00

  costing only a few percent to run it in apps?

• 33:55Peter Diamandis?

  So talk to me about your product, Beagle. By the way, I asked you earlier how to pronounce it and where the name came from. And I think it's worth repeating so people remember it better. Beagle. And Golden Retriever, right? It's a dog name?

• 34:13Speaker 00

  Yeah, yeah, yeah. I mean, we all know the name Beagle, B E A G L E. It's a cute little dog and Golden Retriever. But when the two of them make a little puppy, that puppy is called Beagle, B E A G O. And Beagle, Beagle, B A G L E is very good at hunting. And Golden Retriever is good at retrieving. So it's an apt name for an AI search engine. But I should also point out that Beagle is not zero one dot AI product. Uh, it is a product that I did venture build. And it's actually an American company. And it uses a model very similar to the model that zero one dot AI has built.

• 34:54Speaker 00

  Super fast, super cheap, thereby thinking that AI search could be reinvented.

• 35:01Peter Diamandis?

  So do you want to talk about zero one or you want to jump into a little bit about Beagle? Which you prefer?

• 35:07Speaker 00

  Well, yeah, let me start with zero one, then we go into Beagle. So, um, yeah, zero one, I think, you know, uh, this May, we came up with a very good model. Uh, called E Large. And E Large was a bit behind G P T four O, which came out one day later. And, uh, uh, we, we at the time were ranked number seven. And which is great, number seven model, number four company, just behind OpenAI, Google and Anthropic, something to be really proud of.

• 35:37Peter Diamandis?

  Pull up that chart for a second. Go ahead.

• 35:40Speaker 00

  So that's the May chart. Uh, but I want to talk about what just happened, um, in October. Because over between May and October, lots of models emerged. E Large was no longer competitive. And but we had been working based on what I described as, uh, working super hard, building to match G P T four O. And maybe even be faster. And that was accomplished in October. That we kind of took revenge on the G P T four O, uh, May version, which you can now see on this chart. Uh, as just below us as number seven in the world. We just beat them by a little bit, so. So this is a case in point where we saw G P T four O, we saw what it could do.

• 36:25Speaker 00

  And we knew it could be done and we said, let's go do it. And basically with no hint on how it's done, we figured out how how to do it ourselves. I'm sure the methods are different. But we did match their performance in just five months. Of course, in these past five months, other great models came out, including new version of G P T four O. And Grok and others. So we came out in October with a tiny model called e Lightning. Because we wanted to be lightning fast. This is a much smaller, much faster model. But it became number six model in the world in number three company. And we also surpassed Anthropic this time.

• 37:06Peter Diamandis?

  How big was your team building this?

• 37:08Speaker 00

  Uh, the pre-training team is basically three or four people. Uh, the post-train team was maybe uh, ten people, the infr- that's sorry, the infrastructure team. Maybe another ten people. So it's a twenty to thirty person project. Uh, it's pretty small. And um, the thing we're most excited about and proud and unique about is that we trained this model. The pre-train um, only costs a little over three million dollars. And this is uh, this is three percent of what G P T four costs to train. And we actually beat G P T four in performance. And the inference cost is very, very low. It's around ten cents per million tokens.

• 37:52Peter Diamandis?

  And we sell it. Let's go to that chart. There's a chart here. It looks at inference cost over time. Which is super impressive as well. So explain this is uh, explain this chart here, please.

• 38:08Speaker 00

  Uh, yeah. So back in June, we were a dollar forty per million tokens cost. Which is, which was a lot lower than G P T four O at the time, which was, I think about ten dollars uh, price. And then by September, we came up with a number of breakthroughs, including new ways of doing mixture of experts and uh, better. And ideas of uh KV cash management, etc. So we we had really a big breakthrough not in in launching the e-lightning. Uh because e-lightning was one-fourteenth the cost of the previous e-large model. And at ten cents per million tokens. And and G P D four O had also come down in price, but it was four dollar forty cents.

• 38:52Speaker 00

  So to a developer.

• 38:55Peter Diamandis?

  Yeah, just to just to give folks who are just listening and not watching this um back in June. Um e-lightning was a buck forty per million tokens. Today it's at ten cents per million tokens and next June it's expected to be three cents per million tokens. It's a fifty-fold decrease and comparing to G P T four O, which uh was at, you know, four and a half bucks per million tokens. So I mean, we're we are seeing this precipitous um efficiency gains over time.

• 39:31Speaker 00

  Yes, so you know, another way to look at it is G P T four O dropped ten X in one year. Are we're we actually succeeded in dropping fifty X in the past uh in the past year. So so we we feel we now have the most competitive, lowest-priced um lightning engine. Our cost is ten cents per million tokens. Our price is only fourteen cents per million tokens. So we're also not taking a big margin. So if you look at the performance of G P D four O and e-lightning. Uh their new version is a little better, not a lot, only a little better, but they're four forty and we're fourteen cents.

• 40:12Peter Diamandis?

  Incredible. Um is it all algorithmic gains?

• 40:18Speaker 00

  Uh the um there are a number of differences. I think we actually, I'm sure we use different algorithms because we don't know what they use. Uh we came up with our own.

• 40:28Peter Diamandis?

  But I'm saying the improvements you're the improvements you're making over time, are they al- algorithmic gains there?

• 40:34Speaker 00

  Yeah, our performance gains going from E Large to E Lightning are using a new mixture of experts' model and new ways of modeling and also getting more high-quality diverse data and also having super-fast infrastructure so we can train multiple times to learn more and to do research. The efficiency gains were also mostly by the super-fast mixture of expert model, but also by some inference advancements in terms of KV cache memory management.

• 41:13Speaker 00

  As an example, you know, the way we do our next-gen model design is not go invent a bunch of new things and go make them fast, but it's from the get-go, they have to be fast. So we would first ask the question, where do we project in four months, which is our product cycle, the best chips might be? And how do we get those chips to inference really fast? And oh, these chips with a lot of HBM, which is high-bandwidth memories coming out, and can we turn the inference problem from a compute problem to more of a memory-bound problem? Then should we rewrite our inference engine?

• 41:55Speaker 00

  Then how much RAM can we put on as a second-layer memory? How much SSD can we put on? Can we construct a computer four months from now that is super-fast? I mean, it's not a, it's made out of standard parts, but still it has a lot of memory. Then we put the memory-bound inference engine on top. Then we asked the modeling team, in four months, what model can you build that fits perfectly into this box? Not too large, not too small, use up all the memory, but don't go too far, and use power of two. So a lot of constraints for the researchers, which some companies might face reluctant researchers, but in our case, we are all building a product.

• 42:39Speaker 00

  We're one team in one direction. So we all took, each team took the order and then marched ahead. And out came a very accurate and a super-fast model, thanks to this vertical integration from model to inference engine, down to the hardware and memory.

• 42:56Peter Diamandis?

  I love the old saying, you know, innovation comes from thinking in a smaller and smaller box when you put constraints on on yourself. Right. Everybody, I want to take a short break from our episode to talk about a company that's very important to me and could actually save your life or the life of someone that you love. Company is called Fountain Life and it's a company I started years ago with Tony Robbins and a group of very talented physicians. You know, most of us don't actually know what's going on inside our body. We're all optimists until that day when you have a pain in your side, you go to the physician in the emergency room and they say, listen, I'm sorry to tell you this, but you have this stage three or four going on. And, you know, it didn't start that morning.

• 43:41Peter Diamandis?

  It probably was a problem that's been going on for some time, but because we never look, we don't find out. So what we built at Fountain Life was the world's most advanced diagnostic centers. We have four across the US today and we're building 20 around the world. These centers give you a full body MRI, a brain, a brain vasculature, an AI-enabled coronary CT looking for soft plaque, DEXA scan, a GRAIL blood cancer test, a full executive blood workup. It's the most advanced workup you'll ever receive. 150 gigabytes of data that then go to our AIs and our physicians to find any disease at the very beginning when it's solvable. You're going to find out eventually.

• 44:26Peter Diamandis?

  Might as well find out when you can take action. Fountain Life also has an entire side of therapeutics. We look around the world for the most advanced therapeutics that can add 10, 20 healthy years to your life and we provide them to you at our centers. So if this is of interest to you, please go and check it out. Go to fountainlife.com/peter. When Tony and I wrote our New York Times bestseller, Life Force, we had 30,000 people who reached out to us for Fountain Life memberships. If you go to fountainlife.com/peter, we'll put you to the top of the list. Really, it's something that is, for me, one of the most important things I offer my entire

• 45:09Peter Diamandis?

  family, the CEOs of my companies, my friends. It's a chance to really add decades on to our healthy lifespans. Go to fountainlife.com backslash Peter. It's one of the most important things I can offer to you as one of my listeners. All right, let's go back to our episode. You know, one of the conversations over the last year is we're running out of data to really improve the models. What do you think about that? Do you believe that to be the case?

• 45:35Speaker 00

  I believe it has a bit of a dampening effect on how much we can expect scaling law to continue. But I do think we have ways of getting more data, just not as more as easily as it used to be. Because the fact is that humans were smart to create language as something that could be passed on over millennia. And we have so, once we started doing Gen AI, we took all the language data and put it up. Now, every year, we're generating more language data, but clearly way less than the total collection. So that is incrementally much, much slower. But on the

• 46:16Speaker 00

  other hand, we have video data, we have audio data, and also, we're going to have embodied AI gathering spatial data. So those and also, we have ways of creating synthetic data, which is not as good, but better than not using it. So these are the ways I think we're trying to compensate for the fact that most actual data has been used. If the outcome is, I think we will still get more data benefit, just not as much as we used to get.

• 46:48Peter Diamandis?

  Yep. Let's jump over to to Bego. So it's a US company.

• 46:54Speaker 00

  Um, right.

• 46:55Peter Diamandis?

  And uh, why was it started in US? Uh, is it something that was funded out of innovations? Uh, um, what's its mission? Talk to us about it.

• 47:05Speaker 00

  Right, uh, as I was building up zero one dot AI, I ran into a lot of brilliant American engineers and researchers. Uh, they want to stay in America, but they liked my vision. So I said, why don't I help you guys venture build a company? So they build a company called Rhymes Technologies. And uh, they build an excellent model, um, very similar in uh, approach to, to the model in zero one dot AI. And on that model, they added a lot of their unique multimodal and launched ARIA, which is an open source multimodal. Um, engine, which is one of the best in the world, but only three point five feet.

• 47:44Speaker 00

  So continuing the tradition that companies that I uh, helped build are very committed to open source. And on, on an advanced version of ARIA, they built an AI search engine. So I was pleasantly surprised when they showed it to me. In fact, I was blown away by uh, how good it was already. Um, and also how really, really fast it was.

• 48:07Peter Diamandis?

  What's your hope with Beagle? Uh, to, I mean, to come in and through an app, become the dominant uh, search player?

• 48:17Speaker 00

  Yeah, I always remembered when I joined Google, um, Larry Page came and talked to us and he said, "Google in this current form is not the ultimate form. The ultimate search engine should be one where you ask a question and get a single correct answer." That always kind of stuck with me. And when I venture build the, the, the, the Rhymes and Beagle team in the US, we talked about it. And we feel that the time has come. Um. And in building such an engine, we also consider, well, first, on the, on the mobile phone, it's a very small screen. So you can't have all the tabs. So doing a research-oriented, multi-link, uh, search exploration is a very, very awkward.

• 49:02Speaker 00

  And a single answer just makes so much sense. But of course, the first issue with a single answer is, what if it's not correct? What if there's hallucination or some errors? So we work very hard to maximize factuality. And Beagle is actually better in factuality than a lot of the other AI engines measured by objective third-party, um, queries. So I think those really bring us one step closer to Larry Page's dream. I think right now, the team just wants more people to try it. And they want, you know, really knowledgeable, caring, smart people to try it first and give them the most feedback.

• 49:41Speaker 00

  And it's great that I can be on your abundance program because, um, those are the types of users your, uh, readers are.

• 49:50Peter Diamandis?

  Yeah, I, um, and how do you possibly compete against, you know, companies who've got billions of dollars in this field? Um, is it just that much better in implementation, that much better in alternate?

• 50:06Speaker 00

  Uh, yeah, it's a tough challenge. That's why very few companies go after this space. The fact Prolicity gained some ground is an indication people want something refreshing. Um, and also I think, uh, we're confident about Beagle's, um, factuality and, and engagement. It also has, uh, pictures inside the search, making it more engaging and entertaining. Um, but also I think just the search players, particularly Google, and to some extent Bing, will be hesitant to, uh, replace their search engine with a one-answer engine. Because with one answer, people don't, don't look at ads and the ad revenue will plummet.

• 50:48Peter Diamandis?

  Yeah, uh, they. They will be. I have to ask the question that probably a lot of people are thinking. Is this another TikTok where it's a Chinese-owned company? And it's a way for, you know, what people's imaginations? That it's just a way to get US data into... This is a US-based company, a US-owned company, yes?

• 51:12Speaker 00

  It's actually both US-based and US-owned. So it's not, its employees are Americans, Singaporeans, Taiwanese. I myself, I'm Taiwanese. So it's not a Chinese-owned, it's quite different from TikTok.

• 51:28Peter Diamandis?

  Yeah, I get that. I've been a fan of your work, Kai-Fu, for a decade now. And I've had the pleasure of calling you a friend.

• 51:40Peter Diamandis?

  I, you know, I had Elon Musk and Geoffrey Hinton and Ray Kurzweil, you know them all, on my stage at Abundance 360 last year. And there was a fascinating question that came up. The probability that AI will be the greatest invention versus the probability that it will destroy humanity, to put it very bluntly. And I think Hinton, congrats to him on his Nobel. And Elon said, yeah, 80%, it's good, 20%, we're screwed.

• 52:15Peter Diamandis?

  Where do you come out on that? Do you have an opinion on this? And then how do we protect the downside in your mind?

• 52:25Speaker 00

  Yeah, so, well, if we assume it's like a 10-year horizon, is that reasonable?

• 52:30Peter Diamandis?

  Yeah, I think all of it's going to play out in the next five to 10 years. I think if we get through the next... My belief, I don't know if you agree with me, if we get through the next 10 years, we're fine.

• 52:41Speaker 00

  I totally agree. That's why I asked the question. Okay, so in the 10-year horizon, I would say we have a 5% chance of a disaster caused by AI. And 35% chance of a disaster caused by humans using AI. And 60%, we're good.

• 53:02Peter Diamandis?

  Okay, so now you've written an entire book on this, but I'm gonna ask you to provide some summarization. What do we do? How do we, you know, if we protect our downside, the upside's fantastic. Do you have any advice for, for parents, entrepreneurs, leaders here? How should they think about protecting our downside? Or you, if you're head of, you know, head of the world here, what, what do you do? What do you think?

• 53:33Speaker 00

  Well, I think a lot of technological risks are best addressed by technologies. Like when electricity went out, the invention of circuit breakers, when the internet went out, the antivirus. So technologies are the best likely savior to technological problems. So I would encourage more computer scientists, AI people, to not just work on the biggest next big model, or AI applications, or AI inference, or whatever, but some percentage of them, the ones who feel a responsibility, and their conscience asking them questions, then they should jump into AI safety.

• 54:15Speaker 00

  To find the various types of safeguards and, and, and guide rails that will protect us. I think, to me, that's the most important thing. Regulation comes second. I would actually feel general AI-only regulation to be in uncharted territory and potentially not constructive. I think it would be better to take existing laws, let's say laws about fraud, the laws about other blackmail, and then, and then apply, apply the use of AI to achieve those things. Laws about slander, laws about theft. So we have lots of those laws. It's, those laws are effective, understood.

• 54:59Speaker 00

  So apply them to people who use AI to break those laws, make sure the punishments are equally, if not more severe. That would create some deterrence. To start to regulate AI before it's matured. And while it's changing by governments that are slow moving seems like a futile exercise.

• 55:19Peter Diamandis?

  Yeah, governments are linear or sublinear at best. You're going to be joining me on stage in one of my panels in Saudi Arabia in just ten days or so. Excited to see you there at the FII summit. Yeah.

• 55:34Speaker 00

  Yeah, that'll be great.

• 55:35Peter Diamandis?

  One of the conversations we're going to have is around the potential dangers of ASI, artificial superintelligence.

• 55:47Peter Diamandis?

  But before I go there, you know, I would argue that we passed the Turing test many years ago and no one really noticed. It just, just, you know, it's come and gone. Will we know when we get to AGI? I don't know that there's a good definition of AGI. And I don't even know if there's a good definition of digital superintelligence. I mean, these are, these are challenges when we talk about these words. Do you agree with that?

• 56:17Speaker 00

  Uh, yeah, I think AGI was created to mean that AI could do absolutely everything humans do. And that may not be the right definition because we can't yet project when AI will have love. Or, or even when AI will be viewed as having love. Those are still some distance away.

• 56:38Speaker 00

  But I think thinking generally that AGI just means something overwhelms us. That does almost everything we do so much better, even the most challenging intellectual tasks like inventing a new theorem or something in physics or chemistry. So if we kind of extrapolate that to be the ASI or AGI, then, then I think it's highly likely that it will arrive in the next five to 10 years. And we do need to put in the safeguards.

• 57:10Peter Diamandis?

  I imagine, we just saw a, a number of Nobel prizes related to AI. I have to imagine that in the very near future, every single breakthrough in physics and math and chemistry is going to be enabled or driven or connected to some AI models doing the work.

• 57:33Speaker 00

  Yes, absolutely. Yeah, I I met an um economic economic professor uh on my recent trip to the Bay Area. And he said he already treats um GPT oh one as a graduate student. Um one that's able to challenge him and find mistakes. And he will teach and guide the student and the student learns and the two of them are uh great partners in inventing new things. So it's already happening um and so I would add also economics to be perhaps in another area to what you listed.

• 58:09Peter Diamandis?

  Do you say please and thank you to your AI when you're speaking to it?

• 58:14Speaker 00

  I do say please, I don't say thank you. I'm not sure why.

• 58:19Peter Diamandis?

  Uh it's interesting, right? I I find myself saying please, sometimes thank you uh for uh it but it's it is interesting to to get how uh it's just a very small step away for being a part of every aspect of our lives. You know, people are worried deeply about jobs. You've made some prediction about loss of white collar jobs. Um and of course, all the multimodal AI systems that you're speaking about are being embodied in robots. Uh there is a number of uh fantastic uh humanoid robot companies coming out of China. China needs robotics uh for its aging population, right?

• 59:00Peter Diamandis?

  The one child policy has significant um implications for an aging population. Um so uh talk to me about about uh your prediction about jobs, your advice. I know you wrote about this um as well in your previous uh bestselling books. But if you just have a a few, you know, how should people think about jobs? White collar jobs and then labor with humanoid robots coming. And what should, what do they tell their kids? What do they, what do they do for themselves? How do you think about that?

• 59:40Speaker 00

  Well, I think the fact is that the um, white collar jobs are going to be the first set most challenged by AI because just software can replace a lot of the routine and even non-routine work and they will do so very rapidly in the next five years. Um, that's I think now universally believed when I wrote about it in my earlier books. It was um, um, met with a mixed reaction. So

• 1:00:10Peter Diamandis?

  Yeah, people were surprised. Everybody expected, yeah, everybody expected it was gonna be blue collar work leaving first.

• 1:00:15Speaker 00

  Yes, yes, right. Because it seems, you know, uh, having intelligence in a white collar job is harder to replace, but it turns out dexterity is harder to replace because that's not necessarily solved by the gen AI um, technologies. So, blue collar work, I think going from factory to the type of caring work you talked about for elderly is um, is going to happen as the next wave. I'm among the more conservative on how fast that will happen because I think these technologies are very expensive. Not only do you need the L L M expense, but also uh, these robots um, that Elon Musk has shown are

• 1:00:59Speaker 00

  way out of um, any consumer's price range. So they're kind of um, gonna be a while because before the, the kinks are worked out, before people accept them into their families and lives and offices. And the costs have come, have to come down. So I would project it will not be that soon.

• 1:01:18Peter Diamandis?

  You say that, but you know, I wouldn't, I'm an investor in Figure AI, um, Brett Adcock's company and Figure

• 1:01:25Speaker 00

  Yeah.

• 1:01:26Peter Diamandis?

  And Tesla both are projecting around a thirty thousand dollar price tag. Let's say it's forty thousand dollar price tag. If you can lease that, right, and you lease it at three hundred or four hundred dollars per month, having a twenty four, say, seven um, employee for uh, four hundred dollars a month is, is pretty affordable.

• 1:01:47Peter Diamandis?

  Um, at least here.

• 1:01:48Speaker 00

  Yeah, I can, I can see your point. I can see your point. I, I'm still a little more cautious because, you know, especially used around the home. You know, the, uh, the, just clean my room is one thing that I would predict in three years. This robot cannot even begin to do because every room is different. Every definition of clean is different and every family home is different. Um, but there are mother, many other things, you know, like, um, uh, talking to the kids or, uh, doing more household repetitive work that, that can be done. Um, thirty thousand, I think is probably a reasonable price point for, um, middle-class America. But for China, India, other countries is still way too expensive.

• 1:02:34Peter Diamandis?

  Real quick, I've been getting the most unusual compliments lately on my skin. Truth is, I use a lotion every morning and every night religiously called OneSkin. It was developed by four PhD women who determined a ten amino acid sequence that is a senolytic that kills senile cells in your skin. And this literally reverses the age of your skin. And I think it's one of the most incredible products. I use it all the time. Uh, if you're interested, check out the show notes. I've asked my team to link to it below. All right, let's get back to the episode. Kaifu, looking forward into twenty twenty-five, twenty twenty-six, would you mind sharing some predictions of what you might see

• 1:03:17Peter Diamandis?

  in the AI world coming that would be surprising?

• 1:03:23Speaker 00

  Sure. Um, I think from the, um, product side, we will see basically every category of mobile app, uh, be incorporate AI with many of them showing AI-first disruptive types of, um, uh, changes. So in other words, in about two years, every app we use will be replaced by another app or super upgraded by the same app. I think agents will be a major technology where we delegate what we want rather than just get an answer. I think multimodal will not only be dramatically better because a lot of the super smart people

• 1:04:05Speaker 00

  are working on it and we're seeing text to video, something that would be fairy tale in my youth is now starting to happen. And I would also project that these great technology advancements will find real uses in applications. So it's not just demonstrating, hey, look what AI drew for me in a video today, but wow, I created this marketing video for $10. Those are the kinds of advances I would definitely anticipate to see from a business product and some of the known technology side.

• 1:04:40Peter Diamandis?

  You know, we just, I don't know if you saw this, we just mapped the connectome of the drosophila. Did you see that? There was a, they were able to map 50 million synaptic connections. And it's a step, a step away from a mammal. Let's see the mouse. I think we're gonna probably see the connectome of a mouse done in the next year or two. Where do you come out on the whole, you know, brain-computer interface world?

• 1:05:15Speaker 00

  I, I'm still a little bit more, I would say I'm a bit more cautious about it. I, I think this is one of the areas where there's major disagreement, how fast this is moving and what dangers it might provide. And I think we need to be cautious because it is intrusive to our bodies and it's a kind of a potential, a potentially slippery slope, right? I think people can all get on board with treatments using interfaces and get on board with non-intrusive kinds of BCI.

• 1:05:58Speaker 00

  But as we go deeper and deeper into reading our mind, creating smart tissues, and I just, I think we just have to make sure that people who are being experimented with are aware of what kind of risks they have and that, and that the downside doesn't outweigh the upside.

• 1:06:23Peter Diamandis?

  Let's wrap up with a quick look at something I've heard you speak about, which is

• 1:06:32Peter Diamandis?

  you were there at the, you know, the PC revolution, the mobile phone revolution and the AI revolution. And you've seen those progressions and I think you've modeled what the progression will be for AI. Can you, can you give me that, that summary because I think it's super useful for entrepreneurs listening. If you're looking at starting a company in the AI world, I mean, there's a lot of lessons to learn from the PC and the mobile phone world, yes?

• 1:07:04Speaker 00

  Uh, yeah, I, I think applications always follow a reasonable pattern of being replaced. Because when a new technology, new form of content comes out, you, you, you as users have to first browse them. Then you make the content, then you search and organize the content. Then the content gets richer into multimedia, multimodal. Then you can transact on the content, whether it's by payment, advertising, e-commerce or online to offline. And because these are the fundamental needs of people and the progression of apps that I talked about. Go from fewer users to more users, a small amount of usage to more usage, simple usage to complex usage.

• 1:07:47Speaker 00

  So it's a really exciting iteration of better technology enables the next step on the trajectory. More you people use it, more money is made, more entrepreneurs, more funding, more GPUs, more products, more models. So the virtuous cycle goes on and, and the most, most exciting thing is it took PC ecosystem easily 30 years to play out. Uh, it took mobile maybe 15, but we're going to see AI play out in the next three years or so. So if you jump in to start the company, this is the biggest roller coaster ride you can ever imagine.

• 1:08:24Peter Diamandis?

  What's your advice to the entrepreneur jumping in to start a company in the AI space? What should they do? What should they not do?

• 1:08:32Speaker 00

  Right. Yeah, I use the roller coaster as a metaphor because I don't see it as a rocket ship purely upside. There are a lot of challenges and traps. I would be cautious to probably look at an app company. Because that's the biggest space with the most entrepreneurs and the inference costs are coming down. But when you think about starting an AI app company, be cautious first about can you handle the inference costs? Because those are too expensive. Don't run out of money before the, because inference cost is coming down. So time your launches, time your product design according to the technology you need. And when that technology will be low enough in inference costs.

• 1:09:15Speaker 00

  Secondly, be careful of the modeling companies. Because we've seen companies like Jasper who build great apps, but then the model sucked all their know-how because they saw the data. So find, ensure that you don't do that. The last advice is all the models are getting better. One day they'll be close to AGI. Does that mean my app will be eventually limited to a veneer and with very limited value? I don't necessarily think so, because historically we've seen great platforms emerge. But other apps can often build a moat, the moat that TikTok, Instagram and others have.

• 1:09:54Speaker 00

  Their value were not taken away by the lower level transaction layers or operating system layers or browser layers. So the key is when you build an app and gain some edge and don't sit on your laurels. Think about how to build a moat. That moat could be your brand, your user loyalty, user data or social graph. Things that we have seen or maybe new things in the AI era.

• 1:10:22Peter Diamandis?

  Yeah, I like to think about it and I'm curious if you agree. When I'm evaluating an AI company to invest in it, you know, I'm looking at what unique data do they have? And what customers do they have a very close relationship with? And everything else in the middle will get demonetized and replaced over time.

• 1:10:46Speaker 00

  Yeah, I think your advice is great for B to B. I was thinking more B to C. The two are definitely in concert.

• 1:10:52Peter Diamandis?

  What should people know about, you know, let's, let's turn to the last question, which is, we live on one planet and we've got sort of this bipolar element of China versus the US in AI. And we have this split universe. It would always be better to have alignment and everybody working well together. But what advice do you have there? How should people think about this? I mean, because it's a complicated, way above my pay grade. And I want, I don't want to put you in a situation where you're talking about anything

• 1:11:34Peter Diamandis?

  that you don't feel comfortable about, but I can't not have the conversation of, you know, I see a lot of people feeling like China is the enemy there, or US is being monopolistic there. How do, how do we, you know, navigate the next five, 10 years, which are the most critical?

• 1:11:55Speaker 00

  Yeah, there are some things that we're just not able to change. They are what they are. And, but I think each of us can make our own judgments and decide where we can reduce the impact of this unfortunate geopolitical situation. For example, you know, open source is one area where all the countries collaborate equally and generously. Academic collaborations continue on. Areas of collaborations not involved in the sensitive model or semiconductor can still go on. And I think, you know, connectivity in the world, working people to people, business to business, needs to go on. It has to be good.

• 1:12:39Speaker 00

  Globalism has to be right. Differences between governments is kind of like, you know, when our parents have fights with another parent's, we kids can still get along and, and do something interesting and fun, right?

• 1:12:53Peter Diamandis?

  Agreed. You know, I like to say we all have the same biology. So a breakthrough in medicine in China is The equivalent of a breakthrough in the Bronx. And we all, we all share 24 hours in a day and seven days in a week. That's a, it's something every single human has. And so anything that gains us time efficiency in one place gains time efficiency in another.

• 1:13:17Speaker 00

  And we share the same planet.

• 1:13:19Peter Diamandis?

  Yeah.

• 1:13:19Speaker 00

  Facing his own challenges.

• 1:13:21Peter Diamandis?

  Yes. Thank you for sharing time. Uh, uh, super excited about, uh, their performance. Uh, I've seen in BGO and look forward to, to playing with it. And thank you for joining me on Moonshots to talk about

• 1:13:37Speaker 00

  Thank you.

• 1:13:37Peter Diamandis?

  Your passion, your vision and congrats on going from the guy behind the curtain to the guy in front of the company.

• 1:13:44Speaker 00

  Thank you. Thank you so much.

• 1:13:46Peter Diamandis?

  Be well. See you soon, my friend.

• 1:13:48Speaker 00

  Bye.