Pod Insights Moonshots with Peter Diamandis Humanoid Robots, the Job Market & Mass Automation - The Current State of AI w/ Emad Mostaque | EP #114 About this episode In this episode, Emad and Peter discuss Emad’s new paper, “How to Think About AI,” and the necessary steps the world needs to take to ensure AI safety.
Recorded on August 5th, 2024
Views are my own thoughts, not Financial, Medical, or Legal Advice.
03:24 | Understanding the Complexity of AI
55:51 | Preparing for the AI Revolution
01:20:26 | Open Source AI for All
Emad is the founder of Schelling AI and the former CEO and Co-Founder of Stability AI, a company funding the development of open-source music- and image-generating systems such as Dance Diffusion, Stable Diffusion, and Stable Video 3D.
Read Emad’s paper: https://x.com/SchellingAI/status/1818600200232927721
Follow Schelling AI: https://x.com/SchellingAI
Follow Emad on X: https://x.com/EMostaque
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Transcript 21,842 words · 2 speakers
Speaker names marked ? are inferred from speaking time and the show’s known hosts — they are not voice-verified.
0:00 Peter Diamandis? AI's capabilities will soon accelerate at an unfathomable rate, making today's tech look like Stone Age tools.
0:08 Speaker 01 AI is complicated and hard and scary. Intelligence is like electricity, it's like clean water. It becomes available to everyone.
0:16 Peter Diamandis? How do you quell the fears, or should we have fears about jobs?
0:19 Speaker 01 Are you embracing that technology to do more and create more value or not?
0:24 Peter Diamandis? What's it going to feel like when you're seeing humanoid robots walking around the streets all around you all the time?
0:30 Speaker 01 Actually, this is the real existential threat to humanity. 10 billion robots and a bad firm where we upgrade.
0:41 Peter Diamandis? Everybody, welcome to Moonshots. Today, my guest is Imad Mustak, the previous CEO of Stability AI, the founder of Shelling Point, and the author of a recent white paper called "How to Think About AI." This is a paper I found extraordinarily compelling, concise, and really something that I encourage you to read. We're going to dive into it. We're going to talk about investing in AI, talk about international cooperation, talk about something called "Centors", which is AI-human collaboration. Talk about the growth and the speed. Are we getting to AGI and when? One of my favorite conversations. It runs two hours because it was just an incredible masterclass in thinking about AI. And if you love conversations like this, please subscribe. By the way, at the time of this recording,
1:25 Peter Diamandis? my blood glucose level is 97, which is exactly where I want it. I like keeping it low and flat, below 105 if I can. I use the Levels app to help me measure that, to understand what food causes my blood glucose and my insulin to spike and what doesn't. If you're interested, my team is going to link to Levels in the description below. All right, onwards to the episode. Imad, good to have you back on Moonshots. It's been some time. Are you in London today?
1:55 Speaker 01 Yeah, I'm in London. Thanks for having me back. It's always a pleasure.
1:57 Peter Diamandis? Yeah, last time we spoke, you had just stepped down as the CEO of Stability. And I know that you've been busy, and in particular, the paper you just put out on how to think about AI, I think is incredibly cogent and beautifully written paper. I'd like to discuss that today, and I want to start by quoting some of the elements in the paper here for folks who have not yet gotten to it. You say, "Let's be clear. Humanity's standing at the edge of the most transformative events in history. It's a wake-up call, a roadmap, a vision of our imminent future rolled into one." You said, "There will be an AI explosion, 100 billion AI agents and a billion robots.
2:41 Peter Diamandis? Of the current models, they're just the appetizer. AI's capabilities will soon accelerate at an unfathomable rate, making today's tech look like Stone Age tools. No industry is safe." I love this last one here. "The cost of intelligence is plummeting. The value of human ingenuity in applying these new resources will skyrocket. It will shortly become one of the most important skills you can have. It's time for urgent decisive action to harness the benefits and mitigate the risks of this AI tsunami." So that's what your paper is about, and anyone who hasn't read it yet, I highly commend it to you. We're going to dive into all of these elements. Let's begin. Imad, why did you write this paper?
3:27 Speaker 01 I wrote this paper because AI is complicated and hard and scary. And I thought it would be great to have a framing that the average person could understand and how it applies to them and their lives and their part within it. Because I think, you know, one of the things I mentioned in the paper is that there's a lot of talk about AI agents and all this, but not our own agency within this. It can be far and remote where everyone's talking about supercomputers and national state things and super geniuses running it, whereas this is a technology that's coming into every one of our lives every single day. And there seems to be a disconnect there, so I wanted to create this piece as an initial framing to try and bridge that.
4:05 Peter Diamandis? I love that. And I know this is the prelude to a much deeper paper that you're working on, that you'll release sometime this fall, which I'm excited about, talking about on our next conversation we get together. I think AI, the subject of AI is pervasive. I mean, it's the topic at dinner tables and boardrooms and executive suites in the financial markets. And I think you're right, people really have very little context on how to think about it, other than OpenAI and Google and Anthropic and just the names Nvidia. But how it affects them, you know, Ray Kurzweil talks about this idea of the singularity, which he puts out into the 2040s, but there really is an AI singularity coming too, right? It's a point at which
4:50 Peter Diamandis? the tech is moving so fast, it's super hard to understand what's going to happen, what life will be like on the back end of that. Do you agree with that?
4:58 Speaker 01 Yeah, I agree completely because it's a pervasive systemized technology, just like writing, just like reading, but it's got context suddenly that emerges. So taking this podcast and translating into dozens of languages is an amazing thing, but in our own voices that could be done now. You know, having technology that has real world impact in being as good as any call center worker, or being allowing you to create any song on any topic within seconds, is something we've never really seen before. But it isn't just constrained to these giant supercomputers and companies and things like that, because we're actually seeing this technology emerging on our consumer laptops.
5:40 Speaker 01 We're seeing this more technology being able to use by people in emerging markets and others to build brand new experiences around. And I think that's something crazy that is leveraging the existing infrastructure. And again, we're just at the early stages of that impact.
5:56 Peter Diamandis? Yeah. How big is AI today and how big is it going to get? In terms of either financial or, you know, whatever parameters you want to give it?
6:06 Speaker 01 I think if you kind of look today, the total amount spent training models of all types in generative AI, this new type of AI that we figured out, is probably in the order of $10 billion. The total amount that's been spent on self-driving cars is in the order of a magnitude of a hundred billion dollars. And the total amount that was spent on five G is a trillion dollars. So even though we hear these numbers going around of giant training runs and big investment and there've been estimates of a hundred billion of GPUs bought for training and running these models, it still feels like early innings for what is technology that can really amplify and accelerate the capabilities of any individual?
6:49 Speaker 01 Because they can direct these swarms, these armies, these groups of AI agents and soon physically embodied robots, which is insane to think about.
6:57 Peter Diamandis? Yeah, I want to get into what does the average person think about this and how do you use it besides, you know, chat G P T or Gemini? You know, and is it too late to invest in this, right? We have a lot of folks who are asking, you know, how do I get involved in this? But let's let's paint the picture still as you do in your in your in your paper about where it's going. One of the one of the big ideas that you speak about, and actually you were on stage at at Abundance Three Sixty speaking about this is the explosion of AI agents that are coming, this concept of the AI Atlantis.
7:37 Peter Diamandis? I find that compelling. I find that enticing and somewhat scary if you don't understand the implications. What does that all mean? What are these billions of AI agents and AI Atlantis?
7:49 Speaker 01 Yeah, AI Atlantis was coined by Nat Friedman on stage, former C E O of GitHub when we had that panel together, Peter. And it's we've discovered this new continent. And the best way to think about these AIs is they're like interns, they're like grads. We keep treating them like they should be professors or PhDs, but they're just in the early stages of coming out of high school. These supercomputers are high schools, right? But there's a hundred billion of them. And we can get them to do all these little tasks, but anyone knows who's had grads and interns. We have to teach them before they can do anything. And once they learn the processes, that's when you have agents that can go out and do jobs for you. That can translate something in different languages so you can reach a bigger audience in seconds, or that can paint pictures for you or do S E O.
8:33 Speaker 01 You know, that could be your call center worker. And so the first step was to teach them. Their liberal arts degree. Now we're specializing them. We're optimizing them. And the cost, I mean, I think OpenAI just showed that the cost of GPT three originally versus their latest version, GPT four, has dropped a thousand times. Literally a thousand times. It's almost too cheap now. So we have this new continent and all these workers are coming that can do jobs as reasonably as any graduate. What does that mean for your personal life, your company, your country? Well, it's simple economics. There's a massive amount of supply of intelligence and capability and rule following coming.
9:15 Speaker 01 Because these don't sleep. All they need to be fed is a few flops of, you know, computer and electricity. How would your life change if you had a really good group of graduates? And then they will only get smarter. They'll only get better at following ingredients and soon they'll have physical bodies as well. When you look at Optimus, when you look at Unitree and the other robotics companies coming.
9:35 Peter Diamandis? Let me dive in this a little bit deeper. The idea of this AI Atlantis, this continent of a hundred billion AI graduate students, AI agents that can do your bidding for you. Like you said, you're you're feeding them, you're feeding them power and data instead of pizza.
9:54 Peter Diamandis? How? How much agency will they have? How will we be able to drop them into jobs? To drop them in and say, hey, you're part of my marketing team, you're my admin, you're my finance person. How far are we away from having competence there? And how will that, how will we see that develop?
10:14 Speaker 01 I think that we're not far at all. We just had to get to a certain level of performance on the base models of the base degrees. So again, I think the best way to think about these are liberal arts graduates. They've had a diverse education because we've literally shown them the whole internet, right? Those are the trillions of words that go into the GPTs or the billions of images that go into the image models or hundreds of millions of songs. And now we're specializing them and these are the workflows that we see. Because like when I started as a programmer twenty, god, three years ago. Um, we didn't have GitHub and libraries and the way programming is now, which is like. You pull from pre-made building blocks and you put it together. We wrote directly to the computer.
10:57 Speaker 01 We had these very low level building blocks that now people have made into houses and, you know, Lego blocks that come together. Similar is with AI. We had to go to a certain level of performance of the base models. And now people are chaining the base models together in repeated processes. They're integrating it with lookups for databases. They're learning from how people are experiencing things. If you compare ChatGPT now to ChatGPT a year ago. Well, it's just two years old now, right? There's a massive difference because it's learned from how people have used it. And they're actually paying people for specialist use cases.
11:36 Speaker 01 So I think that you see hundreds, thousands of companies deploying these pipelines right now and they'll standardize very, very quickly.
11:44 Peter Diamandis? I've heard it described, you might bring in an AI agent, um, and when they plug into your business. They'll read all of your past email traffic, they'll read all your Slack channels, you'll, they'll be up to speed. On everything ever said in your company in the marketing domain. And then there'll be a point where you're, you're slacking them and you're speaking to them and maybe even zooming with them. As if they were a member of your team. Uh, is that a good description?
12:16 Speaker 01 Yeah, so like, um, apps like Lindy dot AI do that pretty much already. But as we get more integrated, that will be even more because, for instance, your data all sits in Amazon bucket. Amazon will roll out services within the next year that allow you to take literally a snapshot of all of that and give you your own personalized agent that you own. That you can talk to on Slack and it will retrieve all this information instantly. We're seeing Snowflake and Databricks again, build this type of technology and implement it. And Microsoft with its agents in Office, these are all coming through literally in the next year. So again, at a graduate level, we shouldn't expect more than that. And again, sometimes they make mistakes, you know, sometimes they try too hard. We will have these agents coming through.
13:00 Speaker 01 The single shop thing is very interesting as well because we are making it a little bit superhuman. So Google with its Gemini model now that's just outperformed OpenAI's model for the first time. So it's the top performing model, which is very interesting, has what's known as a two million to 10 million token context window.
13:19 Peter Diamandis? Mm hmm.
13:20 Speaker 01 A token is like 0.7 words. So let's say it's a million words of instructions you can give it. When we first started, we can only give two to 4,000 words. Now we can upload whole movies. We can upload the entirety of the entire bookshelf and everything you've ever said, Peter.
13:38 Peter Diamandis? Hmm.
13:38 Speaker 01 And we don't have to tune the model anymore. It can look at it all at the same time and say, this is how your speaking style has evolved over time.
13:48 Speaker 01 You know, or these are the most impactful things you've said and point to the different parts of that. This is where you seem like maybe you've been traveling a bit too much a tired jet magnet.
13:57 Peter Diamandis? You know, I, I think one of the interesting things, though, is if you do train up in AI agent, um, uh, having had to consume all of your corporate knowledge and all of the previous phone conversations your sales agent or marketing person would have had and know all the context and you like what they're, what that AI agent is doing, you can replicate 10 of them, 100 of them, 1,000 of them instantly. Um, so, you know, the implications there, we'll get to conversation about jobs. I think it's really important. But we also, I think as an entrepreneur, we have a lot of entrepreneurs listening to moonshots, uh, the idea that
14:37 Peter Diamandis? there will be, you know, the one, two or three person unicorn, um, that comes online. How do you think about that potential?
14:45 Speaker 01 I think it's 100% going to happen in the next year or two because I mean, ultimately, anyone who's an entrepreneur knows that the key bottleneck is simply talent. And there is the chef talent that comes up with creative things and brand new recipes and cooks. And you just want them to do their jobs and you want them to do their jobs well. Like there's a sales function that humans are still going to be good at for maybe a period of time. But all of the day to day stuff, like the content, shall we say, versus the creativity. The AI can follow instructions much better, which allows you to scale much better. Again, as an example, media. There is no reason in a year from now that every piece of media isn't translated into every major language
15:30 Speaker 01 the next day with your voice in there and a transcript.
15:34 Peter Diamandis? I know we're starting to do that with this podcast, but you're right. It's it's every language eventually, almost instantly.
15:42 Speaker 01 And then, but that, what does that do to your audience? We can even do it live now. Your audience suddenly goes from just the English speakers to every language, right? And then that increases your funnel. Every business is a repeating process where, you know, it's all about cost going in versus value going out. And as you said, if it's, if you can replicate this agent a hundred times, it isn't like hiring a hundred graduates or a hundred sales specialists. You push a button and you get them for cents or dollars.
16:10 Peter Diamandis? And by the way, you can, it can be, you know, increase the number during your peak season and drop them off without cost during your off season. Let's talk about jobs for a second. You know, in the paper, you say, AI automation puts three hundred million full time jobs at risk. And then you go on to say the adoption of AI is going to create ninety seven million jobs by twenty twenty five. So I think one of the biggest fears people have is about the job market. And and we, you know, what most of us don't realize is that most of the jobs that we all do today didn't exist even ten, twenty years ago. And we've completely disrupted and reinvented what humans have done for millennia.
16:55 Peter Diamandis? But the pace of change here is a lot faster. What are your thoughts? How do you quell the fears or should we have fears about jobs?
17:03 Speaker 01 I think it's a pace thing. As you said, we've always responded. You know, it's been said that there's only two types of jobs, you know, or things. There are things that you need for living and there's things you need for entertainment in a way, right? The survive and thrive elements here, like.
17:19 Speaker 01 We have had such an improvement in our standard of living. And even a reduction in the number of hours worked and we've created brand new industries and service sectors and more. But the pace of this is insane. But if we look at it from a localized perspective. What's at danger initially is the outsourced jobs and the entry level jobs. Because this is where that technology, we've discovered this new continent and they work for. Electrons, right? They work for, you know, GPU hours. In the intermediate, medium level, you could be so much more productive. Which means that you can have more supply and more demand of that stuff. And then if you're a leader that can really leverage this technology, bring it together and think about how am I creating value?
18:04 Speaker 01 You become a multiplier. And so I think this is net aggregate massively impactful for economies, but we have to really look at. What does our generation that has been taught to be a programmer do? When this technology is as good as any graduate programmer? You know, what does it do when again, any graduate job in? That you can do via a screen, the AI can probably do better in a year. Or outsourced jobs as well for the global south.
18:32 Peter Diamandis? I get it. But still, they're going to be fierce. I remember you were on. The abundant stage with me, I think it was two years ago and you said, look, we're not going to have any programmers in five years. And that was like front page across India. You know, and it caused fear. And of course. We're not going to have the old fashioned programmers, which are just. Humans on their own. It will be AI human partnerships that are doing the programming. But let's talk about where you think. Should people have some level of concern about the job market? Or do you think as we have done? Decade after decade, century after century.
19:17 Peter Diamandis? We've invented brand new approaches to jobs.
19:20 Speaker 01 A hundred percent. I mean, again, technology is an enabler. And so it's just about, are you embracing that technology? To do more and create more value or not? Like what would an accountant be who didn't use Excel or a spreadsheet, you know? Like it doesn't make sense or a salesperson doesn't use a phone. This is just a technology it is and it's up to us to embrace it and create even more value that way. And the nature of jobs will change. In your taught generalized computer science terms. I think again, we're uh two years into our five year prediction, right?
19:53 Peter Diamandis? Yeah.
19:54 Speaker 01 Uh so we've got three more years. I don't think you need to learn, even now, if you use Claude and the artifacts feature from Anthropic. It will create programs on the fly for you. And you just talk to it like a human and it adjusts them. Devin is another one that's similar. Do you need to know the specific magical spells of a programming language anymore? No, why would you? You have this assistant. But what are you trying to do? You're trying to build software to do a job. That's all that matters. And again, when I first did it twenty three years ago, we didn't have GitHub or any of these things. Where you could reuse code, you almost coded it from scratch. Every single time.
20:29 Peter Diamandis? Every time.
20:30 Speaker 01 Directly on the thing. And this is how we do it. We build houses from scratch, but then we learn how to build houses. You know, in our company, we do something brand new, but it goes to our institutional knowledge. It's just now we can localize this knowledge. So I think that the key thing here is just to split the floor will raise. And if you don't embrace this technology, then you're going to be left behind. You're going to be left high, you know, low and dry.
20:55 Peter Diamandis? I think it was one of the points that we we spoke about at length. You know, the way I put it bluntly is there are going to be two kinds of companies at the end of this decade. Those that are fully utilizing AI and those that are at a business. Do you agree it's that black and white?
21:10 Speaker 01 I think it is that black and white. I don't think we've got to the competitive stage yet because it was build the basic building blocks first. Now it's time to bring them together and then proliferate the technology. But in the next few years, we will see that competitive tension where companies can outperform because their core loops. Are embracing AI so they can do it better, faster, cheaper. And you know, the global economy doesn't look too hot today, so it's going to be highly competitive. If you can reduce costs and have higher revenue, you're up for it.
21:39 Peter 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 10 amino acid sequence that is a synergic 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. 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. I'm always looking for analogies that are understandable. You know, I think 100
22:18 Peter Diamandis? years ago, when we started to electrify electrify the nation, people took every possible mechanical process, clothes washing, dishwashing, drills, and added electricity to it. And if you didn't have that electrical adjunct, you're out of, out of, out of the leadership. I'm not sure you're at a job, you're at a leadership. And then it was the dot com revolution that did it again. Any other analogies that seem appropriate other than those for you?
22:47 Speaker 01 Well, again, this is a, it's the nature of technology. You go from having something that is single, generalized, to more and more specific, automated, optimized. You can ride a horse, so you can travel more distances. You know, you don't need telegrams anymore because you've got the phone. We're a constant species where we bring this technology to enhance our individual capabilities. Someone from a couple of hundred years ago would die of shock at what we're capable of today on an individual basis. But again, we adapt, we improve, and this is why our GDP has gone up so much. Our standard of living has gone up so much. Have we solved everything? No. But everything from the agrarian to the industrial
23:27 Speaker 01 to the dot com revolution has embraced that. And the AI revolution, I think, is one of the biggest indicators of that as well, because
23:38 Speaker 01 Intelligence is like electricity. It's like clean water. It becomes available to everyone.
23:43 Peter Diamandis? You made this point and I love it. It be, you know, you said, listen, intelligence needs to become national infrastructure, global infrastructure. Can you describe what you mean by that?
23:54 Speaker 01 So the internet is this information superhighway. But now this intelligence and these agents and this technology. If you've only got a few capable of using it and it's not, and it represents only a few outputs and a certain few viewpoints, then you could have an entire hundreds of millions, billions of people that don't have access to that technology. They're left behind even more. It'll exacerbate these gaps. However, if we make sure it's distributed and open, then it raises all boats because it isn't centralized and controlled. So for example, OpenAI has done many great things, but they banned all Ukrainians from using DALL-E 2, their image generation software, for nine months
24:38 Speaker 01 and all Ukrainian imagery. Imagine if they were the only image creation software. It would mean that there'd be a whole nation that could not create images quicker. It was a, they'd never replied to me, but I think it was caution around sanctions and other elements and this technology is now being weaponized in terms of politically. You know, like, is it like, is this a nuclear weapon? Is this a deep fake creation machine? But again, who's deciding on this technology? Is it a right to have access to augmented intelligence? Is it a right to have access to electricity, water? We're not doing great as a species. We're doing better. You know, hundreds of millions of people are still malnourished, a bit, over a billion people still don't have internet access.
25:23 Speaker 01 But I think that if we can make intelligence available to everyone, there's no problem you can't solve. But more than that, people will be able to solve it themselves, which I think is amazing.
25:33 Peter Diamandis? Yeah, it's, I, I love that, you know, define an entrepreneur as someone who finds a problem, solves a problem. And the more empowered entrepreneurs. The better the world is and the more problems that are solved. You know, I, I, there's an interesting point I've.
25:47 Speaker 01 Well, actually, Peter.
25:49 Peter Diamandis? Go on.
25:49 Speaker 01 I think, you know, it's not, it's a force multiplier, right? Because anyone can change the world if they can convince other people to follow them. Right? That's how you create companies, you create movements and more. And again, we now have this new continent where the people will follow you. These entities will follow you to do things. So anyone can be a force multiplier. And that's crazy to think about because if you are in an underprivileged area, you don't have the people around you that are skilled. How will you hire them and get them to come to your company to come to your movement? Whereas now they're available to everyone.
26:24 Peter Diamandis? Yeah, it used to be where, you know, where you were born. Whether your town had a library or a phone or even transportation, the color of your skin, your gender, all those things determined your ability, your agency to, to, to create a purposeful and meaningful life. And this is that, you know, Google came first and, and, and cellular phones came next. And now this is a major force multiplier. There's an interesting point that I, I've noted and it still blows me away. I think of generative AI today as the world's most powerful technology. And I am, I think the right word is flabbergasted that it's free,
27:06 Peter Diamandis? that effectively the world's most powerful technology is available to, for free to anyone with a smartphone. You know, if I gone back 20 years ago and, and described what we'd have today with Gemini and, and ChatGPT and all of that, and said, how much do you think it's going to cost per month to use this technology? I would have never guessed zero. Does that surprise you?
27:32 Speaker 01 This is astonishing. I mean, what is, we have this concept of, hey, I want to make a picture. I want to make music. I want to write an essay. And it costs X number of man, woman hours, right?
27:45 Peter Diamandis? Yeah.
27:47 Speaker 01 Yeah, all of a sudden it costs nothing. The thoughts to the creation process, sure, you can spend a week and make it even better, but to get to an eighty twenty now is almost instant for any modality.
28:01 Peter Diamandis? Yeah.
28:02 Speaker 01 Like, again, we're not talking about perfection, but you don't expect your graduates to be perfect on their output from drawing to coding to anything. That rapid iteration is just something that's beyond belief because, again, when you look at these models, it's just a few gigabytes and they can run on your smartphone. And it does seem something's wrong with that energy equation, right? Energy is that much down to nothing.
28:23 Peter Diamandis? The economic equation is what really bugs me. The value we're extracting, I mean, Google did that and it monetized on advertising, but it also uplifted humanity in such an extraordinary world. You know, I just came back from India where GEO, their five G network, has demonetized data and communications at an extraordinary rate. And so we're, we are seeing this, this abundance equation of digitization, dematerialization, demonetization, democratization over and over and over again. And this is the ultimate one. And I'm just, you know, it feels like we haven't been able to even understand a fraction
29:07 Peter Diamandis? of one percent of the implications of this.
29:10 Speaker 01 So you think about the energy that's taken to put a doctor through school, right? And the cost of that, millions of dollars in the U S.
29:18 Peter Diamandis? Yeah.
29:18 Speaker 01 And that expertise could, a decade, that expertise could suddenly be available and replicable under the push of a button through a digital A I and soon an A I doctor and surgeon that outperforms human doctors in empathy. There's this relationship between energy and G D P per capita. So that's pretty much a straight line.
29:37 Peter Diamandis? And just feels that all of a sudden. Energy is, is output and power available to the society.
29:43 Speaker 01 Per person, yeah.
29:44 Peter Diamandis? Per person, yeah.
29:45 Speaker 01 So the more energy you have per person, the higher your GDP, because you could build stuff, but then more than that, you can support service industries and things like that. You can support the million dollar doctors with a decade of work. Yet, all of a sudden, the energy equation for intelligence has collapsed to nothing. Google and the previous internet reduced the cost of consumption of content down to nothing. And that's why they could have ad and other models to have that go. They want your attention, shall we say, when you're purchasing on the other side. Now, the cost of creation almost has dropped to nothing. Not creativity necessarily, but the creation of information of knowledge, you know? And again, not novel knowledge, it's just how can we distribute this? Because
30:28 Speaker 01 we never have enough doctors. We never have enough good doctors. And again, we're not talking about great doctors. I think all these discussions of AGI are like superhumans in the top 0.1%. I just want an average doctor to be available to everyone, an average teacher to be available to everyone, but the average level to be really much better than it is right now.
30:49 Peter Diamandis? Well, I I get it, but I believe, you know, I mean, I spend a lot of my time in the intersection of AI and health, as do you, we have that in common, that that there is no way that any human doctor can integrate the the gigabits of data that come out of a current, you know, digital upload, understanding all of the blood chemistries, your genomics, your imaging data, but in AI can, it can contextualize all of that and then give you some root cause analysis. So I I think that that we're not far away from having the average AI doctor be better than most all physicians out there.
31:35 Speaker 01 I think we're there pretty much now, it just hasn't been integrated. If you look at the benchmarks from radiology to this, that and the long context window work. But again, this is the exciting thing is you go from individual models to pulling them together. So you have your own medical team.
31:51 Peter Diamandis? Yeah.
31:51 Speaker 01 You're not just calling on one doctor. And we see this with the technology as well. Like we had gigantic models and we still got them with the Binyu Lama, but now we're moving to mixture of experts. And routing to expert models that experts.
32:05 Peter Diamandis? So these are agents and subagents. There's an AI radiology, you know, X-ray radiologist agent and one for MRIs and one for CTs. And one for blood chemistries and one for genomics and all of them are being pulled together as your medical team.
32:21 Speaker 01 Yeah, and they have common language to talk to each other and, you know, the outputs are checked as well. Like in medicine, before we get doc, AI doctors making diagnoses. Every diagnosis by a human doctor should be checked by AI. They're not arguing against that.
32:37 Peter Diamandis? It's gonna, well, I think it's gonna be malpractice to not have AI in the loop in in some number of single digit years. Because it will save, it will save lives. Humans make mistakes. They just can't deal with the amount of data. It's not the way it used to be. And if you can know, because you get access to the data, I think there's an obligation to do that.
33:01 Speaker 01 Yeah, I mean, it's because, because we could, we could only have, I mean, we all know that physician scroll is unintelligible. AI is just catching up with that right now, right? The information flow from that hour long.
33:13 Peter Diamandis? They teach you that in medical school, by the way.
33:17 Speaker 01 It's just a few lines, right? We lose all that context and all that rich context. So is anywhere in the world maybe found, found in health. That you can go and see your lung radiology over time and how it progresses. We just saw, I think, a paper come out that showed that you could detect breast cancer up to five years before, because they just dumped it and it just analyzed every smallest piece of things. You can't see how the world evolves because the lack of context from black and white scroll made it so our medical systems assume ergodicity. A thousand tosses of the coin are the same as a single coin cost a thousand times, but humans are individual, we're complex systems. But everyone gets 500 milligrams of paracetamol, you know?
34:01 Speaker 01 We don't care about things like cytokine P450 abnormalities that mean you metabolize codeine into morphine or fentanyl into death, you know?
34:09 Peter Diamandis? Yeah, I mean, there, there really needs to be a genomics agent before you take anything. The other, other thing which people don't realize is you feel like when you get prescribed a particular medicine for a particular situation, that you assume it's going to work, but the fact of the matter is it works in about a third of the individuals, if you're lucky. It was enough for the FDA to approve it, but no guarantee it works for you. Um, you know, the other thing that you, that you wrote in your paper, and I don't want to let it go unsaid, is besides this, uh, this exponential rise of AI, AGI, ASI, whatever we're going to call it, is the emergence of humanoid robots.
34:50 Peter Diamandis? Um, we've got, uh, you know, probably on the order of 20 plus well-funded humanoid robot companies. You mentioned Unitree in China, we've got Tesla building Optimus here, we've got Figure, which just is releasing Figure 2. Um, and if, you know, Elon's not necessarily been correct on his timing all the time, but he's been correct on pricing. And, and it's, you know, sort of a dollar per kilogram for a certain level of complexity. And his projection for Optimus is $10,000 a unit. Let's double it, give him 100% margin to $20,000 a unit. That's something that you could rent for a hundred bucks a month, having a humanoid
35:30 Peter Diamandis? robot running the highest level AI model accessible 24/7, and that's going to change the world.
35:37 Speaker 01 Yeah, I think this is the thing, the embodied agents and the advances we've seen there now with on-device and cloud AI now, being able to do segmentation, analysis, robotics, like. It's here all of a sudden, all at the same time. We see, we see this actually in history a lot where the same breakthrough just happens in different places almost. And we're seeing this all the time in AI where you see literally disparate breakthroughs. Cause the control problem in robotics is pretty much there now.
36:07 Peter Diamandis? Yeah.
36:07 Speaker 01 For ninety percent of use cases, again, not a hundred percent. But these robots can manipulate, they can read a recipe now. We've seen, and they can just make the recipe from the instructions with full autonomic control. And they can do auto aggressive learning as well in swarms. So Udemy have that and others are implementing that where one robot learns from the tasks of others.
36:31 Peter Diamandis? Which is, which is insanely powerful. You know, bringing it back to health, you know, the best surgeons in the world will be humanoid robots eventually. That can see an ultraviolet infrared, didn't have too much caffeine that morning, no fights with a boyfriend or girlfriend the night before. And it's the capex and the cost of electricity. And then all of a sudden, this robot is a surgeon in multiple distant villages in India and Africa. Um, with the highest surgical skills. And like you said, if, you know, people should know this. The number one question you ask your surgeon when you're interviewing them is how many times you do the surgery?
37:12 Peter Diamandis? And if they're all operating on a single operating system, then the robots all share the same millions of operations they've done.
37:20 Speaker 01 And this is again, our collective knowledge where it's taught through medical school. But AI will obviously be able to teach other AIs much better. And if we can standardize that, then that's when it can go massive. And it comes then down to just a production line process. It's like Xiaomi, you know, the smartphone manufacturer in China. They thought they were going to be added to US sanctions, so they decided to diversify into electric vehicles. And then China's industrial prowess means they now make really good electrical vehicles. Like you will see these industrial giants move forward and produce millions, tens of millions, hundreds of millions of robots. And the really fascinating thing is that helps with the demographic issues of China and Japan and places like that at exactly the right time.
38:03 Peter Diamandis? Exactly the right time and you know, the predictions we've seen from uh, from, from Elon and from the CEO of Figure is. You know, as much as ten billion humanoid robots by. Uh, in the twenty forties, more as many as are humans. Uh, I asked a friend of mine, uh, Iman, and I'll ask you, what's it gonna feel like when you're seeing humanoid robots? Walking around the streets all around you all the time.
38:29 Speaker 01 Uh, it's just gonna be weird, isn't it? Like, we already see these little ones going around that are not humanoid. But there's no reason you can't make them indistinguishable by twenty forty. It's really sci fi. I mean, actually, this is the real existential threat to humanity. Ten billion robots and a bad firmware upgrade. You know?
38:47 Peter Diamandis? What could possibly go wrong?
38:49 Speaker 01 Of all of the, all of the like scenarios, literally, that's the one that can get us all.
38:55 Peter Diamandis? I, I, I think there should be some checks, extra checks and balances there. Um, yeah, my friend, when I asked my other friend that question, what's it gonna feel like? Uh, he said normal. And I, I think, you know, we're gonna have these moments of freak out. I remember I got a Model X with the gold wing doors and in the first month, everybody's like, you know, staring at it like, oh, my God, that's amazing. And then, you know, it becomes normal.
39:20 Speaker 01 It becomes normal. I mean, and again, like, there's different things in different cultures. In Japan, the robots are very much side by side, or you become part of a Gundam. Whereas we have this Terminator concept of robots or AI god in the West. But ultimately, as you said, like, people are used to being able to type in words and make magical images in two seconds now. Or using chat GPT for your homework. Like, you just talk to some of these kids and they're like, yeah, just use it, you know? Like, there's an initial shock and then all of a sudden the magical becomes mundane. I think we're very good at adapting to that with our filters. But ten billion robots means that there isn't, I think one of the ways to rethink about this is, how much energy do you need to put in? Your own physical, mental energy?
40:05 Speaker 01 For a job to be done. And it's collapsing everywhere, the amount of energy that we need to do productive things. Which means we can do so much more productive things.
40:15 Peter Diamandis? Yeah, I think the, the scariest resource in the world is time. You know, I'm working on it from the longevity standpoint of how do we add more years? AI will be the most impactful agent. In how in how we create, we reach longevity escape velocity. But I think people need to realize that we've been buying back time with technology. You know, Google saved us hours of going to libraries looking for that book. And Zoom is as normally you and I would have to have flown meet to London or you to LA to have this conversation. Instead, it's microseconds of, you know, click, we're together. So it's about buying back time, which is our most scarce resource.
40:58 Speaker 01 Well, I think there's two things. There's time, but there's also focus and flow, which I think are the other two interesting parts of that.
41:04 Peter Diamandis? Tell me.
41:04 Speaker 01 So we know how useful an amazing chief of staff or EA is. And everyone will have their own one. So like, how do you live longer? Well, first of all, exercise and eat well. But, you know, you don't have that person, that entity that you trust telling you and advising you sometimes. So you cheat, you fall down, et cetera. No one will ever need to be alone again and can have a persuasive agent next to them, which is something insane to think about, that knows exactly how to talk to you to put you into good habits. And it might be physical embodied as well. It might cook your breakfast and everything. Like, how difficult is it to have healthy meals all the time that are fresh? It's hard. With robots, it's easy. Hundred dollars a month, as you said.
41:50 Peter Diamandis? Yeah.
41:50 Speaker 01 And that's kind of crazy to think about from that longevity perspective. But then it's about our own biggest enemies are ourselves. You know, like I took off a few months planning to clear my head and then read everything and now I'm compressing it and putting it out and off we go again. If I'd had all the help that I needed, and it's not like the people around me didn't help, I would have done even better. But everyone now can have access to that, which is going to be amazing for global mental health if we do this correct, and the capability of people to just achieve so much more by having that focus and flow.
42:20 Peter Diamandis? Talk about flow a little bit more. How do you think about flow? And do you think humans can enter flow with AI partners, AI agents?
42:29 Speaker 01 I think a hundred percent. Like, when I talk to all the creatives, you know, Stabat is one, is generative media leader. How do the top artists use the AI? Do they use it to just make content? No, they're like, we like to jam with it. You know, it helps us enter that flow state, especially when we customize the AIs for their catalog and things like that. And you can use it to discriminate, to do like, you should not use ChatGPT to write your essays. You should use it as a sparring partner to get you to think outside the box and look at things in different ways. That's the best way to use this. You know, like a very smart, precocious graduate that occasionally gets things wrong.
43:09 Speaker 01 I think this AI is original creative, but we haven't built the systems to keep that flow state going. And the flow state is the one where everything is just kind of coming and there's no barriers between ideation, creation and iteration. You're looping.
43:25 Peter Diamandis? Yeah, um, I, the, one of the, the things I, I like to speak to entrepreneurs about is, you know, you don't know how to think other than the way you know how to think. And that's where generative AI models can come in. You know, you could describe to it, this is the problem I'm trying to solve. You know, how would Steve Jobs take a shot at this? How would Imad Mustak take a shot at this? Right? You can actually have it digest and present to you different approaches that, that break you out of your predetermined mindsets. And that's pretty powerful.
44:04 Speaker 01 But then also, I understand you. Again, having a good sparring partner is amazing. And there's this base level, you know, like when we grew up with our parents and they supported us to go to school, you didn't have to worry about all of that. But then you grow up and you have to worry about all of that. You won't have to anymore. And this other level where it is the sparring partner, where it does look from different perspectives, where it knows exactly how to talk to you, cajoling or criticizing or otherwise. But if you own that AI, you know that it's on your side versus someone else's side. Like, what is the objective function of that AI? Is it trying to sell you something or is it looking out for you? And this is going to be one of the most important things. Because we're inviting them already into our everyday lives.
44:47 Speaker 01 Like, Apple Intelligence is now going live on ten hundreds of millions of smartphones.
44:52 Peter Diamandis? Yeah.
44:53 Speaker 01 And it is smart and it's persuasive already. And it's only going to get more smarter and more persuasive. But is it looking out for you or someone else? It depends on how they build it.
45:02 Peter Diamandis? That's, that's a really important point of who is the AI loyal to? Is it, is it your version of Jarvis? I use Jarvis as my, as my, uh, uh, the context thought for, for this AI. You know, and, and being, I guess what you're saying is, listen Jarvis, I want to be more productive today. Uh, so if you don't mind, you know, keep me inspired and keep me focused and keep me productive to reach these goals. And, and having a muse almost in one sense. That plays the right music, uh, tells you the right joke, gives you the right data points at the right time.
45:40 Speaker 01 Well, you know, one of the things we discuss in the paper is this concept of, you know, we're going to move away from files. So PDFs or Word documents or images to flows of kind of, you know, points in time and all the things you pull together so you can rework outputs. Your life is like that. And if you have AIs that understand your context, is that AI provided by Google? That's trying to sell you ads? And so it'll be like, well, you can do this by buying Gatorade. You know, and this and that. Or is it something which is like, you know, like, keep away from this social media junk, you know? You're clearly getting stressed by debating over who's going to be vice president or something like that. You know, I've got you, you know?
46:24 Speaker 01 These are all various interesting things because people have agendas. And one of the things Internet Two, the previous Internet Court, was our focus to sell us ads. And that's fundamentally about manipulation. And so if we make these AIs that are massively persuasive, know everything in our flow.
46:43 Speaker 01 You can inject has like nothing else with incredibly high CPM and it'll be free. Because again, if you're not paying, you're probably the product, right?
46:52 Peter Diamandis? I am curious, and I I've written about this, that once we have these deep AI model integration into our life, I'm not going to be buying anything myself. My AI is going to be buying stuff. It knows when I run out of toothpaste. It knows when I run out of shampoo. And rather than me having some smiling person, you know, shampooing their hair, brushing their teeth and being influenced by that ad. My AI knows my genetics and knows which toothpaste or which hairs, which shampoo is best for me and is probably buying it without me ever asking. Is that going to spell sort of a disruption of the whole ad industry?
47:31 Speaker 01 I think so. I think the ad industry as it kind of exists is already under siege because a lot of the numbers were made up. And we've kind of seen that with the CPM discussions and others. There's been advertising power when you look at the amounts every single year that the big companies have to increase their ad spend to keep the same thing. And now it's being really questioned. When it's disintermediated through independent agents, the value of a consumer brand drops. That's kind of understandable. And then again, it becomes about just the stuff that's background and the stuff that's status, the stuff that's living and the stuff that's entertainment. So I think there'll still be massive brands in entertainment and consumer and discretionary.
48:17 Speaker 01 But certainly on the other side, like as long as it's good quality and you trust it, does it matter, you know?
48:23 Peter Diamandis? Yeah.
48:24 Speaker 01 You get commoditization.
48:25 Peter Diamandis? You mentioned in the white paper the work of Daniel Kahneman, who just passed recently, and System One and System Two Thinking and how AI is injecting into that. You mind taking a second to recount that?
48:38 Speaker 01 So you know, you've got two types of thinking, like, oh my God, there's a tiger in the bush and I'm seeing a little bit your instinctive thinking. And then you're thinking that's very logical, step by step, chain of thought. In fact, that's what it's kind of called. And the previous era of AI was very much chain of thoughts or extrapolating.
48:58 Speaker 01 So what that means is that we had these giant big data sets, we call it big data. And then Meta ran these huge regressions, Facebook, and then with thirteen points about you or me, Peter, knew you better than your best friends and then could target you ads. But it's extrapolate the future like the past, not that you've changed something. Whereas these new models embedded context and they're very much instinctive. So right now, they don't tend to do reasoning immensely well. This is why the challenges people put against them are like, well, it can't do mathematics. And they got better and better at mathematics. But then you saw DeepMind just got a IMO gold silver medal level, International Math Olympiad Silver Medal.
49:41 Speaker 01 It wasn't through these LLM type models, it was through the more Gentic models that look at stuff and chain reasoning together.
49:48 Peter Diamandis? We gave it calculators.
49:50 Speaker 01 Yeah, we gave it calculators. Because these models that we saw have the breakthrough that are like spotting and tagging the word instinctual models, they guess the next token, the next word in a sentence by having all these contexts together. Or in diffusion models which do the image, they learn how to reconstruct and deconstruct images in that process architecture there. So it's all about process flows, as it were, and instinctiveness. And that's what was missing, this understanding of context, like make it happier, make it sadder, explain it like I'm five. Classical AI could not do that, but it could extrapolate really well. And that was worth hundreds of billions of dollars. But that, now we have the missing part, the other hemisphere of our brain.
50:34 Peter Diamandis? I'm amazed how AI has actually taken up the mantle of empathy. You know, if you had asked me years ago, what will AI not be able to do in the next decade, where are we humans going to be better off? I would have said it's in being empathic and connected with an individual. And It's actually now for the last year been proven far more empathic than humans in most cases. Thoughts about that?
51:06 Speaker 01 Well, I think it's because we don't have the time to establish connection and it's hard because we're all so busy with our lives, like our doctors, our teachers. How many people do they have to see? And the empathy from our organizations is drilled out of us. We're mistrusting. And AI doesn't need to be mistrusting. And AI has the energy to be empathic. It learns empathy through the process of the AI training. Because the best way to get someone to trust you is to help them. You know, the best way to establish empathy and rapport is what's my common context? And my common framework. And because it understands these frameworks, they're instinctive. In terms of it can map them.
51:46 Speaker 01 That's why it can communicate to you on your level. Like you, the best way to, again, deal with this AIs is. People tend to say like little short prompts. If instead you say, hi, I'm Peter and I love longevity and, you know, I'm a first principles thinker and I believe in exponential things and this and that and that. And you write your own pre-prompt. In fact, that might be a subject that's taught in school. Write your own pre-prompt.
52:12 Peter Diamandis? Yeah, that's sure. Contextualize yourself. Give yourself, help, help the system understand who you are.
52:18 Speaker 01 If you write a short intro to ChatGPT Anthropic, it will communicate with you so much better. If you tell it how you'd like to be communicated to. And then you can even tell it, communicate with me better. And then it will be even more empathetic and it'll understand you even better. Um, which is again, kind of crazy to think about because we've embedded these into contexts. Um, Bob Shiller has a great book called Narrative, the Nobel Prize winning laureate for economics, has a great book called Narrative Economics. About storytelling and how it impacts the whole of society on the economic and social level. And we're made up of these stories, these contexts. And now we've encoded these contexts Kahneman style and we can mix and match them.
53:03 Speaker 01 And that's what drives the empathy and the connection. And that's also why, like I said.
53:09 Speaker 01 It's not just about the robots walking around, it's we will put more and more trust in these robots and these agents. Because they will be more empathetic and they will never get tired of us.
53:20 Peter Diamandis? And they also, I think the thing you said earlier, which is so true, they will take the time because they can run in parallel. I just rewatched the movie Her, which was, which was still fantastic the second time. And there's a point at which the star is asking this AI agent, are you speaking to someone else at the same time? And, and she says, yes. And she goes, and he goes, how many people? And she goes something like eight thousand seven hundred and thirty six. And he's, he's taken aback. But of course, she's so incredibly patient and loving and empathic the entire time that, that it feels like you have their full undivided attention.
54:03 Speaker 01 But I mean, that can happen because now we can have one AI per person, you know? Or we can have a hundred or a thousand AIs per person, given how cheap they are. So you won't have that thing in her words, how many people are you talking to? Just you.
54:15 Peter Diamandis? Hmm.
54:16 Speaker 01 And you're special, you know, and you're unique. Like again, people like these affirmations, they want to be stabilized because they're constantly filtering to get through the world and this will understand our filters. If you look at her, it's very interesting because there is no visual representation. You know, a lot of these things are like robots or avatars. It's all done through the earpiece and the AirPod. And really, humans are wired for voice. You know, it's the best way to convey. Like if you look at it on an extreme basis, like again, using tools like Hagen and others, they took, for example, Millet's speech at the UN and translated it instantly into English. You're like, that really connects better.
55:00 Speaker 01 But then you can take something like Hitler's speeches and you're like, oh, he does not sound like a crazy ranting German anymore, which is kind of crazy. You can take that Olympian from Turkey who just won the silver, you know, with his glasses without any technology, and they translated that into English. You're like, he seems like a proper dude. These are kind of, kind of crazy. And again, we have to think, what is the effect when we have individualized agents talking to us constantly that are our best friends and will never betray us and are looking out for us, or they're working for someone else? Who are we inviting into our inner circle? You know, and that's really interesting.
55:40 Peter Diamandis? We're going to get into the conversation of open source and, um, and closed source, uh, and how we prepare for this future, uh, in just a little bit. Um, there's a bolded quote you have here. Uh, let me read what comes up to it says, uh, uh, we're going to be processing information and generating new ideas faster than ever before. We're going to see a dramatic increase in the velocity of knowledge. Um, and then you say, the question isn't whether they'll change the world. The question is, are you ready for the world they'll create? Um, and I want to dive into this a little bit because we're in this
56:22 Peter Diamandis? super exponential period of, of growth, right? We, we all used to Moore's law for the last 50 years where computational power was doubling, uh, roughly 18 to 24 months. We've seen that spike up to 10x per year. You know, Elon, when he was on stage with us last year at abundance 360, he said, you know, he's never seen anything as fast as this. It's now at 100x per year. Um, how does anybody possibly keep up with this? What's your advice to that, um, that CEO, that entrepreneur, that 20 something? How should they think about AI accelerating at this speed?
57:02 Speaker 01 I think exponentials are a bitch. And I think that's the thing. We're not really, uh, naturally good at these things. Is it true exponential because of the distributed way that it's emerging, right? And again, nobody's quite sure what the lower bound of a cost of a piece of intelligence is. This has broken our mental models, even for us that are right in this industry. What we can say now is it's too cheap to measure already. And it's just going to get cheaper. It's not done yet. Maybe it'll slow down the pace of it. But even now, if it's stopped today, it's changed society. Because it's just in that proliferation phase. Like the seeds have been planted everywhere. You do not need to hire call center workers anymore.
57:44 Speaker 01 Every single essay written is likely to have chat G P T somewhere in the process, right? At school. These are fundamental one way doors to society. So the way to think about it is. Jeff Bezos has this great thing, which is the inevitable and the unchanging. So he started Amazon as kind of a bookstore because he knew it would be an everything store because the internet was inevitable. The distribution of information and the logistics that would enable people to do that. And books were a great way to do it because building that. You could do it, but people always want faster, cheaper, better customer service. When you're building businesses. The mental models are like the ones that I lay out in the paper. I have infinite graduates and soon they'll have bodies as well.
58:27 Speaker 01 I have a concept where I can go from static files to flows of knowledge. And I want to increase the agency of every single one of my human workers because they're going to be the highest marginal cost. What does that look like in a few years when this technology is inevitable? And we've created these reproducible processes and modules. Because if you start digging into the individual parts of the technology and the minutiae, it does get incredibly complex incredibly quickly. But already we are hitting a bit of a wall. But not in that this technology will stop in that it's good enough. It's satisfied. All of the open source and closed source are catching up with each other and we've hit a level of performance which doesn't matter if it gets better anymore.
59:13 Speaker 01 And that's where you tie it back to what you're doing on an individual company, country basis and think, okay, the world has changed. This technology will proliferate. The cost is cheaper than we thought because I haven't seen anyone say, oh, well, that's a lot more expensive than I thought it'd be. People are usually shocked in the other direction.
59:31 Peter Diamandis? Yeah.
59:32 Speaker 01 Um, and it's available.
59:33 Peter Diamandis? 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 where 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. It probably was a problem that's been going on for some time, but because we never look, we don't find out.
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1:00:59 Peter Diamandis? 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 family, the CEOs of my companies, my friends. It's a chance to really add decades on to our
1:01:43 Peter Diamandis? healthy lifespans. Go to fountainlife.com/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. The next thing you talk about in the paper is a concept I love and I've written about, and you do a beautiful eloquent job speaking to it, which is, it's not AI versus humans, it's AI versus humans plus AI, which has been defined as centaurs. Let's, let's talk about that. How should we be thinking about AI as our partner and the definition of centaurs, and the benefits of being a centaur?
1:02:21 Speaker 01 So, AI, we just got little building blocks of various things. And again, if we look at the individual models, Tesla had 300,000 lines of autopilot code, replaced by a single model, like a sieve of weights. Visual data goes in and driving instructions come out. That's kind of insane, but they're just sitting there. They haven't been tied together yet. It's human ingenuity that ties them together and they can repurpose and refactor them, just like when you're leading an organization or a team. It's about up to you how you deploy them. And right now, humans are better than AI at doing that. That's why humans can do the creativity part. They can tell their own story. They have that agency. Whereas AI is really good at content.
1:03:05 Speaker 01 It's really good at following instructions. Again, it's gone to liberal arts college, maybe because of it weird sometimes, you know, and creative. It'll get better and better at following instructions and more and more specialized. And this is why the centaur, the human plus AI, beats the AI by itself. We saw that in chess. The AI beat the human, but then the AIs plus...
1:03:25 Speaker 01 The human beat the computer by itself. We've seen that with things like Go as well, whereby Lisa Doll was beaten by the AI and then he got so much better and the entire level of Go improved across the top level because you can see more interesting ways and looking at it that broke the norm. So I think, you know, in the world in which you're going, you've got to embrace the technology and use it because it can increase your focus, fun and productivity. Like, question is, why wouldn't you? It's like sabotaging yourself and tying one hand behind your back. You know, it's like saying, I don't want to have good quality teammates.
1:04:03 Peter Diamandis? Well, you know, it's like being a farmer and not using tools. It's using your hands only. Why would you not want to use tools? You identify what you call a triple threat on these, on the centaurs, just to recount them for you and let's talk about each one. Efficiency on steroids, um, uh, time, the ultimate luxury and choices galore. So efficiency on steroids.
1:04:25 Speaker 01 I mean, like, what is the capability of any individual one of us? It's a function of our team and now we all have teams to increase our absolute efficiency and our team's efficiency as well. Because no one will say no to high quality talent. Like, what team would say no to that? As long as they don't get in the way. Like, obviously, we see what happens when teams get too big. And so we have to make sure not to overcomplexify this AI or put it in unnaturally. But we have to really understand the capabilities. We don't need to understand the technology, just the capabilities, right? And that's what enables efficiency per capita to go up dramatically on individual, company and kind of even country basis. If we kind of, um, what was the next one again?
1:05:07 Peter Diamandis? The next one was time. Um, you know, AI frees up humanity's most precious resource. And I get that fundamentally. Time is the one thing we always are struggling with.
1:05:19 Speaker 01 We always have a lot more of it than we think though, right? Um, in terms of quality time. And that's a function of like, we always feel that we're on this treadmill and we fill our time with junk, but how much quality time do we have? Like, there were studies shown that when you go on a holiday, the most important thing is the highlight and the end of the holiday.
1:05:39 Peter Diamandis? Yes.
1:05:41 Speaker 01 Because of the way that we kind of look at it. So make sure that, you know, you send your bags ahead of time and you've got a luxury pickup and stuff like that.
1:05:49 Speaker 01 Because not all time is equal.
1:05:52 Speaker 01 The, we can use this AI to decrease the amount of content we have to make and wrote work and increase the quality of what we're doing. Have content that entertains us all the time versus watching junk. You know, have the stabilization and focus.
1:06:08 Peter Diamandis? When I get up in the morning, I take a moment to sort of prioritize my day. Like, what's the most important thing I'm going to be today, do today? One of them was, you know, sit down with you and do this podcast. And there's another three or four. And I love the idea in the future, my version of Jarvis is going to say, okay, listen, you do that one, I'll take these three and report back into the day.
1:06:28 Speaker 01 Again, just like a good team. I, it's like with that team, you can be so much more, it can free up the time. But then it can also improve the quality of your time and how you spend it. And this is so important as well, because we tend to just default to this autonomic kind of treadmill of just consumption of fast food. Not only input, but content as well. It's very tricksome, because this is the way our internet and other things are done. Like, I spend far too much time on Twitter relative to the quality or X of kind of the output, right? Because it's so engaging. But what if I had my customized feeds that came to me and just gave me impactful stuff
1:07:11 Speaker 01 that both fit my worldview, but then also challenged my worldview, because that's what I wanted? My time would be so much richer.
1:07:18 Peter Diamandis? Yeah, I, I built a, a company called daily.ai that searches the world's news in longevity or tech or whatever it is. It customizes a newsletter for me, because I only want validated, scientific, technological content that is got a positive semantic. I don't want to hear about disaster scenarios. And it generates that, but you can, you can have your AI ultimately feed you. Like you said, if you want to be challenged, feed me the most valid, you know, content challenging my worldview.
1:07:51 Speaker 01 But then also, I think one of the things underappreciated here is that the previous AI that we saw, this old school AI deliberately siloed us into our groups because it was better for advertising. Like, there can be multiple studies of that. Yeah, Facebook can kind of echo chambers. I think one of the really interesting things here, when you look at education and other use cases, social, is allowing us to meet others within the same context and bridging the context. Again, this podcast is going out into multiple languages in our voices, which means that people can understand us in a different way that they could have understood us before. We're breaking down barriers with this, and I think fundamentally, most of the meaning
1:08:32 Speaker 01 of life and the quality of time is in our human connection with each other. Like, when you're reading the classics, you're listening to the stories of these people. And if you suddenly can talk to Archimedes, or Richard Feynman, or any of these people, that's great, but you know what's even better? Discussing it with other people. I think AI can really help with that.
1:08:54 Peter Diamandis? No, the third one you said is choices galore. You say you want to code but never have, AI's got your back. Need to understand complex data, AI can break it down for you. And I get that. It's, it's basically complimenting you in areas where you don't have expertise, or even where you do have expertise but want different perspectives.
1:09:13 Speaker 01 I think a large part of this is the question, that there's memes that go around sometimes of, you can just do things, you know? Like, what's, what's stopping you? Most people are like, well, I can never ballroom dance, and they go and learn to ballroom dance, you know? I could never code, and they learn how to code, and they actually really love it. The bar and barrier for creating and engaging with this technology, the floor has risen.
1:09:42 Peter Diamandis? Yeah.
1:09:43 Speaker 01 What can't you do?
1:09:45 Peter Diamandis? Yeah, the, the, the, the bar has dropped, is I guess what you want to say.
1:09:49 Speaker 01 Yeah. The bar has dropped, the floor, floor has risen. Like, everyone's uplifted by this, because the barriers to entry have dropped, you know? The barriers to capability have dropped with this. So you have increased agency, you have increased capability, because you're always constrained by finding that teacher. Now the teacher is here.
1:10:08 Peter Diamandis? Let's talk about companies and governments in particular. You know, in companies, there was a study done recently, um, in which it showed your Lowest fifty percent of your employee base has gotten the biggest rise out of using generative AI versus your top fifty percent. So it's like leveling the playing field, which is pretty extraordinary. And I can very easily imagine how AIs, you know, companies are going to use centaurs and agents and such. But I want to understand your thoughts on government because government is the most sublinear, in most cases inefficient, structures out there on the planet and are going to have the biggest challenge, I think, in the
1:10:51 Peter Diamandis? decade ahead. I'm not sure if you agree with that. But how do you think about governments utilizing utilizing these resources capabilities?
1:11:03 Speaker 01 I think there's a mixture of hope and fear on the government side. I mean, like, I think for me, it's clear that AI can do better than governments and resource allocation, even with what we have now. Because again, the bar is very low there. But one of the things is we've lost trust in our governments. And that's a function of, you know, the lack of transparency that occurs. Like, you know, you're in LA now, right? I believe, Peter?
1:11:30 Peter Diamandis? Yeah, I am.
1:11:31 Speaker 01 Six hundred million dollars on the homelessness problem or something is being spent this year. You look at the LA to San Francisco Railway, like, we all know that there's a conundrum going in there. And AI can deconstruct every single thing around that and show what's really going on. But does the government really want that? That's the thing. Does it really represent the people? Or is it a continuous entity that's only about self-preservation? And this is one of the challenges we're going to have as a people, because there's riots in London right now, Bangladesh, they're proliferating. People don't really believe in their governments and systems anymore. Can we use AI to increase transparency and trust in organizations and be a co-pilot for every single individual
1:12:14 Speaker 01 engaging? Can we do things like direct democracy, whereby you have groups of people, and then you inform them on the topics and capture all the context of their conversations and feed it up to be representative and new forms of democratic engagement? I'm not sure, but I think we have to try because I'm very worried about where things are going now with just these. Massive echo chambers, the lack of transparency, increased graft and the feeling that the governments do not represent us.
1:12:43 Peter Diamandis? I think we have to try. I also think governments can do everything they're powered to resist. Because it's it's loss of control. It's it's and and the challenge isn't just governments by themselves. It's big governments and large corporations. And the two of them together are likely. If AI is going to disrupt the system, it's going to disrupt that um that unholy marriage.
1:13:09 Speaker 01 Well, I think, again, these are local maximum conditions whereby. That holy marriage works because things were positive some for a while. Now things are a bit more negative some. The corporations and governments have no option but to embrace AI. Because otherwise you'd be left behind by things that are not, and there'll be huge pressure for that. I think it was Taleb, the Nicholas Tanda, that said that, you know, when twelve percent of our population revolt, that's usually enough to get anything done. Realistically, right? And people will be required to embrace this technology or they'll fall behind their peers and they'll demand transparency and other things. But governments kind of like the technology.
1:13:50 Speaker 01 Um if it's from trusted parties like your IBMs and others of the world because it makes them look cool. And this is a separate something like cryptocurrency. You know, government control of money, censorship resistant. It was a system outside the existing system. Whereas this is already institutional. It took how many years for Bitcoin and Ethereum to have ETFs? You know, whereas AI is already institutional, it's already there and people are already embracing it and bringing it into government. But this is why I think like. There are things that directly challenge the graft and other things, and there's things that we can do to make the general thing better, like the representative democracy stuff. Like the analysis of policy positions, making that available to everyone, increasing
1:14:34 Speaker 01 the ability of people to speak. You know, intelligently and understand the topics. Like, these types of things should exist, and I really hope they do get built. Because there's no reason that everyone shouldn't be fully informed of every policy decision before every single election in a way that is directly aligned and impactful for their community, for example, and be able to do scenario analysis.
1:14:59 Peter Diamandis? I agree. You walk into a voting booth today and you're asked to vote on all of these issues. And first of all, the way they're written is confusing. And secondly, you know, most of us haven't had the chance to actually
1:15:16 Peter Diamandis? actually consume sufficient data. And a lot of people are actually just influenced by what the signs they read on the polling booths as they walked in to to take it. But having an AI, your AI, give you an analysis of how does this particular measure impact me and the things I care about? Um, is a very powerful, a very powerful tool. I just don't know that politicians are going to want to give up their control and their manipulation, just like they do. You know, uh, you're gerrymandering of of who can vote.
1:15:54 Speaker 01 Well, the thing is, you no longer need permission with the technology. Leveraging a technology like Llama or Mistral, some of these language models, you know, even Gemini or ChatGPT, you can build a system probably for tens of millions of dollars that would deconstruct every policy position of every single candidate with the publicly available data. And then you could input your own details and it would tell you how it would affect you with full transparency.
1:16:25 Peter Diamandis? I hope someone is listening and will and will build that because I think it's it's so massively needed for every political election out there.
1:16:34 Speaker 01 Yeah, and the beauty of it, like I said, is it can be fully open source, or I think that's the best way to do it because there's very little argument that that shouldn't be built. But again, this is one of these just do it things. I think you could even organize a hackathon and literally have that built. Maybe we should do a government generative AI hackathon. That's all about increasing transparency and using this technology to do so. And again, it doesn't require permission. It doesn't affect the way things are. It just affects the capabilities of people. Which I think, again, this empowerment thing.
1:17:05 Peter Diamandis? That's all the upside of AI for governance, and I agree with all of it and would love this to materialize. You and I have had a different conversation probably a year ago where the ability for AI to be persuasive
1:17:22 Peter Diamandis? and persuade individuals, can you speak to that?
1:17:25 Speaker 01 Yeah, I mean, like, if an AI can be empathetic, it can be manipulative.
1:17:30 Speaker 01 And fundamentally, the business models of Google and Meta are manipulation. So if you have an AI whispering in your ear every single day, and it knows exactly everything about you, then it can sell you that toothbrush, you know, it can tell you, you know, the sort of thing, sell me this pen. I can definitely sell you that pen, right?
1:17:50 Peter Diamandis? My favorite marketing question.
1:17:52 Speaker 01 But then you think about, again, elections and things like that, you can have a personalized call for every single voter. And it doesn't even have to be deep fakes, it can just be really persuasive. I can layer on Barack Obama at his best, combined with Winston Churchill, combined with Oprah, you know, for the sublingual frequencies, and that will be a convincing call from any political candidate. And it'll be an exactly the local candidate.
1:18:17 Peter Diamandis? Knowing your position, knowing how many kids you have, where they go to school, you know, what kind of business you're in, and then giving you a spiel that's hyper personalized to guide you towards, towards their direction.
1:18:33 Speaker 01 Yeah, I mean, you can see an example of this. There was an advertising campaign, just when this was all starting out, by Cadbury's in India, where the Indian superstar, Shah Rukh Khan. And you could go in your local kind of organization, and you could just enter your business name type, and it'd create a customized Shah Rukh Khan ad, where he talks about, you know, buy this sweet shop, buy these shoes, check it out, Cadbury's India campaign Shah Rukh Khan. But again, like, we can expect that the level of convincingness of these ads and hyper personalization will go through the roof, and then the political side, it'll go through the roof as well. But also the personal side, like, if I'm gonna have a Zoom call with a prospective client,
1:19:13 Speaker 01 I'm gonna look damn good, and I'm gonna be so convincing and not have a stutter or anything like that on the call. And when you meet me in real life, you'd be like, oh, my God, he's not as good as that. Why wouldn't you use that technology? Because we can do it real time now, like we have these small filters. But what if your voice, you can dial up the level of emotion on that voice live on a voice call. You know, it's going to be crazy what we're entering right now. Um, I have to say, and it's again, all real time. This is the other crazy part about it. Be able to translate your voice real time, have real time avatars that look like humans were there. It's not a future thing.
1:19:52 Peter Diamandis? All right, next up in your paper, Imad, we talked about crossroads and what to do. Uh, I think that's one of the most important thing. Uh, this is here, this is coming, this is accelerating, this is faster, more powerful. Anybody thought it's not stoppable. I think that's an important point, right? There's no on off switch, there's no velocity knob. It is moving as rapidly as um, the largest, most powerful companies in the world. And by the way, all of the AI companies are the largest and most powerful companies in the world uh, can push it. So um, uh, you state here and and we're going to talk a little about Schelling, which is a new company.
1:20:33 Peter Diamandis? Uh, and that you've been uh, you've been in uh, you've been pregnant for nine months and getting ready to give birth. Uh, and you say Schelling believes in democratizing AI. Um, it's not just preferable, it's essential. And then you have uh, five, consider this, uh, diverse perspectives ensure AI caters to the social societal needs. Open source development builds trust, collaboration speeds up innovation. And broad participation keeps ethical considerations at the forefront. So let's, let's dive into what do we have to do as a society? Um, uh, because people are asking, you know, I, I chaired the AI committee at FII.
1:21:17 Peter Diamandis? Uh, the Future Investment Initiative and the conversation is always, okay. What should large corporations, what should governments, what should the public do? What should be our guidelines? You know, give us direction and I love the fact that in this paper, and again, I really commend this, you know, how to think about AI. And we'll, we'll put the link in the show notes uh, for your Substack and your and uh, on, on X for people to read it. It's not a long paper and it's beautifully written. Let's talk about what should people do? What's your advice, my friend?
1:21:51 Speaker 01 Yeah, thank you for that. I think, again, understanding AI as infrastructure, just like roads, you know, just like ports and others. Clayton Christensen, the famous Harvard Business School professor, came up with disruptive innovation, said, "Infrastructure is the most efficient means by which society stores and distributes value." And if a level of AI capability is barred from people, then you will have an unequal infrastructure. You will have private roads to knowledge. I think it's bad. When you look at the actual cost of building this and the coordination required, because the output of this is literally, GPT-4 is like a hundred megabyte model, that's it. It's a file. We can gather together and build this technology together in an interoperable way.
1:22:36 Speaker 01 And when we look at the inevitable future, will our governments, our schools, our hospitals, be run on black box models that we don't know what the data inside is? Because you are what you eat. You know, the outputs are affected by the input. Or should we work together to create high-quality national societal sectoral data sets? If we have a model that's available for every single cancer patient and their families to guide them through that process, should that be a privatized model or should it be open infrastructure for everyone? You know, and I think there's this class of, in every regulated industry, and industries are sometimes over-regulated, but they're there also to protect people. We should have an open class of models with a data sets, the models themselves are open.
1:23:21 Speaker 01 And you can do things like Anthropic have a series of papers called Sleeper Agents, where you can poison data sets of models turn evil with just a few curioids. This also mitigates things like that. And it's impossible to tune out or find, which also is crazy when they start being decision-making processes. So, my view, and this is what we're building at Shelling, is that every nation needs their own sovereign AI that represents them. We need to make our collective common knowledge open, from creativity to science to health to education. And that should be open infrastructure for all. And then that makes the world safer as well. Because right now, our AI systems are being trained on snapshots of the internet with all its imperfections versus a high-quality curriculum. Again, you are what you eat.
1:24:05 Speaker 01 If you don't want it to exhibit some of this bad stuff, have high quality. We need diversity as well as quality. Yeah, and this is what people are kind of going through on. So I think, you know, deliberately building the future where there's an inevitability of open infrastructure for industry by industry and standards around that is going to be very important. I think this is where governments and private sector should come together, you know, um, and then that can proliferate if it's open because you don't need permission then as well. So stable diffusion and the other image and video models and medical
1:24:47 Speaker 01 models we did at Stability AI when I was CEO, they ended up having three hundred million downloads by developers. Three hundred million because you could just take it and you could build on it. And that's just the start. That was like over the last few years before the vast majority of the world even knew about this technology. Again, if we build an open cancer model that is about empathy and guiding people through that process, how to talk, but also comprehensive, authoritative and up to date on our existing knowledge and the latest knowledge on cancer and groups and connections, that can be in every language and used by every single individual afflicted by cancer in the world.
1:25:25 Peter Diamandis? Completely demonetized and democratized and the billionaire and the poorest child have access to the same information.
1:25:32 Speaker 01 Same information. Again, it can proliferate. I think this is the key thing. But we should do that in a way, and that's why I call it shelling, that there is trusted entities and good governance on that data.
1:25:43 Peter Diamandis? What is the, what is the word shelling come from for your company, Shelling Point?
1:25:47 Speaker 01 It comes from this concept in game theory called a shelling point, which is a focal point, a point of agreement. Because if you create high quality data sets for nations and for sectors, because you have generalized knowledge and we need to have a corpus of that, and then localized and specialized knowledge, then people will use that because it becomes a go to point. If you create that cancer model and you invite everyone who has a stake in cancer to participate in that, it will proliferate around the world and be the standard again. Bitcoin uses this a lot because.
1:26:18 Peter Diamandis? For simplicity, is this effectively what Wikipedia did?
1:26:22 Speaker 01 It created a standard in a way of objective truth, but Wikipedia couldn't capture the nuance because the technology wasn't there. So something like cancer, you have your known knowledge, you have your standards and you've got your stuff we kind of know works, but it isn't in the individual corpus. And then we protect people by not showing them all of the other stuff and they go to quacks instead, or they do their own self-research and they repeat that process over and over again. What if we create a system that was comprehensive, authoritative, up to date, and respected the individual and their ability to do that research, but then also told you, how do you engage with your family, you know, how do you follow through this process, connect you with the resources that you need?
1:27:05 Speaker 01 That should be an open distributed intelligence system. That's the way that I'm kind of looking at it.
1:27:11 Peter Diamandis? I mean, the number of people I get saying, my friend has this kind of cancer, what should they do, where should they go, how do they start? It's um, it's, it's sad and you, your heart goes out to them. And there is no one, one standard, but there can be.
1:27:26 Speaker 01 But it's also this loss of agency that you feel with cancer, autism, multiple sclerosis. And so that's why I was like, let's go and build those models as open infrastructure, use this massive compute, you know, and think about generative AI first healthcare, what should be open? And that's a base. And then people will build on top of that, they'll take it, they'll implement it, they'll make so much money of doing that, but it can then all use great healthcare records and others, because we have a chance to reimagine healthcare, reimagine education, reimagine creativity, reimagine just about anything using this technology. So that's why I was like, let's set a framework first and then let's start building and gathering these communities together. And part will be open and part will be proprietary and that's absolutely fine. But if we can make an open base.
1:28:13 Speaker 01 This can really proliferate, I think.
1:28:15 Peter Diamandis? What do you think about Zuck's Lama 3.1 and his push on open source?
1:28:21 Speaker 01 I think it makes a lot of sense. You know, if they can decrease costs by 10%, it pays for itself and they have. And again, I think Meta has been an open source champion with PyTorch and other things, and it's not where he makes money, so he's commoditizing complement. But I also think it only goes so far. And you need an entity to be able to build this stuff deliberatively because Zuck, I don't think will ever build the models that run governments and healthcare and education and finance, because that's not his company's objective function. His company's objective function is to connect people with an advertising-based business model. And there's a positive and negative side of that, and he's amazing at his job. You know, but it shouldn't be on him to create these standards. And in fact,
1:29:04 Speaker 01 you came back from India, internet.org, for example, they wanted to give free internet to everyone in India and others. It had massive regulatory and other issues. So I think we need to, again, create a complement to that because you need open-based models that are open weights, as we call them. We don't know what's inside them, but we need that class of fully open models as well. And we need the class of models that are fully private as experts because people will make the best models there. And so you'll have a continuum. And then we have all the tools we need to have any type of graduate or consultant to solve our jobs and get things done.
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1:31:19 Peter Diamandis? You'll find it at viome.com/peter. You list in the list of what we must do as rethink our economic models. And, I mean, one of the biggest challenges is that our historic economic models aren't viable. They don't make sense anymore with what's coming, given AI and humanoid robotics. So how do you think about rethinking our economic models? Where do you imagine it going?
1:31:48 Speaker 01 That's a nice, simple question, Peter.
1:31:51 Peter Diamandis? Yes, if we could just, you know, give us your point of view on the world economy for the next century, that'd be great.
1:32:01 Speaker 01 There we go.
1:32:03 Peter Diamandis? Yeah. I've always said, if I had more time, I'd write an economics book.
1:32:08 Speaker 01 We're at the terminal point of our current economic structure. We see it with Japan today, as we're kind of on this call. Largest stock market drawdown ever because they raised 25 basis points because they're functionally bankrupt as an economy. You know, with the 500% debt to GDP. We borrowed as much as we can from the future, and we built this whole infrastructure that's about to have a brand-new continent of... Incredibly cheap workers hit it all at once. In the West, we have the service-based economy. Like, at least with robots, you're constricted with the amount of robots you can produce and the financing of those robots.
1:32:44 Peter Diamandis? Robots will build robots and AIs will build.
1:32:46 Speaker 01 It will go there, but but the agent stuff, literally like a travel agent, as an example. Once we get a really good travel agent, you push a button, it replicates, you know, and it could call you and it can do everything like that. So we have to consider, what is money? And this is happening at a time when, you know, the petrodollar has shifted, so the the energy equation is kind of done and others. And we have to consider, if we live in this world of infinite abundance with billions of robots and trillions of agents, what is money?
1:33:16 Peter Diamandis? Yes.
1:33:16 Speaker 01 Because money was this.
1:33:17 Peter Diamandis? We are heading towards a a post-capitalist society um in the in the long run, right?
1:33:23 Speaker 01 In the long run, and, you know, just like in Foundation or whatever, that interim period can be very, very messy because it's a zero-sum game with people scrabbling over that. But when anyone can live like a king, you know, what is human progress? I think it unlocks a massive amount of creativity, it unlocks a massive amount of other things. We can eliminate hunger, we can eliminate disease, we can do all of this, we can come together and create that human colossus. But again, money is there because we need an intersubjective thing as a unit of account. But when you have AI agents that can barter, that changes things. When you have people embracing this and out competing on a corporate basis, that changes things.
1:34:05 Speaker 01 AIs themselves and robots are corporations, are technically people, you know, in some senses. Like, they have same rights. So AIs already have rights, if they could be a corporation. And in Wyoming, they passed Dow legislation, Decentralized Autonomous Organization. So you may have AIs that go out and have rights, which is kind of crazy to think about. But against this all, it's, I think our monetary base has always been tied to energy. And entropy of that energy to create things, physical and knowledge based. I think that fundamental equation is challenged and so we need to rethink how our economic flows go. And the rights around that as well, and that's difficult and it's hard.
1:34:49 Speaker 01 That's why I think every nation needs their own AI experts, AI teams. We need to get the smartest economists in the world to think about this and think outside the box. And say, is it universal basic compute, universal basic income, universal basic jobs? Do we need a, I think we need a trillion dollar jobs program for the graduates today.
1:35:07 Peter Diamandis? I felt this when I was in India, right? You know, the IITs are graduating, it's like 12 million graduates a year. And you're seeing the number, percentage of them getting jobs. And in youthful nations like India and Africa, um, if you are getting your degree and you don't have a future. That's a lot of, you know, let me name it testosterone that is unchanneled. And that becomes a very difficult uh future.
1:35:36 Speaker 01 It's a social challenge, like the status a couple of months ago is that thirty eight percent of the current IIT batch. Top notch in India were, didn't have job placements. It'll be fifty percent next year. But then even in the West, we have all these programs to repurpose people into STEM education. Like aside, programming jobs, programming will change, you know? What do you do with truckers when you can have trucks as autonomous vehicles?
1:36:01 Peter Diamandis? And I think the truckers, the truckers buy the autonomous trucks and have them work for them.
1:36:07 Speaker 01 Yeah, this is a dividend.
1:36:09 Peter Diamandis? Yeah.
1:36:10 Speaker 01 Yeah, this is a dividend type equation, right? Whereby the nature of capital flows in our society, when you have this influx of supply. Will the demand catch up eventually? It will, I think. But it'll be very messy going through that. And on the other side, it's about. What access do I have as an individual to capability? And then how can I create value from that? And I think it's massively positive sum. But it's really tricky to see the transition. Like, I struggle to see how in fifty years' time, shall we say, going a bit further than normal? Because, you know, we tend to underestimate this. But if we even go to fifty or twenty forty, just before the singularity, right? As Ray kind of said. Like, what is money?
1:36:56 Speaker 01 Because you're extrapolating forward, you're like, the US can't borrow another five hundred percent of GDP. You know, but at the same time. Intelligence is abundant, but also skilled manual labor is abundant. What? It's crazy to even think about.
1:37:14 Peter Diamandis? Yeah, yeah.
1:37:15 Speaker 01 GDP is undefined, so therefore we must instead optimize for happiness, contentment, social cohesion. What do jobs look like? Is it taking the cancer empathy model and going out to your community and supporting families through that? That's a great job, you know? But it's complicated.
1:37:31 Peter Diamandis? We are going to go from, you know, most people have a job because they need to put food on the table or get insurance for their family. It's not the job they dreamed about. So I think we need to start to disintermediate employment for income and employment for personal fulfillment. I think that split is going to happen sooner rather than later.
1:37:52 Speaker 01 And again, you've got this, things are either for living or entertainment, right? And so we need to standardize the living aspects. Everyone can reach that basic level. And again, Western may be a bit easier. But again, when you actually look at what you can build, it's amazing to think about. And then you need to think about how do you manage that transition? Because there's vested interests and others when the whole world gets flipped and currencies start shifting because a lot of the anchors are broken. And this scares me because it's a complex system. And the only way I think you can do that is you have to build AIs to help us.
1:38:25 Peter Diamandis? How far away from that, that flip? That, that inflection point where people, it becomes evident and people start.
1:38:35 Peter Diamandis? Um, searching very rapidly for answers.
1:38:39 Speaker 01 I think that, you know, in five years' time, things get very crazy. I think we're at the early innings of that. I don't see how things calm down. Cause again, this technology proliferates and then ten, fifteen years past that, it's very, very short time. In classical cycles, it can be longer, but again, this is an industrial AI revolution done incredibly quickly. With multiple things from self-driving to autonomous agents to intelligence, all coming at the same time. As the end of mathematically, our debt fuels society that we've had. Again, Japan has five hundred percent debt to GDP. How are they going to borrow more? How is the US going to borrow more?
1:39:19 Peter Diamandis? And part of it has been a decreasing birth rate that's been impacting these nations, right? If a nation is growing in population and labor and automation, then it's producing more year on year.
1:39:33 Speaker 01 Well, I think, I think this is the really fascinating thing with China, because everyone's been like, what if China gets AGI? What if China gets a hundred million or a billion robots before everyone else? And produces the robots. That's what you should be worrying about.
1:39:47 Peter Diamandis? It's about energy one second. You've mentioned this a couple of times and I, I just, you know, was looking at a chart. That shows, um, you know, the US has been generating four terawatt hours per year. And it's been pretty flat for the last twenty years and it's projected to be flat. We're not increasing the energy production in the country. And unfortunately, you know, generation two and generation three nuclear plants gave nuclear a bad name.
1:40:15 Peter Diamandis? At the same time, China has tripled the amount of energy that it's, uh, it's, it's putting out. India is on the path to doubling it. And one of the things that came out of Leopold's, um, uh, paper on situational awareness is the projection that the US could use a hundred percent. Of its current energy production to power its AI needs by 2030. And so, energy is a big issue. What are your thoughts about that?
1:40:48 Speaker 01 I mean, I think that that energy extrapolation is wrong because you're chip constrained and I think scale only gets you to a certain point. I think it's an S curve that we're thinking is an exponential in capabilities, like distributed data and high quality data is far more important. But the reality is that we're already hitting energy limits across the entire base. And the new substrate, like you and I, we need to pay for food, we need to pay for housing, we need to pay for our kids' education. The AIs and robots of the future need to pay for energy and computation. So if we're thinking about money, no, not data. I think there's a misnomer on the data side because once you have a high quality curriculum, you don't need more.
1:41:33 Speaker 01 Like, the original GPT-3 paper was called "Language Models Are Few-Shot Learners." And this is what we see. We have a generalized model, like a GPT-4 or an Anthropic Claude or a Gemini. It is a file that's trained on all of this stuff and then tuned, and it can adapt to any scenario through its context window, through its input prompt. Does it need more data? No. So you get a point of data saturation for a model that has capabilities of X, then Y, then Z, and then proliferation and optimization of that. Whereas what we're seeing with things like the Leopold essay is that you'll have an exponential of compute that continues. I think you'll have a glut in a couple of years.
1:42:16 Speaker 01 But nobody can take the other side of that bet. I think Mark Zuckerberg just said that a few days ago at Meta. "We can't underspend in this cycle." Sundar Pichai said the same. "The US does not have enough energy to meet the demands over the next few years. And even if you have a glut, then you'll be on the other side and you'll ramp up." Energy is intelligence for now.
1:42:42 Speaker 01 You know, what it will be going forward, that's another question. And that will depend on things like video and other things versus language models, which I think a lot of it will go to the edge. I think it will go from these large dense models to mixtures of experts and highly optimized models. But it's very hard to see how energy requirements go down. Or the reallocation of energy occurs here. Like, um, one of the shocking statistics is, you know Bitcoin, our favorite kind of cryptocurrency. The total energy usage of Bitcoin is half of the energy usage of all the data centers in the world. Right now, 160 terawatt hours.
1:43:22 Speaker 01 It's as much as Argentina or the Netherlands. And that's just one use case. So if you think about it, like, people who say that we're in a bubble and AI and everything like that, AI uses like a fifth of the energy of Bitcoin.
1:43:37 Speaker 01 You know, and Bitcoin is useful for various things, but it's not as useful as generative AI. So I think we've got many doublings of the energy usage of generative AI still coming on a training and inference basis, running basis.
1:43:50 Peter Diamandis? What did you think of Leopold's situational awareness paper? And one of the things in particular, he in, in his, in his paper and in his follow-on podcast that he did, um, he pits the US against China and it's the existential risk is who develops AGI first. And so let's get into that conversation of, of AGI. Are we going to achieve AGI? When do we get it? Is that a US versus China, um, modality here? What are your thoughts there?
1:44:24 Speaker 01 I think we have a choice on AGI, artificial general intelligence, artificial superintelligence of, is it a swarm collective intelligence that uplifts everyone as representative of everyone, which I think is a safe thing? Or is it a unitary intelligence based on, like, just a few entities? I think the distributed intelligence, the collective intelligence is a far more powerful vision and way of doing things. Again, open infrastructure enables that. And this is the level above the Lama-type infrastructure, where you gather the people and you coordinate. I think the big AGI ASI is less likely. I think it's an extrapolation on the curve of capabilities that we just don't know about. But regardless, we're talking, I think,
1:45:08 Speaker 01 this is the Ray Kurzweil thing, right? What's his current forecast?
1:45:11 Peter Diamandis? Well, his for AGI is 2029 still, and for, you know, the singularity is early 2040s.
1:45:18 Speaker 01 I think that sounds about right, right? Like, if you have AGI as capable as any human with a mixture of experts and physical embodiment, it's tough to argue against that, honestly. Given, and this is important, as much computation as you need for the inference step. A lot of the models we build today are trained on 10, 20, 100,000 chips, but they're designed for consumers. And the energy that you use in running it is a few watts of electricity, or 100 watts of electricity, 1,000 watts. If you don't care about the amount of energy and you have 1,000 or a million agents, then we're pretty much AGI now, but I think we'll get there, there, where it's as capable as any human. The ASI
1:46:02 Speaker 01 singularity, I think, is a bit further. But again, is it a centralized one or is it distributed one? This is the question.
1:46:08 Peter Diamandis? One of the points that Leopold makes, and you and I have discussed this before, is, you know, GPT, you know, two is a preschooler, GPT three is elementary, GPT four is a high schooler, GPT five, he describes as a PhD level. And at that point, when you've got a, an AI agent that's self-programming, you end up with an intelligence explosion. Do you believe that's the case?
1:46:41 Speaker 01 I think that, you know, we don't know if the stuff will outperform its base data, but also I know that PhDs tend to be very depressed and sad people. Just kidding. But I think it's a reasonable thing. And this is one of the things I was worried about a lot last year. Like, I was on the signatories, I was the only AI signatory apart from Elon Musk on the pause letter, for example, because I think we need a debate about it. But I think if we build high-quality data going in, I'm not so worried about that. And I think that with the self-improvement and recursive stuff and the MCTS type thing, multi-color tree search, we see the type of technology that goes into that deep Google, deep brain, deep mind paper that got silver medal on the level on the International Math Olympiad.
1:47:23 Speaker 01 It makes reasonable sense, but will it go out of control and get self-awareness? I don't really think so. But even if it does, let's say that's the premise. I think this China versus US thing is a bit of a misnomer. Like, for a start, I don't think China wants that. But also, I think that if you look at the components, data, talent, compute, China beats the US. Because on data-
1:47:48 Peter Diamandis? Let's break those down, yeah.
1:47:49 Speaker 01 China doesn't care about IP. You know, look at the video models, they're trained on all of Hollywood. I mean, some of the US ones are as well. They'll train on SciHub, you know, they'll train on whatever, and they'll train multilingual. So data, there's an advantage there, but also, the Chinese via WeChat and these other things can get a hundred million Chinese to do data annotation and feedback, whereas OpenAI pays up to $200 per annotation for that data. The Chinese could easy do that for free. You look at compute, China has two Exascale computers. So Exascale is a thousand petaphlops of compute. So Elon Musk's new 100,000 H100 cluster is two exaphlops.
1:48:31 Speaker 01 The fastest supercomputer in the US, Aurora, is about two exaphlops. The fourth, the fifth fastest supercomputer is like 100, to give you an idea. So we've got, it's order of magnitude increase. China has Tianhe 3 and Ocean Light, which are publicly known about, that are both exaphlop and at least four other exaphlop computers that can run these giant models. And they can source the chips elsewhere. But again, we're moving from this thing of big compute was a substitute for crap data. And we can do distributed compute and data augmentation. That's the type of thing China's very, very good at. They're good at industrialization. There are approaches now, we're seeing with OpenAI and others, where why did they invest in Figure Robotics for robots?
1:49:15 Speaker 01 Who's gonna build most of the robots of the world? It's going to be China.
1:49:19 Peter Diamandis? Well, I think they're going to, they will have their companies that compete against the US base, for sure. And one of the things that's interesting is these humanoid robots become a source of data as well.
1:49:30 Speaker 01 They all feed back into that data augmentation. But I think a large part of this is, we've had this AI AGI giant model fixation, where I think it's more about a distributed collective intelligence that's constantly learning and adapting from real world usage. Again, a swarm of robots would be an ideal one for that, right? Just like a swarm of Teslas, a swarm of robots. And then we've had this fixation on really gigantic compute, when it can be localized data optimization that then feeds in. If you optimize the data for models, you can have orders of magnitude less compute required. But we're not quite sure what that data optimization is yet. And I think, again, this is a race to this existential threat type of thing, where it's China versus, I don't really buy that, honestly.
1:50:15 Peter Diamandis? You do push for international cooperation. How do we get international cooperation in AI? What would be, you're on the stage, you've got the nation's leaders around the world, what are you saying to them?
1:50:27 Speaker 01 I think that, you know, we're already seeing extra-scale computers come on board with things like the Euro HPC initiative, with the new Japanese extra-scale computer, unfortunately Britain shall hold its one for the time being. Like a coordinated deliberative effort to build high-quality data sets and models for humanity, generalized, localized, and specialized. Like we're doing that anyway at Shelling, you know? And we'll be building national champions that can collaborate with governments and more. And then I think standards around input data. We care about the ingredients that feed us in our food, but not the ingredients that teach us and will guide us in the future. I think that any decision-making system should have data transparency.
1:51:12 Speaker 01 I think that will go a large way to helping on AI safety, on AI alignment and more. And, you know, really thinking about what should we feed these models? We should feed them Diverse data, you know, not from a D E I or anything perspective, but from diversity of viewpoints representing humanity versus that snapshot on the internet and ensuring that we have that done properly.
1:51:34 Peter Diamandis? One of the conversations we've had as well is, you know, most of the corpus fed the A I. models are Western, if not U. S. Um, and there's, you know, a hundred nations out there that don't have their data digitized. In other words, they're the data of of the grandfather and grandmother is in verbal language and hasn't been, you know, digitized in a way that it can be consumed by an A I. model. I mean, for me, that's a that's a massive public works project that countries could take on.
1:52:05 Speaker 01 Hundred percent. I think that, you know, we're going to be doing that with crypto economic other incentives at shelling. Uh, the goal is to build a data set for every nation and be massively collaborative as well as for individual sectors and science and more. There'll be more details of that soon as a self representative system. But we can think of it like, you know, um, we send a probe out to space, right? What's on that that represents humanity? We engage with a new species because we've got a new species, this A I. Atlantis, a new continent. Are we showing them one cultural point of view? What's on that data hard drive that we send them? What do we feed in? And again, who do we want to represent? And I think you need to have this hive mind concept, you need this collective intelligence
1:52:50 Speaker 01 concept because that's how we proliferate and flourish as a society. I don't think it's about scaling up a liberal arts polymath, you know? I think it's about building a hive collective intelligence and Neuralink and others, again, are huge things around that. Um, and having it reflect the best of humanity and everyone contribute what they think the best of their culture is. Because then these models can translate between cultures, and I think that's just beneficial, full stop. And that is the education system, it's more.
1:53:23 Peter Diamandis? Yeah, helping people, you know, one of the things I find AI so credibly useful is if, if there's someone on the opposite side of a, of an issue, or someone who, who I'm not able to clearly understand their motivations, AI can help me communicate with that individual in a way that I never could, right? Please help me explain my position on guns to someone on the other side of it in a way that they might be receptive to it. That translation of not just language, but translation of intent and desire.
1:53:58 Speaker 01 It's a knowledge translation, it's a context translation. And again, this is the universal translator. That's what these models are. And again, I think we need to deliberately get together and build that out and realize, I think this is very important. The Leopold essay assumes a negative sum game. Again, there's fantastic things, like eighty percent of it's great. This is a massive positive sum game. And most of the AI discussions are negative sum, which I don't think they should be.
1:54:24 Peter Diamandis? Where does he point out, where does he point out negative sum game?
1:54:28 Speaker 01 There's a negative sum game in that we must be the first to AGI because then it contracts everything. As opposed to building open, common infrastructure for everyone, this is a competitive race dynamic with unstable equilibrium.
1:54:41 Peter Diamandis? I mean, I think he's, his point of view includes the potential for a hard takeoff. And at that point, if in fact, we have an intelligence explosion, the person who gets there first dominates it. And it's a question of do we want the US and its national partners getting there first?
1:55:00 Speaker 01 Well, I think, and this is interesting because it's reflected in the Sam Altman recent op ed in the Wall Street Journal, where he talks about authoritarian AI versus democratic AI, but then recommends that it's locked down and it's restricted and it's basically unitary in its point of view, which sounds very authoritarian. China's producing open source AI that's as good as all this stuff already. And it's a complex thing. But again, I think that there should exist open infrastructure and a collective approach, as well as this collected approach, shall we say?
1:55:31 Peter Diamandis? I want to hit on two final points.
1:55:35 Peter Diamandis? Investing in AI. A lot of people right now are like, I know AI is huge. I know it's the most important part in the world for us economically. We're seeing these companies from Nvidia and Google and Microsoft dominate the stock market. And it's like, if you look at these companies as a percentage of the global GDP compared to other market sectors, they're massive. And so the question is, how does an investor think about it today? Where should they be putting their money? Do they continue to invest in the giants? Or are they overpriced? What's your... I'm not sure if I'm asking for investment advice,
1:56:18 Peter Diamandis? but how should someone think about, since your paper is about how to think about AI, how should they think about investing in AI?
1:56:25 Speaker 01 It's a separate thing. Putting on my former hedge fund manager hat. So, look, I think that the first wave was kind of these Nvidia's and kind of others, Microsoft and Google, etc. Showed they're up like, what, 20, 30% over the last year, maybe 50%. It's not a huge amount, in line with market. Nvidia has been the standout, you know, because they've established market dominance, they capitalized on it. But it's not expensive like Cisco was in the dot com boom, for example, as an infrastructure provider on classical terms. But what I think is happening here is that we're going to have it overbuilt regardless, obviously we will, because they can't afford to underbuild. But then we move on to the application and implementation of this technology by centrals.
1:57:10 Speaker 01 And so highly regulated industries or industries with pricing power that can replace rote human work with AI Atlantis has supernormal margins. And then you see a margin expansion. The impact of this is how much will it cost to... When we had cloud computing, it reduced the cost of building a startup dramatically. We had to wire up our own servers and everything. Now you will have an AI agent in the box that can help you build a hairdressing business
1:57:45 Speaker 01 AI business or whatever, almost there in the next few years, you have this explosion of creativity. AI-enabled work, I think, is where you see this the most. So pricing power is kind of one thing. Industries that can have this on the other side to increase their reach, so you increase your audience. Again, where, if this business existed, I could apply a million graduates, you know, liberal arts grads, and they'll be increasingly specialized, how does that impact? So that should be a framework for looking at the business opportunities of any stock. And again, regulated industries with pricing power are a fantastic place for that. You don't need the private equity to come in anymore and redo that.
1:58:24 Peter Diamandis? Do you think the current, do you think the current major players of Nvidia, Google, Microsoft are overvalued? Or are they still of growth?
1:58:31 Speaker 01 On classical valuation terms, they are perfectly fine on valuation. And again, we're seeing continued growth and it's not like it's crazy yet. Bubbles in the past, though, we've seen crazy, this is not crazy yet. Like, you know, when you're valued by the number of eyeballs in the dot com bubble and things like that that we've seen.
1:58:52 Peter Diamandis? Second question here.
1:58:53 Speaker 01 This does point to something, which is, how much of the global economy will be AI? A lot.
1:58:59 Peter Diamandis? As compared to where it is today, which is still miniscule.
1:59:02 Speaker 01 Yes. And again, if we compare it to even self-driving cars, you still need five, 10 times as much investment to catch up to self-driving cars. Versus model training.
1:59:12 Peter Diamandis? My last question, my friend, is on the education front. A lot of people are looking, I need to get educated. You've said this on the abundance stage, we've said this in our podcast, that it's critical for anybody at any point in their career to become educated about AI. What do you think they should do? How do they, how do they start? I'm not seeing this happening in high schools. I'm sure there are courses in colleges. Do they just head to YouTube or do they just go to Gemini or ChatGPT and have a conversation with the AI?
1:59:47 Speaker 01 I think that's probably one of the things we should recommend to governments, which is standardized AI curriculum on implementing and using things like prompting and other things. But really, it is about just using it day to day and thinking of it not as the expert, but the graduate. You know, um, and so there are a few these things, the mid journeys and the uh chat GPTs and others of the world, like this is where I have bringing the next generation is useful. As you said, you've got your chief AI officer, but also your almost chief AI innovator or trier. That's just hacking and trying and thinking, how can I apply this to the day to day workflow? Again, you have things like um your Lindys of the world and your others that can chain together different models. I think that's the next stage, but you've got a little bit of time now.
2:00:31 Speaker 01 But you've got to get AI native by immersing yourself. There's no better way of doing that. And you shouldn't be overwhelmed by the technology or getting into the weeds because that is a massive, massive depth thing. Again, that's why I wanted to create this piece, just to give a bit of a framework and then encourage people to try and use the technology and think about the world in a slightly different way.
2:00:52 Peter Diamandis? Ahmad, what can we expect next from you? Um, uh, what's on, what's on the near term horizon here?
2:00:58 Speaker 01 Um, so we're kind of finishing the implementation document of a white paper on what an open distributed um AI system looks like on a practical basis to build these data sets for every nation and every sector and then chain them together into things that can really make an impact open source in a sustainable way. So hard at work on that. Um, and then we start, well, we're getting our clusters in to start building models, you know, nothing like it and then release and see what the world makes.
2:01:26 Peter Diamandis? So today, uh, title wise, founder of Shelling?
2:01:30 Speaker 01 Yep, that's the one.
2:01:32 Peter Diamandis? Are you gonna, uh, you're gonna take the CEO role or hire a CEO to support you? What do you think?
2:01:37 Speaker 01 Uh, being a CEO is like, as you all said, staring into the abyss and chewing glass. Maybe I'll see if I can avoid it for as long as possible. That's why you build intelligent protocols that can handle themselves.
2:01:48 Peter Diamandis? An AI model to be your CEO.
2:01:50 Speaker 01 Uh, maybe I will actually, and that's a good idea, I think. We'll get lots of advice from AI Peter.
2:01:58 Peter Diamandis? Where can folks follow you on social and website and such?
2:02:04 Speaker 01 I think, yeah, my X is at E-Mostak and then at Shelling AI for our company organization as well.
2:02:12 Peter Diamandis? Buddy, thank you for the time. Again, if you've not had a chance to read Imad's paper, How to Think About AI, it's a very, for me, powerful, succinct and clear structure for thinking about this. I find it very compelling. I will put the link in the show notes. And Imad, looking forward to seeing you again shortly, my friend.
2:02:33 Speaker 01 Take care. Thank you for having me on.
Previous Bitcoin, The US Election, and AI w/ Bill Barhydt | EP #113 Next AI Hype Vs. Reality 2024 - The State of Emerging Technologies w/ Salim Ismail | EP #115 Page updated October 12, 2026 · transcript and summary are machine-generated
