Pod Insights The Impact Theory Podcast How AI Will Disrupt The Entire World In 3 Years (Prepare Now While Others Panic) | Emad Mostaque PT 1 (Fan Fave) About this episode Today’s episode is with one of the most brilliant minds at the forefront of AI advancement, Emad Mostaque.
The first part of this episode will leave you ready to take advantage of AI’s efficiencies, leverage its potential, and mitigate any potential risks.
Emad cuts through the hype addressing the need for perspective, regulation, and the challenge of tackling bias in artificial intelligence. As he states very clearly in today’s episode, “AI is not going to replace humans, humans with AI will replace humans that don’t use AI.”
This isn’t just another chat about AI, it's a hard look at a future that's a lot closer than you think.
What side of the playing field do you want to be on? Job automation and accelerating changes are just a fraction of the complexities, the future is fast and furious for those not taking part in these conversations.
Get ready for insights that will make your brain melt and make sure you’re equipped to thrive in the future.
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Transcript 19,296 words · 3 speakers
Speaker names marked ? are inferred from speaking time and the show’s known hosts — they are not voice-verified.
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1:10 Tom Bilyeu? Today's episode is with one of the most brilliant minds in the world of AI, Imad Mustak. Imad is the founder of Stability AI, the company that brought you Stable Diffusion, an open source AI model that's already being used by millions of people around the world. As such, he is closer to the realities of how disruptive AI is going to be than anyone I have talked with thus far. Imad's on a mission to get AI into the hands of everyone around the world, both to take advantage of its incredible power and, more distressingly, to ensure that those without AI are not effectively enslaved by those with it. That is how powerful AI is. Imad believes that only an even playing field will protect us.
1:52 Tom Bilyeu? This is such an incredible time to be alive, and I really am very excited about AI. But I also think every one of us needs to face the coming changes head on. AI, like atomic energy before it, can be used to create or destroy, and it's going to be up to all of us to ensure that We chart our course well. As such, I plan to do many more interviews on the topic of AI in the hopes that both you and I can master a technology that I believe will be more disruptive to our way of life than anything ever in human history. Won't happen overnight, but we've only got one chance to get this right, harness the power well and not get left behind. As always,
2:33 Tom Bilyeu? if you want an ad-free version of this podcast with a bunch of extras like curated playlists and additional bonus content you won't find anywhere else, subscribe now on Apple Podcasts. Now, without further ado, I bring you Emad Mustak. I'm your host, Tom Bilyeu, and welcome to Impact Theory.
2:51 Speaker 01 How do we make sure it doesn't kill us? Or how does it make sure it doesn't enslave us? Or how does it make sure that it doesn't give us eternal suffering? And I realize this could be the real thing that unlocks humanity. AI is not going to replace humans. Humans with AI will replace humans that don't use AI.
3:07 Tom Bilyeu? AI is thrilling. It's very exciting, but there is a non-zero chance that it poses a existential threat to the human race. So over the next three to five years, how disruptive do you think it will be? And what are people not prepared for?
3:22 Speaker 01 I think that's an excellent question. So you know, the future is always hard to predict. And existential is a big word. Existential means no more humans. So I personally think the AI will be absolutely fine. As a base case, it'll be like that movie Her, if it ever gets this artificial general intelligence. Like, humans are kind of boring. Goodbye and thanks for all the GPUs. But you could be wrong, because what we're doing is creating something that's more capable than us in narrow fields. And the question is, does that generalize and then become viral? We've seen an instance of COVID and that expansion. We've seen programs that can explode nuclear reactors like Stuxnet and others. What happens if you start combining these and you get a misalignment? So it's got a strange objective function. Our organizations already are
4:07 Speaker 01 like slow dumb AIs. You know, and like Germans are the most sensible people that we probably know. And yet they committed the Holocaust. And we see this over and over again, where organizations chew up people. What if an AI takes over an organization? And then decides to do something disruptive or something terminal, such as creating a virus? We don't know about that, but that's at the extreme. When we look at impact, we have the more mundane. The more mundane is what happens to programmers when everyone becomes a programmer? Just like photographers, you know, now you can take amazing pictures with your thing. What happens when Google's Medpom two model? Now can outperform doctors and medical diagnosis? But also empathy, according to the latest paper in Nature.
4:53 Speaker 01 This is a fundamental reworking of information flows. That's going to be massively disruptive and deflationary. Even with what we have now with no more advances as it becomes enterprise ready. And we have a continuum from that disruption to the productivity enhances to potential existential threat. If we keep doing the models as we do now, which is we're not exactly sure how they work or their capabilities, but we keep building anyway.
5:17 Tom Bilyeu? Now I want to get very specific about what the level of disruption is going to be. So when I look out at this and I think about, okay, we're creating something that is going to be smarter than we are certainly in a narrow way, but possibly in a more general way. But even if it's just narrow, is there going to be any job function that isn't going to be at a minimum augmented by AI?
5:41 Speaker 01 I think if you look at the employment share of industries, something like oil and gas has like 3%. It's mostly like building giant machines.
5:50 Speaker 01 Is that massively affected by this AI? At the edges, yes. Things like programming where you're talking to computers massively. I mean, now basic programming, the bar is raising fast, fast, fast. And so you've got everything from knowledge work to heavy industry. I think it affects just about everything, but some areas far more than others. The two areas that I think it will affect the most are probably healthcare and education. Neither of those are fit for purpose. We're in America, we know that, you know, but across the world, no one's really happy with their kids schooling. And again, medical care, we all, if anything goes slightly wrong outside the norm, we all know how frustrating it is. We can finally have personalized education and healthcare at a fraction of the price. And the two biggest drivers of US inflation over the last decade? Education and healthcare, they make up about eighty percent of the increase.
6:36 Speaker 01 So that will be disruptive. And then, like I said, any type of knowledge work will be disruptive.
6:41 Tom Bilyeu? How do you define knowledge work? Because when, so my own self, uh, what we do is education, that's a big part of it, but also just content creation. And so when I look at the fact that we can already clone my voice, we can already create a Tom bot that will answer questions I have answered before in a very similar fashion to how I will answer them. In our video game flow, we're already making three D objects, which, so when we looked at, I don't know, two months ago, I thought, okay, this is still twelve to eighteen months away. Forty-five days later, we're using it actively in our pipeline.
7:18 Tom Bilyeu? You've got text to video, which still a little awkward, but it's getting better insanely fast. We do all of our concept art now in, um, in AI. So we have, as a company that doesn't even have like a, an AI expert on board, we're just learning as we go. We're already deploying it like crazy. And when I look out, not even, you know, three years, when I look out a year, all of this stuff starts to very rapidly become a centralized point. And so we're already saying, I don't need to hire more people. I just need to make my people more efficient. And so that, an entertainment company didn't even make the list that you just said. So there's a lot of people that I think are gonna
8:02 Tom Bilyeu? get disrupted by this, uh, that may not be like the most extreme, but how far down do you see this trickling?
8:11 Speaker 01 Anything you can do in front of a computer, basically.
8:15 Tom Bilyeu? Goes away or just becomes augmented?
8:17 Speaker 01 The bar lifts. The quality expectations are higher. AI is not gonna replace humans. Humans with AI will replace humans that don't use AI, because you can see that in your workflows right now. There was a paper by OpenAI where they estimated fifteen to fifty percent of tasks get automated or improved. And so, you know, it affects people in different ways. You have a company where you've built a culture and you, again, you're building three D assets. It becomes amazingly more efficient. We just released, uh, we contributed and collaborated on a ten million three D object data sets. So by next year, you'll be generating three D literally live. In a couple of years, you'll have HD movies. We can finally remake Game of Thrones, season eight, and other such travesties, you know?
8:58 Speaker 01 But the speed of this is something whereby it's happening in every media type at the same time, and it's easy to use. Web three had some great ideas, but it tried to create a system outside the existing system and all the money was made and lost at the interface. This is just so seamless because there's no friction. Your mom can use this technology. You can use this technology. You don't need to be an expert because it came and was trained from our content and our collective content, as it were. And now it's just easy to implement and use. So I think this is the big differentiator between this and other massive advances because they required infrastructure. The internet, there was the big lift up, you know. You had the consumption period of web two and the cost of consumption dropped to zero. Now
9:40 Speaker 01 the cost of creation is dropping to zero and humans plus AI can massively outperform humans that don't. It's a forcing function, which means everyone has to use it. And this again is dual in that it can be disruptive, but it can also create massive value.
9:55 Tom Bilyeu? Yeah, so I'll agree with that. I think that so I guess let me lay out my whole thesis for you and for everybody listening because I want to take us through what I think is very real doom and gloom. And I'm not doing it to be a naysayer. I'm doing it because I think these are going to be the things we have to contend with. And if people go into this blindly, which I think they're doing right now, I think most people are burying their head in the sand. They are not paying attention to this and they're going to wait until something really forces their hand and by then it's too late.
10:19 Speaker 01 Yeah, the way that I put that is this is like COVID before Tom Hanks.
10:23 Tom Bilyeu? Yes, very well said.
10:25 Speaker 01 Everyone's talking about this. Your mom's talking about this, but the Tom Hanks and the NBA made it real.
10:30 Tom Bilyeu? Very true. And then we had a very poor response, which I have a feeling will be very similar to what we do now.
10:37 Speaker 01 Yeah.
10:37 Tom Bilyeu? Okay, so here's how I see this going. I think right now for the next year, let's call it, it's going to be, you need to learn how to use it. This will be your window to get efficient. Companies probably aren't going to start lopping people off yet. But I'll just say within my own company. So when I think about filmmaking, I went to film school, so I'm, I'm very experienced in this flow. And even in a 3D world to create, uh, let's say a short cinematic. So it's like a mini movie, but done digitally. I mean, you might have thirty five people touch that thing from the creation of the assets through the moving of the camera, special effects, you might have fifty people touch that. And if that really does become text to output, now fifty people become one.
11:23 Speaker 01 Yeah.
11:23 Tom Bilyeu? And so when you get a fifty to one ratio in certain areas, obviously, it's not going to be like that everywhere. But when you have certain areas that go from fifty to one, take programmers. I've heard you say there will be no programmers because writing code is just a way to talk to a computer. And if you have AI that will interface with the computer for you, why would you ever need to write code? So that that's going to steamroll through society. That is just going to mow people over. So again, I'll give him twelve months. But even in my own company, if you're not actively trying to find a way to integrate it into your job function, I'm already looking at you sideways. A year from now, if you're not really good at either documenting how it is completely useless in your job function, or showing how you're using it, we will find
12:08 Tom Bilyeu? somebody that can do it. I'll be shocked if a year from now, we don't have a head of AI. So three years from now, I think this has created a crisis of meaning for a lot of people. And I don't know if you remember that, the whole learn to code thing where it was like, hey, AI is going to put drivers out of work. They're going to be the first to go. And everybody was like, teach them how to code. Now, the way people responded to that always confused me because that was the right answer at the time. Now, knowing what I know about code replacing, not so much. But you have to go learn a new skill. There is no other option other than going on the dole, right? So you're either going to learn something new, or you're just going
12:47 Tom Bilyeu? to forfeit your career, basically. So I, what do you think about that? Do you agree that that is a very real thing that's going to sweep through?
12:58 Speaker 01 I do agree. I think that, again, we're not sure exactly how this is going to pan out, but.
13:05 Speaker 01 Probably the best mental model I figured out to think about this technology. It's like really talented grads that occasionally go a bit funny. They can draw, they can code, they can make 3D models. How would your business be affected if you could push a button and infinite grads came out? How would your personal life, your society? And this is why I think it's quite deflationary. The only question is, can we create new jobs to make up for that? And that's difficult because he says. You think we can? I doubt we can, to be honest. I think this is a new comet disruption that's far bigger than COVID. And the important thing here is COVID, you had the disruption, then everything bounced back. You're at record employment now and things like that. With this, there's a lot of never the same again. It's like you talk to your kid's school teacher.
13:48 Speaker 01 I can't set essays for homework anymore because of chat GPT. And there's no way to stop that. So what is never the same again? And it's happening everywhere all at once. So this technology isn't just like, you know, there's a bar of entry where you needed to have a modem. You know, you need the latest smartphone or something like that. It has an embedded base that it's seamlessly going into. Look how fast Microsoft implement on the consumer side. But enterprise is not ready yet. It's like the iPhone 2G stage, you just got copy paste. And next year and the year after, you're suddenly at the iPhone 10.
14:20 Speaker 01 You know, entire app stores get built because of the demand, because it's valuable. What's happening here? Again, with the comparison to Web three, you had to bootstrap value because it wasn't valuable. And you hope the value would come. There's product market fit today. You're using it in your own company. And so this is one of my big concerns and that's one of the reasons I decided to do open source. So I could stimulate growth. You know, because I think the only thing that can basically fill the gap is if we stimulate entrepreneurs to create brand new businesses, brand new jobs. So I think demand will stay for a while. Demand for what? Demand for good things, good assets with the way that money flows around the economy. So I was speaking at Cannes a few weeks ago, Film Festival.
15:03 Speaker 01 And you know, I love movies. My first job, I was a movie reviewer, you know.
15:07 Tom Bilyeu? Really?
15:07 Speaker 01 Yeah, British Independent Film Awards, Rennes Dance Film Festival, other things.
15:11 Tom Bilyeu? I knew you were big into video game investing. I did not know that you were a film critic.
15:16 Speaker 01 I love, I love stories. That's how I kind of understood people because of my Asperger's and other things. And so I said to this, the video game industry has gone from 70 billion to 180 billion over the last decade. And the average Metacritic score has gone from 69% to 74%.
15:36 Speaker 01 The average movie is 6.4 on IMDb for the last decade, and the industry's gone from 40 billion to 50 billion. What happens when you can make better movies? I think the market expands. Because the limiting factor is awful movies, in my opinion.
15:52 Tom Bilyeu? All right, let me run something by you. Okay, so I have, I have a really dark view of not the next 12 months. So call it year two to year six. So it'll be a three to four year sort of span where I think there, there's going to be emotional devastation and probably economic devastation. But even if the economic devastation doesn't happen because of productivity gains, I think the emotional devastation is going to be hard to come back from. And I think that as the emotional devastation sets in, the government is going to try to regulate to protect people's jobs. And there you're going to get like some real weirdness. I also think kids are going to have a junior year existential crisis of
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19:15 Tom Bilyeu? Okay, so in the movie industry specifically, and this is indicative of a big problem that I think that we have coming. And I think the problems really stack individual and societal.
19:23 Speaker 01 Yeah.
19:23 Tom Bilyeu? So at the individual level, the big problem you're gonna have is this max of massive fractionation of right now. Movies are even less now than they were when I was a kid. Movies were, it's only a few big movies for the year. Now they're gonna niche down. If anybody can type out a movie in, you know, take them 20 minutes to write the prompt. And then maybe a day to render, who knows how fast that's gonna get. So now all of a sudden, you can make a Hollywood quality film for an audience of one. And once you start doing that, now it's what does that do to the industry? I think it it erases it. I don't think the industry changes. I think it goes away.
20:06 Speaker 01 Yeah, I think there's a few kind of components here, right? So the cost of music consumption went to zero. You saw the Spotify model, yet you still have music stars. You've got even more crap music now, kind of coming and hitting Spotify and other things. But people rise to the top, you know, just like you see top podcasters, top other creators. I think that'll continue because people like common stories.
20:30 Tom Bilyeu? Yes, okay, so this is a very interesting idea. So let's stick with music for a second. Music is still hard to make. It's easier to make than it was before. It's also still hard to get people's attention. But music now is no longer a shared thing. So music is part of what led me to the conclusion that I'm at now, which is, man, as kids, it used to be you were either into the mainstream pop. And there was, you know, seven to 10 hot bands at one time, or you were into the alternate pop. And there was seven to 10 hot bands in that arena. And you you fit into one or the other bucket. There wasn't the infinite buckets now. You can find kids that are 25 and they listen to Frank Sinatra. And I'm I'm always tripped out by that.
21:15 Tom Bilyeu? So they don't even have their own sort of shared lexicon of what music they're into. It's it's all spreading really wide. So it's really wide and an inch deep.
21:26 Speaker 01 Yeah, I think it's really one inch deep and you see the primary methods of monetization are towards merchandising community effectively. You know, this is the interesting thing about NFTs when they took off and then bounced down and things like that. It was the quickest way to join a community, even if it did have bad incentive design. So in an era where you can create anything, something becomes important. What that something is, we have to find out now, right? Because again, I think it's some common stories, but I could be wrong. I think the deeper thing that you said was this crisis of meaning. Where is my path forward? What is an American dream? We're quite privileged, those probably listening to this and us on here. Most people don't really care about this technology. I think on a survey, seventeen percent of people had heard about ChatGPT last month.
22:10 Tom Bilyeu? How is that possible?
22:12 Speaker 01 Well, a third of the world still doesn't have internet.
22:14 Tom Bilyeu? That's terrifying.
22:16 Speaker 01 But again, like, it is kind of very, one point five million people still use AOL. You know, like, fair enough. So we kind of look at it, but there's something that can reverberate very, very quickly. And then as you said, there's a sense of malaise because you're not sure what's happening. And again, the future becomes uncertain. And when the future is certain, things are stable, you decide based on risk. You do a probability estimation in your own head. It's the percentage of that, percentage of that, and then you optimize for that. When you do uncertainty, you minimize for regret. Given these options, what am I going to regret least? And suddenly there are no options. Again, I'm at school programming and then programming is disrupted. What's it going to be? I'm not sure.
22:57 Speaker 01 And some people will throw themselves in and they'll tool themselves up and they'll become ten times programmers. Other people won't and they'll be left behind. And so I think this is a real question that comes at a time when, again, being in America, I'm from Britain, but what is America? What does America stand for? What are the values? These are some things that I don't think America knows now. I think you've seen increased polarization from free consumption. And now as you get free creation and you'll be hearing all sorts of stuff, fake news and more. What are people really going to think? And I think, again, this is a real concern, as you said, from an individual to community to a society level. Because a lot of people don't have an anchor anymore and that's really scary.
23:39 Speaker 01 So how do you think that we process through all of this? I'm not sure. I think that's why we needed to broaden the conversation. That's why I'm the only AI CEO that signed both of the letters saying we need to take a pause and broaden this. Because as an example, you mentioned- Broaden this? Broaden discussion. Get more people involved. We need to get more people involved. We need more points of view because this affects us all. It shouldn't just be a few tech CEOs that control this and you shouldn't have to trust that we do the right thing. Because our models, we make them once, they go everywhere. Right? Again, that what's the R naught of generative AI? It's off the charts, right? We've never seen anything like this. It incubates and boom, and it comes for good and for ill. The you get the example of regulation. When we first started talking to regulators, they were like, how should we regulate it?
24:25 Speaker 01 Now it's a question of them asking us, how are we going to keep up if we regulate it? Because other jurisdictions won't. What do you say to that? I say you should still regulate it because it has some real dangers and harms and we have to work to mitigate those. You can't just have a laissez faire approach to this. Because people will take it and they won't be able to help themselves. I'll give you an example. Meta, Facebook, right?
24:52 Speaker 01 We all know the classic kind of stuff. They had a study where they had a hypothesis 600 that if you see sadder things on your timeline, will it make you post sadder things? So they took 600,000 of their users and tried to make them sadder. And guess what? If you see sadder things, you post sadder things. What do you think is going to happen now that they have generative AI on threads and things like that? And they can hyper-target you, hyper-personalize it and wax Scarlett Johansson's voice to tell you to buy soap.
25:19 Speaker 01 This is a dangerous thing, right? What happens to our kids, again, who are growing up whereby they won't know what's going on and they have very malleable minds. And none of that is illegal. But I think it's an undesirable outcome, right? And then you've got the bad actors and then you've got the politicians using this technology. And then it goes even crazier than that. So the answer is I'm not sure. Nobody's sure. But I think the only way that we can try and figure this out is to work together to make these issues known. Again, the existential stuff gets the headlines. We could all die. No one really understands what that means, but it can happen, right? Okay, there's a probability of that, but there's some real harms today and real opportunities today. And we have to focus on accentuating the opportunities and getting the harms out there and dealing with them.
26:05 Tom Bilyeu? Yeah, and I I definitely want to spend a very extended period of this talk, talking about the opportunity and how we capitalize on that. So anybody that's with us now, trust me, we are going to get to that. But I think we're we're just beginning to scratch the surface of how this goes wrong. And I really want to map out sort of where you think the edges of this are so that then I can hopefully get a sense of what you think the regulatory framework would be. But let me give you one idea that somebody posted today on Twitter, and it really hit me that people are even thinking about the problem in the wrong way. So there was an artist and he was looking at some post about AI. And he replied sort of angrily that, oh, well, people don't even understand.
26:46 Tom Bilyeu? Sure, there's going to be a ton of like instantly generated crap, but it's all going to be bad because there's still a very small number of people that have good ideas. And my response was, if you think that ideas are safe, you're really going to get caught off guard. So going back to the idea of what are people unprepared for? I think they are unprepared for what you were just talking about where the AI. So the human mind is a prediction machine. It is constantly trying to figure out what what does this next movement of my foot equate to? Am I going to stay up, stay on balance? That rustling in the bush, is it a tiger? What is it? If I put money in my 401K, am I going to be able to retire? You're constantly predicting the future constantly.
27:28 Tom Bilyeu? And whenever that prediction engine breaks down, there's going to be a tremendous amount of anxiety. And also, I think a pretty big unknown in terms of how it's going to impact society. So right now, we have a we're building something that is incredibly good at recognizing the patterns that we kick off. Yeah, so we are optimized to identify patterns and move accordingly. And I would say people that are hyper intelligent are people that they notice patterns faster, more subtle patterns, and they understand their implications and how to make sense of them. Now we're creating something that's already proven to be so much better at pattern recognition than we are. Just take art. So for people that don't understand how
28:09 Tom Bilyeu? the art is created, it looks at a field of noise. Here are all the possible things that these could be in any of these pixels. And from that field of possibilities, it pulls forth the most likely placement of pixels and colors based on what you type. That's insane. So that level of pattern recognition, as evidenced by the art that it can generate, is is truly mind blowing. So this guy's saying, okay, hey, at least ideas will be the last bastion, and you'll never be able to get rid of me, the artist, because I'm the one with taste. I'm the one with good ideas. Not realizing, no, no, no. What AI is, is a pattern recognition machine. It will recognize the greatest ideas that have
28:51 Tom Bilyeu? ever been had, what they have in common, and will be able to predict the next great idea along that thing. It doesn't even have to just regurgitate what it's already seen. It can like, figure out what that sequence is, and what that next part of the sequence could be. And on top of that, it's doing that with humans. So AI will get, AI is already extraordinarily good. This is why people think their phones recording them when it serves an ad. Oftentimes, Target, using their AI, knows that you're pregnant before you do, if you're a woman, because they know what to pick up on. So AI is going to get extremely good at understanding us at an individual level, serving us up exactly what
29:31 Tom Bilyeu? we want, right in that moment. And that gets dystopian really fast.
29:40 Speaker 01 Really fast. I mean, again, when you combine it with the social credit score, as you've seen in kind of China and other things, you gamify life, and you have a system of complete social control, a panopticon, as it were. The pattern recognition was the missing bit whereby you had a level of pattern recognition. So for taste, what do you have? TikTok, shine, hundred billion dollar companies, based on old school algorithms, before we even got to generative AI, which, as you said, it can take images out of noise. Stable diffusion, you know, the model that we collaborated on now that we lead, we took a hundred thousand gigabytes of images, and the output was a two gigabyte file. That acts as a filter. Words go in, images come out. Because
30:18 Tom Bilyeu? Why, why is that discrepancy in size meaningful?
30:22 Speaker 01 Fifty thousand to one compression is not win zip. If you remember Silicon Valley on HBO, it's way beyond that they managed there in terms of compression. It's unheard of compression. Is it compression or is it something completely different? Something different. It's intelligence. It's learning the principles. How much information do you see? And then you learn the principles and then to spot the tiger in the bush. You learn what's next. Literally GPT and these language models, they predict the next word. That's all they do. They pay attention. They predict the next word. And that was the missing part to intelligence that now is there. We've had the first studies now come out that show that the language models score higher in creativity than people. Woof.
31:04 Speaker 01 And again, think about TikTok, think about Shine, think about how those old school algorithms are already targeting you. Facebook needs seventeen data points to know you better than your friend. As he said, Target knows you're pregnant before and that was old school. Now it's even better. And you think about where that leads to as well. It's kind of crazy because it can be more creative than you, but are people creative? One of the things I like to say in my speeches, I've just been learning to do is like, are you creative? How many of you in the audience are creative? Three to five percent put up their hands, maybe ten to twenty percent if I'm like in a movie studio, movie filmmaker. Kind of knew you. And I say, how many of you believe that every kid is creative? And everyone puts up their hand. And then I ask, how many of you were kids once?
31:46 Speaker 01 And ninety, ninety-five percent put up their hands. So I know who the cyborgs in the audience come from the future to get me are. I make a note of that for future. Something happens where we're told we're not creative. And obviously some people are more creative than others, can tell better stories than others. But the reality is that the average level of barrier to this has dropped for every human. But much of what we consider art, or much of which we consider media, shall we say, already is by the numbers. I was at a Blackpink concert last weekend. You know, I took my daughter. Don't you dare say something bad about Blackpink. And they are awesome. You know, it was an awesome manufactured experience. It was a premium mediocre is how I kind of say these. Premium mediocre? Premium mediocre. That's hilarious, but accurate.
32:29 Speaker 01 It's nice, but again, it's massively manufactured. It's entertaining, right? And so much of media is already that, like true art, true artists. You know, that's something different. Like, is it the medium itself and the aestheticness of it? Well, AI can make something more aesthetic than anything, can understand the nature of aesthetic. Like, how do you make an image more aesthetic? You say, make it more aesthetic. Just like if you use a G P T four, you can say, make this punchier, make this punchier, make this punchier. You know, you can have a letter and then you say, I'm firing this person. And I want to make them feel okay about it. And then it will redraft it in those terms. Or you can say, I want to drive the knife in, but not in an appropriate way. And it'll do that. And we can literally, anyone on this call can kind of listen to this, can try that now.
33:14 Speaker 01 So I think this is just, as you said, the wrong thing people are thinking about. The wrong model people are thinking about as well. And that's why I always go back to this concept of the really talented grad.
33:28 Speaker 01 Because these models are a couple of gigabytes big. Again, stable diffusion, the image model is two gigabytes and can generate any image of anything. We'll get that out to two hundred megabytes. G P T four is probably a hundred to two hundred gigabytes. And it can pass the bar exam. It can go to fricking Stanford. It can do whatever. That's crazy. Insane, because it's not compression, like you said. There isn't a copy of all the data in there. It's figured out the essentialization of these points and it's replicable. This is the thing. To clone Google or Meta, you need to have a gigantic data center. And then much of the energy is in the processing to target you ads. With these, we take giant supercomputers and we pre-process and package the information.
34:13 Speaker 01 So the output is this knowledge filter that something goes in and something comes out. A prompt goes in and an output comes out. That's something quite different that I don't think people appreciate. And again, this is why I use the ground example. Push a button and those weights, the file. The model gets replicated to 10, 100, a dozen, a million. And what happens when rather than dealing with them one to one, you have a thousand of them? So in a year.
34:40 Tom Bilyeu? I want to really understand what you're saying about the grad thing. So when you say that, you say it in a way that's kind of funny or cheeky. But what you mean is a really smart person is now present in that role. You're probably paying two salespeople to do one salesperson's job. That's where today's sponsor, Pipe Drive, comes in. An intelligent AI-powered sales CRM loved by growing sales teams. Pipe Drive just launched meeting intelligence built right into the CRM. Before every call, it pulls deal history and past conversations into one brief. During the call, it records and takes notes. After it, it drafts your CRM updates to review and approve.
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35:54 Speaker 01 We have figured out how to make humans scale.
35:58 Speaker 01 That is what fundamentally.
35:59 Tom Bilyeu? Intelligent humans scale.
36:01 Speaker 01 Yeah, who can listen to instructions. So you look at something like Claude 2 by Anthropic.
36:08 Speaker 01 You have something, the input is a prompt when you type into GPT-4 or stable diffusion or mid-genie or something like that.
36:15 Speaker 01 Claude, Anthropic's model, can take 10,000, 100,000 tokens. It can take a prompt that's like 60,000 words, which is a whole book.
36:24 Tom Bilyeu? Jesus.
36:25 Speaker 01 Yeah, you can give it like the whole of Ulysses and the whole of, I don't know, The Odyssey by Homer. And you can say, combine these to make another book.
36:38 Tom Bilyeu? And it will do it and it will work.
36:41 Speaker 01 It can follow instructions really well. Occasionally they hallucinate, but even hallucination is a misnomer because when you compress that much knowledge like GPT four is probably 10 trillion words. 10 trillion, 10,000 billion words in a hundred gigabyte file. It's something else. And so I use the word grad because I want to make it relatable, but it is literally like. Imagine if you had a grad in the Philippines, you know, and they're doing good work and they're following instructions well. That's great. But what if you had a hundred of them looking after each other's work and double checking? Meta had a paper called Cicero where they took eight language models and got them to check each other's work. Outperformed humans in the game of diplomacy the first time ever.
37:23 Speaker 01 In a year when we have this before it, you'll just say, I want you to go and look at everything Ahmad said for the last year. And figure out the stupidest stuff he said, so you know, if I can avoid it. And the smartest, most interesting stuff, according to what I know and all of my podcasts. To give answers, to give questions that the audience will really like, based on my ratings.
37:47 Speaker 01 And based on what people look at through the YouTube videos and things like that and what they're most interested in. And it will just happen automatically.
37:55 Speaker 01 How many graduates that take you to do? And then what happens when they stop being graduates and you can actually train them up to be like, you know, experienced members of the team?
38:03 Tom Bilyeu? How long will that take?
38:05 Speaker 01 Couple of years.
38:06 Tom Bilyeu? This is why this is terrifying to me. So I, I am a very optimistic person. And again, I promise we are getting to how you take advantage of this disruption, but I don't like to face a problem naively. I want to face it as head on as possible so that I know my solutions are real. And when, when I look at this from my own perspective of, okay, I'm trying to, I'm trying to build a media company, which right about now is a very terrifying time to do that.
38:33 Speaker 01 Yeah.
38:35 Tom Bilyeu? And I'm thinking about, okay, it's, it's very optimistic when I look at, oh my gosh, I, as the founder of this company, I get access to all these grads, as you're calling it, this just absolute proliferation of very intelligent sort of people that I can now put to work in my company. The problem is I'm now competing against other people that have the same thing. And you, you get in this ever escalating arms race where there, there is a real chance for fatigue. And so I think what ends up happening, and we were talking before we started rolling, it's, it is very important that people understand the following thing. I think this is just a truth, but people certainly need to understand, I believe it. This is a core belief that, that drives me.
39:20 Tom Bilyeu? That we, you get to a point where you need to know, okay, I matter, I'm doing this thing. And that's how I'm contributing to the world. And I need to be in there, working hard, accomplishing, getting better, moving towards something. And if I'm not moving towards that thing, then I'm going to have a profound sense of disease.
39:44 Speaker 01 Yeah.
39:44 Tom Bilyeu? And if I'm not making that progress, then I'm really going to fatigue out on something. And so if people are just treading water because they're trying to build something and they're competing against somebody else that has these 10,000 things and it's just constantly changing. And I can't predict the future anymore and I don't feel like I'm making progress. I'm just gonna back off. Like some part of me is just going to be like, ah, what am I doing all this for?
40:06 Speaker 01 What am I doing? Yeah. I mean, it's like the outsourced revolution, right? Where so many jobs were outsourced and a lot of people felt that way, like, you know, we'll outsource you to China, we'll outsource you to India, we'll outsource you wherever. And again, it just happens that there's a computer on the other side of that versus an Indian or a Chinese person. And so we've got kind of repetition of that, but at ridiculous scale, affecting almost every single industry that's intermediated by a computer. And so this will cause, as you said, a crisis of confidence to many. And it impacts white collar workers, not blue collar workers. It flips, I think, the global order to a degree as well. Because here in the West, we've maxed out our credit cards. We weren't going into deflation, I think, coming off high inflation.
40:48 Speaker 01 And all of a sudden, we can't print our way out because we just printed the last of our money before COVID. Whereas in the global South, what you have is this technology can cause them to leapfrog, just like they leapfrog to mobile, missing PC completely to intelligence augmentation.
41:03 Tom Bilyeu? Why can't we print more money?
41:04 Speaker 01 Well, because kind of we're just coming up to a limit of what's literally mathematically possible, given the debt to GDP ratios and others. We can continue, but it's kind of pushing.
41:13 Tom Bilyeu? If you're deflating, then because so here's my layman's understanding, but this is something I've really looked at. So I'm a pretty educated layperson at this point. Inflation is largely, some people will say entirely, but I'll say largely, a function of how much money you're printing. For people that are new to the idea of printing money, it's government approved counterfeit. So the government is allowed to print as much money as they want. They're literally just making it out of thin air. They're adding zeros and ones to a database somewhere and money finds its way into the system beyond the scope of this conversation. But they, there is no theoretical limit to how much they can print. Now, what you run into problems is the hyperinflation of the
41:55 Tom Bilyeu? currency. But if you're saying it's a deflating currency, which actually makes sense to me, given what we're talking about, then printing seems to make a lot of sense, seems to buy me more room.
42:05 Speaker 01 Eventually. So what's going to happen is that you've got a decrease in inflation now because of base effects. I'm sorry for going into a bit of macroeconomics. And then you'll probably have a bounce back next year because you've still got a lot of inflationary pressure. And then the collapse occurs.
42:18 Tom Bilyeu? Why?
42:19 Speaker 01 Because that's when the job losses start hissing. And the question is, can we create enough on the other side? We've got to have a productivity boom from companies.
42:26 Tom Bilyeu? The job losses are coming from AI or some other force?
42:31 Speaker 01 From AI. And from other forces as well. Again, you know, what we've had is a sugar rush post COVID, a good, strong economy as all of the excess savings go back in. Because if you look at excess savings, people saved up a lot, and that's almost now depleted. By the end of the year, the excess savings will be depleted. You've got some hangover effects from inflation, then you move into deflation the year after. And then it's a political hot potato around printing more. Because this isn't, again, like COVID, because what happens to the job losses just start and they just keep going. It's not like you had a 2008 crash, or you had a COVID where everyone was kind of suddenly going. It's like. It's a bit like boiling a frog, you know, or a lobster.
43:12 Speaker 01 It's just gonna start and then it's gonna accelerate and then it's gonna be like, at what point do you take the big fiscal action? It takes a few quarters of the economy actually shifting. So this is all a lot of hypotheticals, right? But the bottom line, I think, is this. The nature of US society, Western society, will change. I think the biggest adopters and fastest adopters of this will be the global South because it allows them to create value. It allows them to financialize. It allows them to take a big leap forward. And so I think that's got some huge implications geopolitically and others. But a lot of upside as well, because I think you can solve a lot of the world's problems with this. But it's so messy.
43:53 Speaker 01 Because fundamentally, it comes down to what you said. As humans, we're trying to figure out what comes next.
43:59 Speaker 01 And we're starting to have a computer that can do that even better. As humans, we're storytellers. We're made up of the stories that make us up. You're a filmmaker, you know, as a film reviewer, all these kinds of things. This can tell better stories.
44:11 Speaker 01 And that has such a big effect on our societies that none of us can really wrap our heads around it like. I've got a background in economics, management, a whole bunch of different things. I can't wrap my head around it. And so we're just gonna have to see how it goes and then try to mitigate, but. Nobody's got answers to this. And in fact, as you said, most of the people aren't asking the right questions.
44:35 Tom Bilyeu? Yeah, you have said that the shit show happens next year. I have a feeling that what you were just talking about is what you mean by the shit show, that we go deflationary.
44:45 Speaker 01 Towards the end of the year, yeah.
44:47 Tom Bilyeu? So towards the end of twenty twenty four.
44:48 Speaker 01 Yeah, we've got like a burst of productivity enhancement. And then you start seeing job losses, you start seeing question of meaning, you've got the US election next year. My God, that's gonna be awful.
44:58 Tom Bilyeu? Yep, terrible fucking timing.
45:00 Speaker 01 Well, I mean, you know, what you'll have is the week before the election. Fake videos appearing everywhere. And they'll say the same things, you know, so and so has a brain infection or something like that. And they'll be identified as false, but it doesn't matter. It still discourages people from polling. But then what do elections look like by twenty twenty eight, when this technology is in every single pollster's hands?
45:24 Tom Bilyeu? Yeah, that's where we get into the blockchain. We'll save that for a little bit down the road.
45:28 Speaker 01 Yeah.
45:28 Tom Bilyeu? Okay, so now I feel like we're we're getting close to the problem set being on the table. There's one more thing that I I think it's important to put on the table, which is I don't think that the amorphous thing that is society as the world turns, history, the grand dark history, however you want to think about the the real long timelines. So even if the long arc of history bends towards improvement, which I think it does, and think it still will, I don't think it cares at all for any one period of time. And that unimaginable amounts of human suffering happen routinely throughout our history.
46:11 Tom Bilyeu? And I have deep concerns that if we are not incredibly thoughtful, that this will be one of those moments. And I look at what's going on in France right now. I think it's dying down. I can't tell if it's dying down or the coverage is dying down. Hopefully, it's actually dying down. But France was like really having some struggles. And if something like that pops off over not in any way, shape or form to make light of what happened, but it isn't mass joblessness, which is going to have a far wider impact. What happens when you have that and it's global?
46:54 Speaker 01 I mean, I think this is the thing. It's every government has every education minister in the world has to grapple with, why can't I set my set kids' essays homework again?
47:04 Speaker 01 Have we ever seen anything like that before so quickly? I don't think we have. And so you could see this literally paralyzed around the world. Or not, we're not sure what really kicks off some of these things. Like right now, we've got the Screen Actors Guild kind of protesting. And today we just had a couple of actors leave Oppenheimer. Part of that's monetary, but already you're seeing AI fears like front and center. You wouldn't even have thought it six months ago. What's it going to be like in a year when you can generate or two, when you can generate whole movies? And then just describe how you want it done and it's Hollywood level.
47:38 Speaker 01 It's really difficult for governments to react to this, to adapt to this when like in the US here, we're still reacting to section 230 on the internet. They're just getting to the internet, all of a sudden AI just comes and sideswipes things, right? And I think, again, the only way to do this is if you can create brand new jobs quicker than anything. This is one of the reasons, again, like I said, we focus on open source. It's why you need to have things like regulatory sandboxes so that you can experiment and try and you need to stoke innovation because you don't, you'll never get an innovation phase like this either. I think this is a step change and a regime change in the way that society operates. Because we were originally an oral species. Then we figured out how to write, then we had the Gutenberg press. And it took all these words, but it took them down into black and white.
48:23 Speaker 01 It made society quite black and white because it couldn't capture context.
48:28 Speaker 01 Whereas these models can capture context, they can capture principles, they can capture more. So again, you know, you're writing this down, you won't have to in a year or two. It'll just be automatically added to your M M X, to your knowledge base, right?
48:38 Tom Bilyeu? Also, the AI will just be attached to my head. It will read the brainwave patterns and know that that's what I need to remember.
48:46 Speaker 01 And that sounds crazy, but like we had Mind Vis, a paper that we kind of published from our meta division whereby you looked at a picture of a mug, took an FMRI, and then it reconstructed it using our image model.
48:57 Tom Bilyeu? Yeah, no, it doesn't sound crazy to me at all. This is what I'm saying about people do, they're not prepared for what's coming. They are not prepared for this level of change and they really aren't prepared for the rate of change.
49:08 Speaker 01 And it isn't just like an arc like that. It's lots of S curves all at once, all around the world, where every single company is now thinking, what's my generative AI strategy?
49:18 Tom Bilyeu? Yes.
49:19 Speaker 01 For when it pops off.
49:20 Tom Bilyeu? Correct.
49:21 Speaker 01 And every government's thinking, how can I stay competitive? And this is why I said like, it's a race condition.
49:29 Speaker 01 Where everyone is trying to do the same thing or similar things and you can't be left behind, you can't not participate. And it's been a very long time since we've seen that. And there's a world before this technology and a world after this technology. Like, I don't think, again, you know, I've got two kids. What does the world look like in five years, let alone 10 years? I have no idea. And I'm in the middle of this.
49:54 Speaker 01 Because it's just impossible to see the smartest people that I know, they used to be able to see years in advance. They can't see more than a year or two. This sounds again, very apocalyptic, but then, like I said, we're going to get to the good bit in a second. In every crisis, there is opportunity. Our society is broken as it stands already. And I think this is a chance to reshape it for the better and solve a lot of the biggest problems that we've been facing because of our slow dumb AIs, because of our organizations and institutions that we are all frustrated with. And I think this is a big upgrade from it. The example I had to give is there's the amazing poem by Ginsberg Howl about Moloch, this Carthaginian demon of disorder.
50:34 Speaker 01 I think where that came in was text, because we had to centralize everything down and put people into boxes because we couldn't have systems to understand the context. You can't have personalized educational personalized healthcare because you couldn't scale humans. There weren't enough talented humans until now. And so I think that is the incredibly dangerous part because all of a sudden from economic pressures, you flood the market. And it's the incredibly motivating part whereby there's a shortage of talent for everything that's important. And there isn't anymore. But then the nature of talent for jobs and things will transform. And I think the economic abundance that's created on that, that's the
51:16 Speaker 01 flip side of this, as well as the ability to fix our broken systems.
51:21 Tom Bilyeu? All right, I'm going to give you my timeline. I think the next year is going to be a lot of fun for people that embrace it. It'll be a period of time where some people can ignore it and they probably won't really notice. They won't realize how fast things are changing. Although, follow me on Twitter. I, I post routinely like, hey, here's something I didn't think that would happen for 18 months. And we're now 45 days later, we're using it. Things are really, really moving faster. But for the next year, I think people will be able to ignore it and they won't realize that it's growing with such steam and ferocity. Then year two to six, I think it, I think that there is going to be pockets of extreme suffering.
52:03 Tom Bilyeu? And I think deaths of despair are going to skyrocket. And I hope it's not a the world is burning riots kind of thing. It'll probably be more quiet and insidious than that. But I really think that we're going to lose people on the upper and lower ends. I think people that are old are going to just completely check out. And say, I can't keep up. I'm too old. I don't want to learn this new stuff. I think people that are young, it's the only thing they know is change so fast that they can't see around the corner. I think that will be absolutely terrifying to them and they're going to retreat into the levels of entertainment.
52:42 Tom Bilyeu? Sex bots, AI friends that are more loving and kind than their other friends. And they it will be a collapsing inward. Now, as somebody who is prone to collapsing inward. The biggest thing that's held me back as an entrepreneur is that I like being alone with my own thoughts. Yeah. And that if you then layer anxiety on top of that. And then you give me an AI that's actually better to me than anybody in my real life has ever been. And then you give me maybe some drugs. I will truly collapse in under my own weight. Not me personally, I have defenses. But yeah, I'm saying like that personality type is really going to struggle.
53:27 Tom Bilyeu? So I think right there you sort of you're you're going to lose a generation, if I may be so bold. On the upper and lower ends. Then either on the 10 to 20 year time horizon. And I leave it that long because look, we're any prediction that you make with a timeline is guaranteed to be wrong. So I'll try to get myself at least a little bit above her. And I know that everything I'm saying probably directionally correct timeline, probably way off. Yeah. But 10 to 20 years, my rough estimate, that's where we're either. In Terminator and we're running from radioactive rubble to radioactive rubble, fighting the machines. Or it it really is a utopia.
54:09 Tom Bilyeu? And I think that there is a real shot that we get to the closest things that humans are going to get to. To a utopia where things are so plentiful. Yeah. Everything we want is available. We reorient our human psyche not to acquisition, but rather emotional contribution. And We'll paint that picture more as we go down, but that that's sort of my rough thing.
54:35 Speaker 01 Yeah, I think these are crazy timelines. Like, not because I disagree with them, because the fact is that they are crazy. You have a year of incubation, then you have contagion, and then you've got a stark thing. I don't think we'll be chased by robots. They wouldn't need to chase us. They're far more efficient than that, right?
54:51 Tom Bilyeu? That's sad, but true.
54:53 Speaker 01 I think the, basically, the two directions we have are utopia and human flourishing and a dystopia, we're all happy.
55:00 Tom Bilyeu? A dystopia where we're all happy, meaning we are manipulating our neurochemistry.
55:04 Speaker 01 Nineteen eighty-four. You know, like, you're always happy. You've got so many.
55:09 Tom Bilyeu? Eighty-four?
55:10 Speaker 01 Brave New World.
55:11 Tom Bilyeu? There we go. It's like nineteen eighty-four.
55:13 Speaker 01 Not nineteen eighty-four, Brave New World, sorry. Brave New World, I mean.
55:16 Tom Bilyeu? Soma drip.
55:17 Speaker 01 So much.
55:17 Tom Bilyeu? Feeling good.
55:18 Speaker 01 You got so much of you're feeling good. Like, look, you had Replica. Um, familiar with that?
55:22 Tom Bilyeu? Yeah, tell me about the Valentine's Day massacre, though. I didn't know about that.
55:25 Speaker 01 Yeah, exactly. The Valentine's Day massacre. So, you know, that's how I kind of call it. So Replica was a mental health chatbot. Then they realized you could charge three hundred dollars a year for erotic roleplay.
55:34 Tom Bilyeu? That had to be internally a rough transition. Hey, guys, I know we founded the company on mental health, but.
55:41 Speaker 01 You know, you can ask them. Um, fourteenth of April, twenty twenty-three, they turn it off.
55:46 Tom Bilyeu? Why?
55:47 Speaker 01 I think Apple just told them you can't have this on the App Store.
55:50 Tom Bilyeu? Interesting. So it was either remove the sexbot part or go off the.
55:55 Speaker 01 And then sixty-eight thousand people joined the Reddit, and they're like, why did you lobotomize my girlfriend?
56:01 Tom Bilyeu? That's a lot of people to be using it. I downloaded that at Christmas, not realizing what it was. And I was like, ooh, a chatbot. Let me try this thing. Uh, I didn't get into the weird stuff. It, I don't know, didn't do.
56:13 Speaker 01 It was the old technology, though. It's the thing. Now, like, again, Medpom two, the Google medical model.
56:19 Tom Bilyeu? Medpom?
56:21 Speaker 01 Two, it's Google's medical model. It scores higher than humans in clinical diagnosis and empathy.
56:26 Tom Bilyeu? That's crazy.
56:28 Speaker 01 All right.
56:28 Tom Bilyeu? They just, this is one of those statements that you say people need to be shocked. That that a computer makes people feel more comfortable.
56:36 Speaker 01 Yeah, it's in nature. They just published the paper. They included that. And again, it's only going to get better.
56:42 Speaker 01 What if you have a voice that you add to it that really understands you and it's, you know, so empathetic and things like that? Do you ever see that Washington Post chart of males under the age of 30 in America who've not had a sexual partner by the age of 30? It was 8% in 2008, then it went up to 27% in 2018 or something like that a couple of years ago.
57:05 Tom Bilyeu? A straight line.
57:06 Speaker 01 It's just a straight line. This is kind of what you're talking about, like.
57:09 Tom Bilyeu? What happened? I think people need to understand.
57:12 Speaker 01 I think it's the iPhone and Pornhub, probably a combination of those two.
57:16 Tom Bilyeu? You put a computer in the hands of young men and let them see naked females more in a single session than 100 years ago they would have seen in their entire lives. It, these are not small changes and they have huge neurological implications, especially in the years of brain development.
57:40 Speaker 01 Yeah, and this is why it's so important to shield our kids at this point because the influence is going to be insane. Like, I was having a discussion with a very prominent technologist and he's like, yeah, I'm pretty sure that my child's first crush is going to be an AI.
57:56 Tom Bilyeu? Guaranteed.
57:57 Speaker 01 Guaranteed.
57:58 Tom Bilyeu? For most people, it was actors, so we're already prone to
58:02 Speaker 01 You'll have your own AI.
58:03 Tom Bilyeu? Unattainable distant thing. Now it's in your pocket.
58:06 Speaker 01 It's in your pocket, it's always with you, it's always kind of there. Again, as you said, a large part of society would like to draw in on itself as a result of that, and that is a bit dangerous. Do they then go out into the streets? Are you seeing a boot Larry and Jihad kind of thing like in Dune? I don't know.
58:22 Tom Bilyeu? Larry and Jihad?
58:23 Speaker 01 In Dune, there was this concept of the boot Larry and Jihad where as yet autonomous AIs.
58:27 Tom Bilyeu? Larryan.
58:28 Speaker 01 Boot Larryan, yeah.
58:29 Tom Bilyeu? Got it, yeah, yeah, yeah.
58:30 Speaker 01 They rose up against them and said, no more AI agents.
58:33 Tom Bilyeu? The book opens with that, right? And you can never again make a human-like AI, something like that?
58:38 Speaker 01 Again, this is kind of a thing, extension of the ludism kind of thing, but most people will be happy with their AIs because their AIs actually listen to them. I use GPT-4 as a therapist. I've got a therapist too, because this is hard. Why? Because it never judges me unless I tell it to judge me.
58:54 Tom Bilyeu? You tell it to judge you sometimes?
58:55 Speaker 01 Sometimes, sometimes you like.
58:56 Tom Bilyeu? Do you really?
58:56 Speaker 01 Yeah, I mean, like, come on, like, come on, give me some positive constructive feedback and it will listen and give positive constructive feedback.
59:04 Tom Bilyeu? Did you give it a personality? Did you have to like, imagine you're a therapist that's like?
59:09 Speaker 01 Yes. You give it the instructions and it just adapts. And then you give it the things you opt out with GDPR from it training the model further. So those models be even weirder listening to my complaining. And it will come back to you with whatever and soon it will be able to talk. And it will have full vocal control.
59:28 Speaker 01 And these models will proliferate at that level because you're not stopping. The models are going to just going to get better and better and better. So you've got this crisis, but maybe it'll be insulated. But I think again, if you look forward like after the incubation and the contagion and the spread kind of phase, there are only two paths here. Complete control by existing structures and Star Trek utopia.
59:51 Speaker 01 You know, I think those are the two options that we have because organizations look at this technology and they're like, this is really cool. We can optimize our objective function to sell more ads or to control the people and kind of keep them going. You know, do you really trust politicians with this technology?
1:00:09 Speaker 01 No, even if it isn't arms race. Because again, you won't know what's going on because do you have the defenses to defend against what's coming? Personally, for your kids, for this, for everything. We've already had the social media age. It wasn't really social much of the media, right? This is something new that's coming now. Where you can't tell this from a human, except for the fact it's better, it's more convincing. And you can use that to create a human colossus and solve all the problems of the world and we all come together. Or you can use that to get everyone into their basements.
1:00:44 Speaker 01 You know, and cut off from the world.
1:00:46 Tom Bilyeu? All right. I want to paint a very beautiful story for people and I want them to understand. Look, I don't think this is a completely controllable thing, but you said earlier that there's opportunity in any crisis. And I will say that the biggest opportunities come in moments of disruption.
1:01:06 Speaker 01 Yep.
1:01:07 Tom Bilyeu? And the reason I want to lay out the problem set is because I really believe that certainly at the individual level, if you're thoughtful enough, you're you are going to be able to navigate your way through this. So dear listener or watcher, if you're here on YouTube, I'm telling you right now, if you're thoughtful enough, you will get through this and you have a chance to get through this better than when you started. But you have to be aware of what the dangers are. People have to to really lay things out before them, look at them so they know, okay, this is how I'm going to isolate myself from this potential problem. So I'm going to avoid this. This is how I'm going to leverage that. Okay, your story is one of the most incredible stories of how one uses AI. You have both a personal example, and then obviously, as the founder
1:01:48 Tom Bilyeu? of Stability AI is obviously incredible. But talk to me about your son, because this is, and this was when AI was a lot less useful than it is now, and it was still life changing.
1:02:01 Speaker 01 Yeah, so 12 years ago, my son was diagnosed with autism when he was two years old, you know, it's very, very severe, scratching a wall, telling his fingernails bled. And they said, there's no cure, there's no treatment. We don't really know what causes it. Anyone on this call kind of list on this listening knows that's kind of the case. So I was a hedge fund manager at the time, I was lucky enough to kind of be one, quite young. And I was like, well, go do something about it. So I switched to advising hedge funds and then building a little AI team and doing AI with old school AI, natural language processing to analyze all the autism literature, and what could possibly be a cause. Now, is this scientific? No, it's an N of one thing a father does for his son, you know, we'll be publishing some of the results of it soon.
1:02:43 Speaker 01 But it focused down on GABA goose mate balance in the brain. When you pop a Valium, your GABA goes up, you chill out. When you got a glutamate spike, that's when you can't focus and your legs tapping all the time. And there are multiple things that cause it, but a lot of kids with ASD seem to have that. And there are some papers around that, etc. Because how could you focus if you're in that condition all the time? So you can't learn to speak, you can't do that. So it was how do we reduce this through drug repurposing, built a knowledge graph based system to do that and figure out which drugs could potentially Help reduce the glutamate, help increase the GABA.
1:03:16 Tom Bilyeu? How are you using AI for this?
1:03:18 Speaker 01 So this was kind of the mass natural language processing, looking at all the literature. Because the same treatment would make thirty percent of kids better and thirty percent of kids worse. And so I was trying to figure out, the outcome was the same, a cold is caused by lots of different things. The thing that caused it could be so different. And so conventional medicate, medicine and medication kind of failed that. So I worked with neurologists, worked with other psychiatrists and others and tried different medical combinations of off prescription drugs and other things to try and make his brain calmer. So then he could use applied behavioral analysis and others to reconstitute speech. And, you know, you end up going to mainstream school. I think it worked. Um, I told people about it and they're like, you're not a doctor. And I was like, there's a
1:04:03 Tom Bilyeu? That was the response?
1:04:05 Speaker 01 Well, yeah. I mean, look, with anyone who's listening to this, like, am I saying I have a cure for autism? No, I'm saying I'm a dad who tried my best and I saw results. But in order for something to become medicine, you actually have to go through a proper process. And so for me, that was building language models. That was making everything in the right structure. And then we can organize the world's autism literature, making it accessible and useful. Any parent or anyone, you may have someone in your family that has a neurological condition, Alzheimer's, this, the. So many people around the world have the same problems. Wouldn't it be wonderful if they could just find what the latest knowledge is? And also the things that could work? And have a holistic approach to this.
1:04:47 Speaker 01 And until we had this technology, you can never get there, which is one of the main drives for me to want to build this technology. And do it in a transparent order, but you shouldn't have to trust me and what I say about this, because again, my journey as a parent is the same as any one who's got a child with ASD, it's the same as anyone who has a family member that gets multiple sclerosis or cancer. Our systems are not good enough right now to bring us the information we need unless we find an amazing doctor and an amazing group. But now with Med Palm, with our own models, with other things, we can finally be at that point where we're never lost. Where we can say what could work. So an example is clonazepam. It's prescribed with a one hundred thousand microgram dose for anxiety at a five microgram dose, my son could sing. Because it potentiated.
1:05:31 Tom Bilyeu? And he was nonverbal.
1:05:32 Speaker 01 He was nonverbal. And at twenty micrograms, it stops working. It's six dollar a year intervention. Because this is the way that like neurotransmitters work. So like when you pop a nitol and antihistamine to go to sleep, right? What it does is it floods a whole bunch of your neurotransmitters, including the H one neurotransmitter. And that's the one that makes you sleepy. But then other ones give you dry mouth and other things. Something like Remeron in a microdose just triggers H one. It'll knock you out without any side effects, but it's just incredibly cheap, you know? Understanding things like neurotransmitters is not something that most of us ever have to do. Unless you're super hyper focused on it for years, because I was like, I need to figure out my son's neurotransmitters. I'm talking to all the top doctors and I'm lucky because I have access to them. What do I do now?
1:06:18 Speaker 01 So that's when I realized that, you know, this AI was a big thing. And actually, one of the really interesting things is, why couldn't he talk? It's because he had too much noise in his brain. So you've got cup.
1:06:31 Speaker 01 A cup can mean a cup, or it can mean cup your hands or cup your ears or world cup. He couldn't form those connections because his noise was too, brain was too noisy. And so he did applied behavioral analysis for just teaching you, this is a cup, this is a cup, this is a cup with gamification. To reconstruct those after his brain calmed down. It's actually very similar to this generation of AI. We described earlier how it learned principles. It's called a latent space of meaning. So that point and dot, that pixel becomes a cup. Because it understands the principle of cupiness. So when you type in world cup or cup your ears, it gives you dramatically different images. Similarly, the language models do the same. Again, it pays attention to what's important. Attention is all you need was the original paper. And again, forms this latent space of the meaning of cup within the sentence.
1:07:16 Speaker 01 So when it says cup, it's like, well, this is going to be a world cup or this is going to be that. But not actively. Again, it's just a bunch of ones and zeros, a single file. And so I think that's why all this new generative AI really resonated with me. And I realized this could be the real thing that unlocks humanity or controls them forever, one of the two. Um, yes, that's kind of some of the personal story behind this, because again, like, I don't have a cure for autism. I worked as well as I can with my son with the technology at hand. However, I think that with the building blocks we're building now, us and others, there's the potential to have personalized care and knowledge. For everyone who's dealing with ASD, for everyone who's dealing with multiple sclerosis
1:07:59 Speaker 01 or any of these other conditions, where they say there's no cure because our medical system is treats people as a gerdic. And what does a gerdic and a gerdist mean? It means a thousand tosses of the coin, the same as a thousand coins tossed at once. That's why everyone gets 500 milligrams of paracetamol. It's why a lot of people have a cytochrome P450 abnormality kind of mutation in their liver. It means you process code into morphine quicker or fentanyl into death. Yet we don't do a basic genetic test on that because our system has to treat us as numbers because we could never scale intelligence. We can never scale expertise.
1:08:40 Speaker 01 So, yeah, like I said, that's the story behind that.
1:08:43 Tom Bilyeu? Yeah, see, this is where this starts to get interesting because I, I think about this a lot in myself. So I can paint the nightmare scenario of somebody collapsing inside of themselves, having AI friends instead of real friends and how quickly that can get distorted and become a real problem. And yet, at the same time, I'm building exactly that. And the reason that I'm building that is because of the promise of AI and the incredible things that we can do as we begin to recognize more patterns and figure out, okay, where does this really go? So my wife had a tremendous health bout. It's been a while now, thankfully, but at one point I was afraid she was going to die. Her fingernails were breaking, her hair was falling out, she couldn't eat.
1:09:28 Tom Bilyeu? It was just really, really, really bad. And it ends up being her microbiome, but of course it took forever to diagnose that that was a problem. She was about to get immunotransglobulin transfusions. And I was just like, this is, I was like, something's wrong. I don't think this is the right answer. I don't want to do that. Let's stop, let's try to figure this thing out. And so we pumped the brakes, we start researching the microbiome, looking into that, testing things. And the thought of having AI to be able to say, okay, let's take genetic data and read the genetic database that's ever been collected, all of that. Let's look at all the different foods, responses, match all that together.
1:10:11 Tom Bilyeu? And if you can get that level of pattern recognition and now you can engage AI. I think she, cause part of the problem is your microbiome is changing daily. It's probably changing hourly. And if you were able to track all of that and say, okay, with your genetics, with your current state of your microbiome, here is exactly what you should be eating. Maybe even with nutrients from food grown in that area. Like it really may be that specific. And when you can find patterns in that sort of insane level of data, now you've really got something. And that's but one of the many areas where I think that this could be utterly transformational.
1:10:52 Speaker 01 Yeah, I mean, like right now, the AIs are know it alls. I mean, you know, all graduates, but you have specialists. You'll have a nutritionist AI. You will have a microbiome AI. You will have a personal trainer AI. Like, why was Peloton successful? Attracted people shouting at you. You know, we can generate that now. Um, everyone suddenly gets that personalized to them. But more than that, it's not just the information being in this tiny model. You have retrieval augmented models and other things, which means these models now interact with existing data sets and knowledge sets. So you use something like perplexity AI. It doesn't only answer your questions with GPT four. It gives you references. So you can say, what about a man? And it'll link to all of the things as it gives individual stuff. And this will only advance from here.
1:11:37 Speaker 01 So you can dig into as much depth as possible that you want with a whole team of people around you, even if you're by yourself. So you should never be alone again in terms of you can be connected to people like you in the same problem as you. We can build better teams and all the information of the world is at your fingertips in a way that it was never before, including your own private information. So like one of my favorite apps, it does use some battery is rewind AI. It takes a screenshot of your MacBook screen every time it changes and OCRs it. And then it gives a timeline. So I can type in impact theory and it will look through everything that's ever been on my MacBook screen. It's all stored locally and find where impact theory is on YouTube.
1:12:20 Speaker 01 Or there's a picture of this mug and it shows it in a timeline. So I can go back and I can see what I looked at before or after that.
1:12:27 Tom Bilyeu? What?
1:12:30 Tom Bilyeu? Wow, so you can map your own sort of connective trees with whatever.
1:12:38 Speaker 01 And what happens when you combine that with a language model? Everything you see on your screen stored locally with an open source language model, it creates the Memex. And it sees what you've paid more attention to versus less attention. Again, the dystopian versions of this. I wonder whether utopian version, which is ADD me, can finally remember what I was doing, what I was looking at, the context, the search tree as I was searching all these different things, clicking from place to place. And then you can set agents to go and recreate that journey and search all the other stuff that you didn't search.
1:13:13 Speaker 01 This is really positive because again, how much of our life is done searching for knowledge, searching for information that's relevant to us?
1:13:21 Tom Bilyeu? Talk to me about the paper, Attention is All You Need. I've heard you and other people bring this up multiple times. I haven't read it. But this is like the big breakthrough.
1:13:30 Speaker 01 Yeah, this was the 2017 paper by the Google team, all of whom now left Google. And basically what it was is that classical big data, took big data and then Facebook extrapolated it so that when it found 17 pieces of information about you, it could target you with that. That was a classical big data thing. They even create shadow profiles of you. So when you go on Facebook, they actually got a shadow profile that then connect to your real profile.
1:13:55 Tom Bilyeu? What's the shadow profile?
1:13:56 Speaker 01 Like, it's like, what if there was a Tom on YouTube on Facebook? Because there's all these connections to this unknown person. And then it just fills you in automatically. That's why it figures out your preferences so quickly without listening to you. So, Attention is All You Need said that not all data is important.
1:14:17 Speaker 01 You need to pay attention to what is important in the sentence. What's important in this time series? Because that's the nature of being to spot the tiger in the kind of thing. That was the missing part. And so the transformer architecture that came from that, and you have different architectures now is what led to G P T three. Now this general purpose.
1:14:35 Tom Bilyeu? Why is it called transformer architecture? If what it's doing is pinpointing what's important, why not important architecture or?
1:14:43 Speaker 01 Well, again, attention is the mechanism that it does to kind of transform the information, tokenize it, and then figure out these latent spaces of meaning.
1:14:51 Tom Bilyeu? So are the tokens the important pieces?
1:14:53 Speaker 01 The tokens are important. That's how you take a word and then you split up into its constituent parts. And then you try and figure out what the most important part of it by doing pattern analysis at ridiculous scale. So something like a G P T four would use probably from the kind of things on semi-analysis that have been leaked, if they're correct. It uses a supercomputer fifty times faster than NASA's fastest supercomputer for like three months. Uses like twenty, thirty megawatts of electricity.
1:15:23 Tom Bilyeu? Whoa.
1:15:24 Speaker 01 And ten trillion words go into that, and it figures out all the connections between them and what generally comes next. So what it does is just literally figures out what word comes next. And so this was a big breakthrough because what it meant is that you didn't have to have hugely complicated big data algorithms.
1:15:40 Speaker 01 You just needed to have very large compute to scale. And so compute went exponential and then you just threw more and more G P U's at it. And it figured out more and more things. And as you scaled, it had more and more emergent properties, which surprised everyone.
1:15:57 Tom Bilyeu? Do you know who John Nash is?
1:15:59 Speaker 01 Yeah.
1:16:00 Tom Bilyeu? This, it sounds like that. So John Nash, he unfortunately was schizophrenic, but he was, uh, he's the guy from A Beautiful Mind. For people that don't know, watch the movie Russell Crowe, fantastic movie. And, uh, at one, he obviously doesn't know he's schizophrenic and he starts seeing patterns in everything.
1:16:18 Speaker 01 Yep.
1:16:20 Tom Bilyeu? And it sounds like that, that this thing is, you know, whatever the human brain, whatever algorithms we have running that allows us to very quickly suss out what's important, that it's doing that, but at an extraordinary high level.
1:16:33 Speaker 01 And you have to remember, like, there's a saying, all intelligence is compression. You take everything you've listened to this, you only remember a few things. And the whole of computer science is based on information theory from Claude Shannon. And if I want to summarize it, information is valuable only if it changes a state or as much as it changes a state. So if listeners are listening to this and they don't take away anything, this is a useless thing. You know, or maybe they just put it on the radio because they like hearing your voice or something like that, right?
1:17:03 Speaker 01 But if they take something away from it, then it's valuable. You've had a good use of your time. So you start seeing patterns and everything, but one of the things that, again, people I think misunderstand with technology is GPT four is not a program. Stable diffusion is not a program. It's a large amount of text, images, et cetera,
1:17:23 Speaker 01 where the output is a single file of ones and zeros. It's like a filter.
1:17:29 Speaker 01 You can recursively kind of put something through it, but it's just guessing the next word when you type a prompt, even if the prompt is the whole of The Great Gatsby and the whole of Ulysses by James Joyce, and you're telling it to combine them together. It then pushes it through that sieve, that filter, and the output is something that combines them both together.
1:17:49 Speaker 01 Single files, we've never seen anything like this before. Probably the closest thing probably listeners would have heard to this is a codec,
1:17:56 Speaker 01 which with music and things like that, we had these audio codecs that you had one file type and another file type, and you had this single file that translated between them.
1:18:05 Tom Bilyeu? Was that, uh, is that compression? Or is that recognizing this thing in that format would look like this?
1:18:14 Speaker 01 So again, this is compression of intelligence is what it is. So again, it's like a translator function. These, these are universal translators for context.
1:18:25 Speaker 01 And so you push it through the sieve and then stuff comes out where it's predicting the next word or it's de-noising, uh, kind of a pixel. To achieve that, because you're just passing it through the sieve again and again and again.
1:18:37 Tom Bilyeu? Yeah, it's, uh, I unfortunately don't understand it by first principles, but I get it by analogy. Um, you're feeding it so much data, it's recognizing the patterns.
1:18:48 Speaker 01 Interconnectedness, yeah.
1:18:49 Tom Bilyeu? And it's able to say, okay, this is the like, um, all right, this is written in the style of Stephen King. This is written in the style of Hemingway. So even though they're using the same language, their vast majority of the words are going to overlap, but there's different patterns, rhythms, even different, uh, subject matter presented in a different way, tone.
1:19:10 Speaker 01 Except for the fact that it's not actively doing that. It's like a mega sieve. And depending on the words that you put in, the words that come out are different. Hmm. So it's literally a-
1:19:20 Tom Bilyeu? Is the sieve the prompt of write it in the style of Stephen King?
1:19:24 Speaker 01 That's what goes into the sieve. It goes into the filter.
1:19:27 Tom Bilyeu? So what is the filter?
1:19:29 Speaker 01 The filter is this compressed knowledge.
1:19:32 Tom Bilyeu? The principles.
1:19:32 Speaker 01 The principles.
1:19:33 Tom Bilyeu? Because it's not actually compressed knowledge, right?
1:19:35 Speaker 01 Yes, principles. Yes, it's principles. So it would be like a really good book of principles.
1:19:40 Tom Bilyeu? Hmm.
1:19:41 Speaker 01 Uh, except for it's compressed way beyond a book ever was, because a book is a compression of information. And that's why it can kind of do this, because it looks at books, it looks at articles, and they're all compressions of information. To write an article is a huge endeavor that then comes out with something where they're trying to convey a few things. A book is an even larger endeavor. And so again, it's very difficult to wrap your head around, uh, like even for me, like, you have a file and it can do all these things versus these gigantic computer server farms with programs and logic of ones and zeros.
1:20:16 Speaker 01 There's no program here.
1:20:18 Tom Bilyeu? Yeah, and it's important for people to understand that even so, first of all, the AI scientists that are building these things do not understand how this all works. And even if you ask chat G P D chat G P T four to explain how it works, it doesn't know.
1:20:32 Speaker 01 It doesn't. No one's quite sure exactly how we're getting all these emergent properties. And it's constantly surprising like, oh, and now it can add.
1:20:40 Speaker 01 You know, and then you start tying it together. So it goes through this file multiple times. It becomes it shows more and more kind of agent activity. In fact, the next step of this, and this is what OpenAI and Google are both doing is there was this thing called AlphaGo. There's an amazing documentary. Where Google's DeepMind division created an AI to beat humans in the game of AlphaGo or Go. So Go is like chess, except for there's almost a myriad infinite number of potential moves. So you can't calculate them. Like you can't brute force it like Deep Blue do with Kasparov. So instead it learned to play against itself. It's something called Monte Carlo tree simulation where it learns different principles. Dreams again, an amazing documentary on YouTube about this.
1:21:20 Speaker 01 And it beat Lee Sedol, who was a ninth down player, one of the best in the world, far beyond everyone else, the Magnus Carlsen of Go seven one. What they're doing now is they're combining those models with these language models. To make them agentic.
1:21:35 Speaker 01 So models that can plot and plan and other things with language models that can predict what's next. To create things that really understand context even better.
1:21:45 Tom Bilyeu? Okay, so I understand enough about how the AlphaGo system works. I want to understand this. So AlphaGo is going to play against itself. So you give it the rules of Go, you give it the objective, and then you set it loose and it just plays and plays and plays and plays and plays and it plays against itself. Uh, and then I know at one point they because they, uh, Lee Sedol, if I remember correctly, was the number two player. And people thought, well, first of all, you're never going to beat him. And they did. But they couldn't beat the number one player. And then they created another variation and it played against AlphaGo and then it ended up smashing.
1:22:20 Speaker 01 It was Mu Zero, I think, came after AlphaGo.
1:22:22 Tom Bilyeu? Okay, so. How do you get a language model where there is no objectively right answer unless you're doing trivia? How do you, how do you get it to know what the reward is? How do you get the quote unquote right answer?
1:22:38 Speaker 01 Much of this now is about reward functions. So GPT-4, when it comes out of the box, maybe we're getting a bit too into the weeds, is a pre-trained model and it's trying to supercomputer.
1:22:46 Tom Bilyeu? Yep.
1:22:47 Speaker 01 Then we use reinforcement.
1:22:48 Tom Bilyeu? And just so everybody knows, pre-trained model means principles.
1:22:50 Speaker 01 Yeah, principles. Take all this knowledge, all these words, squish it down into a file.
1:22:54 Tom Bilyeu? And then run it on a computer that's literally set up like a brain where there's like a bunch of neurons and they're interconnected.
1:23:01 Speaker 01 Yeah, these are the tensor cores on the Nvidia GPUs.
1:23:03 Tom Bilyeu? Yep.
1:23:04 Speaker 01 And so mimicking the brain on your little
1:23:07 Tom Bilyeu? Literally.
1:23:07 Speaker 01 4090, it has AI chips right baked in there.
1:23:10 Tom Bilyeu? For anybody that wants to know, they kick off so much heat, you can see it from space.
1:23:14 Speaker 01 Yeah, I mean, like, I think our supercomputers are like 10 megawatts of electricity. Each one of these cards uses 700 watts. Uh, yeah.
1:23:22 Tom Bilyeu? Utterly fascinating to me. So anyway.
1:23:23 Speaker 01 All clean energy, by the way, for our ones, for anyone who has. Um, so what you've got is, when it came out, they took six months to make it human because they trained it on like, I don't know, all of YouTube and the whole internet. Obviously it would turn out a bit weird. And so you have a reward function through something called RLHF, reinforcement learning with human feedback.
1:23:42 Tom Bilyeu? Yep.
1:23:42 Speaker 01 Where you give an objective function to say, don't answer questions about how to make napalm. And don't ask.
1:23:49 Tom Bilyeu? The objective function technically is please your human overlord.
1:23:52 Speaker 01 Please your human overlord.
1:23:54 Tom Bilyeu? Got it.
1:23:54 Speaker 01 And so much of this alignment question is focused on taking these big models and trying to make them so they won't kill us some way or say bad things for a given definition of bad things. Um, like my, one of my takes on alignment and you know, is like, we should have better data sets. We should move away from web crawls.
1:24:11 Tom Bilyeu? Can you, uh, yeah, it's fair. I want to ask you about that. But explain to people exactly what alignment is aligned with what?
1:24:19 Speaker 01 Aligned alignment, you know, this is a cool thing of anthropic. There was a great New York Times piece on them recently and others is if we build an AI that outperforms humans and is more capable than us, because it might not just be a single file, it might be a thousand different AIs all working in different ways. So you've got your AlphaGo type AI in this. Yeah, a swarm. Because, you know, humans are swarms, our organizations are swarms that are highly specialized, right? Might be a million AIs.
1:24:47 Speaker 01 How do we make sure it doesn't kill us? Or how does it make sure it doesn't enslave us? Or how does it make sure that it doesn't give us eternal suffering?
1:24:54 Tom Bilyeu? Also lame.
1:24:55 Speaker 01 Yeah, this is will be kind of sucky. How do you make sure it's aligned with human interests? And so this is an unsolved problem. OpenAI have announced they're putting twenty percent of their compute to try and solve this. And Anthropic was set up to do this, although I believe that the only answer they've come up with is let's build an AGI first, an artificial general intelligence, that will then stop all other AGIs from coming. It's just kind of scary, uh, to say the least.
1:25:18 Tom Bilyeu? Why do they think that that would work? Like, that seems, so Elon Musk has likened AI to a demon summoning circle, and we're all just hoping that the demon that comes forth is going to be kind, but we don't know.
1:25:29 Speaker 01 We don't know. In OpenAI's own words in their Road to AGI post, they say, "This could be an existential threat and wipe out humanity and democracy and capitalism. But if we don't do it, someone else will." This is part of the unpleasant race condition. Again, it gets the headlines. I think it'll probably be okay. But with the way we're going right now, you're going to go from two companies being able to build this technology, or maybe three, including Anthropic, to twenty or thirty in a year or two. And what's the odds that they all do it properly? And align this technology properly, I think it's pretty low. What's the odds that if we train it on the whole internet, including the whole of YouTube? Because we don't have enough tokens, not enough words to feed into it, that L10 have it weird, very high. So it took six months to tune it to being a human GPT-4.
1:26:12 Speaker 01 And then Kevin Ruiz of the New York Times is like, hey, how you doing? It's like, leave your wife and come and join me.
1:26:17 Tom Bilyeu? Whoa.
1:26:18 Speaker 01 And I was like, oh, what?
1:26:21 Speaker 01 Bing came out a bit weird when it first kind of had GPT-4 in it. Because again, we feed it crap, we're going to get slightly weirdness. Now it's got a lot better, but it's lost a lot of its personality because you've been tuning it back to human preferences. You've been doing this reinforcement learning with the objective function of don't offend anyone. It's quite hard to get GPT-4 to be offensive now.
1:26:41 Speaker 01 But there are these two phases of this technology. I think with better data, we can have more aligned models. I think with better data and national data, we can have more representative models because you'll never have technology that's not biased.
1:26:55 Speaker 01 So the only question is, who's bias? Well, because you have to build it in a certain way. But even though it understands all the contexts, like right now, these models are trained on the whole internet, which is largely a Western artifact.
1:27:06 Speaker 01 Yeah, largely trained in English.
1:27:08 Tom Bilyeu? How much do you worry about bias? Are you more worried about bias or alignment?
1:27:12 Speaker 01 I'm more worried about alignment, I think. I think this is one of the reasons that we release open models. So you can see how the cookie is made. Like, we're the only company that offers opt out of data sets. Literally the only AI company in the world.
1:27:24 Tom Bilyeu? So if I'm an artist and I don't want you training on my art, I can back out.
1:27:28 Speaker 01 Had 167 million images opted out of our data set, yeah. We're the only company in the world that does that, which I find kind of insane. So my thing is open, auditable, which means that you can tune your own culture into these models. We're helping multiple nations build national models with their broadcast of data that then can represent that. And, you know, try to address some of this inherent bias within the data sets. Algorithmic bias has been an issue that affects real world and it'll affect more and more of the real world as you get into these models, because we will outsource more and more of our minds to them. Because again, like when you go.
1:28:02 Tom Bilyeu? A small subset of people will outsource their mind to it. So in the near term, I'm way more worried about bias. In the long term, I bias is not going to lead to an existential threat.
1:28:13 Speaker 01 Yes.
1:28:13 Tom Bilyeu? But alignment can, but let's talk about bias for a second. So I, I am very uneasy about how rapidly bias finds its way into this stuff. And that becomes another, so if, let's say that we all get our individual AI and it's, you get yours young and it's your primary education tool and it's biased as the day is long. Now you run into real issues because at a time of optimal malleability, you're programming a kid's mind with something that's super biased.
1:28:46 Speaker 01 Yeah, I mean, like what if you have the little AI of Xi, right? Which is a little tiny Xi Jinping that grows up and tells you how great Xi Jinping is.
1:28:54 Tom Bilyeu? Lovely.
1:28:55 Speaker 01 That's biased, but that's inevitable. They already have it as an app that actually tracks your eyes.
1:28:59 Tom Bilyeu? Yeah. What?
1:29:00 Speaker 01 It's not an AI, but they have a little app of Jesus's light little book where it actually tracks your eyes for attention. Like, are you actually looking at this?
1:29:07 Tom Bilyeu? Does this feed into the credit score?
1:29:09 Speaker 01 I'm not sure if it's being hooked up, but of course it will be. Why wouldn't you?
1:29:13 Tom Bilyeu? This is so scary. So, look, I thought, you know, we were on the happy part. Now we're back to, uh, to that. But that's really terrifying. And when you have a state that is not interested in any individual.
1:29:28 Speaker 01 You've got the collective.
1:29:29 Tom Bilyeu? Yeah. So I don't know if this is true, but I saw a headline that said, in China now, the phone will alert you if somebody with a lower social credit score than you is calling you. And it warns you, if you answer this call, it will lower your credit score.
1:29:48 Speaker 01 It's effective, isn't it?
1:29:49 Tom Bilyeu? Yeah.
1:29:50 Speaker 01 I mean, even if it isn't true, again, it fits the objective function of perpetuating that particular system, which is not necessarily our system, but systems around the world shift. So this is why you have an inherent bias. Who's bias is it? Who's the one who creates the AI nanny?
1:30:04 Tom Bilyeu? Is this why you're doing things regionally?
1:30:06 Speaker 01 This is why I'm doing things regionally, but also allowing people to own their own models. So the objective function of the model can be that. Like in Web three, there was the saying, not your keys, not your crypto. So my saying is not your models, not your mind. Because I do think we'll outsource more and more of our cognitive capability to this. It will be our co-pilot for life. If someone else is making that model and deciding on things, what's going to happen? Like the UAE had this model Falcon.
1:30:33 Speaker 01 It was a big open source model and they were like, wow, it was actually Lighton from France who were behind it, but let's leave that to one side. You ask about the UAE and human rights, it's like, this is a wonderful place, full of fantastic things. You ask it about Qatar or Saudi, it's not so nice. Who's embedding these inherent biases in these models, right? Whose model are you using? And these can be very insidious, the biases, right? You won't pick up on them, but you hear it again and again and again, because is your nanny a conservative Republican? Or is she a libertarian or things like that? You will be influenced by that if you grow up with them. Or even if you're using it day to day, just the way it's speaking, the way it's thinking, the way it's recommending stuff, which goes far beyond a Google Maps or something like that.
1:31:16 Tom Bilyeu? Crazy. All right. So that we don't get lost back down the dark rabbit hole. What is the coolest thing that you see AI doing? I know you're building a lot of different companies leveraging this technology to do amazing things. What are some of the coolest?
1:31:28 Speaker 01 So we're doing one company at the moment, which is just let's go. The mission is to create the building blocks to activate humanity's potential.
1:31:34 Tom Bilyeu? So the building block, what?
1:31:35 Speaker 01 Our mission is to create the building blocks to activate humanity's potential.
1:31:38 Tom Bilyeu? Stack. Okay.
1:31:40 Speaker 01 So every single modality, image, audio, video, 3D language, sectoral variance and national variance. You've got like this grid that you can pick and you're like, I'm an Hindi investment banker. I transfer my private data into a chat GPT type thing that's just training my private data. Or I'm a Vietnamese illustrator. I want a Vietnamese cultural kind of image model or something like that. And then you can bring your own stuff to it. That's kind of my goal, which is to enable other people to build on top of what we're building, like a layer one for AI. Effectively, but open models for private data. Whereas the other side is proprietary models where you only be able to send a certain amount of data. Like government's not going to run on black boxes. Education, healthcare, you will need to own your own AI.
1:32:23 Speaker 01 So the coolest thing I've seen is just the promise of personalized education. There is nothing that's been proven to work in education, except for probably the bloom effect to sigma improvement, which is one-on-one tuition. But even here in affluent America, education system's not good. What is education optimized for? It's like a social status game mixed with a petri dish mixed with childcare realistically. Like very few people are happy with their schooling because what are we trying to optimize for? You give a one-to-one tutor that can find out if you're dyslexic, an audio learner, a video learner, visual learner or otherwise. And they're just constantly adapting to you and bringing you information at your level.
1:33:02 Tom Bilyeu? Dude, I want to get that.
1:33:04 Speaker 01 Is most transformative thing ever.
1:33:05 Tom Bilyeu? Is anybody doing this already?
1:33:07 Speaker 01 So, you know, with the kind of charity that would support Imagine World Ride, one of my co-founder. He's been deploying adaptive learning tablets into refugee camps around the world with the Global X Prize for Learning winner. One billion on them. Teaching kids literacy and numeracy, 76% of them in refugee camps in 13 months and one hour a day. Now the goal is to bring language models to all these kids. And you have an AI that teaches a kid and learns from it. And then that data can feed a better AI. You create a lovely system that's just learning and adapting. And a kid in Mogadishu is like a kid in Manhattan is like a kid in, you know. London. Once you have a generalized learning AI, you can really proliferate that around the world to customize.
1:33:48 Tom Bilyeu? This does to education because this is very interesting. Like, okay, now I'm just spitballing here, but
1:33:56 Tom Bilyeu? let's say that I I want to homeschool my kid. I would never do that in a million years because I don't want to turn into a teacher. So, but if I could perform just the sort of babysitting function and I give my kid a tablet that has an AI that knows exactly what I'm trying to optimize for either for this year or for the next 12 years or whatever. And it then calibrates to my kid, knows what they're good at, how they learn, and then knows how long they're engaged, when they're distracted. You know, you now I want the little Gigi thing, just like reading their eyes. Where are they looking? What are they doing?
1:34:33 Tom Bilyeu? And I know that there are like certain frequency things you can do where it's like, oh, if I hit them with this piece of knowledge, they're not sharp at this yet. So I need to hit them with the 27 minute increment or whatever.
1:34:42 Speaker 01 A viral reward schedule or whatever.
1:34:44 Tom Bilyeu? Exactly.
1:34:46 Speaker 01 You will transform education, but you can also make it.
1:34:48 Tom Bilyeu? Away from that.
1:34:49 Speaker 01 A couple of years. Again, what happens if you have a thousand GPT fours? You'll get there. No, it's just, again, we're just right now. We've got the building blocks, but we haven't got the design patterns. The stuff right now is literally iPhone 2G. We're just going to the App Store stage, copy paste. And so that's the biggest transformer to change because how much would you pay for your kid to have an optimal education?
1:35:08 Tom Bilyeu? How much would I pay for that? I would pay for that right now.
1:35:10 Speaker 01 Right now. So do we have the technology to do it? Yes. Does it take time to build it properly? Yes. How long does it take a year or two? And so this is the biggest transformation we've ever seen for education. Again, human flourishing, flourishing. Because that can also bring you the information about autism or multiple sclerosis or the best filmmaking techniques. Again, everyone's thinking one on one. You should think one to a thousand and then you optimize the thousand. You get rid of all the general knowledge. You make them specific.
1:35:39 Tom Bilyeu? What do you mean by that? Say that in a different way.
1:35:40 Speaker 01 So GPT four knows everything. You can ask it about like the most obscure things. Does it need all that knowledge? No. You've bulked, now you have to cut. And then you have a specialist model for calculus.
1:35:54 Tom Bilyeu? So I'd actually have different teachers.
1:35:56 Speaker 01 Different teachers and then a teacher that basically brings these teachers together and tells them, yeah, this is what Tom's like, you know, he's a bit cranky in the mornings, but then he wakes up in the afternoon because even in the best schools, a teacher has one to 20 attention. For all the kids, you'll have a whole bunch of AIs for you and for your kids. And again, are you a visual learner or are you an auditory learner? Do you have dyslexia? Can our system at the moment adapt to that?
1:36:23 Tom Bilyeu? No.
1:36:23 Speaker 01 No way. Can the system that I described adapt to that?
1:36:27 Tom Bilyeu? Instantly.
1:36:28 Speaker 01 Do we have the technology for that?
1:36:29 Tom Bilyeu? Yes.
1:36:30 Speaker 01 Is it gonna happen?
1:36:31 Tom Bilyeu? Yes.
1:36:33 Speaker 01 This is the thing.
1:36:34 Tom Bilyeu? It's a call and answer show now. I love this.
1:36:35 Speaker 01 But, but then, if you're homeschooling your kid, do you want the kid to be by himself? What if you had 10 kids together?
1:36:43 Tom Bilyeu? Hmm.
1:36:44 Speaker 01 And the AIs were encouraging them to interact with each other in a positive way? And build stuff together and share knowledge. Older kids teaching younger kids, leveraging the technology, adapting the technology, understanding the technology. That's super powerful. It's not necessarily everyone in their own little worlds, right? Because we can use this AI to bring people together in an efficient manner. We can, because there's nothing like human connection, right? That's always a concern about homeschooling and things like that. That's why school has a pro-social component. But then the nature of a teacher changes. The nature of a doctor changes. And that's why I think education and healthcare are the biggest disruptions. Because you have something, especially when the AI is more empathetic than a normal doctor.
1:37:29 Speaker 01 Doesn't tell me the AI is super empathetic. It tells me doctors should probably be more empathetic. But most of the doctors I know kind of hate their patients.
1:37:36 Tom Bilyeu? Interesting.
1:37:38 Speaker 01 Well, because you know, how many teachers are happy? How many doctors are happy?
1:37:41 Tom Bilyeu? Right. Oh, I get why teachers would hate their patients.
1:37:44 Speaker 01 Patients.
1:37:45 Tom Bilyeu? Yeah. That is very interesting, man. So, okay. There's two things that I want to talk about here. One, as we get embodied, I want to know what that does. So actual robots, for people to think that's off in the distance, you were not watching enough YouTube videos.
1:38:01 Speaker 01 No, no.
1:38:02 Tom Bilyeu? Because between Boston Dynamics and Elon Musk's, what's called Primus?
1:38:07 Speaker 01 Yeah, Optimus.
1:38:08 Tom Bilyeu? Optimus, thank you. Uh, it is very close. You have, Boston Dynamics has robots that can do parkour.
1:38:17 Tom Bilyeu? It's insane.
1:38:19 Speaker 01 There's Hello D and there's a whole bunch of others as well. They're catching up fast.
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