#1093 Giuliano Giacaglia On How A.I. Will Take Over Your Life

Giuliano Giacaglia is the Author of "Making Things Think". In this conversation, we discuss how A. I. will take over every aspect of our life and how it's doing so already. We break down the A. I. in companies like Tesla, OpenDoor and Tiktok, and discuss how every company will utilize Artificial Intelligence. ======================= Amberdata provides the critical data infrastructure enabling financial institutions to participate in the digital asset class. We deliver comprehensive data and insights into blockchain networks, crypto markets, and decentralized finance. Download our Digital Asset Data Guide at https://www.amberdata.io/pomp ======================= The Pod Pro Cover by Eight Sleep is the most advanced solution on the market for thermoregulation. It pairs dynamic cooling and heating with biometric tracking. Even better? Eight Sleep recently launched the next generation of the Pod. The new Pod 3 enables more accurate sleep and health tracking with double the amount of sensors, delivering you the best sleep experience on Earth. Go to eightsleep.com/pomp ======================= This episode is brought to you by Athletic Greens, the best option for easy, optimal nutrition out there. You take one scoop of AG1 and you’re absorbing 75 high-quality vitamins, minerals, whole-food sourced superfoods, probiotics, and adaptogens to help you start your day right. This blend of ingredients supports your gut health, your nervous system, your immune system, your energy, recovery, focus, and aging. Go to athleticgreens.com/POMP to give AG1 a try today. ======================= If you’re trying to grow and preserve your crypto-wealth, optimizing your taxes is just as lucrative as trying to find the next hidden gem. Alto IRA can help you invest in crypto in tax-advantaged ways to help you preserve your hard earned money. So, ready to take your investments to the next level? Diversify like the pros and trade without tax headaches. Open an Alto CryptoIRA to invest in crypto tax-free. Just go to https://altoira.com/pomp ======================= Bullish is a powerful new exchange for digital assets that offers deep liquidity, automated market making, and industry-leading security. Combining the innovations of DeFi with the regulated environment of traditional finance, Bullish empowers users to trade with certainty and earn passively at scale across variable market conditions, in an environment backed by multibillion-dollar liquidity contributions from the Bullish Treasury. Visit bullish.com/pomp today to learn more. Note: Bullish is licensed by the Gibraltar Financial Services Commission. Virtual assets and related products are high risk. Consult your investment advisor and trade responsibly. Bullish is available in select locations only and not to U. S persons. Visit bullish.com/legal for important information and risk warnings. ======================= The bridge between iGaming, online sports betting, and emerging technology, such as Blockchain, NFTs, fintech, GameFi, metaverse, and AI, is loud and clear. The largest global summit of this kind is heading to Malta from November 15 to 17. Over a thousand exhibitors and 25,000 industry leaders will be there, including top executives from Draft Kings, Bet365, Socios, crypto exchanges, betting software providers, operators, gaming affiliates, and more. Log on to AIBC. WORLD and SiGMA. WORLD to see all our upcoming global summits! See you in Malta, November 15 to 17 for the leading global event in Gaming & Emerging Tech! THIS IS SiGMA! =======================

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

• 0:02Anthony Pompliano?

  What's up everyone? This is Anthony Pompliano. Most of you know me as Pomp. You're listening to the Pomp Podcast, simply the best podcast out there. Now let's kick this thing off. Giuliano Giacalda is the author of Making Things Think: How AI and Deep Learning Power the Products That We Use. In this conversation, we talk about artificial intelligence, machine learning, neural nets, how it will affect various companies and products like Tesla, Open Door, TikTok, and many others. We also discuss the importance of self-driving cars, how AI will intersect with Bitcoin, and many other topics that I think folks who are interested in technology and this disruptive innovation will enjoy. This conversation with Giuliano

• 0:44Anthony Pompliano?

  is one that I learned a lot, and I hope that you guys enjoy as well. Once you've listened to the episode, go to Twitter and tweet at us and let us know what you thought, good and bad. We appreciate all the feedback. Let's go ahead and get into the episode, and I'll see you guys at the end.

• 0:58Speaker 01

  Anthony Pompliano runs Pomp Investments. All views of him and the guests on his podcast are solely their opinions and do not reflect the opinions of Pomp Investments. You should not treat any opinion expressed by Pomp or his guests as a specific inducement to make a particular investment or follow a particular strategy, but only as an expression of his personal opinion. This podcast is for informational purposes only.

• 1:21Anthony Pompliano?

  All right, guys. Bang, bang. I'm here with Giuliano. I thought a great place to start this conversation would be around Lambda, which for those that don't know, there's been a bunch of debate and controversy. Is this computer program sentient? Is it alive? Is it intelligent? Walk us through whether, have humans actually created a computer program that is alive and smarter than us?

• 1:44Speaker 02

  Yeah, so let's just go over how Lambda works, right? So Lambda is a neural network, and a neural network is basically a representation of how our brains work. But it's just basically simple math. It's basically matrix multiplication.

• 2:02Anthony Pompliano?

  Okay.

• 2:03Speaker 02

  And-

• 2:03Anthony Pompliano?

  You realize matrix multiplication is not simple math to somebody like me?

• 2:09Speaker 02

  But for computers, it's very, very simple, right? So how Lambda works is basically you train with, you know, a lot of text and you create this big, big neural network that's just taking basically, you know, taxes input and generating the next word.

• 2:32Anthony Pompliano?

  So you're just taking inputs, all this data, then basically building this neural net and then that neural net is kicking out outputs and almost like a predictive way? Or is it simply trying to synthesize? Is it sifting through it and looking for something? Or is it more so trying to predict some outcome?

• 2:50Speaker 02

  Yeah, so it's trying to predict the next word. It's usually how these neural networks work. But the funny thing is that like there was this Google AI engineer that thought that, okay, we can create actually a computer that is sentient because he talked to it, he wrote some text and the, and the computer wrote some text back. And it fooled the engineer, it fooled the engineer saying that it was actually sentient.

• 3:21Anthony Pompliano?

  So the engineer is literally sitting there, almost like if I type into Google and it does give me answers, but it gives me links to other websites or other, other services. The engineer was almost like in like a chat and it was like literally like talking, but rather than you being on the other side of the chat and responding, the computer was chatting back and the engineer literally was like, the computer has a mind of its own, it's alive, like holy shit.

• 3:43Speaker 02

  Exactly. And the funny thing is that the engineer thought that it was sentient because it convinced the engineer that it was sentient because the engineer asked, are you sentient?

• 3:52Anthony Pompliano?

  That's why the engineer thought that it was alive? Was it because it literally just asked like, are you alive?

• 4:00Speaker 02

  Yeah, exactly. And, and the funny thing is that it's basically grabbing text from the internet, right? And figuring out the relationship between different words and, and seeing which words are connected to others. When you look at text in the internet on Reddit and, and you know, like on Google and Whatnot, you would see words that are similar to what the engineer was asking. And if you ask the computer if like to prove that it was not sentient, the computer would also prove that it was not sentient. And the engineer would be fooled that thinking that the computer was not sentient, right?

• 4:36Anthony Pompliano?

  So this brings us to a question of like this idea of being sentient, right? Like, what are the ways that engineers think about like? When would it be sentient versus, oh, no, it's just predicting? It's just answering with questions off of the internet. Like, what is the difference in a computer program between it actually being sentient versus it's just simply really, really good at using machine learning or artificial intelligence?

• 4:57Speaker 02

  Yeah, personally, I think it's more a philosophical question.

• 5:01Anthony Pompliano?

  Okay.

• 5:01Speaker 02

  Right than than a technical question. There's all all these questions of like consciousness and sentient. If a computer is sentient. But if you actually look at animals, there's a very clear correlation between the number of neurons in a brain and how and its cognitive ability. So if you look at, you know, mammals in general, the number of neurons in the neocortex is very, very predictive of their ability to do things. And that's why humans are like much better doing tasks than chimps. And, you know, other mammals is because we have so many neurons in our brain. And one interesting aspect of humans,

• 5:45Speaker 02

  like the reason why we have so many neurons in our neocortex is because we develop fire. And that's one of the reasons why you see so many people talking about meat. Because instead of digesting all these resources in your gut, you know, we can actually break down the proteins outside of our body. And actually, it consume all these resources and produce that many resources to our brain because our brain needs a lot of resources.

• 6:19Anthony Pompliano?

  So the whole idea is that like as a human being, one, our brains are highly dense in the terms of neurons, which is equivalent in a computer program, the more neurons or kind of, you know, pieces of the neural net, the more dense it is, obviously, the more valuable it could be. But also, there's ways to use external forces or materials to change our body physiologically, right? If we eat really bad, it doesn't just make us fat, it also can have an effect on our brain, you're sluggish, you're tired.

• 6:53Speaker 02

  100%.

• 6:54Anthony Pompliano?

  You lose your intellectual sharpness, like all that type of stuff. If you eat really healthy, there is literally a way to kind of clear out the cobwebs, if you will, from a brain standpoint, and perform better with more mental clarity, more kind of high-performance intelligence, all that type of stuff.

• 7:07Speaker 02

  Yeah, 100%. And if you actually look at other animals, right, the bigger they were, the bigger their brains are. So if you look at gorillas, they have bigger brains compared to other chimps. And even like, an elephant has a big, big brain, but most of their brain, like, the biggest part of, the biggest part of elephant's brain is in their motor cortex. And so they can do really good things, like, they can actually do physical things really well. That's why you see elephants in circus, right? And what happened is that at some point in human evolution, in mammal evolution, we

• 7:47Speaker 02

  we develop fire, and we could actually break down proteins and create so many like neurons in our brain and pack them, and actually eat a lot of food and eat a lot of resources and actually use those resources for our brain. The same thing is true for for computers, the bigger the neural networks, the better they perform. And it's interesting, our brain has around 100 billion neurons. And if, and if you look at the best neural networks today, they have around the same number of neurons.

• 8:20Anthony Pompliano?

  Why is it that if we have 100 billion neurons, and these neural networks have a very similar number, why is it that humans in Some capacity is still much more intelligent than the computer systems. And I'm assuming that just computation, right? The computers are better at other things, but is there just some other way to evaluate their neural nets? Other than just like the density of the actual net itself? And it's missing something that doesn't actually make it sentient or have the ability to compete with a human brain?

• 8:52Speaker 02

  Yeah, that's an interesting question. So if you actually look at how we represent our, like each neuron in the, in our computer system, they are very, very simple compared to our neurons. So maybe we don't map one to one. It could be maybe a hundred neurons represent one of our brain neurons. But if you actually look at different tasks, right? So if you look at Go, if you look at poker, if you look at chess, computers perform better than humans at these tasks.

• 9:27Anthony Pompliano?

  And when a computer is better than humans at, let's take poker, Go, even chess. Is it because there is a set parameter? I kind of think of it like bowling, right? Like, like it has the bumpers and so like it's kind of hard to go without, outside of it. And if you give a computer set parameters, and even though a chessboard as an example, has many different moves you can make. There's only so many combinations. And if the computer understands all of that, then the computer is able to just compute at a faster pace than the human and understand what to do?

• 9:58Speaker 02

  Your intuition is right. So one thing is that computers do really well at games. And why, why do I think that's true? It's because games are simulated environments. So you can play around and, and train your computer through many years of training compared to humans, right?

• 10:23Speaker 02

  Uh, but the universe of possibilities in a Go engine is really, really big. In a Go game. So if you look at a Go game, there are more positions in a Go game than there are atoms in the universe.

• 10:39Anthony Pompliano?

  Wow.

• 10:40Speaker 02

  Even though it's a very simple game, it it's actually like there's a huge number of possibilities.

• 10:47Anthony Pompliano?

  There are more possibility, like possible scenarios in a Go game than there are atoms in the universe?

• 10:53Speaker 02

  Yeah, yeah.

• 10:53Anthony Pompliano?

  That's that is a mind fuck. Like, that is insane.

• 10:57Speaker 02

  Yeah. Um, and if you look at other games, you know, like Alpha, uh, StarCraft, right? We have game engines that perform better than humans at StarCraft or Dota. So these are very complex systems, right? Even though the rules are simple, they're very complex systems. And I would argue the reason why we do much better is because you can simulate the environment and you can play around so many times. And if you actually look at robots that perform really well, you simulate the environments before. So, uh, OpenAI, which is a research company based in Silicon Valley,

• 11:40Speaker 02

  they develop a robot hand that plays Rubik's cubes and it can actually solve it pretty fast. And the way they did it is that they simulated the the environment before. They trained the hand in this simulated environment many, many times. And the hard part was to actually transform that into the real world. And I think.

• 12:03Anthony Pompliano?

  So the computer theoretically could solve the Rubik's cube well before they had built a hand to actually do it. And once they were like, okay, cool, the computer knows how to solve this problem. Then you've got to translate it from pure software into the physical ability to move the Rubik's cube.

• 12:19Speaker 02

  Exactly. And that was according to people that I know work there.

• 12:25Speaker 02

  It was the hardest part to transform that from software to hardware. And you actually, I I think Tesla now will eventually have the the best self-driving cars and their self-driving cars will be better than humans, probably this year or next year. And I think one of the missing pieces that they they had was to simulate the environment, right? They didn't have a simulated environment where they can create all all these scenarios that you wouldn't necessarily see all the time, right? You have this long tail of of of possibilities that you wouldn't see necessarily in the real world, but you still want to train the computer.

• 13:08Speaker 02

  And even though there are a million, you know, Teslas out there and they are grabbing data, a lot of data, it's still not enough because there will be there will be scenarios where you didn't see that scenario before and you want to train and and and figure out what's, you know, what you want to do. And that's a big, that's one of the missing pieces for computers to, you know, reach what they call AGI. I think that's one, like, very important missing piece. Right now, when we train computers, we need a lot of data, right? So if you look at DALL-E, which is this computer that basically you type it out a sentence.

• 13:49Speaker 02

  So let's say a koala dunking into a basketball. You can transform that sentence into an image, right?

• 14:01Anthony Pompliano?

  So it's just text to image and this is the engine to do it?

• 14:03Speaker 02

  Exactly. And the way they did it is that they grabbed 400 million image captions. So that's a lot of data, right? To train the system. The way humans work is that we actually learn through a lot of data, but after some time we don't need that much data. So what do I mean by we learn, we learn by seeing a lot of data? You take a long, long time to learn basic things. When you're a baby, you're seeing the world and you're reacting to it, but eventually you don't need that much data to learn new things. And one example that people usually refer to is you don't need to drive a thousand

• 14:46Speaker 02

  times out of a cliff to know that you can drive out of a cliff. And why is that? It's because we actually imagine that situation, right?

• 14:55Anthony Pompliano?

  And is that the equivalent, that's the human equivalent of a simula- of a simulation?

• 14:59Speaker 02

  Exactly, exactly.

• 15:00Anthony Pompliano?

  And if we go and we look at, so let's go back to the chess example. If I am a chess player, I recently talked to a grandmaster and he was talking about he played two hours a day, every day for eight, 10 years, read a lot, so not just playing, but also reading through different scenarios, different moves, different responses to those moves, studying historical games, all these different things. How many games or how long would it take a human to be able to get the same amount of reps that the computer could just simulate? It seems like the computer is much more efficient, much faster, and therefore, on a compounding scale, it should run away from the intelligence that the human has in a game like chess.

• 15:43Speaker 02

  Yeah, so that's a very good question. So if you actually look at Go, I think the Go engine that beat the best player trained for 1,000 human years.

• 15:56Anthony Pompliano?

  Jesus. And how long do we know did it take to train the 1,000 human years for the computer?

• 16:02Speaker 02

  I don't...

• 16:03Anthony Pompliano?

  Months, years?

• 16:05Speaker 02

  Yeah, less than months, right?

• 16:07Anthony Pompliano?

  So pretty quickly, we were able to pack 1,000 years of one human playing Go into a couple of weeks at most.

• 16:17Speaker 02

  And that's what people don't realize, right? We learn through, like, we pass knowledge through generations. So if you look at Go, for example, there were like three major developments in Go history. And they've learned new techniques, and that was passed down, right? So you have this teacher-student kind of knowledge passed down through generations, and that's through history, right? So we don't only learn in our lifetime, we actually learn what our parents and grandparents passed down to us. And computers can learn much faster than that, right? Because they can simulate all of that.

• 17:00Speaker 02

  And one of the missing pieces for robots is that they need to actually do the simulation. And that's what Tesla is doing right now, right? They can simulate all these scenarios and simulate 1,000 years of... Driving, yeah.

• 17:15Anthony Pompliano?

  So let's take Tesla as an example. If you, it used to be just in Silicon Valley, now across the country, people will see the Google car drive by and it's got the big thing at top of the vehicle and it's spinning around. And you don't know many times, is it like they're collecting data for Google Maps? Is it the self-driving car? If you're in tune with the differences, then maybe, okay, that's the self-driving car. And they're obviously driving with a human behind the driving, behind the steering wheel to capture data. Like that is what that car is doing. And that was a pretty cool idea. It was like, let's put a bunch of cars on the road and capture that data. We'll have our employees behind it and over time, hopefully we can get to the point where we could create self-driving cars. Makes sense. Tesla is like, well, why do we have to have Tesla cars?

• 18:00Anthony Pompliano?

  Why don't we just have everyone who drives a Tesla to collect the data for us? When they're just driving around the road, we'll just collect every single piece of data.

• 18:07Speaker 02

  Yeah.

• 18:07Anthony Pompliano?

  And so I don't know if that's one to a thousand, one to a million, you know, difference. But obviously Tesla is collecting way more data than Google or anybody else. But what you're saying is it's not just about collecting the data from the cars on the road. They're then taking that and then they're running additional simulations on top of it to even accelerate the learning faster?

• 18:26Speaker 02

  Yeah, that's right. And they started doing that recently. They started doing that, I think, last year to this year.

• 18:31Anthony Pompliano?

  Okay.

• 18:32Speaker 02

  So they are running simulations right now and Google has been running simulations for a long time. But I think it's a combination of both, right? When I, when I talked about OpenAI and the Rubik's Cube, they, the hardest part was transforming, like getting the software and then getting that to the hardware. Right? And, and I think both parts are really, really important. For Tesla, they have both parts. They have a million cars running in the road and they also have the simulation where they can see scenarios that, you know, they, they haven't experienced before and they can actually experience that, you know, in a simulated environment.

• 19:14Speaker 02

  So they can train a thousand years in, you know, a few months.

• 19:18Anthony Pompliano?

  And when we think of this, how long will it take for Tesla to have fully self-driving cars? Where I can sit in the backseat, I don't need to be anywhere near the driver's seat and it'll drive me around. And that will be not only possible, like it can actually get me there, but it will be safe and that will become the norm, do you think?

• 19:37Speaker 02

  Yeah. So we are in September of 2022.

• 19:42Speaker 02

  I think it's gonna be either end of this year or next year.

• 19:46Anthony Pompliano?

  You think by the end of this year, potentially, in three or four months, we will have the ability for the car to navigate the everyday driving for anyone who wants to sit in a Tesla?

• 19:55Speaker 02

  Exactly. But they are not gonna allow people to do that.

• 19:59Anthony Pompliano?

  So the technical capability may be there, but the regulatory approvals or the approval to actually do this on the street won't happen?

• 20:08Speaker 02

  Exactly. And Elon Musk has been saying that for years, right? He has been saying, oh, we're gonna have self-driving cars that are better than humans by end of this year. And he has been saying that for five or six years. But the difference now is that they have these simulated environments that make the car know how to drive in all these different scenarios, right?

• 20:30Anthony Pompliano?

  Is the simulations trying to get reps? Because the computer needs to see the situation over and over and over and over again? Or is it if the computer sees a situation one time, it remembers it forever? And it's more so about the super, super long tale of a kid runs out in the road, then a car backs into the road, then a bicycle goes into the road, and it just has to see every single situation?

• 20:53Speaker 02

  It's the latter.

• 20:54Anthony Pompliano?

  Okay.

• 20:54Speaker 02

  So it needs to see all different scenarios that it hasn't experienced before, right? And it needs to know what to do. If he hasn't seen, and maybe it can take some wrong decision, right? So you need to have all these simulated situations so it can know when that situation actually happens.

• 21:13Anthony Pompliano?

  Is Tesla in these simulations, like, are they making the wrong decision? And like hitting, you know, fake pedestrians? Or like crashing into fake cars? And like, that's part of the learning process? Is somebody then sits there and says like, no wrong decision, the car should have done this instead? And it's almost like this like human feedback loop that's correcting the actual computer?

• 21:35Speaker 02

  Yeah.

• 21:37Speaker 02

  And they actually have that information also from the cars running in the real world.

• 21:42Anthony Pompliano?

  If there's accidents, obviously, they shouldn't have gone that way.

• 21:46Speaker 02

  Or if someone, like if the car is turning right and the person driving the car actually takes the wheel and drives somewhere else, they can actually get that data and send back to Tesla. So they actually, when they are gathering that data, they don't send all the video feeds directly to Tesla. They send selected frames because else you would be a lot of data they're gathering.

• 22:11Anthony Pompliano?

  So if you're just driving down the highway and Tesla car, all the cameras on it is collecting all of this data, if there's almost like no outlier situation, Tesla as a computer has seen a million times driving down the highway with other cars around it or whatever, it's when somebody grabs the wheel and jerks it one way or the other, or slams on the brakes, or there's some sort of external or outlier type event, that's when maybe they say, hey, we want to see that. And that may be a situation that we haven't seen before.

• 22:39Speaker 02

  Exactly. And the interesting part of Tesla is that they have the biggest neural network running at scale in the world.

• 22:47Anthony Pompliano?

  Really?

• 22:48Speaker 02

  Yeah. So if you think about it, right, like Facebook, Google, they're a big, like pretty big companies, right? And most of the neural networks they're running are very, very simple. So they're, they're running neural networks to look at an image and see, oh, look, there is a beard there. There is, you know, this thing in this image. Or Google also has a BERT, which is a big network, a big transformer, that actually answers questions. So like if you, if you search some terms at Google, some of the, the, the answers will come from a neural network, right?

• 23:26Anthony Pompliano?

  And this is a dumb question, but like the neural network, a lot of people be like, where does that live? Is that on like Facebook servers or Google servers somewhere? Is that something that's in the cloud? And it's like they're using other services? Is like, how do you think about like the neural network? Where does that actually live?

• 23:44Speaker 02

  Yeah, they're mostly running GPUs.

• 23:47Anthony Pompliano?

  Okay.

• 23:47Speaker 02

  And that's why Nvidia has been doing so well in the market.

• 23:51Anthony Pompliano?

  Selling weapons to the, to the war.

• 23:53Speaker 02

  Exactly. And

• 23:56Speaker 02

  And why GPUs? Because GPUs are really good at doing matrix multiplications, which these neural networks require. So neural networks, they're interesting. The reason why we use neural networks at the end of the day is that a neural network is a circuit. And if you have enough long neural network, it can simulate a computer. So a neural network is basically a computer. And a neural network can actually represent any kind of function that you may like. They are really good at solving the problems that we like. And the way we train them is linearly compared, you know, to the amount of data that we feed them.

• 24:41Speaker 02

  So like, they're really, really good at solving the problems that we give them, and that's why.

• 24:45Anthony Pompliano?

  So the way I have always thought about it is like, you have a computer, right? And the neural network is software that resides on hardware somewhere. We take these massive data sets. We feed them in. Do we tell the computer what to look for? Or do we just feed it the data? And then later, when we're looking for the outputs, we're querying that data set plus the software? Like, how much prep work, I guess, goes into cleaning and structuring the data that we feed it? And specifically telling the neural network, learn image recognition, learn this, and then later we can query it because we gave it that instruction?

• 25:18Speaker 02

  Yeah, so there are different ways of training these neural networks. Google, for example, had this breakthrough at some point where they fed a lot of YouTube videos.

• 25:28Anthony Pompliano?

  Okay.

• 25:29Speaker 02

  And the computer learned what a cat is. Because people...

• 25:33Anthony Pompliano?

  Cats on the internet is an important thing to know.

• 25:35Speaker 02

  Yeah, exactly. But most of these systems, the way they work is that you actually feed like both the data and what you want to see out of this data. So for example... For DALL-E, they fed them images and captions, right? And a big part of these systems is actually cleaning out cleaning out that data. And that's what Tesla does so well, right?

• 26:00Anthony Pompliano?

  Got it.

• 26:00Speaker 02

  Like, they figure out what data and how to clean that data really well. And the best systems is still you need to like actually figure out, okay, this is an image and this is the caption. You need to, you know, tie them together.

• 26:14Anthony Pompliano?

  And if we go and kind of zoom out for a second, right? We say, okay, these neural nets and all this is very kind of technical. It's very nuanced in the larger conversation. There is artificial intelligence.

• 26:27Speaker 02

  Yeah.

• 26:27Anthony Pompliano?

  There is machine learning. And then another term that people may or may not have heard of is AGI, right? Kind of the general intelligence. Walk me through, what is the difference between these three things? Because a lot of times, like when I see pitches from companies or I'm talking to someone, everyone's like, AI, AI, artificial intelligence is going to change the world, right? But there's some nuance and difference between these three things. So describe a little bit as to how people should think about it.

• 26:51Speaker 02

  Yeah, artificial intelligence englobes everything. So artificial intelligence.

• 26:55Anthony Pompliano?

  The high level terminology.

• 26:56Speaker 02

  Exactly. So artificial intelligence are systems that are intelligent, so they can interact with you and whatnot. Machine learning is when when computers learn the features by themselves. So it learns by itself these features that were, you know, interacting with the world. And AGI is basically when computers get better at humans at every single task.

• 27:22Anthony Pompliano?

  Got it.

• 27:22Speaker 02

  So AGI is when, you know, a computer is better than humans at Go, at chess, at driving, at talking, everything.

• 27:31Anthony Pompliano?

  Thinking, dreaming, sleeping, whatever. So is it fair to say there is no true AGI yet?

• 27:40Speaker 02

  Yeah.

• 27:41Anthony Pompliano?

  Is that that's kind of a fair characterization? But that is something that people aspire to create. And if and when we get there, that would be a massive technical breakthrough in terms of computer science?

• 27:53Speaker 02

  Yeah. So one of the interesting things is that When computers get to AGI, right, they will be better than humans at every single task. And we created these computers. So when they get better than us, they probably can create computers that are better than they are, right? So you have this exponential curve that you hit when computers get to AGI, they can actually get better and better over time.

• 28:17Anthony Pompliano?

  Should we be scared of that? Like, that sounds like pretty fucking crazy stuff, right? Like, if we can create computers that are smarter than us in every dimension, not just in kind of mathematical computation, but literally, they can drive better than us, they can play games better than us, they can act like a human better than us. And then those computers can create even smarter computers, and there's this exponential runaway of kind of artificial intelligence. Humans very quickly become the lowest on the totem pole, it seems.

• 28:47Speaker 02

  Yeah, I think we shouldn't worry about it, even though, yeah, even though

• 28:54Speaker 02

  people talk about it and they hype it up.

• 28:56Anthony Pompliano?

  Yep.

• 28:56Speaker 02

  We are the ones creating these tools, and we can control it very well. I think when and if we hit that point,

• 29:06Speaker 02

  we should cross that bridge when we hit it, right? Right now, we have many other problems to worry about, like there are energy problems, you know, in Europe, in California, and, you know, other areas of the country.

• 29:20Anthony Pompliano?

  Are there things that we could do now to set ourselves up better for that scenario? Or is it just something like, right now, kind of everyone's talking and they're worried, but actually, there's nothing that we could do in the moment to change when we get to that situation?

• 29:35Speaker 02

  Yeah, so there are organizations that are actually working on that. So there's an organization that came out of, like, a bunch of people left OpenAI to create this organization called Anthropic, where they're trying to prevent risks that AI eventually can cause, right? But except that, I don't think there's much we can do, right? Like, it's difficult to create policies about a scenario that can happen. It's really, really hard.

• 30:06Anthony Pompliano?

  This episode is brought to you by Amber Data. If you're a financial institution entering the digital asset class, you'll need access to granular on-chain and market data from multiple venues to power research, trading, risk management, and compliance. Amber Data delivers comprehensive data and insights into blockchain networks, crypto markets, and decentralized finance, empowering financial institutions to apply traditional finance methods to digital assets. Amber Data eliminates the infrastructure setup, integrated challenges, and maintenance headaches to access digital assets data, reducing cost and time to market to enter the digital asset class. Learn more and download their digital asset data guide at www.amberdata.io/pomp. Again, that's amberdata.io/pomp. Go check them out today.

• 30:50Anthony Pompliano?

  This episode is brought to you by Eight Sleep. Good sleep is the ultimate game changer, and the Eight Sleep Pod is the ultimate sleep machine. The Pod is the only sleep technology that dynamically cools and heats each side of the bed to maintain the optimal sleeping temperature for what your body needs. With the Pod, you can start sleeping as cool as 55 degrees Fahrenheit or as hot as 110 degrees Fahrenheit. What is the result? Clinical data shows that Eight Sleep users experience up to 19% increase in recovery, 32% improvement in sleep quality, and up to 34% more deep sleep. How do I know it works? I sleep on it every single night, and it works so well that I begged the founders to let me invest in the company. Go check them out today at eightsleep.com/pomp to start sleeping cool this summer and save $150 on the Pod.

• 31:35Anthony Pompliano?

  Again, eightsleep.com/pomp. You get $150 off when you use code POMP. Now, machine learning is here. It exists. We talked about these neural nets. Is machine learning similar or different in terms of feeding it information and what it does with that information to be able to be useful to humans?

• 31:52Speaker 02

  Yeah, machine learning encompasses a lot of other techniques. Neural networks are just one subfield of machine learning, right? And neural networks is... It's basically a technique to simulate how our brain works, basically. And neural networks have been doing well. So and have been solving problems that we like. Most of the new machine learning techniques that we see today, they are using neural networks because they perform really, really well. And I think that's going to be the case in the future.

• 32:28Anthony Pompliano?

  Are there other, what are the other types of machine learning techniques?

• 32:31Speaker 02

  Yeah, there's, for example, SVMs.

• 32:34Anthony Pompliano?

  What is that? Like neural nets we've talked about, what is the SVMs? How do they work?

• 32:39Speaker 02

  You're basically grabbing a lot of data and, you know, figuring out where this data is in a graph and creating a line that divides, you know, some of the points to other points. It's basically, you know.

• 32:56Anthony Pompliano?

  It sounds like you're mapping connections between different pieces of information and also knowing where it is identified in the graph.

• 33:03Speaker 02

  Exactly, exactly. And then you have like.

• 33:06Anthony Pompliano?

  Is it the what this like? I'm going to be really dumb. I already told him that all the questions I asked. People will be like, oh, my God, Pop is so stupid. But is that similar like what a web crawler does?

• 33:17Speaker 02

  No, it's a little bit different. A web crawler usually uses also matrix multiplication.

• 33:23Anthony Pompliano?

  So. At least I got that part right.

• 33:26Speaker 02

  A web crawler usually, you know, figures out how many links to a page there are. And, and, and you can determine the importance of a page if there are a lot of links to that page. And then you can like, yeah, that's basically it.

• 33:45Anthony Pompliano?

  Got it. And so when we think of machine learning, there's neural nets, there's SVMs. There's a couple of these different things. But neural nets seem to be right now, at least the superior way to do machine learning. And that's why we're seeing so many people go and use these in these various applications.

• 34:00Speaker 02

  Exactly. And, and again, the reason for that is that they can simulate computers really well. And they solve the kind of problems that we see in the world really well. And somehow, we're basically simulating how our brain works, right? And somehow we develop this thing, this machine that. interacts really well with the world. So it's not a coincidence that, you know, neural networks and our brains developed.

• 34:25Anthony Pompliano?

  So I think of humans of like the the kind of physical world pre what we think of as software today, humans rose in dominance and we're like the King Kongs of nature, right? Like, we can collaborate, we can think, we can dream, we can organize resources in a very unique way. And for all intents and purposes, we have weaponry and like we are the the dominators of of nature on a global scale. Computers used to be really dumb. Like, there was punch cards. I started out real low with the computers, but they have rapidly been rising in efficiency, efficacy, computing power, all these different things. As we get closer to that being an even playing field

• 35:08Anthony Pompliano?

  between humans and computers, it then brings up this idea of the Turing test, which test for is the computer sentient, is it not? Talk a little bit about what is this Turing test? How did it come to be? And how effective is it?

• 35:21Speaker 02

  Yeah, the Turing test was developed by Turing, which was a computer scientist that developed this game called the imitation game where you had in one side of the I O, you had a human and then you had a black box and then you had a human on one side and a computer. So the human would type questions to both the both the computer and the human. And if the human was fooled thinking that the machine was a human or vice versa, right? The the machine would pass what's called the Turing test.

• 35:56Anthony Pompliano?

  Got it. So I'm sitting here, I'm typing into a chat box. And on one side, there's a human, on another side, there's a computer. If I can't tell that the computer is the computer, then technically, what's the difference? So that would be passing the Turing test. And then we could claim that the computer was equivalent to the human.

• 36:11Speaker 02

  Yeah. And the funny thing is that since 2014, we've been passing the Turing test.

• 36:16Anthony Pompliano?

  Oh, shit.

• 36:19Anthony Pompliano?

  And this is why the Google engineer got fooled.

• 36:21Speaker 02

  Yeah, exactly.

• 36:21Anthony Pompliano?

  And thought that, oh, wait a minute, I'm talking to a sentient thing. Holy shit, I gotta like raise my hand and caution people.

• 36:28Speaker 02

  Yeah, exactly. If a Google engineer was fooled by a chatbot, imagine, you know, the average human. And the funny thing is that humans are fooled by computers much more when they talk about sex.

• 36:40Anthony Pompliano?

  Interesting.

• 36:41Speaker 02

  Yeah, and especially males.

• 36:44Anthony Pompliano?

  Males are dumb.

• 36:46Speaker 02

  And so

• 36:47Anthony Pompliano?

  So that's one of the ways that people actually are able to fool humans is they get males talking about sex to computers and the computers trick the men.

• 36:56Speaker 02

  Yeah.

• 36:57Anthony Pompliano?

  Jesus, we are so screwed. As humanity, we are so screwed. Like if the machines get so smart that they realize all we have to do is talk about sex to men and then they're gonna do whatever we want. That is a bad scenario to go towards.

• 37:10Speaker 02

  Exactly. And so we've passed the Turing test and we've been passing

• 37:14Anthony Pompliano?

  So almost eight years now.

• 37:15Speaker 02

  Yeah, exactly. So we have been developing new tests and new, you know, benchmarks and computers have been doing better and better. So like, for example, Dota, right, the game, computers play better than humans. Starcraft, which is really, really hard, or Go, computers pass humans there. Poker. And we've been doing better and better at exponential rate. So driving, driving is another task that humans do, right?

• 37:43Anthony Pompliano?

  And it's a game to some degree.

• 37:45Speaker 02

  Exactly.

• 37:45Anthony Pompliano?

  It's kind of a test, right, of like, hey, can we figure out this technical breakthrough to actually be able to do this?

• 37:51Speaker 02

  Yeah.

• 37:51Anthony Pompliano?

  You and I don't think about that when we're in the car and like, yo, Elon, make sure I don't crash, right? But to some degree, like, that is how the computer looks at it is you can play the game of chess with all the different possibilities and rules. And then you also could drive down the street. There's, there's differences, but not nearly as many differences maybe people think.

• 38:08Speaker 02

  Yeah. I'm very optimistic in, in the way, I think, you know, in the world, 10, 15 years or 50 years from now. And I actually think these computers are just increasing our productivity, right? So one example of that is that you look at elevators. Before elevators, you had a human actually moving the elevator around. And then we could automate that. And then you don't need humans anymore to, you know, operate these elevators. And the same thing is happening in different areas, right? So you look at farming, for example. 100 years ago, I think 50% of the population was working farming. And now you

• 38:49Speaker 02

  have 1% of the population. A lot of people drive trucks right now. It's like, the most popular Job right now in the US is driving trucks. Eventually, you won't need to drive trucks. And then you develop new jobs, right? So talking in a podcast is a pretty new, like, pretty new job, quote unquote, right? Investing is a pretty new, uh, new job. So if you look at the percentage of GDP that the US, if you look at the percentage of GDP that entertainment has, it has been increasing over time in the US.

• 39:31Speaker 02

  It was 6% in the 1960s and now 10%. So we're spending more and more dollars in entertainment. And as you have more dollars, you spend more in leisure, travel, entertainment, right? And that's what has been happening. So as we develop these tools, we increase our productivity, which leaves us to do like more fun things as a society, right?

• 39:54Anthony Pompliano?

  So one of the things that's interesting to me is if you go back to 1870 or so, about 75% of jobs were characterized as a kind of not fun, not exciting, a pretty tough life, you know, it's farming and that type of stuff. By, let's call it 1970 or so, we're down about 25%. So pretty big reduction in jobs that were not necessarily aspirational. They were tough life type jobs. Now, if you fast forward to today, you already mentioned a couple of these jobs that they're getting automated away, or it's easy to see. Truck driving is one, there'd be many others.

• 40:36Anthony Pompliano?

  How many of the jobs that we see in today's society are not going to be done by humans 100 years from now? Like, when I think of Tesla's manufacturing facility, it doesn't look like, uh, you know, kind of a manufacturing line where people are putting on the car doors and stuff. Like, sure, there are humans there, but it sure looks like there's a hell of a lot of robots in there. If you look in Amazon's warehouse, there's robots moving things around and there's tape on the floor and you start to see like, okay. But then also, if you go and you look at White collar jobs like it seems like software is coming for that as well. And so it doesn't feel like there's really that many areas that are safe from kind of the automation and artificial intelligence that's coming.

• 41:21Anthony Pompliano?

  Is that fair?

• 41:22Speaker 02

  Yeah, and they've never been safe.

• 41:26Speaker 02

  So I think a hundred years from now, everyone is gonna be a TikTok dancer.

• 41:32Speaker 02

  I'm joking, but like we're gonna change the way we work and we always change the way we work. Right? So in the northeast of the US, there are a lot of people who used to work in manufacturing, right? And now they work in service jobs. So like work wise, we've been changing a lot throughout history. But if you actually look at productivity, we've been changing less and less over time. And that's one of the things that Peter Thiel talks a lot about, right? So one of the things that has been happening in the US is that we didn't actually increase productivity so much, but we outsourced jobs.

• 42:15Speaker 02

  So if you look at Detroit, right? Detroit was the manufacturing place here in the US. And we actually outsourced that to other, other parts of the world. So like to China and Mexico. So a lot of the jobs went away here in the US because of globalization and not because of automation. Automation is actually not changing that much. The economic pie of the US. But it will change. A lot of the jobs will be displaced. But that's the way it works, right? You a hundred years ago, most people would be farmers, maybe, you know, maybe 200 years ago, and most people are not farmers today. I think it's a good thing. I think it's a good thing that people, you know, are creative.

• 43:00Speaker 02

  They are like spending their dollars in entertainment and leisure. And I think we're gonna be in a much better place a hundred years from now.

• 43:08Anthony Pompliano?

  This is one of the arguments of, I think, techno optimists, if you will, right? It's like, hey, we freed everyone up from the farm fields so they could go do. Other things. It's not like jobs disappeared and now everyone is jobless. Just new types of jobs showed up and obviously we still have food for now. In terms of being able to feed the global population. Yes, there are areas that struggle, but we still get farm productivity, even though maybe we don't have as big of a portion of the population working in the fields. Now, with that said, one of the areas that everyone has always thought would be safe was the creative jobs. So for example, what is the hardest thing for a computer to do? Everyone was like, oh, like you could be an artist, you could be a musician, you could do this stuff like that's going to be really, really hard for the computers to ever compete with.

• 43:53Anthony Pompliano?

  Well, fast forward to today, like not so fast. The machines sure as hell look like pretty good artists, right? With all of this kind of computer generated art or generative art, like did we get it wrong as a, as like human commenters on the state of automation? Where like, maybe some of those creative jobs aren't nearly as protected as we thought?

• 44:14Speaker 02

  Yeah. So there's this interesting quote that says, like, a human asks a robot, can a robot write a symphony? Can a robot turn a canvas into a masterpiece? And the, the answer was no, but can you, right? You're not Mozart, you're not Picasso, but now, yes, the robots can, can write a symphony. They can create a masterpiece.

• 44:38Speaker 02

  There was recent, recently there was a state fair where a human created a piece of AI art using one of these systems, stable diffusion, and actually won the challenge, right? So AI was able to create a masterpiece, right?

• 44:54Anthony Pompliano?

  Now, that brought controversy.

• 44:55Speaker 02

  Yes, exactly. But it, like, it's just increasing productivity, right? Like, instead of humans creating every thumbnail of a YouTube, like, video, you will have a computer doing that. But humans still play a role because humans will choose which thumbnail to put into an image, right?

• 45:16Anthony Pompliano?

  And, but if we go back to the art for a second, this, I think is an important conversation that's happening right now, is this artist used AI to create a generative piece?

• 45:27Anthony Pompliano?

  Art, it could not be told if you looked at, you couldn't tell the difference between, oh, that was AI and that was a human that created it. And so it went into a competition. It won. It was selected as the best piece of art. The artist community absolutely freaked out. They were like, what are you talking about? This is cheating, right? It's the equivalent of taking steroids or performance enhancing drugs, right? For athletes, like this was the equivalent in the art world. And they were going absolutely crazy. On the same side, the art, the engineer, I'll call them to separate from the artists. And others were like, well, hold on a second. You're using tools to create your art. You are using paintbrushes or whatever other tools you're using. Like, we're using tools also. It just so happens that our tools are superior to yours.

• 46:12Anthony Pompliano?

  And so what's the big deal? That debate is not settled yet, and it feels like we go one or two path. Either people say like, hey, art is art and like whoever can enter it into competitions can do that. Or the second path is like somehow there's a bifurcation. Either the generative art is not allowed to be entered, or there's like a generative art division, and then there's like a human art division or something like that. Like, how does this play out?

• 46:37Speaker 02

  Yeah. So if you allow,

• 46:41Speaker 02

  if you allow people to take hormones and steroids, they will take it, right? So, so like if you look at people driving bicycles, right? Like riding bicycles, if they can take steroids, they will take it. And they will do whatever it takes to take steroids, right? The same will probably be true for the creative, creative type, right? Machines will help and make humans so much more productive that everyone will try to use these tools, right? And one interesting thing that I think, one interesting trend that I think we'll see in the next 10 years

• 47:22Speaker 02

  is that right now, most of, like the internet allowed distribution to go down to zero. And if you look at TikTok, Instagram, YouTube, a lot of the engine that drives recommendation is done by machine learning. It's done by computers that select the next image, video that you are gonna see, right? And they're much better than humans at selecting, you know, what you would like to see.

• 47:49Anthony Pompliano?

  When you do that, though, can the machine pick what will win? Or is it the machine says, here's 10 options and the human says that's the one that's gonna win?

• 48:01Speaker 02

  Both can work, right? So humans still can choose whatever, you know, like you can still have humans handpick, but machines will probably be better at.

• 48:12Anthony Pompliano?

  At some point.

• 48:13Speaker 02

  Yeah.

• 48:13Anthony Pompliano?

  Maybe even today?

• 48:15Speaker 02

  Yeah, so like if you look at YouTube's recommendation algorithm, or even if you look at TikTok's recommendation algorithm, it's really, really, really good at selecting what video people like to see.

• 48:28Speaker 02

  And I would argue it's better than humans at selecting videos, you know, that people would like to see.

• 48:34Anthony Pompliano?

  100% agree.

• 48:35Speaker 02

  And we're seeing recommendation algorithms today, right? But we're gonna see like generated art and videos 10 years from now. So most of the media that we consume today is generated by humans, right? Movies, images and whatnot. I imagine 10 years from now, most of the video, most of the media that we consume is gonna be created by computers. You still have a human determining, look, I have this script, please generate that, right? But a lot of the media that we'll consume will be generated by computers.

• 49:14Anthony Pompliano?

  I went down a rabbit hole, I don't know, six to eight weeks ago. And there's a gentleman who I found online and not in a weird way, but just like on Twitter. And he was tweeting about generative art and all this stuff. And he had created a sci-fi mini-series essentially called SALT, S-A-L-T. And in this sci-fi series, my understanding is that he was able to generate not just the imagery, but also the video using kind of these generative machines, the AI, whatever. He provided his own voice, so he did the voiceover.

• 49:55Anthony Pompliano?

  But what he was building was this idea of a pick your own adventure movie. And so his idea was like, okay, normally for us to go ahead, sit down and create an entire set, get the actors and do all this stuff, tens of thousands, hundreds of thousands, millions of dollars in budget to be able to make a movie, an actual, real movie.

• 50:18Anthony Pompliano?

  But if I can sit here with my computer on a Saturday, essentially, and do something that looks pretty good, you know, close to it in quality, that's gonna have a profound impact. Now, what he then said was if you drop the cost of creating this imagery and kind of this movie, you then can replicate many different scenarios. So rather than watch Top Gun and every single person who watches Top Gun gets the same outcome, what happens when all of a sudden now you go left and pick option one, I pick option two, and our friend picks option three, and you have these kind of multi kind of paths to choose?

• 50:58Anthony Pompliano?

  Like, that's a whole world that it seems like the content, you know, system or industry is not set up to do. But you can also see some creative people saying, wait a minute, there's a lot of things we can do now that previously weren't possible because of this technology.

• 51:12Speaker 02

  Yeah, absolutely. And so if you actually look at games, games are bigger in size than movies. They're much, much, much bigger. And the reason why is because people can interact and they can decide what to do next. And generative art, generative media is gonna be more important to games than movies, right? Because you can create these characters that have their own lives. You can create all these different scenarios, all that long tail that we talked about before can be created for games. And games will be much more interesting in the future because of AI.

• 51:51Anthony Pompliano?

  Yeah, there's another startup called Osmosis Studio. And what they do is they use the same generative machines, and they can do product photo shoots without actually having to do the actual photo shoot. So a lot of people don't know, but these companies will spend tens of thousands, sometimes hundreds of thousands of dollars to literally get two or three models to show up. They bring their equipment, they do the whole setup, they have to hire the photographers or the videographers or whoever, and they go ahead and they do the photo shoot. And that sounds insane because it takes a whole day or whatever, and it costs, you know, a ton of money. Now, what they're basically saying is you could just sit there and say like,

• 52:32Anthony Pompliano?

  I need an African American woman with a Brooklyn street background, sitting on a bicycle in the forefront, holding, you know, product X. And then it takes a second and bam, there you go. There's the image. And now you can go use that as a product placement imagery on your website or whatever. But you didn't have to go find the location, hire the photographer, get the model, bring your product, all this stuff. And when I saw it, I was like, holy shit. Like, these machines are going to decimate industries.

• 53:05Speaker 02

  Yeah, they will create new companies that will be very, very efficient. Right?

• 53:12Speaker 02

  And like the next Pixar, the next epic, epic games will be created by machine learning algorithms.

• 53:21Anthony Pompliano?

  You think so?

• 53:21Speaker 02

  Yeah, it's 100%. Like Pixar right now is not a very efficient company. And you have very creative people that have amazing ideas. But it's really hard for them to translate these ideas to an actual product. Right? With these algorithms, you can actually create a script. You can have your idea, you can actually go from idea to product much faster. And so if you look at it, you're just increasing productivity. If you actually look at different industries, you're going from institutions to individuals. Right? And that's the same thing that's going to happen to games. That's the same thing that's going to happen to movies.

• 54:03Speaker 02

  And you like, if you look at, you know, media, Joe Rogan, Russell Brand, they're bigger than CNN, Fox News.

• 54:11Anthony Pompliano?

  Of course.

• 54:12Speaker 02

  Investors, you have like individual investors, they're bigger than, than VC companies. So you had, you have LadQ, you have Josh Buckley, they're bigger than Benchmark, for example. If you have e-commerce, you have Kylie Jenner, right? You have Tom Brady, you have Yi or like Kanye West. They're bigger, you know, than, than other brands.

• 54:35Anthony Pompliano?

  Right?

• 54:36Speaker 02

  The same thing is gonna happen to games, the same thing is gonna happen to movies, right?

• 54:41Anthony Pompliano?

  So you think the individual creators will actually end up being bigger? And a lot of it is because they're gonna have these tools that will give them way more leverage to create this stuff than even a Pixar or whoever?

• 54:53Speaker 02

  Exactly, exactly right. And you see that in technology overall, right? If you look at the best companies, they didn't have that many people. So if you look at WhatsApp, Instagram, they had 20 people or 14 people when they had significant scale, right? You see unicorns with 10, 20 people. I think 10 years from now, it could be the case that you see unicorns with one person, right?

• 55:19Anthony Pompliano?

  So you think that we will potentially, we will have billion-dollar companies created with only one employee?

• 55:26Speaker 02

  Absolutely.

• 55:27Anthony Pompliano?

  That is wild.

• 55:30Speaker 02

  I mean, like, if you look at crypto, right?

• 55:32Anthony Pompliano?

  Yep.

• 55:34Speaker 02

  Bitcoin, it was a, I mean, you had a set of people, but the initial idea was created by one individual, probably Satoshi Nakamoto, who I think was Elon Musk, but yeah.

• 55:45Anthony Pompliano?

  You think Elon Musk is Satoshi?

• 55:46Speaker 02

  Yeah.

• 55:47Anthony Pompliano?

  No, come on!

• 55:48Speaker 02

  Yeah, yeah.

• 55:49Anthony Pompliano?

  Wait, hold on. Why do you think that Elon Musk is Satoshi?

• 55:53Speaker 02

  There are a bunch of clues why he's Satoshi Nakamoto. So, for example, the codebase was, like, is written in C++, which is this programming language, and it's basically a block. And that, that's usually how Elon, Elon programs his stuff. Also, the way he, he used to type his sentences in, in this forum, there were like double spaces between, you know, one sentence and another. That's how Elon Musk writes. The way he talks, Satoshi Nakamoto writes in the forum, he talks about, you know, porn guys in the forum, and then you have pedo guys, you know?

• 56:37Speaker 02

  There are a bunch of clues that point out to the fact that, you know, Elon Musk is Satoshi Nakamoto. He has been thinking about money for, you know, a long, long time. PayPal, the original idea was to create, like, money to the world, to replace, you know?

• 56:56Speaker 02

  These banks and

• 56:57Anthony Pompliano?

  So, hold on a second. This episode is brought to you by Athletic Greens. I take AG1 by Athletic Greens literally every day. I gave it a try a couple of years ago and now I like it so much that I even became an investor in the business. It is the single best option for easy optimal nutrition out there. Every time you take one scoop of AG1, you're absorbing 75 high-quality vitamins, minerals, whole-food sourced superfoods, probiotics, and adaptogens. Athletic Greens will give you one free year of supply for immune-supporting vitamin D and five free travel packs with your first purchase. Go to athleticgreens.com/pomp to give AG1 a try today. Athleticgreens.com/pomp. I take AG1 every day, so you should go try it today. This episode is brought to you by Alto IRA.

• 57:42Anthony Pompliano?

  They can help you invest in Bitcoin and crypto in a tax-advantaged way. That helps you preserve your hard-earned money. Alto's Crypto IRA lets you invest in Bitcoin and over 200 other different coins and tokens, and it has all the same tax advantages of your traditional IRA. There's no setup or account fees, and it's all you need to do: invest in crypto tax-free. Let me repeat that again: you can invest in Bitcoin and cryptocurrencies tax-free. So, are you ready to take your investments to the next level? Diversify like the pros and trade without tax headaches. Open an Alto Crypto IRA to invest in Bitcoin and crypto tax-free. Go to altoira.com/pomp. That's A-L-T-O-I-R-A dot com slash pomp. Start investing today.

• 58:25Anthony Pompliano?

  I disagree with you that Elon is Satoshi, but let's go down this path and say, if Elon was Satoshi,

• 58:31Speaker 02

  Yeah.

• 58:33Anthony Pompliano?

  Would they just put him in jail? Like, would it just be like, they just be like, hey, man, we gotta stop this guy?

• 58:37Speaker 02

  That's why he can't say he's Satoshi Nakamoto, right?

• 58:40Anthony Pompliano?

  You think so?

• 58:41Speaker 02

  Yeah. Like, for the longest time, I thought, okay, Elon Musk is gonna be the richest man alive, and then it's gonna be Satoshi Nakamoto, right? Because

• 58:51Anthony Pompliano?

  People would have an absolute meltdown if it came out that Elon was Satoshi. Both good meltdown and bad meltdown. There's people in the Bitcoin community who would not be super ecstatic about it. There's people outside the Bitcoin community who would be ecstatic about it. Like, it would just be mass chaos.

• 59:09Speaker 02

  I know. And I think he will never say that he's Satoshi Nakamoto.

• 59:14Anthony Pompliano?

  I don't think he has the self-discipline from looking from the outside. I think that somebody would say something stupid to him, and he would just

• 59:21Speaker 02

  He's underrated. I think people Don't realize how smart he is. One of the things, like, he, he, he talks a lot of shit in Twitter. Because that drives engagement.

• 59:34Anthony Pompliano?

  Yes.

• 59:34Speaker 02

  He knows that that drives engagement. That's why he's so funny on Twitter, right? Like, creating SpaceX, like, it's amazing. He created rockets that learn themselves in their own feet, right? He's, he's creating self-driving cars. This guy is nuts. This guy is completely nuts. People, like, underrate him. He's like, he's Steve Jobs on steroids.

• 1:00:00Anthony Pompliano?

  Yup.

• 1:00:02Speaker 02

  Anyway, Satoshi Nakamoto has a really strong incentive to take out his coins because he's gonna be the richest man alive. Or woman, or whatever.

• 1:00:14Speaker 02

  Elon Musk is already the richest man alive, right?

• 1:00:17Anthony Pompliano?

  Oh, he thinks he wouldn't touch the coins because he doesn't need him.

• 1:00:20Speaker 02

  Yeah, exactly. If it was some random guy, right? He has a strong incentive to take out the coins. But if you're already the richest man alive, like, taking out the coins, you're, you're still gonna be the richest man alive. So it doesn't matter.

• 1:00:36Anthony Pompliano?

  Alright, let, let's put that to the side for a second. When, when you think of, uh, uh, kind of, what is actually happening, though, with a lot of these technologies? It is very obvious that we're gonna drastically drive the cost of creating some of these products to zero, uh, or, or trend towards zero. It will also unleash immense disruption in some of these industries. What are the areas where you think that we could apply artificial intelligence, machine learning, even eventually AGI, most, uh, um, kind of, uh, quickly? But also have the largest impact on society or creating economic value?

• 1:01:13Speaker 02

  Yeah.

• 1:01:13Anthony Pompliano?

  What's ripe for disruption?

• 1:01:15Speaker 02

  Everything.

• 1:01:18Anthony Pompliano?

  But like if you had to pick one of two industries, what would you pick?

• 1:01:19Speaker 02

  I mean, the first one is gonna be media. Like, that's the first industry that's gonna be displaced. And right now, we're seeing technology being displaced by remote, right? So we're seeing Silicon Valley basically dismantling in front of our eyes. And the same thing that's happening to San Francisco and Silicon Valley happened to Detroit because of globalization, right? In the 50s, Detroit was the sec- In the 50s, Detroit was the richest city in the world by income capita. And now it's the second poorest big city.

• 1:01:56Anthony Pompliano?

  So it's the first income per capita in the country.

• 1:01:59Speaker 02

  Yeah.

• 1:01:59Anthony Pompliano?

  And now it's the second worst.

• 1:02:01Speaker 02

  Yeah.

• 1:02:01Anthony Pompliano?

  Jesus, talk about volatility.

• 1:02:04Speaker 02

  I think the same thing's happening to Silicon Valley right now. There's a big migration because of remote work. And the same thing's gonna happen to Hollywood, right? If you look at the biggest stars in media right now, they're not in LA, right? Mr. Beast, he's not-

• 1:02:21Anthony Pompliano?

  North Carolina.

• 1:02:21Speaker 02

  Yeah, exactly. Logan Paul was in LA. He now moved to Puerto Rico. He's all over the place. But the biggest stars in YouTube and elsewhere, they're moving away. Because they don't need to be in Hollywood, right?

• 1:02:36Anthony Pompliano?

  I was wondering the other day, could you name an actor or actress that comes from the traditional movie world? That's under the age of 25, 28, something like that? I couldn't.

• 1:02:52Speaker 02

  Yeah.

• 1:02:52Anthony Pompliano?

  Now, I've never been into Hollywood and kind of entertainment scene and all that stuff. But I know who Mr. Beast is.

• 1:03:00Speaker 02

  Yeah.

• 1:03:01Anthony Pompliano?

  I know who Jake and Logan Paul are, right? I know who a number of the TikTok stars are, right? Like, all of these other platforms, I know who they are. I know the people on Twitter. I know Casey Nistat, right? Like, I know all of these individuals who have built massive online followings. I don't think I could name a pure actor or actress. Now, there's an argument that like, many of the people who have starred in some of these movies that are on the younger side, they built their career somewhere else and they cross over. So I think of, what is the movie? A Star is Born, right? With Lady Gaga, I think is the person in there. Like, that's a musician that crosses over. And so when you start to see this, it's like-

• 1:03:43Anthony Pompliano?

  Either one, you've got to be multifaceted and kind of in different industries, or you actually got to go to where the attention is.

• 1:03:52Speaker 02

  Yeah.

• 1:03:52Anthony Pompliano?

  The attention is not in the Hollywood studio model.

• 1:03:56Speaker 02

  Yeah, the smartest people figure out that they don't need to be in Hollywood. So you look at Joe Rogan, you look at Mr. Beast, they're all outside of Hollywood, and they distribute through the internet, right? So that's a big trend that we're gonna see, right? Like, we're gonna see Hollywood studios outside of Hollywood. The next Disney is not gonna be created, you know, in Hollywood. It's gonna be created outside of Hollywood. But I still think the West is gonna win. Like, I still think America is gonna be, you know, the biggest country in the world. Like, a lot of people talk about Asia, right? But if you look at the biggest companies, the most innovative companies, they're still in the US.

• 1:04:39Speaker 02

  SpaceX, Tesla, you look at Apple, you look at, you know, Zoom, they are all in the US. And they're gonna still be in the West. But they're gonna be distributed.

• 1:04:49Anthony Pompliano?

  As we talk about Hollywood and AI disrupting it in this generative media, you have a very interesting background. You were at Genesis Group at MIT.

• 1:04:58Speaker 02

  Yeah.

• 1:04:59Anthony Pompliano?

  And I have to admit, I didn't know that much about Genesis Group, but I went and I was looking it up and reading a little bit. And then I opened a second tab and a third and a fourth. I think I have like 10 tabs open now in the browser, right? Like, holy shit, this is cool stuff. So talk a little bit about what exactly is the body of work that Genesis Group at MIT is working on?

• 1:05:17Speaker 02

  Yeah, so the group was basically working in trying to simulate the human mind into a computer.

• 1:05:27Anthony Pompliano?

  A small task.

• 1:05:28Speaker 02

  And a lot of it involved studying how the human brain works. That's why I have a fascination about the human brain. And I talk a lot about it in my book, about how the human brain works.

• 1:05:41Anthony Pompliano?

  So we have to understand, if we're gonna try to recreate human intelligence, we have to understand the thing, the brain, where the intelligence lives in order to then to understand how we can recreate it.

• 1:05:50Speaker 02

  Exactly. And at the end of the day, I think with neural networks, we can do that, right? Neural networks, they are basically created based on our neurons. And that's a very, it's the most basic part of our brain, right?

• 1:06:08Speaker 02

  And the reason why I think with neural networks, we will be able to simulate brains is because like that's the only thing that we need. I'm thinking, sorry.

• 1:06:22Anthony Pompliano?

  No, go ahead.

• 1:06:28Speaker 02

  So if you look at planes, for example, right? We understand the physics of, you know, how air flows and whatnot. We don't need to simulate how exactly how a bird looks to fly. And I think the same thing applies to computers. We don't need to exactly know how each part of the brain interacts with each other. It would be nice, right? But we understand the physics, how each neuron works. And a neural network is basically a network of how these simple parts of our brain interact. And because of that, I think that we'll eventually, you know, get to AGI.

• 1:07:06Anthony Pompliano?

  And so Genesis Group is looking at how do we recreate the human brain with computers? Study the brain makes sense. Then what's kind of the next step in doing this? Or what is the body of work that they're focused on right now?

• 1:07:19Speaker 02

  Yeah, so they're basically trying to replicate the human brain. And so you have like different parts of it. You have attention, you have, you know,

• 1:07:30Speaker 02

  how our motor system works, how our neuron,

• 1:07:35Speaker 02

  sorry, sorry, our neocortex works and how they interact with each other. So they're trying to simulate these different parts of the brain, right?

• 1:07:43Anthony Pompliano?

  And when I think of the brain, I think of literally the brain. But obviously, my ability to see is using my brain plus my eyeballs to synthesize what actually my eyes are seeing. How much of the work to recreate these software-based systems takes into account things that interact with the brain, like the eyes or even the ears or whatever? Versus, no, we're just trying to understand how the brain itself works. And so, once the information is put into the brain, that's really what we're focused on.

• 1:08:15Speaker 02

  Yeah, so one of the interesting parts about the vision is that most of the data that's actually being transported is from our brain to our eyes. Like, I think 80%, you can actually see the synapses and see how much of the data is sent from your eyes to our brain. And most of the data is actually sent from our brain to our eyes.

• 1:08:36Anthony Pompliano?

  That's crazy.

• 1:08:36Speaker 02

  Yeah, and we don't understand why is that, right? If you actually look at, for example, our gut bacteria, the way our bacteria interact with our body can interfere how we feel that day. So we don't understand humans that well, right? And so a big part of what Genesis was trying to do is trying to understand why, for example, 80% of the data is sent from the brain to our eyes. We don't understand that, right? In my personal view, you don't necessarily need to understand that to actually recreate systems that do better than humans at different tasks. But it would be nice to understand how our brain and body interact with the world.

• 1:09:21Anthony Pompliano?

  Yeah, and it feels like these machines are only going to get better over time. But what about the engineers behind the machines? So when I think of these neural nets or I think of any of the artificial intelligence, machine learning stuff, like, let's put my knowledge at novice level, right? I have to guess the engineers who are working on this stuff today, 20 years ago, knew nothing about it, right? There's some gap where we went from, hey, very few engineers know anything about this or are focused on this, to now there's a bigger percentage. But the percentage probably is pretty small of the overall engineer population in the United States or globally. Do we eventually have every engineer is an AI engineer?

• 1:10:06Anthony Pompliano?

  Because it's just another tool in their toolbox of computer science to be able to actually do the things they want to do?

• 1:10:11Speaker 02

  Exactly, yeah. So we create new tools and we spread these tools really fast. So like, for example, GPT-3 or Stable Diffusion, we create a neural network and everyone can use that neural network. You just need to create once, right? Stable Diffusion, what they did is that they learned how DALL-E, which is this computer software that takes text and creates images, they learn how they did it and they just replicate it. And they distribute this toolbox to everyone. So that's what's happening.

• 1:10:44Anthony Pompliano?

  How much does it cost to do that? Like to create DALL-E, Stable Diffusion, or even GPT-3? Like, how much money is being spent to create a neural net?

• 1:10:52Speaker 02

  So it depends on the neural network. So GPT-3 costs around $7 million, apparently.

• 1:10:57Anthony Pompliano?

  I don't know if that is a lot or a little compared to what I would have expected.

• 1:11:00Speaker 02

  Yeah.

• 1:11:01Anthony Pompliano?

  Like, it's a lot, $7 million's a lot, but also to create something like GPT-3, you would think maybe they have spent tens of millions of dollars.

• 1:11:09Speaker 02

  Stable Diffusion costs around $600,000.

• 1:11:13Anthony Pompliano?

  Really?

• 1:11:13Speaker 02

  Yeah, so it's not that much. And I think the reason for that is because you need, you have a set of GPUs. So you usually, so for Stable Diffusion, I think they use 256 GPUs, and they need to train for a certain period of time. And, and the longer you train it, the better it gets. But, but people get tired after some points. So you usually train these networks for a month, or around a month, you know, before you actually see in the real world. And I think that creates a constraint. Also, like, these are research labs, right? OpenAI, it's probably generating a little bit of money

• 1:11:57Speaker 02

  because they're selling, you know, access to their API. But they're not Tesla, they're not, like, they're not Google, or they're not Meta. So they can't spend a billion dollars in these neural networks. It's funny, though, because we've been increasing exponentially the size of these neural networks, the size of, you know, these machines. And that trend's gonna continue, right?

• 1:12:22Anthony Pompliano?

  So, to create one of these neural nets, right? Let's say that I wanted to create Stable Diffusion.

• 1:12:29Speaker 02

  Yeah.

• 1:12:32Anthony Pompliano?

  I put $600,000 aside.

• 1:12:35Anthony Pompliano?

  Do I need one engineer? Do I need 50 engineers? Like, how big is the team to build something like that? And then what are the steps of what they're actually going to do?

• 1:12:42Speaker 02

  Yeah, I've done that before, not stable diffusion, but I've I got text and created like recreated Obama's voice with a neural network.

• 1:12:52Anthony Pompliano?

  So you took text from like a speech or something?

• 1:12:55Speaker 02

  Yeah, exactly. So I got a bunch of of data from Obama's speech. And then you have the text and then you have the audio. And then you have a lot of data. So you have hours and hours of text and audio. And then you have this neural network that, you know, you're gonna train on this data. And you train for, you know, two weeks, three weeks a month in a bunch of GPUs.

• 1:13:19Anthony Pompliano?

  And how much does that cost? Let's say a month.

• 1:13:20Speaker 02

  Yeah, so it depends how many GPUs you use. So like eight GPUs for a month, maybe it's like 5K, 10K.

• 1:13:26Anthony Pompliano?

  Okay.

• 1:13:28Speaker 02

  So it depends on, you know, how much money you're willing to spend, right?

• 1:13:33Anthony Pompliano?

  And then you had Obama's voice.

• 1:13:35Speaker 02

  Yeah.

• 1:13:35Anthony Pompliano?

  And you could basically have his voice say anything because of the quality.

• 1:13:39Speaker 02

  Yeah, it's not great. The like, the neural network that train like didn't exactly recreate his voice because I didn't spend as much money. But you can get one person to do it, right?

• 1:13:54Anthony Pompliano?

  And how long would it have taken for you to get it good?

• 1:13:59Speaker 02

  It depends. If I use if I use more GPUs for two weeks, maybe I would get it better, right?

• 1:14:05Anthony Pompliano?

  So let's say if you spent $50,000.

• 1:14:07Speaker 02

  Maybe 100K or 500K would get better. But like, these systems are getting better because the amount of compute that we can use is getting cheaper and cheaper. So every year, Nvidia releases a new GPU that's more powerful. And the old GPUs get cheaper and cheaper, right? So what it costs, you know, today, a million dollars. Five years from now, it's gonna cost one or $10, right?

• 1:14:33Anthony Pompliano?

  That's crazy.

• 1:14:34Speaker 02

  Yeah.

• 1:14:35Anthony Pompliano?

  That is absolutely insane.

• 1:14:36Speaker 02

  Yeah.

• 1:14:37Anthony Pompliano?

  And so when you think of this, let's say that you had successfully recreated Obama's voice. Now, he's a little bit different, he's former president and all stuff. But if you were able to recreate that, what are the things that you would do with it in this world that we're heading into?

• 1:14:52Speaker 02

  I mean, you can create any media, right? You can create any video, you can create any voice. And I think one of the important things that we will need to do is to cryptographically sign real media. So whenever we record with our iPhones, your iPhone will probably need to verify that this is real media. Like, real because, like, it's not real necessarily, because they also do much like-

• 1:15:17Anthony Pompliano?

  Authentic, almost.

• 1:15:18Speaker 02

  Yeah, exactly. Because they have also machine learning algorithms to actually improve the image, right? Like, everything has machine learning embedded in it. But they will probably cryptographically sign, verify that this is authentic. Because at some point, we'll get machines that produce media that's completely undistinguishable from real media.

• 1:15:39Anthony Pompliano?

  And when you think about something like this, it feels like, right now, we have a decent understanding of where we are from a technical standpoint, but the possibilities of how people will apply this were just the tip of the iceberg.

• 1:15:51Speaker 02

  Yeah.

• 1:15:52Anthony Pompliano?

  Right? It is still got quite a bit of runway to go. How can engineers get to the position where they're able to actually train these neural nets? So, let's say somebody's watching this, and let's start with a person who already has some technical background. Are they taking, like, some sort of program online, and they watch some YouTube videos, and like, they're ready to rock and roll? Or are they having to go to, like, some sort of class? How hard is it for them to go from "I'm just kind of a quote-unquote 'regular engineer' to "okay, now I'm armed with, you know, the weapons of information warfare in the 21st century"?

• 1:16:27Speaker 02

  Read my book.

• 1:16:30Speaker 02

  Yeah.

• 1:16:32Speaker 02

  A lot of the information is available online.

• 1:16:36Speaker 02

  So, you can actually find a lot of the information online. So, for example, Andre Karparty was head of Tesla AI, for example. He's releasing videos of how to train some different neural networks, right? So you can find a lot of that information online, like, everything, right? Like, if you want to be the best at something, you can probably find everything online, right?

• 1:17:01Anthony Pompliano?

  And what about people who have no technical expertise whatsoever? I know that you're, I think, an investor in Replit.

• 1:17:10Speaker 02

  Yeah.

• 1:17:11Anthony Pompliano?

  I am as well, and they've blown me away.

• 1:17:13Speaker 02

  Yeah.

• 1:17:14Anthony Pompliano?

  With taking nothing to, you have some technical knowledge, but is that the path that most non-technical people are going? Or are they doing other things?

• 1:17:21Speaker 02

  Yeah. So I think for most people, you're not necessarily going to train these neural networks. For computer scientists, I think most people will also not train these neural networks because you just create once and then you replicate, right? I don't need to recreate stable diffusion to use it, right? And these neural networks are also helping computer scientists write better software. So OpenAI with GitHub, they release a tool that actually completes your code. So you can type it out. Oh, like, you know, can you write this code for me? The machine learning algorithm will actually write. It's not perfect.

• 1:18:06Speaker 02

  So it's still, it's like assisting software developers, right? So these, these machines will help software developers be more productive.

• 1:18:14Anthony Pompliano?

  How, how many companies outside of like Tesla are using some sort of AI or machine learning? Is it every company?

• 1:18:21Speaker 02

  Every company, every tech company needs to use AI nowadays. So if you think about Apple, right? They use machine learning to improve your images. If you think about Spotify, they recommend music for you. If you think about Facebook, News Feed has AI. If you think about Open Door, they're like, they're predicting the price to buy your, your products. Every tech company is becoming an AI company.

• 1:18:45Anthony Pompliano?

  So talk about Open Door is a good example. I think people understand the idea of recommending music or the news feed and the ranking algorithm. But something like Open Door, which in a very general sense, for those that don't know, you want to sell your house, you don't want to wait to find the buyer. And so you can go to Open Door, and I think it's within 24, 72 hours, whatever. They will give you a price that they're willing to buy now. And I think they do it in cash. And basically you're done, move on. They'll hold the house and they'll go try to find a buyer. At some point in the future, but where is some of this more advanced technology coming in into play?

• 1:19:18Speaker 02

  So to determine the right price, they are using AI machine learning algorithm, right? So you take a bunch of images, you you enter all the information that Open Door needs to determine the right price. So they they basically get a lot of information of different houses and their features and their price. And based on all that data, they can actually determine the price of a house.

• 1:19:44Anthony Pompliano?

  So if I ask Keith Raboy, how accurate is it? He'll say it's 100% accurate, which is why I love him. But how do you think about like accuracy? And especially in a complex market where housing, the price today is not the price three weeks ago or three months ago versus three years ago, like it does move pretty quickly. So how do you think about accuracy?

• 1:20:03Speaker 02

  Yeah, I think housing doesn't move as quickly as people think. If you actually look at the 2008 crisis, right? I think housing was moving at 2% every month, down 2%. Housing is going down over time right now because interest rates are going up, right? So I think housing doesn't move as much. I don't know how accurate you need to be because Open Door is giving you cash right now, right? So you don't necessarily need to give the exact offer. You may give an offer that's a bit lower, right? Because if you're willing to sell your house really fast, you might want you you might sell for a cheaper price than you're selling.

• 1:20:48Speaker 02

  You know, if you if it took you a year or like a few months, right? So it doesn't need to be exactly right, but it needs to be within, you know, some reasonable bounds.

• 1:20:58Anthony Pompliano?

  Yeah, it's fascinating to understand how pervasive this technology has gotten and how good it gets, especially not just in a general, oh, AI is getting better. But when one company takes the technology, applies it, and then they begin to compound that knowledge and that experience, they're getting the feedback of we bought this house for X dollars, we were able to sell it for Y dollars. Oh, that's a certain margin, maybe we can actually offer lower. Or later, oh, nope, people won't actually sell at that price. Let's bring it back up. And you just begin to get all these data points to the point where it just becomes a really well-oiled machine.

• 1:21:29Speaker 02

  Yeah, and this feedback loop is really, really important, right? So you have companies that focus in transcription. There are companies that focus on media. There are companies that focus on real estate. There are companies that focus in music, right? So because they're getting that feedback loop, they get better and better over time. And there is like a feedback loop and there's a wheel that, you know, they they're getting better and better. And that's why I think Tesla, for example, is gonna win with self-driving cars. There will be other companies that are gonna do really well, but they already have a million cars out there. So it's gonna take some time for other companies to eventually catch up to Tesla.

• 1:22:12Anthony Pompliano?

  I'm not the smartest guy when it comes to self-driving cars and the the competition between the individual companies. But I do feel like every smart person I know is like, yeah, Tesla is miles ahead than everyone else, which I'll take their word for it, right? Another company that I hear AI talked about all the time, usually in a negative sense, but they're still talked about is TikTok.

• 1:22:33Speaker 02

  Yeah.

• 1:22:33Anthony Pompliano?

  What is TikTok doing with artificial intelligence?

• 1:22:35Speaker 02

  Yeah, I mean, they're just predicting the next video.

• 1:22:38Anthony Pompliano?

  Okay, so similar to Facebook or Spotify or whatever.

• 1:22:41Speaker 02

  Exactly. I I think I think TikTok has relevance, but I I think people overplay how much influence they have, right? At the end of the day, people

• 1:22:57Speaker 02

  people used to say, why is the TV like showing so much dumb stuff, right?

• 1:23:03Anthony Pompliano?

  Yeah, it's making us dumber.

• 1:23:05Speaker 02

  It's because people want to see that, right? If you don't show what people want to see, people just switch off, right? They they transition out of a media channel. And I I think TikTok is the new MTV, is the new media channel. Instagram is the same, right?

• 1:23:23Anthony Pompliano?

  And is TikTok better? I'm gonna put better in air quotes because everyone's like, "Oh, it's so good at recommending. It's addictive. It's all these things." Is that because they're actually collecting more data? And so more data gives them more accuracy? Whereas if you were to take all of the information that Instagram collects, and then all the information that TikTok collects, my understanding, again, from two hours of Googling around a few months ago, is that TikTok collects way more information. And then if you're using these kind of AI neural nets, you should end up getting a more accurate predictive engine than if you had less data?

• 1:23:59Speaker 02

  Yeah, I think Facebook or Instagram is collecting as much data that is needed compared to TikTok. It's just TikTok was forced at using this algorithmic feed compared to your social feed. I think a year from now, two years from now, Instagram will have an algorithmic feed that is as good or better than TikTok, right?

• 1:24:26Anthony Pompliano?

  So in some way, I may go on Instagram and not see a ton of content from my friends and family, or people I've chosen to follow. I could just see like viral videos.

• 1:24:32Speaker 02

  Yeah, and people want to see that. It's funny, because your stated preference is not your reveal preference, right? You may say, "Oh, I don't like that." Like, it was funny, I think King Kardashian or Kris Jenner was saying, "I don't like, I just want to see my friends. I don't want to see these Instagram influencers." But she's an influencer herself, like.

• 1:24:57Anthony Pompliano?

  She wants everyone to see her content.

• 1:24:59Speaker 02

  Exactly.

• 1:25:02Speaker 02

  People want to see algorithmic feeds. That's their reveal preference, even though they say they want to see their friends, right? Some people still want to see their friends, but you know, most people want to see these algorithmic feeds. But in terms of data. I think the company that collects the most data is Google, right? When you're talking about TikTok, you just told me, I Google this. You're using your Chrome browser probably to search for things. If you actually download the data that you have on Google compared to Facebook or anywhere else, Google probably has the most data compared to any other company. They can use that data like differently, right?

• 1:25:45Speaker 02

  But in terms of comparing how much data each one collects, Google collects the most.

• 1:25:51Anthony Pompliano?

  Yeah. When you think of non-tech industries, right? And tech has kind of permeated into a lot of these industries now. So maybe we go to more kind of blue collar type jobs or companies. Are there obvious areas where somebody will build artificial intelligence companies to help the non-tech industries or companies?

• 1:26:11Speaker 02

  Yeah, and it's happening already, you know? Every industry is being helped by AI. So if you think about real estate, you have open door, right? If you think about lawyers, there are companies that are trying to predict how a case is gonna do, right?

• 1:26:31Anthony Pompliano?

  Interesting. Like which argument you could make to increase the odds that you would win?

• 1:26:36Speaker 02

  Exactly, and depending on the judge, right? So every industry, if you look at medicine, you have, you know, AI algorithms predicting if you have skin cancer or not. You have, you know, AI algorithms predicting how a protein is gonna look based on the genes, right? And so like every industry is gonna be helped by AI.

• 1:26:59Anthony Pompliano?

  And are there major risks to this? So we talked about the AGI risk of like, hey, when do the machines get too big? But in the shorter term, are there risks that you've identified around the increased use of AI that maybe people aren't thinking about?

• 1:27:14Speaker 02

  Yeah, I think the biggest risk is around recommendation algorithms. And, and again, when we talk about censorship, right? If you look at social media.

• 1:27:30Speaker 02

  Social media companies are the most powerful companies in the world. And that's why there's so much scrutiny around Facebook. Because the most powerful company before that was the New York Times. The New York Times dictated what people thought, right? If you think about the WMDs in Iraq, right? It was a false story, but it determined the future of the US, right? And right now, social media plays that role, right? So when people talk about misinformation or even these recommendation algorithms, they're talking about power. Why there's so much scrutiny around Facebook? It's because there's a set of people that want to control what other people see.

• 1:28:14Speaker 02

  And so if you look at the New York Post story, when there was censorship around that story, a lot of people didn't see that story. And that may have determined the election results. And so I think this, like, social media and recommendation algorithms are like the most important part of AI that we see today.

• 1:28:41Anthony Pompliano?

  Yeah.

• 1:28:43Speaker 02

  But I think in the long run, if a company decides what media you see and it's not what people want to see, people migrate, right? If you look at these markets, they evolve really fast. People complain a lot about Twitter, but they still use Twitter. I think Twitter somehow is the digital manifestation of San Francisco. People don't like San Francisco that much, but there was a network effect. People used to live there, and people are moving away from there. I think the same thing is happening with Twitter, right? You're creating these social media platforms outside of Twitter.

• 1:29:25Speaker 02

  So I'm investing in Firecaster, which is a decentralized social network. And people are moving away from Twitter to Firecaster to other social media platforms. If you think about YouTube, there's Rumble, there are other social media platforms. If Facebook doesn't serve what people want, they will just move away.

• 1:29:42Anthony Pompliano?

  This episode is brought to you by Bullish. They've reinvented the digital asset exchange. They give you access to DeFi features like automated market making and liquidity pools in a regulated environment. It's a whole new way to generate alpha. Bullish's total trading volumes have exceeded $25 billion, just in the seven months since it launched. And their industry-leading order depth means you can trade confidently when you want, at scale, with better pricing and lower risk, all within a regulated market environment. Good reason to be bullish. Learn more at bullish.com/pomp and follow @bullish on Twitter today. This episode is brought to you by Sigma. The bridge between iGaming, online sports betting, and emerging technologies such as blockchain, NFTs, fintech, gameFi, Metaverse,

• 1:30:24Anthony Pompliano?

  and AI is loud and clear. The largest global summit of its kind is heading to Malta from November 15th through the 17th. Over 1,000 exhibitors and 25,000 industry leaders will be there, including top executives from DraftKings, Bet365, crypto exchanges, betting software providers, operators, gaming affiliates, and more. Log on to aiabc.world and sigma.world to see our upcoming global summits. See you in Malta, November 15th to 17th, for the leading global event in gaming and emerging tech. This is Sigma. Go to sigma.world today. It makes complete sense. I think Dan Romero, who's building ForCaster, has thought a lot about what does those networks look like, what would be something that people would be willing to move away for. One of the things I've always said is, I used to

• 1:31:08Anthony Pompliano?

  get all these pitches all the time, "We're gonna build decentralized Twitter." And I'm like, "Okay, what's the difference?" Like, it's Twitter, but decentralized. And I was like, "Nobody is going to care, because nobody actually is going to move just for the decentralization." Now, if it's a better product, if there's a certain conversation or a certain type of user that they want to engage with, and they feel like it's a superior experience in whatever way, oh, and it happens to be decentralized, sure, maybe they'll be interested. But the idea that we're just gonna take every single thing and copy it, and then decentralize it, it feels like that is elementary thinking and not really where the world's gonna go.

• 1:31:39Speaker 02

  Exactly. You need better products. And that is the same insight that Elon Musk had with electric cars, right? "I'm gonna build electric cars." Nobody cares. Nobody wants to drive a Prius. I mean, some people want, but yeah. But Teslas are the best cars in the market and they happen to be EVs, right? And that's why people are buying them.

• 1:32:00Anthony Pompliano?

  Yeah.

• 1:32:00Speaker 02

  And that's the same thing for, you know, decentralized networks. You can't only just create a decentralized network. Nobody cares, nobody cares. You need to create better products. And that's why people choose one product over another. But it takes time, right? Like Twitter, there is a network effect there. But you know, once you start moving away from, from there, you eventually lose that network effect.

• 1:32:24Anthony Pompliano?

  Yeah, that makes, that makes a lot of sense. Talk to me about Bitcoin and cryptocurrencies in general. Like, how do you view them fitting into this whole world?

• 1:32:32Speaker 02

  Yeah, I, I think Bitcoin is the biggest invention. After the internet, I think will have a bigger impact than the internet. So there is a lot of controversy around social media. Because all these old companies, they lost power, right? Like the New York Times and all these other networks like Fox News, CNN and whatnot, they lost power. And now the individuals, individuals have power. And so like they have more information and whatnot. And all these old companies that held so much power for so long, they're screaming, ah, like, please stop, stop, stop. But you can't stop tech. You can't stop evolution, right?

• 1:33:14Speaker 02

  The same thing's happening with Bitcoin and all these cryptocurrencies. You have better money and, and, and they're private. Why, why was there so much fight over who is gonna be the next president? Because they have the control of the printing press. They have the control of the central bank. So they can actually print so much money and give to other, like different states, right? The US has so much power because of the dollar. And the US is losing power because, you know, other countries are moving away from the dollar. And that's what's gonna happen with Bitcoin and our other cryptocurrencies. You're moving from, you know, the state controlling money to private, like having private money. And that changes everything.

• 1:34:00Speaker 02

  One of the biggest things that people don't talk too much about is decentralized finance, right? When you look at interest rates in the US, they're really, really low. And that's why I think there's so much craziness in big cities, right? Why is real estate so expensive in big cities? It's because interest rates are really, really low and people can leverage up so, so, so much. And then real estate becomes really expensive. And then you have homelessness, you have people like not being able to have their own families and create their own families in these big cities. And that has been happening in the US. And you have some cities that have this problem at the peak, right? And the whole West is seeing that.

• 1:34:44Speaker 02

  So like if you look at the fiat system, the dollar and euro, like European cities are starting to have the same problem, right? When you have interest rates, they're not driven by, you know, the state. So right now, what happens, you know, is that the government issues that at 0% interest rate. Now they're increasing a little bit, but they can't increase too much because, you know, you have the government and even private companies overleverage, overleverage, right? And when you increase the interest rates, like they can't pay, you know, that much debt and they can't borrow as much, right? And with 0% interest rates here in the US, Americans have a huge advantage compared

• 1:35:29Speaker 02

  to developing countries. So if you go to Brazil, where I'm from, interest rates are much, much higher. So the cost of capital in Brazil is much higher. But what's the difference between the average Brazilian and the average American? It's not that much, right? Like they're pretty similar. And so why are interest rates in Brazil so much higher? It's because historically, the government in Brazil is pretty shitty, right? Compared to the government of the US, historically. Sometimes the US has a...

• 1:36:02Anthony Pompliano?

  Goes back and forth, but definitely I think most people would say it's more stable.

• 1:36:04Speaker 02

  It's more stable, exactly. And so that's why interest rates are so low here.

• 1:36:08Anthony Pompliano?

  But is that also a thing where... Where when inflation hits globally, US but also everywhere else, Brazilians know what what's coming, like Brazilians understand inflation and they're like, oh shit, jack up the interest rate to try to get ahead of this thing, whereas in the US, maybe we're a little bit behind the curve. Or is that something that Americans are saying without really understanding the difference of the two countries?

• 1:36:30Speaker 02

  Yeah, I mean, some countries understand interest rates better than others. Brazil is doing much better than the US right now, economic economically and also in terms of inflation. There is less inflation in Brazil right now than in the US, which is yeah, it's crazy to think about, but it's mismanagement.

• 1:36:50Speaker 02

  But yeah, like Brazilians in understand inflation much better, but they also understand interest rates much better. When you have such high interest rates, your cost of money is much, much higher. And I think that's much more important than inflation. Inflation plays a role, but if you can borrow money for much cheaper, why wouldn't you, right? And that's what crypto enables, right? When you have Bitcoin, when you have other cryptocurrencies, you can borrow and lend money in a different, like the interest rates are much, much different than, you know, your state owned interest rates, right? And that will drive a lot of development and I think that will change the the world like much more, you know,

• 1:37:35Speaker 02

  like when you have Bitcoin and you can borrow money at much cheaper in the in Brazil, that will have profound effects. And so you and so you will have areas in Brazil that will be much, much richer. There will be areas that won't be as rich, right? But the individual will have the choice of having, you know, different, who choose their own money.

• 1:37:58Anthony Pompliano?

  Yeah, that makes sense. How do you see artificial intelligence and Bitcoin playing with each other? Are there things that people will use AI around Bitcoin that that are obvious right now?

• 1:38:08Speaker 02

  So I think Naval had this quote that was pretty interesting. I think every person is going to be investor in the future. And now

• 1:38:21Anthony Pompliano?

  They're gonna have to be.

• 1:38:22Speaker 02

  Exactly, exactly, they will have to be. And so when I think about cryptocurrencies in general, I think you're investing in some technology, right? And cryptocurrencies, they enable global investment, right? In the past, to invest in the US market, you usually had to be US-based, right? To invest in Apple, to invest in Microsoft, you had to be US-based. Most people that I know in Brazil, they didn't invest in Microsoft, they didn't invest in Apple. But a lot of people here in the US, they invested in these tech companies. But right now, when we talk about cryptocurrencies, when I go back home, everyone is talking about it.

• 1:39:07Speaker 02

  They are putting money into it. It's a global movement, right? AI just increases productivity. And so when you look at it, AI is just gonna transition this world where everyone becomes an investor.

• 1:39:25Anthony Pompliano?

  Yeah, it's fascinating when you think of on a global scale. And then also, I guess the only way that I could really see right now where it's like already people are trying this, or at least I've seen with Bitcoin specifically, is you have a little bit of artificial intelligence that's being used by the energy companies and the miners of when to actually consume the power for Bitcoin versus go ahead and return the power back to the grid. And then also, I've seen some people talking about like routing liquidity on the Lightning Network and trying to understand what the right path is, what's the most cost-effective, that type of stuff. You've got maybe some trading stuff that people are doing. I'm less familiar or intellectually interested in that stuff, but definitely people looking at tons and tons of data, trying to understand what are the different kinds of things that

• 1:40:08Anthony Pompliano?

  they can pull out a signal and go from there as well, right?

• 1:40:11Speaker 02

  Yeah, so it's just increasing productivity when you think about it. If it's mining, if it's routing money. Right AI usually you have a heuristic, you have a goal and you're trying to improve that.

• 1:40:23Anthony Pompliano?

  Yeah.

• 1:40:23Speaker 02

  And that's what it's doing, right? So if you look at different industries, it's always helping improve productivity.

• 1:40:30Anthony Pompliano?

  Yeah, that makes sense. Talk to me about the book that you wrote. So this book is fascinating to me because it really unpacks a lot of what I think is opaque to people. So where did you get the idea to write a book and what was that process like to write this?

• 1:40:43Speaker 02

  Yeah, so I think AI is impacting every industry, right? So if you're taking a photo with your iPhone, it's using a machine learning algorithm to like predict what's the best image, right? Or when you listen to a song, it's trying to predict what's the next song. When you're watching a video on YouTube, it's predicting the next video to watch. It's everywhere. It's impacting every industry from real estate to driving cars to retail, everything. And most people don't understand it, right? So I explain exactly how it works and the whole history of it, right? So if you want to understand how AI impacts our life, I recommend this book, right?

• 1:41:25Anthony Pompliano?

  And the book is called Making Things Think. And it's how AI and deep learning power the products that we use. And so for the parts that I've read so far, it is essentially looking at things that are already in existence, right? It's not a pie in the sky. Hey, one day we're going to have the singularity and there's going to be a chip in our brain and kind of all that stuff. As much as it's like, here is actually understanding the reality of the technology today and where you are interacting with it in your life.

• 1:41:53Speaker 02

  Exactly. Yeah. If you want to, you know, think about what's going to happen in the future, I give you some hints where we're heading, right? But I think it's the most important aspect is to know what, how is it impacting your life? How is it impacting you today? Right? It's gonna impact your industry, it's gonna impact your life, and you probably shouldn't know how it works, right?

• 1:42:18Anthony Pompliano?

  Yeah, it's it's pretty interesting to kind of contemplate all of the different ways that the all of the different ways that the technology will actually be integrated into this is because we don't understand it all yet, right? Like, there's somebody right now, I think a lot about this, there's somebody somewhere in the world right now who is using this technology, and they're tinkering, and they're trying to explore different ways to actually implement it in a certain industry, or in a certain application, or a product, and eventually, they are going to come up with a breakthrough that then will spread like wildfire across the entire world. And we will all be like, wow, that was amazing. I can't believe no one had ever thought about that, or had ever built using that technology in that way.

• 1:43:03Speaker 02

  Yeah, I think that's great. Could you imagine that Bitcoin was created, you know, like, before that, you wouldn't even imagine about this creation, the iPhone, you know, like, there will be new creations that we can't think about it, that will have 10 years from now, that will impact our lives like a ton, right? And that's exciting, that's the exciting part of our lives.

• 1:43:27Anthony Pompliano?

  Yeah, it is. It is absolutely exciting, but also, I think, something that reminds you, like, there's still a lot of work to do.

• 1:43:36Speaker 02

  Yeah.

• 1:43:37Anthony Pompliano?

  And if you think about artificial intelligence and its ability to go from where we are to where we want to go, we're gonna need a hell of a lot of engineers, right? We're gonna need those engineers trained in the latest technology. And it brings kind of the the conversation full circle, which is, in some way, we are trying to build technology that will put humans out of today's jobs, to free them up to do other, more productive things, and allow the machines to take over some of the repetitive tasks and things that humans do out of necessity today, but maybe in the future won't have to.

• 1:44:11Speaker 02

  Yeah, I think there are two industries that you can have a lot of leverage, and one of them is media, and another one is, you know, software. You write it once, and then you distribute to everyone.

• 1:44:27Speaker 02

  And they impact everyone's lives, right? Like, everyone has, you know, Facebook or Instagram installed in their phones. Everyone has an iPhone or Android, you know? They have a smartphone. And that impacts everyone's lives. It's great, because you're basically, you know, impacting the whole society. And it's usually, you know, to the good.

• 1:44:50Anthony Pompliano?

  Yeah, it is something that fascinates me, because it also requires people to be optimistic about the future.

• 1:44:59Speaker 02

  Yeah.

• 1:44:59Anthony Pompliano?

  It is very hard to build these technologies and build these products and be a pessimist, right?

• 1:45:03Speaker 02

  Yeah, yeah, I mean, if you look 100 years ago, and how our lives were shitty, right? Like, you can't, like, it's hard to not be optimistic about the future, right? Like, the A.C., the iPhone, you know? Like, how our cars evolved. If you look at around, you know, like, just look at our cars. The cars that, how they looked in the 70s and how they look now. They're so much better, so much safer, right?

• 1:45:37Speaker 02

  Medicine keeps improving. Like, everything keeps improving over time, in average, right?

• 1:45:43Anthony Pompliano?

  Yeah.

• 1:45:43Speaker 02

  You have some, like, mismanagement in some areas, but overall, society improves over time.

• 1:45:49Anthony Pompliano?

  Yeah, it is, it's fascinating whenever I see, they say machines can identify, like, x-rays, like, problems on x-rays better than, than human doctors and stuff like, just things that you would never have expected for machines to be able to do.

• 1:46:02Speaker 02

  Yeah, it's great.

• 1:46:04Anthony Pompliano?

  Where can we send people to find the book?

• 1:46:06Speaker 02

  Yeah, you can go to hallway.com, the name of the book's Making Things Think. Hallway is a platform where they have different books. They have really great books.

• 1:46:17Speaker 02

  You can go there and then just buy the digital version of the book.

• 1:46:21Anthony Pompliano?

  Awesome. So Making Things Think, how AI and deep learning power the products that we use, go to Holloway, the website to get that. And then where can people find you online if they want to talk more about these ideas or learn more?

• 1:46:32Speaker 02

  Yeah, you can find me on Twitter. I'm also on Farcaster, which is this decentralized social network, and on YouTube as well. So if you search for Giuliano Giacaglia, which is probably going to be in the show notes, you can find me there.

• 1:46:47Anthony Pompliano?

  Awesome. Well, listen, thank you so much for your time today. I learned a lot, and I think that people are very interested in artificial intelligence, machine learning, deep learning, all those things. And so hopefully this will be helpful to them as they try to learn more.

• 1:46:58Speaker 02

  Thank you so much for thanking me. Yeah, thank you.

• 1:47:03Anthony Pompliano?

  Thanks so much for listening to today's episode. I really hope you enjoyed this one. Make sure you're subscribed on Apple, Spotify, or your favorite podcast player. And if you're looking to transition into a brand-new job in the Bitcoin or crypto industry, we've got you covered. Head over to thecryptoacademy.io. My team and I have been working with the top HR teams in the industry to develop an intensive three-week training program with over 50 live events. We teach you exactly what you need to know to break into the industry, including live interview prep and resume review. Our students have been hired at over 75 of the world's best Bitcoin and crypto companies. Go to thecryptoacademy.io to learn more. Again, that's thecryptoacademy.io. If you enjoyed today's episode, make sure you share it with your friends, and I'll see you all for the next episode.