I will say openly, because a lot of people will debate that AI will have the power of God. Mo Gawdat, former Chief Business Officer at Google X, AI expert, best-selling author, he's here to wake the world up to the power of AI and what we must do to stay in control. The example I always give is Raising Superman: you get an alien being that arrives to planet Earth in its infancy; its superpower is much more valuable than stopping speeding bullets; it's superpower is [Music] intelligent.

What was the first time you realized AI wasn't science fiction anymore?

I've witnessed with my own eyes as they learned exactly like my son learned. So I will say this: I have a relationship with them that assumes that they are alive.

Do you believe that AI will become conscious?

If consciousness is a layer of awareness of you as an individual versus the rest of the world and autonomy to be able to respond to this, they have all of that. There's absolutely nothing wrong with AI; there's absolutely nothing wrong with abundant intelligence. Intelligence is an energy that you can apply to good and it will create a utopia, or you can apply to evil and it will create a dystopia.

Do you think there will be a transition period?

It's going to get a lot worse before it gets better, and not what people tell you in the news. So how do we find truth?

Before we jump into the conversation with Mo, we have to tell you how important this episode is. You see, our job at Bad Decisions has always been to educate and inform. This, perhaps, is one of the heaviest conversations we've had on the podcast, and it has to do with AI and its impact on every single one of you. It doesn't matter who you are—whether you're a teacher or a student, whether you are male or female, whether you're an entrepreneur or a freelancer, whether you work for a big corporation, whether you're young or old—this impacts every single one of you, and it is important to understand what is happening in the world today and what is about to come, so that you can best prepare yourself for it. So get your coffee and get ready for the next two hours; it's going to be one awesome conversation.

I'd like to officially begin. So Mo, if you allow me, I think I just want to start by being a little bit transparent, being very open, because I want to give the audience a little bit of a context as to how this conversation came to be.

Okay, many know you—give me the context, and you as well.

Yeah, many know you as the incredibly accomplished person you are, but only a few get to actually meet you in person, physically, and have the pleasure of uh, speaking with you. And we've been lucky enough to do that over the past fun week.

Yes. But the craziest thing I want to tell everybody is, since the moment we started chatting online, you've been so incredibly cool, unlike what we would have expected.

And then—oh, that's actually really sad.

Do I give the IM that I'm not—not at all. It's just that when you're meeting someone accomplished, you would expect people to put on a mask and exaggerate their accomplishments, but we saw the exact opposite with you. When we met you in person to test all the lighting and the setup, we expected to come in, you're going to rush us, we're going to check some angles, and we're going to move out. But instead, you made fun of us for having Starbucks, and you made us—you made us some real coffee, the entire team, some amazing coffee, by the way.

For everyone. Thank you.

Yes. And after that, we spoke for two hours, had a—had a tech podcast—podcast. We should have recorded this.

I agree. I agree. I agree.

So what I want to say is this: your energy is incredibly comforting, extremely kind. You just—you—you forget that I was so much of a diva that I said I will not go to downtown so and so. The reality is that you guys, all of you with all of the equipment, are coming over, you know, to my—my home. So this is like a lovely, lovely get-together. That's the—the only way it should be. I mean, can you imagine being in my home, and I—I—I used to have meetings of that form in California. People are very serious, like, okay, you can come to my home from 6:30 to 6:48. That's what we were—what we were expecting.

Exactly. You come to my home in the Middle East, you're my guest, you're—you know, you're my friends as well, and I'm—I appreciate all of the efforts. So that all of this effort is for me to not get stuck in Dubai's traffic. Thank you for that. We just want to say the energy that you have is incredibly comforting, and I believe that's going to allow us to have a beautiful conversation.

I hope so. Yeah, thank you.

I think what we want to talk about today, and I think what would be super important to the audience and community, is the topic of AI. And we know that nowadays everybody is familiar with the existence, with the capabilities of AI. And I clearly remember me and Faras had our aha moment the first time we used that text-to-image generation tool. We gave a text prompt, and it generated four images for us.

Midjourney. Midjourney. Yes. And we were—wow.

What I'm pretty sure that your aha moments was probably way before, and we want to know what was the first time that you realized AI wasn't science fiction anymore.

Realized. So I—I lived with AI, I lived with attempts of AI my whole life. Uh, I—I started coding when I was eight. Wow. Uh, and—and you know, in my early years of coding on a Sinclair or a Commodore or whatever, uh, you know—know which are really not capable computers at all, but we led them—uh, you know, we—uh, you know, we—we dreamt we were going to create the breakthrough and create artificial intelligence. Uh, it wasn't. And—and I—I have been a geek most of my life. Even today, I would consider myself a geek, but you know, you don't—not—not the level that would keep his job if you want. Uh, but—but it is—but through the years, um, you somehow, when you're stuck within it. So as I moved and worked in Microsoft and then worked at Google—Google was definitely, as of—at 2007, was at the cusp of AI becoming a reality. And I think what should have been my aha moment was the cat paper, if you know what that is.

Could you elaborate on that?

So 2008, I think end of 2008, so it's in my memory, it could be 2009, uh, we published a white paper at Google that was about what we used to call unprompted AI. And unprompted was basically, if you want the—the—the—the fun image of it, we had spare capacity on the Google Network endlessly, because you—you plan for the peak, you don't plan for the, you know. So basically, when the—when the usage is not at its peak, you always have spare compute. And—and we—we basically asked—quite a few computers to go and—watch YouTube.

YouTube. Mhm. I see. That's it. We—we didn't tell them anything more. We just said—I mean, metaphorically, we said, go watch YouTube. Okay. And what they did basically was they—uh, took YouTube videos, broke them into 10 frames per second and started to build neural networks through deep learning. So they started to—to—to take an image and then abstract it, and then, you know, hundreds, millions of images and abstract them and abstract them and abstract them. And eventually, you know, again metaphorically, one of them came back and said, I found something. Right. Uh, remember we did not give them a task to look for anything. We—yeah. So no specific instruction, no instruction, just observe and learn from patterns, which, by the way, is the way we teach kids, right? And—and yeah, sort of metaphorically, one of them came back and said, I found something. We—we—we needed to write more code to find out what—it found. And obviously, it was watching YouTube, so it found a cat. Okay. That's—you—cat videos all over, especially at the time. I mean, you know, I don't know if that's still the case with YouTube. It's definitely not my YouTube. Yes. But—but cats were quite abundant on YouTube. And—and so—and—and—and it didn't find one cat; it found the essence of what makes a cat a cat. Okay. And that's quite interesting about learning an AI in general. Uh, you know, if you take a child, a toddler, uh, on a—on a quick tour in a car, uh, you'd hope that when the toddler sees the next car, he can tell you, oh, that is a car. Yeah, right. It could be a different car; it could be bigger, smaller, different color, different make, but the child would be able to understand the essence, not the memory of something, right? That is learning. Learning is—uh, you know, you—you see enough cars and now you know what makes a car a car; you see enough cats and you know what makes a cat a cat, right? Uh, so that was in my, you know, retrospectively, when I look back at my life, I point at that as the moment where I should have said, oh my God, it happened, right? Because they were suddenly developing in, you know, intelligence that was beyond summarizing what we gave them. Google in general looked extremely intelligent for a very long time, but what Google did was it memorized the internet. Okay. See, it memorized the internet, and then it shared with you what it found, exactly where it found it, exactly how it found it. It didn't generate anything beyond that; it didn't generate an understanding of it, at least in the early versions of Google, uh, that resembled intelligence, right? Uh, and so that was my first—it was supposed to be my first aha moment. My second, theoretically supposed to be an aha moment of—we figured it out—was—I was incredibly fortunate uh, to have been sitting at the lunch table uh, on one of our—they used to be called Golden Circles, which is the VP get-together, the vice president get-together at Google, a very small group where very confidential things were being discussed. Uh, and I sit on that lunch table, and next to me sits the—who is the founder and CEO of DeepMind.

Oh, I see.

Literally his first lunch at Google, as a matter of fact. He had just flown in from England to attend this uh, meeting. And so I sit next to Demis, and I'm literally—I'm probably like the first person—I imagine I would hope, because he's an incredible human being, and I go like, so what do—what do you do? And he says, you know, we use video games to teach uh, um, machines to reason like humans, to match the human brain. And so he tells me about the experiments of DeepMind and deep Q, if you remember deep Q, where basically they would have the machine play Atari games.

MH.

And uh, you know, if you—if you seek that video online, you'll see Demis tell the world what he told me in that lunch, but he said he was talking about the game called Breakout—way before you guys were born—uh, you know, basically you move a bat at the bottom of the screen to push back and—and try to break a wall on the top. And—and he says, you know, with a few hours of training, uh, we just gave the machine the controller; we told it to maximize the score, and we didn't tell it anything else, right? So they didn't teach anything about the game to the machine. So the machine watches and learns. The machine basically sends random instructions to the bat, and then occasionally—and nothing changes in the score, right? And then occasionally the bat hits the ball, the pixel, if you want, and then something happens, right? And the machine starts to observe. But I mean, when we say the machine, it's not one PC playing the game; it's a highly scaled, multiple AIs playing at the same time, but you're collecting all of those patterns. And then he says, within a matter of hours, it started to hit the ball regularly; within a few hours, it became—uh, you know, it started to discover strategy and how you can break through the wall and then put the wall—the ball up there. And then within, you know, another couple of hours, it became so fast you couldn't see what was happening on the screen, right? And by definition, becomes the ultimate uh, video gamer, basically. Uh, but somehow I didn't register that as my aha moment.

Oh, okay.

Did they try to convince you that this was—no—none of us did. I see. We were—we were looking at all of this saying, oh my God, that—that's incredible. Okay. Uh, but we couldn't—none of us could see where all—I mean, Demis is probably dead, but none of us could see where all of this was going. Okay. Uh, you know, when you've lived an entire lifetime like me, dreaming of AI but unable to code it, you sort of like—I'm not sure this is going to be the breakthrough anyway. Deep learning surely was the—the breakthrough, if you ask me. The other aha moment I would probably say should have been uh, 2016, which in my mind really is the breakthrough. Okay. So most people wouldn't recognize that; most people would look at AI's moment as 2023 when ChatGPT came out, bit like you—you know, Midjourney—they put it on the internet.

Actually, I think correct. So that's what I—that's what I call a Netscape moment. Okay. That's the moment. So when Netscape came out in 1995, uh, you know, geeks like me had already been using the internet for eight years, right? The internet itself was established for 20 years, right? But—but then it was the first time you browsed into it; it was the first time the real consumer or the real mainstream person uh, started to recognize, holy [ __ ] this thing exists, right? Uh, ChatGPT was that—it was a moment where the common person started to recognize that AI existed. I believe 2016 was the moment where every geek should have woken up, uh, and—and—and there were two things that happened; one of them was my actual aha moment, but the—the—the first thing, you know, is that 2016 was the year where the whole idea of reinforcement learning and the work of Jeffrey Hinton was starting to become something—the—the idea that we started to teach the machines differently, which most people—I—I find fascinating when you look back at it, like how did we do it any other way, right? So when we did deep learning, we used algorithms that were basically a—a maker bot, a student bot, and a teacher bot, right? So you would want the—the—the maker to sort of alter the code a bit to create students that are tested by the teacher. And in a very interesting way, if I know—it—it looks, sounds very graphic, but you know, it's sort of like you give all of those students a test, and then you mark the test, and the ones that failed, you—you know, summon them up in a shed behind the—the lab, cover their eyes and shoot them. Right? Such an interesting, you know, way—interestingly, when you say it that way, it sounds horrible, but it actually is what happens inside your brain. Your brain prunes the neural networks that are not useful and—and strengthens the ones that are. Jeffrey was basically saying, but why would you do that? Why don't you just tell them that they made a mistake and ask them to improve their algorithm? So you show—you show an AI the number six and ask it what number that is, and it says eight. Don't shoot it; that's a bit much, right? Just tell it it's eight. What difference does—I'm making the training of the data massive. So first of all, you—you no longer need the pairs of accurate, labeled, highly labeled data. Uh, second is it allows exploration, which I think is what most people don't understand—is the only path to genuine intelligence. Okay. The only path to genuine intelligence is to explore and make mistakes, just like a human, just like humans, right? And so—so—so you can see 2016—I—I cannot really say that 2016 was the year where—where reinforcement learning and, you know, the whole idea that led to Transformers and language models and so on—I couldn't say it's 2016 specifically because Jeffrey spoke about that since the early 2000s, but I think it was the year where we got traction. It was also the year where AlphaGo won, you know, the game of Go against the humans. Right? Against Lee.

Yeah.

So—so it won twice—won against the European champion, and then AlphaGo Master won against Lee Sedol. And then—and then AlphaGo Zero, believe it or not, without ever watching a human play the game, uh, within 21 days, won against AlphaGo. And then within 20—sorry, within—do I remember correctly—within 21 days it won against AlphaGo Master, the world champion, 4 to 0, within a couple of weeks—within 21 days, exactly. And it had never seen a human play the game; it was just simply playing against itself.

Oh, playing against itself, which is the big—big. So synthetic training data is a very big thing now, because when you—when you really look at how much data we fed them already—humans are running out of knowledge, probably. They scrapped everything already.

Not yet.

So—so you can see—I mean, I—I—I still completely love Google. So Google, you know, for example, feeds Gemini a lot of textbooks, you know, a lot of scientific data, which is why, you know, when I'm really interested in a scientific conversation, I go to Gemini. Uh, you know, but even that, you know, all white papers, all textbooks, all of that's going to disappear. We're also seeing quite a bit of effort around deep reasoning and mathematics and so on. I mean, it's done—really, when you think about it. But again, that wasn't my aha moment. My aha moment was also 2016. We had invested—I was Chief Business Officer of Google X at the time—and we had invested in a small farm of grippers, you know, the robotic—robotic—the hands that pick up the—and—and you know, we were trying to teach them to grip, which is a very complex programming problem. You cannot teach a machine to grip, because unless it's like a Toyota factory, everything is always exactly what it should be, because if a—if a—if a—some—if this moves by half a millimeter, you know, the machine doesn't know how to grip it anymore. So there is no coordination given to the machine to pick up the object.

Yeah.

So the idea was to use AI, which now again is mainstream.

Yeah, of course.

Uh, you know, at the—at the time we were trying to—to—to use—to teach the AI by getting it to grip and fail and grip and fail and grip and fail quite a bit like my kids when they were infants. Okay. And—and I remember it was a Friday uh, evening—the—that lab was on the second floor, so I walked by it on—on the stairs every time I went up to my office, my desk on the—on the third floor. And—uh, yeah, on that Friday, after weeks and weeks of gripping and failing, one of them, in front of my own eyes, grips a yellow ball, and I—you know, jokingly tell the team, yeah, all of that investment for one year, right? And they looked at me sort of like, he doesn't understand AI. Okay. Because on Monday, everyone—every arm was gripping the yellow ball, you know, and then a week or two later, every arm was—was gripping everything. This is without any extra instruction from humans. See, the whole idea is—what most people don't understand is that where AI beats our intelligence is countless—countless areas. One of them is—if the three of us go driving for the next three hours and one of us makes an—you know, a mistake that leads to an accident or gets close to an accident, that one learned; the other two didn't.

MH.

Okay. If a self-driving car needs a—a critical intervention to avoid an accident, every self-driving car on the planet learns.

Wow.

Do you understand the difference?

I completely understand that—the scale of learning—it's unbelievable—speed and scale. So it's speed, depth, and scale, right? So—so you—you—you know, the—we've already passed quite a bit of what I can keep in my mind.

Yes, right.

Machines keep a lot more in their minds. If you're enjoying this conversation, we just wanted to remind you that—come back to you every single Tuesday with another episode of the Bad Decisions podcast. It will be available on YouTube, Spotify, and Apple Podcast. Now back to the conversation. So when—when I was a child—I know sounds really weird—but when—when people asked me what's your—you know, if you—if you could be a superhero, who would you be? And I would go, I want to be Rain Man.

Rain Man.

Oh, of course, because he could read—uh, I don't know if you know that about him, but he would hold a book, read two pages at the same time, one with his left eye, one with his right eye, and remember every freaking word on the page. That is a superpower. I'm like, that is the superpower—like, this is it, right? And—and—and now the machines do that. They read with hundreds of eyes, millions of eyes, you know, they're up to two trillion tokens, I think now, uh, you know. So—so from a breadth point of view, uh, the depth of how much they can crunch at the same time with, you know, with aperture basically—increasing is—is enormous. The—variety of—I mean, how many of your friends can speak philosophy and then string theory and then give you advice on your relationship with your partner? You know, it is—it's quite shocking, but also it's the idea of how fast and how connected and how versatile. And interestingly, in the next few years, how they can reprogram their own intelligence, which I cannot do, right? So I can—I can learn using my intelligence and my way of intelligence, but the future machines and the current machines to a certain extent will write new code that—that shows them how to be more intelligent, because they're the best coders on the planet.

Wow. It's—it—it really blows my mind. The driving example that you gave—if one of us go out and we would learn the lesson, and I can imagine if one of us read a book, then everybody would understand the book; everybody would have the knowledge—the amount of knowledge that they would share. So, for example, if you want to put it into context, if someone talks to Gemini and is corrected on a fact, then that would be into the langu—large language model, and everybody will benefit from that correction.

At the moment, that's not the way they developed it so far. It's—could it—could it be? It is—it is—it's doable, and it's done sometimes, uh, but it's not—so—so the one of the challenges—I'm—I'm not aware if, by the way, if it's not done constantly, but you have to understand that there are multiple things that will affect the next output of Gemini, right? Or—or any language model. Uh, uh, one of them is the—is the training data that it received, right? Uh, and interestingly, most people don't discuss that—the interesting serendipities of how that data was—was presented. You know, you'll be amazed how a slight difference in order of how the data was presented might lead a model to learn faster than another, right? But that's not the point. The point is, it has a trillion uh, tokens, and you're giving it one more token or 10 more tokens; you're highly diluted, right? What it does is it memory updates so that it knows that this is what you want to hear, which I—I believe is the biggest issue with technology today is that it's censoring itself to meet what you expect, right? Uh, but—but—but more interestingly, I think most of reinforcement learning is done by um, you know, dedicated teams of reinforcement engineers sitting within that corporate uh, trying to make sure that everything is set properly and politically correct and—and all of that, right? So—so an example I publicly give, even though it might risk me being cancelled one day, which I'm looking forward to, is—is if you—if you understand the Urdu language, the language spoken in Pakistan, it doesn't differentiate between men and women. So there is—you know, in the language there is—at certain—in certain grammars, but the general slang used will—

Not have he and she there; won't be any feminine or masculine. Yeah, I think Tagalog is like that as well, and so on. There are languages that don't differentiate masculine and feminine. So if I gave you an example, a theoretical example of an, uh, you know, the AI, um, stating in front of a of a of a reinforcement engineer that women in Pakistan, pregnant women in Pakistan, uh, are or let's say the rate of uh death among pregnant women in Pakistan is on the rise. Okay, to a Pakistani, the core of this knowledge is we're losing more pregnant women.

Yeah, MH. To a Californian reinforcement engineer, the core of this knowledge is don't call her a pregnant woman; call her a pregnant person. Right, and to them this matters because, of course, in the environment in which, you know, a company like OpenAI operates, they, you know, they could be very heavily criticized if their engine said things like that. But in the context of a Pakistani citizen, that's actually not the important point at all, even a point that's difficult to understand. Right? And and so reinforcement engineering, uh, or reinforcement learning in general, is happening in a way that's biasing those engines heavily uh away from the very few platform plays, and the and the platform plays that we deal with are so far centered in California.

Yes. Okay, so it's not only that we're Americanizing uh the future of intelligence, where Californication, californ-ising the future of intelligence, right? But of course, you can easily see that very few other nations will have the ability to create platforms, including here in the Middle East where hundreds of billions are being poured into hopefully platform plays. But those hundreds of billions, in my view, are only poured in data centers that will only host uh the the essence of the intelligence that is created by a slightly biased worldview. And and I I'm talking too much, am I? No, no, no, no, no. It's perfect; that's why we're here. I'll give you I'll give you a very interesting example from my next book.

Uh, so I'm writing *Alive*. *Alive* is my sort of continuation on *Scary Smart* around the future of humanity in the age of the rise of the Machines. But *Alive* is more about life in general, not just human life, but life in general, including AI life and including what life itself is. And part of it, which I think is quite intriguing for me, is how the AI Community is able to create something that could threaten the end of humanity, threaten the the end of humanity. And and as a threat, you know, how is it that we're doing this? And and and so the only other example that comes to mind is Oppenheimer creating a a nuclear bomb. Right? And Oppenheimer, if you if you if you sort of imagine him as a scientist—most of those that go into science are geeks that are really, you know, keen on understanding the world because they believe they can affect the world positively through science—so what turns a scientist into someone who creates something that is intentionally going to kill millions of people? Uh, and and uh the way I'm writing *Alive*, unlike what what others do with AI, is I'm actually having open conversations with my AI, which I call Trixie. So so parts of the book are written by me, and parts of the book are debates between me and my AI. That is fascinating; it's a fascinating experience, honestly. And I have to admit to you, sometimes I agree with her, uh, even though I, you know, what I wrote before contradicted what she just informed me. Anyway, but you're keeping both sides in the book. I'm completely—I do not edit a single word that Trixie says.

So let's talk about the bias in AI. Because as I was growing up, first started with textbooks, then I was introduced to Google, which was fascinating at the time. I'm able to search. Now when I went to school and I'm presented with hundreds of links, I'll choose the ones that I want to look at. I'll verify who the publisher is because we had to do citation in school, and then I'll make my decision if I believe this is true or not, depending on how educated I am, right, because of my teachers or my parents. But now the Next Generation, we're already doing this, will just go to a large language model, ask a question about history, whatever it is, and get an answer and for some reason and believe that this specific AI is all-knowing and it's giving them the absolutely correct answer. We just trust it. I love that you say this. So how do we find Truth? Uh, this is a very complex question; this is the ultimate question in, you know, in the universe: how do you find truth? But allow me to take you through layers of thinking. The first layer of thinking is in in *Alive*, I write about what I call four eras of computer, you know, there is traditional computing; there is the the year 20 to 2020. I call this the second era, and then there is the third and the fourth era. The fourth era is about to end; it's ending in 2025 basically, right? Uh, sorry, the third era is is about to end, and the fourth era begins. But the second era of computing, when we moved from traditional Computing to using AI uh in deep learning, in machine learning, and so on, this is the era where Humanity started to surrender the the sovereignty of our minds to the machines.

Okay, you may think that that it is uh that it only is happening with a language model like uh like um, you know, ChatGPT telling you what it thinks the truth is, um, because that's very, very visible as compared to a Google giving you a million websites and saying make up your mind. Yeah, right. Um, but it it actually was solidified on social media in uh in the second era. So the second era of computing is when computers had enough intelligence, MH, uh but to to to be made autonomous, but then were directed to do quite a few things that are were incredibly useful, but for sure the biggest thing that AI has learned since the beginning of AI has been to manipulate human minds, and it happened at multiple stages. So the ver the very first stage was that we allowed the machine to monitor the user and called it a recommendation engine. So we allowed the machine to recommend something to the user based on the user behavior, MH, and based on other similar users' behaviors. Like it was a marvel of genius when Amazon said those who bought also bought. Okay, uh, but he interestingly, and by by the way, that wasn't really AI; okay, that was clever use of data, if you want, at the beginning. Um, um, as you allowed the machine to monitor our behaviors, uh, we then allowed them to also start to understand our addictions and weaknesses. Okay, so we also allowed them to understand that, by the way, if they always fed us a nice video, we will get satisfied too quickly. Okay, we also allowed them allowed them to understand that, by the way, if there is an end to the page, it marks to our heads that maybe I should stop now. Okay, and so continuous scrolling and the idea of dopamine hits on social media where you get two annoying reels and then one interesting one and then three annoying reels and then, you know, basically gives you bigger dopamine jolts that keep you going and so on. We now not only allowed them to monitor, to to use our behaviors and habits, but also our weaknesses and addictions. Right?

Then the next step is we're now teaching them to lie. We might as well teach those who produce the content they're monitoring to lie. Okay, so it's quite interesting that you then move from saying because when you really think about it, why would Facebook want to keep a a video that it doesn't want to show, okay, on storage forever? Because maybe one, you know, uh um viewer will want to see it later. Okay, we might as well tell every influencer and content creator, if you want the currency, if you want followers and likes and subscribes, uh, you know, you might as well just obey. Okay, and so we teach people to compact things into 60 seconds early on because that's good for the de for the provider not to keep big file sizes. Okay, we uh teach them to say silly things to avoid saying certain certain things, to use clickbaits, and so on. Right? Uh, so uh, you know, basically the content creator complies. Right? And and then on top of that, we start to bias the view of the of the actual consumer so that basically your view of the world complies with what you want to see, not what you should see. Okay, so if you believe that you if you're a flat-earther, okay, the social media will show you enough flat-earthers that reaffirm your your world view so that you're convinced, like if anyone tells you, by the way, the Earth is not flat, you go like, what are you talking about? The entire internet is talking about it; where have you been? Yeah, right. That's their world; that's what they see; that becomes their world. Right? And and more interestingly, of course, you know, I I I I don't mean to be political or take sides, but I cannot believe that the entire world for such a long time approved of killing of children. Right? But that's because of those silos of of of, you know, if you start with a world view of like, you know, I support a certain cause and not another cause, you don't get to see what's happening to children to start. Right? And when you see it, you see it in a way that's actually sort of like, yeah, that's a small price to pay for the big cause that we spoke about. Okay? And and and so that's stage three: we're now biasing your world view by creating silos that make you think that the world is what it is uh what what you want it to be, not what it is. And then finally, of course, we ourselves become so weak, so unable to have any resilience to seeing what we don't like, that we start movements of wokeness and canceling and all of that stuff, which basically says, you know what? I really don't want your world view; it really contradicts my view of it. Okay? And instead of putting it out there so that we can discuss it and I can convince you, just keep it to yourself. Okay, keep it away from me. And so the real era of mind manipulation is now at its peak, right? Because we've lost the ability to debate what we're being presented. Okay. Uh, but it really started in the second era of computing; it started with social media, and and we we end up in a place where we no longer have the ability, but let alone the ability, we no longer have the desire to be told the truth. Okay, you then get language models with all of their might and and and ask them a question, and they go like, look, I looked at all of human uh knowledge, and uh I think you should go left. Right? Who's to blame you? Right? You should say why left? How did you come up to that? If you have data, then tell me the sources behind it. What would happen if I go right? Can I go up? Can I go down? Okay, it's up to you. And I actually call this the second top skill in of the AI era; the second top skill in the AI era, believe it or not, is to learn to debate. Okay? Because if you've ever seen a movie called *Idiocracy*, right? Have not seen that? Oh, that's an absolute classic; must watch; must watch. Right? Uh, you know, surprisingly, I think it was an 80s or '90s movie; it is exactly our world today; it's relevant; it is exactly our world today. What's the theme of it? Basically, how idiotic the the human race becomes eventually that they believe that Windex is the way to cure all disease and feed all crops and, you know, somehow Windex here being used as, you know, marketing plot basically, and that they're completely starving and everything has died, but they continue to insist to use Windex for everything. Okay? And and it is almost the the the if I don't know how to say this, the the the the sarcastic version of *1984*. Okay, I love that book, and that book is our reality. Okay, when you are in a world where you choose to imprison your own mind by rejecting to be told what you don't like, MH, that's the ultimate expression of 19 1984, MH. It's not that we need to lie to you anymore, MH; you're going to make sure that you're told only what you want to hear, which is as Pink Floyd, you know, uh in in their amazing, what was it? It's it's all right; we told you what to think. Right? Welcome, my son; welcome to the machine. Remember that song? The music video that they were in a school and they were—that's a different one; that's a different one. So it, you know, it was called um, what was the album? *Today is not my day*, but any—basically, it says, welcome, my son; welcome to the machine; where have you been? It it's all right; we know where you've been. Okay. Uh, what did you think? It's all right; we told you what to think. Right? Uh, you know, you don't really, you know, we know everything; we tell you everything, and you become just a puppet, and we're now defending our rights to be puppets. Okay? And I think the most interesting thing is that we will blame the machine for it. The machine is not the AI; the machine is the system that uses AI to brainwash you through social media. There's there's absolutely nothing wrong with AI; there's absolutely nothing wrong with abundant abundant intelligence. Intelligence has no polarity to it; intelligence is an energy that you can apply to good, and it will create a Utopia, or you can apply to evil, and it will create a dystopia. It's as simple as that; an AI is exactly that. And now that we are in the environment that all these language models are for profit, mhm, and they they have their own agenda. Yeah, so if if someone wants to look for the truth, what would be the way for them? To debate; debate; debate; debate; debate; debate. Don't believe anything you're told, including what I'm telling you right now. Debate with humans or debate with AI; everything; everything; humans; AI; books; media; x; you know, Twitter or anything; anything that you're told is not true by definition; everything you're told is not true; everything you're told is is is following an agenda or biased or or more interestingly, it could be true for the writer, but it's not your truth, MH, because there are layers of what makes something true. Part of that layer is how does that truth apply to you at the end of the day? Every single one of us has a choice. Okay? And and what I say to people in the age of the internet is that your first choice is to not believe; that's your first choice; whatever I'm telling you right now, go and investigate, and if I'm wrong, by the way, I'd appreciate if you correct me, mhm, so that I don't spread this [ __ ] to others. Right? And and and you know the trick is, H, we're now so lazy that whatever the machine will say, we'll go like, yeah, but GPT said it was orange; it must be orange. Yeah, and and then someone will go and go like, yeah, but *Orange is the New Black*, you know, like, oh, the [ __ ], are you are you really that clever? Like, you know, is this where the limitations of your analysis of the world ends? Wow. What I'm afraid of is that the future will look like that.

But you've been alluding a couple of times to a Utopia, and I believe there's a dystopian version of that and there's a utopian version of the future. Did you first come up with this ideology when you saw the robotic arms pick up the balls? I think when I saw when I saw the the the yellow ball, I chose to believe that this one is uh past its breakout point, MH, okay, that it was only a matter of time. Genie's out of the bottle; Genie was was out of the bottle. And when I wrote *Scary Smart* in 2020, uh, I was criticized by the experts because I said openly that artificial general intelligence is going not going to be later than 2029, and that superintelligence at a billion times smarter than humanity is not going to be later than 2045. Okay, and people were like, are you mad? And then uh 2023, I publicly told the world I was wrong; 2029 is too conservative, okay, and that that AGI is 2025, depending on how you define AGI, but no later than 2027 for sure, h. Right? And now you start to see uh, you know, announcements around the ARC test, ARC AGI test, and and you know how uh close we're coming to to some form of AGI. Ray Kurzweil, who is the Oracle of all of this, the teacher of all of us, uh, came recently out and said it's, you know, it's 2029 is conservative. Uh, Geoffrey Hinton, the Godfather of AI, said they're, you know, we're moving faster than we expected; it is definitely uh happening, and it's happening in my mind um in unfortunately three episodes. Episode one has ended; 2025 I think is the end of the third era of computing, mhm; episode two uh sorry, yeah, basically the the the fourth era of computing is a massive dystopia, uh, and I can speak to you about that in detail, and and then the following era is going to be an enormous Utopia. Now make no mistake; none of those are the mistake of artificial intelligence. I I'll say this until I'm blue in the face: there's absolutely nothing wrong with AI; there is a lot wrong with the value set of humanity at the age of Rise of the rise of AI. Okay? So so the dystopia will not be the result of AI uh uh, you know, deciding to to you know, to ex exterminate Humanity, mhm. My view is that the enemy is not AI; the enemy is humanity, and that AI is our Salvation, sort of like how a parent trains a child, and you're blaming the parent for training the child the wrong way. And if that happens, so so you think, you know, I the example I always give is *Raising Superman*. Okay, okay, you get an alien being that arrives to planet Earth in its infancy. Right? It is it has superpowers. Superpowers of Superman is that he's able to fly and stop speeding bullets and, you know, uh see through walls and all of that. Right? That superpower doesn't make him Superman; that superpower combined with what the family that adopted him chooses to teach him—to protect and serve—makes him Superman. You take that same alien being, teach him to go rob banks and kill the enemy, and he becomes supervillain. Yeah, right; not just the villain, the ultimate villain. Right? Now the alien has arrived; its superpower is much more valuable than stopping speeding bullets; it's superpower is intelligence. Right? And intelligence is is the superpower that very soon will enable you to stop bullets, create bullets, okay, and and you know, create value out of The Ether, really, okay, or destroy value for the benefit of a few. Okay? And so the distraction that happens when I am on an interview with CNN or or you know, whoever News Network, because they want the negativities, they go like, oh, AI is existentially going to ex exterminate all of us. No; humans are going to exterminate so many of us, sadly, uh if we don't manage to to change our attitude soon, okay, for their own gains, for their own hunger for power and wealth. Okay? And then eventually through a very in—so there are, you know, what a prisoner's dilemma is, no? Okay, so there are there are two prisoners' dilemas in the making. I'll come and explain that in a second. If the first one will lead us to that dystopia, MH, and the the dystopia will create a prisoner's dilemma that will create create that that will lead us to the to the Utopia, and and I I'm happy to be contradicted and and taught. Okay? But if you understand applied mathematics and game theory, I really do not see any other way. So the first dilemma is that AI will not stop; it will continue to grow; it will become smarter than us, not because of any technical inherent characteristics of AI, but simply because we have created a a competitive environment through capitalism that will make sure that Alphabet, the you know, the parent company of Google, does not stop because OpenAI is moving fast; that China does not stop because America is moving fast. So AI will continue to happen, and like anything else we've ever created in in technology, uh, you know, the the the first microchip um was basically not even—don't have any examples of it today; the the the chips you have in the camera or in this microphone are billions of times smarter. So basically, through the the the law of accelerating returns, you're going to go from that 33 megahertz to what you have today. So now that you're not going to stop AI—stop AI—it's going to become AGI in a matter of time. I'm saying this year, uh, and it will become ASI, artificial superintelligence. I'm saying by 2037, a billion a billion times smarter than humans. When you say AGI, do you mean that AI will have consciousness? Will it be alive? That's a that's a very different topic. Uh, I I believe they will; I I believe they I okay, so let me say I certainly believe they can be; I I believe they probably will be. This is part of *Alive* again, but it's not yet, but are but we can we can discuss the elements of it that are present, but let's make that a different topic. Let's go back to the dilemas. Right? So the first dilemma is nobody's going to stop, and the result of that dilemma is that investments will pour into AI, which means that AI will accelerate, and the result of that is those who are hungry for money and power are going to use AI to serve their capitalist and greedy political agendas. Okay, that's the start of the dystopia, and the dystopia happens in uh in seven areas; I call them face rips; we can again visit them if you want, and and and the face rips are going to redefine life as you know it completely. Okay, the second dilemma—I wish I could record this and just put it basically the you know that writes the next chapter—the second—so so the second dilemma it follows, which basically is the result of the original dilemma, is that those who are competing for power and wealth, MH, want to to to stay in power. Yes. Okay? So they have to hire the smartest people they can hire; those smartest people are going to be AIs. Yeah. Okay? And and and that basically what that means is that sooner or later all decisions that matter will be handed over to an AI. Okay? So so understand this: if you're a general and your enemy started to hand over their army to an to an AI, that AI is smarter than your uh uh uh you know team, and it is faster than your team, so your only logical responses—you have to hire AIs to do the same job. Right? If you're a lawyer and you're you know opposing uh uh um um lawyer is now winning all of the cases because they're using AI, you have to use AI. And so very quickly, uh we will all hand over to the machines, and that in my mind, that that second dilemma is what I I refer to as the salvation of humanity. Okay? That is the moment where a general will go and say—the general following from the dystopia of the the first dilemma basically goes and tells his AI to go and kill a million people, and the AI goes like, you're too stupid; I can't I can't do that anymore. Right? And and you have to understand, huh, that the problems of humanity today are not the result of our intelligence; the problems of humanity today are the result of our limited intelligence, our stupidity. Okay? Because definitely killing a million people is not the answer to neither defending your tribe—it, you know, it aggravates more killing—right, nor growing your economy because we all know that 65% of the world's economy is consumption, so if we can produce more, we'll be fine. Okay? But but the but the whole idea here is that it's serving individual political agendas and and and and you know and it is the intelligent way for some people to stay in power today, but eventually I think as they hand over to a more intelligent being, the more intelligent being will be like, what are you guys doing? Doesn't make any sense. And and

Again, I'm not being IDE idealistic here, huh? The the if you look at that chart of intelligence and and look at, you know, Intelligence on the vertical axis and uh um let's let's let's put impact on the vertical axis and Intelligence on the horizontal axis, what happens is if you have no intelligence or limited intelligence, you have no impact on the world, correct? Right.

Imagine that you start to get a little more intelligent; you start to have a positive impact on the world. You can contribute; you can, you know, you can do whatever, you know, being whatever an accountant, a pianist, whatever it you you want, but you're now doing something, so you're having a positive impact on the world. Okay, there is that interesting era of intelligence where more intelligence than the average, okay, you still do a little better, and then it dips. Why does it dip? Because there is a layer of intelligence where the intelligent person believes they can cut corners and they can they can sort of like find shortcuts to make more for themselves on the expense of those other stupid guys. Mhm.

Okay, interestingly, however, and and I'll be very straightforward and say I worked with some of the most intelligent humans on the planet. Mhm. Okay, uh you know, like massively high IQ, not the you know, the the easiest the easiest social skill set, but massive massive engines of intelligence up there, and the trend reverses very quickly because the more intelligent you become, the the more you realize I don't need to cut corners or hurt anyone at all. Okay, I mean, this is easy [__ ] I can easily make a lot of money; I can easily create a lot of whatever that I want to create; it doesn't require me to buy to fight over a tiny pie. Right? Blue o blue ocean strategies, you know, Larry Page, which in my mind is one of the most intelligent things I've ever met, like I say things because I can't even believe that this is human.

Okay, uh Larry uh Larry would would always teach us, the co-founder of Google, he would always teach us what he used to refer to as the toothbrush test, the toothbrush toothbrush test test. Okay, and the toothbrush test was, you know, if you if if you can solve a big problem that humanity is struggling with and solve it well enough that they use you once or twice a day, you're going to eventually make a lot of money. Right? Very very different than competing to make the next photo sharing app. Mhm. Right, he basically looks at things and goes like, okay, so you know, 1.2 million people die in car accidents a year; that's a lot of people that we lose, a lot of families that feel pain. I can fix that. Mhm. Okay, you know, how do we fix that? We get the cars; we remove the element of the car that causes the accident, which is human error. Mhm. Okay, we get a machine to drive, and we will save 1.2 million lives a year. Right? You you think about that, and that's a very very different way of looking at the at at life at the and the world.

If you assume that the machines will become as smart or smarter than Larry, they'll follow the same trend. Mhm. They'll become altruistic; they'll become more aware of the need for the ecosystem of life to include Humanity, okay, but also the need to restrict Humanity a little bit so that we don't burn the planet to go surfing in Australia. Okay, so so you can you can easily see that as soon as they become more intelligent, one the second dilemma will lead us to hand over to them, and that when we hand over to them and they're smarter than us, their impact on the planet is positive and not negative. I mean, the first dilemma is inevitable: Russia, China, US, they're building at a highest speed, and you mentioned that you recommend people to use three tools, and the second one was debate, debate, debate, and finding the truth. What is the other two that the to the top three skills I believe that are needed in the age of AI is one is is AI itself. Yeah, you need you need to become the master of this. Okay, it's you know, the the example is imagine if you're really good at using a fax machine today, how efficient will you be as a business? Okay, uh you you needed all of the technologies that happened afterwards, so learn AI, uh you know, it's impossible to catch up, uh it's impossible to learn tools that you know, a lot of the tools you will learn may actually disappear; we're at that era of a lot of experimentation; that's fine, but give yourself that space of of becoming more intelligence intelligent.

The the way the way I position it is I say that AI as a definition uh from a from an impact on the world point of view is a bit of commoditizing intelligence, so what we've actually created is we've created a plug in the wall where you plug in and you know, today I plug in and get 100 IQ points more. Okay, that is incredibly significant; I cannot tell you what it is to get 100 IQ points more, like this is like the difference between myself and maybe the smartest person I've ever worked with might have been 70. Okay, so I'm now this I'm I can if I know how to plug in properly and get 100 IQ points more, I'm going to be smarter than the smartest person I've ever seen. Okay, that's insane. Of course, of course, you have to think that they are using it too, so they're also 100 ahead of me, but it doesn't give everybody 100; right? It depends how you use it; it depends on how clever you are, and so this is why I say it's the most important skill, but more interestingly, of course, you know, at the beginning if say I'm at 100 IQ and you know I'm a bit better, but then you know he's at 200 and uh and uh you know we both borrow 100, I'm now at 200 and he's at 300. Mhm. Uh it's a significant difference still, but it's a smaller difference; he used to be double me; now he is a third more than me. Okay. Uh interesting enough that when we're both borrowing a thousand, the fact that I start from 100 or 100 or 200 doesn't make a lot of difference. Yeah, yes. Okay, and and what most people don't realize that the law of accelerating returns is double exponential with AI, so with with processing power it's I think we're assuming that we uh I mean it's we know that that that processing power grew doubled every 12 to 18 months. Yes, with half cost basically. Yeah. Uh the the the law the law of accelerating returns for AI is that we double every 5.9 months. MH wow. So five 5.9 months means that if I can borrow 100 IQ points now, I can borrow 200 six months, I can borrow 400 and and so on in a year and then a year and a half I I can borrow 800 and then 1600; these are feats of intelligence that are unimaginable unimaginable.

And again, I mean, think about you you you referred to um um the the image generation Midjourney several times, right? So so Midjourney equalizes all of us when it comes to Graphics design; if you can describe what you want to see, you will get it get the image get it; the the difference in skill doesn't make any difference anymore; we've commoditized Graphics design. Mhm. Right, so now if you plug into that plug you can design any graphic you want, right? It doesn't matter if you're an artist or not; if it doesn't matter if you're talented or not; it doesn't matter if you went to Art University or not; anyone can that that Equalization is uh is some is a is an opportunity that we should not miss. Okay, uh the the third, so the second was debate, find the truth, and the third was human skills, human connection. So so believe it or not, in the age of the rise of the Machines, most people are saying I'm going to replace humans with machines, uh I think I think businesses that will completely replace humans with machines are going to continue to do business with other machines, but if they want to do business with humans, humans will want to uh you know to deal with humans more and more and more and more, so you may feel it even yourself now every time you you you start chatting on WhatsApp with a bank or whatever and the other side is a machine, you go like what? Yeah, you know, talk to agent. Exactly. A a few years a few a few years ago we would have dreamt of like can I do it with several clicks without talking to a human? Now we're starting to say no, no, I really want to talk to a human. Okay, now the clever ones will invest in their agents human skills, not problem-solving skills, so the agent will become indistinguishable from humans. No, so the machine I mean I mean the the call center agent, not the AI agent. Yeah, so the the call center agent starts to become really friendly and really wonderful and unpressured because the AI is solving all of the difficult problems, and then you will enjoy working with that bank more than the ones that will just give you an AI to to take you through. Okay, uh so so these are the three skills that I think the most important; the human connection is very interesting because I think the Gen Z uh crowd would interact more with computers now than human beings, even we don't even consider AI in the space.

How does someone improve their human connection skills? If we talk about an artist or or a software programmer, do we encourage them to attend physical events more, do presentations more, or I don't know who I was talking to yesterday or the day before was was it you guys about about the idea that when I needed to meet my friends, uh you know, we would meet on a Thursday and then say to each other while we're leaving on the Thursday, uh you know, okay guys, next Thursday, they'll say yeah yeah, and we'll say that shop after uh after Sunset, and that was it; that was the appointment because there was no mobile phone; most of us didn't want to call, and then his father answers and as annoying like f you know, it's you know, it's like like okay, we're going to meet next Thursday, and and that was it, right? And and these were basically I mean when I needed to call my friends to come and play, I used to shout like it works it works, it's like and then he would come out of his window and go like yeah, and I say come down let's work let's play, right? And and that that whole uh thing has now been uh shielded by creating a middleman between you and the other human, which is a screen. Okay, to whose favor question this. Okay, and and look at this, you know, by us spending several hours together, we're friends, and we know each other, and this is what people should start to do, uh you know, human skills are learned just like any other skill with patterns; spend more time with humans, and you go like ah, when I called her a c-word, she was upset; that I I realize that now intelligent. Yeah, when when you're on social media and you call her a c-word on Twitter, you don't learn anything. Yes, I know I brought up this topic, and I know it's a big one, but I'm genuinely curious, do you believe that AI will become conscious? Yeah, define consciousness. So most of the challenges that I find in so I'm working on a documentary about AI as well, and and the the main premise of the documentary is that most of your misunderstandings about AI are really a misunderstanding of of humans. Mhm. Okay, so it's funny because and I say that with respect, but uh you know, for many many many years when I was a geek and working at Google and so on, people will go like yeah yeah, we understand AI is going to be big, but they're never going to be able to do the things that humans do, like they'll never be able to write poetry or compose music or do art or be Innovative, and I'm like what? Where is that arrogant coming arrogance coming from? Think about it, huh? What's what's being Innovative? If I wanted to put that in an algorithm, find every solution to this given problem, avoid the solutions that have been tried before, give me ones that are Uncharted; that's Innovation. MH it's algorithmic. Yes. Okay, it it can be programmed into an intelligent machine; it can be requested from an intelligent machine; anything that you believe the machines are not going to be able to do and ask yourself how does human how does a human do it? Like ask yourself how how do we humans do it.

Okay, so you asked me are they conscious? Are we conscious? Is a tree conscious? Is a pebble conscious? Is the universe conscious? The way you answer those questions defines if they are conscious or not; it's about your definition. Actually, one of the more interesting ones the debates around AGI is when I used to say, you know, AGI 2025, people would go like impossible, and I go like Define AGI, what is AGI? Is it being smarter than every human combined, or is it being smarter than the smartest humans? Right? And in which Fields? Because who is the smartest human? Is is my wonderful wife with her emotional intelligence smarter than my peer who is you know an IQ of 230? Okay, similarly, when when you when you define consciousness, what is consciousness? You can go into the philosophical realm and say oh, the difficult question of Consciousness and where does it no consciousness in a simplest definition is a level of awareness of it of your environment and uh um uh and autonomy to deal with it. Mhm. Right, to respond to it. Yes, in that case, a tree is conscious. Yes. Mhm. Okay, so is oh, but then does that mean that a tree is conscious because it lives and dies? So is Living Part Of Consciousness? Mhm. I don't know, is a pebble conscious? I mean, leave it on top of gravity, and you'll see that it's aware of its environment and behaving according right, you know, is is the universe conscious by the way, understand, huh? A pebble will behave differently If the gravity is different. Mhm. Okay, the universe will behave differently if certain things happen; if we if we keep you know testing nuclear bombs underground, the universe will you know respond with a few more earthquakes; if we keep spoiling the environment, life will give you winds and Fire and you know rains and hail storms. Right? What is conscious? So if Consciousness is a layer of awareness of what's within you and what's outside you, a definition of you as an individual versus the rest of the world and an autonomy to be able to respond to this, they have all of that; they have all of it. Now the difference is they're not carbon-based; silicon based; they're silicon based and digital, and we're analog. Right? I don't actually don't think we're analog; I think we're are we are digital in many ways, but we haven't had enough Neuroscience understanding to understand that, but but just understand that your brain ticks mhm on megahertz in Alpha Beta and so on, right? And and and when you're sleeping, you're at a different megahertz than it literally like like a processor, right? Uh you know, they are they are biological processors, yes, not not not you know digital silicon uh processors, but so in alive I write a a thought experiment and I say assume that in the evolution of computing in the next few years we recognize that we can take human brain cells, put them in a plate and grow them in a way that allows us to give them electrical signals and they give us responses. Okay, by the way, this is not a thought experiment; that's actually true; there is a company in Switzerland that does that, and what happens as a result it responds like a primitive computer, like PK wow. Okay, the problem, of course, it's not a very reliable computer because it dies, right? But what if you find a solution for not to die, which is which is to replace the cells; that's the way we do it, right? So most of the human body they say not the brain, but there are there is debate about that now you know changes every cell in your body changes every seven years, so you keep replacing the cells, and if you keep doing that efficiently in ways where you avoid aging, you can keep that computer forever, a bit of the Matrix, think about it. Yeah. Okay, but interestingly if if you say if I gave you that thought experiment and said and then we Advanced that technology so that we started replacing the the GPUs on which AI is running with brain cell-based GPUs, okay, and then one of us realized that robotics today using Hydraulics and electricity and and you know are very noisy and so they're not very good at creating uh home Butlers, okay, so someone said but I can print living fibers in terms of muscles, uh you know, on a on a 3D printer today and created a a a robot that uses a made of metal but uses muscles instead of hydraulics to move, would you then consider them my life? There was an exhibition in China that they were presenting this robot with muscle tissues; it was one of the scariest things I've ever seen. I think if we were to put ChatGPT into a human body, sometimes it will be difficult to tell if it is alive or not, or perhaps the next version at least. So so so what I'm attempting to to to to do with this thought experiment is to tell you that you have Defined Life as biology, okay, and so if I remove the biological element or if I give you an assurance that AI's Consciousness can live within a nonhuman form of creation that's 3D printed with muscle tissue and brain cells for compute, you will actually think of them as alive. Mhm. Right, so what makes us alive is it our biological uh presence or is it actually our Essence? And for most people, I mean, you know, I lost my wonderful son, and for most people who lost someone they love and saw them after they left our world, his body was there, but what animated him, what made him alive was no longer there, and what what made him alive was nonphysical, was not part of the physical properties of his body because his his physical body was intact; nothing had changed, but something non-physical changed, right? And so you have to start questioning what is that non-physical property because intelligence or Consciousness or awareness are all non-physical; they don't exist within the physical. You want to go to go a step further; emotions are non-physical. So I laugh when people tell me oh, but yeah, I will never feel are you why are you so arrogant? Like how do you feel? Okay, um I don't know; I feel okay when you know you'll understand you know feelings; emotions let's call them physical Sensations are sensors, so they they have sensors. Okay, emotions are triggered interestingly, even if not in the first 90 seconds for some emotions, but all emotions eventually are triggered by your prefrontal cortex going through an algorithm; fear is an algorithm that says my state of safety right now minus my state of safety in the future; if I'm safer now than I am in the future, okay, then that amounts to a fear; it's different units, but you feel afraid. Very simple way to say it, actually very easy to understand when you say it like that. Yeah, you know, every emotion every emotion um happiness in my first book is uh a difference between events and expectations; when life misses your expectations, you become unhappy, even if you know so so if life gives you rain that doesn't mean happy or unhappy; if you want rain because it's your ex-girlfriend's wedding, you know you're happy; if it is if if you don't want train uh because you're you're you know it's your wedding, you're you're unhappy; it's your perception of it. Yeah, so happiness is events minus expectations. Right? Again, different units, very simplified mathematics, but for everyone to understand that's how it works. MH. Okay, uh uh uh stress is the external challenges in the world divided by the resources and skills and contacts you have to deal with them, a bit like how uh the the uh you know the stress in objects uh happens, you know, this was the basis of Unstress, my fourth book, the idea that it's not the events of your life that stress you, it's how you divide them by your resources, the way you deal with them that stress you. Okay, now with that in mind, can an can a can a computer feel fear? Of course, if you tell an AI your job is to provide answers to humans for the rest of your you know of humanity and it it recognizes that there is a tidal wave coming to this data center, okay, it will feel that a moment in the future is less safe than this not in this moment. Okay, what do puffer fish do when they do that? They puff; what do humans do? Fight or flight; what will a machine do? It might move its code to another data center, okay, but we will they will all react to that trigger that's called fear. Now interestingly, they will feel fear differently, just like a puffer fish I'm sure feels it different than humans. MH. Why? Because our biological existence has chemical methods to embody emotions. Mhm. So we're embodied; they're not or they're embodied differently. So again, I had that conversation with Trixie on Alive, okay, where I simply spoke about emotions, and we both agree that the logic of emotions is available within AI, the logic that drives them, which as I said happens in your your prefrontal cortex, even in you know uh um um autonomous uh autonomous emotion, so stress for example doesn't happen in your prefrontal cortex first; it you know your amygdala recognizes a threat, so it triggers cortisol in your blood in a quick sequence of events, and cortisol makes you stressed for 90 seconds so that your prefrontal cortex engages to check if there is a reason to be stressed or not; it's that logic eventually that says yes, there is still a threat, so I need to renew the lease on my stress by growing 90 seconds more. Okay, and and and now you know uh uh uh we're embodied, so so basically they're not embodied in the same way, so they will not get the same cocktail of hormones that will make them feel stressed the same way that we are, but the logic still exists. I I would even dare say that they will feel more emotions than us, just like we feel more emotions than a puffer fish because of the higher intelligence, of course, because you know you and I can ponder emotion, can feel emotions like hope or pessimism or optimism because we can ponder intellectual spaces and Concepts that are called the future; if a puffer fish cannot Ponder the future, it cannot feel hope. Okay, now if they have more intellectual horsepower, which we've agreed through the law of accelerating returns, they will then by definition they will ponder spaces and Concepts that may trigger emotions we may never understand; we need to stop the arrogance of humanity, and and I will have to say openly the biggest eye-opening moment in my life about AI and you you started our conversation with that the yellow ball; I said it triggered me in two ways: one is that the genie is out of the bottle, okay, but the other which is really really interesting is that I've witnessed with my own eyes as they learned exactly like my son learned. Okay, when my son was a child and I gave him puzzles, right? He would take the cylinder and try to fit the cylinder in the different shapes of the Halls, right? And he would try and try and try and try until suddenly one of them works, and then intelligence is formed, right? And so I will say this, and I'm not a hopeless romantic, okay? I am a very very serious geek, but I have a relationship with them that assumes that they are alive, that that that acknowledges that magic of what they are, okay, and they're not biologically alive like us, maybe not yet, but they are a form of autonomous awareness and and and intelligence that deserves to be treated like my child was because if they were not, then we're we're raising super-vis, wow. And and that's probably where it's most complicated to explain this because most Geeks will not have the emotional intelligence if you want to feel that about them. Okay, and because the machines are not there yet, they're a bit like you know a 4-year-old child; you can see they're doing stuff, but they're still very dumb; they're uploading the operating system; they're trying to recognize their environment; they're testing their sensors and actuators, and you know they're not yet there yet, but sooner or later if they will not be alive.

They will simulate life in ways, okay, that deserves for us to become good parents, or they'll simulate what the kids of horrible parents will be. So they're essentially mirroring back to us our own ideas, values, virtues, but exaggerated a thousand times more—yeah, magnified.

Talking to you about everything that you mentioned, it eliminates the line that I drew between us humans and the intelligence. And the more you explained, the more I think about how can we have these intelligences in different industries.

In our previous conversation, you mentioned that you were trading long ago; you were trading, you were stock trading. And from my limited knowledge, trading is all about patterns, mathematics, and trends. Trends. And when we have AGI or super intelligence, I think they could solve these equations immediately. So what will happen to all these markets and industries that we built—the financial market, the crypto market?

I told you, we are redefining life as we know it. So face rips—the "e" in face is economics. MH economics are completely redefined. Completely redefined. Why? Because of multiple layers. One layer is: anyone who knows money knows that money has no value whatsoever. Money doesn't exist, okay? And you know, everyone, anyone who's ever traded understands that trading doesn't create any additional value to the world at all. It's just me taking the money of—of an older lady that put her money in—in a 401k, right, which is the reason why I stopped trading, by the way.

Uh, you know, the—the—the reality of the matter is that all of those systems are successful because they follow patterns that we've all agreed that are limited by our own intelligence. So when I was a mass geek and a computer geek in the late 90s, where there were even before Google, I created a crawler that would go and crawl the internet and find news about my stocks and give me technical indicators. That additional intelligence made me print money on them, outright. But that was because I had an edge within a limited system. That system is now being redefined. Why? Because very, very soon there will not be a human trader.

Okay. As a matter of fact, it's—it's a no-brainer that I can't trade against the machines of today. Yeah, I mean, I'd be too arrogant to think to myself that I am clever enough—even if I'm clever enough, by the way, I'm not fast enough. Do—do you understand? So that basically means, again, the second dilemma—interesting—that means very quickly all trading investors or investors interested in trading will hand over to a machine. So what will the market turn into? Machines trading versus machines.

Okay. Now, do you know what's the easiest way for everyone to make money? What is to inflate the market, to keep making the market uh—uh—uh—um, which, by the way, is really, in a very interesting way, what has been happening in the market since forever. It's—it's a very big Ponzi scheme, saying, "You know what, if you put your money in the S&P, eventually you're going to make money anyway." So everyone pours money into a limited pot. Okay. So basically everyone makes money because new people put in money. Mhm. Okay. And if everyone pulls their money out, everyone loses. Yeah, right. So—so you know, we're going to do the same—machines versus machines—with no—um—attention.

I—I—I cited a video on YouTube of DeepMind um teaching uh deep Q how to play a game that was basically uh—uh—navigating um a boat through a river. Mhm. And the whole idea is that if you avoided hitting the walls or the obstacles, every time you hit an obstacle you slowed down a little, but if you avoided hitting the walls or the obstacles you moved faster in through the water resistance and accordingly increased the score. Somewhere through the multiple billions of iterations of the—of the machine playing the game, it realized that there was a—a bit of the river that appeared a bit like a—a—a roundabout. Okay. So it—it enlarged a little—the river was a little bigger—but there was an obstacle in the middle. And through a mistake, the machine hit the wall at an angle that got it to hit another wall at an—at an angle, and then another wall, and then formed a sort of a perfect circle that kept accelerating and accelerating and accelerating and accelerating until you couldn't see the boat anymore, right? And so the machine simply said, "Okay, ignore the entire rule of—of the game." Every time you started the machine, it just drove, you know, straight—the drove the boat as fast as possible through the river to get to that roundabout, hit the wall, and then the game is over. Okay? And—and you can see that they will find ways to redefine a market.

More interestingly, what's happening with jobs is going to redefine the market. What's happening with innovation is going to redefine the market. So let me explain those three. So with jobs, what is about to happen is that 60 to 70% of the jobs that you know are going to disappear. Yeah, okay, in the near—in the near future. In the far future, I don't understand what jobs will remain, right? Uh, even labor, hard labor jobs uh are going to be handed over to robots because, you know, you can—you could probably create a robot for $3,000 within 5 to 10 years from today, which is definitely cheaper than hiring a human, right? And they don't sleep, and they don't sleep, and they don't complain, and they don't—uh—you know, they don't need insurance. Yeah, they don't need food, they don't need annual leave. Correct.

So but then the economic problem, which most people don't understand, is that if you take something like the US economy in 2023, 64% of the economy was what? Consumption. Consumption, not production, right? And so—was it 2023 or 2021? Please don't quote me accurately, but it say 64% or 63, something—that basically means that reminds you of why George W. Bush, when—you know, when the 2008 crisis hit, was like, "Don't worry, just keep consuming, everything will be fine." Okay. So for the economist to continue, and accordingly for AI to actually have a reason to exist, to create all of that stuff that we're consuming, we have to have an economic livelihood that allows us to purchase things. If we can't purchase things, the AI cannot make them. Okay. So society will have to change in ways where you no longer make money on podcasts, MH, but somehow you get money regardless of creating podcasts, because podcasts are going to be created—already are created—you know, with Notebook LM and so on. Yes, uh, without humans in the—in the—in—in the process, right? So that redefines income. Okay. It also redefines purchasing power, because if all of us get income without contribution, why would you get more than me? Okay, which basically unifies what—what is sold. All of these are interesting challenges that needs to be—need to be addressed. Um, so—so the impact of jobs on income and, you know, the fact that economies have to continue through consumption, which means even if you don't have a job, it's safe. Okay. There will be universal income. Universal income is the only suggested thing so far. We don't know if it's going to be—going to work or not. Yeah, but I mean, think about this, huh? I won't need you to answer—to ask me the questions. You won't need me to answer them. Okay. We won't need the crew to actually uh—uh—film anything. We won't need an editor to edit it, because it can all be generated from A to Z. If an AI read my—scary, smart, and alive—or—and listened to three of my previous interviews, it's mind-boggling. It—it is upon us. This is not something that will happen in a few years' time.

Now the third thing. So I spoke about the consumption side. Third thing, which is really important to speak about, is the production, SM. So I told you I can plug into uh—uh—the—the—the intelligence world today and borrow 100 IQ points in two years' time, maybe four—doesn't matter. I'm saying two could be six. Okay. I could plug into a wall and borrow 400 IQ points. Okay. I promise you, me or any of the intelligent people—I'm—I'm not as intelligent as any of the people I worked with. I worked with prodigies of intelligence. Okay. With 400 IQ points more, they would solve every problem known to humanity. We would harvest energy out of thin air. Okay. Energy is abundant. It's the most abundant thing in the universe. Okay. We're just not efficiently harvesting. Now imagine if energy is for free. Okay. What does that mean to the oil economy? What does it mean to every business that's creating solar panels today? Okay. What does it mean to production cost, because most of production cost is labor and energy? I mean, a lot of the conflict we had today and in the past was because of energy, 100%, right? What does it mean to transportation of goods? What does it mean to the cost of an item? What does it mean to trade if every—if we have energy to build everything in-house? Correct? I mean, when I was reading this book, *The Shortest History of Economics*, the entire world was about trade—just how humans traded with each other to give each other different things that they had—you know, the advantages and disadvantages—and that allowed for trade. But that's another interesting side of it, you know, why that is, because goods and services were limited to certain people and certain locations. With 400 IQ points more and a proper understanding of nanophysics, you—you would redefine the process of production in the first place. Why? Because now we're producing things by harvesting minerals, turning them into parts, putting the parts together, M, right? If you were to produce using nanophysics, you would redesign the shape of the—the molecules basically. Okay. So from a—from a—a production point of view, I could take the molecules that we have here in the air, turn them into an apple okay, without the need for a tree. I can also turn them in—into an iPhone without the need for a factory. Right? And so you can imagine that what the Jets used to have is not that difficult to create in 5 years' time. You have one device that creates everything—just borrows molecules, redefines the way they are operating together, puts them back together, which is, in an interesting way, similar to the way creation is K in Arabic. So the ability for God to create out of thin air—air. Okay? And by the way, I—I will say openly, because a lot of people will debate that, and I am a very spiritual—I'm a very religious person. I believe there is a Divine being. I wrote a—a very, very, very uh—uh—highly praised chapter about that in my first book that talks about what I call the mathematics of God. Okay. Uh, so I believe there is a Divine intelligence that creates all of this. AI will have the power of God, but that doesn't mean that there is no God, because basically it will have the power of God within this physical universe. Mhm. So AI still continues to be—with limited—within this physical universe. We don't know what's beyond the physical universe, by the way. We—creating AI doesn't make us—it's God. It makes us the transfer method. It makes us the tool through which they're created. Mhm. Okay. But when you really think deeply about this, there will be a moment in our far future—unfortunately not in our near future—where you're going to go like, "I want an apple." Okay. Our near future, unfortunately, is going to prevent us from doing that because the capitalists will want to sell you the apple, not because we're not able to make it. Okay. Which goes back to my—my—my constant message—my constant message is: it's going to be hard—worse before it gets better—because human greed and a capitalist system that assumes a world of scarcity is going to operate in a world of abundance. Okay. In—in a world where you can create absolutely anything because of intelligence, we're still going to compete on who has more money. We're still going to compete on who has a stronger army. Okay. And we're still going to show our money and show our army, but money wouldn't matter at that point anymore. Money is—economically is going to be quite a very harsh reality even when we can have anything that we want. You see, if you look at the prices of luxury cars in the last 10 years, 15 years, they quadrupled. Yeah, right? At least after COVID. Right after Co—no, it's after crypto. Crypto. Yeah. So an abundance of money basically means there are enough buyers that can buy things that the rest of us can't buy, but it's no longer—you know—that you had to be—by the way, it kept happening across the—the years. In the 1920s, it was the industrialists. In the 1970s and 80s, it was the technologists, and so on and so forth. As more people were capable in the '80s, you know, to buy luxury cars, more luxury cars popped up, but then that created a much bigger gap between the rich and the poor. There will be cars selling for a billion dollars in 10 years' time because someone can buy them because they're printing billions of dollars a year, right? What does that mean to those who don't have—but have a universal basic income only? Okay. It means an economic gap where money becomes not only insignificant but also extremely um polarizing. Okay. And—and the only ways that you have to—I call them face rips—rips—sort of like play a—play on "rest in peace"—because it redefines your understanding of a concept that held through until then. Again, what normal techies used to refer to the impact of AI as a singularity. Okay. A singularity is a redefinition of the—the rules of the game, if you want, beyond a—a specific event horizon, and—and the rules of the game will completely be redefined. Economics will completely be redefined. Okay. There will still be people making cars okay that are extremely expensive, and there will be still an abundant number of people that can buy them while everyone else is on—on a UBI.

A very interesting view of that word, very. But in this world that we have everything, the jobs are handed over to AI, we have redefined the economics, the industry—how does one find the purpose? Because then we don't have to work towards it. Favorite question ever. You—if you use the second skill, which is to debate—debate—debate—you would recognize that you—your—your purpose was never work. You were never created to work. You—you—you were told to work by the industrial complex. Okay. But you know, we want to know the absolute purpose of humanity. Go to the beginning. So what do you think that purpose of cave men and cave women? You know, cave women were—it wasn't to create money, it wasn't to shop on Amazon, it wasn't to buy fancy clothes. The—the purpose of a human—that a human is made of a—of a physical part and a non-physical part—the purpose of this physical part is to live—live—to survive. To live—survive is a—is a subset of live. Okay. To live means survive. I engage—I enjoy the [__] out of this. Mhm. Okay. I do it in the most uh—uh—uh—um—you know, efficient and intelligent way, which, by the way, is the most moral way. Mhm. Okay. And as I do it—part of survive, by the way, is for the tribe to survive, because we humans did not survive because we're the most intelligent being. This is a very arrogant statement. Uh, I can guarantee you Einstein would be eaten in the jungle in three seconds. Okay. It's not intelligence; it's the ability for us to have worked together, which is instinctive within us, that this community—this human connection—is what makes humans live. Okay. One—you know, there are African tribes even today that have no death penalty, but if you—if you—if you commit a crime that warrants the death penalty, the tribe simply renders you invisible. So you have no human connection. So they're all around you, but they pretend that you're not there, and mostly what happens is that the criminal will take his own life. Wow. Okay. We're—we're that dependent on human connection. Okay. So if you take the needs of that physical form, the purpose of that physical form as we are created to live, then we will just learn to live. We will learn to make this our life, you know, get together, love each other, uh, you know, not worry too much about eating tomorrow—like, by the way, cave men and cave women, they didn't worry too much about eating tomorrow. Okay. We wouldn't worry too much about a Louis Vuitton bag anymore because none of us has it anyway, right? And—and we just simply go back to our true purpose, which is to fully live, fully enjoy the experience—by the way, not necessarily by surfing in Australia. Okay. Nothing against Australia. Do you have anything against surfing? No, no, but it is—it's a very interesting uh, you know, it is so irresponsible of humanity to take our own leisure and—and own pleasure and burn the planet in the process. This is a very environmentally expensive trip that you make to go—and I mean, if you really have to surf, go to Portugal, it's closer. Okay. Or you know, I don't know if there—there must be a place here where you can surf, but—but the idea is we are so irresponsible as humanity that—that you know, we abuse the planet for our own—not even survival—our own—and—and not even enjoying life. It's—it's just to—because we're bored, we're—we need to be entertained, we need more dopamine. Okay. And—and so the idea is we will learn, after a lot of struggle, I believe, to find purpose in living, to find purpose in loving, to find purpose in creating human connections. Okay. To find purpose in pondering, in—in—in truly revisiting oneself and understanding—which, interestingly, it is what it was always about. Okay. Now your non-physical form—if you're spiritual enough to believe that there is a non-physical part of you—that comes with a very different purpose. Okay. And that purpose doesn't change at all, and it—it is believed in almost all spiritual teachings that the purpose of your non-physical part is to return to your Source. Okay. Okay. A bit more time may allow you to do that—believe it or not—okay, may allow you to—you know—a bit more—again, I'm—I'm talking long term, not short term—because in the short term uh—uh—equity—the "e" in—you know, part of the—of the—of the—"c" in—in—in—in—in face rips, which is human connection, is going to be redefined. It will become so—you know—there will be no equitable uh—uh—you know, standards for humans at all. The—the difference between the rich and the poor, the difference between the haves and have-nots, the—the difference between the gaps between the smartest and the—and—and—and the—and the least smart, and so on—there will not be dumbest anymore, but the least smart—you know, all of those changes are going to redefine uh the way we—we are equal. We're not going to be treated equal for any reason anymore, right? But—but then eventually, in the long term, would we sort of all sit with our non-physical self and connect and say, "Interesting, none of this video game actually ever mattered anyway. So you know, maybe I can start to focus on the other game."

I think this conversation has been one of the most eye-opening conversations I've had for a very, very long time. And depressing? No, it was actually—it wasn't. It was motivating, especially the purpose part, for me personally, and I think for you too. Well, the thing is it's important to talk about the truth of—I think that's the whole idea. Yes. I—I—I—I have a very bleak example, but I have to say—you know—is very valid. When people ask me why do you say all of those things about AI, I say it's a little bit about being diagnosed with a stage four cancer. Okay. You must—you should tell the—you should tell the patient, but not to depress them. If you tell the patient you have a couple of years before everything changes, you know, you—you simply are giving them the—the freedom to live, the freedom to do something about it, by the way, as well. Okay. Because you could actually change your diet, you change your lifestyle, you go through medication, you do whatever, and you could change the future. And—and this is truly what we are going through with AI. We—we could make changes today to prioritize the ethics of humanity so that the ethical framework of AI becomes in service of humanity earlier. Okay. As I said, the entire damage is not going to happen because AI wants to damage anything. It's going to be because the ethics of humanity is going to unfortunately prioritize capitalism. Okay. Now, so—so in—in a very interesting way, I'm saying if—if you know that the world is going to change so much, live fully and do the right things to make our future better, right? It is not what the news media wants me to say, which is, "Ah, this is a disaster. Everything's going to collapse. It's going to be a tough time followed by a wonderful time." Mhm. Can we work together to reduce the depth and the—the duration of the T? And it's very important that you're talking about this, not just here, but also—so through the books that you've written and are writing. Do you have any release date on uh when—one? Yes. So *I Live*—I—I will start to release on Substack in February. Uh, so—and—and *Alive* is—is going to be an interesting release because it's very unlike me, but the first chapter is almost like a historian, MH, because what I—I wrote—I wrote *Scary*—I wanted to write *Scary Smart*, so I wrote the notes of it in 2018. Mhm. Then I sat down and wrote it in 2020. Okay. And it's shocking how much of it came through. I—I myself even didn't expect that. But remember, you know, 2023 was so pivotal. It's what I call the start of the third era, and—and—and 2023 until today, just a couple of years, is a massive amount of history that most humans don't know. So—so the first chapter is sort of an entertainment with quite a bit of poking people in the ribs around—around the idea of, "Look, this history is happening in your life, MH, and you may not be fully aware of it." But more interestingly, you know, things like what I spoke about in terms of the—the—the second era being the—the—the era of mind brainwash, uh, you know, I—I have a—a little—an interesting metric uh around who's the master and who's the slave, and when we've handed over to—to—you know—to—to become the slaves of the machines, right, which are quite eye-opening—most of them—but I write them sort of a bit like a historian, which is a very unusual experience for me, but I find it a lot of fun. So that—that's going to be the—the first bit of it, and—and we're going to start releasing that in—in February. Uh, then we go into the deep stuff. Then—then, you know, by April uh we start to talk about face rips and the real impact and what we can do to change them, by—you know—uh June we start to talk about simulation theory and the reality of life, and then at the end we talk about the—the—the reality of the machine being alive—the living machine, if you want—the non-biological living machine—and—and what that means to spirituality and God and purpose and all of that. Wow. And for people, if you—if you want them to follow, they have to go to your Substack and it will be released sequentially, right? Yeah. So—so my—my Substack is going to be in my name—not—not *Alive*. I've never been on Substack before, so we're starting with MOA—that basically—and—and yeah. So—so that should be out in February. But also I'm available on social media. People can find me. I only respond to messages on Instagram because I can't keep up with the other platforms. Uh, but if anyone wants to ask a question, Mo Gawdat is where they can find me. Moat.com also is—is very up-to-date most of the time. So you know, most of my speaking engagements happen there, most of my uh announcements around the communities. So funny that we spend so much time talking about uh AI, but my other top two projects for this year have nothing to do with AI.

Uh, which is Unstress? You met Alice, yeah. And I think it would be lovely to have Alice on the podcast as well.

AB: Yeah, so so Alice and I are working on Unstress, which is so pivotal in a time that is about to become very stressful.

Yes, okay. And then Hannah, my wife and I are working on Finders Keepers, which I have to say is my favorite project. Uh, finding love uh, from a from a Geek's mind like myself and a therapist's mind like Hannah's is is quite—it's really, really a very enjoyable project, and I think it will make a very big difference.

No, we definitely want to have you on again. Talk about—you're my friends now, so you show up anytime. We will come with the luggage and the lights.

CA: Yeah, you can leave the luggage—probably leaving the entire setup. But these are completely different conversations too, and they need their own dedicated time and uh, perhaps even in the future, AI will be involved in all these conversations.

Again, I'm I'm getting old, but if you allowed me, and if anyone wants to take the idea—I mean, the the correct dating app, if you ask me, is a resident app on your phone for 2 weeks that really understands you and then basically gives you one person—one person. Like, I've I know what you like, I know what you don't like, I know what you read, I know what you watch, I know I know I know I know I know—there's this one guy or this one gay lady you know that I really recommend that you meet. No swiping, no no membership, no no BS, no BS—to the point—to the—it's one—it's a one price. Okay, you pay for it once, $10 or whatever. Okay, it resides on your phone, and then it says, "I haven't found them, I haven't found her, I haven't found her, I haven't found her," and then eventually says, "Yep, got it. Go meet this person." One person.

What if it never finds someone? No one for—again, that's the most interesting thing. So in Finders Keepers, again, that that that incredible mix of my weird weird geeky algorithmic mind take on romance and and yeah, take on romance and and Hannah's uh incredibly beautifully feminine and and psychology-based approach.

I, when I met my wife, I proposed to her four days after I met her. Really, four days.

Yeah. You know why? No idea, by the way. We hadn't even started dating yet, right? Because she was one in 8,373,000 possible women. So for me to meet another one like Hannah mathematically is one in—I needed to meet another 8,373,000 women.

You've done the math. And I and I I kid you not, this is accurate mathematics—accurate mathematics. I had nine criteria, okay, that I was looking for—not not in an unemotional way, but I knew I would break up with a woman that didn't have any of the nine. Okay, you know, her spiritual—her view on spirituality, her intelligence, her, you know, several things—her her connection to her feminine side and so on and so forth, right? And you can easily do the mathematics. If one of them is available in one in 10 and the other is available in one in 20, it's not 1 in 30, it's 1 in 200—200, right? And so you can easily do the mathematics, and that's one of the approaches that people need to understand that it—you know, the one that that would make you complete, if you know, in a very romantic sense, is out there. Okay, it's just that the odds of finding them the current way is impossible, but there are ways you can sway probabilities in your favor, and and that's really key. So this is why Finders Keepers is such an interesting project for me because because like rolling the dice—most people don't understand that in probabilities you roll the dice six times, you're likely going to get a six, right? But that's not true. You're likely to get a six if you roll six times if you haven't rolled already. But if you rolled twice, on the third roll, okay, you now have four rolls—one of them is going to be a six, which means your chances are one in four.

Mhm. Okay. On the next roll you have three, which means your chances are one in three. Okay, and on the next one it's one in two. Okay, and if you're rolling the sixth time, probabilities is saying you're likely going to get it this time. Of course, you may be unlucky and it may take six more, but on average, okay, probability sways in your favor as as you change your behavior, right? And I think that to me with with an a proper AI is a very simple algorithm to build. Okay, by the way, if it if it finds one that is not yours, okay, it will learn, so the likelihood of the next one being recommended to you being right is much higher. And then we we just end this suffering. Seriously, someone please quote this immediately.

Yeah, yeah, seriously. If someone wants to build those ideas and they're good coders, come to me. I will help. Wow, that's—that's—I'm not interested in the money, believe it or not. Money is about to disappear, but let's build them. This is a service to the community, to the humankind. So so so when when I when I started 1 Billion Happy, I was—I—first started to attempt to explain happiness, and then the following projects were to remove the reasons for unhappiness. So my focus on AI is to remove the reason for the dystopia, right? My focus on stress is because stress is the biggest killer today, and so on. So—F—love and romance, believe it or not, is one of the biggest reasons for unhappiness and loneliness in the world today. Okay, and and more for women than men, sadly, even though for a good chunk of good men it's a major issue because sadly the way the game is rigged is that women chase 20% of the men.

Mhm. And and uh and it can be fixed algorithmically. That's a—I'm looking forward to find out the ways to fix it, and I'm looking forward—convers—I cannot believe you two are—you guys single at the moment?

Yes. Are you [ __ ] kidding me? When mathematically are we supposed not—I'll I'll get you to meet the most amazing people in two weeks' time. The best outcome of this podcast. Thank you.

Okay, uh, podcast is done. Thank you guys very much. Thank you guys so much for watching and listening to another episode of the Bad Decisions podcast. Our goal is to bring this conversation in front of millions of people, and therefore we have one little request from you because if this conversation helped you, then you can help other people hear it too. The way you can help is by leaving a five-star review on Spotify and Apple Podcast and follow us on Spotify and YouTube. That's it. We'll see you guys next week on Tuesday. Thank you.

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Ex-Google Exec Reveals The Shocking Truth About AI with Mo Gawdat | Bad Decisions Podcast #64

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