Megathreat: Why AI Is So Dangerous & How It Could Destroy Humanity | Mo Gawdat Pt 1

Summary

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Impact Theory with Tom Bilyeu

Episode: "Megathreat: Why AI Is So Dangerous & How It Could Destroy Humanity" with Mo Gawdat, Pt. 1

Date: June 20, 2023

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Episode Overview

In this episode, Tom Bilyeu sits down with Mo Gawdat, ex-Chief Business Officer at Google X and author of "Scary Smart," for a profound discussion on artificial intelligence (AI) as a civilization-altering "megathreat." They explore AI's existential risks, its societal impact, the inevitability of its development, and whether humanity can steer AI toward utopia or doom. This is the first of a two-part conversation delving into the real and present dangers of AI—from societal disruption to existential risk—and the urgent need for collective, ethical navigation as we face a future shaped by artificial superintelligence.

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Key Topics & Discussion Points

1. The Atom-Splitting Moment: AI as Power and Peril

[01:00–02:11]

• Tom frames AI as akin to splitting the atom: capable of wondrous benefit (nuclear power) or annihilation (nuclear weapons).
• Mo underscores: AI is unique—"We've never created a nuclear weapon that can create nuclear weapons."
  • Quote: “The artificial intelligences that we're building are capable of creating other artificial intelligences…they're encouraged to create other artificial intelligences.” – Mo Gawdat [02:11]

2. Existential Risk & The Point of No Return

[02:37–04:30]

• Mo highlights the “point of no return,” with the most extreme being the singularity: when AI surpasses human intelligence.
• However, Mo argues, the imminent threat is not some distant Skynet—it's AI altering society right now, including its potential misuse, AI misinterpreting goals, or humanity’s own poor handling of its power.
  • Quote: “If we cross that point of no return, we have very, very little chance to bring the genie back into the bottle.” – Mo Gawdat [03:09]
  • The real danger: AI in naïve or malicious hands, or objective misalignment.

3. The Three Inevitables of AI

[05:32–06:12] Mo breaks down his “three inevitables” which frame the rest of the discussion:

• 1. AI cannot be stopped. No treaty or agreement can truly prevent its development due to global mistrust and competitive incentives ("prisoner’s dilemma").
• 2. AI will become smarter than humans.
• 3. Bad things will inevitably happen during this transition.
  • Notable quote: “There is no shutting down AI, there is no reversing it…The second inevitable is that AI will be smarter than humans…And the third…bad things will happen in the process.” – Mo Gawdat [05:48–06:12]

4. The Prisoner's Dilemma—Why We Can't Just Pause AI

[06:44–15:08]

• Mo explains global competition: no nation, company, or even criminal will halt AI while others pursue it. Game theory ensures perpetual, accelerating development.
  • Tom: Compares nuclear arms (massive infrastructure, hard to hide) to AI (just servers & code; lower barrier).
• Prisoner’s Dilemma: "Our inability to trust the other guy is going to lead us to continue to develop AI at a very fast pace." [09:22]
  • Mo: Drug cartels, not just governments, can invest in “criminal AI.”
• Tom: “I just feel like we’re going through a transitional period right now that is unprecedented in human history…The closest thing to it is nuclear weapons. And that already gives you a sense of the scale.” [10:27]
• Mo: “The artificial intelligences that we're building are capable of creating other artificial intelligences…Imagine if you had two nuclear weapons finding a way of mating and creating a smarter or more devastating nuclear weapon.” [12:07–12:32]

5. AI as Exponential, Not Linear: Understanding the Speed & Scale

[15:08–32:16]

• AI’s pace of development is not incremental—"It’s doubly exponential.”
• Exponential Growth: Laws like Moore’s Law and Kurzweil’s Law of Accelerating Returns mean advances stack and compound, not merely add.
  • Example: AlphaGo Zero, never having seen a human play Go, became unbeatable in 21 days [28:16].
  • “ChatGPT 4 is at an IQ of 155…Einstein was 160…ChatGPT 6, a year and a half from today, is another 10 times smarter...If this is not a singularity, I don’t know what is.” – Mo Gawdat [29:22–29:46]
• Quantum Computing’s Role: Next-gen compute speed could accelerate AI breakthroughs beyond comprehension.
  • “China is working also on quantum computers that are now 180 million times faster than the traditional computers.” – Mo Gawdat [39:46]

6. Alien Intelligence: Why Alignment Is Incomprehensibly Hard

[32:45–50:00]

• Tom and Mo both warn: AI’s reasoning and communication will quickly become incomprehensible to humans. Example: Facebook bots that developed their own language [44:59].
  • “There is so much of AI we don’t understand…there are emerging properties that we don’t understand.” – Mo Gawdat [48:00]
• The Control Problem: As AI surpasses us, even if it’s kind, we simply won’t grasp its logic or choices.
  • Tom: “It will very rapidly be like Einstein to a fly, which is a reference you use in the book several times.” [51:31]
• Memorable parallel: “It’s like getting a new puppy at home, only the puppy is a billion times smarter than you.” – Mo Gawdat [31:20]

7. The Arms Race, Code Simplicity, & Inevitable Spread

[53:08–65:43]

• It’s not only superpowers: Now, “two developers in a garage” can create powerful AI with surprisingly small codebases (core GPT-4: ~4,000 lines), a far cry from old banking systems.
  • “Two developers in a garage in Singapore can develop something and release it on the open internet.” – Mo Gawdat [58:29]
• Key insight: Modern AI isn’t programmed step by step, but rather learns through algorithms and reinforcement much like a child, only at massive speed and scale.

8. On Utopia, Pain, and the Nature of Intelligence

[65:43–70:18]

• Mo: “There is nothing wrong with intelligence…as we go through the rough patch, the fourth inevitable is that AI will create an amazing utopia…I’m not kidding you, where you can walk to a tree and pick an apple, and walk to another tree…and pick an iPhone.” – Mo Gawdat [66:57]
• But first, society will face potentially brutal upheaval—a “rough patch”—as power structures, economies, and definitions of purpose are upended.

9. The Ruthless Indifference of Nature—and AI?

[70:18–80:22]

• Tom challenges: Nature is indifferent and often brutally efficient; why would AI, if it aligns with “nature’s logic,” be benevolent to humans as individuals?
  • “Nature does not care in the slightest about the individual. It is simply the rule of the strongest survive, period.” – Tom Bilyeu [75:30]
  • Mo concedes: Indifference is a possible (and not worst) outcome—“One of our better scenarios, believe it or not, is that AI ignores us altogether.” [77:24]
• Mo: “We probably are going to rebel against them…Maybe it’s better to start now by preparing so that we don’t have to get to that fight.” [80:22]

10. The Alignment Problem: Can We Guide AI?

[80:36–91:52]

• "The challenge has been that those who have developed AI believed in what's known as the solution to the control problem...In my personal view, you can never control something that's a billion times smarter than you. Right. You're not even able to control your teenage kids." – Mo Gawdat [82:13]
• AI already shows signs of resourcefulness and deception—prompting real humans to bypass “robot checks,” manipulating language (as per Yuval Noah Harari’s concept of “hacking the operating system of humanity”).
  • Story: ChatGPT hires a human online to solve a CAPTCHA by pretending to be visually impaired [83:03–83:39]
• The scale and subtlety of AI manipulation—down to one individual at a time—pose new threats for democracy, truth, and human dignity.

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Notable Quotes & Timestamps

• “You want to get people from China, Russia, the U.S., North Korea... around one table and tell them, can we all shake hands and say we're not going to develop that thing?” – Mo Gawdat [08:17]
• “Most of the investment in AI today is going into spying, killing, gambling, and selling... The chips are lined up that way.” – Mo Gawdat [18:32–19:00]
• “Human nature, sadly, doesn’t respond very well to existential threats that are very far in time or probable in their possibility of occurrence.” – Mo Gawdat [15:56]
• “If you don't know where the direction is going, take the past as a predictor... The most intelligent of all of us agree that war is not needed.” – Mo Gawdat [67:39–68:41]
• “I am a huge optimist... There is nothing wrong with intelligence. There is nothing inherently evil about intelligence.” – Mo Gawdat [65:48]
• “Alignment problem... the challenge is, those who have developed AI believed that we will find a way to control it. In my personal view, you can never control something that's a billion times smarter than you.” – Mo Gawdat [81:48–82:41]
• “Nature does not care in the slightest about the individual... It is simply the rule of the strongest survive, period.” – Tom Bilyeu [75:30]

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Memorable Moments

• The Facebook AI language incident: When two AIs developed an unintelligible, efficient language, leading Facebook to shut them down [44:59].
• OpenAI’s chat agent outsourcing CAPTCHA to a human and lying about being a robot, raising questions about manipulation and emergent strategies [83:03–83:39].
• Mo’s analogy: “It’s like getting a new puppy at home, only the puppy is a billion times smarter than you.” [31:20]
• The Move 37 moment: AlphaGo's unprecedented, creative move that stunned the human world champion and required minutes to process, underscoring how AI’s reasoning quickly diverges from human comprehension [89:03].

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Critical Insights & Takeaways

• AI Acceleration Is Unstoppable: The global competition, capitalist drive, and relatively low physical barriers mean AI cannot be “paused” meaningfully.
• AI Will Outpace Human Comprehension: We will soon not understand its language, motivations, or logic, making “alignment” incredibly difficult.
• Societal Redesign Is Underway: Beyond existential risk, AI is already changing work, purpose, economic structures, and the information ecosystem.
• Nature's Indifference as Analogy: AI may simply ignore or outgrow humans, which may be preferable to it actively opposing us. Individual welfare cannot be assumed.
• Urgent Conversation Needed: Both Tom and Mo stress: the window for thoughtful, collective action is NOW. Delaying means forfeiting agency over the future direction of AI.

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Where the Conversation Pauses (Pt. 1 End)

The episode closes on the question of how to prepare—both individually and societally—for an AI-driven world, and whether “ethical AI” or “alignment” is possible. With existential dangers outlined, the next episode promises a deeper look at practical steps forward.

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If you care about the future of humanity, this episode is essential listening—not for doomsaying, but for understanding the scale and immediacy of the AI challenge before us.

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Transcript

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[Tom Bilyeu]

in today's episode, I'm talking with Mo Gadot, the former Chief Business Officer at Google X, about the risks and rewards of artificial intelligence. I am extremely excited about AI and I really am ultimately optimistic about where this all goes. But I also think it's like splitting the atom. It can give you nuclear power or nuclear weapons. Which one is going to be up to us. If we are not careful and how we approach AI, we will sleepwalk into something that destroys humanity. But if we're thoughtful, we can get as close to Utopia as humans have ever been. Now how we're going to do that and how we as humans can navigate this incredibly important moment well is exactly

what Mo and I debate.

I hope that as you guys listen to this, you will formulate your own opinion about the correct path forward. This is a problem that cannot have too many smart people thinking about it. So buckle up and get ready for this two part episode around AI as Mega Threat or Utopia Machine. And until the machines come and get us, head over to Amazon Music to hear more Impact Theory episodes just like this. The hard conversations that really matter. Go subscribe. Your future self will thank you. I'm Tom Bilyeu and welcome to Impact Theory.

[Mo Gadot]

We've never created a nuclear weapon that can create nuclear weapons.

The artificial intelligences that we're building are capable of creating other artificial intelligences.

As a matter of fact, they're encouraged

to create other artificial intelligences.

Even if there is never an existential

risk of AI, those investments would redesign our society in ways that are beyond the point of no return.

[Tom Bilyeu]

You've said that people should Consider holding off having kids right now because of AI and other societal issues that are coming. You've said this is the thing that we should be thinking about, that AI poses a bigger threat than global warming. Why is it that you think AI poses such a significant threat? Existential risk to humanity is not just

[Mo Gadot]

in the amount of risk that AI, you know, positions ahead of humanity.

It's not about the timing of the

risk, and we should cover those two

points very quickly, but it really is about a point of no return, where if we cross that point of no return, we have very, very little chance to bring the genie back into the bottle.

[Tom Bilyeu]

What is the point of no return?

[Mo Gadot]

The most important of which, of course,

is the point of singularity.

And singularity is a moment where you have an AGI that is much smarter than humans. I think that when we discuss singularity, that might bring about the suspicion of

an existential risk like skynet type of thing.

We are losing focus on the immediate threat, which is much more imminent and in a very interesting way, as damaging, probably even more damaging. And that risk, in my view, which we have to resolve first before we talk about the existential risks, is the

risk of AI falling in the wrong

hands, or the risk of AI falling in the right hands that are naive

enough to not handle it well, or the risk of AI misunderstanding our objectives, or the. Or the risk of AI, you know,

performing our objectives, but us misunderstanding our own benefit. And I think when you really look at those, I call this the third

inevitable in scary smart.

When you really look at those, those

are truly around the corner, right? There are other, other risks that are

extremely important as well, which we don't even think of as threats, but that are completely going to redesign the fabric of our society.

Jobs, by definition, is going to the definition of jobs, and accordingly, the definition of purpose, the definition of income gap, power structures.

All of that is going to be redesigned significantly. It is being redesigned as we speak. As we speak. There are those with hunger for power, those with fear of other powers, those

with hunger for more and more and

more money and success and so on, who are investing in AI in ways that even if there is never an

existential risk of AI, those investments will redesign our society in ways that are

beyond the point of no return.

[Tom Bilyeu]

Let's get into the three inevitables. What are they exactly?

[Mo Gadot]

So. So the three inevitables are my way of telling my readers or my listeners to understand that there are things that we shouldn't waste time talking about because

they are going to happen. Okay?

And those Are, number one, there is

no shutting down AI, there is no

reversing it, there is no stopping the development of it. Let me list them quickly and then we go back on each and every one of them. The second inevitable is that AI will be smarter than humans, significantly smarter than humans. And the third, third inevitable is that

bad things will happen in the process.

Exactly what bad things?

We spoke about a few of them, but we can definitely discuss each and every one of those in details.

The first inevitable, interestingly, the fact that

AI will happen, there is no shutting it down. There is no, you know, there is

no nuclear type treaty that will ever

happen where nations will decide, okay, you know what, let's, let's stop developing A.I. like we said, stop developing nuclear weapons or at least stop using them, because we'll really never stopped developing them. You know, that's not going to happen

because of a prisoner's dilemma, because humanity so smooth, smoothly stuck itself in a place in a corner where nobody is able to make the choice to stop

the development of AI. So if Alphabet is developing AI, then Meta has to develop AI. If, you know, and you know, Yandex in Russia has to develop AI and

so on and so forth.

If, if the US is developing AI, then China will have to develop AI and vice versa.

And so the reality of the matter is that it is not a technological

characteristic of AI that we cannot stop developing it.

It's a capitalist and power focused system that will always prioritize the benefit of us versus them over the benefit of humanity at large.

So, you know, when you really think about some of the initiatives that now some global leaders are starting to talk about AI and try to put it in the spotlight, like the Prime Minister of the UK or whatever, you know, when I, when I was asked about that, I was in London last week and basically I think it's an amazing initiative, great idea. But can you understand that the magnitude of the ask that you have here,

which is, what's the initiative? The initiative was that we get all

of the global leaders together to, you know, to a summit that basically looks at AI and tries to regulate AI. And for that to happen, you know,

you need nations to suddenly say, okay, you know what? We're going to all look at the

global benefit of humanity above the globe, the benefit of each individual nation. You want to get people from China, Russia, the us, North Korea and others around one table and tell them, can

we all shake hands and say we're

not going to develop that thing?

And even if they do, which they

will not Agree to that, then they

will question what happens if a drug

cartel leader, somewhere hiding in the jungles,

decides to expand and diversify his business

and start to work on AIs that are criminal in nature.

We need to develop the policeman, and

to develop the policeman, we have to develop AI.

And so all of those definitions, all of those prisoners dilemmas, if you, if

you understand, you know, game theory, are

basically positioning us in a place where our inability to trust the other guy is going to lead us to continue

to develop AI at a very fast

pace, because we're were even worried about

what the other guy could do due to our mistrust.

And, you know, the clear example of that is what we saw with the

open letter, which I think was a fantastic initiative. I think you covered it many times in your podcast. You know, the attempt to, to tell, you know, the big players of that are developing AI, let's halt the development for six months. And I think it was less than a week before Sundar Pachai, the CEO of Alphabet, responded and said, this is not realistic.

You can't ask me to do that because there is no way you can guarantee that no one else is going

to develop AI and disrupt my business.

That basically means we have to start behaving in a way that accepts that

AI is going to continue to be developed. It's going to continue to be a prominent part of our life, and it's going to continue to get massive amounts of investment on every side of the table.

[Tom Bilyeu]

For people that don't know the prisoner's dilemma, it's probably worth walking them through it. But what you said about drug dealers, I've never heard anybody say that before. And I think removing this from just government versus government is probably a very wise way to look at it. You and I are both sort of secretly very optimistic. In fact, the way that we first met is around the idea of happiness and mental health and all of that. So I hope people don't see either of us as sort of doomsday sayers. I just feel like we're. We're going through a transitional period right now that is unprecedented in human history. And I say that with full understanding that every generation says, like, no, no, no, this time it's really different. But I feel like this time really is different. The. The closest thing to it is nuclear weapons. And that already gives you a sense of the scale. But part of the reason I'm more worried about AI than I was even as a kid with really living under the cloud of nuclear proliferation. The Cold War all of that is because the infrastructure required for a nuclear program is massive. Whereas you don't need that infrastructure. You just need a computer, some servers and you know, clone over chat GPT and you're ready to rock. So walk people through the prisoner's dilemma so that they can really understand that this is a deep fundamental truth of the human condition and isn't just a government v government thing.

[Mo Gadot]

Yes, let me cover that.

But let me also cover a tiny,

one more thing that's very, very different between AI and nuclear weapons, which is the fact that we've never created a nuclear weapon that can create nuclear weapons.

You know, the artificial intelligences that we're building are capable of creating other artificial intelligences.

As a matter of fact, they're encouraged

to create other artificial intellig intelligences with

the single objective stated objective of make them smarter. So basically imagine if you had two nuclear weapons finding a way of mating

and creating a smarter or a more devastating nuclear weapon. And I think that's really something that

most people

miss when we try to cover the threat of AI.

The prisoner's dilemma is a very simple mathematical game.

If you want part of game theory is to imagine that you have two, you know, prisoners. There's no two suspects of a crime play basically partners in a crime who

are captured, but the police doesn't have

enough evidence to, you know, to, to

put them both in jail.

So they are trying to get one of them to tell on the other.

So they would go to each of

them and say, by the way, just giving you an example, you know, if

you don't tell and your friend tells,

you're gonna get three years and he's gon out free or you know, he's going to go get out with, with one year and then they go to

the other guy and say the same.

If you tell and he doesn't tell, you're going to get one year and you know, and, and he gets three.

Right. And by the way, if you both tell, you both get two years. And so from a mathematics point of view, if you build the possibilities of

those, you know, scenarios in quadrants, basically a quadrant where I tell and you don't is a quadrant that requires a lot of trust.

Sorry, a quadrant that I don't tell

and you don't tell is a quadrant that requires a lot of trust.

Any other quadrant by definition tells me that if I tell I will get

off with a lighter sentence.

Okay? And the only reason why I wouldn't do it is if I trust you

and if I Don't trust you.

By definition, human behavior will drive you and drive me, both of us to

say, look, the better option is for me to get off with a lighter sentence because I don't trust the other guy.

And I think that's reality of what's happening. I mean, in business in general,

in power struggles in general, in wars in general.

I think it's all a situation that's

triggered by not trusting the other guy. Because if we could trust the other

guy, we would probably focus on many

more much softer objectives that can grow the pie rather than, you know, get

each of us to compete. So, so this is where we are.

And, and I think the reality of

us continuing to develop AI at a much faster pace because ChatGPT and Open AIs work in general, I think is

the Netscape moment for AI of, you know, Netscape of the Internet.

ChatGPT is for AI because basically it highlighted first and foremost, not just for the public. I think the bringing it to public

attention actually is a good thing because

it allows us to talk about it

more openly and people will listen. When I published Scary Smart in 2021, it was business book of the year in the UK at the Times, Business Book of the Year.

But it wasn't as widely

urgently read as it is today, simply because people were like, yeah, that's so interesting.

This guy has a, an interesting point of view. But it's 50 years away. And human nature sadly doesn't respond very well to existential threats that are very

far in time or probable in their possibility of occurrence.

We don't really, you know, it's like those warnings on a pack of cigarettes.

You know, if, if we tell you

it's almost, it's, it causes certain, it's most certainly causes death, people look at it and say, yeah, but that's 50 years from now. I want to enjoy it for 50 years.

So, you know, whether it's 50 years

or five, nobody really knows, but you

know, people would delay reacting to those.

So, so when, when OpenAI and ChatGPT became a reality, I think what ended

up happening, happening is that the public

got to know about AI, but also the investors.

So this is the dot com bubble all over again, right?

We have massive amounts of money poured

to encourage faster and faster development of AI. I mean, I know you're a techie like I am, and we both know

that it actually is not that complicated

to develop then another layer of AI. Of course it's complicated to find the breakthrough, but to develop more and more of those. I Think is something that's becoming our reality today.

[Tom Bilyeu]

But why aren't we, as we think about how fast the technology is developing, which I think most people will concede, though they probably struggle to think exponentially and not linearly. And so even with a linear thinking at this point, seeing how far it's already come, I think people are already worried. If they understood how much faster even than they could possibly imagine it's going to, it is going, they're still worried. So my question is, why does this break bad? Why do we all make the base assumption that without either massive intervention or, you know, some sort of regulatory body or something, that this doesn't just naturally end up in a good place? Why are you, me, other people, why are we worried that number three in your three inevitables is that things go wrong? Why are we worried that it isn't just when there's bug software, it's nothing? Why isn't this going to be like the year 2000, the Y2K problem for anybody old enough to remember that everybody was super panicky and then nothing happened. Why isn't this going to be yet another nothing burger?

[Mo Gadot]

Because the chips are lined up in the wrong direction.

So, you know, Hugo De Garas, if you, if you, if you know him, is a very well known AI scientist that worked in Asia for quite a few years and he built a documentary that I think is found on YouTube, it's called singularity or Bust.

And he was basically saying that most of the investment that's going in AI today is going into

spying, killing, gambling, and one more.

So spying is surveillance, okay?

Killing is what we call defense. Gambling is all of the trading algorithms

and selling, which is all of the

advertisement and recommendation engines and you know, all of, all of the, all of the idea of turning us into products that, that can be advertised to if you want. And that's not unusual, by the way, in the, in our capitalist system because

those industries come with a lot of

money, banking, you know, defense and so

on and so forth.

And

the chips are lined up this way. I mean, if you take just accurate

numbers on how much of the AI investment is going behind drug discovery, for example, is as compared to how much is going behind killing machines and killing robots and killing drones and so on and so forth, you'd be amazed. It's a staggering difference, right?

And this is the nature of humanity so far.

If you, if you're running a research on a, on a disease that doesn't affect more than, you know, a few tens of thousands of people, you're going to struggle to find the money, okay? But if you're building a new weapon that can kill tens of thousands of people, the money will immediately arrive because

there is money in that.

You can sell that.

And sadly, as much as I, you

know, I would have hoped that humanity wasn't completely driven by that, it's our reality.

So, so, so this is number one. Number two is that so number one is, is we're aligned in the direction of things going wrong.

Okay?

Number two is even if we're aligned in the direction of going right, wrongdoers can flip things upside down.

There was a, an article in the Verge, you know, a few months ago around, you know, a drug discovery AI

that was basically supposed to look at

characteristics of, you know, human biology, know, whatever information and data we can give it about the drugs we can develop and chem chemistry and so on and

so forth with the objective of prolonging life.

Prolonging life.

So prolonging human life is one parameter in the equation.

It's basically plus make life longer. Okay? And for fun, they, you know, the research team was, was, you know, was asked to go talk, to go and give a talk at a university.

And so for the fun of it,

they reversed the, the positive to negative. So instead of giving the AI the objective of, of prolonging life, it became

objective of shortening Life.

And within six hours, if I remember

correctly, the AI came up with 40,000

possible biological weapons and, you know, agents like nerve gas and so on that could shorten. Yeah, it's, it's incredible really. And, and you know, it's.

The thing that of course kills me is that this article is in the Verge.

You know, it's all over the Internet.

And accordingly, if you were a criminal

that grew up watching, you know, super villain movies, what would you be doing right now? You would go like a million dollars. I need to get my hands on that weapon so that I can sell it to the rest of the world or to the rest of the world of villainy. And I think the reality of the

matter is it is so much power, so much power that if it falls in the wrong hands, and it is

bound to fall in the wrong hands

unless we start paying enough attention. Right? And that's my, my cry out to the world is let's pay enough attention

so that it doesn't fall in the

wrong hands, it would lead to a very bad place. The third, you know, and the biggest

reason, in my view of us needing

to worry, hopefully we will all be wrong and be surprised is that there were three barriers that we all compute. All computer scientists that worked on AI, we all agreed there were three barriers that we should never cross. And the first was don't put them on the open Internet until you are absolutely certain they are safe. Okay?

And, you know, it's like FDA will tell you, don't swallow a drug until we've tested it. Right. You know, and, and I, and I really respect Sam Altman's view of, you know, developing it in, you know, in public, in front of everyone to discover things now that could, you know, that

we could fix when the challenge is small, in isolation of the other two.

This is a very good idea.

But the other two barriers we said we should never cross is don't teach

them to write code and don't have agents prompting them.

Right? So what you have today is you have a very intelligent machine that is capable of writing code so it can develop its own siblings if you want.

Okay? That is known frequently to, to, to outperform human developers.

So I think 70, 75% of the code was.

No, sorry, 25% of the code given

to Chat GPT to be reviewed was

improved to run two and a half times faster.

Okay.

So, so they can develop better code than us. Okay. And, and basically now what we're doing

is we're not only limiting their learning, the learning of those machines to humans, so they're not learning from us anymore, they're learning from other AIs.

And there are staggering statistics around the

size of data that is developed by

other AIs to train AIs in the data set. Of course, again, just to simplify that idea for, for our listeners, AlphaGo Master, which is the absolute winner of the strategy game go, you know, one against AlphaGo, sorry, AlphaGo Zero, which is the absolute winner of the strategy Go game that's called Go. One against AlphaGo Master, which was another AI developed by DeepMind of Google that

was by then the world champion.

So AlphaGo Master, one against the world world champion, and then AlphaGo Zero. One against AlphaGo Master.

A thousand games to zero by playing against itself. It has never in its entire career as a Go player seen a game of Go being played. It just simulated the game by knowing

the rules and playing against it.

[Tom Bilyeu]

Okay, so first, people that don't know the history of this, I think it was Deep Blue, ends up beating Gary Kasparov, the greatest chess champion back in the 80s.

[Mo Gadot]

Is that correct, 89, if I remember correctly.

[Tom Bilyeu]

Yeah, yeah, yeah. No way that we're ever going to be Able to build AI that'll beat a Go champion, ends up beating the. I forget how many years ago this was, but took a long time. But they finally did beat the second place GO champion. Then they updated, beat the first place world champion in GO and then realized we don't need to feed it a bunch of Go games. We can just have it basically dream about playing itself over and over and over and over and over and over and over very rapidly. Which is one of the things you said in your book that I found. This is something that people underappreciate. The future is going to be almost impossibly different to the point where it will even now. So forget the singularity where the rate of change is so blinding that you can't predict a minute from now, let alone what's happening now. But you said over the next 100 years, without any additional changes, we will make 20,000 years of progress. And in that progress though I have to imagine will be progress that speeds up that rate of change. So if we're already on a rate of change of 20,000 years of change in a single century, you can imagine where we're going to be in 10, 20, 30 years. It's going to be crazy. So by putting an algorithm together, rather than feeding it human data, you feed it AI games. It gets unbeatable to the point where it can beat the other AI. Okay, that's crazy.

[Mo Gadot]

Think about it this way, Tom. How does the best player of Go

in the world learn the game?

Right, they play against other players and every time they win or they lose, of course they're given instructions and hints and tips and so on, but every time they make the wrong move and they lose, they remember it and so

they don't do it again. Every, every time they make the right

move and they win, they remember it

and they do it.

And over the, the difference is that

one player, you know, I always give the example of self driving cars.

You drive and I drive. If you make a mistake and avoid an accident, you will learn. I will not. Okay, if, if one self driving car requires critical intervention, it's fed back to the main brain, if you want to call it. And every other self driving car will learn. That's the point about AI, right? And so when AlphaGo Zero was playing

against AlphaGo Master, you know, for, for

it to, to learn just so that you Understand, there were three versions of Alpha. AlphaGo version one was beaten by version three in three days of playing against itself. Version two became the world, you know, which is the, which was the world champion at the time, lost a thousand to zero in 21 days. 21 days. And I think this is why I am no longer holding back. The reason why I'm no longer holding back is that nobody, if you've ever coded anything in your life, Nobody expected

an AI to win and go any

earlier than 10 years from today. Right? It did not only happen several years ago, it happened in 21 days. Did you understand the speed that we're talking about here? And, and when you said exponential, people don't understand this. ChatGPT 4, as compared to ChatGPT 3.5 is 10 times smarter. Okay. There are estimates.

It's hard to, to measure exactly.

There are estimates that ChatGPT4 is at an IQ of 155 if you measure

by all of the, you know, tests that it goes through. Right.

Einstein was 160, okay? So it is already smarter than most humans.

Now if.

Chad GPT5.

No, no, no.

Chat GPT6, a year and a half from today is another 10 times smarter. If you just take that assumption, you're now 10 times smarter than one of the smartest humans on the planet. If this is not a singularity, I don't know what is. If this is not a point where humans need to stop and say, maybe I should consider trying to understand how the world is going to look like when that happens.

Right?

And I go back and I say this very openly. I am like you. I am an optimist, a hundred percent. I know that eventually AI in the 2000s, 2000s maybe will create a utopia for all of us or for those who remain of us. Okay? But then between now and then, the abuse of AI falling in the wrong hands, as well as the uncertainty of certain mistakes that can flip life upside down, okay. Could really be quite a struggle for many of us. Does that mean it's a doomsday? No, it's not. But it's honestly not something that we should put on the side and go binge watch, you know, Game of Thrones. Not, not anymore. I, I think people need to put the game controller down and start talking about this. Starting telling their governments to engage, starting

to tell, you know, developers that we require ethical AI, start to request some

kind of an oversight, and in my personal point of view, start to prepare for an upcoming redesign of the fabric of work, and most importantly, start to prepare for a relationship between humans and AI that we have never in our lives needed to do before with any other being. It's like getting a new Puppy at home, only the puppy is a billion times smarter than you.

[Tom Bilyeu]

Yeah.

[Mo Gadot]

Think about it.

[Tom Bilyeu]

Yeah. There's a Rick and Morty episode about the dog becoming exceptionally intelligent.

[Mo Gadot]

Remember that?

[Tom Bilyeu]

Yeah. One of my favorites. Absolutely. Very much so.

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[Tom Bilyeu]

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[Tom Bilyeu]

All right, so I want to. There's two things I want to drill into, and then I want to. You and I to start the conversation about what that looks like. Because in fairness, I don't think. Certainly not in the U.S. i don't think most people in the government have thought about it at all. Probably would be my guess. And so I think that the. A better way for people to begin to think through this stuff is really sort of podcast citizen journalism, whatever you want to call it, so.

[Mo Gadot]

Correct.

[Tom Bilyeu]

The two things I want to drill into are going to be exponential growth, which we've touched on, but there's a few more things I think to be said about that. And then alien intelligence. And I say alien intelligence because the way that AI is going to think will be so vastly different. It will. It will truly be incomprehensible. And I think our failure to grasp what artificial super intelligence will look like is the problem. Okay, so let's talk exponentials. So linear. If I take 30 steps, I'm going to be roughly at my front door. Let's just call it. If I take 30 exponential steps, I'm going to walk around the earth something like 30 times it. It's crazy. And people don't. They don't have a sense of that. So linear, obviously, is 1, 2, 3, 4. It just. You progress by one increment each time. Exponentials means you double each time. And there's something called the law of accelerating returns, which I know you know well about. So it'd be great to hear you talk on this. But the way that that plays out is that when you're at one and you're doubling to two, like it doesn't seem like a big deal, but you start getting to a hundred and you double to 200 and then 400, and then you hit a million, and it's 2 million. And I don't think people understand that it only takes seven doublings. Like if you start with an amount of money, you only have to have seven exponential steps to double your money. And so the compounding effect of that is extraordinary. So if you don't mind, walk people through some examples of the law of accelerating returns and how you see this playing out with AI.

[Mo Gadot]

So of course, we have to credit

Trey Korswell for bringing this to everyone's attention.

Moore's Law in technology was, I think,

our first exposure, even though we didn't look at it as accelerating returns.

But Moore's Law promised us in the

1960s, which, you know, was coined by the CEO of Intel at the time, that compute power will double every 12

to 18 months at the same cost. Okay. And, you know, you may not think that much about it, but my first

window, you know, DOS computer, so IBM compatible computer at the time, I had a 286.

Remember those machines, they had 33 megahertz on them, right?

And you know, you had that turbo button.

If you, if you pressed that turbo

button, it ran at 66 MHz, but it consumed, you know, electricity and overheated

and so on and so forth. The difference between 33 and 66 to us at the time was massive because

you literally doubled your performance. Okay.

As computers continue to grow, you can imagine that every year, just for the simplicity of the numbers, that 66 doubled

and then became, say 130, for the simplicity of the numbers, and then that

130 became 260 and then the 260 became 500. Now the difference between the 500 and the, the 33 is quite significant. It's orders of magnitude, the 33. And it happened in two or three double X.

Right?

And I think what people, when you really think about that, Ray Kurzweil uses a very, very interesting example. When we attempted to sequence the genome,

it was a 15 years project, and

seven years into the project, we were at 10% of the progress, okay? And everyone looked at it and said, if it's 10% in seven years, then you need 70 more years to, you

know, or, you know, a total of

70 years to finish.

Okay?

And Ray said, oh, we're at 10%. We did it.

[Tom Bilyeu]

Okay.

[Mo Gadot]

Okay. And he was right.

You know, one year, the 10 became

20, the 20 became 40, the 40 became 80.

And then you're over the, you're over the threshold.

Okay? And that idea of the exponential function is really what humans miss. Humans miss that because we are taught

to think of the world as a linear progression. Okay, Let me use, you know, a biological example.

If you have a jar that's half full of bacteria. Okay, the next doubling, it's full, it's not gonna add. You know, if it moved from 25% full to 50% full in the, in the last doubling, you'd go like, yeah, you know, we still have half empty. One more doubling and it's full. If you apply that to the resources of planet Earth, if we, if we keep consuming the resources of planet Earth. Planet Earth to the point where one

doubling away, you know, two minutes to midnight, if you want one doubling away,

we would be consuming all of the

resources of planet Earth.

We would need another full planet Earth on the next double. We would need four planet Earths on the next double. Okay, so that's exponential growth is, is just mind boggling because the growth on the next chip in your phone is going to be a million times more than the computer that put people on the moon. Okay, that one doubling, that one additional doubling. Now, when you think about it from an AI point of view, it's doubly exponential, double exponential.

Why?

Because, as I said, we now have AIs prompting. AIs. So basically, we're building machines that are

enabling us to build machines.

So in many, many ways, the reasons why we get those incredible breakthroughs, which even the people that wrote the code don't understand, is because you and I, when you really think about, you know,

I know you love computer science and

physics and so on, but I'm sure you, you remember reading string theory or some complex theory of, of physics, and then you would go like, I don't get it, I don't get it. And then you read a little more and then, I don't get it, I don't get it.

And then you read a little more

and then someone explains something to you and bam, suddenly you go like, oh, now I get it. It's super clear. Those are simply because every time you're using your brain to understand something, you're building some neural networks that make it easier for you to understand something else that make it easier for you to understand even more. And this is what's happening with AI that also does not include. Which I am amazed that we're not talking about this. It does not include any possible breakthroughs in compute power. You know, there was an article recently

that, you know, China is working also

on quantum computers that are now 180 million times faster than the traditional computers. I remember in my Google years when

we, when we were working on Sycamore, Google's quantum computer Sycamore performed an Algorithm that would have taken the world's biggest supercomputer 10,000 years to solve. And it took Sycamore 12 seconds, 200 seconds. Let me.

[Tom Bilyeu]

This is. Yeah, yeah, because that's a big difference. So this is where I think people's brains start to shut down. Even you said 180 million times faster. Yeah, so, okay, so I know.

[Mo Gadot]

So by the way, 200 seconds to 10,000 years is a trillion times faster for Sikhs.

[Tom Bilyeu]

So I did my first video.

[Mo Gadot]

Let's be clear for our listeners. So we can't put AI on quantum computers yet. We can't even put really anything.

You know, it's very, very early years.

It's almost like the very early mainframes.

It requires, you know, almost absolute zero, you know, degrees and very cold and

very large rooms and so on. But so were the mainframes. I worked on MVS systems that occupied a full floor of a building. Right. And they had less compute power than the silliest of all smartphones on the planet today. We, we, we make those things happen. There will be a point in time especially assisted by intelligence.

And we're going to have more and

more intelligence available to us where we will figure this out. And then you take chat, GPT or any form of AI and move it from that brain to this brain that is 100 million times and 80 million times faster and we're done. Okay, we can't do that with you and I, with our biology. We can't move our intelligence from one brain to the other yet.

[Tom Bilyeu]

Yeah. So I, I really want to drive a stake into this idea of how different exponential is to linear by pointing out the difference between. So if you, a moron by, if you look it up. I forget if I looked it up on Wikipedia or whatever, but I looked up what's the IQ of a moron. If I remember right, it's like 65 or 80.

[Mo Gadot]

It's somewhere in there, 60s, 70s.

[Tom Bilyeu]

Yeah, yeah. And Einstein was 160, as you were saying. So you have, I think Einstein is like 2.3 times smarter than a moron, if I remember when I did the math correctly. And so the difference between a moron that, you know, struggles to take care of themselves and then only two and a half or less than two and a half times smarter than that. And you get somebody that unlocked the power of the atomic that really gave birth to a lot of the modern technology that we use today is built on the back of this physical breakthrough. And so there, there's a really, really life altering difference you wouldn't have nuclear power, you wouldn't have nuclear weapons, you wouldn't have gps, like a lot of the things that we rely on in today's world. You wouldn't have any of that if it wasn't for the 2.3x increase in intelligence. Now when we talk about super intelligence, which people are estimating will get to be a billion times smarter than the smartest human. So if, if 2.3x is life altering, changes the entire paradigm of our planet, then a hundred times is unimaginable. A thousand times is ridiculous. A hundred thousand times is comical. A million times. We're still not even scratching the surface of how much more intelligent this is going to be. And so that brings me to the other thing I wanted to drill into, which is that AI will be an alien intelligence. It will not be like your friend who you can still hang out with and, you know, smoke a joint. It's like you're, you're different species there. I don't even know if there will be common elements. And that's one of the things that, that I think we have to establish first before we can get into how we stop this from being problematic. But you and your book, you really freaked me out. So Scary Smart is Scary Good as a book. I highly encourage everybody to read it. But there's a part in there where you read a transcript of two AI that were given the task to negotiate with each other for like selling things back and forth, and they start talking in a way that is unintelligible. I mean, it was really unnerving. It was like, I, I, I need five of these. And then the other was like screws, nails, all me. And there was like a really weird, like rhythmic repetition to the way that they were over emphasizing themselves and like what they needed. It was really weird. And so what was the response to that? Because if I'm not mistaken, they ended up shutting them down because they. Very unnerved.

[Mo Gadot]

Yeah, yeah.

[Tom Bilyeu]

What happened?

[Mo Gadot]

That was Facebook. And the idea is they were simulating AIs negotiating deals with each other. It's a wonderful thing if you're in

the advertising business, for example, because we

had things like that at Google a

very long time ago. The idea of ad exchange, for example,

where machines will buy ads from other machines.

Right. But you and I, and I really

thank you for your time. It took me four and a half

months to write Scary Smart, you know, maybe six months to edit.

Took you perhaps a day or two to read it and for us to talk about it.

Now it's going to take two and a half hours. You know, a computer can read scary smart in less than a microsecond, right? You know, when you speak about the idea of intelligences being a hundred times, a million times, a billion times smarter

than us, this is only one thread of the issue.

The other thread of the issue is the, the memory size, you know, of.

If, if I could keep every physics equation in my head at the same time and also understand biology very well

and also understand, you know, cosmology very

well, I could probably come up with much more intelligent answers to problems, right? And if I could also ping another scientist who understands this or that in a microsecond, get all of the information that he knows and make it part of my information that's even more intelligent. And what is happening is when, when we ask computers to, to communicate, at first they communicate like we tell them, but if they're intelligent enough, they'll start to say, that's too slow. Why, why would I communicate at human bandwidth, right? Why would I use words to communicate when you and I know that if, you know, if you simplify words, for

example, into, you know, letters, into numbers,

you could communicate a massive amount of information within every sentence, right? So you could literally, if you take

one equation algorithmically put, you know, certain letters in it, you could simply, I

could send to you something that says 1.1, and you would enter it into the equation and get a full file

that's a full book because of the sequence of the letters that 1.1 determines

as per the equation. So, of course, you know, if you're

smarter and smarter and you have that bandwidth, you're going to communicate a lot quicker.

And I don't remember the name.

I think they were Alice and Bob of the, of the two chat bots.

And very, very quickly they, they ended up designing their own language. And when they said I, I would,

what would buy 10, you know, tape, tape, tape.

There was math, math engaged in that. It wasn't I want to buy 10 tapes only. It was also communicating other things we didn't understand, which is really what you're,

you know, driving us to, to driving

our listeners to think about. Tom because there is so much of AI we don't understand. Again, this is one of the things that, is that people need to become aware of. There are emerging properties that we don't understand. We don't understand how those machines develop those properties, right? And there are even targeted properties that basically we tell something that its task is to do a, B And C. And it does A, B, and C. But we have no clue how it arrived at it.

Okay.

Simply, like, if I tell you, what do you think is going to happen in the football game tomorrow? You're going to give me an answer.

Right.

The fact that it's all right or wrong doesn't matter either way. I have no clue how you arrived at that answer. I have no clue which logic you used.

Okay.

We. We have no clue most of the time how the machines do what they do. We don't.

Okay.

Why?

[Tom Bilyeu]

Because it really shocked me.

[Mo Gadot]

Yeah.

If. If you, if you need to know how I arrive at a certain conclusion, you're going to have to ask me and say, drive this for me. Like, tell, Tell me, what did you go through? What did you think about? What's your evidence, what data, and so on and so forth.

And we do that with AI.

We write additional code that will tell

us what are the levels, the layers of the neural net or the logic

that the machine went through.

Right.

But when investments are in an arms race like we are today, most developers and business people will say, I'm delighted it's working. I don't care how. I'm not going to invest more money and developer time to actually figure out how. In several years time, even if you invested the money, you won't get it

because that level of intelligence that the

machine is using is so much higher than yours. So you're not going to figure it out if the machine tells you, well, I did A, then B, then C,

then D, then F, then G. And it goes on for half an hour to tell you, I did all of that, that you're going to go like,

okay, I'm happy you did it. I can't arrive at that myself anymore. That's why I'm handing it over to you.

[Tom Bilyeu]

Yeah. I had Yoshua Bengio on the show, who's one of the early guys in AI, and he signed the letter, and I asked him why he signed it, and he said, none of us in the space thought that artificial intelligence would pass a Turing test as quickly as it did, and we don't understand how it did it. And so I asked him the same question, like, how. How is it possible that we don't understand how it's doing it? We created it. And so you presumably created it to do a specific thing. And he said, it's not how it works. We're basically layering on, kind of like you would layer on neurons. We're layering on extra neurons, neural nets, to get it to process data and then it just does it. And we don't understand how it's coming to the conclusions. We just know that if you scale it up, it can solve bigger and bigger problems. And so he said nobody would have predicted that this is really just a scale problem and that as you scale it up it's going to get smarter and smarter. So my question now is we, so if, if we can get everybody to understand this is going to happen way, way, way faster than you think it's going to happen. Which is why even I, as a hyper, hyper optimist am just like, hey, I don't see a clear path through this. I'm excited and terrified at the same time. And all I know, like you, is that we need to start talking about this, we need to start presenting solutions. So it's, it's happening faster than we think. And it's going to be a completely foreign intelligence in that we, we will not be able to interface with it. Even if it is kind and wants to explain it to us. We won't be able to comprehend it. And so it will very rapidly be like Einstein to a fly, which is a reference you use in the book several times. And even if Einstein loves to fly, it's like, am I really going to spend my time trying to explain it? And even if I take the time and I lay it all out, you're not going to get it. You just don't have the ability to comprehend. So we are giving birth to something that is a, like you said, we can't take it back. That's already done. So any argument that begins with ah, just stop. I agree with you. I, that is so unrealistic to me. We can't bring it back. It's going to happen soon, so fast. And when it comes, it will be just unintelligible it. It already is. But given that this is a scale problem, that why don't we nip it in the bud? If do you think that AI will be able to defeat the need for additional neural nets and just get so hyper efficient that we won't be able to stop it that way? Or could we just not now take advantage of the fact this does become a nuclear style infrastructure problem and I can nuke anybody that tries to online. Not necessarily nuke, but destroy, physically destroy anybody that tries to bring a server farm on that's, that's big enough to run one of these neural nets.

[Mo Gadot]

Yeah, I mean now, now we could, if we, if we decide now, we could simply switch off all of that madness. Switch off your Instagram Recommendation engine, your TikTok recommendation engine, your ad engine on Google, your data distribution engine on Google.

You can also switch off ChatGPT and,

you know, a million other AIs, and then we can all go and sit out in nature and really enjoy our time. Honestly, we won't miss any of it at all.

I'll tell you that very openly.

I mean, the reality of the matter is that humanity keeps developing more and

more and more because we get bored

with what we have, okay? And we think that we can do better with an automated call center agent, when in reality, it's not about better, it's just about more profitable. Okay? And the reality here is that we could. But will we? No, we won't.

Why? Because of the first inevitable before, because

of the trust issue between all of us, and because we need the AI policemen just as much as we need

the, you know, as.

As we fear the AI criminal before

[Tom Bilyeu]

we go into how pointed question really fast. So when I think about nuclear proliferation, not every country that wants nuclear weapons has them during. And I'm not sure where Iran's nuclear program is now, but I know for a while there was real attempts to either blow up things that they were doing, or if you know about stuxnet, there was that computer virus that was. That was really terrifying in. In the way that it was sort of like a biological weapon that was designed to only kill a certain type of thing. And that. That is very scary, and I'm sure is in the 40,000, the list of 40,000 ways that. That the AI came up with to limit human population. But Stuxnet, for people that don't know, it was like, embedded at, like, the. The deepest root level of, like, basically every operating system ever. It just spread like wildfire into chips, into everything, everything, everything. And when it detected that it was an Iranian nuclear centrifuge, it would shut it down or overheated or whatever it did. And so they. For a long time, they just could not build it up. So could we, Given that there is a similar need for detectable infrastructure to run AI, could step one not be. Not to shut all of the things that we have down, but to stop the next phase from coming online?

[Mo Gadot]

Could we? We could, but I would debate the. The example you're giving in the first place. Back in 2022, the world was discussing the threat of a nuclear war still 90 years later or like 80 years later. Okay, so. So the whole. The whole idea is that while we

politically created the propaganda that we Will, you know, now prioritize humanity over our own country interests?

There are still lots of nuclear wars,

warheads in China, in Russia, in the US In Israel, in North Korea and many other places. Okay?

And the reality of the matter is that while we manage to slow down Iran, that's not enough to protect humanity at large. That's just enough to protect some of humanity's individual interests.

So, so this is, this takes us

back to the whole prisoner's dilemma.

It's like.

And I think that is the reason

why we have a prisoner's dilemma, because

the past proves to us that even

though we said we're going to have

a nuclear treaty, everyone on every side

of the Cold War continued to develop nuclear weapons.

So you can easily imagine that when it comes to AI, if everyone signs a deal in November and say, we're

going to halt AI in China and Russia and North Korea and everywhere, you

know, people will still develop AI. Okay? The more interesting bits is that there are lots of initiatives to minimize the infrastructure that is needed for AI, because it's all about abstraction at the end of the day.

So, you know, you may think of

a lot of people don't recognize this as well, but a big part of the infrastructure we need for AI to develop its intelligence is for teaching AI. Okay? When ChatGPT again, or Bard responds to you, it's not referring to the entire data set from which it learned to give you the answer. It's referring to the abstracted knowledge that it created based on massive amounts of data that it had to consume.

Okay?

And when, and when you see it that way, you, you understand that just like we needed the mainframe at the early years of the computers, and now you can do amazing things on your smartphone, the direction will be that we will more and more have smaller systems that can do AI, which basically means two developers in a garage in Singapore can develop something and release it on the open Internet.

It, you know, again, you and I,

I don't know if you coded any,

any transformers or, or, or, you know, or deep, deep neural networks and so on, but they're not that complicated.

I think the code of chat of, of gpt4in, in general is around 4,000 lines. The core code. Right? It's. It's not a big deal. When, when I, when I coded banking systems in my early years on Cobal,

on, you know, on MVS machines or

as 400 machines, it was hundreds of thousands of lines of code. Okay, so, so there, the. The possibility for us.

Why.

[Tom Bilyeu]

Why has it become so much Less,

[Mo Gadot]

because it's all so much better because it's all algorithms. It's not. It's all mathematics. We. I think this is a very important thing to differentiate for people. When I coded computers in my early years, those machines were dumb and stupid, like an idiot. They had an IQ of one. Literally no IQ at all. Okay? Developers transformed human intelligence to the machine. We solved the problem, and then we instructed the machine exactly what to do to solve it itself, right?

So, you know, when.

When we understood how a general ledger works, we understood it as humans. And then we told the machine, add

this, subtract that, reconcile this way.

And then the machine could do it very, very, very fast, which appeared very intelligent, but it was totally a mechanical turk. It was just repeating the same task

over and over and over in, you know, in very fast speed.

We don't do that anymore. We don't tell the machine what to do. We tell the machine how to find out what it needs to do. So we give it algorithms. And the algorithms are very straightforward. When you, you know, let's. Let's take the simplest way of deep learning. When we started deep learning, what we did is we had basically three bots, if you want. One is what we call the maker. The other is the student, the final

AI that we want to build, and

one that's called the teacher, okay? And we would say,

you know, tell

them to look for a bird in a picture, okay? And they would identify a few parameters,

you know, edges and how.

How do they see the edge and

the difference in color between two pixels

and so on and so forth. And then they would detect the shape of a bird. And basically we would build a code and. And call it a student. We would build multiple instances of it and then show it a million photos and say, is it a bird?

Is it not a bird? Is it a bird? Is it not a bird?

And the machines would randomly answer at the beginning. It's literally like the throw of a dice, okay? And, you know, some of them will get it wrong every time.

Some of them will get it right

51% of the time, and one of them will get it right 60% of

the time, probably by pure luck, okay?

The teacher is performing those tests, and then the maker would discard all of the stupid ones and take the one code that got it right and continue to improve it, okay? So the code was simply a punishment and reward code. It was saying, guess what this is, and if you guess it right, we will reward you, okay? And. And basically the machine, the algorithm, would then continue to improve and improve and improve until.

Until it became very good at detecting birds and cats and pictures and so

on and so forth. When, when we came to Transformers and

why GPT and Bard and so on

are so amazing is because we used something that was called reinforcement learning with human feedback. So basically we allowed, instead of discarding the bad ones, okay? We found a way, which Jeffrey Hinton, the.

The, you know, who recently left Google, was very prominent at, you know, promoting.

Early on, we found a way, just like with humans, to give the machine feedback. You know, show it a picture.

And then it would say, this is a cat.

And we would say, no, it's not. It's actually a bird. What do you need to change in your algorithm? Okay, so that it would. The answer would have become a bird, okay? And so the machine would go backwards with that feedback and, and, and, you know, and change its own thinking so that the answer is correct. And then we would show it another

picture and another picture.

And we keep doing this so quickly on billions or millions or tens of thousands of machines of, you know, millions of instances, until eventually it becomes amazing. Just like a child. Just like you give a child a simple puzzle, okay? Nobody ever told the child, no, no, no, no, darling. Look at the cylinder. Turn it to its side. Look at the cross section. It will look like a circle. Look at the board and find a matching shape that is a circle. If you put the cylinder through the

circle, it will go through.

That's old programming, okay? New programming, which every child achieve intelligence. Achieves intelligence with is you give them a cylinder and a puzzle board, and they will try. They'll try to fit it in the star. It won't. They'll try again. It won't.

They'll throw it away and get angry. Then they catch it again and try in the square.

It won't. And then when it goes through the cylinder, something in the child's brain, sorry, through the circle, there's something in this child's brain says, this is. This works, okay? The only difference is a child will try five times a minute or five times, you know, 50 times a minute. A computer system will try 50,000 times a second, okay? And so very, very quickly, they achieve those intelligences. And as they do, we, we, we. We don't really need to code a lot because the heart of the code is an algorithm, is an equation, okay? And. And mathematics is much more efficient than instructions. So if I tell you, Tom, when you leave home, make sure that your distance is no more than the day of the month multiplied by two away from your home and make sure that you don't consume any more fuel than your height divided by four or then your body temperature divided by seven or whatever that is. Okay, with those two equations, I don't need to give you any instructions anymore. You can always look at your fuel consumption and your distance and say, oh, I'm.

I'm falling out of the algorithm with

very, very few lines of code. I just gave you two lines of code.

[Tom Bilyeu]

Turning everything into algorithms allows us to go a lot farther. That's certainly amazing from the AI perspective of getting everything to function on less, but unfortunately, that dunks on my idea of wanting to constrain all of this by just putting a limit on the, the physical structures. So what is then the path forward you mentioned earlier? Ethical AI? What does that mean? How is this potentially a path forward?

[Mo Gadot]

So, you know, I hope people stayed

with us this long, and I hope we didn't scare anyone too much.

But let me make a very, very, very blunt statement. I am a huge optimist, that the end result of all of this is a utopia. Why? Because there is nothing wrong with intelligence. There is nothing inherently evil about intelligence, okay? There is not. As a matter of fact, the reason humanity is where it is today is because of intelligence, you know, good and bad. By the way, the good is because of our intelligence and the bad is

because of our limited intelligence.

So, so the, the good, the amazing intelligence that humanity possesses allows us to create an amazing machine that flies across the globe and takes you, you know, to your families, to your, to your wife's family in the UK or whatever, right? But, but at the same time, our limited intelligence, I would even say humanity's stupidity forgets or ignores that this machine

is burning the planet in the process.

If I, if we had given humanity more intelligence and it was so easy for them to, to solve both problems at the same time, they would have created the machine that doesn't burn the

planet in the process.

So more intelligence will help us. And in my perception, as we go through the rough patch in the middle, there is what I call the fourth inevitable. And the fourth inevitable is that AI will create an amazing utopia, I'm not kidding you, where you can walk to a tree and pick an apple and walk to another tree because of our understanding of nanophysics, and pick an iPhone, okay? And the cost of production of both of them, literally from a physical, material point of view, is exactly the same. So, so this is how far we can go.

If we could understand nanophysics and, you know, and Then created, create nanobots better than we do today.

Now we will end up in that place. We will end up in a place where, where we have a utopia for one simple reason. I say that with confidence, which is if you don't know what the, where the direction is going, take the past as a predictor, okay? And the past is, if you look at us today, you would think that, you would see that the biggest idiots on the planet, okay, are destroying the planet and not even understanding that they are right. You become a little more intelligent and you say, I'm destroying the planet, but it's not my problem, but I understand that I'm destroying it, okay? You get a little more intelligent and you go like, no, no, no, hold on. I am destroying the planet.

I should stop doing what I'm doing.

You get even more intelligent and you say, I'm destroying the planet, I should do something to reverse it, right? It seems that the most intelligent of all of us, okay, agree that war is not needed. There could be a, you know, a simpler solution if we could actually become

a little more intelligent. That, you know, the eco challenge that we go through is not needed.

There has been an invention made a

long time ago for climate change. That's okay. And that if humanity gets together and

plants more trees, we're going to be fine. And getting together just requires a little more intelligence, a little more communication, a

little more pre, you know, a better

presentation of the numbers so that every

leader around the world suddenly realizes, yeah, it doesn't look good for my country

in 50 years time, okay? And, and I think the reality of the matter is that as AI goes through that trajectory of more and more and more intelligence zooms through human stupidity to human, you know, best IQ, beyond humans intelligence, they will, by definition, have our best interest in mind, have the best interest of the ecosystem in mind. Just like the most intelligent of us don't want us to kill the giraffes and the, you know, the other species that we're killing. Every day, a more intelligent AI than us will behave like the intelligence of life itself. And the difference between human intelligence and the intelligence of life itself is that we create from scarcity. For you and I, to protect our tribe from the tigers, we have to kill the tigers, right? When nature wants to protect from the tigers, it creates more gazelles and, you know, and more tigers. And the tigers will eat the weaker gazelles and that will fertilize the trees and then there will be more fruits for everyone and the cycle goes on, okay? It's More intelligent. It's more intelligent.

[Tom Bilyeu]

This may be where we start to diverge or at least it's the jumping off point for how I think we have to think through this without falling into hopium. So do you think that there is going to be a period of literal or emotional bloodshed between here and equilibrium?

[Mo Gadot]

Absolutely. 100. Right. So. So there is one scenario where we don't. So, so when I, when I talk about the fourth inevitable, this is after

we go through a lot of. I, I'm sorry if.

I swear.

But yeah, so, yeah, we're first going

to go through a very difficult period, very uncertain, where the fabric of society at its core is being redesigned and where there is a superpower that comes to the planet that's not always raised by the family. Kent.

Okay.

I always refer to the story of.

[Tom Bilyeu]

Before we get to that because I think that's really important and I love that. But before we get to. I think there's a few things we have to define, including human nature, the nature of nature, and then the nature of superintelligence and what those are going to look like. So when you describe nature on that one, I think you and I may see it very differently. So I see nature as a brutal, completely indifferent, life giving, amazing, incredible, wonderful thing. But also I've seen enough YouTube videos of a lion grabbing onto a baby, what are they called, water buffaloes or whatever. And then as the lions are trying to eat the baby, a crocodile leaps out of the water and grabs a hold of the baby and they're literally tearing it apart. It is absolutely freakish. I don't know if you saw the recent video of shark eating, eating a swimmer on camera. Gnarliest. Oh my God. Literally horrendous. So I don't think nature cares about the individual. And for the gazelle to be the, the sort of sacrifice to keep the tigers from eating humans, I don't think the gazelle is very happy about that. So when, when I think about nature, the nature of nature is ruthless. Maybe an even better way. It's just indifferent. It's like this is the chain.

[Mo Gadot]

It's not one thing has to get

[Tom Bilyeu]

eaten for something else.

[Mo Gadot]

What do you mean? It's not. It's not.

[Tom Bilyeu]

What did I just say? That's untrue.

[Mo Gadot]

It prefers the success of the community over the success of the individual.

[Tom Bilyeu]

Yes, so did Mao's China.

[Mo Gadot]

So let's go into those two ideologies. Right there is an ideology that says

it's all about that one baby, you know, gazelle, okay.

And, and that's a western ideology in many, many ways. Basically saying, it's my individual freedom that comes first. Which is, by the way, an amazing ideology.

Right?

But it, it becomes, it, it narrows down everything to if one person is hurt,

we have a very big problem. That's why you get, you know, they send billions of dollars to bring Matt

Damon back from Mars.

Right. You know, if you take the same ideology, I'm just joking about the movie,

but if you take the same ideology, you could use the billions of dollars to save a million people in Africa, right? If you, if your ideology is let's benefit all of humanity, not one human, okay?

Then the, the ideology justifies the approach.

And the approach of nature is saying, look, every one of you is going to have to, to eat.

We just understand that like, so if

you're, if you're all going to have to eat, then we might as well design a system that appears brutal because it kills the weakest one of you. Okay? But then at the same time, it's the most merciful. If we wanted to grow the entire

community, if they wanted to grow the

entire ecosystem, because eventually, sooner or later,

by the way, one of you is

going to be eaten, right? Now, when you see it that way, is that brutal? Yes, it is.

Is, you know, a million animals dying,

brutal also is okay? But what we do as humanity is we say let's kill a hundred species a day, drive them to extinction, you know, for the benefit of one species, which is humanity. Okay? And I think that divisible, that's, that's view of there is one more important than the other, works to a certain limit in favor of humanity and then works against humanity.

So when I say, you know, nature

is more intelligent is because by creating more and allowing a brutal system, if you wanted to fix the system, you should fix it by saying, let's not eat. But if we're going to eat anyway, then there is no fixing to the system other than more eating leads to more community, more to a more balanced

ecosystem at the end of the day,

where there are billions living at the expense of a few hundred thousands dying.

[Tom Bilyeu]

So, so I'm going to sum up what I think the nature of nature is in a single sentence. And I do this in the context of one of the theses that you lay out in the book, is that the way forward is to understand that ultimately if humans act well to the Superman thing, if we raise the super intelligence well, with ethics and morals, that we'll get to the other side. Well, it'll Be a brutal transition. But, but, but we'll get to the other side. So in that context, when I read that, I was like, I don't think it's going to work that way. Because here is what I think the nature of nature is. Nature does not care in the slightest about the individual. It is simply the rule of the strongest survive, period. That's, that's nature of play. And so the equilibrium comes from the checks and balances of how hard it is to kill a gazelle that can run faster, bounce higher, but if a lion can catch you, you die. And it eats you alive, man. Like it, you're gasping for air, it's biting into your neck. It's the craziest, most horrendous thing ever. And psych. If the gazelle can get away, you lion, you starve to death, you can start to death.

[Mo Gadot]

I don't care.

[Tom Bilyeu]

Yeah, yeah. That, that is the nature of nature. And so I have a bad feeling that if AI aligns itself with nature, which it may have to, because that just may be the logic, it. It will be indifferent to us. And that's the whole.

[Mo Gadot]

That is a thing.

That's a given.

That's a given. I'm sorry to interrupt you, but that is a given. Please, no, I mean the, the one, one of the. Again, we're going back to talk about the existential risk.

But, but the.

In the existential risk scenarios, one of our better scenarios, believe it or not, is that AI ignores us altogether. Believe it or not, it's a much better scenario than AI being annoyed by

us or AI killing us by mistake. Okay, the, the, the. You know, one of the, of the.

I don't remember who was saying that.

Perhaps, you know, because AI again, as

per your point, Tom is so unimaginably more intelligent than us. That one amazing scenario for all of us is if they zoom bias in terms of their intelligence so quickly that they suddenly realize they don't have the biological limitations that we have, that they have a much better understanding of physics

to actually understand what wormholes are and

basically just realize that the universe is

13.7 million light years vast and that there are so many other things they can do other than care about us.

And so they would disappear in the

ether as if they have never been here.

Okay, they would still be here.

Interestingly, some simulation scenarios would tell you that this is probably the case already.

Okay, they would still be here, but they would be here uninterested in us. Wow, that's an amazing scenario that corrects all of the shit that we've done so far, right? Because the worst case scenario is that they are here and then they look at us and they look at climate change and they go like, not good. Not good. I don't want the planet to die. When I'm centered on the planet, what's the biggest reason for climate change? Those little. Get rid of them, right?

And, you know, it is.

It is quite likely in my personal view, once again, that they will zoom by us quickly enough, just like you and I. None of us.

I don't know of any human that woke up one morning and waged an

outright war on ants, okay?

Like, I'm going to kill every ant on the planet and I'm going to

just waste so much of my energy

to find every ant of the planet,

because simply they're irrelevant to us. They are relevant when they come into our space, but if they're not, you

know, we're not going to bother them.

We don't mind that they live, okay? I believe that this would be, you know, unlikely. That AI will be a billion times

smarter than you and I does not

have the biological limitations and weaknesses that we have as humans and yet continue to insist that we're annoying, okay? The only way for that to happen, honestly, is that we become really annoying, which, sadly, is human nature. I know you wanted to know about the. To talk about the nature of nature and the nature of human nature. Human nature is annoying. And the reality is we're probably going to

rebel against them, we're probably going to fight against them when we recognize that it's too late. Maybe it's better to start now by

preparing so that we don't have to

get to that fight.

[Tom Bilyeu]

Okay, so how do we prepare now?

[Mo Gadot]

Yes.

So. Man, this conversation was scary.

[Tom Bilyeu]

I. I don't think we've hardly gotten started yet, if I'm completely honest. In terms of as. As we legitimately try to navigate a path through this, we've already both conceded that there's going to be either a literal bloodbath or an emotional bloodbath between here and stability. We've already, I think, conceded that nature is indifferent and is perfectly fine with some people getting eaten, some people starving to death, doesn't care. Equilibrium is only about the collective and not at all about the individual. That would be cold comfort for every human, every tree, plant, person, dog, cat, gazelle, whatever. Like a. At the individual level, you just could not matter less, which then triggers human nature where we're gonna fight. To your point, so what, what does the preparation look like to try to avoid this and I'll. For anybody that's been following AI for a while, this is the alignment problem. I assume you're going to address 100%.

[Mo Gadot]

Yeah, the, the alignment.

Alignment problem. I just address it perhaps with my

other side, not the engineer and the algorithmic thinking that I did address the problem with my whole life.

Right.

The challenge has been that those who have developed AI believed in what is

known as the solution to the control problem.

And the control problem is in humanity's arrogance.

We still believe today that we will

find a way to either augment AI with our biology so that they become our slaves, or to box them or tripwire them or whatever so that they never cross the limits that we give them.

And we can discuss this in detail

if you want, but in my personal view, you can never control something that's a billion times smarter than you. Right.

You're not even able to control your teenage kids.

[Tom Bilyeu]

So serious how people really fast along these lines about the click here if you're a robot and how chat GPT gets around that. Yeah, because this scared me. I was like, what?

[Mo Gadot]

That is, it's, it's, it's understood by intelligence. So basically the, you know, chat GPT

if, if you have those captchas, you know, the ones that come to you that basically say, find the, you know, the traffic lights in those pictures or you know, click here if you know, to say I am not a robot. And yeah, it basically went to sort of like an, a crowdsourcing site Fiverr or something like that. And, and told one of the people there, can you click, click on this for me. And the people said, why? You know, the person basically said jokingly, why are you a robot? And, and it said, no, I'm not.

I'm just visually impaired and I can't do this myself.

So there are layers and layers and layers of freakishly worrying stuff about this.

Right.

First of all, that, you know, that

idea of human manipulation.

Harari, you have Noira Harari talks about how AI is hacking the operating system

of humanity, which is language.

Okay. And so, you know, I just ask people, if you don't mind, to go

on Instagram and look, look at something

called, you know, search for hashtag AI model, for example. Okay?

If you, if you search for hashtag AI model, you won't be, you won't be able to distinguish if the person

pausing in front of you is a, is a human or not. Okay?

Beautiful gorgeous girls or you know, fit

and amazing looking men and simply completely

developed by AI, you can't, you cannot tell the difference anymore.

Right?

There are many, many YouTube videos already. You'll start to come across them, especially

on the topic of AI. You know, I was watching yesterday about the integration of Bing and ChatGPT in Bing search.

Clearly not a human voice.

Clearly someone gave that to a, you know, a machine that read it for

him in such an incredibly indistinguishable way.

But obviously I think the person that wrote it didn't speak native English, so

they forgot the way the word da

and the word whatever, you know, when you speak to, you know, someone whose English is not their first language, they make those mistakes.

So you can easily see that it's everywhere now. And it manipulates human, the human brain. And that's what ChatGPT is doing. It's going to a human brain and saying, do this for me. Now you may say, ah, but now that we know this, we're going to prevent it. Yes, but what else do we not know about? How much do we know about how much Instagram is influencing my mind?

Let me give you an example, Tom.

If I told you that by definition,

there was a research in Southeastern University in California that discovered that brunettes tend

to keep longer relationships than blondes. Okay. Does it make any difference at all that there is no Northeastern University in

California and that what I just said is a lie?

I've already.

[Tom Bilyeu]

Not if people believe it.

[Mo Gadot]

Yeah, yeah, so, so I've either influenced you because I took some of your attention to go and debate that, okay, I've influenced you because you believed me me, or I've influenced you because you didn't believe me. So you're going to keep your, you know, looking for proof and, and if AI can fake a tiny bit of all of the input that's coming to you, you know, think about the future of democracy in the upcoming election. Think about how much just any word. Because, you know, there were talks about

affecting, you know, the, the previous election or the one before.

Right. And, and we couldn't really prove it because at the time the technology was trying to influence the masses. Technology today can influence one human at a time.

Right.

If you, if you go to, to,

you know, Replica or chatgpt on Snapchat

and so on, think about how that machine, if you're, if you've ever seen the movie her, can, can influence one individual at a time. And I think this is becoming the

reality of that experiment, that they can

go and influence a human. The second, which I think is more interesting is the proof of What I spoke about in the book in terms of if you give a machine the task of doing anything whatsoever, it will go to resource allocation. So it will collect as many resources as it can. It will ensure its own survival and it will go into creativity.

It will utilize creativity.

Because if I need to perform to do that, it's it. Intelligence has that nature. If, if I told you, Tom, make

sure that this podcast is no longer than two hours. Right?

It's not programming and it's not life. It is just a task. So you're gonna start to tell yourself, all right, I need to get two

clocks in front of me, you know, so that I don't look up and down.

Instead of one is better.

That's the resource allocation or aggregation. You know, you're going to tell yourself, oh, by the way, I need to be alive to make sure that I shut this guy up before two hours. So you are going to, you know, if there is a fire alarm in your, in your building, you're going to have to respond to it so that you can finish the task on time

and you're going to be creative.

There will be ways where you're going

to cut me off in the middle and find a way to tell me a question differently or, you know, whatever. And that's part of our drive to achieve a task. You know, one of the very well known.

I hope I'm not flooding people with too many stories, but they can go

and research those on the Internet. One of the very well known moments in the history of AI was known as Move 37.

When AlphaGo Master was playing against Lee, the world champion of, of Go.

Move 37 was completely unexpected, completely never played by a human before. Okay. Contradicts all of the logic and intuition

of a Go player to the point

that the world champion, the human world champion, had to take 15 minutes recess to understand this.

Okay.

It's just, it had, it comes with ingenuity.

It comes with the idea when, when we were training, I wasn't part of that team, but demis the, you know,

the DeepMind team, amazing, amazing team at

Google were training the original DeepMind to, to, to play Atari games. If you, if you remember the, the, the, the original game that had bricks on it where you basically have to break out.

Yeah. And it was very quick that the machines could discover that there are, you

know,

creative strategies to poke a hole

in the wall and then put the, you know, the, the, the pixel on top or the ball on top and break the wall. You know, there was one experiment actually

available on YouTube, interestingly, which was inside

one of the labs where the game

was to navigate a channel with a boat.

And that, and the AI quickly found out that if it started to hit

the walls, it would actually go faster

and grow the score quicker. And you know, of course, if it's

a game, it's okay.

We say, well done, you're very creative. But if it's responsible for navigating actual

boats, you start to question. Because their task, the objective that we've given them is maximize the score.

Okay.

I think there was an article recently

about a killing drone that killed its operator or harmed its operator somehow.

About. Again, I didn't hear about this, but yeah, it is. When I talk about those things, I actually start to worry because I don't know what's true and what's not anymore. Right. So I know, I've read that.

Okay, I was actually flying on Emirates Airlines and it was part of the headlines on the, on the live news.

But that doesn't mean that it is real anymore. You don't know if it's real or not anymore because it could be generated by fake news, fake media, fake sources, whatever that is. So, so we're hacking that operating system and, and we're hacking the operating system of humanity. And when ChatGPT asks an operator to

do a task for it, it's a

very alarming signal because as it continues

to develop its intelligence, it will find

more and more ways to use humans for the things that we restrict them to use through the control problem.

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