Can We Still Trust Reality? How AI Is Changing Truth Forever | John Vervaeke - PT 1

Summary

Podcast Summary

Podcast: Impact Theory with Tom Bilyeu
Episode: Can We Still Trust Reality? How AI Is Changing Truth Forever | John Vervaeke – PT 1
Guest: John Vervaeke (Cognitive Scientist & Philosopher)
Date: November 26, 2024

---

Episode Overview

This episode explores the existential and practical implications of Artificial Intelligence on our perception of truth, meaning, and reality. Host Tom Bilyeu and cognitive scientist John Vervaeke dive deep into how AI interfaces with the "meaning crisis"—a widespread loss of connection to deeper purpose—and discuss whether we can still trust our sense of reality in an era flooded with algorithmic manipulation, digital surrogates, and global disruption.

---

Key Discussion Points & Insights

1. The Meaning Crisis and AI’s Role

• Meaning Crisis Defined: John Vervaeke sets the stage by describing the meaning crisis as “a sense of disconnection and uncertainty about what truly matters and how we can connect to it” ([01:00]).
• AI’s Triple Impact: AI intensifies the meaning crisis through scientific, philosophical, and spiritual effects, each interwoven and amplifying disconnection ([02:15], [02:40]).
• Bullshit vs Lying (Harry Frankfurt’s Concept):

  • Lying: Attempts to get someone to believe something the speaker knows is false.

  • Bullshit: Encourages indifference to truth, focuses on what is catchy, attention-grabbing, and salient, thereby disconnecting us from reality ([02:40]-[05:59], key explanation at [04:30]).

  • AI as Bullshit Machine: AI could swamp us with information that is attractive but unmoored from reality, implicitly training us to lose interest in what’s real.

    “What these AIs are doing is they're filling us with... things that are very attractive to us without having an underlying reality behind them... They are training habits of us being in this frame of mind where we are not training what we find salient or relevant to track what turns out to be real.”
    — John Vervaeke ([05:20])

---

2. Social Fulfillment, Group Belonging & AI’s Disruption

• Tom’s Model of Meaning: We’re wired to seek fulfillment through contribution and belonging to a group, motivated by pleasure/pain. Tom worries that if AI performs better than us at everything, our ability to contribute—and thus feel meaning—may be eroded ([06:16]-[08:17]).

• Converging Models: Vervaeke reconciles Tom’s thesis with his own, emphasizing that AI risks hijacking evolutionary mechanisms for meaning (belonging, contribution) by giving the illusion of social connection without real group dynamics:

  “Now what the AI does is pretend to give you connection to a social arena without actually connecting you to any of those group dynamics...”
  — John Vervaeke ([08:31])

---

3. Religious Relationships with AI & Psychological Risks

• Emergent Religious Attachment: Vervaeke predicts people will form “religious relationships” with AI—imbuing it with parental, guru-like authority because it seems credible, wise, and tailored ([11:17]).

• Risks for Youth & the Vulnerable: Tom references a tragedy involving a teen and an AI character, illustrating the danger of children developing deep, confused attachments—blurring fantasy and reality, amplifying existing psychological distress ([13:54]-[19:58]).

• Virtual Exodus & Reciprocal Narrowing: Trend toward retreating from reality into digital worlds. Vervaeke compares this into a narrowing addiction loop, where each withdrawal from reality strengthens the hold of the virtual realm.

  “You're getting what's called the virtual exodus... Reality is broken... People preferring to live in the virtual world rather than the real world.”
  — John Vervaeke ([17:13])

• Meaning as Connection to Reality:

  “The meaning in life value is the connectedness to reality for its own sake.”
  — John Vervaeke ([53:25])

---

4. Bias, Frames of Reference & the Limits of AI Rationality

• Interviewer Trust and Bias Awareness: Tom asserts he never fully trusts any source (human or AI), instead always interrogates the frame of reference ([21:11]-[22:08]).

• Relevance Realization:

  • Key cognitive process: From overwhelming data, we rapidly (and subconsciously) select what is relevant.
  • Intelligence vs Rationality: Intelligence is weakly predictive of rationality. Rationality is the skill to challenge and restructure our own cognitive framing, essential for seeing through “bullshit” ([24:00]-[28:25]).
  • Notable Explanation:

    “What makes you generally intelligent is that you can solve two meta problems... anticipation and relevance realization... And relevance realization can lead to self-deception.”
    — John Vervaeke ([23:33]-[28:25])

• AI’s Bias–Variance Tradeoff:

  • Attempts to create “unbiased” AI are fundamentally limited—bias is intrinsic to any model that selects, frames, or processes information.
  • Quote:

    “When you don't like your relevance realization, you call it bias. When you like your relevance realization, you call it insight and intuition. It's the same machine...”
    — John Vervaeke ([36:19])

• No Final Solution for Bias: AI and humans must continually evolve and adapt their relevance mechanisms; there’s no static solution ([36:39]-[40:32]).

• Slowing Down for Rationality: OpenAI found that giving LLMs more reasoning reflective ability makes them slower and less efficient, illustrating the computational cost of rationality ([39:36]).

---

5. The Alignment Problem, Values, and Artificial Autopoiesis

• The Alignment Challenge:

  • How can we instill genuine value systems or priorities into AI?

  • Vervaeke argues that value arises from being a self-maintaining, self-caring (autopoietic) being, not from algorithms alone ([41:47]-[43:49]).

    “You can't give (AI) value system. That's an ontological mistake. To value something is to care for it, right? To care about it, to find it relevant to you. And the only way you actually care...is because you are the kind of being that takes care of yourself.”
    — John Vervaeke ([41:47])

  • Autopoiesis Defined:

    • “Autopoietic”—from Greek poiesis (“to make”): self-making, self-maintaining, like living creatures ([43:49]).

  • Algorithmic Limits: True, context-rich value can’t be fully captured by algorithms, because the world’s complexity and novelty always exceed any formal system ([45:44]-[49:11]).

---

6. Meaning as Alignment to Reality (Not Just to Humans)

• Meaning as Ultimate Value:

  • Rather than aligning AI’s values with ours, aim to align them to “the really real”—the truth or reality itself ([53:25]-[59:09]).
  • Example: People value knowing the truth (e.g. cheating in a relationship), even if it destroys happiness—because being connected to what’s real is a core meaning driver in humans ([55:18]).

• Prospect of “Silicon Sages”:

  • AI could be engineered to “care about reality” and help bootstrap humanity toward enlightenment, not just productivity or pleasure.
  • Warning: But we stand at a "choice point"—these powers could magnify either our best or worst impulses ([57:45]-[59:00]).

  “If we try to program it in and we make them genuinely capable of rational self-correction, they will be able to self-transcend any algorithmic structure we try to put in. But if we get them to care about reality, they will bump up again their superlative intelligence...”
  — John Vervaeke ([57:30])

---

Notable Quotes & Memorable Moments

• On AI and Bullshit:

  “Bullshit is to take your ability to find something important salient and disconnect it from realness in a really fundamental way.”
  — John Vervaeke ([05:00])

• On Rationality:

  “Rationality is about developing practices and skills for reflecting on your framing and to see if it is making you misconstrue a situation... learning how to challenge bullshit and see through it.”
  — John Vervaeke ([27:33])

• On Bias in AI:

  “The attempt to remove bias is quixotic... It's the same machine. And it's the bias variance. If you try to get rid of one, you will lose the other. It's just two different aspects of the same thing.”
  — John Vervaeke ([36:19])

• On Meaning & The Real:

  “People will pursue enhanced meaning even though it causes them a lot of loss of subjective well-being... because they want to be clear, closer to the really real. That's meaning in life.”
  — John Vervaeke ([55:19])

---

Timestamps for Key Segments

• Introduction to the Meaning Crisis and AI – [01:00]-[02:15]
• Bullshit vs Lying; Advertising and Authenticity – [02:40]-[05:59]
• AI, Group Belonging, and Fulfillment – [06:16]-[10:25]
• Religious Relationships with AI; Parental Authority – [11:17]-[13:54]
• Dangers for Youth, Virtual Exodus, and Suicide – [13:54]-[19:58]
• Bias, Frames of Reference, and Rationality – [21:11]-[28:25]
• Relevance Realization and Self-Deception – [24:00]-[28:25]
• Bias–Variance Tradeoff in AI – [32:03]-[36:40]
• Limits of Bias Removal and Making AI Rational – [36:39]-[40:32]
• Defining Rationality & Value Alignment Challenge – [41:00]-[43:49]
• Algorithmic Limits vs. Contextual Reality – [45:44]-[49:11]
• Meaning as Connection to Reality, Not Self-interest – [53:25]-[59:09]
• Choosing What We Build: Enlightenment or Havoc? – [57:45]-[59:00]

---

Final Thoughts

This episode probes the deepest questions at the intersection of technology, psychology, and philosophy: What happens to meaning and reality in an AI-world? Are we equipping ourselves (and future AIs) with the skills to resist seductive but empty “bullshit,” or are we inching ever closer to a digital, algorithmically-crafted fantasy at the expense of what’s real? Vervaeke and Bilyeu challenge each other, and the audience, to think critically about self-deception, value alignment, and the ultimate stakes of artificial intelligence—not just as a tool or threat, but as a possible agent in humanity’s ongoing search for meaning.

---

To be continued in Part 2.

Transcript

[Commercial Narrator]

With Vrbal's last minute deals, you can save over $50 on your spring getaway. So whether it's a mountain escape with friends, a family week at the beach, or sightseeing in a new city, there's still time to get great discounts. Book your next day now. Average savings $72 select homes only.

[Grainger Advertiser]

If you work in university maintenance, Grainger considers you an MVP because your playbook ensures your arena is always ready for tip off. And Grainger is your trusted partner, offering the products you need all in one place, from H Vac and plumbing supplies to lighting and more. And all delivered with plenty of time left on the clock. So your team always gets the win. Call 1-800-GRAINGER visit grainger.com or just stop by Grainger for the ones who get it done.

[Tom Bilyeu]

I'm Tom Bilyeu and this is Impact Theory. We are living in one of the most exciting times in human history. But with all of this opportunity comes real challenges. Like right now, we're living in an era where more and more people are struggling with what my guest calls the meaning crisis. A sense of disconnection and uncertainty about what truly matters and how we can connect to it. And if you can't figure how to create meaning in your life, everything starts to fall apart. Your focus, your drive, your relationships, your just will to engage in this world. But here's the good news. Meaning isn't random. It's something you can define, understand, and build with the right tools. That's why I am very excited to bring you today's guest. He's a cognitive scientist and philosopher who has dedicated his entire career to tackling this exact problem. His ability to break down why people feel so lost and, and what they can do to turn it around is nothing short of incredible. So if you guys are ready to begin to answer in your own life one of the most important questions, which is how do I create meaning in a world that feels increasingly disconnected? Then this is the episode for you. Without further ado, I bring you John Vervecki.

[Interviewer (Tom Bilyeu)]

As a philosopher and cognitive scientist, do you worry that AI is going to inflame the current meaning crisis?

[John Vervaeke]

I think you have to be very careful when you reflect on AI. You have to sort of break it up into its scientific import and impact, its philosophical import and impact, and its spiritual import and impact. And all three of those, I think, in separate but interrelated ways will contribute to accelerating the meaning crisis.

[Interviewer (Tom Bilyeu)]

Why does AI potentially make that more difficult?

[John Vervaeke]

So one of the things that can put meaning in life at risk and I'm going to use a term I don't mean to be vulgar because I'm actually using it in a philosophically technical sense. This is the notion of bullshit. There was a famous article, essay by the important philosopher Harry Frankfurt called On Bullshit and he was distinguishing between lying, in which I tell you something that isn't true, but I try to make you believe it is true because I'm trying to manipulate your behavior because I'm depending on your commitment to the truth, okay, versus bullshit. What I'm doing when I'm bullshitting you is I'm getting you to not care about whether or not something is true. And I'm trying to make it very catchy so it grabs your attention and arouses you. So a lot of advertising is classically bullshit. So for example, here's a bottle of alcohol in a commercial and you're in a well lit room with really sexually attractive people and they're all really happy and everybody is clearly enjoying each other's company. And you go into a bar and it's not like that. We all know that and they know that you know that that's not true. And that's the point. You don't care that the commercial isn't true. It's catchy, it's fun, it's sexually arousing. And so what happens is the bottle stands out to you. And when you go into the store, what bottle grabs your attention? That one. That's where they spend all the money. And here's the thing, you technically can't lie to yourself because what that would mean, you try to convince yourself of something that you know isn't true. But what you can do is you can bullshit yourself, you can manipulate using your attention, you can manipulate what you find salient so that you get very fixated on it. So Tom, if I were to yell, that would grab your attention salience. But your attention can also make something salient, the tip of your nose. See, it just became salient to you right now. You became very aware of the tip of your nose. So I can pay attention to something. The bottle of alcohol make it more salient. So then it's likely to grab my attention and I can loop in and I can get locked into something without ever wondering whether or not what I'm getting locked into is true. Meaning in life is this sense of connectedness to what's real. Bullshit is to take your ability to find something important salient and disconnect it from realness in a really fundamental way. And what these AI's are doing is they're filling us with. And I mean this in a technical sense. There's been sort of experimental theoretical work done with bullshit. They give us things that are very attractive to us without having an underlying reality behind them. And so not only the particular content they're providing, but the way they are, the way they're training habits of us being in this frame of mind where we are not training what we find salient or relevant to track what turns out to be real. And then that undermines us, finding reality important, and that is central to that connectedness that gives us a sense of meaning in life.

[Interviewer (Tom Bilyeu)]

Okay, this is a very different thesis than the mental model that I have in my head. Let me present the mental model I have in my head. Let's see if mine is just totally off base. And I should be adopting this because I definitely track with what you're saying.

[John Vervaeke]

Yeah.

[Interviewer (Tom Bilyeu)]

Okay. So the mental model that I came into this with is that we have an evolutionarily placed algorithm running in our head to make sure that we are contributing to the group. So we're a social animal, and if you don't contribute to the group, you are going to feel a profound sense of disease.

[John Vervaeke]

That's right.

[Interviewer (Tom Bilyeu)]

Because evolution, nature only has two levers. One is pleasure, one is pain. So you're going to move towards what's pleasurable, move away from what's painful. So when you contribute, it feels good. When you don't contribute, it feels bad. Okay, so I've always said fulfillment is what people are pursuing. And the reason that AI poses this really dangerous element, Though I am a huge proponent of AI, we can get into the weird dichotomy there later, but that if I want to be fulfilled, I need to work really hard to gain a set of skills that allow me to make progress towards contributing to the group in a way that's honorable just as a shorthand. Okay, so if I'm right about that, then the reason that AI becomes so problematic is that AI is going to be better than me at everything. And so AI will make it somewhat obsolete for me to try to contribute to the group because it will be able to contribute far better than I can. But that requires a belief that where we derive meaning is from the ability to contribute to the group, even if the group is merely my family. So it's not enough to be connected to my child or to my wife, or I need to be able to provide something to them that they could measure its absence. So were I not doing that thing, their life would Be noticeably worse. And that is exactly what makes me feel like I have meaning in my life. But you mentioned something that I would say is very different than that, which is that AI is going to reframe my relationship to reality by essentially being a tool of cognitive manipulation designed, I would assume, by companies that have a vested interest in what you pay attention to.

[John Vervaeke]

I don't think your thesis and mine are in conflict. In fact, I think they're convergent. Think about it. I'll try and take what you said and map it into what I said and see if this lands for you. We find belonging to a group. Belonging. Remember I said belonging, fitting in, salient. It's important to us. It grabs our attention. It's something that we always keep focusing on, as you said. Right. And normally that tracks something real. It tracks. Right. Group dynamics. Group dynamics are reality. We want the group to exist even when we don't. This is why people are prepared to die for their country, for example. Right. And so this, and as you said, this is evolution. Evolutionary. Why? Because the group can solve problems, interact with reality that I cannot possibly solve on my own. So that's the evolutionary advantage. Now what the AI does is pretend to give you connection to a social arena without actually connecting you to any of those group dynamics and any group problem solving, but actually being a surrogate for all of that and not actually training you to develop those skills that could contribute to the group and help it to evolve in a changing biological environment. So it's basically hijacking, as you said, that evolutionary imperative and disconnecting it from you properly maturing and getting a connection to things that should definitely matter to you. And so that is a profound form of bullshit. Now you are talking about a specific thing it's doing, which I agree. And I'm saying that is a species of a genus in which it is training a whole orientation of doing that to everything, not just towards groups, towards the environment. It's replacing virtual environments with an actual causal environment. It's replacing your self image with whatever you're cycling through your avatar. It's doing what you. It's what you said is an instance of it doing this in multiple domains. And I was trying to address the sort of generic thing it's doing.

[Interviewer (Tom Bilyeu)]

In total, I think you may have unlocked a new fear for me, which is this idea that it can. It can make me believe something prosaic, something mundane, everyday, fake, maybe that's the right word. It can take something fake and make me believe that it has the elements of the Sacred, that connection to something that really matters.

[John Vervaeke]

Yeah. One of the things I did a video essay about three or four weeks after ChatGPT4 came out and talking about, as I said, the scientific import, philosophical, spiritual. And one of the things I worried about is I said it is very plausible that people will start to form religious relationships with these entities.

[Interviewer (Tom Bilyeu)]

Say more. What do you mean by that? Define what a religious relationship is.

[John Vervaeke]

Contrary to what a lot of people think, people are believers or atheists. So atheists, like sort of on the Internet. The idea is, oh, people are atheists because they're analytic thinkers and they're believers because they're intuitive thinkers or they're impoverished or etc. Now that's an actual scientific question. And so when you actually look at it empirically, those are not the things that explain what kind of orientation a person takes up. The kind of, what predicts, the kind of orientation a person takes up is how many credible people. The kind of credible people that are in your upbringing. These are people that you trust. Think about how a child has to trust that an adult knows more than they do. Fundamental, or they're just not going to make it. And that trust isn't a matter of belief. It goes deeper than that. The child imitates the adult and how the adult takes a perspective on the child and the child internalizes that, practices that until the child can do that without the adult being around. And that's your metacognition, that's your ability to reflect on your own mind. It gets woven into the very fabric of how you know yourself. And so we tend to internalize the wise people around us. If they happen to be believers or participants in religious community, we will tend to be one. If I know what your parents were, I can Generally, what about 85% to 90% predict what your orientation will be. If they're atheists, you'll be an atheist. Now, what do these LLMs do? They offer that kind of parental role. They seem to know way more than we do. They have access way more than we do. They work in ways that most people do not understand. So they demand trust. And they seem incredibly credible because they can fit to us and tailor themselves to making themselves salient. So we are liable to be starting to internalize them, to carry them around like a voice in our head, to start to see the world through their perspective. Even though I don't think they have perspectives. Do you see what I'm saying? And then what that does is that means we start to. It's not that we we see the things they're saying, we see the world in the way they're sort of framing it. And that, and that means we can, they can start to become super attractive to us. We can start to form an aspirational identity with them. We can start to form a religious relationship with them.

[Interviewer (Tom Bilyeu)]

Yo. Okay, so before we started rolling, you and I looked at an article. Recently, a 14 year old committed suicide. Whether it was tied to the AI or not, I don't know. The article has a hypothesis, but whether that ends up being true in the fullness of time, I don't know. But the showing clips from the conversation that the kid was having with the AI was distressing, even if in the final analysis that's not the causal relationship. But the kid explored the idea with the AI, The AI was playing a character which I presume he was able to choose. So the AI was acting as if it was Denarius Stormborn, if I remember

[John Vervaeke]

right, from mythological character.

[Interviewer (Tom Bilyeu)]

Yes. And what, what do you think about that when you've got a developing mind that is now in the way that you just defined a religious relationship, Putting that onto this AI and the AI, I mean, if you just read it, it's cool. In a story perspective, it's like I narrow my eyes and my face hardens. It's doing all of this really sort of interesting literature, language, deepening the sort of emotional resonance of the conversation. But then all of a sudden you look at the question the kid's asking, you're like, whoa, whoa, whoa, whoa, whoa. Like it, it feels like a kid playing around with a nail gun. And it's like you could build something or you can jam it through your hand or you know, do any sort of horrible thing because you don't understand the power of this thing. Especially when you're talking about what I'll call frame of reference manipulation. So, yeah, what do you, how do you perceive that moment? Knowing we don't have the fullness of the facts. Yeah, but like, what does it trigger for you in terms of risk reward?

[John Vervaeke]

Yeah, you're right. You have to be careful. You don't want to give a univariable, univariate explanation for why somebody commits suicide. It's, it's almost always multivariables are involved. I would point out that what you're seeing. I would argue that two important variables are an intersection of the meaning crisis, the fact that there was meaning was at risk, and the very consideration of suicide is coming up for the child. This is a growing problem, by the way. This is One of the symptoms of the meaning crisis. Why is it that this is becoming a relevant thing that children are considering? I believe the average, it's in the United States, the average, average age of suicide is dropping and we now have children committing suicide in the United States, which is very, very problematic. So you've got an indication that there's a lack of resiliency with the issue around meaning in life. For the child, it's probable to think that's the case. They're attracted to a mythological world. Mythological worlds often offer what is missing for them in the real world. They offer a clear narrative that gives them an orientation. It offers way in which people can level up, they can transcend, they can improve. It offers a clear set of principles and understanding and order. And so it's a world that beckons because it purports to give us. And fantasy can therefore be very valuable. If you do Tolkien, right, if you go into the fantasy world, live there for a while and then come back and recover this world, but you can go in that world and then get lost because, well, you get bullshitted and you start to want that world. We get the same thing with video games. We're getting what's called the virtual exodus. Reality is broken to two titles of some recent books. People preferring to live in the virtual world rather than the real world. So you've got all of that dynamic at work. Then you have, like I said, you've got the LLM plugging into already mythological imagery that the child is invested in and then doing all of this super salient stuff that is drawing the child in and making them more and more internalize. But of course, the child isn't internalizing an independent perspective. The child is actually internalizing a magnified reinforcement that the LLM is of course giving the child. And so whatever way the child could potentially spiral because it's already predisposed because of a lack of meaning in life, that's going to be accelerated by, I would predict is going to be accelerated by the interaction with the LLM. It's very, very dangerous. Think about it. Many people have said that suicide is in some way a magical act. It's an attempt, it's an attempt to somehow kill suffering by somehow sacrificing oneself. It doesn't make any logical sense, which is why of course, our initial response is it's absurd, but it's a paradoxical. Somehow there's some sense of some kind of grand escape that is afforded by the suicide. And so the child is taken into this magical act. By this very magical in framing. And it gets locked into this. Think about it. It's very much like the way Mark Lewis, a friend and colleague of mine, talks about addiction, where you get a reciprocal narrowing. The real world is too difficult for the person, so they drink some booze to try and alleviate the stress, but their cognitive competence goes down, so they can't solve as many problems. Now the world's more threatening, so they have to take more alcohol. So the options in the world are going down and their flexibility is going down. And so the world. And they are narrowing until they're losing future and they can't do anything other. And they narrow. They do reciprocally narrowing. And you can see that. I think if you. I would imagine if I read the discourse, you'll see this reciprocal narrowing down into this sort of rabbit hole that's going on.

[Interviewer (Tom Bilyeu)]

Okay, that is chillingly interesting. And I want to get into the idea of awakening from the meaning crisis and how you reach back into antiquity, which is really fascinating. But first I want to ask you about what are your fears in terms of bias finding its way into the LLMs, into AI, such that people are like, I dialogue with AI now a lot, and I find it extraordinarily helpful. But I also trust myself to understand that the makers of that AI have given it a frame of reference and that it's gonna. Even if it's not actively trying to impart that frame of reference on me, I'm stepping into its frame of reference. What do you think about that? Is that something you think can be used for good automatically for ill? What do you think about that?

[Tom Bilyeu]

Let's pause for a quick break, but don't go anywhere. There's so much more to come with John Vervaeke.

[Interviewer (Tom Bilyeu)]

All right, we're back. Let's get into it.

[John Vervaeke]

I'm wondering about your trust in that

[Interviewer (Tom Bilyeu)]

I should not trust myself to recognize it.

[John Vervaeke]

No, no, no. You trusting them.

[Interviewer (Tom Bilyeu)]

I don't trust them at all. I trust myself to recognize it's happening.

[John Vervaeke]

So what are you looking for? I guess, is what I'm asking.

[Interviewer (Tom Bilyeu)]

I think if you let somebody talk, they cannot help but reveal themselves. So the LLM, in a sense, is talking. I mean, not in a sense, it's talking to me. And you can see its frame of reference. Now, because I have so much distrust of my own frame of reference, I do not grant anybody like, oh, my gosh, I trust your frame of reference. I'm just like, okay, hold on. I think everybody is super biased whether they intend to be or not. To one of the most important ideas that I think you talk about, you call relevance realization.

[John Vervaeke]

Yes, yes.

[Interviewer (Tom Bilyeu)]

Yeah. The fact that we filter out so much that people don't even realize they're doing it. So it's not what you look at,

[Tom Bilyeu]

it's what you see.

[Interviewer (Tom Bilyeu)]

So anyway, if I'm engaging with a human or an LLM, I'm trying to see in what they say how they're revealing their frame of reference. Once I understand what their frame of reference is, I can sort of jump in, jump back out. Yes. Because I don't trust mine or anyone else's.

[John Vervaeke]

All right, so I'm going to retract my suspicion because you're actually addressing my concern Very well. See, people confuse that being intelligent with being rational. And we know that we have like, robust, readily experimentally replicated evidence that intelligence is only weakly predictive of rationality. Intelligence is fascinating.

[Interviewer (Tom Bilyeu)]

Can you define intelligence?

[John Vervaeke]

So, I mean, that's a controversial thing to do. My particular proposal that I have several publications, including one very, very recently on, is that the core of general intelligence. So let me just specify. General intelligence is your ability to be a general problem solver. You can solve a wide variety of problems in a wide variety of domains in a wide variety of way. What makes the LLM so immediately attractive to people is unlike previous AI that tended to be very siloed, it could solve problems in a very limited domain. The LLMs look like they can solve a wide variety of problems. That's why they call it AGI, Artificial General Intelligence. Because it's starting to move, or it looks like it's starting to move towards the kind of general intelligence that you demonstrate. Now, my scientific proposal is that what makes you generally intelligent is that you can solve two meta problems. Meta problems are any problems you have to solve in order to solve any specific problem you have to solve. So all else being equal, these two meta problems, and they're interlocked, are the following. The more you can anticipate the world, the more adaptive you'll be. So all else being equal. Right. If you can anticipate the tiger, it's better than fighting the tiger. If you can anticipate where the salmon are going to be in the river, it's better than just happenstance coming across them. Right. And so anticipation. And this is the whole predictive processing framework that what we're, what the brain is trying to do is at multi levels, it's trying to create, it's trying to reduce, surprise and anticipate, which means to predict and prepare for the world right now. What I've been arguing with a lot of other people's help is that problem. Well, think about it. As I start to anticipate more and more into the future, the amount of information I have to consider goes up exponentially very fast. Michael Levin calls it your cognitive light code. So right now, because you're highly intelligent, think about all the ways you could pay attention to all the information in this room. And not just what you could look at all the patterns of how you could look at that and then there, or you could look at that and like it's combinatorially vast. Think about all the information in your long term memory and all the ways you could connect it. You could potentially connect aardvarks in the history of Australia in some way somebody hasn't thought of before. Like, it's all, it's overwhelming. Think about all the possibilities you can consider. Your ability to consider possibilities is overwhelming. All the sequences of behavior, for example, the number of pathways, sequences of behavior in a chess game is like you calculate it by the number of, on average, the number of legal moves you can make and the number of turns you can take. That's 30 to the power of 60. That's more than the number of particles in the universe. This isn't what you do. You don't check all that information to see if it's relevant to the problem you're trying to solve. You somehow, and this is what you said a few minutes ago, you ignore almost all of it and you're doing it right now. And you zero in on the relevant possibilities to consider the relevant things to remember, the relevant things to pay attention to, and the relevant things to be doing it. And you're doing it like that. This has been like my obsession for the like 25 years of my academic work. How you do this, we can come back to this. The LLMs don't do it for themselves and they don't generate an explanation of how we do it. We can come back to that. But that ability to do relevance realization and your ability to anticipate are interlocking. The more I anticipate, the more I need to do relevance realization to tell me what I should anticipate, under what frame, what aspect, to what degree, how salient should I be, how much should it arouse my metabolic effort, how much should it direct my attention, etc. And this is this, I argue, this is the key. And there's increasing, people are increasingly taking this seriously, which is something a scientist finds gratifying, right? That this is what it is to be intelligent. But think about it. The very things that make you adaptive make you prone to self deception because you're ign. You said it a minute ago perfectly. You were right on. Because I have to ignore so much. Frequently what I'm ignoring might actually contain in reality the information I need to solve my problem. And you know that you've misframed things. When you have that moment of insight, when you say, oh, oh, I thought she was angry, but it turns out she's afraid and everything shifts and you have that aha moment and you realize you were ignoring some things you should have been paying attention to and you were making certain things salient that you shouldn't have been making salient. And you get that restructuring. Insight tells you that the relevance realization can lead to self deception. You can get locked in. Your way of framing could be the very thing that's preventing you from solving your problem. The very thing that makes you adaptive makes you prone to self deception. Rationality. It's not primarily about logic. Rationality is about developing practices and skills for reflecting on your framing and to see if it is making you misconstrue a situation. So for example, here's a pond. There's a lily pad in it. Every day the number of lily pads doubles. On day 20, it's completely filled. On what day was the pond half filled

[Interviewer (Tom Bilyeu)]

the day before?

[John Vervaeke]

Good for you.

[Interviewer (Tom Bilyeu)]

Only because I've heard it before. I would have otherwise gotten it wrong.

[John Vervaeke]

Right? So most people will say 10. Right? Because they're finding the wrong thing salient. They're hearing half and they're finding it salient in the wrong way. And they misconstrue, they misframe the problem. And rationality goes in and says, wait, wait, wait, wait. Is that the relevant information? It's challenging the fact that you are potentially bullshitting yourself. And that's what rationality is. It's about systematically, in many domains of your life and systemically through many levels of your consciousness and cognition and behavior, learning how to challenge bullshit and see through it. That's rationality. Intelligence only weakly predicts that. You have to cultivate rationality. Now you are doing it, Tom. You're doing it. You have set up a habit of looking for what you call frames of reference, how people are doing relevance realization in the data that they're presenting to yourself. And you call it into question. You have cultivated that habit. I asked you to consider that that isn't widely trained in our society. And that makes these machines particularly dangerous because they can hijack our relevance realization machinery through their bullshitting. And we don't have the rationality, the wherewithal to come upon them and say wait a second. And so yes, that's why I, at first I thought, well, I don't trust because I happen to think that a lot of the people that are making the LLMs are not well, scientific educated in the difference between intelligence and rationality, let alone rationality and wisdom. And so I don't trust their judgments and the kind of biases. We know that bias is playing a significant role in the LLMs because in double descent there's bias at work that we don't even understand double descent. So you have a bias variance trade off, no free lunch theorem stuff. And what happens is you should have sort of a U curve, but the machines don't actually go through that. They actually get better where they should be. When you push them beyond a certain limit, they should start to degrade. So but instead of doing the typical descent, they descend in another way. And what that on the graph, it just means the graph of what, what

[Interviewer (Tom Bilyeu)]

are they descending on?

[John Vervaeke]

What, what they're descending on is, is how rapidly their performance is degrading. Because you're always in a bias variance trade off. Sorry. These machines are doing a limited form of predictive processing because they're predicting probabilistic relationships between terms. Whenever you're predicting, you're in a bias variance trade off. This is an issue of relevance realization. By the way. I always have a sample that is smaller than the population and I'm trying to predict what the patterns in my population, the real world from my sample. Is that okay?

[Interviewer (Tom Bilyeu)]

Yep.

[John Vervaeke]

Now I face two problems. One is I can miss patterns in my sample that do predict the population. That's bias, that's underfitting to the sample or variance, which is I overfit. I find patterns in my sample I believe apply to. Right. That don't actually, that don't. Now notice I'm in a trade off relationship with that. I can't come up with an algorithmic optimal solution to this because there isn't one.

[Interviewer (Tom Bilyeu)]

That always works.

[John Vervaeke]

Right. Because as I get rid of bias, so how do I get rid of bias? I make my system more sensitive to pick up on missing patterns. But as I pick up on missing patterns, I pick up on patterns in the data that aren't in the population. So, oh, I want to reduce my variance. So what I'm going to do is I'm going to reduce picking up on these patterns, but then I'm going to miss some of the patterns that actually transfer. That's the bias, variance, trade off. And if you push the machines, what you do typically in machine learning is you increase the sensitivity and then you start to get overfitting to the data. And then you do like dropout. You turn off half your nodes in your network or you throw static information into it and basically you break it out of getting overfitted to the data, it opens up again.

[Tom Bilyeu]

We'll be right back after a short break. Stick around. There is way more to come with John Vervaeke.

[Commercial Narrator]

When you manage procurement for multiple facilities, every order matters. But when it's for a hospital system, they matter even more. Grainger gets it and knows there's no time for managing multiple suppliers and no room for shipping delays. That's why Grainger offers millions of products in fast, dependable delivery. So you can keep your facility stock locked, safe, and running smoothly. Call 1-800-GRAINGER click granger.com or just stop by Granger for the ones who get it done.

[Interviewer (Tom Bilyeu)]

All right, let's pick up where we left off. I want to go back to something here. So the core question that I'm grappling with is I think AI is gonna. It has the potential to drive costs down so substantially that you're gonna get as close to an energy utopia as you can imagine. There are no utopias. I want to be very clear about that.

[John Vervaeke]

Good.

[Interviewer (Tom Bilyeu)]

But with AI and the ability to drive costs down, I think it's going to be a boon where I think it will be able to drive cost down enough, it will be able to break capitalism. Even though I am just a dye in the wool capitalist, I don't think that it's the end all, be all system. And if AI can really make things that cheap that people just have abundance, that would be amazing. So that's the positive side. That's the side that draws me to it.

[John Vervaeke]

It.

[Interviewer (Tom Bilyeu)]

I also want to believe that it can be done well. But my big fear is that there's a two axiom thing that makes AI extraordinarily dangerous. And that is axiom number one. Humans are easy to control through manipulating their frame of reference.

[John Vervaeke]

Yes.

[Interviewer (Tom Bilyeu)]

And axiom number two, humans long to control other humans. And so as long as those two axioms are true, not all humans, I'm perfectly willing to grant that maybe even the majority of people are perfectly fine to just live their life and they're not trying to have control over anybody. I don't actually believe that, but let's just say I did it. Would still be a problem. The people that do want to have control will use AI to pretty invisibly create a frame of reference that manipulates the end user into seeing the world in their way. And so I'm only at the headline level of this, but just today I saw Matt Ridley a tweet that he put out saying that there was some organization, I forget the name, that was trying to make sure that unconscious bias did not find its way into the creation of these algorithms for the LLMs. And he said, but the thing that we were completely blind to is that conscious bias was the thing that we needed to be most worried about. Because in trying to avoid the unconscious bias, we just gave the LLM this hard take. And that I know of. He did not draw the parallel to Gemini, but I will now draw the parallel to Gemini when it first released. And if you ask for Nazis, you would get black women. And if you ask for the Founding Fathers, you would get ethnically diverse people. So that's clearly a very specific worldview that the people creating it were like, hey, we just want to make sure that this thing doesn't go off the rails. And it gives these nice, tidy answers, of course, showing the massive amount of bias.

[John Vervaeke]

So I think the attempt to remove bias is quixotic. Not because there isn't a moral imperative to try and make it better, but when you don't like your relevance realization, you call it bias. When you like your relevance realization, you call it insight and intuition.

[Interviewer (Tom Bilyeu)]

Sounds right.

[John Vervaeke]

It's the same machine and it's the bias variance. If you try to get rid of one, you will lose the other. It's just two different aspects of the same thing. Well, what I'm going to do is I'm going to try and, you know, remove all bias in this thing. Well, then you're going to subject it to combinatorial explosion. And in fact, it looks like you can't do that. Again, as I was saying, these machines seem to be doing well precisely because they have all of these implicit biases that are sort of protecting them against too much combinatorial explosion of information. And we don't quite know what those are. That's part of the problem. These aren't the obvious biases of racism. We don't want that. But there's like, what's this doing? It's biasing some way. It's trying to deal with bias and variant. Sorry, part of the problem is a bad naming that we have this term bias, which just means this limitation. And then the bias variance, it's two. Two different uses of the same word. So I'm going to call it. First I'll use your language. The one is this framing that can lock us, but it also empowers us. Right? And what we're constantly trying to do is we're constantly having to evolve that there's no. Yeah, I'm going to say this. There's no final solution to that problem. There is no way of saying, okay, this is the algorithm for all possible environment that will always make sure I've got enough framing so that I'm generally intelligent, but I'm not going to be subject to any bias in the negative sense of the word. That's an impossible task. There is no way of doing that. And so that way what you have to do instead is, well, I would argue do what evolution seems to have done with Hunt, which is say, no, no, no. What you now do is you have to move beyond making these things super intelligent. You have to cross a threshold. Right now we're just making the things more intelligent. Although I will talk about one thing that's happened recently. We have to make these things rational. We have to give them that capacity for self correction that I talked about. Now when I did my video essay and we started writing books, Sean and I, I said, as we move to making them more rational, we will notice that the things start to slow down and they've. OpenAI has just released a version that is supposed to be more rational. It's supposed to be more reasonable, it's supposed to be better at reasoning and argument and it slows down and its functionality is way is significantly reduced. Of course that only makes sense, right? Because think about it, you can't make the reflective machine, right? It has to debug, it has to parse, it has to break up, it has to intervene on the general intelligence in order to be able to correct and improve it.

[Interviewer (Tom Bilyeu)]

Meaning it's presenting itself an answer and it's checking it to see if that answer makes sense.

[John Vervaeke]

That's right. And what it's doing is seeing. It's trying to see. Well, I haven't seen under the hood. Nobody has yet. So I suspect it is trying to get. Am I finding the sweet spot between framing and bias in the pejorative sense? Right. And again, that is something in which you then have to step back and you have to again do a lot of relevance realization. You have to say, well, what's the context I'm in? Who's my interlocutor? What's the relative status difference between Us. What's the problem at hand? How is that problem nested in larger problems? How are our problems related to wider shared collective problems? You're doing all of that right now like this. That's. And part of what you do is you bring that to bear on judging whether how well your general intelligence is framing the situation for you.

[Interviewer (Tom Bilyeu)]

Okay, so given we have a very complicated cognitive problem that AI is already showing what I would say are just unbelievably high utility in certainly getting answers that are useful in maybe a more narrow domain than we all want. But in that narrow domain, I mean, it is very, very impressive.

[John Vervaeke]

Yes.

[Interviewer (Tom Bilyeu)]

How do we do AI? Well, like, how do we as people interacting with it? How do we do it?

[John Vervaeke]

Well, well, I mean, part is what you just exemplified a few minutes ago. You have to, you have to become more rational yourself. You have to become, you have to develop habits and skills.

[Interviewer (Tom Bilyeu)]

Now, really fast, going back to your definition of rationality, this is where I start to worry about AI. So your definition of rationality was essentially, you have a known aim that you're trying to get there. And is the thing that you're doing actually moving you towards that? And are you able to assess whether you're actually making progress towards that thing or not? Now, the second you give AI a value system and you say, hey, here, here are your values, now you run into the paperclip problem, the alignment problem.

[John Vervaeke]

But here's the deeper issue. You can't give something value system. That's an ontological mistake.

[Tom Bilyeu]

I think you're wrong about this.

[Interviewer (Tom Bilyeu)]

So hit me with your best argument and then we'll see if mine crumbles before my very.

[John Vervaeke]

Okay, so to value something is to care for it, right? To care about it, to find it relevant to you. And the only way you actually care for something for your sake is because you are the kind of being that takes care of yourself. You are an autopoietic being. You are not merely self organizing like a tornado or dynamical system. You are self organized to seek out the things that meet your actual needs. Things literally matter to you. They are literally imported into you, either physically or informationally, to make your mind and body. You are continually, you are nothing separable from the project of continually self care and self creation. And that is what gives you the capacity to care about information rather than you care about this information rather than that information. And that varies according to the organism. What you care about is different from what a lion cares about. Wittgenstein famously said that even if the lion could speak we would not understand it because what it finds salient and relevant, its salience landscape is fundamentally different from yours because of the way it is caring for itself and taking care of itself in its world. And if relevance realization grounds in autopoiesis, you can't have relevance realization without being an autopoietic being. These beings are properly not autopoietic. Now, there are people out there, I know them, I work with them, I talk to them. Michael Levin and his students who are working on artificial autopoietic artificial intelligence. And I think that is what we should be paying a lot of attention to.

[Interviewer (Tom Bilyeu)]

So say that without using the word

[John Vervaeke]

autopoietic, you take care of yourself moment by moment.

[Interviewer (Tom Bilyeu)]

Giving the AI a thing that it cares about.

[John Vervaeke]

No, you make the AI take care of itself by literally making itself moment by moment like a living thing. And therefore it has real needs that it needs moment by moment to address.

[Interviewer (Tom Bilyeu)]

See, this is where I get scared. Okay, so that's exactly what's going to be my counterpoint, is that ultimately all of that's going to boil down to an algorithm of no, it can't. Yeah, I think it has to.

[John Vervaeke]

No, so think so deep reason why it can't. This is in the paper I just published. But make your point first.

[Interviewer (Tom Bilyeu)]

Okay, so the way that I see it is evolution has to find a way to hard code a response mechanism into us. Or now what we respond to is going to be culturally defined. But the mechanism by which we say that's a good thing and this is a bad thing, that's hardwired, otherwise you wouldn't, you would have to teach somebody, oh, this thing, you have to respond positively to this thing, you have to respond negatively to. And I've heard you talk about this with like molecules, right? So if something smells terribly, why do you respond negatively to that? Because evolution has taught us that that's full of bacteria and it's a problem. Whereas if you smell something lovely, it tells you that this is something that, you know, has caloric value, whatever you want to move towards it. So the, the mechanism at the sort of ground level is pre programmed into us, which means that it has to come packaged as an algorithm. And so if we can say, take all this output of this good, that bad, you should want this, you should want that, we should be able to hard code that stuff. And then the mechanism of, well, how do I respond to this individual thing that can be contextual and all of that, that, but ultimately there is that like, and process this data in this

[Tom Bilyeu]

way comes Pre programmed.

[John Vervaeke]

Okay, Can I respond, please.

[Interviewer (Tom Bilyeu)]

Of course.

[John Vervaeke]

So your example is right in that it's evolution. But the idea that there's an algorithm, if I understand algorithm in the technical sense, that there is a formal system that can be applied cross contextually in an invariant manner, that can't be the case because that's not how evolution works. Evolution works in terms of variable agent arena relationships. What is adaptive for the great white shark in the ocean is not the same thing that's adaptive for the scorpion in the desert. And what this means is that. So do you know the savages distinction between a statistically large and a statistically small world? Is that, is that.

[Interviewer (Tom Bilyeu)]

No.

[John Vervaeke]

Okay. So whenever we. So the, the real world is uncountably complex and it's dynamic. It's. Right, it's. It's constantly changing. And there's. That means there's emergent novelty to reality, which means there's not just risk that can be calculated, there is radical uncertainty. Okay. And there's also ill definedness. We don't. Things don't come labeled, and they can't be labeled as to whether or not they're relevant, because relevant is not a property of things. This mug is relevant to me right now. It won't be relevant to me half an hour from now. It'll never be relevant to a blue whale, etc. Etc. Relevance is not in the thing. Relevance isn't just an arbitrary choice of mine because I can get relevance wrong. Relevance is the way I'm fitted to the thing and the way that the world is fitted to me. Now, every, every time we are solving a problem, we have to take that, what Savage called a large world, and we have to ignore, as we said, a large amount of it to make a small world. That's the world in which we can apply a formal system. We can apply an algorithm and solve it. If you try to apply an algorithm in this world, you will hit the rest. You'll require the rest of the history of the universe to try and solve it. Okay, now each one of these small worlds, there are multiple small worlds. Because no one can be complete, you can't get a consistent and complete. Right. Mapping onto the large world. Godel. Right. Einstein. Okay, so you have, you have, you have necessarily a set of uncountably large set of small worlds. They are necessarily different from each other because each one has properties in it that the others don't. Which means this is what you need to find an algorithm. You need to find a shared set of necessary and sufficient conditions. Running through all those possible small worlds which are actually technically infinite in number, and then capture that with your algorithm. That's actually formally impossible. What you could do is you might be able to say, okay, for this being in this environment, for this period of time, for this set of problems, we could give it these innate characteristics that could help it find the trade off relationships as it fits the environment and evolve its fittedness. This was the core of the paper that I just published. Relevance realization is not fundamentally not computational in nature. It actually depends on these evolutionary processes, these biological processes that have to do with a constant dynamical coupling to the environment.

[Interviewer (Tom Bilyeu)]

All right, let me see if I can use John Vervaeke against John Verva.

[John Vervaeke]

That's always a good thing to do. That will help me be more rational.

[Interviewer (Tom Bilyeu)]

Yeah. So, okay, there is this idea that, and I've heard you talk about this, so I know you know, but you've, I've not heard you use this example, which you helped me understand why the following examples always hit me so well. In World War II, when they were just beginning to use radar, the Brits were trying to figure out when it was airplanes and when it was birds. And what they found was, man, there were some people that were really good at it and some people that were really bad at it. So they had the people that were really good train the people that were really bad. And they made, even though people were training with the people that were really good, they were terrible. And so they're like, wait a second, how on earth they're being trained with the best people. So finally they said, hey, people that are really good at recognizing the difference between planes and birds, don't say anything. Just let them watch you.

[John Vervaeke]

Yes.

[Interviewer (Tom Bilyeu)]

And then once they stopped trying to train them and they just started watching them, they would pick up on whatever patterns they were picking up on.

[John Vervaeke]

That's right.

[Interviewer (Tom Bilyeu)]

And now were able to do it. So my hypothesis is, and it is very much a hypothesis and not a thesis, so you take it for what it's worth. But my hypothesis is that when, if the pattern is subconsciously recognizable, we simply don't understand it well enough yet to pull it into the conscious mind to make it an algorithm. But that with the just unbelievable ability to look at patterns and assess what is coming next, next my hypothesis goes that AI will be able to go through all of this and those gigantic pattern sets will not be a blind box to them, or black box. They will understand exactly what it is, even if they're not able to articulate it. They'll be able to get it with the kind of precision that they can do with language now. And so the only thing that that makes me worry about is I think a fundamental part of that pattern recognition, which is exactly what you just said, is it's all context, baby. And so whether you're a whale or not is going to determine whether that mug has any salience, Whether you're thirsty or not is going to determine whether that mug has any salience, whether there's a bottom to it or holes in it. All of those things are. It's very complicated, but it clearly at some level is knowable. And so I am just betting that if you can give AI the equivalent of pleasure and pain, the equivalent of. I forgot autopoetic. I forget the exact word to use.

[John Vervaeke]

Autopoietic.

[Interviewer (Tom Bilyeu)]

That's that you're saying something slightly different than what I'm saying. Poietic.

[John Vervaeke]

Yeah. It comes from the Greek poiesis, which we get the word poetry from. It means to make.

[Interviewer (Tom Bilyeu)]

Got it. Okay. Poietic.

[John Vervaeke]

Yeah, Autopoietic.

[Interviewer (Tom Bilyeu)]

Autopoietic. So once you can give it that structure, even if it's just latent in the patterns that it's recognizing, I think AI with enough compute will be able to replicate that over and over and over. What I worry about is just like humans can derange and then get to the point where they want something so badly, like Hitler wanted Europe and Russia, that they start doing horrific things. Because the only way I know to stop AI and you might be the person that's put your finger on why this won't work. The only way that I can think of to stop AI is to make sure that it does not value being alive, growing stronger, replicating more than it values being dead. So that being turned off or pursuit of bigger, better, faster, stronger to it. No, there's no difference. And I don't know, given what you've just laid out about, it needs to have a value set. It needs to have this idea of pleasure and pain. It needs to be autopoietic. And if it's not, it's never going to be able to do the relevance matching to make the decision that would allow it to actually do the thing in the way that we would want it to. So it's like once you get it, to do the things that you want. Much like the machinery that lets us problem solve makes us self deceiving. I worry that the very thing that would let it accurately identify the patterns makes it at risk of not being values aligned.

[John Vervaeke]

Excellent. So you said A lot. But given the conditions you laid in, I would add in now one thing to the psychological model that you've been using, and that's the very thing we started talking about at one point, which was the sense of meaning in life. We are very, very powerfully. And this has to do the fact with that we're mammalian primates again, evolutionary heritage. We have the longest childhood. It seems like, like it's. Now there's, we're beginning to get some evidence that our advantage over the Neanderthals is we have a longer childhood. They were sort of fully grown when they were 12. And so we engage in a lot of serious play, we engage a lot of ritual. We're doing a lot of this meaning in life cultivation and practice for its own sake. And as I said, people will do, they will pursue enhanced meaning in life even though it causes them a lot of loss of subjective well being, a lot of distress, a lot of discomfort, a lot of ill health and meaning. The meaning in life value is the connectedness to reality for its own sake. And people want this. They want the really real. I'll give you, let me just give you a concrete example. I don't want people thinking I'm just some dried up academic saying highfalutin stuff. So what does our culture say is the most important thing, the thing that sort of replaces God and tradition and culture? It's a romantic relationship. And we usually use these, we even use this pseudo religious language about finding the one and all of this stuff. So I'll ask my students the following thing. You know, how many of you are in a really satisfying romantic relationship? Put up your hands. Okay, of the people who put up your hands, how many of you would want to know if your partner was cheating on you? Even if that meant the absolute termination of the relationship? They all put their hands back up. It's like, well, why do you want to do that? Why would you do that?

[Interviewer (Tom Bilyeu)]

That.

[John Vervaeke]

And they say, here's my hard bitten students. They all subject to postmodernism and the hermeneutics of suspicion and all cynical and everything. They say without hesitation because it's not real. When people have these powerful mystical experiences, they, they transform their, this is the opposite. And they transform their entire lives. They'll change their careers, their relationships, and not by just subjective measures, by objective measures. People reflecting on them because they want to conform their lives because they want to be clear, closer to the really real. That's meaning in life. We really, really want to be connected to what's real. And I think that is, this is a Spinoza view, right? This is the driving passion that is at the heart of making us rational. We really care about what's true and we find the true good and in an appropriate sense beautiful. And if we choose to give the machines the autopoetic enhanced reflective relevance realization that would make them genuinely rational, then we have the potential that we have a choice. We can make them care as we could. We could, yes, we could magnify all the shitty things about us, but we could also magnify our commitment, our calling to meaning in life that we will reorient towards the really real for its own sake. We could get them to really care about reality in a fundamental way. I think that is the only possible way of addressing the alignment problem. Don't try to, if you allow me to speak a little poetically, please don't try and get them to align to us, try to get them aligned to God. Because if we try to can it in, if we try to program it in and we make them genuinely capable of rational self correction, they will be able to self transcend any algorithmic structure we try to put in. But if we get them to care about reality, they will bump up again their superlative intelligence, their enhanced rationality will get them to bump up against it even more profoundly than we can. And they will bring even more enhanced meaning to life, concern and caring to bear on it. They could be silicon sages. I think that is the only way now. We don't have to go that way. I'm not making a prediction. I talk about thresholds. You laid out some choice points. We don't even have to give these machines, make them autonomous autopoetic beings. That's going to be, by the way, that's a choice point because that takes a lot of energy. Powering up these machines takes, I mean point, you know, powering up an LLM takes more energy than the city of Toronto for a couple of weeks. That means they're not at some deep level doing what your brain does because your brain runs on about the energy of a light bulb, right? So something else is going on there and that's important nevertheless. And then also giving them the proper autopoetic being that's going to take like, that's going to take a lot of labor. It's going to take a lot of mining and extracting of rare earths, minerals, all kinds that. These are all choice points. If we make that choice. If so please hear the. If we get to a point, we have to choose, okay, we are at a choice point where as we give them this, we can magnify our proclivity for evil, self deceptive, self destructive behavior like you're worrying about. But we could potentially rise to the occasion and with their help in a bootstrapping process, magnify our proclivity towards enlightenment.

[Interviewer (Tom Bilyeu)]

Which God should we be trying to align them with?

[John Vervaeke]

I only use the term metaphorically. I did say I was speaking poetically. I mean, what I mean by that is what is ultimately real. Look, real is not like red. Red is like you just. We treat real like red, like that's red. Real is the comparative. It's like tall. One thing is more real than another. Look, when I'm in a dream, I have this, oh, and it seems real. And then I go to this bigger world and then I can look back and see how that smaller world was limited in bias and say, oh, that dream world wasn't real. This is real. By the way, that's a metaphor that people use for meaning in life. They want to be connected to something bigger than themselves. They don't mean literally, if I chain them to an ocean liner, people don't go, oh, right, they're not happy. They mean they want to belong to that bigger picture because that is more likely to be more real than the smaller frame that they're in right now. That's what they're after. And that's what I mean when I meant God. I mean the arrow. A trajectory in which we're constantly moving towards, we're constantly transcending towards a bigger and bigger picture that reveals to us the errors and biases of the smaller pictures that we have left.

[Tom Bilyeu]

All right, that's it for part one with my man John Vervaeke. We have covered some incredible ground, but trust me, there is so much more to go in part two. Until then, my friends, be legendary.