Unlocking Reality: Donald Hoffman on Consciousness, Simulations, and the Limits of Space-Time | Impact Theory

Summary

Impact Theory Podcast with Tom Bilyeu
Episode: Unlocking Reality: Donald Hoffman on Consciousness, Simulations, and the Limits of Space-Time
Date: May 18, 2026

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Episode Overview

This episode features cognitive neuroscientist Donald Hoffman, renowned for his radical theories regarding consciousness, reality, and the structure of space-time. Hoffman and host Tom Bilyeu delve deep into whether reality is as we perceive it or merely a “headset” placed over a deeper, computational, possibly conscious substrate. The conversation traverses topics like free will, Markov chains, simulation metaphors, the boundaries of neuroscience, and the pursuit of a scientific “code” underlying the universe. Technical at points, philosophical throughout, they explore both the current limits and future possibilities for understanding what’s truly real.

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Key Discussion Points & Insights

1. Free Will and Consciousness – The One Behind the Avatars

• Hoffman's Position on Free Will:
  • “The consciousness, the one consciousness, is infinitely free. And the avatars ... are free in the sense that the one consciousness is free to act through the avatars as it wishes.” (01:12)
  • Hoffman acknowledges neuroscientific findings (e.g., brain activity predicts decisions), but argues: "Neurons do not exist when they're not present, perceived ... they're just a headset representation." (01:36)
• Nature of Neuroscience:
  • Neuroscience is the study of the “headset” (our interface) rather than the underlying ‘software’ driving conscious experience.
  • "Neurons and our brain are just a headset representation of how this headset is engineered." (01:36)

2. The Markov Chain Universe: Algorithms Behind Reality

• Markov Chains as the 'Software' of Reality:
  • Markov chains, which model probabilistic transitions, may be the first layer of “software” rendering our reality.
  • “We have to figure out ... the first layer of software outside of space time that's being used to render what we call brains and neural activity.” (03:59)
• Probabilities and Free Will:
  • Hoffman links probabilistic transitions to the potential for free will.
  • "In the Markov chains, there are probabilities ... Does it mean a free choice? Does it mean some physical event that just happens with some absolute probability?" (04:42)
  • Bilyeu presses: “Probabilities to me does not equal free will. Even the fact that the choices that you have are finite would say that you already don't have free will.” (05:10)
  • Hoffman offers that free will may be defined or pointed at via "ostensive definition" – i.e., the experience of awareness itself.

3. Pointing to the One Consciousness

• Ostensive Definition & Direct Experience:
  • Hoffman uses the gap between thoughts as the "pointer" to the One Consciousness.
  • "That gap when you were completely silent between thoughts, that's the best pointer I can give to what I mean by the one." (07:38)
  • Tom reflects: “It feels like the lack of something versus a thing in and of itself … to finally go to true neutral … would be tremendous relief.” (08:45)

4. Simulation, Headsets, and the Limits of Metaphor

• Tom’s Simulated Reality Metaphor:
  • “For me, that metaphor of this is a computational universe that's simulated for a purpose … the why we’re being simulated becomes incredibly interesting.” (09:37)
  • Acknowledges ultimate metaphysical questions – "Why something instead of nothing?" – have no clear answer.
• Hoffman’s Ocean Metaphor:
  • Cites the "drop returning to the ocean" as poetic imagery for the One Consciousness manifesting myriad forms. (10:35 leveling off through 12:53)
  • On the difference between their metaphors: “Some people hearing this might say … How can you tell these different views apart? … I would say that there is something real that will be testable scientifically...” (12:53)

5. Testable Science, DMT, & the Future of Headsets

• Scientific Testability & Expanding Dimensions:
  • Hoffman claims that soon, science will "write down the software outside of this headset," allowing us to design new “headsets” with more dimensions, resolutions, and perceptions.
  • “When science actually writes down that layer of software...it will give us new tools that will do magic inside the headset and open up new dimensions of perception.” (12:53)
  • DMT as a hint: “You just up the quantity just a little bit and all of a sudden people ... will say, I’m in a higher dimensional world ... the resolution is higher ... it feels far more real.” (13:53)
  • “This is not just hand wave ... if we pursue this line of research, ... we will transform this headset, period.” (15:53)

6. Modeling the Observer: The Next Scientific Revolution

• Current Limits:
  • Science has never properly modeled the observer in experimentation—Newton ignored it, Einstein reduced it, and quantum theory still grapples with it.
  • “All of the data ... comes from observation. And we have no theory about what's going on in observation.” (20:36)
• Leibniz & the Monadology:
  • Introduces Leibniz's “monads” – perceiving entities united by “pre-established harmony.” (22:25)
  • "Leibniz ... realized that we needed to build science on the foundation of observation because that's the fundamental thing ... It's you and me and other scientists looking." (23:06)
  • Modern Markov chains provide the mathematical substrate to tie all possible observer states (monads) together.

7. Trace Logic – Tying Observer States and Building Physics

• Trace Logic and Pre-established Harmony:
  • Observer states (Markov chains) can be mathematically projected (traced), mimicking what happens if you cannot perceive certain states.
  • "Whatever three by three you give me, when I take the trace, I will get exactly the right two by two ... it's called the trace. It's a mathematical theorem." (29:10)
  • Trace logic is the “logic of zero surprise” – predictive, matches all possible observed transitions.
• Building Relativity from Markov Chains:
  • Counters in the Markov process correspond to the experience of time; time dilation can arise because some observers see more of reality (higher counters).
  • “That is going to give us time dilation. In relativity theory, that's the precise way that we will be able to go from Markov chains to the time dilation ... in Einstein special and general relativity.” (33:01)
  • Dirichlet forms – mathematical structures – can analogize spatial distance and contraction (relativity effects).

8. From Mathematical Models to Algorithmic Reality

• Procedural Construction – Parallel with Game Design:
  • Tom draws parallels with procedural generation in video games, acknowledging that deeper Markov logic would be essential for building simulated worlds. (41:58–43:07)
• Trace Institute:
  • Hoffman announces work to formalize these theorems at his upcoming Trace Institute, aiming to provide such algorithms within five years. (43:07)

9. AI and the Limits of Physical Substrates

• Can AI be Conscious?
  • “If you're framing it … to make consciousness emerge ... you can't do that. It will never [emerge].” (45:32)
  • Hoffman rejects consciousness emerging from unconscious computational systems; instead, consciousness is fundamental, and all avatars (humans, AIs, rocks) are “interfaces” to some deeper conscious reality.

10. The Radical Humility Required

• Limits of Our Headset:
  • “Things that look trivial to me, maybe it's because I'm an ant to them, right? ... Do not mistake the limitations of the headset for an insight into the nature of reality.” (48:00–50:55)
  • On reductionism: “The stupid components are just the headset giving up. They're not the fundamental nature of reality. That's the headset saying I give up.” (51:58)

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Notable Quotes & Memorable Moments

• On Free Will:
  Donald Hoffman (01:12): “The consciousness, the one consciousness, is infinitely free. And the avatars ... are free in the sense that the one consciousness is free to act through the avatars as it wishes.”

• On Neuroscience:
  Donald Hoffman (01:36): “Neurons do not exist when they're not present, perceived ... they're just a headset representation.”

• On the Nature of Probability:
  Tom Bilyeu (05:10): “Probabilities to me does not equal free will. Even the fact that the choices that you have are finite would say that you already don't have free will.”

• On Experiencing the One:
  Donald Hoffman (07:38): “That gap when you were completely silent between thoughts, that's the best pointer I can give to what I mean by the one.”

• On Reality as a Simulation:
  Tom Bilyeu (09:37): “For me, that metaphor of this is a computational universe that's simulated for a purpose … the why we’re being simulated becomes incredibly interesting.”

• On Scientific Testability:
  Donald Hoffman (12:53): “When science actually writes down that layer of software...it will give us new tools that will do magic inside the headset and open up new dimensions of perception.”

• On Consciousness and Reductionism:
  Donald Hoffman (48:00): “Do not mistake the limitations of the headset for an insight into the nature of reality. It's just a limitation of my headset, period.”

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Important Timestamps for Core Segments

• Free Will, Avatars, and The Headset View: 01:12 – 03:59
• Markov Chains, Free Will, and Probabilities: 03:59 – 05:10
• Ostensive Definition: Pointing at Consciousness: 05:24 – 07:38
• Rendering Queue and The ‘Gap’ as the One: 07:29 – 09:17
• Simulation Metaphors and Layered Realities: 09:37 – 12:53
• DMT, Higher Dimensions, and Building New Headsets: 12:53 – 16:13
• Modeling the Observer – Science’s Last Frontier: 20:36 – 23:06
• Leibniz, Monads, and Trace Logic: 23:06 – 30:00
• Relativity from Markov Chains (Time Dilation, Length Contraction): 30:00 – 39:24
• Procedural Reality and Sim Game Parallels: 41:24 – 43:07
• AI, Consciousness, and the Headset's Limits: 44:56 – 50:55
• Radical Epistemic Humility on Reality: 48:00 – 51:58

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Conclusion

Donald Hoffman’s theories challenge the very foundations of how science, philosophy, and common sense treat reality, mind, and perception. If his predictions about mathematically reconstructing the “code” behind our headset of space-time prove correct, not only will neuroscience and physics be redefined, but our understanding of self, free will, and even AI consciousness could be upended. For both the scientifically curious and philosophically intrepid, this episode offers an intense, thought-provoking journey beyond the common-sense world—straight to its possible source code.

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Further reading by Donald Hoffman:

• The Case Against Reality: Why Evolution Hid the Truth from Our Eyes
• Visual Intelligence: How We Create What We See

Follow Donald Hoffman:

• On X (formerly Twitter) for updates about the forthcoming Trace Institute and publications.

Transcript

[Donald Hoffman]

I sold my car in Carvana last night. Well, that's cool. No, you don't understand. It went perfectly. Real offer down to the penny. They're picking it up tomorrow. Nothing went wrong. So what's the problem? That is the problem. Nothing in my life goes as smoothly. I'm waiting for the catch.

[Tom Bilyeu]

Maybe there's no catch.

[Donald Hoffman]

That's exactly what a catch would want me to think. Wow. You need to relax. I need to knock on wood. Do we have wood? Is this table wood? I think it's laminate. Okay. Yeah, that's good. That's close enough. Car selling without a catch. Sell your car today on Carvana. Pick up fees may apply.

[Tom Bilyeu]

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[Donald Hoffman]

I would say that the consciousness, the one consciousness, is infinitely free. And the avatars then are. These are avatars and they're free in the sense that the one consciousness is free to act through the avatars as it wishes.

[Tom Bilyeu]

Okay. So because we are the one consciousness, we do have free will.

[Donald Hoffman]

Again, the notion of free will is a little complicated. But if you wanted to say, Don, do you think in free will or no free will, I would have to go with the free will side of things in this context. As a neuroscientist, of course, I know about the neuroscience of activity in the brain that predicts seconds ahead what you're going to do and so forth. That's all very well known. And I think that that's just no problem because again, the neurons don't even exist when they're not perceived. So neurons. So I'll be very, very clear. I'm a cognitive neuroscientist. I love neuroscience. I've studied it heavily. Neurons do not exist when they're not present, perceived. We should study neuroscience. I'm all for studying neuroscience. It's very, very important. But neurons do not exist when they're not perceived. They cause none of our behaviors but we should absolutely spend even more money on neuroscience because neurons and our brain are just a headset representation of how the headset is engineered. That's I need to say. It's a simple sentence, but it's a complicated idea. Your nervous system, the brain, the 86 billion neurons and trillions of synapses, is just a headset representation of how this headset and this body are being rendered. Right.

[Tom Bilyeu]

But they're really just Markov chains. Right. So outside of space time, it is basically a sequence of transitions from one thing to another, which is a Markov chain as a reminder. And in the right sequence, that's Donald Hoffman. In a different sequence is Tom Bilyeu. And another sequence sequence, it's a mosquito. So what I'm trying to pin down is whether you think when we're studying neuroscience, like, what exactly are we studying? Are we studying Markov chain relationships? And that there, that pattern is actually so detailed that it replicates the firing of neurons and all that. And then I would say, okay, fine, but where is free will in that? Like, that Markov chain is the way that it is. And so I don't see how you exit out of that Markov chain with free will somehow to now change that Markov chain, it. Yeah, I'll stop there. Right.

[Donald Hoffman]

As I mentioned, we should spend more money on neuroscience, because what we have to do is not only understand the neural connections in space and time, we have to reverse engineer them. We have to figure out what you're saying. The Markov chains, the first layer, I'll just say the first layer of software outside of space time that's being used to render what we call brains and neural activity. So neuroscience is much, much harder than we ever thought. The 86 billion neurons, trillions of synapses is hard enough. But that's just the data that has to be reverse engineered for us to understand the software. So neuroscience is going to need lots more money because we have to actually figure out the software that's doing this.

[Tom Bilyeu]

And do you think as we do, that we will discover somewhere there is hiding free will?

[Donald Hoffman]

Yes. To be simple, I'll say yes. In the Markov chains, there are probabilities. So every transition is typically a probabilistic. It's not just 1 or 0 probability. It could be 0.3 or 0.7 or 0.8. So as soon as you get probabilities in there, you have to ask, what does the probability mean? Does it mean a free choice? Does it mean some physical event that just happens with some absolute Probability, some objective probability. And so I view it.

[Tom Bilyeu]

But even if so, probabilities to me does not equal free will. Even the fact that the choices that you have are finite would say that you already don't have free will.

[Donald Hoffman]

If you want the best description I can give of this infinite, there is a way to do it. And it's by something called ostensive definition. And it would take a minute to just talk about it, but it might be worth doing this. So most of the things you know, almost everything you know, is because someone pointed and said a word like this is a rabbit. This is a rabbit. That's the color green. And it's not like they gave you, you know, a lesson and materials and they're fuzzy. That's right, they, they just. Your mom or dad pointed and said rabbit. And at the right hand, I've already

[Tom Bilyeu]

done all the categorizing myself.

[Donald Hoffman]

And that's right, you already. So all you. So all the colors someone just pointed and named and you. And by the way, I don't know that Tom's colors are the same as mine. I just don't know that. Right.

[Tom Bilyeu]

They're not. Mine are way cooler.

[Donald Hoffman]

I might have to grant you that. But if you think about it, almost everything that's important, the taste of chocolate, the feeling of heat, the smell of garlic, the feeling of love. No one teaches you those things. You have them. Someone just gives you a word to name what you've already got. So they'll give you some way of pointing. Like I'll point and say cup. And that's all I can do because I can't if you can't get cup. If I do that to a bacterium, say cup, I don't think the bacterium is going to get it right. So all I can do now is use ostensive definition to point as best I can to what I mean by the one. And the way I would point it is to say, we'll just be quiet for a minute and I want you to ask yourself the question. I wonder what my next thought will be. What happened?

[Tom Bilyeu]

I have a go to thought. So I immediately, as soon as you said that, I immediately went there. But of course that's all from my perspective. It's hardwired, pre programmed. It is the result of my NPC ness.

[Donald Hoffman]

So maybe if I ask you again, dropping that thought, what's the next thought that you will have? That's not that thought.

[Tom Bilyeu]

No idea. It will just appear out of nowhere.

[Donald Hoffman]

So let's try that.

[Tom Bilyeu]

So I have one.

[Donald Hoffman]

But was there A little gap, though, before you had it.

[Tom Bilyeu]

Yeah, probably.

[Donald Hoffman]

And that's my pointer. That gap when you were completely silent between thoughts, that's the best pointer I can give to what I mean by the one.

[Tom Bilyeu]

Okay, so the space in which thoughts appear.

[Donald Hoffman]

That's right. The awareness. The silent awareness.

[Tom Bilyeu]

But do you think that fully and accurately captures the presence of the One Consciousness? So whatever that feels like, whatever that qualia is, if I were to take off the headset, that's what it would always feel like?

[Donald Hoffman]

No, I think that that's the best pointer I can give you right now. I think it probably transcends that. That you, as the One Consciousness, transcend even that pointer. But at least that pointer is a step in the right direction. It'd be the way I think about it.

[Tom Bilyeu]

That's interesting. To a hammer, every problem is a nail to me, because of the dominant metaphor that I use of computation being inside of a simulation. To me, that's basically you're in a rendering queue and you're waiting. And that's what it would feel like to be in the rendering queue. But it doesn't give me agency.

[Donald Hoffman]

Um, yes.

[Tom Bilyeu]

It feels like the lack of something versus a thing in and of itself, which can be deeply pleasurable, especially if somebody has high anxiety or depression or whatever.

[Donald Hoffman]

Right.

[Tom Bilyeu]

To finally go to true neutral, I'm sure would be tremendous relief. So I get why people chase that feeling, right? Yeah, I get why people chase that.

[Donald Hoffman]

But eventually, I think it even transcends even feelings.

[Tom Bilyeu]

It's.

[Donald Hoffman]

It's a matter of just.

[Tom Bilyeu]

I agree, to me, it's the absence. It is a render cue.

[Donald Hoffman]

You.

[Tom Bilyeu]

You are waiting for something.

[Donald Hoffman]

Very interesting. Yeah, so I see your take on it, and I'm suggesting that that is. So we just differ on this. Right. So where we agree is I think computation is critical for this particular rendering of the avatars in space and time. So computation. I'm all good with that. I'm a computationalist.

[Tom Bilyeu]

I really do think that we're quite far apart. And when. So here's why I think we're far apart. I am desperate to acknowledge that I just can't imagine what the world is outside of my simulation. I make the base assumption that they're simulating something like what they know. And so even if that thing is simply the principles of evolution. So not humans. Maybe the humans were a super surprising life form. They were like, whoa, I thought we were going to get dinosaurs like us, but the dinosaurs got hit by a meteorite. That's so crazy. So it's very possible that in sort of form and function we would not recognize it. But I don't think they would simulate something that wasn't interesting to them. But you're always going to terminate it. Unmove mover. But for me, that metaphor of this is a computational universe that's simulated for a purpose. And the why we're being simulated becomes incredibly interesting for me.

[Donald Hoffman]

But do you have an answer for the why?

[Tom Bilyeu]

It's all guesswork. That's what I'm saying. Like people need to understand. I fully recognize that a. This may only be a metaphor. And even if it's not a metaphor on the outside, I have no visibility. I have just pushed the miracle one step away. And that's why I say this is all. The final question is why something instead of nothing? And since I can skip all the way to that and just tell you I have no answer whatsoever. So I am a total blank. I cannot be of assistance there. I just no idea that one is befuddling. But to try to draw a stark difference, at least how I feel when I think about this is how this is the metaphor I'm operating on or the reality that I've acknowledged. And then the metaphor that you're operating on or the reality that you have acknowledged there is. This is bizarre, but I have like a ghost meets worm sense of what the one consciousness looks like. And so that is. And the Markov chains, for whatever reason, are on a big board in front of me with like all these infinite but like tessaracty, like so many different permutations. And that's not what I imagine on the other side on my metaphor. So when I think about sort of the ground reality of there's a show called White Lotus. I don't know if you've seen it, but when I saw it, I thought of you. And so there's a moment in the end spoiler ish alert. But a guy is having a conversation with himself. He's in immense emotional pain, and he is talking to a Buddhist, I believe, and the person is describing that we're all just drops of water and it's the ocean splashing. And for a brief second, you're a drop of water and you're separated and you're having your own experience, but then eventually you fall back and you just become one with the ocean. And that's how I sort of imagine your. When I get poetic about it. Your vision of like the one consciousness is this big ocean, but for whatever reason, it can pop up all these different mosquitoes cockroach you, me, everything in between, all through human history, evolution, dinosaurs, whatever. Why it's doing it, I don't know. But that is a very different thought when I try to hold on to what I hear you saying.

[Donald Hoffman]

So I think one way some people hearing this might say, look, they're now talking about how many angels can dance on the head of a pen. There's not much. How can you tell these different views apart? Is there anything real here? And I would say that there is something real that will be testable scientifically, and that is if the view of the one is correct and spacetime is just a headset and there is a layer of software outside, then the proof will be that when science actually writes down that layer of software and shows how the headset is built, it will give us new tools that will do magic inside the headset and will open up new dimensions of perception that we hadn't imagined before. So I'm claiming that this is not just how many angels can dance on the head of a pin, this is a scientific claim that we will, and I think within just a few years, write down the software outside of this headset and we will show that this headset, three dimensions of space, one dimension of time, is actually one of the more trivial headsets that's possible. And we got stuck with one of the cheaper models and we will figure out how to reverse engineer it and we will build much more interesting headsets. And we already get little hints of that. For example, like in DMT research and so forth, you put in just this molecule in your brain that's endogenous. DMT is in your brain all the time. You just up the quantity just a little bit and all of a sudden people who have mathematical degrees, mathematical training, will say, I'm in a higher dimensional world. The three dimensional limits of this headset were transcended and I'm now seeing in more dimensions than I've ever seen before. I'm seeing more colors, I'm seeing entities that I've never seen before, and the resolution is in fact higher than the resolution of this actually looks now like a low resolution, cheap headset. When I go to this DMT world, the resolution is higher, it feels far more real and I'm seeing more dimensions. So, so, but that, of course, that is not the proof. I'm just saying that's the kind of thing that would be consistent with the. The proof will be when we write down the mathematical equations and we show exactly how to build the headset and we show exactly how for example, the DMT molecule. If we can reverse engineer this, we may actually show that in the software there is a dimension parameter and DMT changes that dimension parameter in our software. And that's why we're and maybe even the resolution parameter. Instead of having, you know, 120 million photoreceptors, now we're up to, you know, 500 trillion or something like that equivalent so that we get the higher resolution. So, so it's not how many angels can dance on the head of a pin. The claim is if you pursue my approach, there is software that we're going to get. Probably within a few years we'll be able to prove theorems. Space time arises this way, quantum mechanics arises this way. Exactly. Here's the parameters, here's how we play with it, and here's how we beef up a headset beyond three dimensions to ten or fifty or a billion. So it's not just hand wave. This is. So in that sense it's very different from the religious approaches that just require faith. I ask for no faith whatsoever. I'm saying if we pursue this line of research, you will win. You will build the software and we will transform this headset, period.

[Tom Bilyeu]

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[Donald Hoffman]

I think that what science has not done yet is what science needs to do. We have not yet modeled the observer that's collecting the data in science. So under Newtonian physics, you ignore the observer. You don't even have to talk about them because we assume it doesn't affect what you're getting. In Einstein, you talk about the observer, but the observer is reduced to clocks and measurement pointers. In quantum theory, now the observer becomes front and center because there are two different regimes in quantum theory. There's a unitary evolution when no one's looking, when you don't observe. And then there's what they call the collapse of the wave function when you observe. And so all of a sudden in quantum mechanics, the observer comes front and center. But quantum theory has no universally accepted model of the observer. And we haven't had it for now a century. We've had quantum theory for a century. That's one of the biggest open problems in science, which is what is going on when we make an observation. And this is not a trivial issue. All of the data that gives us evidence for or against our scientific theories comes from observation. And we have no theory about what's going on in observation. That's universally accepted. We're still. So the reason our science is still horse and buggy, I think, compared to where it's going to be is because we haven't done what Leibniz said in 1700 we need to do. We need to have a model of the observation process and how all the observations are tied together. He called it monads. And pre established harmony was his term in English. Pre established harmony. So I think what science, science has

[Tom Bilyeu]

done deeper on that. So monads, an observer, pre established harmonies, simple geometric shapes. I have no idea what he means.

[Donald Hoffman]

Well, and neither did Leibniz. So. So because Leibniz, the monadology is a very, very small book. He. And basically he doesn't know what else to say. And by the way, he was a genius, right? He invented the calculus as independent of Newton, maybe even prior, who knows, he did all sorts of math. So this, this guy was a genius, complete genius. But he, and he realized that we needed to build science on the foundation of observation because that's the fundamental thing that's going on in science. It's you and me and other scientists looking.

[Tom Bilyeu]

Was he trying to grapple with consciousness or something else?

[Donald Hoffman]

Well, I think he had a spiritual point of view. So consciousness probably. Absolutely.

[Tom Bilyeu]

But is that it? That's what he's trying to get to?

[Donald Hoffman]

Well, I wouldn't want to say just that because he was also hard, no scientist himself. So he had spiritual views.

[Tom Bilyeu]

And did he articulate why he thought this was so important?

[Donald Hoffman]

Well, yes, he actually, he's very famous for his parable of the mill, which is he was basically saying our conscious experiences could not be built from physical systems. He understood that. So what we call today the hard problem of consciousness. Leibniz knew it, and he knew it so well that he figured it only needed one paragraph of his attention. It was so obvious to him that no physical system could build consciousness, that he figured any smart guy can get it in one paragraph and we should move on. So he really. So what we're doing right now is what Leibniz dismissed so quickly that he only thought it was worth one paragraph. And he just says that if you go into a mill and make yourself small so that you see all the cogs and wheels of the mill, you won't see anything in there that would give you any clue about how consciousness is made. And he says same thing. If you go basically inside the brain and look, how is that going to give you, it's just another physical system. How is it going to give you consciousness? So he clearly understood what we call the hard problem of consciousness back in 1700, roughly. But he invented so much math. But we needed several more centuries of mathematics before Markov chains didn't come around until 1905. So we've only had them for 121 years. So he didn't have them. He gave us the mathematics, a lot of the mathematics that let us eventually get Markov chains. But you can't do it all, even if you're a genius like him. So Leibniz clearly understood and he couldn't say how to do it mathematically, so he just said, look, there are things, I'll call them monads, but they're these perceiving entities and they have to be tied together mathematically somehow. So there's got to be a pre established harmony. And all you can do when you don't have anything else to say. God did it, but clearly he wanted to do more, but we didn't have the math. Now I, I think that he's right, but we have to, we have to restart science.

[Tom Bilyeu]

What?

[Donald Hoffman]

Right about what?

[Tom Bilyeu]

Just that, that we have to start with observers. Important.

[Donald Hoffman]

We have to start with observers and understand how observers are tied together.

[Tom Bilyeu]

What does it mean to start with observers?

[Donald Hoffman]

So I, when I say that we're going to have these Markov chains and each one is an observer, so either one, so like the three, but so the observer that can see red, green and yellow so that the Markov matrix, which Says if I see red now here's the probability I'll see green next or yellow next and so forth, that matrix, I'm going to call that an observer or an observer window, so that's a monad. Call that a monad in Leibniz's terms. And there's an infinite number of these monads. They can be as big as you want. You can have three, you can have a billion, a trillion. Go as big as you, Markov matrix as you want, go off to infinity, literally accountable infinity of states. And then look at all this whole set. These are all the quote unquote monads. So there's an infinite number of them. And he wanted a pre established harmony. And which is what? So this will take a couple minutes, but I can give it what I think the pre established harmony is. So if I have a, and it's a very simple idea actually. So if I, if I have a, if I'm looking at the red, green and yellow lights and I could write down the probability, say it's a bad traffic light, so sometimes red turns to yellow instead of green and so forth. So it's a bad traffic light. So but you write down the matrix of whatever this traffic light is doing. You know how it changes from red, green and yellow. Now suppose I put on a pair of glasses where I can't see yellow. All I can see is the red and green transitions, but I'm still looking at the same traffic light. Then I will write down a different matrix, only have two elements, red and green. That's all I can see is red and green. I'll have a probability. If I'm seeing red now, I'll see red next or green next. And if I see green now, green next or red next, so I'll get a two by two matrix. But since I'm looking at the red, green and yellow light, but I'm only seeing the red green, the red, green and yellow transitions are of course controlling what I see in the red and green when I can only see red and green. Right. It's clear all the transitions that I'm seeing there are because I'm seeing this bigger one, but I'm missing the yellow. It's hidden from me. That's the key idea in mathematics. We say if you give me the matrix for the red, green and yellow, then I will tell you exactly what the matrix has to be for the red and green. There's going to be only one. It's called the trace. That's the key. This was discovered decades ago. Between 1905 and now, it was discovered that if I have a big matrix and I say I'm only going to look at a subset of the states, then there will be a unique, in general, a unique matrix. There are technical terms, you have to be ergodic and so forth, but that's beyond what we need to do here. So, but I'll just say in general, but for those who know the math, if it's ergodic. But, but anyway, so, so I have a big matrix and I look at a subset of states, then I will get a trace matrix which is exactly the zero surprise matrix.

[Tom Bilyeu]

What do you mean zero surprise?

[Donald Hoffman]

Zero surprise means that the probability transitions that I predict are exactly the probability transitions that I will have. So this is probabilistically exactly what I will see. So the trace logic, I'm sorry, the trace operation gives you a zero surprise. It gives you exactly what you will

[Tom Bilyeu]

see because you have limited the options so profoundly that there's just a very finite number.

[Donald Hoffman]

Well, because it's just a mathematical theorem, basically that wasn't it a mathematical theorem

[Tom Bilyeu]

with red, green and blue or so.

[Donald Hoffman]

So, so I should be careful. So, so with the red, green and blue, you can pick any matrix you want. Pick any, any three. And there's a, there's.

[Tom Bilyeu]

When I reduce by one color, all of a sudden I get to a no, surprise one.

[Donald Hoffman]

That's right. So pick, pick, pick one of those three by three matrices. And if you, if you fix that matrix, then, if you only. Then if you restrict red and green, I will give you a two by two matrix that is matched to that particular three by three. If you give me a different three by three, it'll be a different two by two.

[Tom Bilyeu]

Yep.

[Donald Hoffman]

Okay, so whatever three by three you give me, when I take the trace, I will get exactly the right two by two, which gives you exactly the transitions on the red and green that you will see if you can't see the yellow. So that's called, that's called the trace. And it's just a mathematical theorem that.

[Tom Bilyeu]

What's it telling us?

[Donald Hoffman]

It's, it's telling us exactly when you have a bigger system and you can't see all of its states. Yes. It will tell you exactly what you can see. What exactly you will see probabilistically. Exactly.

[Tom Bilyeu]

Why does that matter in the headset analogy?

[Donald Hoffman]

It turns out well. So this has been known for, for many, many decades. This, and what I, what I discovered about two years ago was that this gives us a logic on the set of all Markov chains on the set of all observers, it ties them together, it gives a pre established harmony that unites all of these observers. So logic. I'll talk first about a boolean logic, a simple logic, a Boolean logic that we have. Like in computer science, you can take things and take their ands and their ors and the knots and so forth. So that's a kind of logic. And the logic of the. So the trace logic is not boolean, it's more general than boolean. It's a non boolean logic, but it's locally boolean. So if I take any particular. If you fix a matrix, say it has 1,000 states in it, and you look at all of its possible traces and there's lots and lots of traces. If you look at just those traces are just subsets, subsets plus the matrix, right? The subset and the matrix on that subset, which is telling you the transitions that you're going to see on that subset of states. Like when I went from the three by three, red, green and yellow to the red green, I was telling you what's the probability of transitioning from red to green or green to red and so forth. So it was telling you exactly what those probabilities are. So that's what I'm saying. You fix any big matrix and then look at all of its traces, and then all of those traces form a Boolean logic or Boolean sublogic of the trace logic. But the whole logic itself is not boolean. So that is the key. We now have a theory of an observer, very, very simple. It's the Markov matrix. We have the pre established harmony which is this trace logic. And then we can start to use these matrices to build space time. So I'll give you just a concrete clue, just a concrete clue about how this is going to go. It's standard in Markov chain theory to have in addition to the matrix, a little counter. Every time you change an experience like from red to green, you just increment the counter. It's really as simple as I'm making it. So maybe start the counter off at zero. Now I'm seeing red. Oh, that's one. Now, oh, now it's green. Two, now it's yellow. Okay, three and so forth. You just keep adding the counter. Notice what happens to the counters. Now if I have the red, green and yellow matrix, and then I can only see red and green, notice the red green has its own counter, right? That matrix has its own counter, but it doesn't count the yellows because it doesn't see yellow. So the counter that's bigger on the bigger matrix, so the red, green and yellow is going to count faster than the counter on the red and green. That is going to give us time dilation. In relativity theory, that's the precise way that we will be able to go from Markov chains to the time dilation that we see in Einstein special and general relativity.

[Tom Bilyeu]

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[Tom Bilyeu]

Thanks for sticking around. Let's get right back into the action. What's missing that causes the counters to be different?

[Donald Hoffman]

You just aren't seeing everything. The idea is going to be the

[Tom Bilyeu]

why is it mapped so specifically to the speed of light?

[Donald Hoffman]

So that's a good question. So If I'm going to make this kind of claim, where does the speed of light come out in all of this? Right.

[Tom Bilyeu]

Well, speed more the computation of energy equals mass is probably what I'm asking.

[Donald Hoffman]

The speed of light is going to come out as the fastest way that you can transition among states in the matrix. And there's something that are called cyclic matrices. So you've got a cyclometry matrix for red, green and yellow would say, for example, if I'm red now, the probability is one that I'll see green next, and the probability is one then that I'll see yellow next, and then the probability is one that I'll go back to red. So you cycle in and that's going to be the fastest way that you loop among all of. And that will effectively be. That will, that will correspond to a matrix that corresponds to the speed of light. Okay, but for example, if I'm in a different matrix that says I'm stopped at red right now, I might go to green with the probability 0.5, but I might stay at red with probability 0.5. Now I'm going to be moving much more slowly around the states. So there is a maximum speed at which you can, in this case you call the commute time. There's a fastest commute time, the smallest commute time, and therefore fastest speed. So you can use that for starting to build up notions like time dilation. And then now it gets a little technical, so I'll just hand wave a little bit because we don't want to go into the math. But there are then on these matrices there are mathematical structures called Dirichlet forms. Dirichlet forms, Yeah. D, I, R, I, C, H, L, E, T. Dirichlet. French, French name. Dirichlet forms that will allow us to use effectively how states diffuse in a Markov chain, two states diffuse in a very similar pattern. We're going to put them close together in our space time headset so we can build distance from Dirichlet forms on these matrices as well. So the counters dilate, the Dirichlet forms change. Because when you take a trace, the distances that you're going to get on these Dirichlet forms contracts. So we're going to get time dilation, length contraction from this. Now this is just top level.

[Tom Bilyeu]

Why would the changes be so predictable? So there is a precision with which things change that can be mapped out very eloquently when you're talking about the speed of light as we approach it and how time and distance react very differently, which allowed Einstein, and I'm perfectly willing to accept he was incorrect, but his approximation, by calling it a curvature of space time allows you to very precisely measure this stuff. But if, if I'm understanding him. Jesus, this is a big if. But if I'm looking at this gigantic matrices of all the different Markov chain combinations that there could be, and I'm saying I'm looking at now subsets of that, but there's still the sort of connective tissue of the whole of this matrices. If I'm looking at two different traces, subsets, then the distance will appear to be collapsed, the time will appear to slow down, but different ones are going to behave differently. That's right. And so I don't see, and this is just my ignorance, fully accept, but I don't see how that no matter where they are, no matter whether we're going from 12 things down to two or 12 things down to 10 or whatever, that it would collapse the distance in the same predictable fashion that would trick Einstein into thinking he was seeing the curvature of space time.

[Donald Hoffman]

And that's what we have to prove. So that's, that's what.

[Tom Bilyeu]

But that's what you are saying.

[Donald Hoffman]

I'm absolutely saying. So we're not saying that Einstein is wrong. We're saying, you know, Einstein, well, space time is fake. So you are saying that. Well, saying. Well, I won't say that spacetime is fake. I will say that space time is a, is a wonderful data structure. It's just not the full, it's not all of space time is not the final reality. It's just a headset. But it's a good headset. And, you know, and what I have to do as a scientist, if I'm claiming that I'm using these Markov chains outside of space time to get the next layer of software outside of spacetime, I have to show you exactly how I get Einstein's special and general relativity. I have to show you exactly how I get quantum field theory out of it.

[Tom Bilyeu]

Do you think that the Markov chain is being consciously manipulated to create the effect of the bending of space time?

[Donald Hoffman]

Well, so I don't, I don't need to say that at all, but I can allow it later on. I should say that I can get the curvature of space time, I believe, from certain kinds of Markov. Chains. So, in fact, the way I think about it right now is there's a wide variety of Markov chains, and I think that there is this very special subset, probably what we call a measure 0 probability 0 subset of Markov chains that can be used to build the kind of headset that we have. We have such a sort of a low grade, trivial kind of headset that the, that most Markov chains just won't do it. They're off doing other interesting things. But there is a subset of Markov chains that you can use to build flat space time or curved space time. And then there are others that will, that will basically, that kind of smooth space time just won't work for them. They just won't. So Markov chains give you the chance to build everything that Einstein has, but then infinitely more.

[Tom Bilyeu]

Hmm. It's very interesting. And I'm probably, I. I'm very at risk of oversimplifying what you're saying. So I'm hearing it for the first time and I may just not understand it. Uh, and I've heard so many of these interviews where you can tell that the interviewer is like, not quite tracking what's being said. So I run that risk right now. But I will say, as you were describing all of this again to a hammer, every problem is a nail. But when I look at this, the metaphor or reality that this is code and it's simulated would make sense, because you really want to build procedurally.

[Donald Hoffman]

Right.

[Tom Bilyeu]

But to build procedurally, you have to know what the probability space is not quite. Right. What are all the permutations? So you get into this idea of that. Is it decorated permutations? Right. So when I looked up what that was and was like, oh, yeah, you guys are just trying to account for more variables. So if you're doing a, a procedurally generated world, not only do you have to come up with a terrain, but you've got to, like, place trees and stuff like that. So then you go, okay, what orientation is the tree? How tall is the tree? Roll, pitch, yaw. Like you have to determine all of those different things.

[Donald Hoffman]

Exactly.

[Tom Bilyeu]

And so it feels like what you're describing with the markup chains, with the trace.

[Donald Hoffman]

Right.

[Tom Bilyeu]

With the elements. You didn't call it trace elements. Trace something.

[Donald Hoffman]

Trace logic.

[Tom Bilyeu]

Trace logic. Thank you. All of that stuff is still fitting within my layman's understanding of this absolute simulated reality.

[Donald Hoffman]

It's. We, when we're. If we succeed at this, we will have theorems that tell you exactly how to write the computer code. And you could write computer code? Yeah.

[Tom Bilyeu]

Literally, as you were talking, I was like, man, if this is going to be a breakthrough that I can use in my game, I'm going to be very excited.

[Donald Hoffman]

That's. That's Put it this way, if I cannot give you a breakthrough that you can put in a game, then I'm wrong. This is fair enough. So that's what we have, this Trace Institute that we're building. That's the goal is within the next five years to basically nail down these theorems with proofs. The proofs will then give us effectively the ways to build these algorithms and we're off to the races. So that's the goal. And the idea will be that once we figure out how to build this headset, then we can start to play with it, we can start to reverse engineer it. And using the trace logic now we have the ability to do some kinds of magic. But I should just put this in a framework right now there's a lot of work that's being done on, that's very, very good work by Carl Friston and others on the free energy principle and trying to minimize surprising surprise and maintain markup blankets for, for self versus outside and so forth. And, and this is very much similarly related to that kind of thing. The trace logic is related to the minimum surprise in the following sense. The trace logic is the logic of zero surprise. So it this, so what I'm claiming is that the pre established harmony that Leibniz was looking for is this logic of zero surprise, the trace logic. And then it leads to a very different approach to maintaining self versus outside than the Markov blanket kind of thing. We, we, we get something that's, that's broader than the Markov blanket. So but one of the key, we can go into the, there's more, more details if you want to. We can, I can show you how we build agency by re recursion of the trace logic.

[Tom Bilyeu]

So well, before we, we go there, because I do worry that we're at risk of losing some non technical people. One thing I want to talk about is AI. So when I think about the way that you describe like the neurons aren't real, this is all fake, I go, well then why can't an AI agent become conscious? Why isn't an AI agent already conscious? Like if consciousness is fundamental, what is it that stops AI from being conscious?

[Donald Hoffman]

Okay, so it depends on how you want to frame the question of the emergence of consciousness. If you're framing it in the case where you're saying I'm going to start with a physical system that's not conscious, and my question is, what can I do with that physical system to make consciousness emerge, Then I say you can't do that. It will never.

[Tom Bilyeu]

But your Take is that everything is already conscious.

[Donald Hoffman]

That's. That's right. So. So I'm giving a. So you can see why we have to be very careful about our assumptions. Most of my colleagues are. Are starting with space and time as fundamental, and they're saying that they're of course, they're not conscious. And so we're trying to figure out what neural structures or neural activities or properties are going to be required. What functional properties?

[Tom Bilyeu]

Blender consciousness.

[Donald Hoffman]

Well, that's the kind of question they would have.

[Tom Bilyeu]

What do you think?

[Donald Hoffman]

So, so, so I. I'll first say I reject that entire framework that consciousness does not emerge from physical systems. It doesn't emerge from neurons or anything like that.

[Tom Bilyeu]

But does the one consciousness, the one consciousness does pop into Don? It does pop into Tom.

[Donald Hoffman]

Right.

[Tom Bilyeu]

Why doesn't it pop into the blender?

[Donald Hoffman]

So, so I would say that no matter what I see, this cup, this table, an ant, in every case, I'm fundamentally interacting with a conscious reality, and my headset gives me more or less insight into that conscious reality. With Tom, my headset has given me quite a bit of insight into what Tom is thinking, what he's reacting.

[Tom Bilyeu]

Am I conscious, though, from your perspective, or are you materializing me?

[Donald Hoffman]

I would say that. That what I'm experiencing is an avatar that's giving me insight into consciousness. I'm calling that avatar Tom. Tom is calling this avatar Don. And I am getting some, but not complete insight into the conscious experiences that Tom is having. By no means do I expect to be, you know, knowing everything that's going on in your mind and your feelings and so forth.

[Tom Bilyeu]

But if the consciousness is stepping inside of us, broadcasting whatever that is, is biology. The thing that makes it viable is a nervous system. The thing that makes it viable as a place the consciousness would want to go.

[Donald Hoffman]

So. So I would say that that way of putting it is the wrong way to put it. It's. That's assuming again that we're in the game of starting with non conscious physical ingredients and trying to figure out where consciousness can emerge.

[Tom Bilyeu]

But you said that that space where I'm observing the emotions or whatever is the indication of where the one true consciousness is.

[Donald Hoffman]

Right? Because my experience in my headset, the avatar in my headset of Tom is detailed enough to give me some insights into the experiences you might have with my cat. I have less insight, but some insight into what that cat might be wanting. Like, I know when it's hungry and it's rubbing against my leg and so forth. I can guess with an ant I'm getting less information. If I really studied, I might be able to understand what it might be like to be an ant. But if I turn it around, ask how much do I think the ant knows about my world, my consciousness? My guess would be, I mean, I can go up there to an ant and I can go like that and be ready to kill it, and it wouldn't even know that I'm doing that. So how much of my world? Almost nothing. I would imagine that as complicated as I feel I am, the ant, its model of me is probably trivial. If it has any model of me at all, it's pretty, pretty trivial. So take that insight and turn it around. Things that look trivial to me, maybe it's because I'm an ant to them, right? I'm trivial to the ant. Not because I'm trivial, but because the ant can't see me. So the things that I think are trivial, that's not because they're trivial. It's because my headset is just not smart enough for me to see the true complexity of that consciousness. So what I'm saying is quite radical here. When I see something looks as dumb as a rock, that's because I'm dumb. It's not that the rock is dumb. The best I can do is come up with a rock. What I'm actually interacting with. If I could actually understand what it was, I might fall down in amazement before it and worship it. So. But all I can see is a rock, just like all the ant can see of me is almost nothing of my complexity. So we have to not mistake. And this is the. I'm going to say this slowly because this is the key point. Do not mistake the limitations of the headset for an insight into the nature of reality. It's just a limitation of my headset, period. It's a dumb move on my part to think that because it looks dumb to me, therefore it is dumb. No, that's just. You're not smart enough to realize that that was just a headset. And your headset's got limitations. What's really dumb is to think I'm seeing the truth. I'm seeing through a filter of a headset. What I need to do is be very, very humble, very modest, and ask, what are the limitations of my headset? How can I understand the limitations of my headset? I think I'm seeing a world of space and time. Oh, space and time is fundamental, really. Why should I believe that I should be very, very modest and say, space and time is what I'M first given in my impressions. That's just the first give. Let's reverse engineer what's really behind that. And again, I think if you always think about what can the ant see of me? Almost nothing. Just turn it around. When I see something looks like almost nothing, then I am probably the ant to that thing. That's the way to look at it. So I'm an ant to this table. So whatever I'm interacting with is transcendent. I'm just getting a table. It's not because the reality is stupid. It's because I have my limits in my headset. So that's the key difference here. So the whole game of saying how do I start with stupid components and then build up consciousness is just the wrong game. The stupid components are just the headset giving up. They're not the fundamental nature of reality. That's the headset saying I give up.

[Tom Bilyeu]

We could do a whole episode just

[Donald Hoffman]

about just on that.

[Tom Bilyeu]

Donald, this was incredible. Where can people keep up with you?

[Donald Hoffman]

I have. Well, pretty soon we're going to have an institute called the Trace Institute coming out in June. So I'll be announcing on X in a few weeks the website for Trace. So that will be out and I do have papers if you know. I've got a book called Visual How We Create what We See and also the Case Against Reality, why Evolution Hid the Truth from Our Eyes. So there's two books, the Case Against Reality and Visual Intelligence and my X account. And then there, if you just Google on Google Scholar, if you're a scientist, then of course you'll know that you can just Google my name and there's dozens and dozens of of papers that are published.

[Tom Bilyeu]

That's awesome.

[Donald Hoffman]

I love it.

[Tom Bilyeu]

Thank you again for taking the time out.

[Donald Hoffman]

Thank you, Tom.

[Tom Bilyeu]

I really appreciate it. Boys and girls, if you have not already, be sure to subscribe. And until next time, my friends, be legendary. Take care. Peace. Let's talk about a pattern that is guaranteed to be killing your progress. You know what you need to do. You need consistent nutrition. We all do. You need vitamins, probiotics, greens. We all know that we should be doing more of it. When your morning gets chaotic, you skip it. When you travel, you skip it. When your routine breaks, everything tends to break break. And that inconsistency compounds against you every single day. AG1 is designed to solve the execution problem. One scoop, eight ounces of water and you're done. You're getting 75 plus ingredients, vitamins and minerals, pre and probiotics, nutrient dense superfoods. Everything that used to require six, seven different supplements and perfect planning now happens in one drink that takes about 30 seconds to make. Right now, AG1 is giving you $87 worth of free gifts. With your first subscription, you get a welcome kit, travel packs, vitamin D3 plus K2, and flavor samples. Click the link in the show notes or visit drinkag1.comimpact to claim this offer.

[Donald Hoffman]

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