Consciousness in the Quantum Realm

I actually have a fun analogy for LLMs that I was just thinking about last night. You might like this. So imagine that, kind of like, consciousness is something that comes first, and then it generates more and more and more training data for whatever large model you want. And I think this is actually what happened even from evolutionary perspective. So we, like, organisms started with a few neurons, and then they evolved like a tiny brainstem, and then they evolved like more brain regions and more brain regions. And then eventually mammals and us evolved this massive cortex. So we've been building this giant cortex which gives us like weirdly sized head compared to other animals. And what I think is happening is we're just basically building like a bigger and bigger and bigger LLM-style thing inside our head, um, and, and, and that's what evolution has done. And we're doing the same thing now with LLMs. We're just building this like massive neocortex, but everyone thinks the neocortex is the seat consciousness? What if it's not? What if it's that, like, really deep thing that started off in individual cells that's been throwing off training data, throwing off training data for millions or billions of years, and it's grown this giant model?

All right, um, We're gonna get a little weird today. We are talking to Suzanne Gilder. She's a, she's a quantum physicist who I've known for a long time and has a new startup called Nirvanic Consciousness Technologies, which is an impressive name and an impressive field of research. Basically, we're going to get into consciousness today, obviously, and, and the limits of LLM technology, current AI technology, whether we're on a dead end and the really interesting stuff is off on some other path that Suzanne is chasing. So I think you'll be— takes a little minute to get into this, but then I think you'll be entertained and your mind will be racing as we dive in now to explore consciousness in the quantum realm. Hello, Suzanne, how are you?

Hi, Ashlee Vance. I am very good, thank you. Loving the new brand.

Oh, thank you. We try. We try. I saw you, you have a Substack as well. I know.

Actually, well, it's kind of funny because I went on your YouTube channel and I saw like your banner thing and it's got the core memories with the actual wires going through them. It's like a, some computer history geek must have designed that.

I, well, it was me. I mean, I didn't do the design, but I was, I was like, we gotta, we gotta have these bits and pieces in there. Yeah. We're trying to, um, it was a hard mandate because I was like, I want it retro. To acknowledge the actual core memory, but then, but then kind of futuristic for the stuff we cover. So yeah, I was happy with that. And I'm so happy to know somebody who actually knows what core memory is. Where are we catching you?

Well, I'm in Vancouver at the moment, so yeah, yeah, in my home office.

Okay. All right. And I see we've got, we've got some of your robots behind you.

Yeah. I thought these might actually be a bit of a throwback because some of these robots you've sort of seen before and they, they kind of come back back over and over again.

Yes. Yeah. Well, I'm glad because we, we're going to be talking robots. Um, well, there's all kinds of places I want to go to chatting with you, but, you know, I thought there's probably a vague unifying theme in some of this, which is, as long as I have known you, you have been pursuing this idea of, of where is AI heading, where is robotics heading, and, and I mean, please obviously correct me if I'm speaking out of turn, but I feel like you have been very open-minded to this idea of, of what do these new forms of intelligence and machines look like in terms of consciousness, in terms of some of these more philosophical thoughts, like, like these are beings that are, that are coming into this world, and, and do they have, um, rights, and, and how do we interact with them? And I know you've been I don't think your view on this particular part has changed. I mean, you've always been pretty pro-embracing these new life forms and living with them. Is that fair?

I think so, yeah. I mean, I've always been interested in trying to understand what the human mind is and how it works. And as an engineer, I've always tried to understand something by building it. So the idea of understanding the human mind is like, can we build something equivalent in a machine, in a robot, and get all those pieces put together in the right way that it sort of comes together as a mind. So it's really a fascination with the mind, I think.

But I remember I was interviewing you one time, and I don't remember the exact quote, but it was something like, like you were kind of looking forward to the day that you're hanging out with your robot buddy at home.

Yeah.

And how— and you've been thinking about that type of future since you were a kid, or that's more in your professional life?

I think I've been thinking about it for quite a long time. Early on, it was about exploring electronics and what can I build? But as soon as I realized that you can actually make systems move, you can make them do things in the world, then my mind started to go kind of crazy. And well, where does this end? Will we have machines that are eventually like us that are moving around and have minds like us? And at that point, I started thinking about interacting with those machines and would they think differently? Would they feel differently to us? Which I guess brings us to consciousness at some point.

Yeah, I was going to say, lots of people have these thoughts. You happen to have dedicated your career to actually seeing if— exploring.

I think I have a weird way of thinking about this that's different than most people because I have a very optimistic outlook on most things. So when I think about robots living amongst us or machine sentience or what could those systems do and look like, I tend to be more utopian and optimistic rather than the dystopian sci-fi kind of horror fantasy that a lot of people think of. So that's generally the first barrier I come across when I talk to people. They say, oh, this, you know, they're going to kill us all, or this is a horrible future. But I don't think that way. It's like, why are you thinking like that? Right.

Yeah. I mean, I tend to go a little more that way as well, although, gosh, it is, you know, I've been reporting this book on AI technology for the last couple of years. It gets harder because all the people I'm sitting with, the people who are developing this technology, and gosh, you know, I feel like the vast majority of them are like, seem to be deathly afraid of what they're making at the same time as they're making. I don't know, really, it does get in your head a bit.

Yeah. But then on the other hand, people are still buying products. That are promoting AI and making AI improve. And so if we didn't want this as our future, I think we wouldn't be voting with our wallets. So I do find a little bit of a paradox there.

Yeah. Well, yeah, I mean, we've seen this many times with technology. It's weird, you know, something like Facebook where, you know, you're making this deal with the company to hand over all your data in exchange for these free services.

I don't know.

I go back and forth on whether I know that people are are really how aware they are of these decisions they're making as this stuff is foisted upon them. All right.

Two things I know for sure is that most people think ChatGPT is really helpful and useful, and they use it all the time. And then whenever I ask someone, would you want a robot to do all your domestic chores for you and do all your grocery shopping and cooking and cleaning? They're like, hell yes. So those are two good data points.

Well, let's, let's kind of, let's go there for a second. Um, I know with your new startup, which we'll get into, you've, you've changed some of your thinking around this stuff, but your, your previous two startups, Kindred and Sanctuary, you were sort of building helpers like that in, in a lot of ways. I mean, some of it was evolved into robotic arms and then, and then I think it's fair to say with Sanctuary getting more to like a, more embodied form of AI. But that was the quest that you were on for a bit, right? Was to use, I mean, yeah, can you explain to me, I mean, using advancing AI technology and to give it an embodiment?

Yeah, so the original idea of Kindred back in 2014 was actually a really simple idea. It was, oh, we now have this like VR technology and we have like sensors and things that we can put on our bodies. So could we move a humanoid robot using all this gear? So I was kind of imagining this advanced suit that you would wear and it would map to a humanoid robot, a bit like Ready Player One, you know, that movie where they go into the VR and then they're actually controlling avatars, but it would be a humanoid robot instead. And my thought was, if you could do that and the robot could then do anything the person could do, then it's immediately generating training data. And with enough training data, you could automate that process. So you could have an AI like watching, learning in the background as a person moved a robot or say thousands of people moved thousands of robots. And that was really the core idea of the whole thing. And then that played out over the next 8 years, that idea.

Yeah. So, I mean, you end up selling that company for hundreds of millions, if I recall correctly. And then, so that's good for you. And then, and then, and then with Sanctuary, it evolves, it evolves into something, yeah, that I would say is, comes with bigger, deeper questions attached to it, right?

Yeah, so the original idea of Kindred was kind of the same, like we wanted to do this in humanoids, but at that point humanoids weren't quite advanced enough, like the hardware wasn't there. So what happened at Kindred was the idea ended up morphing into a niche version of itself where the robot ended up being a robot arm in a warehouse. But the technology idea was exactly the same, that people had trained that robot arm by showing it what to do through these VR teleoperation gear. And then at Sanctuary, it was kind of a reboot. It was like, okay, it's 2018 and humanoid robots are getting better and better. So can we try the same thing again, but with a fully general-purpose robot this time? Yeah.

But you, but in the background, I mean, you were always a scientist and a researcher and you were thinking, even unless I'm wrong, even on Sanctuary is like, how is this evolving towards this thing, not just doing tasks, but becoming kind of its own being, right?

Yeah, that's right. That's right. And we designed a whole bunch of different, what are called cognitive architectures. So it's like, how do you put the pieces of a mind together to bring a machine to life? And that's the goal of people who design cognitive architectures is they're trying to, I guess, deconstruct the brain into pieces, simulate each piece in software, put it back together. And like, watch the thing boot up and see how it behaves. But what happened over that time, like between, I guess, 2018 and like 2023, is deep learning kept getting better and better and better. So now in this field, everyone thinks you just train an end-to-end deep learning model on multimodal data and you don't need to build the mind piece by piece anymore. You just have, like with large language models, you just have a giant amount of data, you train one giant network, and then the robot will kind of like behave as it's been shown with its training data. Yeah. Yeah.

But like, what do you make of that, that thesis? Because I, I spent some time with Physical Intelligence and with some researchers in NVIDIA's group and with Figure AI and, and they're all exactly to your point. I mean, it was this idea that we were doing this robotics work, then we solved math, math, math, we solved language mathematically. Um, and, and that We could piggyback off all this LLM work and infuse it into robots, and they would somehow gain this, this general-purpose knowledge of the world where you, you like set something on the table and now you just have to say in language, pick up the cup, and, and the robot, um, you know, discerns a very different way than the way we used to train this, which is like you'd have to show it thousands of cups, have it pick it up tens of thousands of times, and now you're kind of using language to get this head start about context in the world. Do you, I mean, this, this in the last year, hundreds of millions, if not billions of dollars have been pumped into the startups taking this approach. I was so curious what you make of it.

Yeah. So I have two main thoughts on this. Have to be careful not to get into like hot water here and, you know, say anything bad about these companies.

Welcome to hot water.

My first thought is it might work, I think the amount of data required is going to be ridiculous. If you look at how much data was used to train some of the large language models, it was already all the data we have on the internet. It was just an immense amount of data. But if you think about a humanoid robot now, it's not just one modality like text, like letters of the alphabet. It's like high-definition camera feeds, audio, touch sensors, motor position sensors from all over the body. So the data is much richer. So in order to learn a model that predicts and controls all those different kinds of modalities, not just text, you need an exponential amount of data, more than what we needed for language models. So I'm not saying it won't work. I actually think it will work in a weird way where if you stick within the training data, the robots will actually be able to learn to do things autonomously and they'll generalize a little bit. But what I'm nervous about is they won't It won't be like a person. So in my mind, there's still something missing because people can go into a general environment that they've never seen before and start making smart decisions and doing things without having seen training data of that type. And I've just never seen an example of a humanoid robot generalizing in that same way. So you could say, oh, well, maybe humans have just seen like trillion gazillion episodes of training data from robots haven't yet. But there's another way to think about it is maybe we're actually using a different process at that point. Maybe we are not just using trained model. Maybe there's something else going on. And that's my second point, which is I think there's something else going on and I think it's related to consciousness and our conscious awareness.

Yeah. Which I 100% want to talk about, but really quickly. I mean, maybe we don't have to call out any of the startups by name. If you don't want to badmouth them. But, but, but generally, well, so I've had the same questions because I've gone into some of these labs and I see them. A big part of their thesis is, you know, I can't remember when I went to visit Kindred, but it must have been for the first time. It was probably like 8 years ago or something.

And yeah, that's right.

Yeah. And you had these arms doing, doing very similar tasks to what I am seeing now. When I walk into these startups, they just have arms picking up objects, folding shirts, you know, just doing these, these closing microwave doors, just repetitive things. And when I talk to the companies, I mean, they're arguing that, that even though companies like Google and Kydra did this kind of thing in the past, that they are going to do this, to your point, on, on a higher level of volume so that we get more amounts of data. But I don't know if I'm sold on that, on that, that thesis. I mean, even, even that part is, is that even possible that like, are they right that even though companies doing this type of work existed before, they weren't doing it at kind of like the scale that we could get to now?

Yeah, it's, it's a really interesting question because if you'd asked the same thing about like language models back in 2013, we would've been wrong.

Yeah.

Because language models weren't able to do what they do now. And so maybe it is just a volume of data. Problem, but I don't know, I'm skeptical. I mean, you've mentioned you've been to a lot of these robot companies. Have you ever just been seeing the robot demo and then it's moving thing from place to place and then it suddenly stops and turns around and looks at you and goes, "Hey, Ashlee's here." Right.

No.

I've never had that spark of life moment with any autonomous robot I've interacted with. The only time I've had it is when a robot's been teleoperated by a person and the person is hyper-aware of what's going on around them. They imbue that into the robot and then the robot cat comes to life. There's something fundamentally different when a person is teleoperating that you just don't get.

Okay. So you're, you're, it sounds like you're skeptical, but, but my sense is you don't see this as total smoke and mirrors. It's like possibly a thesis worth exploring because, because they might crack the code on some sort of store of data that's locked.

I don't think it's smoke and mirrors. So what I think is it's a question of generalization. So these robots will always be able to do something if you make the task narrow enough. And just as a really limiting case of this, there's something called pilot replay, which is you teleoperate, and then if everything is in exactly the same position, the robot will do the task perfectly autonomously. So that's one extreme limiting case of zero generalization. Then what you can do is you can maybe move all the things you have to pick up by a millimeter and ask, okay, is the model generalizing? Maybe you change a lighting condition slightly and say, does it generalize to that? But what I think is going to happen is this circle of generalization is not going to widen as fast as people think it is. So the robot will have had to have seen everything perfectly before in its training data. To automate it. Now, that's fine if you're on a factory assembly line or something like that where things don't change very much. But you take that robot and you put it out into the street in the middle of traffic, a bustling environment with people trying to kick it and all this kind of stuff. I just don't think it's going to work in those kind of really ultra-general environments. Okay.

I mean, that would be a problem because that seems to be where the Android-type robot folks want. I know, sorry. That's right.

And the home, I often say the home is the ultimate test of AGI. It's like we're so familiar with our homes and we think things like cleaning are so boring and mundane, but these are actually what I call AGI-complete tasks. Like try cleaning an average American household with all the completely random things you could come across. Yes. Trying to do that with kids running around and dogs and all this kind of stuff. It's a very hard problem. It's very general.

I saw a couple of these companies doing the demos where the robot's going to the fridge and like picking things out. And then I, on X, people were like, oh, this is like your, your stoned roommate trying to, trying to make a sandwich after a late night out or something. Um, but, but, uh, okay, well that's, and you must have, you must have reached this conclusion in many ways a bit Okay, let me come into this slightly different way, because what you're working on now, I think, is not just what we were talking about. I mean, you've been hunting for consciousness as well. So there's two parts to this puzzle, which is the robots doing interactions out in the world. And then there's been this other idea, which is if we build these neural networks sufficiently large that they start to take on a level of complexity that starts to resemble the brain. And obviously, as far as I'm aware, nobody has a super clear answer on where consciousness lights up or the soul or anything like that. But this theory that, well, maybe we're just heading on this path where we make things complex enough with classical computers and, and and it does light up one day. And your philosophy, as I understand it, and your new company is kind of a rejection of that idea. Is that fair? And did you maybe walk me through what you try to do? And you must have seen limitations on these multiple fronts that led you here.

Yeah. So, okay, there's a lot to unpack there. So maybe let me start by, I guess, posing a question to the audience is So first of all, we have to sort of define what consciousness is because people have different definitions for it. Some people think it equates to thinking or reasoning or something like that. So my definition of consciousness, at least from the what is it, what does it look like to us, is it's this concept that we have this subjective experience. So we feel like we're inside a movie that's playing out around us. It feels like something to be us. That's kind of what consciousness is. And we see it visually, but we also have all these feelings inside of us, like emotions, sensations. We're aware of things. So that's how I think of consciousness. And we can do things unconsciously and not be aware that we are doing them. So a good example is driving. So if you've been driving for 20 years, sometimes you even forget that you drove to work. You can't even remember being in the car. You just somehow got there. So that process, after having seen a lot of examples of driving, has become, it's sort of gone into your unconscious. It's gone into your muscle memory. It's gone into your autopilot. So the models I'm working with are, it's like the first time we see something, like the first time you ever drive a car, you're like hyper-aware, you're hyper-conscious of that. But if you do it over and over and over again, it becomes automatic. So the question is, why don't we think about AI in the same way? We are giving AI all this training data, all this training data that comes from people. And we're creating this unconscious automatic routines, but we've completely skipped the part where those systems are able to do that for themselves to create that training data in the first place in an online way. So let, in my world, if I had a conscious robot and I put it in a car, it would be like its first driving lesson ever. It would be like, oh my goodness, what am I doing? I feel terrified. It's grabbing the wheel, it's trying to learn to drive the car, and it's using that that conscious decision-making process to kind of now create its own training data that over time can become these unconscious subroutines. And we're sort of doing that part for it. So my question is, well, can we create that like conscious module that in the first place allows it to create its own training data? Does that help? Does that make sense?

It does. I was just, I was, I like the way Yeah, the order of operations that you laid out is really fascinating. Clearly, you know, in the last year or so, we've moved from, from some of what you were talking about with LLMs to this reasoning era. I mean, it's my sense is that people think, seem to think that, you know, like on one level we could argue that these LLMs, they're just these, these statistical machines and, and they're just prediction machines and You know, there's, there's not, it's amazing. Like, there's a lot that goes into that. But other than that, there's maybe not that much under the hood that at all resembles thinking or consciousness on any level. And then now we've moved to this reasoning era where, again, it's based on these same principles, but it seems to be working through problems in a kind of human-like way. I mean, even with, with that stuff, you're not seeing hints that convince you otherwise, that that something's changed in the last year or so?

No, I don't think any AI today is conscious. That doesn't mean that they can't do useful things. And again, this is where this definition of consciousness comes in. I think we can, if you took the consciousness of a person away, they'd still be able to do a lot of things because they've already learned how to do those and those things have become unconscious routines. But that system will be kind of like a zombie. It will be doing things that it's done before in the way it's done them before, and it will have no experience. It will have no feeling. It will have no subjectivity. And what I'm saying is I think it's that subjectivity that gives us this ability to go into a new environment and do something in a different way. So this is where consciousness research gets a little kind of crazy and well, interesting, if you like the other word for crazy. But I think when we are in this conscious awareness mode, I think we're using a different kind of decision-making process that allows us to make decisions in places we've never been in before where we don't have training data. You might call it intuition, I think is the closest word we have in the language, but it's certainly not prediction. It's not memory. It's sort of like gut feeling or intuition of what to do.

Yeah.

Okay.

So where LLMs and current AI is very brute force, it's, it's missing all of this nuance and those, those funny feelings that we have. And you don't see a path even as the current models add complexity for that just to infuse itself in or to, to appear out of, out of nowhere.

I think it's the other way around. I think it has to come from the foundation. So we're, we're building these increasingly complex hierarchies of unconscious behaviors and expecting consciousness to pop out of them when I think it's more of like a fundamental layer. Like consciousness is what creates those in people and in animals. I think consciousness is what creates that training data in the first place. It allows you to create those unconscious routines. So it's sort of like flipping it on its head. And maybe a fun thing you can think about is, so I have like a microscope at home and I love looking at little microorganisms.

Of course you do.

I know, I thought you might like this actually, but just another one of my weird hobbies. Yes. But so you go to the pond down the road and you scoop some pond slime and then you put it under the microscope and you see all these little creatures and you can interact with them. You can poke them with a needle and some of them are really small and just watch what they do. They are very smart. So they're zipping around looking for food. But if you start interacting with them, they go into a different mode. They start hiding. They'll run away from you. You will not be able to catch them. It's kind of crazy. And they'll have all these super interesting behaviors. They don't have a single neuron. They don't have a nervous system, right? They're making decisions somehow without any of the things we would think are now going into massive trained AI networks. So, if you think about it, it's kind of an interesting question is what inside that little dude is making those decisions and having that little 5-micron guy be hyper-aware of what's going around it so it can survive. I see. So, this is— And I'm pretty sure that The guy, from an evolutionary perspective, has never had a pencil poked in its face. So it's a totally novel situation. Yeah.

And so this has to be built in even at that most basic level.

And I think consciousness is in that level. I think it's a very deep thing that gets deep into biology and physics. So I think consciousness came first and then learning came from consciousness, not the other way around.

Okay, okay. And so like, let's, let's run with, run with that. And, and you are a quantum physicist, and, and I'm about to enter territory where I'm going to embarrass myself terribly. But, but, you know, I've seen you with your new company, I've seen you make this, this argument. So why— you, you seem to be saying that, that quantum properties, possibly quantum computing elements lend themselves better to what you're describing. And it's okay. So for the rest of us, why, why would quantum properties be at the heart of that fundamental consciousness possibly, whereas as what we do with, um, these classical computers, just these transistors going on and off, are not?

Yeah. Okay. So there's different ways of tackling this problem, uh, different ways of thinking about it. What got me interested in it first of all was, um, some work that was done by Sir Roger Penrose and Stuart Hameroff back in the day, and they jointly came up with a theory. Like, Penrose came up with a theory that maybe consciousness is non-computable. It's like not something you could simulate with a digital computer, if you, if you like. And he has a bunch of mathematical reasons for why that might be. So he was thinking about it from a very abstract perspective, sort of mathematical, philosophical argument. And then Stuart Hameroff came along and said, hey, there are these structures in the brain called microtubules that might actually have a mechanism through which, uh, you could have consciousness happening, and it relates to quantum physics. And so Penrose really liked this, and it's related to this not being able to compute things using a digital computer. Because if you think consciousness is not a, like, digital computable phenomenon, then you have to say, well, what is it? It has to be something else. And the only other thing we really have right now is, like, something that goes deeper than digital computation, which is, like, quantum information. So you have to find a type of computer that you can't simulate with a digital computer if you're looking for consciousness being something that's not digital, if that makes sense.

I mean, I get where you're going. I guess, I mean, couldn't people just argue like, okay, if it's not this digital phenomenon that we know, I mean, couldn't it be anything? Why does it have to be microtubules and these quantum properties?

So it's kind of two different parts to the argument. So the Penrose part is just, like forget about the microtubules and the biology. Uh, Penrose thinks that our brain— okay, I just said forget about the biology, but then the brain— like Penrose thinks that our mind, our thoughts, are not able to be simulated on a digital computer. So then the question is, well, what's— what physics are they using that's different than classical physics? Because all classical physics can be simulated on on a digital computer, on a Turing machine. So then Penrose thinks that there are certain things in quantum physics that might fit the bill. And there's one thing in particular, which is— so there's some weird properties of quantum physics like superposition, which means a 0 and a 1, they can be in the same state at once. So it could be like a 0-1 combination state. So that's one interesting thing that goes beyond standard computation. Then there's something called entanglement, which basically means if you've got 2 bits of classical information and you put them on opposite sides of the galaxy, they're going to have to— the only way they can communicate is via sending information at the speed of light. If you have 2 quantum bits and you put them on opposite ends of the galaxy, they can still kind of interact with each other, or they still have correlations even though they're really far apart. So that's entanglement, and that's kind of weird. And then the weirdest one of all is called the measurement problem, or the measurement process,— so you take a highly quantum mechanical system and then you measure it or you observe it, it picks, it chooses a classical reality to end up in. So before you've done this measurement process, it's sort of in a weird fuzzy probability of all possible things that could happen. And then once you observe it, it chooses one particular reality to end up in. So the reason people thought this might be related to consciousness is that we seem to have this agency. This ability to consider lots of options and then choose, like make a choice. And so the— in the quantum consciousness, quantum agency community, that's the process by which this choice is made, is this quantum measurement process.

So this is like this bond of our concept of free will or something like that with computing.

Yeah. So people think that there might be a loophole in that— give— that lets us have our free will if we switch to quantum computing. Because in classical computing, there's absolutely no room for free will because everything is just like clockwork, right? It's just computation.

So like, I'm, I'm way out of my depth on the quantum side. What you're saying though, I mean, there is like this intuitive part of me that is, I don't— you know, there's always been something weird to me about LLMs, that it is some of the things I was talking about before, this brute force statistical Um, like it doesn't, it doesn't quite feel like intelligence, right? I mean, I mean, it is on some level, clearly it can do, do very interesting things, but it feels like, yeah, you've reverse engineered this, these properties. And it, it's clearly, um, like when I, and I feel like this is borne out when I, when I was reading the stories about that, that Google guy freaking out when he was talking to the LLMs at first. Like, to me, it was just— it's— I don't know. To me, it's clear that this— I feel like it doesn't have a personality. I don't think I'm having this, this super existential metaphysical experience with these things. And so, so I don't know, just like intuitively, I can see, see where you're going. And then, and then we're in this, we're like in this interesting time where after all these decades of quantum computing being chased after and, and, and sort of overhyped, I guess, these days, it seems like we've kind of turned a corner, right? I mean, so like, are the— do you have to— would you be playing off some of these quantum computing chips people are developing right now, or is this— you have to follow like a totally different different path.

Yeah, absolutely. So I think what's happened recently that sort of made this, what I'm trying to do possible, is that quantum computers are now mature enough that we can actually run a reasonable number of different algorithms on them. They're not there yet. The quantum computing industry is still playing catch-up with the classical. And in fact, it's kind of a weird race because the classical computing industry is still accelerating with GPUs and things. So they're sort of racing against something that's trying to get away from them at the same time. So that's interesting, right? Because people have been trying to cope with quantum algorithms and show that quantum computers are faster and better. But what they're doing is they're applying them to the same problems that people are solving with classical computers. So it's this, this like arms race constantly. And what I'm saying is, well, maybe this consciousness thing is a problem that just can't be solved by classical computer at all, because a classical computer doesn't have the ability to like choose reality through this measurement process. There's not— there's just no way to simulate that classically. So if, if we can get these algorithms to a mature enough state where they're able to control, say, physical robots and make choices and make decisions, I think the quantum computers are doing something fundamentally different at that point than the classical computers. Now, people might say, oh well, that's not consciousness. But even if, even if you don't believe that, they're still doing something radically different. They're using some resource to make a decision that might be smarter than a classical computer. We can put these algorithms to work. And so we have chips now that we can run these things on.

Okay. And I mean, I was trying to imagine, I mean, you have a startup. I saw in a couple spots you were giving a talk about, it seemed like this Okay, part of it seemed like, uh, very researchy, and then part of it seemed like, no, yes, you actually want to make products that you would sell based off all this stuff, right? What on earth is like a day-to-day— what are you working on? Like, okay, if we're gonna bring this, this chase, this idea of consciousness, um, appearing in these quantum machines, what is your like day-to-day work like? What— I have no idea what this would even look like or how manifest itself.

Yeah. So the way I'm testing it is, firstly, I think you have to put the quantum system in something, like you have to put it in control of an AI or in control of a robot to see this sort of effect. You need to set it up so it's making decisions that allow it to take action, and then those actions affect a feedback loop. So it basically needs to be in a feedback loop with its environment. And that's what we see in life. Like all life we think is conscious or has consciousness is always in a perception-action feedback loop with its environment. So what's happened up till now is quantum computing researchers have taken quantum systems and tried to isolate them from the environment. And there you can see these sort of like weird quantum properties. They have the quantum properties in their potential states, but they're not actually doing anything. Right. So they just sit there and be weird and quantum. If you can connect them to a physical system, you can put them in this like cyclical loop where they take in information put that into a quantum state, and then that quantum state collapses. And now what reality it chooses becomes the action option that the robot takes. So very practically, what does this look like? Well, I have some small robot systems that I'm working with, and I've worked with these kind of things for many years. So it's easy to get the perception data coming into the robot and actions out of the robot through the motor system. That works in real time. So it's very important that it's online. And then instead of just having, say, a neural net that takes in perception and does an action prediction, you have a quantum neural net. So again, how do you do that? Well, there's quantum computers available today.

That was my question.

Yeah. So there's quantum computers available today that you can use. There's a variety of different ones that offer. And if you go onto Azure or AWS, or sometimes the company's homepage, they have their own like private cloud thing, you can actually download an SDK of your choosing, whichever code, you know, whichever programming language you like, and you can actually program quantum algorithm and then it will send the problem or the program off to the quantum computer and the answer comes back. So, it's a cloud-enabled system. Like quantum is not a weird future thing, it's here today. Now, it might be noisy, it might be like small numbers of qubits, you know, it might not have scaled yet, but the end-to-end thing is there. So I just like code in Python, I write my code and then I'll have a quantum algorithm and then I'll send off the actual part that queries the quantum state and does this collapse process. I have write some code that does that. And then the quantum computer in the cloud actually does it, sends me back the answer, and then I use the answer to control the robot.

What kind of robots? Like the ones behind you or arms?

Yeah, this one I can show you. These are small, small robots. This is— you may remember this guy.

Oh yeah, yeah. I remember those running around.

Yeah. So this is just like, it's kind of made from a kit, so it's not an off-the-shelf robot you can buy, but we had some of the similar things at Kindred and Sanctuary. They're kind of really good test platforms for prototyping code on.

So it's just a small quadruped. Like, where do you order that from?

Well, it's just parts. So I build them from scratch.

Oh, you just— when you said a kit, I didn't know if you could buy the whole—

Oh no, I just buy the parts individually. But you can actually buy some similar kind of quadrupeds, but they don't have exactly all the right specs that I want. So it's better to just build my own.

Okay. So you have this kind of animal, like, for people who are just listening, Suzanne—

Oh, sorry. I should be more descriptive.

No, no. I think most people are probably watching, but you held up, I mean, a vaguely looks like a cat, I would say, or, you know, and is robotic. And, um, and so you're, you're sending the, the, the quantum operations off, off to the cloud where it's getting calculated on a quantum computer, and then it's coming back to this machine, and you're trying to see, and you're just trying to create this, um, and then there's going to be some interaction between that cat that has sensors and, and things like that with the quantum code. Is that the right idea?

That's right. So think about it, if your audience is familiar with neural nets, you can think about it as the information comes in through the robot sensors, that information goes into the neural net from the low layers, and then you run something like an inference mode. So you do what's called a forward pass of the neural net. So the neural net, some of its inputs units will be fixed by the perception feed, and then you run forward pass, and then some other output neurons will be the action. And so you're doing exactly the same thing, but now the neural net is a quantum neural net. It's running on a quantum computer rather than a classical GPU.

Okay. So this is like, I mean, this is the research part. You're exploring what this looks like and if it—

Yeah, that's right. Yeah. So just to come to your question about the kind of like research versus business side of it. And because one of the big questions I get asked is, why are you doing this as a company? Why?

Yeah. Are you going to be selling consciousness that we can buy?

Yeah. Well, we like to think of it as consciousness as a service.

I kept picturing it in a jar, but I don't know what I would do with that.

Kind of half joking here, but I think there is— Okay, it's very researchy. So what we're doing now is we're in this R&D phase. So mostly what we're doing is scientific research, but the reason it's a company is because there's a lot of interesting innovations here to be discovered along the way. So when you're doing something completely new, okay, you don't have a product on day one, but you're like discovering things as you go along. And they're not just scientific discoveries, they're actually technological or engineering discoveries. So you can kind of generate a lot of intellectual property around those ideas. And because there's literally no one working in the field, I think we're the only company, like the only quantum consciousness company in the world. I think I could say that as of today, February 2025. There may be more, there may be more coming soon, but I think we're like the first one. So the weird thing, the dangerous thing about that is it's very risky, right? Because what you're doing is very researchy and there's still open science questions. But the advantage is that you're in this green field of innovation there.

Yeah. And are you— I mean, I totally Ket, what you're saying about the innovations along the way, do you feel like, is the expectation they come more from the, in the near term, from pushing the limits of quantum programming and where we are with quantum software, or is it more advancing the state of neural nets or both?

Yeah, that's a really good question. I think initially it will be quantum algorithms. Discoveries. So, if you go to— actually did this last night. If you go to AWS and get an AWS account and sign into Amazon Braket, it's called, it's like their quantum computing platform. And you can actually, there's a tab on the side that's quantum algorithms and there's like 5.

Right. Yeah. It's early days, right?

There's really not very many quantum algorithms that have actually been innovated and people have run them on quantum compute. And there's even fewer that are useful. Useful. Most of them are just mathematical curiosities, like random circuit sampling and things like that. It's not actually useful for anything. So, I think there's a huge opportunity here to come up with really new innovative quantum algorithms that no one's thought of before in this whole area. And then how does that tie into the kind of more classical AI stuff? I don't think we're going to innovate in any classical neural nets anytime soon, but there might be innovations in how to connect connect classical AI into this quantum conscious decision-making system. So one of the things I'm really interested in is how do you get the best of both worlds? So we trained all these classical AI models to do unconscious things really well from training data. But what if the consciousness module could actually generate that training data in the first place? So when the robot suddenly finds itself in a completely novel situation, very complex, very dynamic, it switches into conscious decision-making mode. And then switches over to the cloud platform I'm offering, it starts making decisions tentatively, finding its way around, and it's throwing off training data at the same time. That can now be improving or training the classical AI model. So then the next time it's in that environment, it can just use the unconscious module. So that's the kind of idea of the two halves of it.

It must be an interesting experience to make a slide deck where you're like, we are, we're going to, we're going to pursue quantum consciousness and nobody else is doing this.

Well, it's actually fascinating though, because the kind of people I talk to, they fall into very different camps depending on what they've heard about this before. Like some people have heard about Penrose's work and so they're kind of already open to it. Some people think, some people believe in strong AI, like they're like, oh, AI is already conscious. Some people like, it'll never be conscious. And so you have a very different starting point depending on people's not just their background, but their actual philosophical perspective on reality.

Yeah. Well, and I mean, it's not like— I'm sure you've— I know you've thought about these things and I'm sure you've heard them before. I mean, a couple things went through my head, like when you were talking about the example of the organisms on the slide under your microscope and it having this— like, I get your argument that maybe you want to start this from this— some of this stuff is built in at this fundamental level, but wouldn't other people just argue, I mean, you know, if it, if that creature doesn't even have a neuron, I mean, this is like, whatever you're chasing is so basic and, and just, you know, it's just like a, like chlorophyll reacting to light. I mean, there's, you know, there's just nothing that interesting going on there that you're chasing. And, um, yeah, I mean, and, well, God, maybe I should start with that one bucket. And then, you know, whether, whether something like free will and consciousness are just illusions for us anyway. I mean, I, I guess you have a thesis and you're exploring it and you've thought about all these things.

Yeah, well, the interesting thing about consciousness research is I agree most of it is very vague and hand-wavy and fuzzy. And the reason I like the quantum consciousness hypothesis is it sounds like the weirdest of all, but there's actually a mod— there's actually a way you can test it experimentally. So what I don't like about other models of consciousness, like, um, IIT or global workspace and stuff like that, is they don't really make very good testable predictions that I could turn into a technology. Whereas the quantum consciousness hypothesis, it does. It says, like, if information that's coming in from an organism shapes a quantum state, and then the collapse of that is an act, a decision, that's something I can actually build and test. So although on the face of it quantum consciousness sounds like totally wacky, even amongst consciousness theories, in a way it's the most analytical, it's the most scientific, it's the most— it's the closest we have to something we can actually engineer and test. So that's why I'm interested in it.

So it's, it's like this, this idea of this interaction between the the basic particles of the universe and life. And that when you, it's the Schrödinger's cat kind of thing that then us as an organism are influencing how those particles are interacting with the universe. So this is the part where it's this union of the, physics of the universe and the organism living in that universe.

That's right.

Yeah.

And this is not a new thing. Like the early founding fathers of quantum mechanics thought about this. Like Erwin Schrödinger wrote a book called What Is Life, which I would definitely recommend anyone watching reads. And it was all about how life harnesses quantum mechanics to make decisions. And even in things like DNA molecules, Are they— do we think of these things as random mutations or are they actually purposeful quantum decisions that are being made at the molecular level? So this stuff goes really deep. And there's people now like Michael Levin who are working on uncovering really interesting properties of biological systems where they're somehow orchestrating and acting as a whole in ways we can't really explain. So, yeah, I think there's a lot. There's just a lot to pick at here.

And like, for you, I know you said there's going to be discoveries along the way, but for you to somehow be sitting in your lab, in your office one day, and now your robotic cat is doing things we've never seen a robotic cat do before, like, where does the field of quantum compute— do we have to, you know, some people are predicting, oh, by 2030 we're going to have the first truly usable quantum computers in kind of general purpose, like, you know, they're going to have enough qubits to do much more interesting things. I mean, do we have to reach that point? Do we have to be at like something far beyond that? Or do you have a sense as to what you need to do this work?

Yeah, I think we already have quantum computers that can do interesting things. I think maybe they're just being applied to the wrong algorithms. So, if you look at what quantum computing companies have been doing over the past few years, they've been trying to prove something called quantum supremacy. And the idea is, can the quantum computer do a calculation that cannot be done classically? So, the Google and D-Wave and a few others have claimed now that they're able to compute things that would take longer than the lifetime of the universe to compute on a classical computer. So, we already have quantum computers that are doing that appear to have an amazing computational resource inside them. It's just that no one's applied that to a real-world problem that actually matters. So this idea that could this be put into a robot and then that robot is able to make intuitive decisions that say increase its learning rate or have it learn faster than a pure reinforcement learning system. That's a way you could now maybe harness quantum supremacy resource and actually put it to use. So I think we have systems today that can do that. But in 2030, if we're up to like a million qubits, then it'll be doing that on steroids, basically.

Okay.

So I think we can see what I call a spark of life today if we get the experiment set up just so. But then, yeah, we're going to need the scaling to get that to something that could be like usefully implemented in robots.

So, okay. So that's fascinating. So you're just, you're just like pushing on all kinds of things you feel like other people are ignoring or going about kind of the, the wrong way at the moment.

Yeah. I kind of feel like the quantum computing industry needs something new injected into it, right? Like why is everyone just constantly trying to break cryptography? It's so boring. Like why don't you do something different, right? Do something that has never been done before. And I think that's where quantum computers are going to find their like the, the kind of like the magic bullet or like where they really fit. It's gonna be in something we haven't thought of yet. Let's just not do computation faster. Let's do a different kind of computation completely.

And I'm again, way outta my depth. I mean, Roger Penrose is like, you know, such a, a legend. If I remember right, I mean, but is this is kind of one of his more controversial—

It is, yeah.

Opinions, right? Yeah. And, and, can you— I don't know enough to like explain to people why, but I mean, he's a, he's a genius. Didn't he— didn't Stephen Hawking study under Penrose?

Or— yeah, I think there was a link there. Yeah, yeah. And, and they're both like black hole aficionados.

Yeah, like regardless, Penrose has, has done pioneering, all kinds of pioneering work. But so why— as I remember from hearing somebody else talk about this, isn't this one of these things where they thought Penrose was like a kind of the kookier side of things he was chasing after.

Yeah, it's very— I mean, it's probably one of the most controversial theories in science, but that doesn't mean it's a bad theory. It just means it goes against everyone's intuitions. And I think the reason it's so controversial and so polarizing is it's not just a scientific hypothesis, it actually changes your metaphysical understanding of reality. So if you fall in the Penrose kind of camp, you basically have to let go of materialism. So what you're saying is consciousness doesn't just arise from certain types of networks of brain electrical signals. If you just put them all together in the right way, boom, consciousness emerges. This theory is saying, no, consciousness is like the fundamental thing that everything else is built out of. So you've got to flip your philosophy on its head. To take this seriously. And most people don't want to completely change their philosophical worldview, right? So that's why it gets super controversial.

Okay. And if we buy into what you— if what you're— this path that you're down and what you're laying out is true, I mean, then the— you know, I've been talking to people all year who are like, oh, well, now that reasoning's here fully by the end of 2025, we're at superintelligence and I mean, based on what you're saying, you have no expectation that something like that would happen and that people should at least pump the brakes on how far LLMs can even get us towards truly interesting things.

I actually have a fun analogy for LLMs that I was just thinking about last night. You might like this. So imagine that consciousness is something that comes first, and then it generates more and more and more training data for whatever large model you want. And I think This is actually what happened from evolutionary perspective. So organisms started with a few neurons and then they evolved a tiny brainstem and then they evolved more brain regions and more brain regions. And then eventually mammals and us evolved this massive cortex. So we've been building this giant cortex, which gives us weirdly sized head compared to other animals. And what I think is happening is we're just basically building a bigger and bigger and bigger LLM-style thing inside our head. And that's what evolution has done. And we're doing the same thing now with LLMs. We're just building this massive neocortex. But everyone thinks the neocortex is the seat of consciousness. What if it's not? What if it's that really deep thing that started off in individual cells that's been throwing off training data, throwing off training data for millions or billions of years, and it's grown this giant model? I think that's more like what's happening. So we are building this weirdly a disconnected cortex. It's disconnected from a consciousness module. So it will be able to do things, but it will never be able to do anything new. It doesn't have that kind of, whether you think of it as free will or true creativity, I don't think it'll have that. It'll be stuck wherever you've finished training it forevermore. It'll never go beyond that. And so here's a really, you might like this actually. So Space, right? So if we want to send machines out into space, because it's going to be hard for us to send human bodies and human biology and things out there, we're just not designed to live in space. So we'll have to end up having machine civilizations to at least support us, if not be our legacy. I think those things are going to need to be conscious because what we're going to do, we're just going to train like GPT-7 and then fix the model and then send the model out there. How is it going to ever, like, generate its own training data and come across a completely new environment like the edge of a black hole or something? It's like, it's going to need that same intuition and creativity and decision-making we have when we're highly conscious. And so I think in order for us to become a spacefaring civilization, we need to solve consciousness.

You said you, you've talked to people people in Silicon Valley, in the field, and that it kind of depends on their perspective, you know, how open they are to this. But like, when you're going to the top 5 or 6 companies that have hundreds of billions, trillions of dollars at stake at this point on, on, on this arms race towards LLMs, I mean, when you're talking to the, the top researchers from those companies, you know, are they open to this idea, or are you getting more pushback that they feel like they're very strong in their conviction?

I think it's a mixed bag. So I've talked to some deep learning researchers who say that they don't think it's going to scale to full AGI. And by AGI, I mean something that could do anything a person could do in an embodiment. So I should be very clear. I think language models are going to solve all of our non-embodied problems.

Okay.

Like all of our research and programming, we're still using them as tools on our computer. I think when we start trying to put them into robot bodies and then have those robots go out there and say, do original science research, I just don't think that's going to work. And I've spoken to quite a few people who agree with me on that. They just don't see how these robots are going to ever get that kind of capability without all the training data in the multiverse. So, but I do think some people are then very bullish on it. The other side, they think it will, you know, the reward is just enough and all this kind of stuff.

Yeah. What about the idea of like, where do you fall on reverse engineering the brain? Like if, you know, there's efforts underway now to put record numbers of electrode sensors into brains to record out how the neurons are firing and precise detail that you're, you know, we've got a long way to go, but that maybe you're replicating the exact electrical computation behavior of a physical brain and it's at a high enough fidelity that you can put that into a computer and get intelligence out. I mean, obviously, according to your theory, that would still be lacking this other element.

Yeah. So maybe I can make a prediction or kind of a fun scientific experiment here.— you know when you're just in everyday waking life, you can switch your conscious attention. So you can be conscious of something and then you're not conscious of a whole bunch of other things. So if I focus on, I don't know, the feeling in my left hand, I'm not focusing on the feeling in my right foot. So I just don't even notice it's not there. So what I predict is, say we're able to open up the brain and have all these connections to every single neuron and we are looking at the activity of all the neurons, what I think think is when you switch conscious focus to different parts of like your nervous system or the environment, the neurons that are involved in that will become unpredictable, and the neurons that are involved in the stuff you're not focusing on will be predictable. I see. So I— the way, the way I think of this like model of consciousness in the brain is that we have two parallel processing paths running at all times. We have a classical computer which is just our neurons firing in a way that we can model and predict, like, you know, neurons 3 and 4 fire, and then the neuron fires 60% probability or something. So we can model that. But when that process goes under conscious control, I think things are going to start changing in things like maybe the firing rates we see in neurons, and the model no longer works. Right?

Right.

And I think what's happening is we have this other parallel quantum computing process that's going on, and it's probably inside the neuron. Neurons or inside the synapses or something. Like, it might not even be microtubules, it could be something else. But I think that, that takes over when you go into conscious control mode, and it subtly affects the final output firing of the neuron. So I think all this brain-computer interface stuff is strongly related to this. Like, we'll actually be able to test this scientifically. If a neuron is doing something consciously, maybe it just starts firing in a different pattern if it's doing it unconsciously.

Consciously. This is— I love this. Um, I feel like microtubules needs a rebranding. I mean, that sounds— that, that definitely sounds like some conspiracy theory.

That's right.

Sort of, uh, yeah, yeah.

Let's call them quantum cores or something, right?

That, that's where you need to go. I think you should, you should definitely work on that. Um, just a couple last questions for you, just being mindful of your time. The I mean, I think the answer to this might be yes, but, you know, I know you were so— you were art and science inclined as a kid from the influence of your dad on one side and your mom on another. And this blending of the two, I know you've been, you know, pursuing very complex, sophisticated scientific research for a long time, and you worked in quantum computing and then all these other fields. But, you know, Did you ever imagine you'd be like sitting here in 2025 trying to build quantum consciousness?

It's funny you should say that because I think the answer might be yes.

That's what I thought it would be.

Because, okay, so I don't know if any of your audience like believes in synchronicities or anything like that. And trust me, when you start researching consciousness, you find all these like weird things going on.

Carl Jung, I'm here for it.

Carl Jung, man.

I'm deep on it.

But anyway, there's a bunch of— now I like look back, there's a bunch of synchronicities I think happened in my life that ended up with me taking this path. Back in 2010, I think, I was talking already about a quantum brain because I was doing quantum computing at the time and thinking about quantum neural nets and stuff. And I just read On Intelligence by Jeff Hawkins and thought it was the best thing ever. So, I was like, "Well, could we build a quantum brain?" And back then, it was just a silly idea, but it actually ended up being what I'm doing now. So, there's a bunch of things like that. And I also think that I finally figured out a connection between art and science, and it comes through this consciousness angle. So if you think, what is art? It's difficult to define, but I think art is a way of thinking through problems and expressing and representing things in a non-language way, like a non-symbolic way. I mean, okay, some people think poetry and stuff is art as well, but it's kind of like a weird use of language. So I think art is a way of trying to solve problems or have intuitive creative breakthroughs that can't be expressed using normal thinking, normal words, normal logic. So I think art might actually be a deeper expression of our consciousness, or we are tapping into those deeper conscious intuitive processes to solve a problem. So I think that way art and science are quite synergistic with each other.

No, I like that. You were just talking about On Intelligence by Jeff Hawkins. That must be, must be like one of the most, like among the circle of people that I run with and probably you run with, you know, like obviously influential book, but, but, you know, not, I think it sold well, but it's not like a totally mainstream book, but I just feel like it must have impacted. I hear it come up again and again and again for people, you know, like it's definitely helped shape the last 15 years or so. And then I haven't heard a lot from Geoff lately and we're like in the thick of like all these things that he was kind of pursuing.

Oh, they're still going. And they actually, Numenta recently announced like a spinoff nonprofit organization called the Thousand Brains Project. So they're open sourcing.

Oh, okay.

So it's all of Geoff's like theoretical work. They're turning it into an open open source code project. So you can go and code these sort of online neocortical-inspired algorithms and things. So I'm definitely keeping an eye on that.

Oh, okay. So the Palm, it's crazy. He was like the Palm Pilot guy, right?

Yeah.

Here we are.

I actually think, I know deep learning and transformers and big neural nets are the big thing today, but I actually think in the long run it's going to be these more online cortically-inspired algorithms that are going to take over, especially for embodied systems. Because when you put something into a robot body, you need something more like a hierarchy of cortical controllers that all work together online. And you don't get that from modern transformer networks.

Yeah, you're just making me rewire my brain and my timeline on all kinds of things. I think as I've been reporting this book, I've been so— San Francisco's like an LLM cult right now. And you if you're talking to everyone in this field every day, all day, it's just like you start to— even though I'm skeptical, you start to be convinced. You're like, oh, by the end of this year, I guess this is just like solved and we're off on some, um, crazy superintelligence singularity timeline. And, and even though part of me is like, okay, you know, it's probably not— possibly not gonna happen. And, and, um, but now, I mean, because you've opened up all these other avenues that, that, um, now I'm going back to my— where I started out on all this was like, oh, we mathematically solve language. That's like interesting, but it's very limited in how interesting it is. I don't know. This is helpful. You're making me recalibrate. I appreciate it.

Well, one piece of advice I'd give to any, I guess, budding startup entrepreneurs watching your channel is like, just don't go into what is really hot right now. Don't go into LLMs, don't go into transformers because you've got your super cool idea for a startup, you're going to start pitching it, and then Google will just release some open source side project next week that totally wipes you out of the water. It's crazy to go into that. It's like a giant shark bath at the moment. You've got to pick something. If you want to innovate, you have to pick something completely different. You need to pick something that people are like, "That's crazy and ridiculous and insane." because that— it means no one's going to be working on it, right? And, and so I give people this advice.

I'm with you. If you're going to take all this risk and do all this work, you might as well make it worth it. I, I feel like quantum consciousness— you've done it. Let's— I'm excited to see where you go. Um, and, and, um, thank you. It's always so fun talking to you. You always make me think, um, a lot. And, and, um, thank you so much for— what, what's the best thing for people to check out?

You guys' website or your Substack Yeah, the website's got all our links to our other socials on. So like nirvanic.ai is probably the best place to go at the moment. We have, yeah, Substack newsletter and we also have, we're quite active on X as well.

Okay.

Yeah. And I promise you, Ashlee Vance, next time you come to visit, I will have a robot look you in the eye and you'll be like, holy crap, that robot's alive.

Okay, I'm here for it. What does that mean? When would this visit? Take place.

Oh my goodness. Okay. I'll sort that out. I can't promise any timelines right now, but that's, that's my dream.

All right. Well, we will, I will show up with a camera if that, if that can happen. I mean, I'm here for it.

Awesome. Thanks.

Thank you. Thanks so much, Suzanne. It was great talking.

Thanks.

Today's episode was hosted by me, Ashlee Vance, our Our guest was Suzanne Gilbert. Core Memory is produced by me and David Nicholson. Our theme song is by James Mercer and John Sortland, and the show is edited by the great John Sortland.