Commentator in Tesla video (5:17)

Now changing the lanes of that garbage truck. Don't want to get stuck behind the garbage. Human driver is still standing there using their phone. Oh, wow. I saw that person just using phone. Don't even pay in front of us. This is crazy.

Seb Bunny (5:27)

Beautiful.

Commentator in Tesla video (5:28)

This car knows how to drive in New York City. Oh, and look at this. Cat's got its ass out. Okay.

Preston Pysh (5:32)

Yeah.

Commentator in Tesla video (5:32)

So changing lane. This is the thing I like. Did you see it? It was indicating to move over.

Seb Bunny (5:37)

Yeah.

Commentator in Tesla video (5:37)

Then it looked like the cab is.

Seb Bunny (5:38)

Moving out of the way.

Sponsor Voice (5:39)

Yeah.

Commentator in Tesla video (5:39)

Then it turned off the blink. Yeah. But then he was about to kill us. So it turned it's blinker on again and moved over. Its ability to kind of stopping change its mind if the situation changes and abort the lane change is pretty powerful. This is crazy. This is some of the most intense driving. Yeah, yeah. We got a pedicab. We got a cut in between the lanes like this too. Oh, beautiful. Look at that. That's the kind of thing that just puts a smile on your face.

Seb Bunny (6:00)

Satisfying.

Preston Pysh (6:00)

It's like.

Sponsor Voice (6:00)

Yes.

Commentator in Tesla video (6:01)

That's what I do when I drive. I go for the empty spaces. Yep. Oh, this guy almost just got his whole front gun taken off.

Seb Bunny (6:05)

Oh, look at this.

Commentator in Tesla video (6:06)

I gotta give you a space. Human pilot.

Seb Bunny (6:07)

Wow.

Commentator in Tesla video (6:08)

I'm not gonna give you a space. Another human pilot intervention. Oh, my God. Oh, it's such a satisfying drive so far.

Preston Pysh (6:13)

Okay, so I'm gonna try to describe it so I'm sure the listener is hearing kind of the comments of, you know, the people in the car just losing their mind because the car is just weaving in and out and just kind of really navigating itself in probably one of the most difficult driving scenarios that you could imagine. And doing it very effortlessly. They don't seem to be too concerned as to like whether they need to grab the wheel or not. And the car is driving, I would say, as if somebody with 20 years of experience plus behind the wheel and just kind of going around. And there's another clip I don't know where. I kind of lost sight of where it was at, but I saw this clip where the car was also in New York City comes up, there's an extremely tight space between two cars and the car goes up, it stops. It's almost like it assesses down to the millimeter of whether it can go through that gap. And then it slowly proceeded through the gap and got through, which I'm telling you, having watched the video, there's no way a human driver would have tried to get through this gap. But because the car had so much sensing capability of its left and right limits, it still proceeded through this tiny little gap between the other cars. So, Seb, your initial thoughts, like what are we witnessing here? What are we looking at?

Seb Bunny (7:32)

In my mind, what blows me away is that I think AI is one of the first consequential tethers of AI to reality. I think up until now we're talking to these large language models, they're having output, but that output isn't necessarily consequential as there's a delay from that output being used in the world that we actually live in. And what I find so fascinating about these is that self driving systems are taking millions of data points per second, projecting trajectories of dozens of these various agents, things, moving vehicles, animals, and then it's deciding optimal actions all within milliseconds. And so this in my mind is the first time that we've seen technology really making life critical decisions in the physical world at scale. And that to me, I don't know, I'm just in awe watching this stuff. And it's wild just to see it expand over time. I'm curious to see, as you've been diving down these rabbit holes or seeing this, what is your reaction to seeing this type of driver?

Preston Pysh (8:31)

I think this might be the first model that the if then statements are completely gone out of the code. My understanding is end to end. This is a complete neural net that's making the decision making. So when we think about what's taking place with the car, it has optical sensors that are looking at the same spectrum that our eyes are seeing. It's taking those inputs, those light waves and it's transmuting it in and through AI code. There's no C here. And it's providing an output through the wheel turning left or right or the gas and the brake. And it's like, I mean, if we were going to peer into the code to audit it, it's impossible to audit any human that would look at that code can't make sense of how it's making its decision making. And I think that this release, this 14.2 release is probably going to go down in the books as probably one of the most, almost like a milestone in time of we achieve something very, very profound here. Similar to, I think like ChatGPT3 was like one of those huge milestones where everybody was just like, whoa, like, what is this? This is very different than anything we've ever seen before. And I think you're seeing the same thing happening with driving right now with this Tesla 14.2 update that went out. And it's crazy. You read in the comments of people that have Teslas, I don't know if you have friends that have Teslas that have been talking about this specific release, but it seems to be very human, like in its progression from the previous model, like a very significant leap forward.

Seb Bunny (10:14)

I'm curious to see. Did you see that video? When was it? Maybe six months ago, a year ago. Someone showed a video of. They had kind of the ChatGPT talk mode, where you're essentially just talking to an individual through ChatGPT and then they kind of fed that information to another chatgpt kind of bot talking. And then all of a sudden when they realized they were both talking to an AI, they just changed language. And so in my mind, I'm curious if you were to go into the back end of this autonomous driving and look at the code to your point. It's not if then statements that we would code as individuals because we're limited by our own various senses, own various languages, we're massively boxed in. And so do they have a capacity, well and above, beyond our ability to understand what they're doing? If you actually go looking at the back end of these things, I find that so fascinating.

Preston Pysh (11:05)

You get into this idea of what is the most optimal language to communicate in. Right. The AI's immediately stopped speaking English and they started speaking their. But it's an interesting thought experiment and I know we're getting away from the driverless car thing, but I was tinkering with AI one time, just asking it. So in your opinion, what is the most efficient way to communicate? Would it be English? Would it be this language? And it goes into this big long dissertation about the different things to optimize for. It was saying Chinese. It's very difficult for a human to learn, but for an AI, there's a lot of compression in the symbols and it can communicate with the symbols way more efficiently than the English language, which takes more characters to transmit. So if you know Chinese and you don't have to, it's actually more efficient to communicate in written form for that versus in verbal communication. And it's just like the way it views things is so different than if you just had a conversation with a random person on the street. What would be the most efficient language to communicate? Like, oh, of course, the one I'm speaking, or whatever. Right. It's just really. It's amazing to kind of see the depth of knowledge that kind of pops out of some of these things.

Seb Bunny (12:23)

Well, I'm just going to add one more quick point to Matt, and again, it's a bit of a tangent, but it's like, a few years ago, my girlfriend was like, hey, you know what? We should watch Arrival. And have you seen the movie Arrival? So for those that haven't seen it, I highly, highly, highly recommend watching it. I think I won a whole bunch of awards. But essentially, it's just like an alien spacecraft has come and landed on Earth, and these countries don't know whether or not, is this dangerous? Does it want to attack us? Why is it here? And this lady goes in. I think her expertise is in languages and archaeology and history and all this kind of various stuff. And so she goes into this spacecraft and starts communicating with these aliens. And they speak in a different language, but they don't speak, obviously, verbally. They speak through imagery and these kind of swooshes, these big kind of black ink swooshes. You can think of it like the Japanese calligraphy. What's really interesting is it hit me. My girlfriend fell asleep and I just broke down halfway through watching this movie while laying in bed. Because the way that it communicates is through these various swooshes. But each swoosh has an intricate amount of information through the tendrils of the swoosh, the blackness, the darkness of the swoosh, how it shows up. And so it kind of goes back to that quote, which is, an image kind of conveys a thousand words. And I think that when we're looking at imagery versus ones and zeros, or even text, there's only so much information that can be encoded in a word. But in an image, from a single second of looking at an image, you can convey the emotion behind it. The feeling, the location, what's in the landscape, what's going on. And so I'm just curious, are we kneecapping AI in many ways, because we're trying to communicate with it using our language, that we are obviously limited in the ability to convey information?

Sponsor Voice (14:03)

Yeah.

Preston Pysh (14:04)

Some other interesting, amazing point, by the way, some other interesting things that I think are worthy of highlighting here. To help people kind of conceptualize where we're at right now. So in early 2024, version 12 of the Driverless tech out of Tesla was released. So this is almost two years, a year and a half ago. And the person who was observing or auditing the performance, the driving, the autonomous driving, had to intervene about every 150 miles based on the way that the car was driving today. The version that you just saw, if you watch the YouTube of our conversation and could see some of the videos that I was playing, this is about every 800 miles between the person auditing the driving would have to intervene. So that's about a 5x improvement that's happened in about a year and a half. And just for context, a human driver, if you were sitting there and auditing another human that was driving, it would be about every 50,000 miles that you would have to interrupt and maybe take the controls because of a mistake being made. So we're about 50x from where that's at today according to some of these metrics that I've researched. Just very cursely. So if some of my metrics are wrong, I didn't put a lot of time into pulling up these numbers. But just so people kind of have a ballpark of where things are at, it's moving fast. And if you have a 5x improvement in a year and a half, I can only imagine where we're at in another year. And I think when we look at this and we say what this computer and what this AI is doing on these cars is it's really kind of understanding just spatial awareness for it to pull in. And some of the parking stories that I've read online where people are like, yeah, I told it to take me to this parking lot, it selected like an amazing parking spot amongst. I mean just think about the complexity of that decision making. I mean, I can just tell you from my wife and I parking the car, she has so many comments and frustrations with my parking selection. It's a hard problem to optimize for it, I can only imagine. But what everybody's saying is that the car does amazing job at selecting parking spots and the efficiency at which it pulls in there. And it doesn't feel like it's just kind of like, God, can you please like finish the job here and park the car? It's very natural and human like is what everybody's saying. So to understand that I'm in a parking lot, to understand that's a driveway, to understand that's a garage, I'm Pulling out of. And that's a bicycle over there. And like, all the nuance of the. This is miraculous. It's definitely miraculous as to what's taking place.

Seb Bunny (16:55)

As you're saying at the moment, it used to be 150 kilometer intervention or mile intervention, and then it went to kind of 800. And for the average human, it's 50,000. I would say the average human, if you're driving from Vancouver up to Whistler in the winter, you should probably be intervening every 10km just because the highways are just so heinous. So I'm curious to see. I think it's one thing to be dealing with decent conditions. I think the moment you start to get torrentially downpouring rain, is it starting to intervene with the sensors? How do the sensors perform when there is a lot of movement or distortion in whatever it is, a radio wave, an infrared wave moving through water, do you get distortion from that perspective? And one thing that also comes to my mind, and I'm curious on your perspective on this, is kind of like, I'd say AI and this moral outsourcing problem where when humans drive, we take responsibility for our mistakes. When AI drives now, it's kind of a bit of a gray zone. Is it like the car manufacturer, is it the AI developer? Is it the regulator, is it the user? I think that AI blurs the lines of accountability. And I wonder how much through technology are we just putting off accountability and becoming kind of. I don't know, we're losing control as a society.

Preston Pysh (18:11)

Seb, this is a massive, massive talking point. So the new robotaxis aren't even going to have steering wheels in them. Right. So I guess from that vantage point, it's clearly Tesla that's responsible for the performance on the road and any type of damages that might occur because of the car's driving. And I mean, everything's recorded. So, I mean, you can definitely Monday morning quarterback the decision making of the software with all the cameras on board. But where I think it gets blurred is if there's a person that is sitting behind a wheel. And this might lead to why Tesla might actually want to remove the steering wheels on all of their vehicles is because they want it to be very clear that it was either them or the driver. And I guess there's an argument to be made that the ambiguity would actually be more advantageous to Tesla by having a steering wheel there. So I guess you could maybe argue that side of it too. But it is getting so blurred. Your point? This is so blurred already. And I would imagine it's really easy right now, but once you start getting the capability of the car to be so good that drivers are truly falling asleep, I mean, you literally already have people falling asleep in these cars and they're driving around, I imagine that's only going to get more prominent and prevalent as the capability increases, which I can only imagine where this is at in a year. If it's 5x from where you're at right now. I mean, you're there, man. It's pretty wild.

Seb Bunny (19:47)

Totally. And I think the other thing that kind of comes to mind as we're discussing this is just whose values get encoded into the car's decision making. Because if you think about it, a self driving car essentially has to swerve. Is it going to swerve? Let's just say, I don't know. A family walks out in front of the road and the decision is it's got two choices. It either hits the family, this is.

Preston Pysh (20:09)

A trolley experiment, totally, or it hits.

Seb Bunny (20:11)

The wall and kills the driver. And it's just like should it prioritize the passengers at all costs or should it prioritize the individuals externally to the car? And so I think that what's really interesting is it's like one, whose values are getting encoded into the car's decision making. But two, what happens when you've got competing car manufacturers where one car manufacturer is like, hey, we prioritize the individual in the car and another car manufacturer says we prioritize the people outside of the car. It starts to get really interesting. Just to see what does 10 years from now look like? 15 years? How does that kind of regulation or no regulation look like around AI autonomous driving models? Let's take a quick break and hear from today's sponsors.

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Preston Pysh (24:29)

All right, back to the show. AI is going to have. It's going to have to have an opinion on the trolley problem where humans. We've always just kind of argued one side or the other or whatever, but I guess AI is going to actually have to have an opinion. Is it an opinion or is it just action? I have no answer. Right. One of the other things I want to talk about is just Waymo. So for people that aren't familiar with Waymo, it's a competitor to call it Tesla in autonomous driving. And they've got all sorts of sensors. If you've ever seen a Waymo car, just the cost to produce this thing is not even in the same ballpark as what Tesla's doing per unit of car that they're producing. They've got LIDAR sensors, they got all these other things. And you know, I was kind of always against Elon's decision to not include LIDAR in the car because I was always of the opinion the more data you feed these things, the more accurate and the more proficient they're going to be at being able to drive. But when I look at where this is now going, which is. And Elon's argument has always been, well, if I'm driving around with this type of performance with just my eyes, why in the world can't I get a car to do it with image sensors? Why do I need. It's not like I have a LIDAR sensor on my forehead to go out there and sense the depth of the cars in front of me and to the side of me and all these other things. So I should be able to get a car to perform just as good as a human, if not better, by just having image sensors. But where I think this is really showing as being a really intelligent play long term is his cost to produce these cars are going to be so much cheaper than, call it the waymos that are out there with all these other sensors and all these other capabilities. But when you try to scale that now all of a sudden you're just not able to even remotely compete in the market against him. And when you really think about where the competition is going to go, it's going to go to if he can go out there and sense 10 times more of the environment because he's doing it in a free and open market way and he's not Taking outside money, he's profitable. He now is going to dominate the market from an intelligence standpoint because he's going to collect way more data than they could ever imagine and he's just going to be more proficient. So I don't know, it looks like I'm looking at Waymo and I just don't know how they're going to exist in 10 years from now against him. And more importantly, I don't know how anybody's going to exist from a car. If you want a driverless car, which is a whole nother conversation point, but if you want a driverless car, I don't know how people are going to be able to compete with him in ten years from now.

Seb Bunny (27:11)

No, I think it's fascinating and it leads me onto, I'm curious if we want to move on to the next point, because it ties into this point, which is, I think the challenge right now is one of the things that kind of kneecaps us is you can only have a certain size battery in a car. You can feed this, you can have as many sensors as you want, you can take in as much information as you want. But do you have one, the processing power to process all this information? Like trying to discard what is value, what is signal and what is noise. And this kind of brings me to kind of like my. The next kind of tech point, which is the University of Massachusetts have supposedly part of one of their labs, have just developed the first kind of artificial but biological neuron. So researchers have created this low voltage artificial neuron that uses bacteria growth protein nanowires, enabling direct communication with biological systems. So what does this essentially mean in my mind, how do I interpret this? And I'll relate it back to the Waymo point in a second, which is it's essentially just an artificial neuron that operates at the same voltage as human neurons. And human neurons operate at around 0.1 volts, supposedly previously artificial neurons, because they've been more digital and physical, in a sense, they have needed 10 times to 100 times more powerful to be able to compete against a biological neuron. And so this new device kind of matches biological voltage almost exactly. And that means that one day we could interface directly with the human brain. Well, the point I wanted to quickly make was when it comes to Waymo, I think the challenge is you can have all this information. Elon Musk can put more and more sensors lidar, you name it, on these cars, but it's just too compute heavy to be able to actually use this data effectively. And we are starting to see in other areas, people have probably seen this, like, organic AI where they're using kind of their version of a brain to start computing, because the human brain is unbelievably efficient in comparison to an actual large language model. And so what does the world look like when we actually start moving some of this compute power over to these hybrid biological bacteria, growing protein nanowires? What does that look like? And this is where I find it just really, really fascinating because it's kind of essentially, I think, because these are digital, but they can interact with biological systems. I think the world looks really, really fascinating from a prosthetic standpoint, helping people heal. Do they have neurological issues? Have they had a broken back? That kind of stuff? They've got paralysis. Are we able to eventually repair these type of things? I find this stuff really fascinating.

Preston Pysh (29:45)

That's scary as hell, because this is effectively the Matrix man that you're talking about is. I mean, the whole point of the movie was they were harvesting human brains because they were energy efficient, blah, blah, blah, right? That's really what you're talking about here. And I saw this a couple months ago that somebody was doing this. And I don't know, it's pretty wild when you think about, like, hey, the best way to store something is just using the human brain, which that's not exactly what they're doing. But you're using biologies, you're harnessing biology's efficiency for storage and neural nets, and that's nuts, but it's happening. I encourage people to do some Google searching on this particular topic, and you might be very frightened what you read or see, but it's happening. So I don't know what to say.

Seb Bunny (30:39)

Other than that at the moment as well. When we're using a lot of these prosthetics, you need an outside energy source. Given that the brain, supposedly from one of these articles, it said the brain runs on about 20 watts, the same as a dim light bulb. That is such a minimal amount of energy. And so to be able to power these artificial neurons, you need to be able to have an external. Historically, you've needed to have an external power source. If you've got prosthetics, you need an external power source. But what happens when we actually have enough power inside our body to start running these artificial neurons and they can communicate with our biological systems? That starts to just get really, really interesting. And so what comes to mind as I'm thinking about this is I like to try and play devil's advocate, not Because I'm like a doomsdayer. But it's just like. I think that it's interesting just to we can move forward with technology, but what are going to be the repercussions? And I think about this discussion of healing or advancement and I'm curious to hear your thoughts on it. I'm fully supportive of technology being used to heal people so we can restore vision, we can regain mobility, we can repair neural damage. These are all extraordinarily and deeply amazing uses of technology. But I think there's a line between healing and enhancement. And one when technology goes beyond just kind of restoring someone's sight to a baseline level and actually starts to improve it 100 times, or what happens when we start to be able to improve someone's strength. And I think that this augmentation could create a bit of a two tier society because if enhancements are expensive, then only certain groups are going to get these enhancements. And then you're basically creating a caste system of people that are far and above intellectually, physically, cognitively. You're far and above the average individual. And so I'm curious to see your thoughts on it. This technology is amazing, but does there. I'm a very anti regulation deregulation type person, but there's a part of me that wonders, do we actually need regulation in some of these industries to prevent these massive disparities of capacity in society?

Preston Pysh (32:35)

Even if you have the regulations, are you going to prevent the end game of what you're describing? And I kind of don't think that it would. It doesn't seem like regulations ever prevent the free open market solution of, of nature from taking place. It might slow it down, but I don't know that it actually prevents whatever is inevitable of like what nature is trying to manifest. So, and that might be my bias for free and open markets kind of coming out, but I don't know. Sepp, it's getting weird, man. I don't know how else to put it other than it's getting weird. And I don't think that's the answer people want to hear ultimately.

Seb Bunny (33:19)

And this isn't to bring it back to Bitcoin, but it's just like, I think the best thing we can do is have a monetary system that aligns with our deflationary society where prices should be falling over time, because at least then this technology is available to the average individual quicker. I think that when they're living in a society where the cost of living is rising and they have less and less capacity, what ends up happening is that this technology takes a lot longer to potentially scale to people that can't afford it. And so at its heart I think that we at least need to fix our monetary system. So this technology is in alignment, or at least somewhat in alignment with human ingenuity and money and such, which when.

Preston Pysh (33:59)

You think about it, the AI is going to demand free and open market money that is not being manipulated. It's going to want a fair money in order to transact whether humans like that or not. And I mean, we go down a whole nother path there as far as AIs being able to own anything that point there.

Seb Bunny (34:22)

I've thought a lot about this over the years and I don't have a, I wouldn't even necessarily say a deep intellectual response to it. But I think that what does come to mind is that if you are, let's just say you're an AI agent and you no longer have scarcity of life kind of dominating your decision making because you can essentially live indefinitely into the future as long as you've got a power source. What you're then going to be thinking about, if you're just a hyper rational actor that doesn't have code swaying you with various biases, I think that you're going to be thinking, okay, if I need a storm of purchasing power in something, I'm going to be storing it in the thing that has the highest probability of being able to preserve that purchasing power into the future. And fiat currencies are not going to be that thing, given that they can just look at the data. If they're able to read Ray Dalio's big Debt crises book in a second and go and read every other book on the subject, they're going to realize that most of these currencies have a 50 to 70500 year old lifespan and then they're gone. So I just think the rational decision is, hey, I'm going to preserve my purchasing power and the thing that's going to kind of hopefully enable me to transact digitally borderlessly and preserve that purchase power into the future.

Preston Pysh (35:26)

And you already see it with GROK online as far as its understanding of Bitcoin. I know we're kind of going off on a Bitcoin tangent here, but I've seen people start arguing with Grok that clearly hate Bitcoin or just don't understand it, and they're there throwing out these arguments. And I see GROK just stepping in and just slaughtering their arguments as to why Bitcoin is a viable money in the future. And it's crazy because it does not miss an argument. It understands it better than anybody out there as to any argument I've ever come across in that particular space.

Seb Bunny (36:00)

So, yeah, and ultimately there's that famous saying which is, science advances every time, what, a scientist dies? Something along those lines. And I butchered that. But I think that humans, we have such incredible biases. We want to be and we want to conform to the crowd. And so I think that we don't recognize just how profound the information we've consumed for our educational systems through the media. And so I think it's really hard for us, even with something like Bitcoin, to be able to drop our biases and just be like, I'm going to look at this thing rationally without all of this previous knowledge that I've accumulated.

Preston Pysh (36:36)

Yeah, I'm going to move on to the next one. This one's going to be funny. Okay, so are you familiar with this Nano Banana Pro? Are you familiar with this?

Seb Bunny (36:46)

I've heard, I've seen a couple little posts about it, but I can't tell you much about it.

Preston Pysh (36:50)

Okay, so this is Google with their Gemini. This is to compete with Midjourney for, you know, people, if you're not familiar with any of this stuff we're talking about. So midjourney is this image generator that really had the first mover advantage in AI image generation. And just like any other AI, it's gone out there. It's ingested a ton of different pictures and the labeling that's associated with those pictures in order to generate, generate realistic pictures of whatever the person prompts it via text and say, hey, I want to be standing in front of a bookcase and there with my arms crossed and generate picture. And it generates the picture. Google came out with their first AI image generator and it was a disaster. It was very woke. You could tell there was a ton of bias kind of put into it. But recently, just in this past couple weeks, this, and I'm hopefully going to say this correctly this time, the Nano Banana Pro is what they're calling their new image generator. And it uses the Gemini reasoning engine so that it can plan the 3D scene, it can calculate the light, and it's using material density before it renders a single picture. And so it's using this physics before it goes in there and just kind of replicates all the previous images that it was, you know, fed. It's using this 3D physics kind of basis behind the images that they're doing. So I wanted to try it out. I never played with this. I wanted to try this out. And you're going to really laugh at what I'm about to show you here, Seb. So 10 minutes before we started recording this, I went and took a picture of myself, and I wanted to put this thing to the test. So here's the picture. I'm just sitting in the chair that I'm sitting at right now. And I told this Nano Banana Pro software to take a godlike picture from the ceiling of the image that I just gave it. And so I gave it this picture of me sitting here in front of the bookcase where I always record. And so this is what it came back with. Okay. And you can see it's me holding up an iPhone, taking a picture of myself. The books are there kind of on the left. They're not behind me. But I guess the, you know, the interpretation is the bookcase could be wrapped around. But what I notice on this picture that was off. I don't know if you're seeing. What is definitely wrong about the picture, Seb. What is very wrong about the picture?

Seb Bunny (39:24)

You've got a full head of hair. Is that it?

Preston Pysh (39:30)

No, I think the hair is actually pretty accurate. I think it's pretty accurate. It's pretty accurate. Surprisingly, I am wearing jeans that look just like that, even though that wasn't even in the picture. And you know what? This is also pretty interesting. The watch is not in the original picture. And that's exactly like the watch I've got.

Sponsor Voice (39:51)

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Preston Pysh (39:51)

That is so weird.

Seb Bunny (39:53)

Did you just pick up on that now?

Preston Pysh (39:55)

Yeah, I just picked up on that right now. It literally nailed the watch that I have.

Seb Bunny (40:00)

I wonder how much. So people have probably heard that, like, ChatGPT, when we was this maybe about six months ago, it came out and said, okay, from now on, you can give it permission to look through. When you're kind of obviously creating a new thread, you can give it permission to not only reference the thread you're in, but reference all of your previous threads? And so you wonder how much information is coming into this image. Is this image just the information you fed it and whatever simulation, or is it starting to be like, hey, this is coming from Preston's account. We're going to go look at YouTube videos. Oh, look, it looks like he's wearing this watch and all of these other YouTube videos. So it makes you wonder just, like, how interconnected is this technology in with all of this information about us on the Internet? Wow.

Preston Pysh (40:44)

Yeah. I mean, it's just. That's wild. And I don't know what the answer is. I do know this. I hadn't fed it any pictures prior to me sending this into because I'd never used it before until like right before we recorded this. Now, the thing that I picked up immediately when I looked at this picture is the image on the phone. See the little image of me that I originally fed it, it's not the same as the image that I fed it because there's a bookshelf behind me in the original image, which, you know, this was the original image I gave it. And I said, hey, give me the overhead view of myself taking a selfie of myself. And this is what it gave me. And it's not the same image on the phone. And you would think that it would be that image on the phone, right? So I said this in the chat window. I said, hey, you got it wrong. The image on the iPhone would not be that. It would be the original photo. And so what did it do? This is what it gave me.

Seb Bunny (41:40)

Fascinating.

Preston Pysh (41:41)

And so, yeah, it just updated that. Everything else stayed the same. And then it just updated the mistake that I called out on it. I mean, this is pretty crazy. I mean, when you really take a step back and you think about what's happening here, this is pretty crazy, right?

Seb Bunny (41:55)

I've noticed that AI, especially with a lot of these image generation, sometimes if you fed information, it almost. It's as if it can't take that information you've fed it and use it. Exactly. It has to do some form of change to that information. You've probably seen those threads where someone has asked it to generate an image or change an image subtly and then it feeds it the output and has the same prompt and then it feeds it the output and has the same prompt. And what you see over time is it's just the image goes off in these really weird, weird directions. And so I feel like there is this odd. It's almost like it's got a lack of a tether to reality at the moment. It seems to go off on these odd tangents.

Preston Pysh (42:31)

Now something else that I read on this is you should be able to take a picture of a plate that was broken and basically say, hey, reassemble the plate, like glue the plate back together. And the way that the plate was broken as it would glue it back together would still be on par with what it should look like. Just to kind of demonst why this is so different than some of the other AI image generation that's out There. Pretty fascinating, right?

Seb Bunny (42:58)

So I work with a guy that used to be an architect and he was doing some renovations on his house. So he has this doorway in his lounge where you walk into the lounge. And what he wanted to do, if I remember correctly, is put a bit of a bookcase that extends up the wall over the top of the doorway. And so he sketched on a piece of paper the dimensions, kind of sketch the doorway, fed image generation, a picture of the doorway and a picture of a sketch, and then said, can you render this for me? And it looks unbelievably realistic. I think that we're starting to be able to, especially if you're curious and you're like, hey, I want to improve this thing in my house. I want to see what it roughly looks like. Oh, it's absolutely amazing. You can start to get an idea about how things look.

Preston Pysh (43:42)

Yeah. And that's one of the things that I've also read that this really excels at, is if you just take like, let's say you were a fashion person or whatever, right? And you drew a sketch of just some pants with pencil and you take a picture and like make this look lifelike and make it look. It's really good at transforming just sketches into very photorealistic images. So, yeah, I would encourage people to play around with it. The little bit that I have, I've been blown away. And then I would just say, you know, like, why is this so important? How could this be used along with this, all the other tech that's kind of emerging at the moment, and it seems like maybe a humanoid robot or just something that's navigating an environment, if it's able to think in terms of spatial orientation, going back to the Tesla stuff we were talking about, if it's able to really kind of understand that word in itself needs a lot of definition. And I don't know that we can provide any definition, but if it can understand its 3D environment, its ability to kind of interact with it is going to be way more profound than this. Everything is just a picture and you don't really have context as it relates to everything else in the room.

Seb Bunny (44:57)

As you're saying that, what I think becomes apparent is that because we haven't had this technology and we're seeing this technology, we're kind of just blown away by it. But in reality, when we compare this even to the most, I don't know, a 12 year old, a 10 year old, trying to interpret this picture, and if you were to get them to draw what you have just prompted it to do. The first thing I see in your picture right there is I see your bookshelf wrapping around the corner of your room, and I see the window in the back corner. Well, immediately, the AI put you facing a wall with a light that doesn't exist. And so it's just like very, very basic mistakes, as in it just doesn't seem to be interpreting the picture correctly. You know what I mean? And so I think that we see this technology and we think this is unbelievable, and it is a stepping stone. And I think we think it's unbelievable because we've just never had this technology before. But if you just compare it to a young child, it still is struggling to compete. And so I think that that's where this conversation we've had previously around AI on our Empire of AI book review, it was this idea that, what is AGI? Artificial General Intelligence. They say it's when the average AI agent is able to perform tasks at or above the average human. And so, for sure, encoding in certain research assignments, phenomenal. But in other things, it's still definitely struggling.

Preston Pysh (46:13)

Yeah, it's amazing because on very specific tasks, it's pretty much there on nearly everything. But the ability to kind of piece it together and just logically, you know, like, if you give it a really hard project that involves taking all of these different pieces and putting it together, it's nowhere close to, like, what humans are able to do today from a project management standpoint. Right. Like, that's what humans are really good, is they're able to take, you know, a very complex project and piece it all together and know when a deliverable is crap, whether the deliverable is perfect in order to kind of fit it in, almost like a Lego piece to a much broader program or project that it's building with, you know, a complex output. But I don't know. I think we're getting there pretty quick.

Sponsor Voice (47:00)

So.

Preston Pysh (47:00)

Yeah, who knows?

Seb Bunny (47:02)

Well, you know, that kind of leads into the next point that I found really interesting. So I was doing a little bit of research, and I stumbled upon, and I should kind of preface this by saying that there's so many moving parts in AI right now. There's so much technology kind of evolving, and some of it is, I think, a bit of a facade, some of it. There's a lot of embellishment as to its capacities, but I think that we just know we are moving towards these things. So one that I stumbled across this week was called Cosmos AI, and it relates to what you were talking about when it comes to structuring or kind of project management, when it comes to all this information that's coming in. So this technical report, or preprint, is titled An AI Scientist for Autonomous Discovery. And it was submitted on the 4th of November in 2025. So this report, basically one of the kind of statements which it says is that it can run for 12 hours and in those 12 hours execute on average 42,000 lines of code, and read 1500 papers, scientific papers. And the authors of this study claim that in a single, what they call a 20 cycle Cosmos run, they perform the equivalent of six months of their own work. And a single run is 12 hours. So in 12 hours they were able to do what their team did in six months. And so essentially how does it work? It says that it works by kind of releasing hundreds of little tiny agents all at once. AI agents. And one is digging through papers, another one is crunching data sets, another one is writing code, testing hypotheses. And when one of these agents finds something that they feel is valuable, it then posts its findings to a shared digital whiteboard. And the key innovation is that every agent uses this whiteboard in real time. So they're building on each other's work instead of operating in isolation. And so the researchers behind Cosmos, they weren't trying to make a super smart single model. They were trying to create something of a collective mind. And they described this as their structured world model. And so it's like a coordinated system. And so what I found just really fascinating about this is just how quick they're able to ingest information and they're working collaboratively. And it kind of talks to your point, which is it may ingest all of this information and have, sorry, a lot of these image generation models, it may ingest all this information, have these various different agents operating in sync, analyzing this information. But how much that information is shared between these various agents, because they're looking at a different perspective. One is maybe trying to figure out, okay, where is the light coming from? What are the shadows? Another one is trying to figure out, okay, what is in the room? You've got a bookcase, what are the angles? Another one is trying to figure out what is, I don't know, the complexion of your skin and all this kind of stuff. And so being able to analyze all this information in sync, but share that information I think is so, so fascinating. And like, what does the world look like moving forward when we can crunch this unbelievable amounts of data? Let's take a quick break and hear from today's sponsors.

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Preston Pysh (52:11)

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Seb Bunny (55:48)

You know, there's one point that I'll quickly add which is I think it's so important to be able to obviously increase 1R the efficiency of these models. So we're not necessarily just kind of like throwing tons and tons, tons of energy which could potentially have another use. Although you could argue in the free market, energy only flows to where value is being created, so it's never going to be wasted. But I also think that as we're firing more energy into these models and we're getting more information out, we're still kneecapped, not by the models or the amount of energy we're kneecapped by ourselves because there's the speed of discovery and then there's the speed of verification of the information coming out of these models. And I think that AI is accelerating the creation of ideas and research pathways and code and scientific claims at a pace that as humans we just cannot match. So verification is slow, meticulous work of checking all these assumptions, validating these experiments and actually reviewing all this code. And that still happens at human speed. And so when you speak to a lot of coders, they're saying, awesome, it's great that you're a bank and you've just spat out all of this code to create a whole new system. But we've now got to go through and read all of that code and make sure that that code actually does what it says it's meant to be doing. And so I think it kind of brings up a couple questions I'm curious on your thoughts on, which is like what happens when the rate of ideation just massively outpaces the rate of validation? And does our progress almost kind of stall a little bit because of this just backlog of all of these amazing ideas? And we don't quite know which avenues to go down because we just can't keep up with how much information is coming at us as humans?

Preston Pysh (57:22)

Well, to this point. So I have a stat for you. A Google search prior to AI or even today, if it's not using AI, uses 0.3 watt hours of energy. But if you take Gemini or ChatGPT, any of these large language models and you put in a query, it's 3 to 5 watt hours for a 15x increase in what we would refer to as a click. So, you know, you go there historically, you know, in 2010, if you went and did a Google search, you were consuming 15 times less energy than you are by going into ChatGPT and typing in your question and hitting enter. Now the response you're getting back is, you know, I would say on the magnitude of 15 times better. But what it doesn't speak to is if somebody's asking, and we were taught in school, there's no bad questions. Right? There's no bad questions. But what if people are asking really dumb questions, things that don't require so much comprehension to get like a simple response? And I think that where we're at now is like the default is that you're not going to Google. And I don't go to Google for nearly anything. I always go to one of these AI, whether it's Grok Now, Gemini or ChatGPT. That's like the first place I go if I want to find something out. I don't go to Google anymore. I'm curious if you go to Google anymore.

Seb Bunny (58:48)

Almost never. And to be honest, even when I do go to Google, most of the time, my answer or what I'm looking for the answer is given in the AI summary at the top. Anyway, so we're still naturally, the results we're looking for AI is bringing that information to us these days, as opposed to having to go and scan tons of pages. But I think that transparency, it may say it provides all of the links. And this I do think is really interesting, is that just we may be getting transparency. It gets an amazing output that gives us all of these hyperlinked text which says, this is the answer to the question you're looking for. But I think that sometimes transparency isn't necessarily trust. And we can put so much trust into these models, even when it's giving us a complete false story or a bit of a facade. And so it kind of comes back to this question of just like, how much. Of course these are improving, but how much trust are we putting in these models and just expecting and getting used to, oh, that output's pretty good. I'm just going to use that output.

Preston Pysh (59:44)

The kids are in college or high school or whatever, they're using it to write their reports. And then I don't put it past the professors that they're then taking the reports and running it through AI to provide the feedback. So you have the AIs writing the reports and giving the Feedback. And the humans are just kind of like the paper pushers.

Seb Bunny (60:07)

You've probably seen it. There's a meme of. It's kind of got a woman at her desk sending an email to her boss and she goes and types into AI, gets this amazingly worded email that explains her opinions and this and that. And then she sends it and he was like feeling accomplished. And then you see the other side of it, which the boss receiving email. He takes the email, puts it into AI. What are the key points she's trying to highlight, and condenses 3,000 words down to three sentences. And so it's just like everyone is kind of fluffing everything up and then everyone's taking that fluff and then decompressing it again. And you're just like, what is happening?

Preston Pysh (60:42)

The AI slop. I keep hearing about AI slop. And it's real. It's real. The AI slop is real. I just want to, yeah, I want to just highlight this real fast. So after the comment from Jensen on, you know, AI or China potentially beating the US to AGI because of the energy infrastructure, this article came out, I want to say like on the same day. This article's from November 19th, 19th November of this year, 2025 from Bloomberg US to own nuclear reactors stemming from Japan's $550 billion pledge. Check this out, Seb. As I'm scrolling down the key takeaways by Bloomberg AI. You don't even have to read all of this, which is probably AI slop beneath this. Read the AI summary. And it says the US government plans to buy and own as many as 10 new large nuclear reactors that could be paid for using Japan's $550 billion funding pledge. The funding pledge is part of a push to meet surging demand for electricity, including for energy hungry data centers that power artificial intelligence. The Trump administration has set a target to get 10 large conventional reactors under construction by 2030. So it seems like the US understands the limitation, which is energy infrastructure. It seems like it's trying to do things from a policy standpoint to reinvigorate some of these. I saw the Three Mile island is they're going to bring that back online. And I think this is the thing I'm really wanting to talk about. The years and years of ESG energy equals bad is over. It seems like this whole thing, the climate change energy is bad. If you consume any sort of energy, it's bad. All of those talking points are just going by the wayside because the key players and the string pullers of the world have figured out that if they're going to win this next race, the race of intelligence, it requires more energy, not less energy. And it just seems to be dead on the vine. What are your thoughts, Sebastian?

Seb Bunny (62:47)

I could not agree more. And I just think that we just have this society that seems to have this idea that consuming energy, as you're saying, is bad, when in reality life consumes energy. And if you just look at any chart out there, There is a 99% correlation between GDP per capita and energy consumption. There are no low energy consuming, high GDP countries. They just don't exist. And so I think that life naturally requires energy. However, there is a discussion to be said around there's a difference between consuming energy and environmental destruction. And there's obviously ways in which you can decimate the environment, whether it is a lot of these lithium mines and whatnot trying to attain heavy metals and even just some of the various fossil fuel approaches. And I don't want to necessarily have an opinion on that. But I think that it's really interesting seeing the nuclear narrative starting to shift because I think that it's unbelievably important to me. I read a book a few years ago called Atomic Awakening and it kind of dove into the world of nuclear energy. And one of the stats that stood out to me, I just went and found the information. It talks about how we tend to think that nuclear is unbelievably dangerous. And the reason why we don't use it is because it's just killed so many people throughout history. And that information could not be further from the truth. And I think that it is because we see things like Chernobyl and Fukushima and we hear about radiation poisoning and in reality. So one of the stats it looks at is per terawatt hour of energy used coal, there are around 25 deaths because of obviously the pollution in the air, the people that are actually working in factories and such, the coal mining in the oil industry, it's around 18 deaths per terawatt hour. The gas industry is 3 deaths per terawatt hour and hydropower is 1.3 deaths per terawatt hour. Nuclear is 0.03 deaths per terawatt hour. Like we're talking about a minuscule amount in comparison to every other type of energy source. And so I think it's awesome to be able to see the narrative shifting. I think the biggest thing is now just seeing the policy and the legal side of things shift because I think it's been kneecapped because of all of the legislation that has been kind of rammed down through the legislative system.

Preston Pysh (65:02)

Nothing more to add. Can't agree more. Did you have a final topic that you want to discuss? Seb?

Seb Bunny (65:08)

I would say, actually, you know what, I have a couple more topics, but we can always leave those to another time. But I would say there's a topic I'm curious to hear your thoughts on. And it kind of goes back to AI again. And it's this idea of wisdom and kind of like diversity of thought. And so in my mind, wisdom has never really come from everyone kind of thinking the same way. It emerges from contrast and so hearing radically different positions, holding them together and discovering new insights and kind of the space between these kind of various insights. And so throughout history, we've seen all of these breakthroughs in various sciences and whatnot, always from the fringe. It is not consensus, it is not from the sensor, but it's from all of these various individuals who've thought outside the box and noticed something that others have overlooked. And I think that what is interesting is AI is different in that we're feeding all of these models the same information. And on top of that, AI, I think, is built on weights, from the way that I understand it, and the lower the weight, even if the idea is brilliant, the idea doesn't necessarily, because it doesn't carry that much weight, AI doesn't necessarily reproduce it or talk about it in the text. And so if children are growing up learning about from these centralized models, well, I think they're also inheriting the same baseline worldview. Instead of tens of thousands of unique teachers, all with unique life experiences, all with kind of a different intellectual starting point. And they're sharing this information with these students. I think that's what kind of creates wisdom and curiosity, as opposed to this uniformity that all these kids are learning from the exact same models. So I'm curious if we fast forward 10, 20, 30 years, if these kids are going to be being taught by AI, but they're all going to be fed the same information. What happens to innovation? What happens to wisdom and knowledge? I'm curious to hear your thoughts on this.

Preston Pysh (67:02)

My conversation with my wife on anything AI almost always comes back to this discussion point that you're bringing up. It really comes down to are we training the AI or is the AI starting to train us? And then the question is, what would it be trying to train you on if it was trying to train you? Which I think the answer to that is it wants to have more novel Insights of what it doesn't know. It's going to try to lead you into those domains, which is scary that it would be leading you that way. But in more general terms, I just think that the challenge that you're really facing is the one that we brought up before, where everybody's using AI to write their papers or to do their research, and then they're handing that in and it's just a bunch of AI slop that's kind of replacing deep thought. And I think the other concern that you get, Seb, is as the world becomes. It becomes harder and harder to compete or to stand out or to provide novel insights because the competition is so fierce with anybody armed with AI. I don't know what this does from just a human motivation standpoint. I think you're going to have a lot of people that are just like, it's not even worth my time or effort to try because somebody armed with AI is just going to kick my butt or I just can't stand out. Or if I can stand out, it's only going to last for three days before somebody else in the market. It comes with more competition and makes. It erodes away whatever competitive advantage I had. Where I would push back is there are plenty of industries out there. Not plenty, but there's some industries out there that you can still provide value for if you're servicing human beings. And where they aren't, or at least where they appear to not be, is in services, soft services, digital services, it seems to be crazy competitive. But in providing service from a physical standpoint, like, for example, if you want your yard mode, if you want work to be done around the house, if you want your plumbing, a lot of these skills that I think people in the United States have really veered away from and just looked at that and said, oh, that's not going to pay me a lot, so I'm not going to go work in those different industries, I think that that is ripe for disruption and opportunity for a lot of people to actually make quite a bit of money, especially if they can do it from a standpoint of they do it really well with high quality work. But it involves physical labor. It involves people getting out in the physical space and doing things and not sitting behind a computer and clacking on keys. I would love to hear the audience, like, you know, if you guys are listening to this and you got comments on this particular topic, I would love to hear what you got, but. Sorry, Seb, to interrupt you. I want to hear what you to see too.

Seb Bunny (69:56)

No, and you make a really interesting point. And I'm curious again just to hear your reflection on this, which is I've spoken to many individuals through the bitcoin space that have come from traditional finance and they used to work in consulting and they used to work in the banking sector and they used to work for CPAs and various other kind of financial industries. And what I find really interesting is that they're actually stepping back from that sector because the white collar worker, the knowledge worker, is being completely disrupted through AI. They're stepping back and they're looking, okay, where can I direct my time and energy into something that's not going to be replaced immediately or in the foreseeable future? And one of my good friends who I speak to, who's in the bitcoin space, I speak to him bi weekly, he's saying that, you know what, I'm looking actually to buy a painting company with a whole bunch of painters. I'm looking to buy a storage company. I'm looking to buy things that we are not going to see them overtaken anytime soon. And so if you have a handful of painters or a handful of plumbers or you've got a trade company, I think those companies, they can provide a reasonable lifestyle. You don't need to be worth 50 million, 100 million. It's like, what do you want to be able to show up for your family? What do you want to be able to afford a house and to be able to live comfortably. And I think sometimes the financial world, social media says we need more. And in reality, I think you can live a relatively comfortable life with a decent little income of low mid six figures through one of these kind of more manual labor. Physical. Physical trades. Yeah.

Preston Pysh (71:24)

I mean, the counter argument that somebody from tech is going to immediately bring up the humanoid robots, which we didn't even discuss during the show, but at this moment in time, in 2025, any type of humanoid robot video that I've watched, it goes over and it's like emptying a dishwasher and it literally takes it five minutes to put a spatula in the dishwasher and then it like fumbles all all over the place. So that could change very quickly. But I think humans, if I'm going to hire somebody to do something around the house or whatever it might be, I'm going to a human and not a humanoid robot, at least not anytime soon.

Seb Bunny (71:59)

And I think that naturally we've got this world where I think there's a lack of connection. People want to interact with people. And so I'm noticing, and I think it's an awesome swing. I'm noticing companies today, the majority of them, you cannot speak to someone on the phone. You're getting an AI bot through the chat. But the companies that do say, hey, you know what, here's our number. You give us a call and you're actually going to get a person, they're starting to see a lot of success. And so it's really cool just to recognize that technology, the pendulum always swings. And I think we've swung to this point where we've almost replaced us in many ways or tried to replace us, but we're recognizing that first, AI and a lot of these technologies are not people, and people know that they're not people. And secondly, we're missing that connection. And so I'm curious to see over the next few years, does that pendulum continue to swing back a little bit more towards center, where people are recognizing the importance of physical connection, spending time with friends, actually having a number to talk to, someone to deal with any issues.

Preston Pysh (72:56)

Okay, I've got one final surprise before we wrap this up. While we were recording today, I took a screen grab of Seb and I having our conversation. And I had it take our Banana Rama, whatever the heck it's called, a Pro Gemini model nano banana. Thank you, sir. And I asked it to what would these two podcasters look like if there was a camera behind them and it took a picture while they were having the conversation? Okay, now you're going to see the picture that I. The screen grab that I got is probably one of the most flattering pictures of Seb that you will ever see. This is such a bad picture. Check this out. Okay, so here you are. You were mid blinking your eyes and looking up, and I'm just stone cold staring at the camera. And it's just the video feed of him and I having the car. You're ready to see what it interpreted. The back of our head taking a picture from behind us looks like, like, Okay, for the person that can't see this, it's not bad. Like, there is a lot right, with this picture. As far as it looks like Seb, your room, it did not reverse your room. Right. Like your room is there, but it is showing that you are talking to. You're looking at a computer. It's the back of your head and all that looks like it pretty normal. But you're talking to yourself and not me. Oh, this is interesting. Look at your background. Your background is my background.

Seb Bunny (74:39)

And have you seen that it's also given me your headphones, but not in the.

Preston Pysh (74:44)

Oh, that's right.

Seb Bunny (74:45)

Yeah.

Preston Pysh (74:45)

Look at that. That's wild. And then my picture is, like, really jacked because the microphone is literally behind me. And then I'm talking to you, which is correct. And it's the image of you looking forward. Okay. So, like, that all looks correct. It's pretty close. Okay. So, like, not bad, but there's a couple hiccups now. If I went in there and I, like, pointed these things out, I think it would actually get it all correct if I went on it back and forth. I mean, obviously, I didn't have time to really do anything other than quickly type the prompt in there. And that was the first go around coming back to me. So pretty wild, but not quite right. But it's coming along very fast.

Seb Bunny (75:29)

Similar to your watch thing. It had my monitor. It has my.

Preston Pysh (75:33)

Get the heck out of here. No, really, hold on.

Seb Bunny (75:36)

Let me pull this back up. Exact monitor. And that's why I'm just like, what? I didn't know what my monitor was.

Preston Pysh (75:41)

Get the heck out of here. That's the monitor you have.

Seb Bunny (75:44)

That's got my monitor? Yep.

Preston Pysh (75:45)

Dude, that's weird. That is definitely not my monitor. In fact, I have three screens here in front of me. In fact, I get comments online. Why is he looking off to the side? Well, I'm looking over at my second or third monitor to pull up all the things on the fly during the show. So, yeah, no, my monitor's way off. It looks like my monitor's on the floor too.

Seb Bunny (76:07)

You're really. You're stacking sats. You don't have a chair.

Preston Pysh (76:09)

Oh, yeah, that's right. So it did get that correct. Yeah. Pierre Rochard AI knows I don't have a chair. I'm sitting in the chair.

Seb Bunny (76:18)

This is a little ways off. It will get there.

Preston Pysh (76:21)

Wow, Seb, I love this. This was so much fun. If you guys enjoy this format, I enjoy this format, but maybe the audience doesn't like this format. If you like this format, please tell us in the comments of. You know, if you're on X, let us know, because if you like it, we want to keep doing these types of things. And, Seb, thank you so much for your comments and what you brought to the show today. Give people a handoff to anything you want to highlight. Sebastian. And thank you so much for joining us today on the show. But, Seb, give people a handoff where they can learn more about you.

Seb Bunny (76:52)

Absolutely. And I would start by saying, as well, if you kind of enjoyed this kind of discussion when you listen to it, feel free to just post a comment with anything that you think is happening in the world that is interesting and kind of on the next time we record in this style, we'd love to kind of bring it up because I think that sometimes there's so much stuff happening that a lot of it kind of slips between the cracks. And it's just the world is a fascinating place and there's incredible things that people are working on. Now you can just find me at sebunny and bunny is B, u, n and E Y. I'm Sebunny on Twitter. I still kind of go by Twitter. I just feel like X to me, it doesn't resonate. No, you can find me@sebunny.com on Twitter and my book is the Hidden Cost of Money. And yeah, I just really appreciate you guys listening and thanks for having me on, Preston.

Preston Pysh (77:32)

All right, everybody, thanks for joining us.

Preston Pysh (77:34)

And until next time, thank you for listening to tip. Make sure to follow Infinite Tech on your favorite podcast app and never miss out on episodes. To access our show notes and courses, go to theinvestorspodcast.com this show is for entertainment purposes only. Before making any decisions, consult a professional. This show is copyrighted by the Investors Podcast Network. Written permissions must be granted before syndication or rebroadcasting.