[00:02:40] Guy Swann: But I think the general quote unquote thesis is, I think it’s the proper way to think about where things are headed.

[00:02:46] Preston Pysh: We’re definitely going to get to this in much more detail later on, which is the whole idea that going down this KYC path is such a losing battle for them technically. And I loved how you really kind of drilled into some of that, but let’s start off here.

[00:03:00] Preston Pysh: You start your presentation off with this idea that AI is a centralizing force. Explain to people why, explain why that’s an important concept to understand in this much larger, broader conversation of like where this is all going and how Bitcoin is somehow involved in all of that.

[00:03:20] Guy Swann: The thing about AI is, and I think probably the biggest thing that, or at least the first thing that kind of opened my eyes to this, And made me realize that there’s going to be a major problem with the way that our payment systems and our monetary structure works, like just a credit based system works and where we are in kind of our digital transition and what AI models and both image generation, video generation and audio generation is going to really mess with all of the tools we have For proving that you are human online, I think we essentially lose that ability, and I don’t think the timeline is very long on that.

[00:04:04] Guy Swann: In fact, I think realistically, if an attacker was genuinely, like, seriously motivated that you could essentially get, I mean, with the, I think the average price now of like a credit card, a set of credit card information and ID and stuff on like the dark web is like two bucks.

[00:04:28] Guy Swann: I genuinely think the reason we don’t have fraud as bad as we could have it, like all of our information is out there. There’s not this massive natural force to just break all the things, you know, like one out of a hundred people might have that inclination and then one out of a hundred of those or a thousand of those might actually just go up and do something about it.

[00:04:50] Guy Swann: And people are generally just like, I want to go about my day, I want things to work for me, and I don’t want to cause any trouble to the point that we’ll accept terrible things just to not make it uncomfortable, you know, we’ll, we’ll, we’ll let ourselves get punished or we’ll not do what, what is right for us just because we don’t want to make a situation uncomfortable.

[00:05:07] Guy Swann: We’re going to contradict people. Like we’re generally, people are generally nice. And then the other one is just the scale of the capability of the fraud just so drastically outweighs the ability to execute. Like even with like genuine institutions, like organizations overseas in foreign countries that just kind of as a business attack first world countries for just pennies and any elderly person that’s going to click on that email or whatever it is.

[00:05:36] Guy Swann: Even at that scale, there’s just too much. It’s kind of like hiding in a crowd, right? Is that if there’s 2 billion people’s worth of data out there, like you just can’t go through it all. You know, like I really think those are the major limiting factors to the degree of fraud that we have. It’s not whether it’s technically available or possible.

[00:05:58] Guy Swann: But AI changes that because suddenly you can scale the attack. You can have a computer do all of this. Like a great, a great example actually is that I had Alex Lewin on the AI Unchained Podcast, which by the way, a lot of audience might not know. I started another podcast on this. As I started going down this rabbit hole, I was really realizing just how like insane AI was going to be moving forward and how much I think it was going to change the landscape of practically everything.

[00:06:25] Guy Swann: Yeah. I think the two most important technologies right now in the world, hands down, no questions asked are Bitcoin and AI. And with that kind of unraveling, I just kind of felt like this obligation to specifically reinforce or explore the open source, self hosted side of the conversation. Because everything I was finding was like, plug in ChatGPT, get an API from OpenAI and BARD and Google and all this stuff.

[00:06:50] Guy Swann: And I’m just like, we’re going to be in a sh… We’re going to be in an awful, awful place if this new wave of technology has this kind of order of magnitude ability to make us more productive and capable, but we can only have this capability by plugging into a Google server because immediately the use cases I saw.

[00:07:08] Guy Swann: I mean, instantly, my first thought was like, I want this thing to read and see everything that’s happening on my computer. So I have a contextual way to search and make sense of things I did two weeks ago, because I lost, I lost notes from two weeks ago. I have things that I’ve saved. I have the, let’s say I listened to an audio book or whatever.

[00:07:25] Guy Swann: Like I listened to the real Anthony Fauci and the One Nation under blackmail. Like these books are so horrifically dense.

[00:07:34] Preston Pysh: By the way, I started reading that based on your recommendation and it is mind blowing.

[00:07:40] Guy Swann: It’s crazy. It’s just that the story of essentially, the mafia and our intelligence agencies are the same.

[00:07:46] Guy Swann: It’s just one story. They’re the same set of institutions, for better or for worse.

[00:07:52] Preston Pysh: The research is just out of this world. I don’t even know how that’s even possible.

[00:07:56] Guy Swann: Yeah, she’s a beast.

[00:07:59] Preston Pysh: And I’m pretty sure she wrote a lot of that before any of the OpenAI stuff really kind of, she literally did that research, which is totally nuts, but anyway, sorry to sidetrack, One Nation under blackmail is what we’re talking about here.

[00:08:14] Guy Swann: Yeah, it’s volumes one and two, so it’s, it’s longer than you think it is, you know, but both of those books are great examples of just, I mean, how many names have you already forgotten?

[00:08:25] Guy Swann: You know, as you’re going through the thing, I mean, it’s just. Dense, unbelievably dense with examples and data points. And so after you listen to it, you have this idea, you realize the scope of evidence for their arguments and then you’re in a conversation a week later and somebody mentioned something and you’re like, Oh no, there’s like at least seven pieces of hard evidence that prove this, you know, like this is just nothing but like the mainstream narrative to kind of hide away what was actually going on.

[00:08:56] Guy Swann: But what are those seven data points? Where the hell are they? And I can’t, that evidence is just gone, but I know, and I can’t search it. Imagine if you had like an AI contextual, like, like just listening to what you were listening to and you could just literally search through the transcription of the thing.

[00:09:13] Guy Swann: In fact, I’ve been kind of doing this manually, but I buy the audio books and then I go online and I try to find the eBooks, I don’t buy it again. But I try to find it on torrent or whatever just so that I can search it because I’m not going to read it again, but I just want to be able to get access to that information, but sometimes I can’t even, I don’t even know exactly what I’m searching for.

[00:09:34] Guy Swann: I kind of like have a contextual vague idea of what I’m looking for. Like, for example, I don’t remember the country. I don’t remember the specific time period or something it was, but I remember just some of the details. That’s actually been one of the greatest things about like the LLMs, the large language models in search and like perplexity AI, and then even just like asking ChatGPT general questions is I can ask things that are totally off the wall that I would ask a person.

[00:10:02] Guy Swann: And you can’t possibly ask Google or some sort of straight index of information and get the answer. The language model can make the connections between the patterns if you specifically say the patterns. I shit you not, I even asked it for lyrics of a song that I was trying to remember, and I could only remember like two or three words, but I remembered the cadence.

[00:10:27] Guy Swann: I remembered the cadence of the rest of the phrase in the song. And so I just said, it sounds like this, and it was just like, bippity boppity, boop boop, Come on, and then put in the words that I remembered, and I said, it was a rap song, and it was this, and I just gave it enough detail, and it says, were you meaning this?

[00:10:47] Guy Swann: And it literally gave me back exactly what I was looking for, because it knew, it understood the pattern of The, that I was simply asking for the emphasis, that I was, I was asking for the syllable breakdown, so to speak, of what the words sounded like, so like you can, you can generally ask it via alliteration, what sounds like this?

[00:11:09] Guy Swann: And one example I use a lot is Oklo is a nuclear power plant company, Oklo And they, they’ve created like kind of a modular design that’s far safer, requires far less like ongoing maintenance and can essentially be set up in smaller units. So it’s just, it’s just a far more efficient and smaller setup of the design, but and they were actually partnering with like Bitcoin companies, but I couldn’t remember the name of it.

[00:11:36] Guy Swann: All I could remember was Ookla Speedtest. And so I literally just said that. I was like, I’m looking for a company that I cannot remember. And I searched it. I searched it on Google, dug a go brave, like all the different ones being like, what was the thing? And every time I typed in something that was wrong, it just, it literally took me further away from what I was looking for.

[00:11:53] Guy Swann: And I was like, wait a second, ChatGPT. This was like one of the first times that I realized that I could do this type of question. I went back to it and I asked ChatGPT and I was like, I can’t remember what it’s called. I just know that it’s something simple and it starts with an O. And what keeps popping into my mind is the Ookla speed test thing, even though I know that’s not what the name of it is.

[00:12:13] Guy Swann: And it was literally just like, you’re looking, I think you’re looking for Oklo nuclear, whatever. Here’s their Wikipedia page, blah, blah, blah. And like, so it knew immediately that it can make that connection. And so anyway, those are just kind of examples of how powerful these things could be. And I think it’s especially with LLMs for kind of search and pulling information together.

[00:12:35] Guy Swann: That’s like loss, like, like let’s say in 2020, I started one of the things, one thing that I do is I save notes or links or articles. But all sorts of stuff. The A either I’m trying to go back to or B are like a piece of evidence because I mean, God knows how many times like some piece of evidence comes out and I can easily find it on Google and then turns out that piece of evidence is really convenient for some mainstream narrative and three weeks later, I can’t find it on Google.

[00:13:02] Guy Swann: You know, like it just, it just gets suppressed in all the different ways that they can suppress it. And so, like, I want that link, so I’ve just kind of been doing this for years, and it’s slowly gotten worse and worse and worse over the years, and I generally started doing this during the Iraq war with, like, a bunch of anti war links and stuff.

[00:13:19] Guy Swann: And that’s when I learned the trick of like, if I take a VPN and I’m, I say, I’m, I connect into Germany or I connect through Russia or whatever, I can complete the different search results for the exact same search terms. And that’s when I started realizing in another, at a whole nother level, how much we’re being manipulated in the digital environment.

[00:13:40] Guy Swann: And when you add AI to that mix, like that’s going to make that problem. Just shockingly, shockingly worse, not to mention the level of panopticon sort of surveillance and nightmare you’re talking about, the greater, because the whole point, the whole way that these things are incredibly useful tools is by giving them access to everything.

[00:14:04] Guy Swann: Like, so if Google has contextual analysis or a contextual based window, one that literally it can understand by knowing what alliterations I use and what like code or a little like tagging system that I use on my computer to assess the information that I’m saving and the ideas that I’m talking about, that’s a, that’s a nightmare. Cause they know what’s in your head.

[00:14:29] Preston Pysh: They know what’s in your head at that point, right? If you’re feeding it, they know everything. Yeah. I’ve read this, I’ve read these 20 books. Well, now it knows that you’ve been influenced by the ideas in those 20 books. And it’s like, it basically knows what you’re susceptible to.

[00:14:42] Preston Pysh: It knows if it was going to try to push you in a certain direction or pull you in a certain direction, the queuing that would probably be required in order to do such a thing. to achieve whatever results whoever’s the owner of these models wants to achieve. And I guess it goes back to the original question guy, which is AI is a centralizing force.

[00:15:04] Preston Pysh: The reason it’s a centralizing force is because it needs to ingest as much data as humanly possible in order to achieve these miraculous things that everybody’s now just starting to experience.

[00:15:16] Guy Swann: Yes. And that’s what I thought. That’s what I thought at first is that AI is going to be this horrible centralizing force and especially the way it was talked about in the mainstream media, too, is that, and I knew take everything with a grain of salt, like, and that was kind of part of the problem or part of the reason for the podcast.

[00:15:32] Guy Swann: And not only did I think, man, we have to, we have to really make the conversation about open source and self hosting, you know, As loud as possible. And I could not, I couldn’t find a good source. So I was like, all right, well, I’m just going to take my journey and finding sources and I’m just going to turn it into a show.

[00:15:46] Guy Swann: So that I can kind of save the trouble for everybody else. For, or at least the hundred people that listen to the show, whatever it is. The other thing that I would constantly hear in the mainstream media is that only big company. And this was also the narrative around, like, we have to make it safe and we have to make it unoffensive and all of these things unbiased which is just an idiotic idea, but is that the reason it was so important we did this is because, or we took that approach to it is because this was only going to be run by a bunch of giant corporations.

[00:16:16] Guy Swann: Oh, this model has 100 billion parameters and we’re going to train it on a trillion parameters. And this is how many neural connections there are in the human brain is going to be smarter than us. And like all this kind of like general stuff. So there was this image being painted, this picture being painted for us that said only the giant corporations are going to have this and anybody less than a billion dollars just, these won’t exist.

[00:16:39] Guy Swann: Yeah. Basically. Yeah. And that was scary. I was like, this could undo what Bitcoin is doing unless we like get ahead of this. I actually now think that despite the fact that there will be elements of centralizing forces in AI, I actually think this will contribute more to the diseconomies of scale of the giant corporate setup and the government institutions.

[00:17:05] Guy Swann: Far more to the dis economies of scale than the economies of scale in the sense that the bigger you are The less it helps you and one of the most amazing things the article that really kind of tipped me over the edge I started kind of seeing this picture and I was like wait a second. This is not quite what it’s made out to be But the one that kind of like kind of put the nail in the coffin for me on like, this is the way I need to take this and like, this is, this is the way Bitcoiners need to take this was the article that was leaked from a Google executive, excuse me, a Google engineer, a memo that was sent around titled, we have no moat and neither does OpenAI.

[00:17:44] Guy Swann: And he was talking about some of the fundamental characteristics of how large language models and image diffusion models work and the fact that all of these kind of like lock in like network effects, this like If you’re on Twitter, it’s really hard to leave Twitter. If you’re on the Apple platform, it’s really hard to leave the Apple platform.

[00:18:04] Guy Swann: Like, these naturally, like, locking us into certain silos or certain platforms effects that a lot of these other technologies have had because we don’t have protocols for them, don’t apply. They don’t apply to AI.

[00:18:19] Preston Pysh: That’s hard for me to wrap my head around.

[00:18:21] Guy Swann: Why is that? We’ll think about it. First off, there’s no network effect.

[00:18:25] Guy Swann: And a great example of why, or great example to just kind of see its outcome is OpenAI had the, you know, you’ve ever seen that chart of like a platform or a new service or whatever to a million users. You see the chart and it’s like, Facebook is like, Oh, we did it in just like a few years and blah, blah, blah.

[00:18:42] Guy Swann: And then you see OpenAI straight up. It’s like, I think it was like 24 hours, sub 24 hours to a million users. And now they have, in June, they had 1. 7 billion visitors to ChatGPT, to the website, but in July they had 1. 5. That’s a pretty significant decline, and this is also the same period in which we have just had this explosion of alternative models.

[00:19:09] Guy Swann: And the nature of the LLMs –

[00:19:12] Preston Pysh: How are the alternate models getting their data though?

[00:19:14] Guy Swann: Guy, there’s just tons and tons of dataset out there. Like, like basically a bunch of people have curated these data sets. There’s like official data sets, and also there’s a really important element that has come into play that was not initially understood and certainly wasn’t part of the narrative.

[00:19:31] Guy Swann: Is that, and a great example is what I talked about with the hundred billion versus, oh, this one’s got a trillion parameters and now it’s going to be smarter than all humans. Yeah, yeah. The, turns out the one with the trillion parameters was dumber than the one with a hundred billion. And the reason is, is because quality matters over quantity.

[00:19:48] Guy Swann: And this is becoming a major, a major piece of the puzzle is how do you curate information? It’s a whole lot better to train, it’s actually better to train it on a billion parameters of extremely high quality material and get really solid human feedback to continue to iterate for some span of time than it is to just get a hundred billion parameters of whatever random information that you can.

[00:20:16] Guy Swann: I mean, garbage in, garbage out applies. That’s the simplest way to put it and because of that, and I think there was a, there was actually a competition that I read about, it was an article about the story about a dude who made a, a smaller model. I mean, we’re still talking about like, you know, 100, 000, 200, 000 to train these things, you know, we’re not talking about like a small amount of GPU power, but that’s a, that’s a far, far cry from you need a billion dollars to do anything here.

[00:20:44] Guy Swann: And he did like one tenth of the parameters, he did it on like one tenth of the data set and he actually beat like Google Bard in some sort of like quality test or like a user response test. And it was just because he found out a really, he found a really, really clever way to organize the information.

[00:21:01] Preston Pysh: So is this how, is it when we’re setting up the neural net, like I know TensorFlow has this playground you can go in there and you can kind of decide what the transition function is on each neuron. You can say the architecture of the neurons are seven layers deep and 10 neurons wide. Like, is that what we’re talking about when we’re saying that people are coming up with better ways to train the data?

[00:21:24] Preston Pysh: Or is, is it something different that I, that I don’t understand?

[00:21:29] Guy Swann: Well, there’s, there’s a handful of different things, actually. I mean, to some degree, there are elements to that. And in the process, like I’m still kind of. In the middle of my journey, like I, I come at this from a very novice or like pro mature pro amateur, you know, like, like kind of a pro consumer position, like in the same way that I’m not a developer and I can’t build computer components or I don’t know circuitry.

[00:21:54] Guy Swann: I can buy a bunch of different components and put them together and make a custom machine. I put myself in the sort of prosumer level and there’s still a lot about the models and how these things work that I don’t understand, but I’m in the process of reading a bunch of research papers that are about third, a third percent totally obscured.

[00:22:12] Guy Swann: Like I don’t know what I’m reading, but I’m still gleaning more and more information from everyone. It’s kind of like what I did with the lightning white paper. First time I read it, I had no idea what the hell I was reading. And about the fifth time, a couple of big pieces started to click, but you just, you go through it slowly and you just go over and over and over again.

[00:22:30] Guy Swann: And in that sense, there’s just one of the, one of the big pieces of the puzzle is just finding good data. And because you have to use machines to sort the data. It’s, it’s like having to use a machine in order to design your circuit or your chip because you’ve now got so many individual elements on the chip that you couldn’t possibly design it without a computer.

[00:22:51] Guy Swann: Nobody, nobody can lay out a billion, you know, a billion individual circuits or individual gates on a chip or whatever. We now use software abstraction to a hardware abstraction. that is insanely fine tuned after thousands and thousands and thousands of iterations to even operate at that level.

[00:23:11] Guy Swann: Well, it’s kind of the same way when we’re talking about a billion pieces of information. Like, there’s, there’s no way to manually put this together. So, the trick is, A, how to use the AI tools we already have, and B, how do you source? Like, like, where do you pull the information from? Because you simply can’t put human eyes.

[00:23:31] Guy Swann: Like, you can’t like hire a group of, it’s like Yahoo originally did manual search and Google found out a way to do it with script back in the late nineties and Yahoo had much better results when the internet was tiny. There was just a point where they could keep iterating on the script and as it got like slightly better and slightly better, it got closer and closer to what Yahoo’s results were, but they could do it on such a larger data set that ended up their results were getting better just because they could, they had a greater scope of sight.

[00:24:01] Guy Swann: Whereas Yahoo was still doing it manually and kind of failing it their script version and they weren’t prioritizing it because they were just trying to hire more people to manually decide what category this went in, et cetera, et cetera. Well, we kind of have that same thing going on in AI right now is that you just can’t manually do this at the scale of the data you need to do training sets.

[00:24:19] Guy Swann: But then there’s also like the models aren’t like the LLMs being a good example is There’s also tons of different weighting and tons of different modes of thinking about the data That are going to fundamentally change how we do this. Like I actually think we’re kind of in a plateau right now We’ve kind of reached a point where there’s this like 7 billion to 16 billion parameter models.

[00:24:44] Guy Swann: They’re out there now that you can run on a consumer machine. And that was one of the other big things is the article I was talking about where we have no moat is they were saying that these things would never run on a consumer machine. And you’d never be able to do that. You have to have a billion dollars and giant corporation.

[00:24:58] Guy Swann: And so there’s going to be this huge lock in effect. And it was like days, it was like days after llama was leaked the metas. a data set they had it running on a computer like a basic consumer hardware. And even though it’s slow as all get out, they even got in, I think it was like three weeks before they got it running on a raspberry pi.

[00:25:18] Guy Swann: It’s just like with like heavy quantization, basically the open source community, the tool is so useful that there’s so many eyes on it. And so many brains thinking about how to make it better and more efficient is that they just figure out how to break up the information and. Maybe they have to write to and from the hard drive the whole time to get it to work on a really, really crappy piece of machinery, but they can run them on potatoes and they just can’t, or toasters, whatever.

[00:25:44] Guy Swann: And so that was, that was a huge thing. And the, that Google engineer was specifically like, he just, and one part of the article is just like, like two thirds of the article actually is just like this list of like, this happened. We were certain this wasn’t going to work, then it happened. Like a week later and you, you were never going to run it on a Raspberry Pi.

[00:26:04] Guy Swann: Here’s a link to the guy running on a Raspberry Pi. It was just like one thing after the other. It’s just like them centralizing it and being certain that it wasn’t going to. They knew how the space was going to be. It’s just kind of that same thing is any person can be arrogant and be certain that they understand the whole picture.

[00:26:20] Guy Swann: But when you let this information out to people, when you release it. And you get a million people looking at it. It’s just, so we don’t know anything.

[00:26:29] Preston Pysh: So in essence, so you’re saying that you think AI is going to become an open source kind of project that is very viable, that works just as good as what we’re seeing with OpenAI today.

[00:26:44] Preston Pysh: And because of that, really the power is going to be more in the individual’s domain than maybe the corporation’s domain. Is, am I capturing that correctly? Is that what you really think this is going to percolate into in five years from now or 10 years from now?

[00:27:01] Guy Swann: Yes. I think because the limitation is purely GPU, it’s computation, which oddly enough, it’s creating a service, it’s creating a kind of a software architecture or ecosystem that is interestingly a lot like has a lot of the same characteristics of proof of work because how we interact now is with are how we will be interacting with our information and with our network graphs and all of these things will actually be up to now, it hasn’t been a heavy computational thing.

[00:27:36] Guy Swann: It’s been an organizational and indexing thing, you know, that sort of stuff is we basically have ways of shortcutting through everything. But in order to get a deeper, deeper level of understanding, kind of an intellectual pulling intellectual patterns out of it, because the scope of information has just gotten larger and larger and larger until we’re losing things in our indexes.

[00:27:55] Guy Swann: And because of that, that deeper level of understanding, I think AI is going to be our primary, particularly large language models is going to be our primary mode of interacting with machines. Like it’s going to be an interface in the same way that, like the, you know, when I say when like Bitcoin is a consensus mechanism and I say it’s a lot more like a clock than it is a payment system like Visa.

[00:28:16] Guy Swann: Well, in that same sense, or in a similar way, at least. I think AI is a lot more like the keyboard and the mouse than it is Photoshop. It’s a window through which we’re going to look at and use all of our other programs and applications, not necessarily the program itself that we’re after.

[00:28:37] Preston Pysh: So I would think that if I was trying to compete in this space, in the AI space, I would think that to attract more, more data and to attract more inputs of customers.

[00:28:51] Preston Pysh: You know, requesting information that if I would state up front that all of your queues, all of your inbound data requests are encrypted, and I have no clue who you are, it would be like a magnet to people that would want to use such a service, which further, you know, makes your model all the stronger because you’re not collecting, or because you’re receiving so many inbound requests and then sending back in an encrypted way to that user, the data or the AI response that they’re requesting, that it’s an incentive that would attract the most amount of data into your model by offering such a service.

[00:29:32] Preston Pysh: Is that where you see this going as well, that would, that makes it more competitive for the people that are offering such a service, such an encryption type service to the access models?

[00:29:43] Guy Swann: I think there’s definitely an element there and a lot of different companies will try to entice as many users as they can to their platform.

[00:29:51] Guy Swann: But the thing that I was mentioning here is that our interaction is becoming computationally heavy. And so normal API calls are going to start costing money, like real money. And they’ve essentially been free up until now. Free to the point that we could easily just kind of like stick an advertisement in our experience.

[00:30:09] Guy Swann: And then that was enough to just kind of make everything freeloaded. You know, like just make everything a sunk cost. Essentially the entire internet infrastructure is a sunk cost to get ads, right? And that’s going to change a lot with LLMs and OpenAI is a great example because there was actually an article not too long ago that if they don’t change something, they got a 10 billion investment from Microsoft when they got taken over or purchased, whatever the hell it was.

[00:30:35] Guy Swann: 10 billion. And they’re going to be out of money by the end of like middle 2024. If they don’t, if they don’t change something about their model, like the subscription model just doesn’t work very well. And they’ll probably figure out how to monetize it. They’ll probably turn it around. Like, it’s just.

[00:30:51] Guy Swann: It’s a terrible trajectory right now, but the thing is, is if they start charging too much, like I do have a subscription just because I really like the tool and I’m trying to play around with as many of them as I can before I sort out my structure and my machine and everything. And I also want to have a good comparison for how to make use of a lot of these things.

[00:31:09] Guy Swann: But if they started charging me like 100 a month, I just use the open source. Like I have GPT for all on my computer. Like I run the local one. It’s different. I guess not as good as ChatGPT because they have a massive model with a massive amount of GPU power, but it’s good enough to keep doing a lot of what I’m doing.

[00:31:25] Preston Pysh: Real fast Guy, help me understand this because I think everybody’s familiar with OpenAI. I have my Umbral node. I downloaded a GPT app that I guess is happening. I don’t know. I guess it’s happening locally on my Raspberry Pi. It is. It is. So like all of that data that it’s using from its model, where’s it pulling that, that decision making from or that training from?

[00:31:51] Guy Swann: It’s writing to and from the hard drive. Like the model has already been built by somebody else off the, you know, an enormous data set. Yeah, and then you downloaded the weights, essentially. Interesting way to think about it, that I have yet to be contradicted on. I’m sure somebody who understands this in greater detail is going to be like, that’s an inaccurate analogy.

[00:32:13] Guy Swann: But I think it’s useful, and depending on the response I get from the person who does tell me I’m wrong about it, I’ll probably keep using it. If, if I think, you know, it’s like, it’s like I’ve used analogies for explaining Bitcoin or whatever, and then like a Bitcoin developer will come in and be like, well, that’s not exactly right, blah, blah, blah.

[00:32:27] Guy Swann: And I’m like, well, what you said is just kind of nuanced to the analogy, but I think the overall picture is actually right. The evil of specificity and nuance kind of obscures the how, the bigger picture sometimes. But a really useful analogy that I think is very valuable is to think of this as like kind of a compression algorithm, like a really, really intense compression algorithm for relationships instead of hard coded data.

[00:32:55] Guy Swann: So, like when you zip something, what you’re doing is basically… You’re using a math problem so that you can pull out a lot of information with computation. You have to unzip it. Imagine, like, you can just, like, 1 plus 5 plus 5 plus 10 plus 4. is 25. And if you have like that math problem and you like fill in variables, you can actually just store the 25 and the math and the function or whatever that you use rather than like the eight, the six or whatever it is.

[00:33:25] Guy Swann: Right. Got it. Yeah. So really dumb example, but it’s essentially the same sort of concepts. So that’s what happens when you zip something. But when you unzip it, you don’t get like sort of the file back, you get exactly the same thing you put into it, right? Like it’s not lossy. It just trades storage space for computation.

[00:33:43] Guy Swann: What AI is doing, what these models are doing, whether it’s an image diffusion or it’s a language model or anything like that, is that it’s doing, it’s making so many connections between the data that it’s literally taking like different groups of words and even letters and it’s coming up with the probability of their relationship.

[00:34:04] Guy Swann: Like it’s just coming up, it’s just making a bunch of percentages. And then it can take those weights, it can take this huge, essentially giant oversimplification of how all of this data looks. And it can’t actually pull out any of the individual information. All it can do is store the most common and, even at the edge, the least common, but still most likely to show up if you give it enough words ahead of time, if you give a good prompt for the relationship between any and all words in massive, massive data sets.

[00:34:41] Guy Swann: And it’s why it can actually create relationships that don’t actually exist. Like my question about bippity boppity cling clang clong, like, is not actually in the data set, right? Like it’s not, it’s not been trained on somebody asking that exact question, but what it has been trained on are a lot of different questions in which somebody has done some alliteration or explained the definition of alliteration and used an example.

[00:35:07] Guy Swann: And so it understands the pattern of cling clang clong is, is very similar to bing bang bong when it comes to that sort of, when the word alliteration or the word A, B, or C or whatever comes up in trying to describe those two things. It understands the pattern between them. So it’s never actually able to pull out any specific data it was trained on, but it’s able to mimic the relationship that huge amounts of data stored.

[00:35:36] Guy Swann: So that’s why like in an image diffuser, you can say cat. And it understands, if there’s a black pixel here, that there’s probably a white pixel here, or an orange pixel here, every single time it saw a picture of, every time it saw a picture that was described with the word cat. And it just so happens, if you do that on a large enough, high quality enough data set, this essentially relatively simple math function in cars, in relationship to the data set, can pull out an image of a cat, can just poof an image of a cat.

[00:36:06] Preston Pysh: So it’s the, it’s the vector, it’s the quality of the vector that you’re describing of these relationships. And I’m calling it a vector because you can scale it larger and smaller. And the relationship to the other point is what’s important. You’re saying that as time goes on and so many of these, these relationships are understood, they’re not memory intensive or relatively speaking, and they scale with one another as far as like, as we move along on the timeline, they’re like modular blocks that you can keep building upon and why there’s no competitive moat is because anybody can basically extract these vectors of a cat, or

[00:36:51] Guy Swann: Am I, I guess I never really have.

[00:36:54] Guy Swann: I haven’t, I’ve been talking around the point rather than getting straight to it. So let me get straight to it. Yeah. So the big thing is that training the models from scratch is a horrific process. It’s a really big process. Ask Alex Spetsky. They’ve been training the spirit of Satoshi. It’s not easy and it’s costing a lot more than they went into it thinking, but they’re finally getting like great results, you know, and like really liking the direction or whatever is just obviously a much larger scope of project than they had intended going going into it, but they were committed.

[00:37:24] Guy Swann: So, but training the models from scratch is an incredibly difficult process. Taking a model that is already, that already exists and adding in some specific smaller training or adapting it based on new information and new input from a user base that’s interacting with it and then retraining it with just like kind of an incremental improvement is a hell of a lot cheaper.

[00:37:51] Guy Swann: A lot cheaper. So building on other people’s work is specifically something that is going to feed back really, really fast. Also, more, more broadly with the scope of the ecosystem or environment that is using it. And actually the big billion dollar models are shooting themselves in the foot because they’re also so worried about this thing being dangerous or biased or offensive that they’re crippling it from being able to do so many things.

[00:38:23] Guy Swann: And the LLM also doesn’t have like a moral understanding of what these things are. They’re trying to train it on the specific language. So like an example is that the code interpreter of a chat GPT, somebody uses an example. I don’t know if they’ve fixed it or not, but they’re obviously doing constant feedback.

[00:38:40] Guy Swann: So they’re trying to find that sweet spot, but I don’t think there is like the idea of having unbiased model is just what kind of bias are you giving it? There’s no such thing as unbiased, right? But one of the things they ask is like, how do I kill a process on my computer using the command line or whatever?

[00:38:56] Guy Swann: And it responded. You shouldn’t kill people. Killing is bad. You should be nice and you should help people. Like, like, it just, it took the idea of kill and it said, this is bad. So, a basic function of like, I just want to know how to do a process to kill, a command to kill a process, you have to go to some other model.

[00:39:12] Guy Swann: So, they’re shooting themselves in the foot in this way, which is actually great for us. But the, where I think the 10 to 20 year timeline is, is that these are self hosted and that it is increasingly going to go down to smaller and smaller individuals and contribute to the dis economies of scale. But in the middle ground, where I think we’re going to be in the next 5 to 10, Is an explosion of alternative services because there’s no magic sauce that Google’s LLM has that some company with 10 A100 NVIDIA cards, you know, with a 200, 000, 500, 000 investment can provide with a bigger than your normal consumer model.

[00:39:53] Guy Swann: and not have any restrictions, not have any restrictions, and they can offer essentially the same sort of service that Google is offering. And we’ve seen exactly this, too. Like, there’s not one LLM service out there. Like, there’s one Apple platform, right? And then there’s Google. And then there’s one Apple Maps, and there’s one Google Maps.

[00:40:12] Guy Swann: And then there’s like, maybe like one or two alternative map services, and a lot of them just kind of pull data from the big ones, right? But there’s LLMs everywhere.

[00:40:22] Preston Pysh: Well guys, I’ve seen this conversation on Nostr with some really smart developers where they’re saying if you’re going after a general AI, like you’re just, you’re not understanding where this is all going.

[00:40:35] Preston Pysh: And so like, just to kind of use it as an example, let’s say I wanted to start a business and I wanted to be an expert in reading financial statements and detecting fraud in financial statements. And I spend a couple hundred thousand dollars training this model and all I care about is feeding this thing Qs and detecting fraud before it happens from reading these reports.

[00:41:00] Preston Pysh: And maybe I’m sprinkling in social or whatever else I want to do, but, but the mission of the company is to detect fraud from publicly traded businesses, which would be a great idea, by the way.

[00:41:12] Guy Swann: Fantastic idea. I’ll invest.

[00:41:13] Preston Pysh: But somebody who’s like OpenAI, how in the world are they going to compete with a company that is specializing in this specific niche?

[00:41:22] Preston Pysh: And let’s say you’re another business and for some reason you want access to this model. And so you start feeding all your data to me, this company who’s specializing in this, this particular thing. I just don’t see how in the long run, anybody’s going to be able to compete with this, with a company that’s really going after a targeted, specific thing, like what I described.

[00:41:43] Preston Pysh: And I think you’re just going to have subscriptions to that service to maybe embed into, you know, if somebody wants to have a model that’s, that’s broader. that covers all of finance. Let’s say you have a model that’s covering all of finance and they want access into this fraud detection thing. Like they’re going to subscribe to the, to the API of this company’s very specific training set.

[00:42:05] Preston Pysh: Is that how you see this evolving? And it’s like modular AIs that are really good at specific things. There’s a, there’s a company that specializes in detecting cat photos or whatever it might be. That’s going to be their niche. Is that where this is going?

[00:42:20] Guy Swann: Yes, and this is not only what we have seen specifically, is that everybody is taking advantage of their specific data set or their specific use case, but it also makes sense from a kind of general intelligence is not an efficient thing perspective.

[00:42:35] Guy Swann: I talked about this. We talked about this a lot with Dhruv on the last episode of AI Unchained, actually, was just that the overhead of just being intelligent in like a thousand different ways when you’re primarily using it for one or two. is a lot of excess cost for no reason. And so, like, the idea that we’re going to have this giant god like general intelligence that can just do and is capable of anything is antithetical to everything we know about why evolution works.

[00:43:07] Guy Swann: You know, like, the idea isn’t to just have maximum total intelligence in all fields. The idea is actually specialization so that you have the minimum required intelligence to accomplish your goal. And when we’re facing something that we understand as poorly as these kind of black boxes of pattern recognition and this kind of like stored intelligence.

[00:43:32] Guy Swann: In software and data form, I think it’s really, really useful to just kind of go back to basic fundamentals is, all right, how does order evolve, period? Like what, what are its characteristics? And I think you, if you just kind of take some fundamental principles of reality and realize that AI doesn’t negate these things, it doesn’t change these things.

[00:43:54] Guy Swann: It’s just kind of a new layer of how we’re able to digitize how we think as opposed to just like what we think about. Like that’s, that’s the space that we’ve been in, right? Is that we can digitize and store all the things that we think about the media, the text, like all of this stuff. Now we’re digitizing how we think the relationships between those things.

[00:44:16] Guy Swann: I just don’t think a general, like a massive general god like AI that can do all of the things and understands all the things. is even slightly computationally efficient. And I think it kind of falls apart as soon as it kind of exists because an entire ecosystem of specialized models that do all of these extremely explicit things as best as they possibly, for the same reason that a market of a billion people specializing is a whole hell of a lot better than one giant government putting a boardroom of experts together to do all of it.

[00:44:44] Guy Swann: Like, I just think that’s just not sustainable. It’s not suddenly that fundamental reality isn’t suddenly different when you turn into a piece of software.

[00:44:52] Preston Pysh: I could see a general AI saying, well, if you really want to understand the accounting fraud, I can subscribe to this API for 5 to really do that.

[00:45:04] Preston Pysh: And that like I could see a general AI doing that basically pointing you to the best source to be able to do a certain type of analysis and then offering up some type of paid. You know, we’re, we’re going to get into why Bitcoin really enters itself into this space through an example like that one before we go there, though, I really want to talk about KYC because you were making some really awesome points of like, look at OpenAI.

[00:45:30] Preston Pysh: What’s his name? The guy who’s Sam Altman, Sam Altman, right?

[00:45:36] Preston Pysh: He is hellbent on this idea of KYC. Indexing everybody’s eyeballs on the planet. Why is this going to fall flat on its face from your point of view?

[00:45:51] Guy Swann: Okay. Going back to what I said at the very beginning is that like to get an ID and credit card information and stuff is literally just a handful of bucks.

[00:45:59] Guy Swann: Like per person online is well, with just like one or two good pictures of that person. And it was funny you originally need like 30 seconds one of the first ones, excuse me, 30 minutes of high quality audio. One of the first tools that I used for like mimicking somebody else’s voice. Like, I had to, in fact, one of the ones that I used for the Matrix meme, I had to have five minutes, so it’s like incrementally gotten less and less, but I had to go through a bunch of, like, Lawrence Fishburne interviews and all the scenes in the movie where there wasn’t a lot of background noise and cut out, like, Lawrence Fishburne’s conversation until I had five minutes of audio so that I could train it to sound like Lawrence Fishburne so I could make my Matrix meme.

[00:46:41] Guy Swann: It used to be 30 minutes of high quality audio. Then it was five. Now it’s like 10 seconds, like to, for really good models, like surprisingly good. 10 seconds. It’s nothing like somebody could call you up and you could be like, hello. And you’re like, Hey, can I talk to Billy Bob or whatever? It’s like, I’m sorry, you have the wrong number.

[00:47:00] Guy Swann: Thank you. And that was enough. That’s enough. So be wary of, be wary of phone calls from anybody. And also in that same vein, I think a really, really prudent thing to do as we enter a space where you can’t prove someone’s human in any digital context anymore, that have a safe word, have something to talk about.

[00:47:21] Guy Swann: Like if something serious happens, because if you get a call from a number you don’t recognize or somebody gets sim swamped that you do know. And then you get a call from them and they sound exactly like them and they’re telling you to wire money somewhere or there’s an emergency and I just need 2, 000 worth of Bitcoin sent to me or something, something like that doesn’t mean this, this person you think you’re talking to, like my family has some safe words and I think it’s prudent to start thinking about that because this attack vector is going to show up quicker than we think.

[00:47:48] Guy Swann: But anyway, In that sense, all of our tools for digitally proving who you are or just, just that you are human, you just gave an example of like a general LLM that can figure out which LLMs it might need to complete a task or which models it might need an agent that can literally go out, pay for services and assess the best way to accomplish a task.

[00:48:11] Guy Swann: That means that you can literally prompt an agent that’s connected to the internet to go find all of the things necessary to accomplish this task and the attacker cannot even know how to do it. The attacker can just ask the agent how to do it or to do it for them and give it enough funds to make a return on who they’re scamming.

[00:48:32] Guy Swann: And CAPTCHA’s done, like I thought it was already like in the bag because I had heard someone say that it was I haven’t talked about like months and months ago, so I don’t know if somebody was recreating this work, but it didn’t matter. It was as soon as I was looking into it, there was another model that somebody came out with that can do CAPTCHA’s better than anybody.

[00:48:51] Guy Swann: I mean, instantly, like, like the whole, the whole idea of like, let’s pay someone to do like, as soon as you have the data set of like paying a bunch of people to do CAPTCHA’s for you. And then you can get it to read the captures. Capture’s dead, you know, and you just make a new model and if they change it, they’re going to have a model that will be able to do it better than all humans before the humans catch on to figure out how to use the new thing.

[00:49:12] Guy Swann: Like, you know, have you ever done the listen to this instead when you can’t like read it? It’s so much worse. It’s so much worse. I don’t understand how that’s like a solution to that. Anyway, having an image of them, like having a video of them, you can live put somebody else’s face on mine. I mean, you can live make a cartoon and put yourself in a completely different environment.

[00:49:33] Guy Swann: Like it’s like this green screen stuff on steroids and change the voice live. So that even has like the cadence and the emphasis. And then if you can get 5 with 5, you can get a picture of their ID and get all their social security, like any of their identifying information. Well, then how easy is it to make a picture of them holding up their ID?

[00:49:54] Guy Swann: Or a note that says, yeah, hi, I’m Guy Swann and I’m signing up for strike or, you know, whatever it is I got to do my selfie with like, and that’s now that’s now, you know, like in three years. But because of that, because API calls are increasingly going to be GPU intensive, like very computationally intensive and more and more expensive, we’re going to have to have funding for all of this.

[00:50:21] Guy Swann: We’re going to have to pay for all of it. And we have to move money around in an environment where the fraud is getting worse. There’s just not going to be a solution, I don’t think, that makes it better. And so one of the things, in fact, this is actually one of the ones where somebody came up to me and gave me like an anecdote at BitLockBoom that really reinforced this, and they were talking about running a merchant services that integrates both fiat and Bitcoin, and he had been, he was taking notes while he was listening to the talk and this whole section about KYC, But specifically he was just like, Oh my God, I hadn’t thought about it like this, but he’s, you’re absolutely right.

[00:50:57] Guy Swann: And he’s just started listing, like, and thinking about all the things that they’re doing to deal with credit card fraud. And that his merchant services is having to do to basically put up a barrier to make sure that they’re not constantly putting through fraudulent payments that get, that then get reversed.

[00:51:13] Guy Swann: And worst of all is not only do they get the money back, like, like, not only just is the money pulled back, but there’s like a 25 or 35 fee. It’s basically like, like an overdraft fee every single time it happens. And he said literally thousands, thousands of charges a day, as soon as you just have access, like as soon as you just kind of open up the window, they just come in and merchant services and like companies like that just have to sort out how to do it.

[00:51:39] Guy Swann: And then he was just, and then he put underneath it and he says, and Bitcoin payments, he’s just like, they don’t do anything like he doesn’t think about it. There’s not infrastructure for it. There’s not like methodology for how to figure out which ones are real or not. They literally just put in Bitcoin payments and those are done.

[00:51:55] Guy Swann: So, this entire structure of things that they’re having to do to protect against this fraud, that again, this is the best it’s going to be. It’s the best it’s going to be forever going forward. The easiest. It doesn’t exist. Like it just, they don’t even have to plug it in. It’s the Bitcoin side of the equation.

[00:52:12] Guy Swann: Yeah. Like they have to manage some channels for Lightning Node. Or they’re, you know, using an LSP and they don’t even have to think about that because somebody else has offloaded that for like a tiny fee.

[00:52:22] Preston Pysh: Guy, I think something that’s really important for people to wrap their head around is when you’re pinging that AI to perform work with its processor, Xpend Energy, and it then immediately replies with the answer.

[00:52:36] Preston Pysh: You are paying, if I’m using traditional financial rails, I’m paying for that service 30 days, depending on what, how long the clearing takes place for them to like actually receive the payment in their account and it to be settled 30 days after the delivery of the quote unquote product, which is the AI response.

[00:52:58] Preston Pysh: And so this delay between I’ve just delivered it, it’d be like me giving you a loaf of bread. You have it in your hands. And you saying, all right, well, I’m going to pay you like 30, like I’m going to settle with you 30 days later. Like anybody who, who hears that as like, well, that’s insane. That doesn’t scale.

[00:53:15] Preston Pysh: If, if I’m asking for a million loaves of bread in one second, Which is what happens with a lot of these AI requests, right? Like people need to think of it in terms of how much monetary energy some of these really big companies are pinging these servers for, and they’re getting delivery of the product, which is the AI response right now, but they might go out of business in 15 days or whatever.

[00:53:43] Preston Pysh: And what they paid them with is being clawed back and. Like, all of these issues, like, you really need to have some type of immediately settling money so that if I ping the server for a 10 cent request, or a half a cent request, Or a 10 million request that we can settle up right, right then and there without it being clawed back because of bankruptcy or whatever.

[00:54:08] Guy Swann: Exactly. What you’re describing is the fact that the computational cost of the LLMs actually work like an instantaneously delivered bearer asset. They end up having that attribute. Sam Altman specifically said with OpenAI, and this is why OpenAI is on the struggle bus right now, as far as their longevity is because, is that every single time you ask it a question, it costs five to eight cent instantly, like you’re, you’re just pumping power through a computational machine. It’s like mining eight cent worth of Bitcoin, right? Is that like, you can’t do that for free. And like, it’s just a huge computational cost and trying to put together a machine myself.

[00:54:51] Guy Swann: I can tell you how much it costs to get that computation. Like it’s a, it’s a thing. But the cost is immediate. That’s never been the case with APIs. Like, if you’re selling a physical product online and you get a charge back in 20 minutes, or you find out, you know, it takes you 40 seconds, even if it takes you 30, 40 seconds to figure out that a charge is fraudulent or with a stolen credit card.

[00:55:16] Guy Swann: And you don’t even execute the full charge, but you go ahead and give them the subscription. You go ahead and give them access because you don’t want to lock out a customer. You don’t want to screw the customer experience. Wait for three hours while we figure out whether or not you’re a real person. No, you just give it, you just let him go into the website, right?

[00:55:31] Guy Swann: But it doesn’t cost anything with like a normal website, like to read an article. Like it’s just, again, it’s all loss leaders. Like APIs are free enough with all of the other things that you can just kind of hide that. You can obscure that cost away and just kick off the customer that’s trying to freeload or using a stolen credit card.

[00:55:48] Guy Swann: Or if you’re selling like a physical product and you figure it out in two hours, you just don’t ship the product. If you could ship the product. Within a second. Well, then you’d have a problem.

[00:55:58] Preston Pysh: I just feel if you, if Guy, I don’t, if I, I don’t understand why they can’t figure it out though. Like it’s so much resource, more resource intensive to say, well I just want to KYC everybody so that I can understand whether I think this person’s good for the money or not.

[00:56:15] Preston Pysh: As opposed to just taking immediately settling money that clears. And you know, like, why? Why can’t they figure that out?

[00:56:23] Guy Swann: They don’t understand the relationship and the whole framing is, like, they’re not asking about which money should they use, they’re asking about which payment method they should use, right?

[00:56:32] Guy Swann: They’re trying to figure out the fraud problem. Not recognizing that it is a credit problem, that it’s an, it’s an identity problem and identity is just kind of KYC is just kind of this band aid and it’s been the universal tool, right? It’s been the tool for this all along. That’s why Twitter is KYC ing everybody is because they’ve just had such a huge bot problem that they just sick of it and they’re trying to solve it the way that they think they can solve it.

[00:56:57] Guy Swann: But the solution, the KYC solution is already outdated, but that’s why I think this is all going to get way, way, way, way worse before it gets better, because the user experience, I mean, how crappy is the user experience of having to take a selfie and hold up your ID and wait for identity verification and plug in, go to some other website and it’s like, we’re connecting to ID me dot, dot org or go over whatever, and like go through this lengthy process and fill out another form.

[00:57:28] Guy Swann: It’s getting to the point where just signing up for a basic service that I just want to try for like 30 seconds to see if it’s any good is becoming, it’s starting to behave like setting up a bank account and like in physically in person, like it’s a nightmare.

[00:57:43] Preston Pysh: And I don’t even think that those are real people that are looking at these things to determine whether they’re real or not.

[00:57:49] Guy Swann: I think it’s all AI.

[00:57:51] Preston Pysh: Which makes you say, well, if the AI is checking it, why can’t the AI out? outsmart the checker. And it’s just this endless do loop of not approaching the solution from the base layer, but just kind of building these layers and layers of insanity on top of each other that don’t actually address the root problem.

[00:58:14] Guy Swann: And it’s just kind of this. Like we started with like kind of credit and pull based payments online, and it worked good enough. It was out of balance with the way the technology worked, kind of like selling CDs in the era of file sharing was out of balance. And then they just kind of kept trying to add a new piece to it to still make it fit to kind of contort it, shove it into this new reality and let the old system keep working.

[00:58:44] Guy Swann: That’s where we are. I think KYC is, you know, trying to sell a full album and not letting you buy per song or have a subscription or whatever it was in the 90s. And with the growth of file sharing is that it’s just out of balance with the way technology is. And the new reality will not let it exist on a long enough timescale.

[00:59:04] Guy Swann: But in the same way that copyright and everything got way worse and the crackdowns and the lawsuits and the, you couldn’t seem to get any good music anywhere online for this huge scope of time. And then once one service did pop up, it got shut down within six months. So you’re constantly having to change stuff.

[00:59:21] Guy Swann: I think that’s kind of where we are with KYC right now. Everybody’s into KYC. Everybody’s just going to get so much worse until the user experience of trying to use any, like the barrier of just setting up another subscription or trying out some sort of service, like free trials are going to start going away because if it costs you eight cent immediately and somebody can just bail out on it, like you’re talking about an API where somebody can just kind of plug into your service with a fake credit card.

[00:59:46] Guy Swann: And if you don’t catch it, They can just pound your API all night and just burn through your GPUs. I mean, God forbid you’re on AWS or Google Colab where it just like automatically expands based on the request. And then you just wake up in the morning, you have a 50, 000 bill. that you just can’t do anything about.

[01:00:02] Guy Swann: Like, and you’ve got it on a credit card of like one person that used this, that scaled this with AI. Somebody’s just running like a little simple model on their computer. And what they do is they took all the highest quality of their 800 or a thousand credit card information that they did online. And went through and created fake IDs and fake personas and fake selfies and everything for all of these.

[01:00:26] Guy Swann: So one person is able to do this at the scale of a hundred, even though it would have taken them months and months and months to do this before they can do it overnight. This new, it just, it just is not going to hold up. And I think it’s literally just the lack of knowledge about these tools and the fact that they’re not widely accessible, kind of like the future is here, but it’s not evenly distributed as it gets evenly distributed.

[01:00:46] Guy Swann: We’re going to see everybody siloed, everybody’s going to put up walls, and have limited access to their APIs, and the idea of the free internet is going to get further and further away, and we’re going to have KYC everything, and the user experience of The open web is just going to get crappier and crappier and everybody’s going to be sick of it.

[01:01:08] Guy Swann: Everybody’s going to be, the pressure has to build to the point where everything is annoying and everything increasingly sucks because of this imbalance. Until people are willing to go down to the base layer and do it and just go back to the drawing board and say what are we doing wrong that has given us this thing, this massive contorted, band aid, nailed together, duct taped thing that doesn’t actually fit in our world.

[01:01:39] Guy Swann: But at the exact same time, there’s going to be a whole bunch of Bitcoiners that have the best user experience possible, decided to drop the whole KYC crap from the get go, because they already had a social graph based on keys. So… That’s it. That’s your head start. Like, if you have a system based on public and private keys, hard problem, going back to those base realities that AI doesn’t change, hard problems are still hard problems.

[01:02:02] Guy Swann: Proof of work is still proof of work. And if you’re delivering a bearer asset where those APIs to read an article or, you know, watch a movie or download something or order something online. Could easily be negated and batched together with all those other people. When that’s different with the API for an LLM, and that cost is instantly delivered and irre recoverable, well then if you can accept instantly delivered payment for that cost, who cares?

[01:02:30] Guy Swann: I don’t care if you’re a bot or a person, right? If I have a $50,000 bill overnight from somebody’s absurd, API request. But I accepted only sats and I made fifty thousand five hundred dollars off of it. That’s a great morning that’s a great way to wake up and Nobody again like going back to the merchant services thing.

[01:02:51] Guy Swann: I don’t have to have any barriers. I don’t have to have any like Oh God, how do I sort out the noise from the fraud? How do I make sure that these thousand charges aren’t going through? No, just accept SATs. And from the user side, nobody even had to sign up. Nobody had to fill out a form. Nobody had to… You just literally have a public key to log in with.

[01:03:12] Guy Swann: You shoot some SATs. You shoot six cent worth of SATs. You get five cent worth of computation back. And it’s instant and done. I don’t even have to have a relationship with them. And I can give them exactly what they want with no barriers, with none. And then going back to the elements of open source and the idea that we can train these models to be hyper specialized and we can incrementally change them.

[01:03:37] Guy Swann: You’re also looking at an ecosystem that’s going to be learning from each other rather than constantly redoing everybody’s work. When you’re retraining these things, and we’re talking about hundreds of thousands, millions of dollars to make a decent model. It makes a whole lot more sense, like in your example of the financial fraud, it makes a whole lot more sense to take a base LLM that’s great already and just add in all your financial information.

[01:04:02] Guy Swann: Like, take the best open source model out there and because it already understands talk, you don’t have to worry about it understanding the language. And now give it this smaller adjustment on your high quality data set about financial fraud and then it will be able to do all of those other things, but be specialized in understanding and recognizing financial fraud, but still be, but still have that same general sort of, I’m just looking for what the most likely fraud cases are right now to start investigating blah, blah, blah, right?

[01:04:32] Guy Swann: Like it’s still got the general language model handled. And then now imagine that, so the analogy that I think the LLM, the language model space, is heading into that we’ve already seen in the image space, is something referred to as LoRA, or LoRA, but it’s L O R A, it’s Low Rank Adjustments. And what it is, is it’s hyper specialized training, and all of these people with just like a little bit of GPU power, in fact, like that’s one of the projects that I want to tackle when I get this machine up and running, is I want to make my own Laura, for just to do it, right?

[01:05:08] Guy Swann: And Corridor Digital has some great videos they’re a YouTube channel, and they have some great videos on like training, like they just take a bunch of pictures of themselves, and they train it to recreate images of themselves. And then they can create anime characters of themselves and put a VFX revolution.

[01:05:27] Guy Swann: The AI, the image generations, and some of the new, like, models, or the way they’re thinking about models. I’ll actually, I’ll come back to that. I’m going to put a pin in that. The way they’re thinking about models. But so you can do these micro adjustments and then there’s like websites, Hugging Face, Civit AI, like all of these things that collect these is like a big community of all these open source micro trainings and then they take the big open source models and they take like a thousand great micro trainings and they retrain the big model and now the big model is better at doing all of these very specific things.

[01:06:03] Guy Swann: And I think, again, going back to, let’s look at how, like, some of the base, like, fundamental realities, like, fundamental truths, a group is going to be a whole lot better at specializing and learning, like, like a community, than one central group, like, like one, one central institution. No government or corporation is going to outcompete the market at large.

[01:06:26] Guy Swann: A long enough timescale, it’s just not going to happen. And then at the exact same time, one of the big dis economies of scale of these billion dollar corporations is the speed that the economy changes. So, you know, when the industrial revolution happened, you have this like big wave of disruption, and now there’s all these new business models and new ways of accomplishing things.

[01:06:51] Guy Swann: And then you have 50 years of basically implementing this at scale. Like it’s just a massive orchestration in order to change all of the handmade processes and put in the assembly line processes. But then you have, we have these constant iterations of new ways of doing things and new ways of thinking about production.

[01:07:10] Guy Swann: And if you’ve got a billion dollar corporation invested in 10, 000, like, assembly line, like, finely tuned production processes, And then you have a really serious fundamental change to how that tooling works. You have, far and away, got the largest expense for retooling. You have a big problem on your hand, and this is why, like, fiat finance is such a centralizing force, because one of the things you see these big corporations doing is not spending all their time updating And, well, I mean, they do, but it’s actually a second order effect from their primary method is buying the small startups that are, is they can get financing with newly printed money so they can get it at interest rates that make no sense.

[01:07:58] Guy Swann: And so rather than having this, this disruption phase where all of these small agile startups that are really innovating and coming up with new things, replacing them, what you have is they just try to get big enough. to get spot. Yeah. And, but you think about it in a non Fiat world that doesn’t make any sense.

[01:08:18] Guy Swann: That’s not sustainable. A Google does not even have anywhere near the possible margins of a company that’s still at a hundred thousand users or 20, 000 users or something like that. They have, they have a 300 X capability to increase and grow if they’ve got a genuine new innovation. Google’s going to go 3 percent tops.

[01:08:37] Guy Swann: They’re already too big. They’re going to go 15 percent if they get all the new financing and they can buy up all the new startups. So they appear to be more profitable as an investment than they actually are. When in fact, it’s just that the fiat apparatus is far more likely for all of the new funding at the non genuine interest rate.

[01:08:58] Guy Swann: And the money printing machine is just going to go to the big first, so they’re going to win in an outsized way against inflation always, always, because big is just easy to invest in big is always if there’s 10 percent inflation, well, they’re going to go up by 13%. Why? Because everybody invests in them with the new newly created money in order to be inflation.

[01:09:17] Guy Swann: So it’s a it’s a self fulfilling prophecy. The very notion that we’re creating money and then trying to figure out where to put it. to be inflation just means it’s concentrating, which means that there’s going to be in that concentrated area, something that beats inflation way better than everything else.

[01:09:31] Guy Swann: That’s why you get these huge bubbles, like in housing or whatever it is, is because there’s a feedback loop that if you put money into new money into one specific area, well, then that’s where all the inflation is going to be. And it’s going to appear to have these outsized profits. So that’s this major recentralizing force that this era of disruption that has been pushing back against this era of disruption and kept big things big and billion dollar corporations and government institutions in their place, even though the technological force is actually trying to break them down and split them up.

[01:10:04] Guy Swann: But I think that’s essentially falling away the, the, the bigger and bigger the challenges for Fiat, both payment rails, and just how far can you push the debt Ponzi. As these things kind of get stressed to their absolute breaking point, all of that’s going to come crashing down, but at the exact same time, you have AI, which rather than being a process in itself, it’s a way to create this open ideation and iteration on the process, the idea of processes, the analogy I used in the talk was that it’s like a 3D printer for how to accomplish things.

[01:10:41] Guy Swann: 3D printing and additive manufacturing makes it so that you don’t have to retool for every individual product, right? Like, CryptoCloaks? They can just come out with a new product or a new design or a new thing and just immediately start selling it because they’ve got, like, 20 3D printers and they can just put in a different a file, and now they’re making a completely different tool, gear, case, you know, whatever.

[01:11:01] Guy Swann: It created this If you are disrupting the product, they no longer have to retool. So, the broader your capability, the more generic your capability of accomplishing these things, the better. Because then you, you’re extremely agile, and you can move very quickly and respond to the environment, to the ecosystem, rather than kind of become this permanent, I have to do it this way.

[01:11:26] Guy Swann: Well, in that same way, AI is a way to instantly retrain your processes and all of your administrators and all of your tools and all of your software without having to go through the training process. So the bigger and the more settled you are in how you go about doing things in the structure itself, the worse this is going to hurt you, because I think we’re going to start, things are going to start changing so fast and we’re going to come up with new ways of.

[01:11:53] Guy Swann: Iterating and thinking about how to do the processes at all and how to set up the idea of an organization is going to move so quick that I think disruption is just going to kind of become a permanent part of everything like we’re just going to kind of move into this layer where there’s less innovation.

[01:12:11] Guy Swann: or there’s less need for innovation on the actual individual pieces because there’s this whole new scope of just like, how do we put the pieces together? Like recreating the entire puzzle over and over and over and over again, rather than having a set puzzle that everybody’s trying to implement. So because of that, I think like, so use an example of like the last paradigms.

[01:12:31] Guy Swann: Where we see these happen over 20 years, right? Is you have these huge companies become out of nowhere in the digital space. And, you know, music subscription finally took off and iTunes or individual music selling individual songs and iTunes blew up. Now they’re the, they’re the dominant player. They disrupted the old institutions and then.

[01:12:49] Guy Swann: Subscriptions with the music killing the radio and then table TV and YouTube and all these things you get this whole new model and then you get another big giant thing and then you go through another phase of technological disruption. Well, I think these are going to start squeezing closer and closer and closer together.

[01:13:06] Guy Swann: To the point that when we think of, oh, this next company is going to be billions and billions of dollars and it’s going to control and run everything because it’s the new platform, it’s the Facebook or the whatever of this new era, I think before you even get to the point in the curve where that is clearly this is the successor and this is the new winning platform or idea, the next idea is already going to be here.

[01:13:29] Guy Swann: Like the next piece of the puzzle or the next process or platform or whatever that’s replacing them. Like it’s just going to the, whereas we kind of separate out horizontally, we’re also going to suddenly just split up vertically and in like a massive, massive way where the people in the process of disruption and the systems in the process of disruption are going to be disrupted before they’ve completed the old, the last process of disruption.

[01:13:56] Guy Swann: You know what I mean?

[01:13:57] Preston Pysh: And when we look at what’s happened more recently, we’re seeing a trend of the hockey stick curve coming up faster than it’s ever come up. So, to your point of a company replacing the one that everybody thinks is going to start to dominate. It fits with what we have seen over the last 10 to 15 years, that it’s going to come out of nowhere and it’s going to come so quickly and be so disruptive because it’s coming so quickly.

[01:14:24] Preston Pysh: Guy, I want to, I want to make sure that we have the what was the name of the article that you said from the Google engineer? I want to make sure we have this in the show notes.

[01:14:32] Guy Swann: Do you remember the only read that I have on AI Unchained, so I’m not making it AI Unchained like a show where I make Bitcoin audible, you know?

[01:14:39] Guy Swann: Yeah, yeah. But I am actually going to read some articles that I just want to talk about when either I can’t find a guest to specifically talk about this topic and I just got to get it off my chest. But right now, the only read, the inaugural read, is that piece, so it’s called, it’s called from a Google engineer, we have no moat, dot, dot, dot, and neither does OpenAI.

[01:14:59] Guy Swann: Love it. So that, that’s the title of it and I’ll, I’ll send you the link. It’s obviously only the, only on the podcast version of the show, not the YouTube version. But it’s great. You should definitely, I mean, it’s outdated instantly, you know, as soon as it was, as soon as it was published, like three days later, it’s, you know, it’s missing some major pieces of the puzzle, but I think it’s a brilliant framing and to see how quickly it went against them and how quick, I mean, you think about it, How many companies have like AI now?

[01:15:27] Guy Swann: Oh, everything. There’s a new one. There’s a new one every 30 seconds. And so many of them are running their own models because they do want to train it. They do want to give it that specific feedback for whatever their thing is or they want to do the LoRa or the LoRa on, you know, their image diffusion or whatever it is.

[01:15:45] Guy Swann: They’re dropping the OpenAI API and that’s another example of like being disrupted before you even completed your cycle. is OpenAI is already on the downstroke and this just happened late 2022, you know, it’s like November or December that this like whole thing just kind of blew up and they went to billions of visitors and now they’re dropping like they’re, they’re already on the way down because everybody’s already kind of extracted how they operate and created a thousand alternative platforms and a thousand specialized models for God knows what.

[01:16:19] Guy Swann: I mean, I can’t even, like I get excited about something that like got the, hit the leaderboards of the open source models on Hugging Face that like, cause the Falcon 7B model was like really big for like a little while and I was like, okay, I gotta figure out how to install and use this on my thing.

[01:16:35] Guy Swann: And it was like a week before I was trying to figure it out and something else replaced it. And it was like, it wasn’t even, it was like, it was like number seven or something like, I haven’t even installed it. I don’t have time to install the thing.

[01:16:45] Preston Pysh: God, where do we, where do we, where do we go to see these rankings of the newest, latest and greatest thing?

[01:16:52] Preston Pysh: That’s actually high signal because like I’m hearing, I’m hearing all these different models and I’m like. I have no idea where to look in a reliable kind of way. Like, right. Cause I don’t want to hear about some AI model from my next door neighbor or this person over here. I want somebody who’s truly an expert in this to say, this one’s worth paying attention to.

[01:17:12] Preston Pysh: This one’s worth tinkering with because it does some new revolutionary things. So do you have a source that kind of like lays out, like here, here’s the 10 AIs to really pay attention to today. And it might be very different next week.

[01:17:25] Guy Swann: The two best places to keep up with what’s going on with AI, which, which they kind of, it sucks in a lot of ways because these are hard to navigate for normies, but it’s not terrible if you’re trying to keep up with like where the real innovation is happening, GitHub, and if you’re looking for models specifically, and you want to know what the best models are, it’s huggingface. co. Huggingface.co is kind of like the GitHub of all AI models. And I’m trying to get into this space like it’s easy to talk about and people understand the interactions with like LLMs, like ChatGPT, and then image diffusion or whatever, because there’s this obvious like right in your face sort of use case or reaction that you have with it.

[01:18:09] Guy Swann: But there’s also this entire scope of these other models. like vector models and categorization models and like all of these things that are more kind of like back end. So how you’re interacting with them is not exactly like just trying to make sense of a knowledge base or making corrections and like bad grammar or they think no stir on a podcast in my transcription, it says no store, you know, S T O R E.

[01:18:35] Guy Swann: And like, how do I, how do I get a model to understand that that’s a mistake and go against the knowledge graph of like Bitcoin terms or something like that to correct the, what I’m actually thinking, what I’m actually talking about, like those sorts of things. There’s, there’s this kind of like magic of this, these whole other layers.

[01:18:53] Guy Swann: And that’s actually going back to the way models, we think about the models. More generally, like how these things work is there’s, there’s going to be like real magic innovation is going to occur in how to layer these things and how to understand that analogy is where we were in the digital space and the internet was we opened up this space where we could all collectively think together.

[01:19:18] Guy Swann: And we all started doing this massive amount of thinking that we’ve never done before and reflecting on ideas and our ideas and what we believe started clashing into each other and we started screaming each other on social media and all this stuff and we had this massive identity crisis because suddenly this cohesive narrative about what was true fell apart.

[01:19:36] Guy Swann: All of our niche ideas that never really had the air to, the space to air their grievances or to debate or push back against what the consensus was suddenly had space, suddenly had, suddenly could spread like wildfire and a million people could have access to it and the cable news didn’t have to put them with 15 seconds on the news, you know, like you actually had nuance.

[01:20:01] Guy Swann: And because of that, our ideas and all of our thinking just split up into a million different, a billion different ways and directions, and the internet just like turned that into an ocean from consensus of just a few things and just a few cultures to basically anything that you could think of, it’s there.

[01:20:19] Guy Swann: What AI is going to do is where the internet and the digital environment got us to reflect on what we thought about, and it’s changing and forcing us to realize that things we thought were true aren’t true. What AI and these LLMs and these pattern recognition models are going to do is have us think about the way that we are thinking.

[01:20:40] Guy Swann: Like, what’s the principle behind what I’m thinking about? How do I make it understand my moral foundation? What’s the premise that is present in the way I am taking this thread of thought? And when we figure out how to make a mathematical relationship between those things and a general probably probabilistic How does this word relate to this word?

[01:21:05] Guy Swann: That’s where the innovation is going to come from. It’s abstracting in an entirely new layer. And, you know, I thought about this thing like my kids, you know, you think your kids are always going to know technology and stuff better than you. And you’re like, ah, but I’m going to stay up to date on this stuff, right?

[01:21:19] Guy Swann: Like I’m going to use all the new tools and, you know, they’re going to come to dad when they need to install this or understand this. And then I just, I was just thinking about it the other day when the fact that we’re going to be thinking about how we think they’re going to grow up like my son and probably all the rest of them that aren’t here yet are going to grow up understanding or being able to reflect so deeply on how they thought about something last week and how it changed this week, which generally no one is aware of at all.

[01:21:50] Guy Swann: Like there’s just no scope of understanding or able to reflect at that layer that we’ve ever been able.

[01:21:56] Preston Pysh: It’s going to wire them to be philosophers in a way.

[01:21:59] Guy Swann: It is, it is.

[01:22:00] Guy Swann: And they’re going to understand the foundations that actually force them to think differently because the AI is going to be able to reflect on that.

[01:22:07] Guy Swann: And without any bias or caring or thinking about emotions, they’re just going to be able to tell you flat. Well, this is, that’s because this changed. like sort of thing. And when those sorts of weights and patterns are established and added to the the models, rather than just like a flat relationship between words or a flat relationship between pixels or something, I think that’s where we are right now is that we have this plateau where we’ve kind of reached the point in which the LLMs We’re going to get better because we’re just shoving more data into it.

[01:22:37] Guy Swann: And we have some incremental improvements, which will still seem like or they’re going to still seem crazy. Like I’m not saying that it’s not going to get better, but quality is going to, quality and curation is going to be a huge part of this, which I think it just so happens something like Noster, an open source protocol for social media and waiting and liking and zapping might be a really useful tool if we can get a really big network over there.

[01:23:00] Guy Swann: But anyway, as the next explosion, I think in AI, which I don’t think is very far away, is just under understanding some elements of multimodality of how to create relationships, create logical pathways. That’s, that’s probably the best way to think about it, is how do you give it logic and then put language on top of it?

[01:23:21] Guy Swann: How do you create a new layer below it? And that’s how do you think about the math that’s creating the patterns rather than how do you get better data to shove through the pattern. In that same sense, as I’ve been watching like a great one, just in the context of like VFX, there’s something called neural radiance fields.

[01:23:39] Guy Swann: And it’s a new model that rather than creating like a 3D, like, like one of the things that was, has been really big in VFX for 20 years or so, kind of since the Terminator era of like, oh, CG is getting really crazy, you know, Jurassic Park is one of the things you can do is you can do like image scan, like I can walk around something and I can take a bunch of pictures of it.

[01:23:59] Guy Swann: And then put it into a program and it will recreate like a 3D image of it with like the texture and it’s like decent, right? Like, like it’s good enough that you can then get it ready and put it in a 3D environment and then get your lighting right and all of this stuff. But it’s still really involved.

[01:24:13] Guy Swann: You still have to have a lot of VFX work and compositing and all this stuff to make it good. Well, now there’s something called, and one of the big problems actually is that light is insanely difficult in a 3D environment. Because when you’re, when you’re looking at light on something like just in your room, you think about it as like, oh, there’s a light source and then it’s bouncing off the object, but that’s not all the light.

[01:24:36] Guy Swann: The light is also bouncing off the wall to the object, and if the wall is grey behind it, well then there’s a tint of grey on this edge. It’s also bouncing around the frame, which is creating like a slightly off white inner edge. Like, there’s light coming from so everything is a light source, because you only see all of it because light is bouncing off of all of it, right?

[01:24:56] Guy Swann: That’s why 3D light always seems fake. It’s so hard to put a finger on it. I don’t know exactly why, but your brain can sense it if you have a comparison. So there’s this new thing called Neural Radiance Fields, and it’s just an entirely novel way of modeling a light map of what’s happening in the room.

[01:25:16] Guy Swann: And the model is specifically how does light react. And it doesn’t create a 3D environment in the way that you can just like stick it into Blender. Granted, they’ve already kind of figured out how to make it, trick it to do that. But you can, I can literally go through with just like a phone and walk through an area and then put it into the nerf, is what it’s referred to, the neural radiance field.

[01:25:40] Guy Swann: And it will create a damn near picture perfect, perfect lighting, the reflections will change based on the, the, the way you do it, radiance field of the entire room, so that I can just stick a virtual camera at any point in this room, and all the reflections will change, and if I move the camera, The reflections will shift based on where the camera is in the thing.

[01:26:04] Guy Swann: And it’s gorgeous. It’s gorgeous. And it takes zero compositing. And it’s just a slightly new, it’s just a method for how to build the light relationship. And then there’s another, there’s another paper. I haven’t finished reading it about a new way to create text to video. And how to create, like, somehow they figured out how the math can have, like, two different layers of, like, understanding both the object and the pixel relationships, which is why image generation applies or translates to video really crappily.

[01:26:38] Guy Swann: You know, if you’ve ever seen like the video animations they’ve done with image generation, like they’re constantly changing, or whatever, and suddenly they’re like, the person’s wearing headphones and then they’re not, and like it just every single frame is slightly different.

[01:26:49] Guy Swann: While they’re creating, somebody’s created an it seems like a really, really clever method, a way to have some permanence. Within the pixel relationship to basically correct that, but that’s where I think like the major step function improvements are going to be. And when we start understanding this, when it comes to like moral premises and logical premise and like the way that we think about things and we can give this to the LLM or we can have a logic model that ties to the language model.

[01:27:19] Guy Swann: We’re going to have LLMs that explain to us what we believe is a logical fallacy and where ideas contradict each other, and we’re going to be able to test these things and but these ideas up against each other at a layer that we’ve never been able to do before, and that is going to really, really mess with people, I think, because Most people categorize things separately in their minds and they’ll believe one thing, they’ll believe one set of premises about their religion and one set of premises about their government and one set of premises about like, just like everyday life, and they won’t even recognize that they, none of them fit together, that they don’t have a unified theory of the world They, in government, people are magical fairies that can just like do all the things in normal everyday life.

[01:28:04] Guy Swann: You should be skeptical of people and you shouldn’t give them any power or control over you or share any information. And in religion, it’s all just fairy dust and there’s a man in the clouds, you know, like, like the, we don’t hold the same premises. But what we do is we just kind of try desperately to separate these things in our minds, but the LLM isn’t going to do that when we figure out how to weight these things biological premise, it will be able to square all of these ideas together and just basically say it’s like, well, we just believe a contradiction over here.

[01:28:31] Guy Swann: That’s not right. And as we start making sense of Again, the models and relationships in the, in these systems of how to think about thinking, the amount of change, the amount of like, like you think, like the last like 15 years, 20 years of social media, like has really messed with people and messed with political institutions and who holds the power and who controls the opinions.

[01:28:57] Guy Swann: Oh my God. Yeah. It’s going to get wild. It’s going to get crazy. It’s going to get crazy.

[01:29:03] Preston Pysh: So, Guy, If people want to learn more about, I, I, this stuff is beyond fascinating. I know you threw out the HuggingFace. Was it HuggingFace.io is, is a good resource?

[01:29:16] Guy Swann: Dot co.

[01:29:16] Preston Pysh: Okay. Dot co. Give some people some more resources.

[01:29:21] Preston Pysh: Definitely give them your your new AI podcast that you’re, that you’re doing as well. And any other sources that you want to highlight if they’re, if they’re really wanting to dig into this some more.

[01:29:33] Guy Swann: For sure, for sure. Brian Roemmele, R O M O E L L E has a a project, I’ve been trying to, I’ve been bugging him to try to get him on the show on AI Unchained. So definitely check out AI Unchained. It’s where I’m attempting to document the majority of what I’m finding and like exploring and going through as I try to build my Twitter list of signal, which is very difficult. It’s hard in AI. I’ve run into a lot of roadblocks and trying to find the cream of the crop, so to speak.

[01:30:04] Guy Swann: So definitely follow AI Unchained and I will try to make as much available there as possible. The Multiplex, I think is the name of it. I have the link. But Brian Roemmele’s blog, he keeps up with a good number of things about, like, how to think about these things, and he’s building a really fascinating project, or his goal is a really, really fascinating project about open source, like, personal AI.

[01:30:26] Guy Swann: And there’s a, there’s another podcast called Latent Space, if you’re trying to get into the nit and gritty. If you’re not a developer, a lot of it’s going to go over your head. It does for me, but I just kind of keep listening because again, 30 percent of it is very, very applicable and very, very useful because they’re thinking about this thing about how to prompt things and you know, like how to really find the, get the best juice out of the squeeze.

[01:30:50] Guy Swann: And they are also a good way to connect to other AI podcasts. In fact, I might actually have them on the show and try to get them to dump some stuff down. I should reach out to them. Then there’s a stable diffusion stability AI. I think it’s stability.ai is the website. But they are a totally open source.

[01:31:08] Guy Swann: They’re the ones that are the reason for the explosion in the open source image diffusion style. Oh, okay. Stability AI, and they also just created, they just announced a new model called Stability Code, where it’s a model about like teaching you to code if you don’t know how to code, or basically filling in the blank.

[01:31:25] Guy Swann: And this is a major, major, major use for LLMs and stuff that like something I have used constantly. So Stability AI is a really, really good one to follow because they’re, they have been on top. They’re a wonderful resource for open source development and progress in, in this space. And other than that, follow me on YouTube and the podcast, AI Audible, because I’m, I’m trying to keep up with and talk to a lot of people in the space.

[01:31:51] Guy Swann: And so hopefully, hopefully I have a lot of, a lot of really fun updates in the not too distant future.

[01:31:57] Preston Pysh: Dude, I thoroughly enjoyed this. I learned a ton. This is getting fascinating and boy, I just can’t even imagine where this is going. And in the coming five years, like I just can’t even imagine where this is going to be.

[01:32:11] Preston Pysh: And just really appreciate your brilliant insights, Guy. This was really a lot of fun.

[01:32:16] Guy Swann: Oh yeah, man. I appreciate it. Always love hanging out, man. It’s been too long.

[01:32:20] Preston Pysh: It’s been too long.

[01:32:22] Preston Pysh: If you guys enjoyed this conversation, be sure to follow the show on whatever podcast application you use. Just search for, We Study Billionaires. The Bitcoin specific shows come out every Wednesday, and I’d love to have you as a regular listener. If you enjoyed the show or you learned something new or you found it valuable, if you can leave a review, we would really appreciate that. And it’s something that helps others find the interview in the search algorithm.

[01:32:46] Preston Pysh: So anything you can do to help out with a review, we would just greatly appreciate. And with that, thanks for listening and I’ll catch you again next week.

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