[00:03:00] Jeff Booth: One, two generations from now, my kids could actually ask me questions. What would Jeff, or sorry, my grandkids, great grandkids could ask questions. What would Jeff have thought? But in the meantime, if you think about something gaining more and more capability over time to be able to do a whole bunch of things that I just naturally do, that seems to have pretty enormous implications.

[00:03:25] Jeff Booth: But then if you extend to that to say, what if everybody did it? And everybody will do it over time. What does that what does that world look like? So anyways, you and I have had lots of conversations as this has progressed about what this looks like. And and so we thought we would do a podcast on it.

[00:03:44] Preston Pysh: In the meantime. So Jeff, you’ve been working on this for a few months, I want to say.

[00:03:49] Jeff Booth: Yeah, but the idea probably two and a half months ago and then really kicked off in earnest.

[00:03:59] Preston Pysh: For people that are hearing this, I think the initial reaction, I know whenever I’ve told various family members about like this idea of being able to do this, the first thing that people, you know, come back with this, that sounds, one of the funnier responses I heard was that sounds really egotistical that you would want to.

[00:04:18] Preston Pysh: And I was like, well, so like, here’s kind of the rationale of like why somebody would want to do this. And it’s not just for generations or family members to interact with it. If some say something would happen to you, I think it’s much more, well, maybe not for you or for somebody else, but I’m just, I guess, speaking on my own behalf, where I would find this extremely valuable is.

[00:04:39] Preston Pysh: If you pull this thread far enough, now all of a sudden you could have this agent sit in a Zoom call on your behalf. It could look like you. It could respond to questions like you. It could automatically take notes. It could synthesize the meeting, the Zoom meeting. And then email you the, the main points, if there was any key decisions that were, that were needed from you or that were decided during the meeting, it could synthesize all of that into an email response and then you could pull up like, Hey, I want to know more about this decision that you’re talking about.

[00:05:15] Preston Pysh: Can you pull up the video of where that took place? And then I could watch that two minutes of the zoom call. and be high definition, understand exactly what was discussed and what was said and how my AI agent responded. Like all of these things I think are so much closer to where this is all unfolding than people think.

[00:05:38] Preston Pysh: I guess at the essence, you’re able to literally copy paste yourself and expand it through space and time, which makes you more efficient and more productive. So like, what are some like-

[00:05:48] Jeff Booth: Well, you know, I, because in training my own AI, I had my AI assistant following me around. Yeah, so it’s on all my Zoom calls, as long as I ask the counterparty or whichever person I’m doing the Zoom call with, if that’s okay.

[00:06:09] Jeff Booth: But even before it’s in my essence, it’s already recording everything, giving me a statement, what decisions were made, giving me a feedback, the video, what, when people said what. So that, that exists right now at scale, and it’s incredible. It’s a time saver. But that’s just the start where this, where this goes and what that means and what we’re going to have.

[00:06:32] Jeff Booth: We will literally have agents doing business on behalf of us with other agents, a Preston agent and a Jeff agent making a decision that then we, then, then we can make it if we can agree or not agree and quickly come to a different conclusion or the same conclusion, but the things that things are going to get weird, things are going to move so fast or they’re getting weird because they’re moving so fast.

[00:06:57] Preston Pysh: Have you received like your agent that, that, and I have seen this in some of the zoom calls that I’ve been with you for various meetings. Have you got any type of benefit out of that, or has it just been ingesting all that data and then supplying it into the model? Like, what can you say about it?

[00:07:14] Jeff Booth: So right now, right now that agent makes me way more productive.

[00:07:19] Jeff Booth: It gives me every meeting, it gives me a summarized high level notes, I can go into those notes at whatever level, the entire transcript is there, plus the video, and it picks out key decision points. And it tells action steps. So it’s like a personal assistant and steroids. And then it gives me a weekly summary and I can go into it, whatever level of any of these calls or any of these zoom meetings or anything that I’ve done that I want to to look at that.

[00:07:48] Jeff Booth: So the added benefit is I’m using it to train my own agent.

[00:07:53] Preston Pysh: I want to tell you about my experience and the main reason why I want to talk about the one that I’ve built is so that we can kind of compare and contrast notes because Jeff is building one that is way more and like you’re dealing with real engineers, you’re dealing with people that are using various AI models in like synthesizing and you are much mine is very turnkey because I’m just using open AI and I’m using this new thing that they literally rolled out last week to train mine where you have been literally working with a team of engineers in order to build yours out.

[00:08:25] Preston Pysh: So there’s much more granular approach and much more engineering heavy approach that you’re using than this really basic thing that I coded up. When I was so OpenAI, for people that aren’t aware, OpenAI rolled out what appears to be almost like an app store for these general purpose trans what’s the T, trans, ChatGPTs?

[00:08:48] Preston Pysh: Yeah, the ChatGPTs, but the transformer, the T is a transformer. I just, it was very intuitive the way that you interacted with it. So if I want to create a GPT, that is a Preston GPT, and that’s literally what I named it. It has a keyboard chat interface to begin coding this thing up. The way I started, I was just like, I want to create an AI bot that basically replicates my persona and knows what I know and can respond the way that I would respond.

[00:09:17] Preston Pysh: And I said, what documents, I literally just asked it like, what documents should I provide that would help you and help me build this thing? And it responded back, well, if you have any chat logs, if you have any books, if you have anything that were highly influenced Preston, you can provide this. And so I was like, okay, I wrote a book.

[00:09:38] Preston Pysh: It’s about my time at West Point. Would that be helpful? And I said, that would be very helpful because it would help me understand how Preston writes. It would help me understand. So I uploaded the book. Next I said would my chat logs from being on Twitter help understand like how I communicate?

[00:09:55] Preston Pysh: Definitely, please upload those. So I have all of my Twitter logs that I uploaded into, into the model. Long story short, I started feeding this thing over, I mean, I literally started doing this on Saturday and here we are on Tuesday, like three days. And you said that you interacted with it, Jeff, and like the model is crazy how accurate some of the stuff is that, that it’s responding back with.

[00:10:19] Jeff Booth: Yeah, it’s incredible already after three days of feeding it data. Yeah. What does it look like after two years of feeding it data? And how much more helpful is that in your life? It’s, it’s mind boggling, but it’s, but yeah, when that came out last week, And you could go in to ChatGPT plus with an API key and start playing with that and how fast you think about all of that innovation that moves into it and feeds more data into it to make, make it better.

[00:10:50] Jeff Booth: It’s, it’s incredible the rate of growth of this.

[00:10:54] Preston Pysh: One of the things that I was just kind of blown away with is I took like five podcast interviews and then I did more, but the first five transcripts of these very hour long podcast episodes, it was like five hours worth of transcripts of podcasts that I’ve done, I uploaded them into the, into the model, and then I asked the assistant that helps you build these models which is just another AI bot.

[00:11:20] Preston Pysh: I said, are you able to determine what I said versus what the guest was saying? And like, how are you discerning like which speech you’re incorporating into the model? And it came back with this response of like, I can definitely see when you’re talking versus your guest and I’ve only incorporated your speaking style, your writing style based off of your words and not your guest’s words.

[00:11:45] Preston Pysh: It then talked about how it only used, like, as far as, like, weighting goes, that if your guest is talking and providing an opinion, that it’s treated at some kind of weighting, like a smaller weighting, than if you’re saying it yourself. what your opinion is, but what the other person is saying is influencing the model.

[00:12:04] Preston Pysh: It’s just not as, as strong of a waiting as what you, this was literally the response back that I got from the agent after uploading my podcast episodes.

[00:12:13] Jeff Booth: It is mind blowing, right? Because how do you, how do we, how do we learn? We interact, right? We come away, wow, that was insightful. That was something different.

[00:12:23] Jeff Booth: And then you might’ve thought one thing before and that’s soft interaction. It changes your mind over time. And if you have a lot of those interactions to get more and more data of those interactions, then all of a sudden you change. It’s crazy that this is doing this right now.

[00:12:43] Preston Pysh: One of the things that I wanted to make sure that the model incorporated, and sorry to talk about mine.

[00:12:48] Jeff Booth: Because I think what we’re doing right now is remember that this came out for general use. last week. You’re already on it. I’m on it. And the space is developing so fast. So I don’t think most, most of your listeners won’t even try it at or think it’s hard, but what you just described is how easy it is.

[00:13:09] Jeff Booth: And that’s the model, how to help you create the model.

[00:13:13] Preston Pysh: The one thing that I, you know, in Bitcoin, I’m really passionate about proof of work versus proof of stake. And so there was some interviews that I had done with, with Saylor and some others. You were one of them. And so I took some of the interviews that I was involved in with respect to Proof of Stake, Proof of Work.

[00:13:31] Preston Pysh: So I pump it into the model or I, I’m there working with the assistant to build the model. And I said, one of the things that I think is really important is this differentiation between Proof of Work and Proof of Stake. Here is a document and it was all the transcripts from, from those discussions. That I want the model to really have a deep understanding on and I supplied the, the transcripts.

[00:13:54] Preston Pysh: And I even told the assistant, I said, even though I’m not talking in these interviews, the people that I completely agree with their opinions on this particular topic, proof of work versus proof of stake. So ingests it and it replies back and it’s like, okay, I’ll make sure that these are part of the way that Preston responds in any type of questions that come across with respect to proof of stake, proof of work.

[00:14:18] Preston Pysh: And sure enough. So I started like playing with the model just to see how accurate it was. And I’m just like, This thing responds better than I can respond. These topics, because it’s, it’s ingesting all that data and all like the context of all those conversations, but it can actually remember the conversations unlike I can.

[00:14:36] Jeff Booth: So the wild thing you’re talking about was really funny is when you shared your Preston bot with me, the first question I asked was the question. No way. Yeah, the first question I asked was, it was that question because I wanted to see how you your Preston bot would explain that and it was wild. The only, the only, the only problem with it is it was eventually so detailed, it timed out.

[00:15:02] Jeff Booth: It stopped because probably you don’t have enough money in your franchise to review it so many times because it was so thorough. It was, but it was amazing. That was the first question. Of course, that was the first question I asked. That’s why I was laughing at you.

[00:15:17] Preston Pysh: Wow. So your model, you’re able to interact with it in just like right now, I still have mine in a private mode that if I share the link, like I shared the link with Jeff, he was able to interact with it, but I can make it public.

[00:15:29] Preston Pysh: How about yours? Like, so how are you, how are you thinking through, is this something that you’re even going to make publicly available? Like what’s your thoughts around making it publicly available?

[00:15:39] Jeff Booth: Again, part of it was curiosity when I wanted to understand this and the space that’s moving and be on the front edge of that.

[00:15:47] Jeff Booth: And then I wanted to engineer it in such a way, which what I believe is going to happen is, and I, and this is a longer tied into the same talk as Bitcoin, all these prices fall to zero eventually, right, including open AI or the gap between the open source models. And the private models closes rapidly.

[00:16:09] Jeff Booth: And so there is no moat with AI. There’s no moat with AI without regulatory intervention. Why do you think the big AI companies are trying to drive regulatory intervention because it creates a moat for their products? That’s why. So we should discuss that topic a little deeper in on this podcast, but I’ll keep going with the with your comment first or why.

[00:16:33] Jeff Booth: So I built it in such a way that the vector database that I’m creating and all of my own data to be able to sit on top of the other LLMs is abstracted so that I could tie it into multiple different LLMs over time and be able to see the difference in my responses. So I’m building it in a way that’s, that is essentially infinitely scalable as the underlying models change.

[00:17:01] Jeff Booth: And then with kind of easy to use tools, and so here’s what, here’s the question, rates the question, I can rate the question in a simple interface. Or rate my response to the question in a simple interface. And so I can constantly do that and kind of constantly upgrade this this model. It’s mind blowing how accurate it already is that’s on, I did it in a staged way where I would upload, I think I have 20 videos and 20 books or things I’ve written, content pieces I’ve written into it now, I first tested it and refined all of the answers from, from that.

[00:17:40] Jeff Booth: and this week I’ll be extending it to thousands of pieces of content and books and such. So my Twitter files are in it as well. Then all of I probably working with entrepreneurs. probably have at least five deep conversations per week of training, mentoring, and probably more than that of different things that I’m helping them with.

[00:18:02] Jeff Booth: And what you realize is a lot of the things through pattern recognition that you think are just natural because you’ve seen them so many times. are not natural for everybody because they haven’t, they haven’t been knocked down by those things over and over and over again. So some of this very training that I do and the help that I, that I give is literally through tens of thousands of hours of pattern recognition, making some, in some cases, making the same mistake myself, some cases seeing the same mistake over and over again.

[00:18:33] Jeff Booth: And as you’re training your model, it just gets better at doing this.

[00:18:37] Preston Pysh: One of the things that I noticed with this turnkey, zero cost to train solution that I’m doing, which is different than yours, where you’re paying for engineers and you’re working on it, is you were talking about the architecture and the data anchors to like that architecture, right?

[00:18:55] Preston Pysh: So if I was going to describe some people, like they might hear me say that and they’re like, what the hell is he talking about? So when I was young, when I was in my twenties, I go to West Point, then I’m in the military, then I’m doing finance things. So like each one of these like major events in my life, there is an environmental like document or books.

[00:19:15] Preston Pysh: If you think of like those events in your life as books that anchor to these events in my life. that can be used to train the model. The architecture of this, basically the turnkey architecture for me to basically create, here’s my timeline, here are the things that influenced me at those key moments in my life.

[00:19:35] Preston Pysh: This was a really important one, of a magnitude out of 10, this is a 10. This over here was important, but it was like a 5. Like that waiting and that timeline in the open AI does not exist. So it’s me just trying to like talk and chat with this assistant, like, Hey, this was an important document, but I can’t like wait it like this is a 10 out of 10 and this is a five.

[00:19:58] Preston Pysh: But with your model, what it sounds like, and correct me if I’m wrong, it seems like you have done almost like a wire diagram of your life, the key documents and the key data that corresponds with those events. And then you can run it on as, as AI models get better, then you can rerun all of that architecture and that data with the new LLM models that come out.

[00:20:20] Preston Pysh: Is that what you think?

[00:20:21] Jeff Booth: I think a little different to the, because, because doing that, I guess if you did it that way, you wouldn’t even remember. The things that actually changed your mind that were insightful, some of them you would through a story, through an emotional story or something like that, that stood out for you because of that time.

[00:20:38] Jeff Booth: But through a series of, through asking the model, the various questions and then rating those questions and saying what your response would be and giving it tons of data, you get to the same spot. So that vector database becomes extraordinarily large. And then kind of making it efficient to be able to tie in the LLMM is another piece, but it effectively, in time, it’ll be almost you.

[00:21:08] Preston Pysh: So Jeff, I think the, if I was listening to this conversation, the number one thing in my head would be the security of all this. I know everything that I provided the model that, you know, if I make it public or whatever, I’m perfectly comfortable with people knowing and having access to. And it’s very, like, like I said, three days of training is like nothing in the grand scheme of, of where I think a lot of this is going.

[00:21:30] Preston Pysh: So how do you think about that particular thing moving forward? I think people that are hearing us training these models with like personal information and data is probably like, those guys are idiots. Those guys are crazy. So like, what do you say to that person? How do you think about the, just the security of these?

[00:21:51] Preston Pysh: How does that factor into, from a business standpoint, from a business model standpoint, when you think about like how Apple handles your private security, like these AI models, it’s going to be of prime importance at a certain point that people figure out that this is this could turn into a very scary world if that’s not safeguarded against.

[00:22:12] Jeff Booth: Yeah. So, so open AI, the reason they’re coming up with the app store type of model where you can get paid for some of this stuff is the same reason Apple did the same reason Google did is so they can get all of the incentives to people, get people to move to money, to get more data, become bigger models, and people are locked into their ecosystem.

[00:22:35] Jeff Booth: That’s why they’re, that’s why they’re doing that. That’s actually part of the reason that I’ve created this in a different way. I’ll use this to be able to test it, but as the open source models emerge and everything else, I own my vector database. I own that in a different format. And I actually can put that on my own server and I can air gap it from the internet, and I can put that into my own model on my own, own server in an open source or in a, an open model where I don’t have to send that everywhere.

[00:23:07] Jeff Booth: But the point is, as this proliferates, even if you’re not on it, It’ll be able to infer who you are because you’re not on it. You’ll have a stamp. The people that are, look a certain way. Will be categorized by the way they look like these things are so powerful that you’re not going to be able to hide from AI, no matter what you do, this is going to be a part and that’s why Bitcoin is so critical in where we’re going.

[00:23:36] Jeff Booth: This is the future. This is where the future is going, and there is not going to be no hiding from it, no matter where you are in the world.

[00:23:44] Preston Pysh: Explain what you mean by that, as far as Bitcoin.

[00:23:47] Jeff Booth: So ultimately, there are real simple premise. This is but because money is distorted today, and who wants to win out of that distorted money?

[00:23:59] Jeff Booth: So let’s use the top tech companies. Why do you think you’re in that people are investing in the top of the magnificent seven because they’re stealing money at the fact and they’re not stealing money They’re incented by a system that is more and more efficient and they can remove labor faster than anybody else than anyone else And you use their things because they can remove labor faster because they’re cheaper, more effective for you.

[00:24:24] Jeff Booth: So you race to them, and then you store your wealth in those same companies to make it happen faster. Well, jobs are being, jobs eventually over, over the course of time will go down. And that whole thing keeps people on a, on a spiral, which they in turn reinforce. That makes costs higher and higher and higher measured in fiat.

[00:24:47] Jeff Booth: And it’s stealing their, it’s, it’s literally stealing the productivity that should flow to them in the form of lower prices and transferring up into control monopolies and governments who move into essentially business together to provide control over a population. That’s what’s happening. And it’s happening at an alarming rate because the natural state of a free market is deflation.

[00:25:12] Jeff Booth: And if you have technology moving as fast as it’s moving, the natural state of a free market is, is massively deflationary. In other words, we should probably be, if, if all things were equal today, we didn’t live in this kind of manipulated money system and the debt was it was repealable. If all things were equal today, I’ll bet you, you would have about 5% annually, deflation rate in economy, meaning people, the entire world would be getting richer at 5 percent per year as they worked less and got more.

[00:25:48] Jeff Booth: And that would, that would go faster as AI moved faster. As AI moved into robotics, that would, rate of deflation, in other words, everyone would have to work less and get more. are folding down. For every year, forevermore. And so we have that system, that is essentially the Bitcoin system. Where prices, because of the 21 million cap, right, you can’t manipulate money.

[00:26:12] Jeff Booth: It means in the Bitcoin system, all prices are falling. Exactly like I said, it’s measuring the free market, it’s measuring prices falling to the marginal cost of production, it’s measuring exponential productivity, and as long as it stays decentralized and secure, which we both think is inevitable, then no matter what anybody does, it doesn’t matter what people yell at it, governments try to stop it, as long as it stays decentralized and secure, it is measuring the productivity flowing to society in the form of lower prices.

[00:26:44] Jeff Booth: Well, the existing system must steal that productivity to pretend to pay back that it can’t pay back and the outcome of, of stealing that productivity, essentially creating more monetary units, transfers all control into big state. big tech. And it does so because the incentives are completely backwards to complete it.

[00:27:07] Jeff Booth: It’s not a free market at all. There is no free market in the world we’re moving in. So the, so the control structures, the only way to control people, or the only way to get away from that financial repression, stealing their money at an ever greater rate is through control and coercion, which you’re seeing play out around the world.

[00:27:29] Jeff Booth: So that’s the, they’re two completely different systems. So people in Bitcoin are moving the world into a free market where there’s abundance, where where, where nobody can manipulate your money. And that, that abundance flows broadly to society. If you’re measuring your life or reinforcing the other system, you’re doing the exact opposite.

[00:27:51] Jeff Booth: And I will debate anybody in the other system that wants to have a first principles debate. on how that happens because I can’t believe people can’t see it. They’re totally asleep at the switch. They’re using the same thing in the technology that is stealing out from the existing system that is stealing their productivity and transferring it to others.

[00:28:14] Jeff Booth: And they think they’re winning. They’re picking out their, their, their literary and so you, you have AI will be used to control you in the existing system, or AI will be used to free you in the Bitcoin system. It’s that simple.

[00:28:28] Preston Pysh: So that’s a powerful statement. It seems like it’s just so generalized, but obviously I agree with you, Jeff.

[00:28:35] Preston Pysh: So when when you say that. Bitcoin will be, will leverage AI to free you. Explain how, explain why, why you say it in that way.

[00:28:44] Jeff Booth: And, and, and what, what a lot of people in Bitcoin are doing, which will happen too, are talking about, okay, I’m going to use AI to use. AI to be able to, I’m going to pay it with Bitcoin, right?

[00:28:57] Jeff Booth: To be able to create these models so I can pay pennies on these models and to have an interface. And a lot of that’s happening in Noster. A lot of that’s happening and that’s exploding, which is really exciting. I’m talking about something at a way higher level. It’s way more important that people aren’t measuring.

[00:29:12] Jeff Booth: If AI, we wouldn’t use AI, nobody would use it unless it was a productivity enhancer. Is that fair? Absolutely. If it’s a productivity enhancer at the scale that we’re talking about, then that means prices fall because that productivity comes from somewhere, right? Yeah. We’ll hire less people. People are making, it comes from, that productivity is there as a result of you not needing to do it anymore, your job going away.

[00:29:45] Jeff Booth: So that productivity in a free market flows to society through the form, through lower prices in a free market. And from there, where that goes next, we’re talking about right now in the digital world, but that’s going to merge in the next three to five years, even faster, maybe with robotics that we’re going to do.

[00:30:04] Jeff Booth: They’re going to be the Preston bot, Jeff bot. They can do everything we can do, but in in, in robotics, they can do anything physical that we could do and never sleep, never recharge the batteries, but continually, and it’ll swarm out into society and those will remove all of the physical labor things in, in time.

[00:30:27] Jeff Booth: Why? Because if a business doesn’t use them and hires people instead, No one will use the business because it’ll be too costly. So, the same things we’re using, the same things we use every day to try to save money in our lives should be flowing to us in the form of lower prices. And those lower prices ensure we don’t have to work.

[00:30:49] Jeff Booth: So what’s happening today is as you’re doing, as you’re driving the system and making people work harder and harder and harder, what they’re really scared of in AI. is what am I going to do when I lose my job or what are my kids going to do? What is their role? So they’re petrified. So what they do, so then you have some tech company, tech monopoly, who makes their money from this theft say, don’t worry, we’ll save you through regulation.

[00:31:16] Jeff Booth: And then a whole bunch of people are fearful of that and they regulate these industries. There is no monopoly in a free market. So go deep, go deeper on that, especially in a global market. Yeah. Look at, look at the food systems today. Look at the health systems today. Look at all of the systems today.

[00:31:34] Jeff Booth: They’re getting more and more centralized. And they’re more and more centralized through regulation to protect the monopolies. Regulation favors the monopolies. It’s not for, it’s used to convince a whole bunch of people that you need to regulate this through fear. But ask yourself this, if prices fell from AI like they should, and robotics like they should, Where would the fear be from AI?

[00:31:59] Jeff Booth: How would it control anything? What would it do? Right? It has nothing to go after because the point is the prices fall and the technology serves us rather than serving somebody who controls us through it.

[00:32:12] Preston Pysh: But for when I’m listening to that, Jeff, if I’m one of these people that have lost my job because my expertise was X, Y, and Z, how do I even get access to Bitcoin if I can’t be employed?

[00:32:24] Preston Pysh: And like, there’s. 10 or 30 percent of the rest of the population that’s in the same boat as me.

[00:32:31] Jeff Booth: Okay, so now you can see why this is going to be such a long battle as people understand it. Now, point one, Bitcoin doesn’t care. It’s not trying to do this to anybody. It’s just an honest ledger that’s pricing the real market.

[00:32:46] Jeff Booth: That’s all it is. It’s pricing what we’re describing flowing to you. Now, whether you have any Bitcoin and all prices fall to zero or near zero and you don’t have any Bitcoin, that still helps you. because you’re able to forward access for, because all prices fell versus somebody telling you they’re going to control you by paying paying you money, you going around the monopoly board and constantly needing more handouts so that they can control you.

[00:33:16] Jeff Booth: So either way, if you lost your job, it’s a simple thought experiment is this. If you lost your job and all prices are going up forever, and there’s less and less work. What would that impose on you versus you lost your job and prices were going down forever. And every year it was easier. What would that impose on you?

[00:33:36] Jeff Booth: It’s so simple, but, and we’re not explaining rocket science here. We’re just explaining what an honest ledger would do to the free market to work versus a dishonest ledger. What it would do is just steal your time. And so that point is so critical because, because even in the question people are, That’s what drives the fear is people are thinking in a light switch moment.

[00:33:59] Jeff Booth: One day is going to be like this the next day, everything’s gone. And it won’t look like that at all. This is going to be a long transition for anybody that’s living in Bitcoin already. They’re already living in the future. They’re seeing more hope. They’re seeing more abundance. Every price in your world came down by 42 percent in the last month.

[00:34:18] Jeff Booth: So, it’s measuring everything coming down over that time. And as this system decays, or the existing system that we live in, or most people live in today, decays, more people are moving over to the open monetary network that is Bitcoin. And they’re just moving their time. And as they move their time, it just transitions.

[00:34:38] Jeff Booth: Now, carry that forward, and you know this from ego death and what we’re investing in and, and, and such, the model is so different on the entrepreneurs that are building into this new model, you’re providing value on top of Bitcoin and that value you can make more because you’re providing value, but over time, the same, very same thing that you created at first will probably drop to zero too.

[00:35:04] Jeff Booth: And you’ll have to create more value for society if you want to, if you want to increase your Bitcoin holdings, otherwise the business will fail because those things, and here’s a really good example. Look at wallets today. Hard to make money on wallets. And so all of these things, there’s a whole bunch of things that already exist in Bitcoin, open source, and moving, that are already giving value, if you think about Lightning transactions, and what’s happening, they’re already giving value, and as the competition moves there, those entrepreneurs are going and creating more value, and bringing on more people, and the output of that work is prices drop.

[00:35:44] Preston Pysh: Jeff, I want to, I want to talk again about this idea that AI isn’t necessarily going to be a winner takes all by the, the big open AIs or the apples or the Googles of the world. We had Guy Swan on the show. He was, he was suggesting that that’s not going to be the case and that there’s this huge market for localized models and intelligence.

[00:36:09] Preston Pysh: And I guess if I was going to push back, I’m looking at open AI, right? And how I just spun up this, this model and how they’re going to have an app store an app store of GPTs that it’s going to be very obvious, which models are very value accretive to people using them. It’s a week old. So obviously we’re not seeing that right now, but give it a year, give it two years, and then all of a sudden, like.

[00:36:34] Preston Pysh: There’s going to be a huge network effect and a huge interest for people to just go to the open AI app store and say, Oh, what are the top 10 AIs that people are using today? Oh, wow. I understand why everybody’s using this one that’s number one on the list. I should probably be using that as well. So isn’t there some kind of network effect that’s going to happen for basically the new app store, these AI app stores that because everybody’s there, because everybody’s using this model, that it’s value accretive because of the network effect that that’s associated with it versus somebody who’s creating their own localized model and doesn’t have this app store access.

[00:37:15] Jeff Booth: So this is going to sound different to people that are used to the way that the existing model has worked for the last 20 years and even for 30 years. And I know it very well on how these network effects drive into, into this and they create the person who sits on top of it, who controls over 70 percent of all value of the technology companies are created by a network effect, giving more value.

[00:37:37] Jeff Booth: But that more value is extracting wealth from society because of the printed money. If you don’t have printed money, if you can’t make up more money, the network effect that you’re talking about constantly moves out further and further. In other words, it’s a network effect for us. And every node makes it, because what would happen in, in every market is if the number one thing was providing that much more value and getting paid that much more, a billion competitors would try to compete and those competitors would constantly drive the price down and they would attack industries with the highest margins.

[00:38:15] Jeff Booth: Try to attack Google today. I could track Microsoft today, try to attack Facebook today, try to attack these today through the existing system of manipulated money. And there’s no young entrepreneur that can go do that in Bitcoin. The world is competing against that. And so, so these things constantly derived, Auster, or Bitcoin, the network effect is at the protocol level rather than the company level.

[00:38:45] Jeff Booth: What, what the companies that are, that are winning out of the existing monetary system, that, that, why they’re winning is because they’re at the company level on top of a protocol that’s on top of a broken money protocol that is transferring that wealth to them. But having them, having the network effect at the protocol level as money too, and information, ensures that that network effect moves in favor of us.

[00:39:14] Jeff Booth: But, so that’s part of it. That’s a huge part of it in, in Bitcoin. So will all of these companies try to create the next app store? Absolutely. But you know what FedE is doing, they’re going to create essentially an open app store on top of FedE, and they’re not going to have to charge anything for it. So as people move into this new world and they’re able to do more and drive more value, and that, that will create a whole bunch of things we can’t see today.

[00:39:41] Jeff Booth: It will explode in favor of us, right? Prices will continue to drop. It’ll get more and more innovative. More people will move there. Lots of new lots of new ideas, but there will be no control structure to drive all of the 30 percent take rate, let’s say, like what Apple does. to run the app. So, and that’s what OpenAI would try to do too.

[00:40:02] Jeff Booth: As well, it’s moving so fast. The industry’s moving so fast that I suspect what’ll happen is right now OpenAI has a lead in these models, but that lead will narrow. Like, it won’t get better at a rate that’s, it’ll feel better. But you know what it does right now. That rate of how much better can I get than all human intelligence, right?

[00:40:28] Jeff Booth: So it’s some sort of rate limit. It stops getting better at that rate. It doesn’t need to get better and better and better at that rate. And as it’s, as that slows, the open models. Are are catching just as fast. Like you, you, you know, you use ChatGPT for all test your the open AM models ChatGPT for versus some of these other models.

[00:40:55] Jeff Booth: So it’s not a massive gap. So there is a gap like the ChatGPT 4 is better, but actually, if you go, if you go to the new one that they just released.

[00:41:05] Preston Pysh: Plus it’s or whatever it was, yeah, it’s worse.

[00:41:09] Jeff Booth: It actually performs worse. It hallucinates more. It has a lower rating on performance. It’s worse. And so as some of these LLMs get bigger is not necessarily better.

[00:41:23] Jeff Booth: And now, now, now I think if you’re consuming content and you’re consuming more and more content that is content that was created by you. And so like if the model is consuming more and more content that from other bots that were created by the bot. You actually have some areas where it is kind of that engineering to make sure to remove some of that noise would get exponentially harder.

[00:41:49] Preston Pysh: To kind of piggyback on this idea, like if I was going to try to argue the point with you, Jeff and correct me if anything that I’m saying here isn’t accurate, if your understanding of it is different, but one of the things that I was really fascinated by with all of this is just that a lot of this is just compression.

[00:42:07] Preston Pysh: So you’re feeding it all these ones and zeros, and through these GPUs, and what pops out as a model is a compression model, just like if you were going to take a file and you were going to compress a file, and it used to be 50 megabytes and now it’s 5 megabytes, the way that that algorithm compresses it is it’s looking at patterns in the ones and zeros, And it’s turning it into less data.

[00:42:31] Preston Pysh: It’s saying, okay, when there’s five ones in a row, then it just becomes five, one, instead of the five characters of one, one, one, one, one, these models are just that they’re compression models. Only it is figuring out through the transmission or through this transistor that was discovered. Most of this dates back to a really famous article that was written by some Google engineers called focus is all you need.

[00:42:57] Preston Pysh: And it really lays out how these machines can go about compressing all the ones and zeros and data that it’s being fed into a much more efficient way. One of the things that I found interesting is they said that they took a wave file, which everybody knows is a higher form, like way more data intensive than an MP3 file for audio.

[00:43:16] Preston Pysh: And they took the wave file and ran it through one of these AI models. And what they got was something that was way better compressed than the MP3 compression mechanism. And so, the idea of that is insane because it was never, it was never optimized or trained for compressing music files. But, it can do it just because that’s what these models, these intelligence models do, is they compress data.

[00:43:41] Preston Pysh: I’ve heard, and there’s a point I think to all of this, I’ve heard that the GPT 4 model is compressed to just 200 gig. Approximately, which Jeff that’s to be able to take a file that’s 200 gig and I can ask it some obscure legal question and it can pass bar exams better than 95 percent of people or I can ask it some type of medical, some obscure medical thing and it pops out an answer that’s right and the file that’s doing this is only 200 gig, which could be stored on pretty much any computer or even smartphone today is bananas.

[00:44:18] Preston Pysh: That’s like, this doesn’t even. Like, I can’t even comprehend how that’s possible. No, no.

[00:44:23] Jeff Booth: So that’s, that’s why. And, and it’s, so I’d recommend anybody who’s listening to this, go download ChatGPT for all for, is it GPT for all, GPT for all to your computer. And you can, you can download a whole bunch of models, a whole bunch of various models to your computer.

[00:44:41] Jeff Booth: And it means if you never had internet access again. You have the world’s knowledge of the information of the world, history of the world, on your computer. And so, that’s kind of what I’m getting at. This, these things, why I keep coming back to over and over and over again is prices fall to the marginal cost of production, no matter what.

[00:45:02] Jeff Booth: That’s why. So these things move as, as, as this innovation comes up, they drop, they drop, drop, drop, and that gets, it propagates everywhere. And you can rest assured that if we can do it here, that you’ve just opened up to innovation to billions of people. All over the world that now can participate in that innovation and they will find a way and there’s nothing you’re going to do to stop them from, from finding a way.

[00:45:28] Jeff Booth: And these things will proliferate more and they’ll become, and people go to the best one for a while. So there’ll be a, they’re constantly for the best people that there is pricing mechanism for the best. But really quickly that will narrow. It won’t be as good anymore or it’ll narrow into the best in certain domains for a while, which will then narrow and everything else.

[00:45:52] Jeff Booth: So even when you go to, if you think about, cause right now people are talking about AGI and we have AGI today, we don’t have AGI today, but we will get there. When you extensively talked about this in the book, but it almost doesn’t matter, it matter because in narrow AI, how we’re paid for our jobs is arguably more important than broad.

[00:46:15] Jeff Booth: And in narrow AI, this is tackling domain after domain, after domain, after domain, and it’s better than us in many, many domains already.

[00:46:26] Preston Pysh: I’ve got a story to tell you on the narrow AI. So I recently went to a, like a robotics thing that was happening in my local area. And there was a guy that had a, some type of like robotic arm with like a pencil on it.

[00:46:40] Preston Pysh: And he had a, these where’s Waldo sheets. that he would put down. He, he would, he had, he gave me one, he gave the robot one, and then he flipped them over and he said, all right, let’s see who can find Waldo faster. And I’m there looking around and the robotic arm with the pencil on it, like immediately, as soon as he flipped it over, just went straight and pointed at Waldo.

[00:47:01] Preston Pysh: And he said, that was pretty fast. Why can’t you beat it? And, and I’m not saying this response to, and maybe the response wasn’t even intelligent, but I thought it was smart. I said, well, I’ve got, you know, over 40 years of environmental conditioning that has made my, my model like way slower than probably the, the 50 sheets of paper that you trained it on.

[00:47:22] Preston Pysh: And it’s dealing, it’s taking longer to process all of that, right? He says, Oh, that, yeah, that’s, that’s really good. Like, obviously you’ve, you’ve read up on some of this stuff a little bit. I was like, well, just a little bit, but like the, the takeaway for people not to tell you a story of, of that, but the takeaway is, is narrow AI.

[00:47:40] Preston Pysh: If that robot is trained on just pictures of finding Waldo and nothing else, and it was trained on it and that’s expertise focuses is what you need going back to the the Google paper. That is going to always outperform some other general AI or something that’s trained to be able to answer medical questions, right?

[00:48:05] Preston Pysh: Like there’s no way you’re going to compete on time and speed and efficiency and all these other things that make you win. right? Or to create market value. So I just, I bring it up because I, I think it’s just an easy way for people to visualize why there is always going to be a need for narrow AI, why there’s always going to be a need for people to create narrow AIs to accomplish tasks that general AI is always going to be worse at or slower at and dealing with way more training and just information that it can’t compress to a much smaller, more efficient file.

[00:48:40] Jeff Booth: The question is, and the only, the only thing I would disagree on that is always, eventually the art, the agis will be able to compete on everything.

[00:48:49] Preston Pysh: Because they’ll be referencing individual files.

[00:48:52] Jeff Booth: Be be because the, the compute is growing and the rate of compute is growing. Its. It’ll be meaningless. The difference will be meaningless.

[00:49:01] Jeff Booth: So it’ll be able to merge these things and it’ll be able to see other things that the narrow can’t see and then infer from those other things. Oh, here, here’s how to change that.

[00:49:12] Preston Pysh: So you’re talking into like time timeline, which you’re talking about there is probably 2030 plus. Right?

[00:49:19] Jeff Booth: Yeah, probably, but this is why, this is why, remember, we and I have talked about this a lot of times, but but in my book I wrote about Kind of that exponential rate and the paper folding and what was, what was happening.

[00:49:33] Jeff Booth: People have to remember that AI has been, has the same exact rate of growth for 70 years. Same rate of growth. It’s, that’s why I use the paper folding analogy because on fold one, on fold two, you can’t see the rate of growth. It feels like we’ll get disappointed and nothing’s happening. The researchers still think, Oh, it’s going to be, and then, and then that created the first AI winter and then the second day I winter and that rate of growth, it’s just an exponential function.

[00:50:03] Jeff Booth: And now we’re, we’re on fold 30, we’re full 35 going from. And just, if people haven’t heard that before, about 50 folds of a piece of paper goes to the sun. And so on the first folds, you don’t see anything. But now we’re on the deeper folds. We’re on the thicker folds. So, fold 35. Essentially in a year and a half, you’ve doubled all the previous history and then full 36 doubles it again.

[00:50:32] Jeff Booth: Almost, it’s hard to comprehend how big these steps are now. And so it feels like when people are wondering, even right now, when people are talking, most of the talk we’re doing right now is talking about right now in AI. Right? Can’t even imagine. Yeah. In other words, you’re measuring your, and this, which will blow people’s brains on what we’re talking about.

[00:50:54] Jeff Booth: We’re not talking about in 18 months, we’re not talking about that logarithmic, that exponential trend on what changes in 18 months, which we’re talking, we’re, we’re right now. looking almost backwards, what will this look like? And it’s so, because it breaks your brain so badly, you have to think exponentially.

[00:51:14] Jeff Booth: You have to think exponentially to be in front of it, because otherwise you’re going to get wiped out by it. You won’t be able to, to be able to put together everything that’s coming and how fast it’s coming. into a system that you’ve always lived in. The world’s moving too fast.

[00:51:30] Preston Pysh: So there’s, if you go online to YouTube and you watch any AI stuff, almost always the conversation is about, is the world going to be this dystopian nightmare?

[00:51:40] Preston Pysh: Is, are we going to be pets to the AIs like a lapdog is to a human? We’ve talked about this a few times. I’m curious if you’d be willing to kind of share your opinion on, on what your thoughts are as far as like what this means for humanity moving forward.

[00:51:58] Jeff Booth: And we’re specifically Preston, like, we’ve, we’ve taken this rabbit hole everywhere.

[00:52:03] Preston Pysh: Yeah, well, I guess, are you, are you viewing this from the dark dystopian outcome.

[00:52:11] Jeff Booth: No, I, I, I think the, the way forward through this transition is going to be, depending which system you’re measuring by, is going to be chaotic, like. Like no time in history. I think everybody thinks that their time in history was the time in history that mattered most.

[00:52:30] Jeff Booth: I say that with I said, we actually mean it. I say that with a certain reservation that but we’ve never had, we’ve never had machines that could do that. That could do what we’re talking about right now. We’ve never had this, this singularity. that we’re talking about is the, is also the merge to a, to, to Bitcoin.

[00:52:52] Jeff Booth: And what, what’s scaring most people is they can’t see themselves in a future. That from the system that they’re in how fast this is moving and they don’t even understand how fast it’s moving Yes, they could be themselves and what they would do in a new system They’re so there’s and they’re so petrified that they’re they’re actually yelling and they’re marching against People and they’re spending all of their time in this system that’s stealing their designed to steal their life force and their life energy and concentrate it up.

[00:53:24] Jeff Booth: That’s what’s happening in society today. I see a very prosperous world. I see a world where, where prices continue to fall and you can do it, do do more and more on an ever increasing basis, but it’ll be moving to Bitcoin and the fight against Bitcoin in that. If you just think about how much control is in the existing system that you’re giving it more control.

[00:53:50] Jeff Booth: When people go and buy, okay, I’m going to go buy some rental Airbnbs, I’m going to, I’m going to create a whole bunch more wealth for myself in the existing system. When I go buy Apple stock, when I go and I need to put my money in that. What they’re really doing is reinforcing the old existence to try to lose money less fast.

[00:54:10] Jeff Booth: That’s what they’re trying to do. That’s what they’re doing. And Bitcoin is taking all of that energy because it’s repricing everything, but they think they’re winning a system that doesn’t have negative externalities. The negative externality is they’re giving away their freedom the entire time and AI will be used to control them.

[00:54:29] Jeff Booth: And so imagine how many people Right now, the people that are going to be listening to this podcast, imagine how many people that actually are doing that and spending most of their time giving the system that is, that they know, that they know for sure prices fall to the marginal cost of production and we have exponentially increasing productivity.

[00:54:51] Jeff Booth: So they know so well, and they think they’re escaping something by trying to make more money in the short term within that same thing they’re trying to escape. But most people will stay there. Most people will, will spend their time there and what’s happening, and that’s why it’s going to be chaotic. It doesn’t have to be chaotic because if all of those people listened to your podcast and moved just their attention over to the new system tomorrow, this would emerge way, way way faster and it would.

[00:55:23] Jeff Booth: It wouldn’t be as chaotic on the way through, but most of those people are going to elect leaders that they think are better for them under the same system. Most of those people are going to polarize themselves against those other people that are creating the problem in that same system. Because human, besides everything that we’re talking about, we’re talking about something that imposes new rules.

[00:55:44] Jeff Booth: It doesn’t impose new rules on human nature. Human nature is going to consistently try to, to react in the, inside the existing system and make it stronger. Well, well, this imposes new rules. So the faster people move, realize what we’re saying and kind of if technology is, if technology is deflationary, I said this on one of your other podcasts, I could have stopped the book there, deal with it.

[00:56:13] Jeff Booth: Right. So the only thing that deals with that. The only thing that deals with it, you can just say any time, not even, not even inflation, even if you, if, if the natural rate of deflation was 5 percent a year and you, you had negative 1 percent deflation or 0%. It still means the government is stealing or, or you’re transferring 5 percent a year unnaturally, but you’re stealing that productivity from the population.

[00:56:42] Jeff Booth: So I don’t understand how people can concurrently agree with technology as deflationary, and we live in a free market. And then what, what would look, what would the world look like in that system? And again, I’ve said this millions of times too, but we have regulation in a system, regulation to protect your money.

[00:57:05] Jeff Booth: in a system designed to steal your money. And then people are throwing stones at different sides of that regulation without realizing the primary thing is you have a regulation in a system to protect you from stealing, from somebody who’s stealing your money.

[00:57:23] Preston Pysh: Speaking of which, do you have I know we’re past our time here, but do you have time to talk about the FinCEN situation?

[00:57:31] Jeff Booth: Yeah, I would, I would love to, because this is, this is completely connected. Yeah. It has to be connected. All of these things are connected.

[00:57:39] Preston Pysh: So, real general overview for folks. So Senator Warren came out with this letter to the White House, to the President, addressed to the President, talking about mixing services and how she thought that Hamas was funded 120 million through you know, crypto and mixing and she had this Wall Street Journal article that she was wielding around that talked about how, you know, that was the number and those were the facts.

[00:58:06] Preston Pysh: The next day, FinCEN comes out with a proposal that leverages the, the Patriot Act to put this broad sweeping rules and laws in place for mixing services, running nodes. How anybody that’s doing these types of activities are basically conducting financial crimes. And anyway, so like that’s the kind of the background on all of this.

[00:58:33] Preston Pysh: The fact that the FinCEN thing rolled out a day after Senator Warren’s letter to the White House seems like it was very coordinated. But Jeff, how is it all related to what we’ve been talking about? Why is it important for people to even care? Because we just got done talking about how if you save in Bitcoin, You’re basically not voting for the old legacy system and it shouldn’t matter.

[00:58:55] Preston Pysh: Why should a person be interested in this proposal and why is it important?

[00:59:00] Jeff Booth: Yeah, so I’m going to read something that I posted on on Noster because I know a lot of your audience isn’t there. And so, because it talks about this and so said, Elon Musk started PayPal and as a result of that and his first principles thinking it is highly likely, likely that he perfectly understands money similar to how it’s highly likely Elizabeth Warren perfectly understands money.

[00:59:25] Jeff Booth: Thank you. They’re sitting at the top of money. Fiat money. Yeah. Fiat money. He must, so he must also understand that the natural state of a free market is deflationary because he’s a technology leader. Yeah. No doubt. That’s what he, that’s what he is creating. If both these are true, then he also must know as one of the top AI and robotics companies.

[00:59:48] Jeff Booth: That he, he is stealing the productivity gains that should flow to society in the form of lower prices by pretending he is solving planetary issues through a broken system that actually makes them worse. He must know that. By the way, Elizabeth Morton must not know. That must if she, if she know if she knows that.

[01:00:08] Jeff Booth: In other words, Elon is not advocating for Bitcoin because he is stupid. It’s because he believes you are, and you can say the same thing for Elizabeth Warren. That is why she is advocating for a system that steals money from the very people she says she’s advocating for, and making the problem worse.

[01:00:25] Jeff Booth: And what will happen with FinCEN, and this is a really dangerous, we got a, you and I got off calls with lawyers this morning on this, on how dangerous this is. The U. S. can enact, or if after comment period, they can enact this and what, what that will do is entrepreneurs will, the free market will emerge everywhere and entrepreneurs will move their businesses elsewhere to be able to deal with the free market and the government will do this under the Patriot Act or some other erosion of individual rights and freedoms granted by, to the U. S. through the constitution that is, that has been ongoing eroding those individual rights and freedoms to protect you from the same thing that is imposing the pain on you. And most people will follow, fall for that trap and they won’t, they won’t realize what’s happening. That slow moving erosion of their individual rights and freedoms will happen in many, many places.

[01:01:23] Jeff Booth: And it will provide an opportunity for other countries. Moving the other way, like El Salvador is doing, to be able to take advantage of people wanting to live in a free market. And then the free market will grow faster, because you’re not going to, you’re not going to out China by being more authoritarian than China.

[01:01:42] Jeff Booth: So if you want to live in that world, where that’s happening. Then you should continue to down this path and you should continue to advocate for people or like everything in that system ends there. There’s no way to stop it because the, because. There is no free market in a market that must be manipulated through money.

[01:02:03] Jeff Booth: It’s just, it’s who gets to win in it. And that’s the control function. I can’t believe people can’t see it. I can’t believe people are yelling on both sides of these aisles. And there’s no, there, it’s believing one person’s a hero and one person’s a victim. Like, look at, look at even Elizabeth Warren and Elon Musk through that lens.

[01:02:23] Jeff Booth: How many people hero worship him when he must know? I’m trying to say something very different than he’s a great entrepreneur because he is a great entrepreneur. He’s a great, he’s created a whole bunch of really great products. I can, I can equally respect that. But if you’re using, if what we’re saying is true and he knows it, then you’re saying he’s using a narrative to gain more control.

[01:02:47] Jeff Booth: And why is Elizabeth Warren any different? She’s doing the exact same thing. So this is really, this is really important. that people stand up for this, but it’s more important than if you think a government can stop you from this and that’s why you’re not moving or not moving into Bitcoin. Think about what you’re saying.

[01:03:06] Jeff Booth: I’m going to stay into the burning building as AI moves faster and faster and you use to control me because I believe that somebody else has these rights over me.

[01:03:16] Preston Pysh: Well, a person’s a person’s effectively saying, I think I’m helpless. I think that there’s no way I can, I can solve this problem. So I’m just going to burn in the building.

[01:03:26] Jeff Booth: And so I have empathy for those people because they just don’t know. What ends up happening is they’re living in such a perpetual state of fear. that they will believe something from this state that alienates or divides people to be able to get more, give more control. They’ll believe it all day long.

[01:03:44] Jeff Booth: In fact, he wrote a whole chapter on this in the book, right? Us versus them. Cause you could see that that would, that’s where this would go based on a control structure that was stealing money.

[01:03:55] Preston Pysh: If people were hearing that and for, for me, it’s just. I don’t want to vote with my feet, but I will if I’m pressed up against the wall, right?

[01:04:05] Preston Pysh: So like, I want to try to do everything I can to try to have the most favorable outcome to have these types of businesses in my country and for my country to basically lead the charge with respect to Bitcoin and Bitcoin businesses. So like that’s why I’m responding to this FinCEN in the way that I have Jeff and I and some others are working on trying to publish something that lays out where all of their rights are being violated in this proposal we have till I think it’s January 22nd to reply to the public registrar on this particular proposal.

[01:04:37] Preston Pysh: So we’re going to try to pump out some document for people to look at, that they can pull the research and the work that we’re doing to provide a comment. It is insanely easy to add comments to the registrar. And my understanding is that FinCEN has to respond or address all the comments in the registrar that’s there.

[01:04:57] Preston Pysh: So take action, like get out there. It may have no impact at all. I don’t know, but at least it’s some type of attempt to allow this technology in this, this freedom of choice of money to be in your domain, right? Like it’s better to try to ensure that it’s successful than to just give up and say, Oh, there’s no way.

[01:05:19] Preston Pysh: I’m just screwed. Right? Like, I just can’t stand that mindset. So that’s why we’re talking about it. That’s why we think it’s important. Doesn’t necessarily mean we’re going to be successful, but at least we can kind of raise our hand and say, Hey, we tried. And if it gets bad enough, well, sure as hell, I will vote with my feet.

[01:05:35] Preston Pysh: You better, you better bank on that.

[01:05:37] Jeff Booth: So yeah, and there’s a, there’s a bunch of paths there too, because a lot of people will vote for their feet and they’ll vote for free markets, but again, from our lawyers this morning, this will go through a number of things, even if the order comes in, it becomes law, it will be challenged in court all the way up to the Supreme court, and it’ll be challenged on a whole bunch of different merits all the way up to the constitution.

[01:05:59] Jeff Booth: So this is going to play out for a long time. And when we said it’s going to be messy and chaotic, And all the way through. Here’s the more important thing you can do. That’s important. Comment, make sure you comment, make sure you’re part of this, make sure you’re driving for it, but more important, advance people onto this network as fast as you can.

[01:06:19] Jeff Booth: Let them know why, because as the free market emerges here, there is going to be no choice, because try to find people on it. It’s just, even if every government in the world locked down on this and said, we’re not doing this. An underground economy would explode. It actually might move faster, but it’s going to be important for, it’s going to be important for the people that you care about to understand why that’s so important, important to self custody, move into, into this and start, cause you’re going to have, you’re going to have circular economies building on this that are untouched by all of that’s going on in the existing financial system.

[01:06:58] Preston Pysh: All right, Jeff, this was fascinating. I just can’t even imagine what this looks like in even three or five years from now. It’s crazy. It’s crazy. I really, I just truly can’t. You know, that’s one of the things that amazes me about Ray Kurzweil is just how he… It’s almost like he naturally thinks in exponentials and he’s been so right through the years on some of the stuff that he’s called.

[01:07:21] Preston Pysh: And I just look at this technology and it’s so exponential and it’s so difficult to even imagine where we’re going, but boy, it’s an exciting journey. There’s going to be a lot of bumps and bruises along the way, but hopefully conversations like this is helping people kind of oh, geez, I’m knocking over my, it’s so bumpy here.

[01:07:37] Preston Pysh: I’m knocking over my mic. But hopefully some of the stuff we’re talking about is helping people kind of prepare and think about what’s coming next.

[01:07:43] Jeff Booth: So by the way, I got one quick one for you. Yeah. You’d want to read the maniac. It’s a story about John von Neumann. You want somebody who’s prescient and all of this stuff.

[01:07:52] Jeff Booth: And and I wrote a little bit about but just worth a read. I mean, really fascinating read.

[01:08:00] Preston Pysh: Jeff, give people a hand off where they can learn more about you.

[01:08:04] Jeff Booth: Probably best is my website, jeffbooth. ca or an Oster. Okay. Awesome. Or more important, maybe why don’t you tell them what you’re doing in our new fund?

[01:08:15] Preston Pysh: Yeah. So Ego Death Capital, people can check that out. I’m going to be coming on as a GP on the second fund that they’re getting ready to roll out. So I was part, I was an advisor on the first one and very honored to be included in that, Jeff.

[01:08:29] Jeff Booth: So it’s a, I’m having the time of my life there and I can’t believe I get to work with you and Lyn and Nico and Andy and it’s just been, it’s just been awesome.

[01:08:41] Jeff Booth: It’s been a blast.

[01:08:43] Preston Pysh: Awesome. Well, thanks for making time and coming on the show. The very interesting topic. So thank you for making time, Jeff.

[01:08:49] Jeff Booth: Thanks.

[01:08:51] 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.

[01:09:04] 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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