00:02:16 · Tillman Holloway
[00:02:16] S3: Yeah. I think the beauty of the merger of both traditional finance and AI and crypto is that we can all learn from one another. I think we've seen that in the cold storage, and the storage side of Bitcoin. But from from a Bitcoin specifically, the proof of work network taught us a great deal about how networks should be built. And I believe that we're headed down a similar path to what we saw when we saw mining start to get over concentrated in the publicly traded mining stocks and companies. And I think you're gonna see the pendulum swing back the other way because it's such a tax. I mean, if you see the negative headlines about AI and the data centers that are being built, it's because they're concentrated. They're they're they're trying to put so many in one geographical location that there's a tremendous amount of unintended consequences that come along with that, including natural resource taxes, just the political fallout from a from a optics perspective when one facility is that big in these small rural towns. There's just a lot that goes with that that I think can be avoided, quite frankly. If we take a step back and we say, okay. Is it good to have concentration risk in those utilities that we're so dependent upon? No. There's there's not. The the more decentralized we can make that utility, the better off we all are. And so if you look at public utilities as a whole, you know, they're decentralized at the telephone pole. There's literally access points throughout the entire network. And, you know, you can contribute, for example, like the solar grid. If I put a huge solar farm on my property, I can contribute to the grid. They'll actually pay me for that. There's no there's no reason why we can't do that with compute. You could put as much online or as little online, but everybody's online. Everybody's contributing. Even household appliances should have some compute power built into them. And the reason why I say that is because I don't believe there's going to be a way to secure data moving forward unless it's tied through a proof of work network to natural resources and consumption of electricity or some other, scarce natural resource. And so, you know, moving forward, how are governments gonna secure themselves against this AI threat that's expanding and growing at an infinite pace? You can't stay ahead of it.
00:04:43 · Tillman Holloway, Anthony Pompliano
Well, you tie it to something like Bitcoin has shown us to be the most resilient, largest, fastest growing natural law network that's ever been built. [00:04:52] S2: Now when you think about this idea of securing data, are you talking about it in terms of, like, if you go and listen to Apple, Apple will talk about how your data stays secure on the device. It's really hard for other products to tap into Apple Health data as an example. If you go and listen to Tether, they've got something called QVAC, which is basically run your own local intelligence or keep it kind of in this confined environment. Is that what you're talking about? Or are you talking about more about, like, encrypting data for big companies that already have your data and you don't have much access to it?
00:05:20 · Tillman Holloway
[00:05:20] S3: Well, it's it's both. So, you know, the decentralization of data on the devices is a is a powerful tool, especially for verification of identity. If you've seen some of the most recent developments on some of the most secure sites, there's liveness checks that are through your phone that are connected to the website that are verifying who you are, that you're alive, that you're not under duress. That's an example of security reaching the device at a device level that's very powerful. But we also have to have databases. We also have to have large enterprise efforts that that are aggregated, you know, databases and libraries of sensitive and non sensitive data. And the question becomes, how do you keep bad actors from exploiting that data? And if you look at the data breaches that, you know, are on every headline right now, if you look at the the credit card data breaches that end up being, you know, 3,300,000,000,000 worth of fraud every year in the credit card space, Like, all of this is only gonna accelerate. So how do you secure data? Well, individual die... Device based security is fantastic. But then how do you back up that? How do... Where does that answer? Where do you get push pull updates and notifications? All of that's gonna be answered, in my opinion, through Oracle technology, which a lot of blockchains are focusing on, coupled with proof of work. If you combine those two things, you get a blockchain that's very, very difficult to breach from a brute force perspective, but then you also get real world data being onboarded in secure measures in real time.
00:07:03 · Anthony Pompliano
[00:07:03] S2: What I find also very interesting about this whole idea of, like, securing data and maybe the intersection of AI and Bitcoin or AI and crypto more broadly is we have a ton of data that is, like, exhaust from our lives. So I always talk about, okay, you used to walk around. Now you put a smartwatch on and it counts the number of steps. Well, that has been, you know, quote unquote monetized or become useful because you have something like an Apple Health, a Garmin, whatever it is. And now I start to say, okay, well, there's a correlation between the more steps I take, the healthier that I am. Sleeping. Well, I have an Eight Sleep mattress. What? It tells me how much did I sleep? What was my deep sleep? You know, all these different kinds of components. We were having this data exhaust. It just wasn't being captured. And now we're starting to use it specifically around health. What I find very fascinating is there's going to be a very similar trend. So one of the areas that I am very excited about, I'm starting to invest a lot of money in, is this idea of how do we capture first party data to then train models. And so I've seen things in the health care industry. I've invested in a company that's doing this with blue collar workers. They're actually going out. They're giving smart glasses to, auto mechanics, and they're saying, hey, we can make a better experience for the consumer, but also we can go and we can solve this problem of capturing this information. It feels like that data capture or that data brokering that's happening, you know, like a micro one or something like that, those, that industry never existed before. And so not only do you get this explosive revenue growth, but now you have to also say like, Hey, how do we capture, clean, structure, secure all of this data? That almost feels like the security of this is going to become a huge industry by itself.
00:08:38 · Tillman Holloway
[00:08:38] S3: Yeah. It is. And real world data hasn't... We've we've never even approached that because the limitations of the technology itself and what's the use case behind it. How do you monetize it? Well, that's becoming really easy with AI. And the amount of data that you can sift through and harvest and analyze, it used to be the most burdensome part of the collection process. And now it's absolutely, you know, the snap of a fingers, and any kid that's 15 years old that has, you know, Grokbot on their phone can basically do it. The the... It's democratizing innovation at a level that I think we are... If you thought America could innovate in the past, wait until we get, you know, proficient using these new tools in this new toolbox that we've created. And it's... I I agree with the notion that, like, AI is not gonna take your job. People that know how to use AI will take your job. And so how proficient you are... And I've also said this, it's like the analogy of, like, if you wanna be a professional dancer or a professional figure skater in the, you know, the doubles category, You know, you can skate your perfect skate, and your partner can fall, and y'all lose the gold medal. AI is the perfect partner. It will never fall. So your only limitation is your performance against it. And that's an incredible ceiling breaker that I think we're... We haven't even scratched the surface on. So I I think that we're gonna see innovation over the next ten years in terms of data collection, usage, aggregation, and then, you know, spitting out some useful value or use case around the... That data is gonna be exceptional. And I think natural resources are where you start, quite frankly, because it touches everybody, gets the government involved. There's lots of... And, like, for example, demand management is the problem of all of our grids right now. If you know anything about power generation is you have to generate for what they call peak demand, but peak demand doesn't happen very often, and so you're over generating all the time. Managing through Tesla Powerwall and battery capacity that is improving at the rate that it is, you're gonna see a future where demand management is going to be, really in the forefront, especially as it relates to AI and immediate adjustments that are needed. And that opens up so much capacity on our grid. It's, it's it's pretty...
00:11:14 · Tillman Holloway
It... It's gonna touch every single area of our lives, essentially.