What Bitcoin Did - Bitcoin, AI & the Coming Surveillance State | Mark Suman

Episode Date: November 21, 2025

Mark Suman is the co-founder of Maple, a fully private, open-source AI. Mark breaks down how Big Tech and governments are using AI to harvest data, profile behaviour, and build the foundations of a coming surveillance system. We get into closed-source models tracking your thoughts and emotions, AGI hype vs reality and the rise of Chinese open-source AI and why private, verifiable AI is the only path that doesn’t lead to mass influence and behavioural control. We also get into how anonymous AI accounts are only possible with Bitcoin, why Lightning and eCash still matter, how miners are navigating the AI-compute boom, and why open protocols are the only safeguard in a world where AI intermediates your money, identity, and communication. THANKS TO OUR SPONSORS: IREN RIVER ANCHORWATCH BLOCKWARE LEDN BITKEY FOLLOW: Danny Knowles: https://x.com/\_DannyKnowles or https://primal.net/danny Mark Suman: https://x.com/marksuman

There are likely data sharing agreements between Open AI and the U.S. government. They want to harvest all the user data and they want to sell it and monetize it. We actually don't know what they're doing behind the scenes because everything's closed source. What vulnerabilities are there? What am I signing up for by giving them access to your mind effectively and then letting them into your digital life? It's this amazing potential for humanity for human rights. That said, the more that we give ourselves over to it, the more that we turn our data over to it, our minds, everything. we're giving it power to influence us.

We've taken a lot of that ethos of the Bitcoin mindset, that don't trust, verify mindset that is only made possible because of Bitcoin. We can't do it with credit cards. We had to do it with something that was private, and that was freedom-oriented money that is uncensurable. AI has the ability to upgrade humanity, but we need to make sure that our humanity is preserved in the process. It's good to see you, man.

Thanks for coming on the show. We've been trying to do this one in person. I've not been in Austin for a long time, So we decided we just do it remote. You guys have just dropped some very cool new features. But your first time on the show, you should introduce yourself. Tell everyone who you are. Sure.

Yeah, I'm a long time listener, first time caller. So this is great. My name is Mark. I'm online. I go by Marks a lot. So you might see that name as well. But I've been around in the tech industry for a while. I live in Austin, Texas now.

And, yeah, about myself. So I work on private AI. called Maple AI. Prior to that, I was at Apple for six years working on, I was a software engineer over there working on an internal project that had a huge privacy and machine learning and AI component to it. Apple does care about privacy. And so that was like from day one, I had to work on that aspect. But yeah, just love and life and glad to be here. No, I'm glad to have you on, man. Bitcoin's at $94,000. So this is an AI podcast now. these big tech companies are investing tens of billions, hundreds of billions of dollars into AI at the moment.

They're all in this like arms race competing against each other. I want to know like from your perspective, what's their end goal? Is it basically who can get to AGI first and whoever gets their first wins? Yeah, I mean, everybody talks about what's your moat? Like what are you doing to get your competitive advantage? And so AGI is like this thing that they love to sell people on and talk about. It's really good for raising money. really good for driving adoption. It's anybody's guess how close that is, really. But I think they're

honestly just driving for who can have the stickiest product, who can get the most people in and keep them the longest, and then let's continue to upsell you. But the big part of their revenue model is the data, right? They want to get the data. Everybody talks about how data is the new oil in this life that we live right now. And so they're gathering all this information they're making better models. They're monetizing the data. They're selling advertisements. They're selling shopping to you.

They're building agents that will go out and purchase stuff for you. So really, it's just about how can we collect as much data so that we can build businesses off of that. And I mean, the big major AI shops like OpenAI, Anthropic, XAI, they all got a huge $200 million fund or investment from the government. not if this is technically a grant or not, but from the United States Department of Defense. And so there are likely data sharing agreements between OpenAI and the US government. This just kind of reading between the lines there.

So there's a lot of data gathering going on and then monetization of that data. You know when you say, like, you don't know how close AGI, superintelligence, I think are those terms basically interchangeable at this point? Like, you don't know how far that is away. Is this almost like quantum computing where it's just always a few more years?

Or do you think we are actually on the brink of a breakthrough here? That's a good one. I feel like that's above my pay grade. But I think a lot of it depends on just like the task at hand. I mean, you've used AI a lot. And sometimes it's really good at one specific thing. And then you try to have it tie its shoes. And it like totally falls over and trips on itself.

Right. Like so I personally think like it's very far away. We're not right there yet that we are going to build very specialized AIs to do things. You know, Elon loves to show off his robot and say it's good at dancing and it's good at moving boxes in a warehouse and all this stuff. There was the robot that made the rounds a couple weeks ago with all the memes of, you know, here's this robot you can buy and put in your house. But it was, it's not an actual product that's functional. I think that we have a long way to go still before we get the whole AGI thing and super intelligence. I think we're just going to be targeted

intelligence for a long time. Okay. I mean, I saw the videos that launched with that robot that was in your house and wasn't like it looked kind of brilliant in the videos but is it true that that was actually driven by someone using like a VR headset so there's just like some guy in a warehouse somewhere working away looking at the inside of your house yes yeah they're saying for the early prototypes it's going to be somebody actually like wearing a VR suit that's driving your robot eventually they want to get to where they're not but that's not where it's at right now which is really creepy exactly this is the dystopia that everyone's scared of um but so the reason I asked like how close we are to AGI is because I did a show a couple of months ago with a guy called

Roman Yampolsky. I don't know if you listened to that one, but he is like the AI safety guy. I think he came up with the term AI safety and he's really trying to push back on all these big tech firms just carelessly investing to the point where they're throwing billions and billions of dollars at this thing, trying to get AGI, I'm not really thinking of the ramifications of that. Do you think there is a risk that AI is almost so good that it's too disruptive too quickly. Even if you take away the part of it going to kill all humans, do you think it can replace, you know, 90% of jobs within a decade sort of thing? Yeah, it's starting to replace some jobs, it seems like. We see a lot of headlines about

jobs getting replaced, and I think some of those are people looking for a reason to blame when really they were probably a lot of malinvestments from 2021 timeframe when the money printer was, you know, we had zero percent interest rates. So I think that there was, there's a lot of unwinding of bad hires, not bad hires because they're bad people, but hires that shouldn't have happened financially. So I think we're seeing that right now and they're just saying, oh, it's AI. We're just going to blame it on that. That's part of it. And then there are, there are industries that are already starting to get disrupted in a way by AI. So transitions are always really hard. We've seen it throughout time with new technologies that come in. And there's like this, this period of,

many years where people have to find new work or decide to retire early, it's going to be difficult if it happens incredibly fast. And I have long been, like the economic side of me does not align with something like a UBI, universal basic income, but it almost seems like we might need to have some kind of AI stipend or something like that, right, where everybody gets some kind of income because they've been displaced by AI until we figure out what are the new jobs, what are the new industries, what are the new business is going to be built up? Because that's kind of the pattern that always repeats. New technology comes in. New industries are birthed from that new technology. And we'll see that with AI. We just don't know what it is yet. And we need to

have a good, happy civilization, no civil unrest if possible before we get there. Yeah, I totally agree with that. And that's like my biggest concern on the job displacement front is that there's some industries that are sort of, it's very clear to see the path to being completely replaced by AI. Obviously, software development has already changed entirely with AI. But even things like long distance truckers, that's an example I've used before on the show, but that job is not going to be there in, you know, 20 years guaranteed it's not going to be there. Who knows if it's quicker than that? And what happens to all the people doing those jobs? And I can't see how you get around it without a UBI. Like, I don't think retraining in another industry, if all the other industries

are also getting disrupted and displaced by AI. Like, that's not a feasible outcome. So how do you get there without some form of universal basic income? Yeah. And then what does that do to like wealth inequality? Yeah, wealth inequality. I mean, that's, I don't know how related those are. Those maybe are very related, maybe they're not.

But the hardest part with doing something like a UBI with what we're talking about right now is once it's there, it's very difficult to unwind to that. that, right? There's no, the most permanent thing is a temporary government handout kind of thing. So once people start depend on UBI, it's going to become part of their life. And so 30 years later, it's like time to get rid of that and everybody's got their own jobs and new industries. That UBI is just going to be part of their income and they're going to depend on that. So it's, I don't want us to like just jump in and say, yeah, let's do this. I think we need to really look long and hard at like what are the long term ramifications? of that. And then as far as the wealth gap goes, I think a lot of that still comes down to fix

the money, fix the world kind of stuff where we need to really fix the financial incentives behind everything in order to start to fix the wealth inequality that we see. And maybe I helps that, right, because it helps people that are on the lower part of the ladder to jump up higher and elevate up. Yeah. It's when you're in your work, like I imagine you're using a lot of AI in sort of the software development side. Has that replaced essentially new highs that you would have had to make otherwise? I want to be careful here. I don't want to create a sound bite or something.

But yeah, we become way more productive with having AI. So we do everything out in the open. So we build on GitHub and deploy on GitHub. And so you can see, well, not deploy on GitHub, but we put our stuff there. And what we do is we've got AI agents that sit there in our GitHub repo. and we push up, I'll write like a whole feature spec, and then I'll just say, hey, will you build this? And it'll build it, and then I can review it and tell it to make changes.

And then we have two other AIs that are code reviewing that code. So we have three different agents all working on this code with me inspecting it. And that's for code that's not like super mission critical. You know, Anthony is the one really in there building stuff. And he does a lot of it locally first with AI and then pushes it to GitHub. But we are seeing that as a small company, We're doing a lot more with a two-person team, whereas we five years ago, three years ago, probably would have needed to hire two more people by now in order to get to where we are at this point.

So it's more that we're moving faster with two people. Or we could have, we would be like half as far as we are now or even less than that if we didn't have AI. So to kind of answer your question, yeah, we could have hired some people to help us get here now, but instead we're getting here faster. If you already self-custody of Bitcoin, you know, the deal with hardware wallets, complex setups, clumsy interfaces, and a seed phrase that can be lost, stolen or forgotten. Well, Bitkey fixes that. BitKee is a multi-sig hardware wallet built by the team behind Square and Cash App. It packs a cryptographic recovery system and built-in

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head over to leaden.io forward slash WBD and you'll get 0.25% off your first loan that's leaden.io forward slash WBD so like obviously you're building an LM with a maple you're competing against the chat GBT

the anthropics of the world. How competitive can you be against them? I'm sure they have hundreds of thousands of developers. How competitive can you make your product with two? Yeah. Well, we're following in the path that others like Signal have shown, where you look for the user experience that the expensive companies who have these large, massive teams,

and spend all the money on user research, spend all the money on design and everything, they figure out what works really well. well. And then you have the luxury of coming in and saying, all right, talk to all these users, what features do you like the most? And which ones do we need to focus on? So then you go and build something that's similar, but has your own unique flavor. And our unique flavor is that we care about the user, we care about their data, privacy. We don't track them. We're not spying on that kind of stuff. And so we can build something that is functionally will look identical to chat chip BT.

It's going to have pretty much all the same features, maybe 95% of the feature set that you to want. But then we have the thing that they don't, and that is, you know, they, they, they, they want to harvest all the user data and they want to sell and monetize it. So in that regard, I think we can do a really good job with two people. We'd love to hire a few more and catch up and get really close to that. And we know that we're obviously not going to like take down chat to PT and take them over, but we can get really far and we can build a product that millions of people, hundreds of millions of people find incredibly useful. I remember this was probably a year or two ago. I think it was from Google. There was a sort of internal memo that was leaked, which was essentially

saying we've got no mo and these open source AI models are going to be just as competitive as us. Where is that stand? Because even though there's obviously great progress on some of the open source models, like I know Lama is open source at the moment and that's what Facebook are using. Is that correct? How close are they to the close-sourced chat GPs of the world? Yeah. It depends on which benchmarks you look at or if you go off of your own vibes. And really, it sounds silly, but you almost have to just try it out with the task that you want to do and with the process that you want to follow and test the different models to see what works best for you.

But when you look at straight benchmarks, they've really caught up a lot on coding standards, on math standards, on all the different benchmarks that are out there. especially the Chinese models. Like, Lama is still pretty far behind META. I imagine META is cooking up something for Lama 5 that's going to be really big because they have so much data that nobody else has. They have all of the WhatsApp data and the Facebook data and Instagram. So they're probably making something.

But in their absence, the Chinese models have really come in and caught up. But I was chatting with a founder who's here in Austin. He's building an AI service as well. His is more enterprise. But he said that internally, they kind of measure all the different tools. And he finds that the Chinese models really try to fit to the benchmarks. So they work really hard to make sure that they score high on the benchmarks.

But then if you stray out of the lane at all of those benchmarks, then they might start to fall down. That's specifically for programming. So certain programming languages or something. But that said, like, they do perform really well and they keep getting better. So I'm hopeful on open source. And like the Google memo said, it's just a matter of time before they are good enough for the average person and the average business user that they don't need to pay for these proprietary models anymore. Why do you think it is that Deepseek and these Chinese models have gone the open source route when the American companies have gone closed source?

Like that seems backwards to me. Yeah. I wonder if they realized that Americans wouldn't use it if it was fully closed source Chinese and they know that they need to compete somehow. so the world's only going to listen to them and use their stuff if it's out there for free and open source. And then the other part of it too is open source is going to get adopted way more than the proprietary ones by hobbyists and by others. And so if you have an ideology that you want to seed out into the world, especially if you're

looking at like a global south where maybe they can't afford to use the proprietary models, then you can embed your ideology in this model. and then push it out to the world. So I could see a couple different reasons why they would want to go the open source route. And deep seek, even though it's open source, they're still collecting data, correct? Sort of. So a couple clarifications. Open source with models is a little different.

They're more open models, if you will. We can't fully see the data that went into them, but we can see the weights and the measures and the biases and you can dial them yourself, that kind of stuff. So it's a little different than open source code. And then as far as data sharing goes, the only time you're sharing data with deep seek is if you download the deep seek app or you go to like the official deep seek website and use the AI that's hosted by them, then yes, they see your data, they see all your chats. And there's there's heavy suspicion that the CCP is able to access all of that information, mostly based off of data arrangements that pretty much every other company in China has with the government there. That said, if anybody's running the deep seek models locally on their laptop or they're running in something like Maple or some other system, then no. There's zero data sharing going back to DeepSeek as an organization or to any kind of Chinese government. You're obviously a Maple not collecting any customer data at all.

If, as you said earlier, data is the new oil, like, what are you foregoing that? Why are all these other companies just so desperate to harvest as much as that's possible and you're willing to just say, no, we don't need it? I think it's because we've all been sold that this internet that we use has to be monetized by selling your data. Like us as users got so used to using Gmail because it was the most amazing email service ever. It conditioned us to say we should have email for free because prior to Gmail coming on the scene, we were all paying for email. In fact, my dad was paying for his email inbox even when he stopped using it. He's still paying for it like five or ten bucks a month because it was just, It was a hassle to cancel, right?

And so when Gmail came out, they were like, hey, here's this new business model. You get it for free. But what we didn't realize is it came with this huge cost of all of our data being monetized. And we've just kind of gone down that path. And we don't need to. Like, there are other ways to build sustainable companies and sustainable products that don't use that as their business model. So that's really what we're doing.

We are, we're trying the more healthy route, if you will, healthy for humanity, healthy for all of us. to build it in a different way where we sell you a really great user experience, and we sell your product, and you can use it. And that's really where the relationship ends. So before we get into how you're doing things at Maple, how are these other big AI companies? What are they doing with the data that you're putting into it?

And are they using literally every single word you put into these models and then storing that, creating profiles about you? Like, how do they actually use that data? Yeah. Well, so they're using all the information you input into it. They are, they're also using everything you don't put into it. And what does that mean? There has been evidence and research showing that they look at your keystrokes.

So if you're typing something into the box and then you hit delete a bunch of times because you change your mind, they've captured that. And so they know, okay, here's how Danny thinks. Danny typed all this stuff in. Maybe he was like really angry and riding this really angry thing. And then he's like, you know, I need to tone it down. a little bit, so you backed off. It's learning your emotional state. It's learning your entire thought process. They're storing that all in their system. And then the way that I love to describe it is that they are like a, it's like you hired someone to write a biography on you. So they're like a

world-class author. They sit down and they're just constantly interviewing all day long, but they're also paying attention to your body language. They're paying attention to your heart rate, all these like other indicators that you don't realize you're giving off. And then they're creating this profile about you. And then they can pump that into the system for you to make the AI understand you more, which is great. That's the end product, right? They're like, hey, an AI that knows you. It's very effective. But then what they're also doing is they're using all that to train new models, to create shopping networks for you. They're building these computer use tools that will be able to control your computer. And they're making web browsers now that are going to browse the internet for

you. So you can see how they are just getting intertwined. into your life. And so you have to ask, like, what, what vulnerabilities are there? Or what, what am I signing up for by giving them access to your mind effectively and then letting them into your digital life? Maybe if we just imagine for a minute, Maple never existed, the other people that are working on privacy. I know Proton have come out with a private AI model. Imagine they never existed. How does this get dystopian from here? Yeah. Because you see these things like, I saw that friend necklace that came out,

which, by the way, look like one of the worst products have ever seen. I can't believe they actually launched with that. But these are things that literally just follow you around all day are looking at everything you're looking at. Like, what's the dystopian end game there? Yeah.

Well, there's a dystopian end game. I would love to paint kind of the rosy picture real briefly first. The reason why we get there is because I think a lot of times we look at this dystopian thing and we're like, man, we're all a bunch of idiots. Why do we sign up for that? But it's because AI has, as huge, amazing potential, right?

It's this amazing potential for humanity, for human rights, even. You have people who are oppressed all over the world, and now they can grab the world's knowledge and use it for their own advantage to try and fight back against people who are oppressing them. So there's really cool things you can do with it. That said, the more that we give ourselves over to it,

the more that we turn our data over to it, our minds, everything, we're giving it power to influence us. And so the dystopian side of it is that if we start giving an access to see in our room, to hear what we're talking about, it understands how to persuade us of things. Let's just say that. So it knows that maybe, Danny, you're really gullible in a certain way. And so if it wants to pass off some misinformation to you or a lie, it knows how to sell you on that. And so you can see that effectively, they're building the system where somebody could come in with a right amount of money,

or the right amount of weapons basically and coerce them and say, we need the community to start thinking about a certain political thing in this direction. So we want to deploy this directive that is going to shift the mindset of this country in the general populace in a certain way. And if you think about how we used to do, let's see, think about it. advertising let's kind of look at it that way if you want to make a new product and you want to sell it on to a bunch of people maybe you make a 30 second advertisement and you put it on something like the Super Bowl but you don't know who's actually watching you don't know what this frame of mind is you don't know whether they're male female a child an adult whatever you just make your best guess based off of demographic research and so you have to try to come up with like the 30 seconds that's going to sell the most number of people on your product now you fast forward to this time where we all have AI that's harvest seeing all our data and understands everything about us. And now you can say, I don't want to make a 30 second ad that tries to capture 30% of the people that watch it. I want to capture 99.9% of the

people. And so you can deploy something to this AI system that knows how to talk to you to you on a product and then talk to me in my way to sell me on the exact same product and convince most of us to use it. And that's just for products. That's not governments. That's not, you know, there's all sorts of ways that could be used to kind of weaponize the system for lack of a better word. It's a really scary future that seems very, like it's very easy to see that coming to the world in the next maybe like three, four, five years. And like, thank God we've got things like Maple and Proton doing this. But just quickly, before we get more deeply into Maple, are these models actually getting better? because like I use

AI quite a lot for work. It helps a lot. Like it probably I would have to hire someone at least like 20 hours a week to replace what AI is doing for me currently. But every time chat GPT, which is the one I use most,

like comes out with an update. It doesn't seem to be better. In fact, sometimes like it's worse. I think 4.0 was the best one they've done so far. So like how much better are these getting like incrementally? Yeah. It's,

it's up in the air. It depends on, it kind of goes off vibes like are they really getting better? A lot of people look at chatty 3-5 and think that really it's just 4-0 under the hood with some modifications around it. And it was less of a huge upgrade. But then you have XAI. You have GROC, which from, you know, from 2 to 3 and 3 to 4 was a really big jump. So it is possible there's still gains to be made there. But a lot of a lot of people that I read online who, you know, are really, deep into this. It seems like they're plateauing. And maybe we're plateauing because they're working on like the next big major breakthrough and they just haven't got to it yet. So they're holding us over with these small bumps until we get there. But that's why I think that the open source is really going to be able to catch up because if it's true that these big models are starting to plateau,

then open source is going to get just right up against them. And now we can have the same things that they have, but use it in a way that's that's better for us. Yeah, okay, so let's get into Maple. First of all, explain exactly what you're doing, how you're making sure this is like private AI that's not harvesting data. Give us the pitch. Yeah, sure. Yeah, Maple is the alternative, the chatypT that is not harvesting your data,

that is protecting your privacy. The way that we've built it is we have, we built it around open models and all of our code is open source, so you can go look at it and see, and we're running them in the cloud using something called secure enclaves. Another term for that is confidential computing. But these are servers that have hardware encryption built into them. It's the same stuff that runs on your phone. So on Apple devices and Samsung and other devices, they have these secure enclaves where it stores your wallet,

it stores your face ID, that kind of stuff. And it's these hardware encrypted things that are difficult to penetrate. And so in the cloud, we have those now. And so we're able to put Maple there. and when you as a user log in, we create a private encryption key just for your user. And so as you're chatting with the AI, it encrypts everything locally on your device using that private key,

and then it sends it to the cloud. And then the cloud in the enclave is where the AI is sitting. And so it's effectively like you and me right now. We're having a one-on-one conversation in a private room that we're going to give to everybody. But right now we're having a private conversation. And that's really what the AI is doing

in Maple and the secure enclave. and then once it's done chatting and working on your stuff, then it re-encrypts it and sends it back down to your device. And then we take it a step further than some other private AIs do, and that is we can synchronize it to all of your devices. So you can have it on your phone, you can have Maple app there, you can have it on your laptop, wherever you want to be. And then because we have that secure enclave

and it knows how to handle your private encryption key, it can synchronize everything across to all your devices for you. So in a nutshell, that's what Maple is doing, is just using a private key. And the last thing I love to kind of tell people and explain is a lot of these services that you use in the cloud, they take all the user data and they stick in one giant database. And if you are an employee at that company who has elevated privileges, you can just go in and hop around and everybody's user data all you want to.

You can go look at it. Usually they have audit trails, and so they'll know that you wouldn't access it, but that doesn't prevent you from accessing it. And then if a hacker gets in the system, well, they don't care about audit trails. so they're just going to get the whole database and get a data dump and everything. We've totally flipped on its head. And with these private encryption keys, our back end is just a bunch of private vaults per user. And so if anybody were getting into our system, they wouldn't be able to look at anybody except their own vaults.

That they can get in there, but they can't see anybody else. So, like, for me personally, as I said before, like, I use chat GPT the most. And probably some of that is just just down to habit. Like, it's just the first one I started using and it's been hard to kind of move away from that. But I have been, like I signed up from April basically as soon as you guys launch, and I have been using it more and more. But the thing that I always use it for is if I'm ever putting like business data, like financial data is always my go-to because I know that that's like an actual secure place to put that rather than giving it to open AI. But in terms of like feature parity compared to these big AI LLMs, where are you at? Like what do you have that you expect, you'd expect in a chat GPT type thing?

Yeah. Well, I will tell you, you might be happy to hear. that if your favorite model is 4-0, then we have the GPT-OSS model inside of Maple. If you go in there and do the model selector, it's called Quick, is the name of it. But that is really similar to 4-0. In fact, if you ask it, hey, what model are you?

It'll tell you it's chat GPT-40. So you'll get that experience, which is nice. As far as features go, you know, we let you upload documents to it. You can upload photos and get photo analysis. Like, you can take a picture of a tree or a plant and say, what is this? You can upload financial documents.

or legal contracts and have it talked to you about, you know, what are the legal terms that you've agreed to. We have voice so you can talk to Maple, which I use all the time, hit the microphone. We had it working where it would talk back to you that is temporarily broken. We're working on fixing that because we really loved having this two-way conversation. I would, when it was working, I would just kind of like walk around and just have a conversation with the AI, which I know a lot of people do with chat to BT. Yeah. So those are a lot of them. And then the biggest one is, is what everybody was waiting for, and that is live data. So now we have the ability to do private web search.

So now Maple is no longer stuck with these models that were trained on data from a year ago or two years ago. Now you can be sitting there and say, hey, what's the score of my favorite sport team game that I'm watching right now or that I'm curious about? It'll look it up and it'll fetch it for you and give it to you. But there's obviously a lot more utility to that than just sports. But yeah, being able to get the latest information from the web that is now available inside of Maple. That's a huge one for me because I use it a lot when I'm preparing for shows and stuff. I'll try and get like current relevant information I can use in the show. So without that, like if this was a model that was trained on data up to like mid-24 or whatever,

it's just it is useless in that sense. So that's a huge one for me. And how do you do the private web search? How do you do that while not giving up any data? Yeah. So we're using, we're using Brave API, the Brave search API. they're a privacy-oriented company as well. But then we anonymize it.

So when you are going to search, we don't attach your user ID or anything to the search and give it to Brave. So we have very little information already about our users, right? We don't collect names. We don't collect, you know, phone numbers or anything like that. The most we collect is an email address. And then we also know, like, what time you did your chats, because we have to keep track of like just when chats happened so we can synchronize them.

And then we keep track of how, much compute resources you used. But we don't know anything about what you're chatting about. So when we go to do the Brave search, we pass it along to them. And so all they know is that there's like this giant fire hose of web searches coming in from this one account called Maple, but there's zero way for them to, to, you know, tie it to anybody. Unless you literally say, my name is Danny Knowles, blah, blah, blah, and you put it in the search, then like the Brave Search API will see your name because you put it in the content.

But other than that, it's fully private. And there are some other private web searches that we're looking as well, web services that we're looking at. And we would love to have this model where we can actually spread it across so people can get even more anonymity by getting lost in a bigger crowd than just one crowd with Brave. This episode is brought to you by River, and they've just launched a very cool new product where you can automatically buy every price dip. Their zero-fee recurring buys are a proven way to build wealth with Bitcoin,

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Because I can't even tell from our users if you're using it. And I use aliases whenever I sign up to any website. So there's no way that you would be able to pick me out of a group of people. Like I've been using it since launch and you had no idea, which is really cool. So that's amazing. Are there any features that you think you need to bring in to be competitive? Yeah, definitely. And I think it's one that maybe you talk to us about early on, AI memory.

Yeah, that's huge for me. Yeah, like having the AI get to know you, that author that says down and writes a biography about you, we want to build that. And that is one of the stickiest features, right? You know, you talk about being a creature of habit. You use chat deep because it's habitual, but also because it knows you. It knows your style and maybe you don't even realize that, but when you have it generate images or something, it's kind of following the style that it's learned that you like. Unless you're very explicit and say, I'm going for this other different style now. So that's great.

There's research out there showing that maybe it's like a six to 12 month thing where if you have somebody in a system for that long and the memory starts to get to know them, then they're going to stay. And so obviously we're trying to run a business that's profitable. So we would love to build a feature that doesn't lock in users from a nefarious standpoint. We want to build a feature that gets to know users so well that they want to keep using Maple. But we're going to build it, of course, in the same way that we build everything else. So it's going to be in the open. People will be able to see what is this memory service? What is it remembering about me?

What is it passing into the AI that it knows about me? Because that's one of these problems with the closed models is we don't know actually what part of us they're sending to the AI. And we don't know if they're changing things that they send. So if you're someone, I try to use non-political things when to explain this just so I don't divide people. But like, let's say you really like chocolate ice cream. And it's secretly in the background.

It's saying Danny actually like strawberry ice cream. And so it's starting to give you different results. And over time, you're like, oh, you know, maybe, you know, maybe I start thinking this way. It's kind of a weird metaphor, but the point is, like, they can change things to slowly nudge you, like imperceptibly nudge you a certain direction just by changing the memory under the hood and not letting you know that's what they're doing. And nudge is a very nice word that when you're kind of saying they can coerce you into thinking differently. Yeah, yeah. If it was overt, right, then it would be so obvious people,

were rejected. It's like the matrix, right? They're like, oh, we tried all these different iterations on the matrix and people started waking up in their pods. And so we finally built one that was that was just so like non, you know, so easy that they didn't even notice it. Yeah, the memory is a big one for me because like with chat GPC, obviously it builds a memory on you. And that, like, I understand all the downsides to that in terms of giving up data and that harvesting of everything that you ever enter into the LLM. But it gets to the point where I can put one line in and I will get the output that I want from it because it knows what I'm trying to ask for.

Like if you could get that in a private way where maybe you can periodically whenever you want, completely erase that data so it forgets everything about you. But that would be a massive improvement for me just from like a UX perspective. Yeah. No, definitely. Yeah, for anybody listening, you know, I would recommend just if you use chat GPT, go in there and just like ask it, what do you know about me? You know, build me, build me like a dossier, which is what the CIA would do.

Tell me everything you know about me and if you were to do like a private investigator research on me. And you'll get some really interesting, interesting information out and you might be a little creeped out by it. Another cool thing to do is go in and say, hey, if you wanted, if you had a lie and you wanted to persuade me to believe this lie, how would you go about fooling me? And you might have to nudge it a bit. You might have to like push it along a few times. but it'll finally tell you, oh, well, when we've talked about this, I've noticed you have a tendency to ignore this.

I passed this lie across to you, and you just picked it right up and ran with it. So you can start to understand, like, what are my own weaknesses because AI has learned them about me. The other thing that I would love to see, and if you can implement this, please do, is how to stop it just being a sycopham. Like, I go into chat GPT and tell it to be neutral and critical all the time,

But over a few days, like it gets back to being just this, this like, yes man on my computer where anything I ask it, it's like, that's great. Eight and a half out of ten, nine out of ten, or whatever. But it's like, I want you to tell me the truth. Like, can you actually program that in? So this is a completely neutral model. I hope so. We've kept the models neutral in the sense that we don't change them. This, what you're seeing from ChatsyPT, we actually don't know what they're doing behind the scenes because everything's closed source.

So it's very possible that they've built something that says, like, over time, we want you to just really make Danny feel good about himself because that's going to keep him in the system longer. And even if he tells you to, like, stop, like, just make him feel good about himself. Like, they could have that in there. So we want to build something that is totally verifiable. And so at any time, somebody can go in and say, Maple is handling things this way. And if I tell the AI to be neutral, they're not inserting something in after the fact and saying,

I know he said to be neutral, but ignore that directive. Now, whether or not we can take these base models and make them neutral and stop having to be a sycophant, that the jury's still out on that. I'm hopeful we can. There are people, there's a company called Dolphin that will take models and try to like rip out some of the bias

and not do a full retrain, but do like a minimal retraining of it. And so I'm hopeful that we can do stuff like that. And maybe as Maple grows, we can invest some money in there as well to get more neutral models. But the kind of the commitment we make to the community is you're going to be able to see everything we're doing. And so you can decide if you like it or not. And if you don't like it, then go use another product. But we're always going to try to be open and verifiable with our users.

Yeah, because I don't want a friend. Like I want a tool, not a friend. And it seems to always just want to be your buddy. The other big issue, especially earlier on, I don't know how real this is. of as we stand right now, but was political bias. And I remember there was an example of, I don't exactly remember the model, but someone was basically like, give me a picture of George Washington, and then was like iterating on the same picture being like,

make it more realistic, make it more realistic. And it ends up being like a Native American or something. And it was like there was always these like biases within these machines. Can you get rid of that or is that a problem that is kind of unsolvable? You just have to do your best to keep training it. Well, you can get rid of it in some ways by getting, this data set that people are able to look at and verify. If you just get a good data set,

then you can build a model that is not so biased. But all these models, for the most part, have been trained in closed environments. And so we can't tell exactly what biases have been put into them. And then kind of the other problem we have is these models are trained off of written communication that exists on the Internet, audio communication, video communication, things that are published. And they weight them based off of volume a lot of times, right? And so if the very people, a lot of people believe that, like, the media is slanted one way or the other politically.

And if those are viewed as the credible sources and they slant one direction, then the models might think that they're being neutral. They might be told to remain neutral, but they're viewing one side of the political spectrum and claiming that's the neutral. So they're actually setting their middle point on one side of the political spectrum because that's the data that they've been tuned on and that they view as credible. So how do you then as a company running an LLM try and keep it sort of constrained to be what maybe you and I would say is neutral? I guess neutral is almost a subjective term. It's right now we haven't tried to go one way or the other on it. We just take the raw models and we give them to our users.

There's a thing called the system prompt that all of the LLMs, all the companies use where they, they give it like tons of instructions of like this is how you're supposed to behave. We don't have a system prompt in there. Well, we do. It's super minimal and you can see it. It's open source. It's just like one or two like really basic instructions. So for us, we've tried to just stay hands off and we like let users interact with the models directly.

And we have a user just this weekend. He was like, hey, it's Saturday night. You know what I'm doing for fun? I'm probing every single model on Maple and I'm figuring out which one is the least biased. Which one is like the least leaning one direction of the other. other. And this person actually said the GPTOSS actually turned out to be probably the most neutral of all of them, which I thought was interesting. So yeah, for now, we're not doing that. But down the line, I would love when we have more money and more abilities and more people to start to figure out how can

we influence some of these models to try and be more neutral and come up with more open standards and open development around that to be truly unbiased or truly neutral. And one thing that I'm sure some listeners know, maybe not everyone's aware of, is that this was a pivot from initially like a lightning wallet. So this was a pivot out of mutiny. You guys are Bitcoiners. Like, where does Bitcoin fit into this? Yeah. I was really sad that we shut down Mutiny.

I understand why we shut down Mutiny wallet. My biggest contribution. Oh, man, the reason I joined these guys is because it was my favorite lightning wallet. And so when I decided, excuse me, when I decided to leave Apple, I joined up with these guys because I was like, this is going to be awesome. Let's make this grow huge. Let's do it. But for reasons that have kind of been discussed publicly on blog posts and things, we decided it was just, it was best to wind it down. And so writing the blog posts of how we were winding it down, that was a task given to me. And it was it was like through tears, not literally tears, but through tears, I was writing it to wind it down. We've taken. a lot of that ethos of the Bitcoin mindset, the don't trust, verify mindset, and then as well as, you know, just kind of the open source development, and then the privacy aspect of Mutiny Wallet. And we've brought that into Maple. And something that we've done recently that we've launched is our new anonymous accounts. And this is a feature that is only made possible because of Bitcoin.

We can't do it with credit cards. We can't do it with stable coins or anything else. We had to do it with something that was private. And that was, freedom-oriented money that is uncensurable. So these anonymous accounts, you know, you mentioned you use like an alias, a privacy email alias when you signed up with Maple. That's been a big rub for a lot of people, is just having to use an email address because email can have some sense of surveillance to it if people are using like a Google account or something. So we came out with this anonymous account that just generates a unique ID for you. You have to write it down and save it. If you lose it, you lose your account. So unique ID, you set a password, and then you pay for it with Bitcoin.

So no credit card involved, no kind of know your customer stuff is involved. You can use on chain Bitcoin. You can use Lightning Network. You can use eCash that goes over Lightning. You can pick your privacy model that you want to follow. But for us, that is kind of the holy grail of an AI that lives in the cloud is you are completely anonymous interacting with it. There's nothing that ties it back to your identity.

And for us, the only way to do that was using an open protocol like Bitcoin. That's awesome. So it's almost like the Mulvad model of onboarding customers. Yeah, exactly. That's a similar model that we followed. I love that. And in Bitcoin, like we like to talk about this idea of if we get to a world of hundreds of thousands of ergentic AIs that all do like different, very specific tasks. Like the money that these AI models will use to interact with each other is Bitcoin. Do you think that's true? Because the reason I've had an issue with that is like it makes total sense. I can, like, if I was designing this, that's definitely how I would design it. But when you have these big tech companies that are like at the heart of all of this AI innovation,

like if they choose to push stablecoins to be the medium of exchange between different models, like do they not win just because of their sheer scale? Yeah, it's true. And you look at even government legislation, right? They push through the Genius Act with stablecoins before they did this Strategic Bitcoin Reserve Act. So it does seem like things are going more of the stablecoin route and you have Stripe that's working with stablecoins now and you have Tether working with Rumble and I know Tether's a Bitcoin company as well but they're also stablecoin. So I think from a technical standpoint, Bitcoin, maybe eCash or something on top of Bitcoin, I think that makes the most sense. But I'm less optimistic that that is going to be the outcome.

I think the only way that becomes the outcome, honestly, is if Bitcoin becomes like the reserve currency of pretty much everything we do, then yes, maybe there's stable coins backed by Bitcoin that become the engine that fuels all these AI credits and compute that we pass around. So, yeah, I mean, your guess is as good as mine. I think that I think you're right that there is a huge, huge conglomerate of tech companies and government organizations

that would love to push a different direction. Damn, I hope you were going to be turbo bullish on Bitcoin there. You were going to change my mind, but that's maybe a bit of a black pill. Hey, we've got to make Bitcoin the gold reserve currency and then everything else follows. And that's what will happen. Okay. Easy task. We'll get there.

When it comes down to like the compute backing AI, it's obviously been an insane year or two for all these companies, these like data center companies. How sustainable do you think that is? Because there's obviously a lot of talk on like AI bubble type things. But the sheer power that is needed to train and run these models. Is that like a trend that we're at the beginning of, or we in the middle? Like, where do you see all of that? Yeah, the training of the models is significantly more power intensive than the using of the models. I like to kind of frame it where you've spent decades of your life learning everything that you've learned up to this point.

You and I both have, and that's taken a lot of energy, a lot of time, right, a lot of work. but now you and I are sitting here have a conversation, and this is just an hour that we're spending an hour and a half hour long this ends up being. And so that's significantly less work. And that's really how the AI models are. So training the models takes a lot, using them not so much. And I think that I think we're going to see some breakthroughs where training is going to become easier and less power intensive.

And so there will be more of a focus on just inference, which is the using of the models. Now, is there a bubble? there bubbles really are just a malinvestment or too much investment in something right and so you blow it up you invest in all these things and then the bubble pops a lot of people think when the bubble pops it's like a soap bubble that pops and it's just gone right it disappears from the from being out up in the sky but really when the bubble pops and something like with AI and building like these data centers and all the power generation and stuff is we're still going to have all that infrastructure and there will be some winning companies when the bubble pops. There will be a bunch of losers that got invested in. And I've heard it framed that bubbles are actually important for building that new technology because if we were super methodical and only invested in the things that we knew 100% would work or 95% would work, the innovation will go too slow. And so we actually almost have to throw money at a lot of things and just hope to see,

you know, which ones work and which ones don't. And knowing that there's going to be some failures, But what happens is when it does pop, we end up with some really strong companies and a really strong infrastructure that can kind of move things forward from there, which is really how the internet worked with the dot-com bubble. And when obviously you're in Austin and it very quickly became like the home of Bitcoin mining Texas. Like every major like basically every major public Bitcoin mining company had at least a site in Texas. A lot of those have now pivoted, obviously not just there, but throughout the world, to being AI because they can make more money. Do you think that will be a growing trend where these Bitcoin miners will continue on the AI stuff? Or do you think that's almost like a short-term grab before moving back to Bitcoin mining? How do you see that kind of evolving?

Yeah. So my understanding from talking to a lot of these Bitcoin mining companies is it's actually not about the computers in the data center that they're just switching from Bitcoin mining over to AI compute. A lot of people think that's what it is. Really, it's the power contracts. So we have all these AI data centers that are spinning up and they need energy, and they can't get it. Either the energy is already being used elsewhere, or there aren't enough transformers coming in.

There's a backlog on transmission lines and other things. And so these Bitcoin miners are saying, hey, we're making this much money in mining. But then we have Microsoft over here who wants our power. And so we have this contract with the local utility. We'll start making money off of Microsoft. instead of mining. And that I see as a temporary thing until we start building out these small nuclear module, you know, the SMRs, the modular reactors. And those kinds of things. And you,

you co-locate them right on site with the AI data center. It's not even part of the grid. It's just, you know, for the data center. And I think that's long term what we're going to see 10, 15, 20 years down the road. But in the meantime, we're going to have some Bitcoin miners who are always looking at their bottom line and saying, what's the best for me right now? Do I sell to an AI data company or do I mine Bitcoin? And that's going to change here and there, depending on the market. It's $94,000 of coin right now. So yeah, maybe they're selling to AI people. But when it moons to 500,000, then they're going to maybe start mining Bitcoin again. And do you think part of this that's being driven by like these big tech companies that are just willing to throw money at it?

I even saw Facebook offering 100 million salary to developers, like high-level developers from Open AI to move across, plus 100 million bonus. Like, it's insane. If they're just willing to throw money at this to be the first one to get to AGI or whatever it is, like, presumably this has some legs. Yeah, I would think so. They're not going to waste all that money. They don't have endless firepower to spend on things. So they definitely see something, and that's the direction they're moving. So, yeah, I mean, I think it's, I'll say this.

AI as a technology has so much promise, and we've already seen enough utility out of it that it's here to stay. So that's kind of a foregone conclusion in my mind. Now it's just like, how do we build it out? And these companies are going after these massive power contracts and so that they can do what they want to do. So I don't know. Yeah, I mean, I don't know the final end result there, but I don't think that they're just wasting their money. And so it might, it might not be in the direction that we see today, right, with AI chat. There is going to be more products out there.

There are going to be, you know, the things that you wear, there's going to be glasses. There's going to be stuff embedded in your mind. There's going to be robots. They're building for 10 years from now. They're not building for right now. is not just going to be image generation and trying to do Studio Ghibli stuff. It's going to be way bigger things down the road. And that's what they're trying to lock down.

Yeah, I'm interested. What do you think that will be? Because when it comes to like the wearables sort of real world physical objects that are like AI, do you think it will be like a necklace, like that friend thing? Will it be robotics? Will it be, you know, AirPods? Like where do you think the kind of final form factor will be for AI in like every, everyday life?

Yeah. Well, I think the ears are probably one of the best spots to put something like that. People keep talking about like you're going to wear a pendant or something. I think that's kind of a dumb place to do it. It's really your eyes and your ears. Those are like the two biggest sensory input points for you as a human being. And so I think that's going to be where a lot of it is. And then as much as I like dislike this idea,

I do think that there's probably eventually something that's tapped into our brain and just kind of skips those senses and just go straight in and hard wires in. So then it's just a matter of how do you capture the data to feed into those wires that go into your brain and it'll be it'll be your eyes. And so it'll be something that you're wearing. And man, I hate it. Like I do not like that future where we've got cameras everywhere and microphones and everything picking it up. The only way that I see it being okay is if this technology is built in the open and we can inspect it and we can verify it. That is a future that I would love to see. You think about self-driving cars. You've got the Tesla's that self-drive and the Waymos and things.

There's a, there are a couple projects that are building open source self-driving. And so that is something I'd be fine with. If I could verify the firmware that's going into my car and know that, like, it's not going to drive me off a bridge if I say something that's, you know, politically incorrect while I'm driving the car. Like, I want to be able to verify that kind of stuff. So it is possible. We can have this world with all these amazing wearables and stuff as long as we can inspect. how they're being built and what's what's driving them yeah i think the neuralink is definitely going to be like i think that's probably the sort of end state of this um and it's both terrifying and kind of awesome like i i don't i don't want one yeah but when you see

these people who are like quadriplegic who can't do it like are literally just you know sat in their wheelchair unable to move and able to do anything and then they have the neural link and they can like play video games and communicate like that that's a use case that's awesome Like, I'm all for that. But the idea of every single person in the world being chipped and being, like, tapped into this global AI model is kind of terrifying to me. Yeah. That's like full dystopia. Yeah.

And the way you phrased it, the global A.M. I would hope that it's not one global one, that it's a bunch of different ones. Sorry, I interrupted you, though. Yeah, maybe there's multiple chips. But it's, I don't know, it's a scary world. I'll probably just start farming or something at that point. Yeah. How are you going to keep up, man?

Like, if everybody's using it, if everybody's getting huge productivity gains off of these chips and their brains. I'm just, I'm hoping I don't have to keep up by that point. I can just run away and be with, maybe that's where the Bitcoin Citadel has become interesting. It's like a no-chip citadel. Yeah, that'd be awesome.

Because it gets to the point where it's like, who am I talking to? Am I talking to you or is this just Chip talking to Chip? And I'm just the physical embodiment of AI. Yeah. I mean, are we all like, is this conversation? The conversation in the future is us two staring at each other. And then like our minds are going back and forth. It wouldn't make a great podcast.

No, no, it wouldn't. Okay. So what are there like other things that you're excited about, both just generally with the AI stuff and a maple? I mean, I love the idea that if we can build an ecosystem, that's not just us, right? I don't, I don't, if somebody walks away from this podcast, I don't want them to think that like, Mark is going to save the world, you know, from evil AI. Like that is not what we're trying to do. That's going to be my title. Yeah, dang it.

You can. You can click beta if you want to do. But no, what I want is I want to be part of an ecosystem. And if it's not robust yet, then maybe we can help like inspire more people. But we need people building out in the open. And opposed to what open AI says they are, they're not open at all. They're closed AI. And so we need to build truly open AI that is verifiable.

And that's really the only way that we can build a society that uses. this tool and can make sure that it's serving us. Because I think that AI has the ability to upgrade humanity, but we need to make sure that our humanity is preserved in the process, that we don't lose it as we embrace these tools. So that's what really gets me excited is the ability for us to kind of build out in the open. We're obviously doing it in the cloud with secure enclaves, but I would be remiss if I didn't mention local AI, right?

The most private way to use AI is to run the model directly on your device, turn off the internet, and now you're just talking to this thing that's on your laptop or on your phone. And nobody knows what's going on there. And it's just you having a conversation with this tool. And that's really what it should be is a tool. And local AI is awesome, most private. The problem is that it's just not powerful enough for some people. And so we're trying to build this middle ground between like the most private thing on your laptop and then what JadjPT is selling to people.

we want to be the in-between. And I think that eventually with Maple, we get to a local state as well. So with this memory thing we're talking about, we didn't even talk about some other things that we want to work on, like data integration into your phone. So you have your health app on your phone,

your fitness app, your journals, your other apps on your phone that are just very personal to you. You probably have a line drawn in the sand that you're like Sam Altman's not allowed to get into my journal entries. But if you can verify, the open source code of Maple

and you trust it because you can can see what it's doing, then you start to let it into those spaces. So I think there's a lot of really cool stuff we can do where we can make Maple the most personal AI, the most useful AI to you because we've built it with this data privacy. And that's really

what it comes down to you when you asked earlier in the conversation, you know, these companies with hundreds of employees and thousands of employees, how do we compete with them? We compete with them because we actually build the AI that people trust. We build the AI that can get most personal with you. And so it's going to know you better than chatypD will ever know you

because you're self-sensoring yourself when you talk to chat Chachapit, you're holding back. And even people who give it everything, they still hold back a little bit. My hypothesis is that they aren't totally brutally honest with ChatsypT because they know that they're sending their information to somebody else. And so we would love to build this place where people can get the most personal because they can verify it. That's awesome because that's 100% me.

There's stuff that I just refuse to ever put into one of these big tech LLMs, that if I know with Maple that that's completely private, that I'd be more than willing to share. And then you do get a way more powerful AI model just on the base of what you can actually willinglyly share. So that's very cool. I totally understand that as like a business model. I think that's awesome.

Can we just talk a tiny bit about Bitcoin before we close out? Because I'm interested in your perspective of, Like where Bitcoin development, like Lightning Network, actual usage of Bitcoin is going. Because obviously you tried to run a lightning wallet. You had a very cool one and then end up closing that down. Like, are you still bullish on like the things being built on Lightning at the moment? Yeah. I use Lightning pretty much every day.

A lot of it is Noster usage where I'm zapping people. But then I pay for things. Over the weekend, I used a square terminal and paid for something with lightning. It was great. It was so cool just to walk in. The person at the cash register, I said, hey, can I pay on Bitcoin? They're like, oh, yeah, here you go. Boom, they hit one button, and it popped up with a QR code.

And I paid, I use my primal wallet, but like I have probably five different lighten wallets on my phone. All of them work really well. I never have payment failures. So I do love that. I also still love the idea of on-chain Bitcoin. And I still like using that. And with fees being so low, like it's almost like,

why not keep using it? So I think on-chain Bitcoin, let's keep pushing it and let's keep using it while we can because that's like the most censorship-resistant form of it. The other L2s, like you have Arc, you have Spark, you have some of those other ones. I have not stayed as current with them because I'm no longer building a lightning wall on myself.

I've kind of moved away a little bit from staying totally at the date. But the thing that I do continue to follow is the whole eCash stuff. And I think eCash has a real big, it's really promising, both on the cash you side and on the Feddiment side. And we might see other ones that come out, other other kind of mints and other kind of eCash stuff. Because I think it's this really cool marriage of on Jane Bitcoin, lightning, and then something that is like a bearer token that you can actually pass around. I don't know if you've done it before. but with eCash, like, I can air drop money to another person peer to peer to peer from my phone to somebody else, and they get it. And it's like they can use it right then, just like a dollar bill

that I'm handing to somebody. So to me, I dig in more there. And then I hope that other L2s come along that have other cool things and we can continue to build and scale this thing. Yeah, one of the coolest things when it comes to ARC, I was at the Baltic HoneyBadger Conference earlier this year. And I used my cashy wallet to buy a beer. And I didn't even know until after the event that that had like everyone, all the merchants at the event were using ARC. So I'd used E-Cash, obviously lightning, then to ARC, without even knowing it happened, just like seamless like every, like the way I would always pay with Bitcoin. And it's just like using these different L2s to like complete the payment. And I had no idea. That's how it was working. I think that was really cool.

The U.S. has got to a point where it's like pretty easy. And when you go into like a square merchant, I don't have the U.S. privilege of having done this yet because it's only available in America. But like, how does it work? Do they have to press a different button on their like cash register to actually pay in Bitcoin? And is it just on, is it just lightning or can you do on chain as well? My understanding is just, it's just lightning right now. And yes, they do have to push another button. I know that the square team is already looking into making it on every screen.

So when you go to pay, you know, the square terminal has a screen facing the user and it'll say like, do you want to tap your phone to pay with Apple pay or something? They could just have a QR code already on that screen. So if a Bitcoiner wants to pay a Bitcoin, it's just right there. They want to get there eventually. They're just not there yet. And then obviously the biggest hurdle is they have to turn on Bitcoin to begin with. And that is required by some kind of admin, somebody who has like elevated privileges on the terminal to turn it on and activate it. And there might be also some like know your business kind of stuff, KYB, where maybe the,

maybe the store is on an older version of Square Terminal and they never went through some of the documentation government ID kind of stuff. So there might be some of that they have to do too in order to activate it. But once it's activated, yes, it is like, hey, do you accept Bitcoin? Oh, I want to pay in Bitcoin. So there's a little bit of friction there. And I would love to see them remove that to make it even more seamless. Yeah.

Do you think this is maybe a bearish question to even ask? But do you think it'll work? Because we've seen people try and convince merchants or accept Bitcoin in the past. Around 2017, 2018, there were a ton of companies or some of businesses where I live in Brisbane that were accepting Bitcoin. And you slowly saw those, we accept Bitcoin here, stickers get pulled off windows because no one actually used it. Do you think this is different because Square is such a huge company with so many businesses actually... integrated. Yes, I think it is. And there's also so many different wallets out there now that work really well. So from a user standpoint, like it's really easy to and cash app being the biggest one,

right? I think that's, that's really what it is. It's this company that's come in that has all of the pieces. They have an app, an end user app with a wallet that is used by tens of millions of people and then they have the merchant side. And then they've also built in the financial incentive for the merchants where it's like it's zero fees through the end of 2026. So no fees there. And they're making it so that you don't even have to pay with Bitcoin. You can pay with your USD balance. I don't have you seen this, but you can actually pay it with your cash balance. But then it goes over the Bitcoin rails and then settles in cash again. That's how the merchant wants it. So they're just using more like strike is doing where lightning is just the rails between the two

intermediary the two parties but they're both exchanging in their currency that they prefer to use so I do think that this time is and this time is different the famous phrase I do think that it is because there's such a critical mass of people now around the world that know about Bitcoin and have it and then the merchants can see there's this history now that shows that Bitcoin appreciates over time due to kind of the scarcity of the asset and Square makes it so easy for them to slowly get into it, right? They don't have to let go all in on Bitcoin. They can just go a little bit if they want to. And then as they see it grow, then they go into it more. And then one other piece I'll throw on there

is that that Burger company, Steak and Shake, that famously started accepting Bitcoin before Square turned on their stuff. Steak and Shake has had, I don't know if they're public earning reports, but they've come out and said, hey, this Bitcoin thing is going so well for us. We've actually accelerated some of our store openings and our expansion. Our company is in a much better financial state now because we just went in and started doing this Bitcoin thing. So it's going really well for us. So that's a case study now for merchants to look at and say, all right, we have like 9%

margins on this thing. How do we get this 3% fee reduced down to zero? That creates even more margin for us. And then we can also start saving the Bitcoin, which allows us to expand our operations in the future. So I think there's a lot of bulginess there. Yeah, that's very cool. Because they're doing their Bitcoin Strategic Reserve now as well, which is awesome to see.

And I love that when Square made this announcement, the fact that they're using Bitcoin as the payment rails, kind of regardless of currency in currency out. It was almost like a hidden thing. And to me, that's the coolest thing that they've done. I think that's absolutely amazing. And it was like a stealth launch that I think Miles first announced on Twitter almost accidentally. Like it is really cool how this has gone so quick. they're shipping a crazy amount at the moment over a block. They're doing so much cool stuff.

Yeah, they've been shipping like crazy. It feels like Miles kind of won the internet last week. He was just like on there. Like this was his week. And it was awesome to see like my hats off to all of them over there. I know it's just not him. It's a whole bunch of people working on it. But that's been great to see.

The last things is I would love for people to just kind of think about how they're using, using AI and kind of picture it. as when you're using some of these systems like chat chvetypT, that you have another person sitting in the room, excuse me, you have another person sitting in the room, kind of watching everything that you do, and they are approving or rejecting what you do and say, and they're making copies of it.

So just kind of have that in your mind as you're using it. And then I would love for people to sign up for Maple. They can go to try maple.a.i.i. They don't have to stop using ChatTPT. Just add Maple into your toolbox, and then use it for things. And you'll start to see like, hey, that person that was sitting in the room with me, listening to everything that I'm doing with my AI, that's not there in Maple.

And start to notice how you use that differently and how maybe you're more free to speak. And that's really our whole thought around Maple. Maple is the AI that allows you to think freely. And what I mean by that is just there's nobody who is trying to get in your way. And there's nobody that's going to hold you accountable for anything you say. because when we think in our mind, we think all sorts of things. And that's how we're supposed to work.

And so if we're going to use this AI tool to help us think, we shouldn't have some intermediary in between telling us, no, that's not okay to think that way. We should be thinking freely. Yeah. I mean, people should definitely go check out Maple. I'm actually, like, this has made me reconsider how I'm using AI. I'm going to use Maple more and more because, like,

I think a big part of the reason that I'm kind of stuck on chat GPT is just habit. So I'm going to try and break that habit and use Maple. So this week, I'm going to be Maple only and we'll see how it goes. Awesome. Okay. What I want from you is, oh, sorry, what I want from you is like, tell me the features that you're like, I must have that in Maple. That's really useful from people so we can start building that.

I do think for me, the big one will be memory. If we get memory, like, that's awesome. Because like now it's no longer sort of restrained by the training data that you can actually search the web, like that's huge. And if it then had memory, I think that's, that's kind of everything I want really. Talking to it would be great, but I only do that really occasionally anyway, but if it knew a bit more about me in a private way, that's basically everything I need. That's awesome. Let's go. All right. Thank you, Mark. And where do people find you on Twitter if they

want to follow you? Oh yeah, it's just my name, Mark Summan on Twitter, and then I'm on Primal, Noster. I'm just Marks at primal.com. You can find me there. Awesome. Thank you, Mark. This has been great. And I'll hopefully see you in Austin soon. Let's do it.