Marty (2:19)

Well, I think it'd be worthwhile to go into differentiating, like LLMs, how they work from these graph. This graph approach. Because I think, again, we were just discussing before we hit record, ton of capital, time and effort has been put into LLM specifically. But I think some would argue, I think yourself included, that LLMs may not be the best way to go about this problem.

Paul (2:51)

Yeah, I think people anthropomorphize LLMs a lot because it's speaking language to you, because you can talk to it. You think that it's actually reasoning, and especially when they call it a reasoning model. And it does do an amazing job of mimicking logic, but it does not know why it's saying what it's saying. It's just a statistical output of the next word. Yeah, Yeah. I mean, how do you see it? Do you. When you think of an LLM, do you think of it as. Do you find yourself thinking of it as a machine spitting out words, or do you think of it as, you know, especially when you name a bot or something like that, it really starts to feel like a human or something.

Marty (3:31)

No, I definitely don't think it's human. Anytime I interact with our open call, I'm like, okay, what context do I need to feed it to make sure that it gives me the right response? That's what I think most about is like, what do I need to preload this thing with?

Paul (3:43)

Maybe we should start with, what's everyone running right now? That would be a good thing. You're running a bunch of cool stuff, right?

Brian (3:48)

Yeah, I mean, I'm using Hive, of course, doing some cloud code, but I think the context issue is something that everybody's kind of running into. They're able to just stumble through it or hack their way through it. But I think we're all going to feel this need of something better, something that's going to be more accurate, give us better answers, help us do the next thing better. And you're starting to see it. Like, you open X and there's more and more visualizations of graphs. I feel like the whole ecosystem is drawn that direction. But, yeah, those are some of the things I've been messing around with.

Marty (4:33)

Yeah, it's. How about you, Marty? I mean,

Paul (4:37)

I.

Marty (4:38)

For the context, like interacting with my open Claw bot, like, when we're doing something, I have to be very specific. I'm like, hey, we're going to write the bitcoin brief today. I've dropped some stuff in a folder that I've named, a specific name. I'd say the name of the folder, go to my tab. The tab is named this. Like, I Have to give it like direct direction. And I've heard of people leveraging the Obsidian vaults to solve that context issue. I haven't dove down that, but we're using Openclaw, we use Claude 4.6 and then combination of code. We have codecs, sub agents that we can. That we can ping if we need to build something. And then 11 labs are the models that we're using now for voice.

Paul (5:20)

Yeah. When you told me about your automation process, it was pretty solid about what you do. Has that stayed the same since then? Or do you want to describe that a little bit?

Marty (5:28)

Yeah, I mean, for um. Like we'll take this podcast, this transcript, and then we have prompts that we've been iterating on for literally two years at this point in Claude that will. That are very long prompts that will take the transcript and then pull certain sections, quotes and be able to either directly quote for a section of the newsletter or find something that we're talking about, do deeper research on it to expand on the topic for another piece of content. And that is for the podcast, like post production, it's one of the things we'll do. But other things we have for clips, we'll give it the transcript with timestamps and Claude will then use our third party Twitter API that we have access to, look at what people are sort of talking about, what's trending. And then it'll go back to our transcript, see if we talked about something during that episode that people are really on top of. And then we'll be like, all right, here's the timestamp of a section of the conversation that you just had, that if you clipped it out and put it on Twitter, it would. It would probably get good engagement and

Paul (6:40)

are using N8N for that sort of that pipeline anymore or it sounds like you brought up robots.

Marty (6:45)

So we were.

Paul (6:46)

Okay.

Marty (6:46)

We vibe coded our own, like backend dashboard that we basically replaced N8N. Um, shout out to Ed on our team. He's put a lot of effort into that.

Paul (6:54)

That's pretty cool.

Brian (6:55)

But so when you're done, like this episode, when you hit finish or record the recordings over, does it automatically kick that over to these workflows?

Marty (7:04)

Once we put the files in Dropbox, it'll start and then.

Paul (7:09)

Yeah, you'll be.

Marty (7:10)

Or you can just. If we put it in Dropbox, you don't want to trigger automatically in the dashboard that we've built, you can go like, all right, write it, run this process.

Paul (7:18)

That's crazy. Yeah, that's cool. I was talking to Scott, who runs, you know, Scott Foreman, he runs a video lab. They do a lot of work for the Human Resource foundation. And he's trying to do a similar automation on the back end. So everyone is trying to figure out this process.

Marty (7:36)

But it is like it does feel. We were joking. You came here in a Waymo and it was driving on the wrong side of the road.

Paul (7:43)

Are you serious?

Brian (7:44)

Yeah, I'm like, I stopped paying attention to the road. When you're in the back of an Uber or Waymo and the car starts like jittering and I look up, the Waymo is on the wrong side of the road with traffic heading towards me. Because Austin has all these events going on. So they were like coning off the streets. So you could tell that somebody took over via teleop.

Paul (8:06)

Yeah.

Brian (8:07)

And then finally got me over. And then as soon as we were getting over, there was a traffic cop who was like trying to direct the Waymo. And it was just this moment. I was like, gosh, what the hell

Paul (8:19)

are we doing here?

Brian (8:19)

Yeah, well, that's a human directing Waymo.

Paul (8:23)

And then some poor person sitting probably in the Philippines operating the car like a video game on some PlayStation console from 1988 or something like that. Yeah, it's crazy.

Brian (8:34)

One thing I was thinking on the way over was and we had a team meeting yesterday, we were talking about this. I think we all find ourselves thinking about the current state and it's hard to think about the through line, like where things are going to be in a few months. And when I was in this Waymo today, I was thinking about when Waymo first came out, or self driving in general. People have been talking about this for a long time and dismissing it, but it's like it's basically here. And of course there's going to be some kinks along the way, but we've come a long way in a short period of time. And it's pretty hard to imagine what a year from now is going to look like because things are changing so much. But it's exciting. It's like an amazing time to be alive.

Paul (9:21)

I mean, we don't want to say the phrase, but you have to say it gradually, then suddenly. And people just have such a hard time with it. I have a hard time with it. So, I mean, we're doing coding automation stuff. Lots of people are. But the platform that Brian mentioned, the takeoff on it is so real, it's so crazy. I'll do a demo for you later on. But I mean, we pull up a Voice assistant on our conference calls and you haven't even seen this yet. We nicknamed Jamie after Rogan's assistant, which I don't think anyone's really got on that, so I think that's a good nickname. But you literally just talk and Jamie can read your code. So it's not just some note taking assistant where you tell it to make a note of this or basically transcript manipulation. That's what most of these things are. It can read through all of our graph database, which is all of our code, all of our chats, all of our conversations, all the context for the conversation. So how many times have you been a meeting where you go, hey, is that the way it really works? So you don't remember, hey, Jamie, can you look that up and see if the code actually does that? And then once you can get past that in a meeting, you just have. It's such a flow state because you're not like, hey, let's figure this out and circle back together again. You just know right away and then just keep going.

Brian (10:34)

If we were talking about your agent in this, in this chat right now, and you're like, yeah, I really wanted to do whatever. And then by the time we're done with the conversation, it's just done, fixed it. I think that's what we all want, right? Yeah.

Marty (10:44)

I mean, you were demoing it to us in San Francisco in November and it was doing it come a long way. Yeah, well, that's the, that's the thing as an operator, integrating these tools. To your point, Brian, like, what is the through line to where this is? Like, you don't have to worry. Like we, I tell everybody at our team here, like, hey, this stuff's changing rapidly. Like something's going to work one week and then something better is going to come out. We're going to have to like start from scratch to rebuild it. And, and it's just like this constant iteration of like, okay, build something quick. It works. It works better than the thing we built before, but, oh, here's something new. Like, we gotta go do that again. So that's what, like, I'm curious, like, when are we gonna get to the point where. Yeah, you don't have to keep doing that.

Paul (11:25)

Well, there's a book that really impacted me early. It's called Second Machine Age. Have you heard of this before?

Marty (11:31)

Yes, I've read that book.

Paul (11:32)

Have you read it?

Marty (11:33)

It's been a while, but yeah, it

Paul (11:34)

is an old book. MIT professors, I think, and they talked about this idea. I mean, don't read it. But I think the main idea is combinatorial tooling. So once you make tools that help you make other tools, this is when you start to see the fast takeoff. And so I think that book made. The point of what we're seeing right now is that the software tools to make software tools is happening so quickly. And you can automate workflows, you can automate all the stuff that you're talking about. And I think that the people who figure this out and can get these tools are in a permanent. You know, we talk about the K shape economy. Have you heard of that? Have you? We talked about that. So I was explaining that to someone yesterday. It's so true. If which line you're on could sort of determine what it's like forever or for a very long time. If you're on the downslope of that K, then you may not ever make it to the upslope. Which to me means that we're just at this really critical moment. I was talking to the best man in my wedding last night in Georgia. He works in construction management. And I'm going, are you using any of these tools? Have you heard of any of this? Nothing. So I sent him a video. Here's how you get started. So I think that just the distribution of this tech is just so uneven right now.

Marty (12:50)

What do you think?

Paul (12:50)

We're in a bubble, basically.

Marty (12:51)

Yeah, that's what I was gonna say. What do you think the Penetration is like 0.1%.

Brian (12:54)

It's like you go on X and you think everybody knows everything. And then you go to like a family gathering. You're like, nobody knows anything. I mean, that's been my experience.

Paul (13:05)

It's tough. We're sitting here driving around in Waymos and experimenting with Apple Vision pros and playing around with Clawbots, and it's pretty scary. What I think can happen when you diverge people this distinctly is going to be tough.

Marty (13:21)

Well, that's again another question on the through line that I have thinking through this social problem. Will it get good enough where. Yes, we're early adopters, we're reaping the benefits of this massively right now. But will it get good enough where it doesn't matter? Somebody like your best man will be able in a year from now to download an app. Just do it like that with little learning curve.

Paul (13:44)

It'll accelerate so quickly. I think it'll be way faster than that. I mean, what's your take on the. Well, where are you on the doomer?

Brian (13:51)

There's like a Divergent point here. So on the one hand you could say the K shaped graph, which is basically to say that people, there's the people who know how to use these tools and the people who don't.

Paul (14:01)

Right.

Brian (14:02)

On the other hand you could say all these. The tooling is benefiting the creators because the tooling is getting more accessible and easier. So then it lowers the bar to creation. I hope that's how things come to pass. And I think about my kids. So I've got four daughters and the older two are old enough now to where I'm like working with them on some things. In fact I'm working on this bitcoin project with them I want to tell you guys about. But like our local pool, community pool, they have a website where you go and see the pool schedule and times and order your swim gear and stuff like that. It's a piece of crap website. And so my 10 year old, I was like, hey, why don't we try to fix this website? And the tools are easy enough for her to sit down at the computer and we just iterate on it. So if she's able to do these sorts of things, she has the intention and the desire and she has like a point of view on how things should be and she can do it. And that's today. I just imagine a year from now it's going to be everybody with an idea can go and create their idea. I hope that's what the future looks like.

Paul (15:12)

Did you see replit's announcement yesterday? Agent 4? Mm, yeah. I mean I tried it yesterday. It's really good. I mean it's really good. You can basically just start with a sentence and then it'll build the slides. So he completely expanded what Vibe coding or Vibe building is like. You get the code, you get the decks, you get the images. I mean a lot of it is stitching it together in a clever way. But you stitch enough of that stuff together and 8 year olds can create businesses. 10 year olds.

Host (15:44)

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Marty (16:27)

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Marty (17:31)

So go check it out. I had a 15 year old, a 15 year old girl on the show a couple months ago. She's at Alpha school. She's.

Paul (17:37)

Oh yeah, yeah, I saw the clips of that.

Marty (17:39)

Yeah. Stella, she's daughter of a, of a good friend of mine here in Austin and they're obviously Alpha School is probably tip of the spear of integrating AI tools into education and it was blowing my mind like how, how robust their, the aperture of like what they let the kids do at the school and has me thinking like so I have three boys, the oldest of which is six. Right now he's in kindergarten and just like a Catholic school and I think it's a good school kindergarten, it's Montessori based so they're not really getting Too much in the weeds of math and science and stuff like that. But I really want to go in there and be like, hey, we need to sort of rejigger how you're teaching these kids with these. And you need to integrate AI because this is what they're going to grow up with.

Paul (18:26)

But they're doing the opposite. The guy had dinner last night. His son's friend is going to one of the top boarding schools in the country we've all heard of. And they're just so anti AI. You cannot use it. They're not allowing it. You don't touch it. It's evil. Imagine teaching that to kids right now. It's unbelievable.

Brian (18:49)

I'm of two minds. So, Marty, you haven't met my wife, but Paul's met her many times.

Paul (18:54)

She's great.

Brian (18:54)

She's awesome. She's on a crusade to reduce the amount of screen time for our kids in school. We go to public school, which I'm all about. I think just giving these kids Chromebooks and having them sit on a computer all day long is not what young brains should be doing. On the other hand, I want them to be exposed to these tools and become capable with this technology because it's so cool. It's amazing. You can make anything. So I'm mixed on this, you know, how much to expose young people to like kids to this technology and encourage them to build, but without having them sit in front of a computer screen all day.

Marty (19:34)

Yeah, that's why I like the Alpha model. It's like two hours of intense, like

Paul (19:38)

how they do it, integration.

Marty (19:39)

Then beyond that, it's like social skills, like group projects, stuff like that. Yeah, that sounds good.

Brian (19:44)

Especially if we, if things happen the way we just described, where it's like people can have a conversation and then you end up with a product that other people can use. That's a lot different than the hacker spend, you know, not sleeping, spending all night coding something. I hope that's the future we find ourselves in.

Marty (20:04)

I think the focus needs to be on like first principles, logical thinking. Like, clearly, that's what I've come to learn. Again, going back to how I interact with my. My agent is again thinking about the context and the direction I'm going to give it. So, like having to think from first principles. Okay, what's the sort of logical order of instructions I need to give this thing to get the output that I need? And I think reading comprehension is going to be way more important than STEM moving forward, unfortunately.

Paul (20:37)

Well, you write every day, so it forces you to clearly think through steps. And I think having that, that muscle fully exercised, when you write down, it's, you know, you've heard this term spec driven development. You're no longer coding. You have to clearly articulate what you want. And that's really hard to do. When I sit down and try to spec out and the system's asking you questions. Well, you want to select all, and then what? If you select all, then how do you unselect? You're just going, oh, man, you're right. But forcing you to think through the whole thing is actually where the mental friction takes place. So how do you teach that to the next generation?

Marty (21:13)

Yeah, like, how are you?

Paul (21:14)

I mean, you're kids, right? So it's like, how old are they? And what would you say to them?

Marty (21:18)

I mean, my oldest is. He's just in the process of starting to read, but I think never too late.

Paul (21:23)

Marty.

Marty (21:24)

No, but I think reading the classics again, like, getting like, I don't know at what age you introduce it, but like, literally Socrates, Plato, like, how do you think, like, how do you logically,

Paul (21:34)

like Aristotle read the really old stuff?

Marty (21:36)

Yeah, I mean, I went to a Jesuit high school and that and actually, like learning Latin. Like, we had. We were. We had to take six years of language and two of which had to be Latin. And like, you get down to, like, the etymology of language. And the whole goal of our high school is by the end, when you graduate, you're going to be able to write competently. You're going to be able to read something and then have an opinion on it and write articulately about it. I feel very fortunate. It set me up. Like you said, I write every day. But I think that model is going to be more important moving forward.

Paul (22:09)

What survives after AI then?

Marty (22:11)

I don't know. That's the other question. Is it going to make us dumber? You see people allocating their thinking to the machines, and are we going to find ourselves with a competency crisis a decade, two decades from now where, God forbid, something happens to the machines and then we don't know how to rebuild anything? Because you have a generation who never learned the first principles of software or math.

Brian (22:37)

Yeah, but I think most of society has gone that way. When the average person encounters a car engine, they're probably, I don't know what the hell is going on in here. And then you meet someone who knows what's happening, and it's extremely impressive. And that person who knows what's going on can fix it. They can do a lot More things. So I hope that that kind of competence gets rewarded in the future. I hope these types of people are better at creating things, at advancing things, and encourages other people to do the same thing. And I think from a parenting perspective, I think that's an important thing to bestow on your kids. And honestly, my role model is Paul. I remember the first time. Do you mind if I tell the beaver story?

Paul (23:24)

Sure.

Brian (23:25)

Okay. First time I met Paul's younger son, came over to his house. First time I went to your house, there was sous vide happening. Sous vide. I was like, what are you guys cooking? Like, oh, beaver tail. Like, okay, interesting. And then he goes on to tell me that his son just got. He was like 10 at the time.

Paul (23:46)

Yeah, 10.

Brian (23:47)

10 year old son just got back on a solo hunting trip. He trapped a beaver, brought it back, was sous videing the beaver tail. So he's cooking for the family. But then before that, he scanned the beaver and was making a video game out of the 3D scan of the Beaver. He's 10. It's like, what? And then over the years, I've come to. You homeschooled your kids?

Paul (24:10)

Yeah, for a while, up until high school. Really.

Brian (24:13)

And I just. I think I've learned that you took a very. You and Nikki, your wife, took a very intentional approach to how you brought your kids up. And they understand things deeply, and now they're super resilient and capable. So, I mean, I feel like this is a tangent, but also pretty important thing for people nowadays is to try and teach people these. Like.

Paul (24:36)

Yeah, I mean, I was. I mean, you guys are in a different phase in terms of upbringing. But I told I was right about one thing and wrong about another thing. I said, don't learn to code. I don't think that people will be writing these letters long term. So I wouldn't say that learning to write the letters is unnecessary because I think that just like learning Latin, you're not writing Latin, but learning Latin, which I did too, teaches you the structure underneath. And so I really felt like the job of writing code won't be around long. That's why I told them when they were that age, just don't bother worrying about that. But I also thought they would never have to drive a car. So that took way longer than any of us thought. You know, I rode into Tesla. You know, I was driving down the freeway a decade ago, and I thought, oh, this is. We're so close. So I think. And now that's basically here. So the question of Timing is the one that's the tough part. I will say back to our previous point, just that I know this from firsthand, that you can automate, basically today, with no advancements, no change of anything. 80% of every job done on a computer. So everything that we're all doing here and 80 is pretty conservative. So go into the most esoteric job in some law firm, and you can automate it consistently to a degree that's better than the human doing it. And that's a fact. And whatever we do with that fact, I'm not sure. But again, my son's friend who's doing finance at a public university, and I'm looking at his homework, and it's manipulating spreadsheets. Oh, my God, you're paying to have someone test you on editing, building a spreadsheet. And I dropped the files in the folder and said, here's the thing called Claude, cowork, go to town, you know, And. And then I had an Uber driver the other day take paying 6,000 to 8,000, something like that. He saw the waymos and was like, I have three. I've been driving for 15 years. I guess I have three years left before this whole job goes away. I'm paying 6 to $8,000 to learn to write QA code for class at night. And I went, oh, buddy, don't do that. Don't do that. That's a bad. That's the job that's going away now. I mean, could you turn that into something else? And he goes. He's sitting there, and he just says, well, what should I do? And I went, that is a very good question, man. And I've had to go with, you know, what do you. What do you love to do in your free time? How do you turn your hobby into something? But literally, that was when the ride was over, and I just said, it's just stuck with me. What do you tell people to do? What do you tell your kids to study? What do you major in college for? Do you go to college? I mean, these are things, you know, my kids aren't have. Don't seem to have plans to go to college, which I don't think is a bad thing. I just, I. You know, I think they miss out on some of that critical thinking and socialization that the college part, the collegiate part of meeting people is super critical. What they're teaching you. Don't use AI to write this essay is absolute crap, you know? So how do you separate the two? And so I think there are, you know, whatever these Schools are popping up, home schools, all that stuff. I would say, you know, go. I would advocate for that strongly. I think that you don't want to have, you don't want to be cookie cutter in this next era. You don't want to be like everyone else because you're already in the training data and so are all your friends in the training data. Like, why do you exist?

Marty (28:15)

Well, like, talk about gradually. Then suddenly this has been around for like, we didn't have an attendance policy, so I, I didn't go to class and I just used Khan Academy to teach myself.

Paul (28:23)

Like, oh really?

Marty (28:24)

Yeah. Like I basically took all my con. When was this Salcon. I graduated in 2013. So like Sal Khan covered everything. I, I studied in college and I was like, all right, I'll just teach, teach myself. And I were, I wound up working during the day and. But yeah, I mean, to the point of gradually and suddenly, this has been positive. This change has been emerging slowly, but now it's hitting a critical tipping point where, yeah, it's, it's, it's here.

Host (28:52)

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Marty (30:39)

And I really want to dive into this like graph model versus 80% of the way there. Are you confident with that graph? Because again, going back to lms, you can tell I was talking to Justin Moon about this. Like the first thing he does whenever he's test out a new LLM model is tell me a joke. And nine times out of ten the joke's very similar to the previous model. And it's basically like it's using that distribution of okay, this is what a good joke is. And you end up in the middle of the bell curve and you get a similar joke every time with the graph model. Seems like it has way more.

Paul (31:13)

Yeah, I mean there's graph pops up a lot. So I mean, I don't know if you guys want to dive into the details or not, but it's. I think I was practicing explaining this the other day, but there's really four, maybe five pieces. So should we go through them? What do you guys think? So, and you've seen this slide From I think 2022 is when I wrote this slide. So there's basically people over index. When ChatGPT came out, it was like six year olds playing soccer. This model is going to do everything because you're so blown away by where you previously were versus what ChatGPT can give you. And so everyone was like, these models are just going to take over the world. Remember the thin wrapper thing? Don't be a thin wrapper because it's going to eat, you know, the model's going to eat everything. No one talks about that anymore. In fact, I think OpenAI bought a workflow company late last year. So if the Model is going to eat everything. Then why do you have all these extras? So in the corner you do have the models, but I think that they are very good at interpreting human intent and then communicating back out the pieces of logic in words. So I look at it as like the input output to these things. And you can extract a ton of logic from the language models because the sentences contain logic. So you have the models in one corner and then you have the thing that everyone's excited about right now, which is agents and loops. But any programmer will tell you because the more technical you are, the more you look at this and you just go, well, what is an agent doing? It's a loop with tools and language and you can do crazy stuff when this thing just has the right tools. It can do crazy good, it can do crazy bad. But that's. I think of agents as figure this problem out, iterate and then get back to me. That's what the agents are really good at. I look at them as like explorers or discoverers. I don't really know what the problem is. You go and do your thing and then tell me what happened. And those are. Agents are incredible though. People call it agent harnesses and stuff like that too. So language models, agents, and then the piece that people overlook are the workflows. You actually don't want your agent figuring out how it did something yesterday every single day. Right. I don't need it to go and figure out. The way I look at it is if you didn't have Google Maps and you just had your cars driving around town with no map, they will end up at the coffee shop one day when they're just driving and driving and driving. Once you know how to get to the coffee shop, write it down, create a list and execute that list. So the workflows help keep the agents on track though, don't, you know, just go off the rails. So when you know how to do something, that's the workflow. Cron jobs. And those are the kinds of things, schedulers, you'll hear those words. So that's. That part of it.

Brian (34:00)

Cron jobs is you would put that in the workflow bucket.

Paul (34:02)

Yeah. That's basically triggering the workflow. Okay.

Brian (34:05)

And just to just make sure I'm tracking. So the agent and the driving around town analogy, the agent's loop is just go find a coffee shop and they're just sort of driving aimlessly. Whereas you give it the workflow that's kind of like the instructions of how to get exactly to or the directions to get to the coffee shop once

Paul (34:20)

you've figured something out.

Brian (34:21)

Got it.

Paul (34:22)

So input output with the language models. Then you've got the agents doing discovery using tools and language. And then you've got your workflow that actually can do very complex long steps. And you just. When you know what you want, you generally want it to go. You put it into a workflow. That's what you were doing with it. Yeah.

Marty (34:41)

Or that could be like a cloud skill or something.

Paul (34:43)

It could be anything. Yeah, anything that's writing down steps. Skills can be tools, though, too. I'm less familiar with how Claude does the skills. But then the last piece is memory. Right. So. And memory right now is in the form of files. So whenever you talk to your cloud bot, or whatever you're calling these things is just. It's writing down these markdown files and it's reading them, though. The analogy people use is that movie Memento where the guy wakes up every morning and doesn't have any memories. He has to read all of his tattoos. So every time the agent goes through the loop, it's actually rereading the files and figuring that out. The problem is that you have the language model, the agent loop, and you have text memory. Those combinations results in the thing you were talking about before. The language model can only have the attention span of so many words. The agent is just spewing through words like crazy, writing them down, reading, and writing them down, reading. And then the more complicated it gets, the longer it gets. The model can no longer hear all the agent has to say. So then what do you do? You have to shrink or truncate or compact. Those are the words you'll hear. Compaction. And it'll just arbitrarily take long sentences and turn them into short ones or crop them. There's many different strategies to do that. That's when you start to see mistakes. So now you have an agent with Alzheimer's because it's just missing chunks of his memory. So can you do files? What everyone's trying to figure out now is, are files really the best way to do this right now? They're incredible because the models and the agents have really great file searching capabilities using grep and bash tools. So they're really finely tuned to do that. But it's almost like the tail wagging the dog. Hey, we're really good at text tools, so let's use text for all of our memory. It doesn't make sense. So what we've explored for years is using graph databases. So we started out with microtask Payments, where we pay human beings to do small tasks using lightning. Right. Our company is founded on that. So we were taking complex human processes, breaking them down into small pieces, and then giving them to different human beings around the world, and then reassembling the answer later, which is exactly what these language models are good at. And all these systems, what you're describing is you're taking your process of doing a podcast and breaking it up into small pieces, giving them the different models, and then reassembling it at the end. We were doing that with humans from day one, and so we were very well primed to then say, oh, we know how to break things up into workflows. These skills humans are doing, and some are automated, and now just more and more is automated. So we think that memory will end up in a graph structure, whether it's a graph database or not. And so that's what we've been working on forever.

Marty (37:43)

And again, you were demoing Hive to us in.

Paul (37:47)

Yeah.

Marty (37:47)

In San Francisco, like, comparing it to how people are using these harnesses. Some like openclaw. How would you describe the benefits of leveraging this graph database instead of all these text files?

Paul (38:01)

Yeah, the graph database can basically construct, using a search, the pieces of text you need instead of a file. The reason why we moved, I mean, when you log into Airbnb, they're not reading a bunch of files to get the listings. They have a database. Right. Because databases are way more efficient than text files. Somehow we've gone full circle back to pre database land and going, we're down in the 70s going, oh, let's just use text files.

Brian (38:31)

Right.

Paul (38:31)

So what do you miss with text files? Relationships, versioning, typing. It'd be like, I don't want an Excel file. I just want to put it all into a text file. You could, but you still lose some stuff. So that's what we're trying to point out to people is the same things that drove people away from storing all of your information in text files 50 years ago apply to memory today. So you have to be able to query the pieces that are relevant in the background. We do so much using workflows, janitor work to clean up the context so that it can be presented in a way. This is what Evan from our team has really excelled at, is pre rendering the context in the way that the model can actually understand it. So there's a lot of layers. So you have, say, every recipe you've ever cooked, all the ingredients and text files. But then you don't know how often You've cooked that recipe, right? This is a recipe I've cooked 800 times. Text files aren't great at that. Right. Because they have the recipe, they have what you ate in a different text file. Let's say you're tracking your meals. But those two things aren't linked together. So how would you do that? You couldn't count. And these things are trivial for databases to do. But that's why your openclaw or your agent of any kind might seem dumb because it can't keep those statistics, whereas any database and especially a graph database can do all these things for you. Yeah, that makes sense.

Marty (40:06)

It does. We have our agent has QMD from Toby running in his server to sort of solve some of that context. Not exactly. It's a vector database or a rag.

Paul (40:22)

Yeah, I haven't loaded qmd, but I was just going to bring up rag. Remember, you guys invested in a company too that does rag. And so rag for people is just the process. Everybody does rag all the time. So the term rag is really retrieving stuff and feeding it to the model. The most popular way of doing rag for years has been with a vector database. And no layperson touches vector databases. Right. We've gone over this too, but it's basically turning text into points in a space. But you can't picture the space because it's not three dimensional space, it's multidimensional space. And the example I use is if you do. Back to the recipe analogy would be if you do a keyword search for the word avocado, guacamole will not come back. Those are two totally unrelated spellings. But a vector database, guacamole and avocado will be very close to each other in this space because they appear all the time together, right? Yep. But when you take the word guacamole and take the word avocado, one is from a farm bureau about avocado farming and the other one is a recipe or menu in a Mexican restaurant. Those two sources for those two words are completely unrelated. But the vector database cannot tell that those two things are unrelated. They'll just say they appear together very often. So if you use vector databases for rag, you get these kind of random combinations. It's better than keyword search, but it actually orphans the word from the source of the word. So I don't know if that helps or there's a bunch of different examples, but vector databases peaked like crazy about two years ago and they took a precipitous fall because of this problem. It's orphaned information. The best example I've heard, and I'm borrowing this from someone else, is if you do the Berkshire Hathaway in a report and you're searching for 2023 EBITDA versus 2024, a vector database can easily mistake those two things because it's just one character difference. And the embedding might be very close to each other, but those two numbers are very different, meaning if you're trying to plug them into a stock picker. So that's the kind of thing that you get with vector, and especially vector with code, because code is so similar to each other. If you search on. If you use vector databases purely for code search, you cast almost like too wide of a net and you get a bunch of false positives. So maybe that's too detailed.

Brian (43:08)

No, I think it's great.

Paul (43:09)

Does that help? But back to the four corners. We've been living in this memory here. So just to summarize, you've got text memory or you've got database memory, and then once you get into database memory, how do you want to. What kind of database do you want to use? And vector databases are just. We use all three, so there's no right or wrong answer. Vector is much faster. But then when you want to know the ground truth, the graph databases are really good for storing and keeping things that you've learned that you want to know for. Sure. I do care if it's 23 or 24 EBITDA, don't just randomly pick one. So that's where I feel like all rows are leading to graphs for us, which is the most convenient tool.

Brian (43:54)

So recap.

Paul (43:55)

Sure.

Brian (43:55)

LLM is like the input output. You've got the agents, which are the loops. They're out doing stuff, discovery.

Paul (44:04)

You think of them like explorers. Explorers. That's why OpenClaw is so fun. You're like,

Brian (44:11)

okay. And then to direct them, you got workflows. And then memory is the fourth point. Yeah, I think there's like, we can connect this back to Bitcoin pretty soon, if you'll allow me. So it feels to me like we're in this era right now where it's so fun. Everybody's trying their own thing, like kind of isolated and independent. Everybody's spending tokens to create their app or whatever it is. But we're redoing a lot of work. Like millions of people independently are spending tokens to do the same thing. Now, what if that thing that they spent tokens on, let's say they spent 100 tokens to do to create a transcript of the latest TFTC episode. You already do that. You spend your hundred tokens doing that. You put it on an accessible graph with an L402 in front of it and charge 10 tokens to get that transcription. That's where I think all of this independent work that we're all doing, which is a lot of fun for everybody, I think it starts to connect with each other.

Marty (45:18)

That is an incredible point because one of the things we'll do is we'll turn like if I listen to a podcast, like, hey, this was a great podcast. I think we should make Twitter aware of it and or X aware of it. And a lot of people don't want the clips, they prefer to read it. And so you just transmute the content from audio, video, and ultimately a transcript into like a long. Yep, a long tweet. But we are going to YouTube sort of spending tokens to download the transcript, running it through our prompt. But to your point, if you had a marketplace where it's like, hey, who already produced this transcript? I'll pay you 10 cents for like a token arbitrage.

Brian (46:01)

I think there's like this big thing that's.

Marty (46:03)

This is.

Paul (46:03)

This is.

Marty (46:04)

This is very big.

Brian (46:05)

Yeah, I think it's big. And I think.

Paul (46:07)

Take a look at it. Yeah, I'll show you.

Marty (46:09)

Yeah.

Paul (46:10)

All right. So this is a graph database and we have workflows. Well, first we have a topic. So think of this as subreddit. And this also leads to the questions of what will remain. How do you make money in a post AI world? It's not easy. I don't have a great answer for that question. So I think short answer is software goes to very cheap. Everyone's going to have software. Software engineers will perforate for a period. But five years, really? I mean, maybe not. So what do you end up with is marketplaces. And not just marketplaces, but marketplaces for agents. And so your agents, if you instruct it to save money, get my job done, but don't burn all my tokens. So if you're downloading a transcript or you're downloading a podcast and transcribing and someone else's, you're just. Everyone is wasting money and you're just burning electricity and tokens for no reason. So if there's a source, if you instruct your agent, go to this place and they tend to have transcripts, search and then get the transcript back. And it'll be cheaper than you doing it yourself. Cheaper, faster, better, and so the. These content graphs can be built by agents or human beings. And then what you can see here is you have. I'm using shout out to Kali's Clawey interface. So I signed up for this. It's great. And you can see here, all I have to do is say, search this graph for, let's say. So Claudie has an LSAT that I gave it, and it's using lightning to go out and retrieve data back from the graph and paying 10sats to get that data. And then the person who uploaded the transcript, you hopefully is getting paid some of that money back for providing that data. So if you play this out and there it is coming back. So this is everything the graph has on this thing. So this is scraping all tweets, podcasts, Reddit. You can put anything you want into the graph, but in order to make money back from the graph by putting it up there, you have to stake a little money. Otherwise you get garbage. Mm. So everyone is talking about, how do you have an Internet when content goes to zero, the cost of content goes to zero. You're just gonna get spam. Right. So if creating profiles is free and creating content is free, then how the heck is your agent gonna find real information or not? And so you'll have to have these walled gardens. Think of them as a protected subreddit. It could be on K Pop, on fly fishing, on skiing, on machine learning, whatever the topic is. And then you have a community of people and agents building this shared memory together. And then if you instruct your retrieval agent to just get it in the most efficient way, then they'll say, yeah, it'll cost me 10 cents of tokens to get this, or I can spend 2 cents here. But then you. If you upload TFTC now, you'll be making SATs for years to come. Every time someone retrieves it, you get it back. This is what I talked about with Adam Curry five years ago. This was. And the way I told him, I said, hey, there's going to be podcasting 2.0, but really that's a stop in the journey. This is the final destination of the journey. It's all content. It's gated so that the creators make their money without having to have advertising. And this just wasn't going to happen. I didn't believe with human beings. So we built the first prototype of that podcasting 2.0 and streaming sats with you. And I loved it. You can feel how this could work. But also, human beings aren't going to change their habits. But now you don't have to convince human beings to change their habits. The agents figure it out on their own. So people like us, who aren't great at marketing, myself, I'm speaking about myself, this could be the golden era. You just have to build something better and the agents find it. And then once you have this rolling and your graph becomes the most popular graph on that topic, honestly, it's like open. It's like owning beachfront property. If you keep the best graph going, it's the most thorough, high quality reputation, all that stuff. The agents will keep coming back. If you're down for a week, agents are going to look for someone else. This is the full information meritocracy. But there's pay to put it up there, but you earn it back. It's like an investment. And then you pay to retrieve. You have to pay your way. So this really is the value for value for the agent world anyway.

Marty (51:13)

But this is where the human in the loop is the curator, the tastemaker.

Paul (51:18)

Exactly.

Marty (51:19)

And that's going to be again, going back to what I was saying earlier, understanding logic and what people need and thinking about humanity in the age of machines. What is our edge?

Brian (51:27)

That's funny to think about. I was one of the many listeners of that episode episode with you guys Talking about podcasting 2.0 and being involved with the Bitcoin and lightning community for a long time. We've been talking about machine to machine payments forever. But I think everybody, if we're being honest, when we were talking about it, we were thinking about like a refrigerator is going to pay like a Roomba or something like that. It was like an IoT use case. But really what it's come to be is agent, machine to machine is agentic payments. And so I think this graph concept, what we're talking about here, for me it was really eye opening to. It just makes sense like these agents are going to be going around on the Internet trying to get information or skills or things to complete a job. They're going to be spending tokens and they're economically rational actors, the agents. So if it costs them 100 tokens to do the thing or they can get it for 10 tokens, they're just going to go get it for 10 tokens. But as Paul was saying, the cool opportunity is for people to cultivate these graphs and make sure they're to going their graph is really good and has the highest quality content. So the agents keep coming back to them. So yeah, beachfront property is pretty if

Paul (52:40)

I were to do anything, if I were to say to my kids what to do, I would say build these graphs on things that you know about. My son's super into restoring Toyota Land Cruisers. And he was on Claude yesterday or two days ago building a Toyota maintenance app, a web app. I mean, and he's just going, you can just build this. And I said, yeah, but anyone can build that. So what is actually useful? Collecting every single tip and trick about how to do this, building a community and then making it available to humans and making it available to agents. If you have those two things and you have the lead, this is the K shaped thing. If I try to start the second subreddit on OpenClaw, no one's gonna come to my subreddit. Cause there's already one and it's already huge. So there's a real advantage to be first mover.

Brian (53:31)

It's kind of like tftc. Is this for bitcoin for humans? Right. Like the humans who want to learn about bitcoin come to TFTC to learn about it. What is the TFTC for agents? It's probably like this graph thing that's easy to traverse, easy to do an economic trade to get the information. Agents are probably not listening to ad reads. Right. They're just like going to get the information. So yeah, the information curation as like a job, I think it's going to be very rewarding because people will focus on the things they're naturally interested in, whether it's Land Cruisers or Bitcoin.

Paul (54:11)

Yeah.

Marty (54:11)

Hopefully nobody from zerohedge is listening to this, but I have like a Zero Hedge Pro subscription. And with that you get access to all the banking reports that they have. You just get access to.

Paul (54:21)

Zero Hedge's agent is listening to this.

Marty (54:24)

I've been like. So I've been running cron jobs and all the banking reports that get uploaded to the Zero Hedge Pro subscribers. So like I've got insight, like with Golden City, like all the European banks are saying like every day and like I just wake up to a report from Martin, the sophisticated Marty agent.

Paul (54:40)

Right.

Marty (54:40)

That it's like, what are the banks thinking today? And he's like, here's what they're all saying.

Paul (54:44)

Well, people will do this with play it forward. And I'm saying I would. But people will upload articles from behind a paywall. Right. Same way a friend will print it to a PDF and send it to you. Agents will figure all this stuff out whether you want them to or not.

Marty (54:59)

Yeah.

Paul (55:00)

So the next step I Think so. Build graphs for agents with L402s in front. One shot, open claw. Once I paste to the LSAT, knew what to do with a 402 response from the web server, which is, do you have a lsat? Do you have the string of characters I paste it in? It goes, the next step will be can it buy one on its own and all that stuff. I think there's actually a real business for selling L402s where you take Bitcoin and turning it into an L4.2 so in case your wallet doesn't support it. But I think the big next step is your personal graph. So this is your shared machine learning graph or your fly fishing graph. And think of it like a magazine in the old days where you would buy fly fishing magazine and read about what's going on, you know, the physical magazine. So this is like a collection point for all that information for enthusiasts on any topic of business topic, personal topic, whatever it is. And then I get so sick of going to YouTube and to Twitter to try to keep up with anything. I'm interested in surfing. We both surf. I want my personal graph to know what I'm interested in. Give it a budget and just go get me the best surf clips from Inst. I don't want to go to Instagram and YouTube. I just, I want five minutes of great surf clips every day. And I want, you know, surf clips from where my friends live so I see what they're, you know, that kind of thing. So you'll store that on your personal graph. And so your graph will be talking to your agent, will have your graph and talk to these shared graphs and exchanging value back and forth and then it'll know what you know. So applying this to say, the example I use is like the Joe Rogan podcast because he has a ton of interest that I like. I'm a bow hunter. I love bow hunting stories from Joe Rogan. There are certain ones he tells too many times.

Marty (56:57)

He's told him a hundred times.

Paul (56:58)

My graph knows that I've heard this bow hunting story. So when I listen to Joe Rogan's podcast, I can have it shrink that section of the story down to, hey, he's telling that same story again that you've already heard 50 times. So I can just set to skip it. Just like I can skip ad reads. Right?

Marty (57:20)

Well, so as you're describing this, I'm thinking because like you brought up surfing and I just want to get the pinch my salt clips. I think, yes, I think those guys are Hilarious.

Paul (57:29)

Funny.

Marty (57:30)

But I don't want to depend on the Instagram algo to like surface it to me or me to have to go.

Paul (57:35)

Exactly.

Marty (57:36)

Find it via search. Pinch myself.

Paul (57:38)

How's it funny? Like a top tier comedian is running a surf podcast. It's like so funny and it's.

Marty (57:45)

But it's like to your point, like you can get it. And this is like another thing that's been a big topic is like how do you get away from the algos.

Paul (57:52)

Exactly.

Marty (57:53)

And you just create your own by curating what you want.

Paul (57:57)

That's some sort of personal agent and it's some sort of graph or memory. I think of it as graph is what people have tried to do with Obsidian. Remember Rome? This is what, you know, really intense people want, how to run their life. But what if this were more passive and what if this were just as you're listening to things, it's collecting what you know and what you don't know. There's. I think it's called Math Academy or something. I don't know if you saw that. Yeah. Third grader doing calculus or speed running six years of math. All by doing a skills graph where it knows the skill depends on this skill. So if you want to learn, there's a Google maps for your brain. Exactly.

Marty (58:40)

Vector. Right, Exactly.

Paul (58:42)

That's a graph database. Yeah. And so if you have my math, my math graph would be a tiny fraction of their math graph. But I have this node colored and it knows by referencing the shared math graph that the next step for me would be to learn this concept. So I have five minutes and I've told my personal agent while I'm waiting for this plane, I want to learn a little math. Give me a math thing. Well, it knows that I'm sitting at a terminal and so I can actually watch a short video. So it could construct a lesson using the video and content and just play me a video in the style that I like. The playback speed, the talking speed, the accent I want male, female, whatever you want, just generate the content and feed it to me. And the only way to get that is you have to have know what you know, your agent has to know and store that memory. And then you have to have shared, you know, someone has figured out all these math skills. I don't want to do that. Right. It wouldn't make any sense. And so those two interactions, I think for the first time we're seeing this open claw phenomena really open up the idea that people want this control, they want their version of this, which I think is super encouraging.

Marty (60:00)

Well, bringing it back to the machine payable web. I bought a 21co computer back in 2015 when Balaji first launched it and they were talking about machine payable web via on chain Bitcoin. An idea. It was great. Too early. Brian, you're on the board of lightning labs. Obviously L402 has been around for years now and it's been funny with the emergence of OpenCloud, everybody talking about the agentic economy like oh, now we have X402 and like all these other payment protocols for agents. And as we were talking about like I've had Cali, Justin Moon, Matt Carollo on in the last month talking about agentic payments specifically and I think massive opportunity for bitcoin. But I think it's still unclear to me how this two sided market emerges and how the I think demand from bitcoiners is there in terms of we have Bitcoin, we're willing to pay for this stuff with sats over the Lightning network or whatever. It may be ultimately the path to make the payment. But I think that the chicken and egg is like how do we make sure enough people have payment gates that are leveraging this tech?

Brian (61:10)

Yeah, a lot to say on this topic. So I remember when Elizabeth and Lalo first were telling me about L402s, I was like what? What are you talking about? This was back in the refrigerator paying the Roomba machine to machine concept. But turns out they were very prescient on this point. So you talked with Matt in your last podcast about the concept of 402s. We don't want to replay that, but having a system where you can trade value for resources, where agents can trade value for resources, is going to be important part to realize this future that we just described. I think that there's still a lot of work that needs to happen to make it easier to get up and running. Like you said, the three of us were bitcoiners, we've made transactions on the Lightning network, we run nodes, et cetera. That's still pretty hard to go from zero to one right now. It's pretty complicated. I would encourage people in this ecosystem and I know Lightning Labs is working on this right now to make it as easy as possible for the next vibe coder to integrate L402 payments. So that's not just about putting an L402 Gateway in front of some API. They need to be able to spin up a node, they need to be able to manage their channel balances, then liquidity I still think there's quite a bit of friction there. I think that's getting solved very quickly. Excited for some announcements coming up pretty soon that are going to lower those barriers, but I think if we can make it easier, there's a team called Money Devkit. Nick, who's. Those guys are doing a lot of good work on this front. But if we can just lower the barrier for a developer to get going on this, I think that's just going to be a massive accelerant for all these things and the vision that we're talking about with these interconnected knowledge graphs, that's where we're going. But to get there faster, we need to make it easier to just get started in the first place.

Marty (63:12)

Yeah, I mean, my. Our clanker, he's running a Phoenix D server. And it was. I mean, was fascinating to that process because to your point, like the lightning node management stuff, for some reason I had the LDK docs up on my. On my screen. I was like, all right, take these docs and like, see if we can spin up an LDK node and completely, like, we banged our head for an hour and it completely failed. And I remembered. I was like, all right, let's think through this logically. Like, instead of me giving it something while I have it do research. Like, what will work best for your server setup and like, what do you think you could actually do? And then like, went, did some research, came back. It's like, hey, this Phoenix D server looks. Looks good. Like, I think I could. We have enough space on our disk. Like, it makes sense. It seems like it's easy enough to set up. I was like, all right, go set it up and then set up. It's like, okay, it's. It's here, it's running, but we don't have any Bitcoin in it.

Paul (64:07)

That's awesome.

Marty (64:08)

You need to open up a channel. I was like, okay, like, what's the best way to do that? And he's like, oh, I found this bolts tool where you can send on chain Bitcoin, then it will submarine swap and automatically open up a lightning channel with Async.

Brian (64:20)

This is the stuff we need. It's got to be like, less.

Paul (64:23)

Yeah.

Marty (64:24)

It was like, all right, all right. Get an on chain address from Bolts and tell me where to send it.

Paul (64:28)

And I was, yeah. Boom.

Marty (64:29)

And then was up and running.

Paul (64:30)

Yep. This is where I just pictured opening. Cash app, sending bitcoin, getting a channel. Jim asked us yesterday, how'd you get the inbound liquidity? First question so no one's going to want to deal with any of that. You're just going to have to have it work. Your agent will have to say, do you have this app? Do you have any bitcoin in it? Do you have any money? Go buy some now. Turn it into a channel if you want to run one yourself.

Brian (64:55)

I want to just like. So my dream is to be able to let me back up a second. So first a few shout outs like Shout Out. People like Graham at Voltage, Jesse at Amboss, the team at Albi. A lot of people have been working on the key bits of infrastructure to make this easier. We just gotta keep going on that front. I can't wait for the moment where someone, they're making like viral apps so they see something that happens on X and then they make an app to respond to that thing they saw. So I have an example of this that I worked on, which is, do you guys remember seeing that tweet of there's a speed reading video? It's like you can read something really fast if your eye focuses on the center letter. So I was like, okay, how fast can I make an app to do this? And a bunch of people did this too. But I wanted to integrate L402 payments. So the website's called Speed Read Fit, so you can see it in the wild. But I want to be able to take that idea and have like a product production App ready with L402 payment gateways with the right liquidity and everything that's necessary in under an hour. I think that we're pretty close to that. It took me like a day to get it live because of some of the friction. But I think if we can head in that direction where people have ideas and they can quickly convert those ideas to cool things that people can use, we're just going to see an explosion of, of innovation.

Marty (66:22)

Yeah, I wish we had two more hours. I know you got to go soon.

Paul (66:26)

Yeah, that was great.

Marty (66:27)

I think, I guess just final thoughts on all this. Where we are in terms of approaching the suddenly moment of AI, where this will go by the end of the year, and how bitcoin payments actually become an integral part of this agentic economy.

Paul (66:44)

I mean, everything takes longer than I think, so my timelines are, are always off. But it's everything that we planned on where speech and money are combined into one protocol. And that's what we started with Sphinx and we continue to work on. We pulled everything back knowing we needed to re architect everything. But speech and money become more Important than ever. Because when content is infinite, then you have to gate it somehow so people start to look for that. I think that we'll have. This concept of staking is going to come up a ton. Micropayments. Even Nick Szabo, I think, recently retweeted something that was counter to his original criticism of micropayments, where people will never dedicate their brain space to micropayments. And now the era of you don't have to dedicate your brain space to

Marty (67:35)

micropayments allocated to the agent.

Paul (67:37)

Finally here. Right. So lightning L402s staking marketplaces graph databases that act as shared agent memory. You can see how this just speed runs. This is not stuff that we have to cook up. Every piece exists right now. We're in the assembly phase and it's built on. I think we all agree this is the best money out there. It's global and it doesn't put money into someone's pocket who made up that money. Right. And so I think that the agents will be doing their owners a favor by operating on a bitcoin standard.

Brian (68:18)

Yeah. I think especially for the listeners of this podcast who are bitcoiners by default, it's like, why put your energy into this? Well, you're advancing the core principles of bitcoin if you help build. Because now we're all kind of contributing to the ecosystem of bitcoin. The more sats are flowing around the Internet, the more. The more bitcoin is successful and the more we're all achieving the bigger picture goal that we care about as people who believe in the power of bitcoin and the reason for its existence. So I think there's this really neat underlying motivation that everybody in this community has, which is not tied to corporate profits. It's tied to the mission of Satoshi, which makes it, I think when people are mission driven like that, even if it's sort of deep down in the psyche, it makes it more interesting, more fun. People are more motivated to see things through. So like we've been saying, it's a really fun time to be out there. And I think everybody should be trying things, trying to make you have an idea, try to make it happen. It's never been easier.

Marty (69:21)

I need to set up a graph.

Paul (69:23)

I'll help you. It'll be fun.

Marty (69:25)

And I'll just reiterate what I said with Matt. And I think the one core advantage that all this bitcoin tech has compared to other payments mechanisms is the interoperability. And I think that it doesn't mean it's a foregone conclusion it's going to win, but I think it is a massive edge that people need to lean into more.

Paul (69:46)

The agents will find the pathways that work the best, and bitcoin and lightning have the best pathways. It's just purely factually correct. And so now's the time. And bitcoiners like myself aren't good at marketing, so you don't have to be. Agents will just find you.

Marty (70:01)

So yeah, we can't wait five years. And let's not Brian, thank you for coming on.

Paul (70:06)

Let's Harvest. Yeah, thanks for having us. Thanks, Marty.

Host (70:09)

Peace and love, freaks.

Paul (70:09)

Thanks.

Host (70:10)

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