Richard Robinson:

And we are back. Welcome, ladies and germs, boys and girls, to the Intercognitive Foundation Podcast. My name is Rich Robinson. I am the proud and eager Founding Chair of the Intercognitive Foundation, with the mission and vision to make the physical world accessible to AI.

And we are joined today by one of the founding members of the Intercognitive Foundation from peaq, an incredible company that's really putting a dent in the multiverse. And the co-founder and CEO, Leo Dorlöchter, is joining us. Willkommen! Vielen Dank! Thanks so much for coming on the pod today.

Leonard Dorlöchter:

Thank you so much, Rich, for having me. It's a pleasure to be here.

Richard Robinson:

Wonderful, excellent. Hey, so let's talk about your origin story. You're originally from Germany. That's where you're based right now. Still, yes. Tell us about your... no, where are you calling from?

Leonard Dorlöchter:

Yeah, not anymore actually. Yeah, moved to Lisbon actually, a sunnier place. But—

Richard Robinson:

You did. Terrific. Well done.

Leonard Dorlöchter:

It's true, right now I'm in Germany.

Richard Robinson:

Excellent. Yeah, I think Portugal has become this incredible catch basin for builders from around the multiverse, especially since COVID. And sure, why not? Why not go there with the lifestyle and the vibe and the prices and the weather? Yeah, well done.

So tell us about that path from growing up in Deutschland to ultimately moving to Lisbon and how you came up with the idea for peaq and all the other things you did in between. Let's talk about your hero's journey.

Leonard Dorlöchter:

Yeah, let's do it. I mean, the journey goes back all the way to end of 2016, actually. So when I grew up close to Cologne, Western Germany, but then moved to Berlin because Berlin was just the place to be if you wanted to found a company. It was clear for me. I wanted to become a founder.

And when I was 17, I read the book Rich Dad Poor Dad from Robert Kiyosaki. And it kind of was like, "Hey, there's a different career path than studying and just climbing the corporate ladder." So that's kind of, that was my intuition. That was my path, and that's why I moved to Berlin.

And I was just really scouting for ideas because I knew I wanted to become a founder, but I didn't really know yet with what. But then an old friend of mine, Till, who's now one of my co-founders, I knew him from close to Cologne and he also moved to Berlin.

And he saw that I'm in Berlin on Instagram and was just like, "Hey, let's meet up for coffee." And that was end of 2016. And yeah, then on that meeting him and his business partner back then showed me Bitcoin and how you make a transfer between Bitcoin and Fiat and basically how this magic internet money can be converted to real Fiat back then.

Right now it's much more established. But it wasn't actually Bitcoin that caught my attention. It was Ethereum, the ability to build decentralized applications on chain. And yeah, I really fell down the rabbit hole then and started working with those two guys on multiple projects.

And also notably, back then Berlin was truly the crypto capital of the world. All the big people were there, the early Ethereum people, and it had this cyberpunk magnetism because the spirit of the city, the history of being part of the Soviet Union as well and having all this privacy infringement and all that really created a culture of privacy. People like cash, people like anonymity and being able whatever they want to do.

And yes, Berlin was the epicenter back then. So we went to many meetups in the early days, and we stumbled upon also IOTA. IOTA was one of the early big projects in the space and they were early in focusing on IoT and blockchain as well.

And we started working with them back then and did events and so on. That's kind of, that was the first time we thought about the concept of IoT and blockchain coming together and the vision of machines eventually paying each other for goods and services and transacting autonomously.

And of course, back then that was really futuristic and visionary.

Richard Robinson:

Preposterous!

Leonard Dorlöchter:

Yeah, exactly.

Richard Robinson:

And now with X402, it's just another Thursday. It's amazing how it's become part of the zeitgeist. But that was a decade ago. Wow, okay, interesting. And what were the first things that you were building to address that?

Leonard Dorlöchter:

Yeah. So initially we thought, okay, we need to see, can we solve real problems and create real business value with that technology? So back then enterprise blockchain was quite a thing. IBM was investing a lot of money and so on.

And we were like, yeah, let's go and sell enterprise blockchain to corporates and see if we can make revenue and if we can solve real problems. And we did that quite well for a couple of years.

Most notably, we worked with Audi and the Volkswagen Group because they basically realized our charging infrastructure is fragmented. Our users have to have nine or 10 different charging cards in order to charge everywhere. And they also wanted to enable their customers and users to offer their private charging stations to the public or to their neighbors.

So it's kind of an early DePIN vision that they had for this. And yeah, they found us, called us up one morning and said, "Hey, we might have a project, please make an offer." And yeah, we were able to win that offer against big established companies and went down that path in building this decentralized charging platform pilot.

And that was kind of the first time we're like, "Okay, we have machines that have identities on the network. They can authenticate each other. They sign the data on the machine so they can prove, 'Okay, I really served you that much energy.' And then they transact peer-to-peer and settle the payment there."

So it was kind of like a mini machine economy vision and the foundation for DePIN. And yeah, that's what we did until 2021, where we then realized enterprise blockchain doesn't really make sense because in the end, you still have a centralized platform that's owned by a centralized company.

Why should another car maker adopt another car maker's centralized blockchain platform? So structurally, that didn't make sense, even though we signed the MOU for mass production.

But we then said, "Okay, we need to go fully open source and decentralized and build a layer one blockchain that is coming with all the machine-native functionalities to enable this machine economy vision that we had all along."

Yeah, that's what we started doing in 2021. And I think early 2023, DePIN became a thing, or late 2022. That was perfect for us because we knew DePIN is—many DePINs together enable the machine economy. That's kind of the conclusion we came to back then.

And yeah, really had built a layer one for DePIN all along. And that was great to see, like the first narrative in Web3 which actually suited well to what we were building. And that helped, of course, with building traction, finding DePIN projects, having a clear target audience, of course, also attracting capital.

Because as you know, Rich, in the industry, capital is always going to narratives, right?

Richard Robinson:

Jesus.

Leonard Dorlöchter:

Yeah, which is unfortunate, but it's also just how this industry works.

Yeah, so this is what we did then. I mean, fast forward, robotics started to become a thing. And robots are the prime actors of the machine economy, the prime physical actors of the machine economy, I should say, because AI agents are the digital actors, and they also merge together.

And yeah, I mean, now we're in 2026, and literally the machine economy is unfolding on a daily basis, as you're saying, right? Every day there's something new. And it's crazy how we've had this vision for a long time and now it's just unfolding out there.

And yeah, maybe last sentences and then I stop my long monologue. One thing that we also realized at the end of last year is machine economy is a multi-omnichain thing. It's not going to emerge on one chain only. We can see this already with Base, Solana and so on.

And our thesis for a long time was, we built the perfect layer one for the machine economy, and then everyone—or like the entire machine economy—will happen there. But also that's not the case, right? That's not how it's unfolding. And in hindsight, it's obvious.

So yeah, I think by the time the podcast is out, the next evolution of peaq will be out there as well. And it's going to be an exciting one, where we take all our stack and knowledge, OmniChain, to serve all of Web3 and bring the standards and infrastructure to all chains and all machines and all applications.

And yeah, I'm very excited about this.

Richard Robinson:

Wow, beautiful. What a terrific origin story. You know, Berlin really is this very sexy, edgy city with so many layers of culture and history in which to build. I think Silicon Valley builder communities are being more decentralized all over the world as well too. And Lisbon, and here in Bali, Dubai, everywhere else.

But you got to have a front row seat at the show in Berlin, in the heyday. And what a great access point to the giant automobile makers.

I was a "Getriebe-Anbauer" during my Wanderjahre, as they say in German, my wander years in German. I was working at the BMW factory back—I was a physical nomad before digital nomad—and I worked my way around the world.

And I was really blown away working in the BMW factory as an assembly line worker connecting 3 Series engines to transmissions. And it was pretty terrific.

But yeah, you were able to really tap into that machine economy. Certainly electric cars are one of the first foray. And now you look at Tesla and what they're doing with their charging stations and how they optimize that and have full self-driving, and you know that's all folding into this much broader smart city machine economy.

And, you know, we talked to Mike Horton from GEODNET and I like the way he framed it. Like, you have so many appliances and electronics in your home and a lot of those will become smarter and more autonomous, but you probably only have one, two cars or maybe a motorbike.

So you'll probably have one or two humanoids that will be in the home helping you out. And that's going to come much sooner rather than later. I think it's really—

Leonard Dorlöchter:

Yeah.

Richard Robinson:

—being fueled by the Chinese humanoid industry and AI and so many things converging all at once. When that starts to become much more of a reality, tell me and the listeners how your Omnichain vision will plug into that.

Leonard Dorlöchter:

Absolutely. Yeah. So our thesis has always been, and I think this is really unfolding now, that blockchain's greatest product-market fit is being the financial infrastructure for machines.

And machines and agents, we see that already, they're not going to have traditional bank accounts, right? Because in order to have a traditional bank account, you need a human identity and so on and so on. You can have a wallet and start transacting, right?

And blockchain is just—it's very complicated to use for humans. Like the user experience ain't great, but AI doesn't care, right? For AI, this is super easy.

So I think the native users of blockchain will be and are—it's happening already now with agents—machines. And the beauty about it is it's not just being able to send and receive value.

You build up an identity, you build up reputation. You can claim certain things and go into certain contracts and you can facilitate all of that on chain.

So in a nutshell, what we are doing is we're giving machines everything they need to do business, right?

Richard Robinson:

Mmm.

Leonard Dorlöchter:

The same way humans have a financial identity. Like I have a passport. With my passport, I can get a bank account. With my bank account and history there, I get a credit score. Once I have a credit score, I can get a loan and I can get credit and credit cards and so on, right?

So we humans, through multiple layers, we build a financial identity with which we then can act and transact. And you can really translate that to what we are providing, basically. We're building the layer that is giving machines everything they need to do business across any chain.

And in our thesis, every machine eventually will do business on chain because it's a much better way than doing business on closed centralized payment rails from old companies.

And additionally, you can make a machine liquid, right? It's not only that it can do business, but it can also be co-owned by people. It can be tokenized. People can crowdsource capital to initially buy it, to own it, to fund it, right?

And all of that happens natively on chain. And we're building—and we've been building—that stack for peaq only, and now we're opening it up to the entire space. And yeah, that's super exciting.

Richard Robinson:

Sure is. And you said something interesting, which is how almost intuitive it is for AI robots to use tokens, to use the tokenized economy, to use blockchain to transact.

And it's interesting to me how I've been in this industry for five years, and it doesn't seem to be getting much easier. I still hit roadblocks after a half decade where, "I need Ethereum to be able to pay the gas fee, but I have ETH in this wallet, which is connected to a hardware wallet, which I don't have with me right now. Even though I have thousands of dollars in this wallet, I can't move it to you because I don't have it, forget it," right?

But I started playing with AI agents and X402 and 50 bucks of USDC into the account and, "Hey, I need you to do some social media campaigns for me."

All right, well, I'm going to work with this other agent to do a survey and to be able to increase engagement here. And I have another agent creating an AI video.

And it's fascinating, agents paying other agents and things really happening. And I think I'm still very much baby steps, but I try to do something every day in that space to be able to get me even more comfortable with where embodied AI is going to be.

Leonard Dorlöchter:

Yeah, that's the best thing you can do every day, right? Experimenting, seeing what's possible already. Because it's moving so fast.

And I think most people in the world don't yet realize what is happening. And the only way you can is actually being hands-on engaged.

Richard Robinson:

Certainly. And so when you're going to be launching this after we launch this podcast, where do you see the initial use cases and the uptake happening in the early days?

And then let's take us to like a little bit more of a mid- and longer-term vision of where you see it growing into.

Leonard Dorlöchter:

Yeah, absolutely. So I mean, initially what we've been pioneering for quite some time now is tokenized machines, right? We have in Hong Kong the first revenue-generating robot that we tokenized last year. And it's literally autonomously producing salad and selling the salad to everyday people, which is amazing.

So it's like real-world revenue coming on chain and the whole concept around making machines liquid and being able to, one, solve the CapEx problem, right?

So you can crowdsource capital and at the same time enable people to make money and earn money through tokenizing revenue-generating assets.

That's really the entry point of, "Why should people care?" Right? And I think you know better than anyone that in the end it's all about, how do you, as a project, make people more money or how do you save people money? As in a general business context when you build a product.

And yeah, that's really the initial entry point of enabling people to make more money through the whole tokenization aspect. But the system we've built, it's literally end-to-end everything you need for a machine to do business at scale.

But we're starting with, okay, why should you care? You can make more money by that aspect of tokenizing a hardware asset and a robot. Yeah, that's kind of the starting point.

But then, of course, as it unfolds, right? I said earlier, credit score. Credit score is going to be so crucial. Reputation—we just call it credit score to make it more relatable.

But what's the reputation of a machine? What's its history, its financial history, its actions and all of that? Because if you want machines to transact at scale, you will need that reputation to be there.

And there are initial standards out there like ERC-8004 already in the market, which is great. But this is just raw data thrown into an open registry.

But to make that data intelligent and really track what the machine is doing and so on and so on, and apply some validation methods, there's more needed than just the registry.

And yeah, ultimately, mid- to long-term—I mean, who knows how fast it goes because everything is moving so fast—enabling machines to have a unified onboarding to Web3, all of Web3, enabling them to economically coordinate with each other, then validate that coordination and the reputation of those machines, and also facilitating orchestration.

Meaning if a machine wants to buy something or sell something, it can offer this through a streamlined way and then find the best deal for energy, compute, storage, geospatial intelligence or data, location data and so on and so on, enabling this to basically do the service discovery for the machine.

Yeah, and that's kind of the four steps that are part of the whole stack.

Richard Robinson:

Fantastic. That's a perfect segue into the Intercognitive Foundation. You talked about all these different parts, literally and figuratively, these moving parts to be able to make the physical world accessible for AI.

And as a founding member of Intercognitive Foundation, along with, you know, Mawari, the guys at Auki and GEODNET and Tashi and etc., tell us what really got you excited and some of the problems that you see the industry group solving.

Leonard Dorlöchter:

Yeah. So I think that the initial problem really is about, how do we agree on standards, right? If we're building an open system that can—like an open machine economy—we need standards that work together.

Luckily for us, standards are emerging more and more. You mentioned X402 out there, right? There's ERC-8004 and so on.

And I really see now how we as Intercognitive, right, we can take those standards, package them together and basically enable robots, machines to transact between each other no matter which manufacturer they are coming from.

Opening that up and really enabling the world to become AI-accessible because right now every manufacturer, every robot OEM is producing their own system and so on.

And we as Intercognitive, we can make that—and we are making this—interoperable and open. And we're using Web3 rails and standards to do so and are coming together.

And I think by packaging that together well and also carrying this into the market, right, we can create a better—we will be creating a better machine economy than it would.

Like, it's going to be more efficient because, you know, machines can transact with each other better. And at the same time, we make it open, accessible and, yeah, more democratized than if it was just closed systems working individually or not enabling that access.

This is really, I think, the mission of Intercognitive, right? Like, we need to make sure the infrastructure and the protocols that the machine economy is running on are open and accessible. Yeah, because it's crucial for the future of humanity.

Richard Robinson:

Future of humanity indeed. That's one thing that really silver-pilled me into getting involved with Intercognitive because you can throw out words like humanity or mankind or civilization, and it really is true.

This is going to be the biggest industry in the history of the multiverse, and I'm happy to have some small contribution to that.

And your first small contribution was to set a standard around timing or time, machine time. And now you've put forth a suggestion for other standard settings.

Tell us a little bit about the timestamp. Maybe it may seem simple, but it's an excellent sort of atomic unit, so to speak, of the whole machines being able to coordinate, dance together.

Leonard Dorlöchter:

Yeah, totally. So, if, as you're saying, we're envisioning many machines coordinating together, they need to run on the same time.

And it needs to be nanosecond precise because in order to make any statement—or like for data sets also to be compatible, right? Like if you source data sets from different machines and you need to plug them together to form a whole picture of the reality, they need to run on the same timestamps so you can merge them, right?

Because if they have slightly different internal time clocks, you can't merge data that's originating from different machines. So that was the first problem to be solved there.

But then, really coordination at scale, right? If you imagine robots, machines, drones navigating our cities, doing business and having to be in constant communication with each other, they need to all run on the exact same time in order to do this effectively because—

Richard Robinson:

Mm-hmm.

Leonard Dorlöchter:

—if there's some time delay, there's a miscommunication, an accident happens, whatever.

So yeah, time is really foundational. Like a universal machine time is how we called it. It's foundational for machines to coordinate at scale together.

Richard Robinson:

Yeah, I see my sons playing video games and sometimes we were based in China and sometimes there was lag and then it comes back and then they're dead.

And I think it's not so dissimilar from the real world where drones and other vehicles or autonomous or humanoids are moving in the wild and they need to be able to do that cavorting and dancing in a very coordinated manner, or there's going to be collisions or something even worse, possibly.

And you've also suggested in our talks about focusing on identity, because of course, we're humans and we're documented and can get bank accounts, but how do we put some sort of verification and identity onto not meat robots, but metal humanoids? What are your thoughts around that?

Leonard Dorlöchter:

Yeah. Yeah, that's a really, really great question. Because right now what's happening is that agents get a wallet. They just get a wallet address and then they can send and receive funds.

And in standards like the ERC-8004, the identity of an agent is the NFT address of the owner, basically. Like the owner is holding an NFT and that's the agent identity. And that's all good and fine.

But that's not enough for a real comprehensive machine identity that can scale, especially when it comes to physical machines as well.

Because the physical machine needs to be able to authenticate itself, meaning it needs to have the private key on the machine itself in a secure way, where it can sign transactions and authenticate that it's really the owner of that public address.

And at the same time, it needs to be able to sign statements, initiate transactions, sign data that originates from the machine.

And therefore, like a physical machine especially needs an identity where the private key is on the machine itself and not an address where someone is holding the NFT in a wallet and that's the machine identity, or just having a payment wallet address. This is not enough.

How we are thinking about machine identity and what we've been building for a long time is really, it builds on the DID method, decentralized identifiers. It works with verifiable credentials, and it's really a machine-native identity where the machine is signing things on itself.

And what we're doing now is basically we take all those emerging standards like ERC-8004 and so on, and also emerging authentication methods that are around, and add them to the identity.

So the identity you can imagine as some sort of container, which is holding different authentication methods, which is holding all kinds of wallets, wallet addresses across different chains.

And it's then basically this container holding all the different things. And yeah, this is how we—because then the identity can scale, right? New standards come out. The identity is flexible, can incorporate them.

It's not too opinionated in terms of which authentication method to use and so on, because there will be so many emerging.

But yeah, we're really building this flexible machine-native identity that can scale with any emerging standards.

Richard Robinson:

Fantastic. Yeah, I think when the coffee test is finally passed—you know, of course, Alan Turing had the Turing test, and some people think that we passed that 10 years ago under maybe some conditions. You only have to be able to think it's a computer a third of the time.

But for sure last year the Turing test is in the rearview mirror. But the coffee test, where I'm summoning somebody to my house, that somebody being a metal humanoid, not a meat robot, and it has to be able to find where I am and be able to transport themselves there, and they have to be fully charged, and have mapping and have compute, and have internet access.

And then be able to enter my building or home, and then have the wherewithal to have the manipulation skills to be able to grind coffee and then brew coffee and bring it over to me, and give us the cup of coffee, and then little salute and then back onto its next duty.

And it probably won't work like that necessarily, but I think it's a very nice rubric. And think about all of the moving parts, literally and figuratively, to make that happen.

And I look forward to the day that's passed. And of course, identity is just table stakes to be able to enable one—

Leonard Dorlöchter:

Yeah.

Richard Robinson:

—portion of that to be able to happen.

Leonard Dorlöchter:

100%. Yeah, so many things have to come together for this to happen and we truly believe it's going to be a lot of open source working together that's making this happen.

Especially when the robot is switching between different environments. You know, like if it's just one closed environment, you can have the Apple-style experience where everything is out of the box from one provider and it's all closed.

But if you have humanoids that need to navigate the world and, you know, they're part of so many different environments and they need to interact with so many different people and devices and machines, I don't see how this is happening in one big closed system that's controlled by one big company.

I do think that's—

Richard Robinson:

Hmm.

Leonard Dorlöchter:

—naturally going to be, you know, open and interoperable. And I think this is Web3's greatest purpose.

Richard Robinson:

Wow, wow, well said. Interesting. And it really excites me.

I've never—you know, standards are something that are ubiquitous or either ignored or insufficient. But it's not something that I really put that much thought and effort into until I got involved with Intercognitive.

And now I really see the sexiness of it. It kind of lubricates. It's the grease that makes the gears interoperate in that very precise Swiss watch or whatever kind of mechanism it is.

So we talked about time, we talked about identity. What are some of the other areas where standards will need to be set or need to be set right now, that you're working on or you can foresee?

Leonard Dorlöchter:

I think an important area, which also not too many people are thinking about yet, is the whole concept of access control and role-based access control.

And it sounds trivial, or like it sounds not so important, but if you think about the world, access is an integral part of society. Like who's allowed to access what, who is allowed to see which kind of data, going into which kind of physical spaces and so on.

And when you think about machines, access is so, so important. Like, is the drone allowed to fly in this area? Is the humanoid allowed to access your house or like walk there? Like who is allowed to access the humanoid and see its data and so on and so on, right?

So like in order to have a secure machine economy as well, we need to engrain role-based access control. It's kind of foundational to any security system as well.

If you build up a security system on AWS, for example, you have very clear access roles and permissions and rights, right?

And we see this as super, super important as well. And that's maybe something too many people don't have on the radar yet. And yet it will also be crucial.

And really, you know, when a robot or machine also does something wrong or maybe has malicious intentions, we need to be able to have mechanisms to revoke certain rights, certain access. And yeah, that's going to be important too.

Richard Robinson:

Certainly do that with humans in some Twitter feeds. So I think I'm a little bit more concerned about not so much artificial intelligence, but genuine stupidity around that.

I think maybe it's the humans that are overstepping their bounds. Maybe that's more of just a little more of a tweak rather than a spanking by revoking access.

But all right, more, more, more. I want to hear more. So time, identity, access control—what are some of the other areas where we need to be able to do the tango together very fluidly?

Leonard Dorlöchter:

Yeah. I think reputation is another big one, right? Okay, like credit score, as I called it before. So how do we—it's a better word. Yeah, it feels better. It's less—I think with credit score, most people have negative associations.

But yeah, like it's true, like reputation. So how do we measure the reputation? And how do we compute the reputation of a machine?

And based on that, right, we know, is it a good actor? Is it a bad actor? Is it someone that can be trusted, is reliable or not reliable, right?

So I think this is really, really key also to enable machine economy at scale because how should two actors interact with each other that have never met each other before, right? You need trust.

And reputation is also the prerequisite for accountability to a certain degree. Not fully, but like, actually it is, because if you then do something wrong or like you're malicious as a machine or robot, your reputation goes lower. You're being held accountable for your actions.

And I think that's also very crucial. And I mean, in the human world, you know, we have the law and we have the police and that is trying to hold people as accountable as possible to what's good and what's true.

Or like what's good behavior, at least in open, yeah, in societies where there's a good legal foundation.

But in a machine economy, there's no way humans will be able to keep up with the speed of robots and agents interacting, right? It's already too fast to handle for us. We need, yeah. Yeah.

Richard Robinson:

Fascinating. Fascinating. It's philosophical in many ways.

And there's a book, a science fiction novel called Down and Out in the Magic Kingdom written by a guy named Cory Doctorow, from boingboing.net. He was in China and I helped him on a book tour.

And he has this imaginary future where the currency, it's called Whuffie, and it's your reputation. And it's actually indeed already something that we have as a currency. It's just not quite as palpable and measurable.

It's something that's a little bit—you know, this credit score, but there's also that triangulation where you have to talk to somebody and be like, "I'm going to introduce you to that guy, but I'm introducing you because, you know, somebody asked me to do it, I owe him a favor, but you don't owe that person anything because I don't know them. Or even worse, I think they're a little unreliable, so be careful."

And you think about robots in the future where you're like, there's robots that are maybe not malicious. I think a lot of problems are not really necessarily caused by maliciousness, but just by incompetence.

And you think about—

Leonard Dorlöchter:

Mmm.

Richard Robinson:

—a robot that's optimized for making a delivery really fast, and maybe it's around your small kids or around your grandmother. It's moving so quickly that it could injure them or trip them or something, right?

And it's something where, okay, wait a second, you have to dial back the speed when you're around them and then you can go fly down the road later, right?

And there's so many use cases where it's really fascinating how that reputation.

And—

Leonard Dorlöchter:

Hmm.

Richard Robinson:

—in some ways I look at, like, I lived a long time in China and China is a—it's not a judgment, but it's a little bit more of a low-trust society because it's just sort of, there's so many people there, and it's a little bit difficult to navigate.

I love my time in China, but you're always kind of watching your back in a way. You have to really make sure you have connections and guanxi with people.

And then, when e-commerce launched, people said, "It's never going to work because number one, people don't have credit cards. Number two, it's difficult to deliver. And then number three, the low-trust aspect is going to make it really difficult."

And—

Leonard Dorlöchter:

Mmm.

Richard Robinson:

—all those three weaknesses turned into strengths because somebody really figured out to do payments by mobile through Alipay and through WeChat Pay and it became the first, you know, mobile-first economy in the world.

And then number two, delivery companies became really, really advanced to find places that weren't even on maps or named well.

And then number three, because there was accountability in e-commerce, the Chinese consumer's the most spoiled in the world because if you make any complaint, people are all over it because they don't want there to be anything out in the wild that they have a bad reputation, because then it's going to affect their score.

In some ways, I think a lot of this transparency and accountability is going to make—if you think about historically, we lived in tribes and it was probably 150 people, the Dunbar number, and you couldn't get away with much if you were like, "I'm just going to hang out with the kids while you guys go hunting."

And you know what? You get the scraps, you don't get—you know, because you've done that three times already.

You know, if you're doing something, if you're stealing or you're doing something else, it's all very visible and your reputation—and you don't want to be pushed outside the tribe because you're going to die and you're not going to be able to procreate.

I think reputation is something that's really important to humans personally, but also writ large. And it's just been difficult as we scale the world.

But putting things on chain and having things much more automated and accountable and transparent, maybe that could bring us to a better era of accountability.

Leonard Dorlöchter:

100%, especially as machines are rising and we need a better, faster system for them to, you know, have accountability between themselves, but also for us humans to be able to see, okay—

Richard Robinson:

Mm.

Leonard Dorlöchter:

—which machines have great reputation and which don't. And yeah.

Richard Robinson:

And then why, and then that's transparently shared and then they're all recalibrated—

Leonard Dorlöchter:

Yeah.

Richard Robinson:

—so that they become much more in favor. And then that frees us up to be more creative and collaborative and connected.

And yeah, on that note, I really appreciate you connecting with me and our listeners. What a terrific hero's journey you've had and I really appreciate all the work you're doing with peaq.

I look forward to your product launch. I really appreciate also you working with the Foundation to help identify and set these standards. Thank you very much.

Leonard Dorlöchter:

Thank you so much, Rich, for the work you're doing bringing us all together, leading us in the Foundation. Yeah, thank you for having me on the pod. Amazing.

Richard Robinson:

Fantastic. Thank you.

‍