Ep. 345 - How Artificial Intelligence (AI) is Changing YOUR Life w/ Fetch AI founder, Humayn Sheikh

In this episode of CRYPTO 101, we speak with Humayun Sheikh, founder, and CEO of the biggest crypto project focused exclusively on artificial intelligence. As a former algorithmic commodities trader and pioneer at DeepMind, which was acquired by Google for $500 million, Humayun gives us a fascinating perspective on exactly how artificial intelligence is changing our lives. A futurist and a hands-on builder, Humayun explains things in easy-to-understand terms and paints a very bright picture of the next decade. We finish our conversation off with a discussion of the FET token and how the Fetch AI blockchain network provides a secure and decentralized digital economy.

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Episode Transcript:

Bryce Paul: Alright everybody, it is time for another episode of the Crypto 101 podcast. But before we dive in to our awesome guest in conversation today, I want to remind you guys of two things. And the first one is that if you go to crypto101insider.com, you can join our private community. Here’s where we have our model portfolio and all of our top picks.

We also have Crypto 101 University, where we have hours and hours and hours of written and video content that explains blockchain and explains cryptocurrency in a very bite-sized and easy to understand way. And we have a weekly newsletter that goes out in quarterly state of crypto addresses that go out. There’s just a ton of value packed into this every which way.

So I want you guys first to go to crypto101insider.com today, if you haven’t already. I also want to remind you guys that Pizza Mind and I recently just finished a book. It took 11 months of our lives to write and we’re calling it Crypto Revolution: Your Guide to the Future of Money.

We walk you through this fascinating world of cryptocurrencies and blockchain and its part history book, its part instructional guide, and it’s going to really show you guys why cryptocurrencies are globally disruptive and how they’re going to actually change in real life and in real terms, the way that we buy and sell and even live. We include a bunch of how-tos on getting started with your first exchanges.

We give you tips on how to safely buy and sell in store cryptocurrencies, as well as how do we evaluate potentially good cryptocurrencies. And the best part of the books is that we’re giving it away for free. All you have to do is pay for shipping and handling. So go to cryptorevolution.com and pick up your copy today.

Alright, everybody, all you good, wonderful citizens of crypt nation. It is your host Bryce Paul and Pizza Mind coming at you with another kick butt episode here at the Crypto 101 podcast. Pete’s, are you hunkered down over there, you’re surviving these Southern California fires. We’re lucky that we’re not in San Francisco or further up north. But you’re good over there in your neck of the woods?

Aaron: I honestly don’t feel so good today, Bryce. I think I invested in some bad sushi and my stomach is just churning really bad. I was up all night. Oh, just awful. But anyway, if you got something that can make me feel better, can you go in the cabinet and find me something that’s fundamentally sound and actually is doing good things for the world?

Bryce: You know, it’s funny that you mentioned that because you know, we’re recording this. It’s September 15. Sushi is being completely falling off a cliff here. But it’s one of those projects with anonymous founders, and kind of behind, you know, very cloaked and veiled. And I don’t know, I don’t think that it’s got a long staying power. And one of the things that we talked about when we talk about just fundamentally strong coins and projects and stuff, we always look for conversations with the founders and people that have defensible reputations and stuff on the line, right, like, “That’s a real company.” So, Pete, why don’t you introduce our guest here today, CEO and co founder of Fetch AI, Humayn Sheikh.

Aaron: I would bet you just did.

Bryce: I just did.

Aaron: Humayn, welcome to the Crypto 101 podcast. How are you doing today?

Humayn Sheikh: It’s been great to be invited and really appreciate your time, guys. It’s a pleasure to be here. Yeah, I’ve not had any sushi but I’m thinking about it. So let’s see how that one goes.

Aaron: Fantastic. We’re glad to hear that you’re safe. Yeah, so tell us what were you doing that inspired you to co-found Fetch.ai?

Humayn: Yeah, it’s a quite interesting story how things come about and I think most founders will have these stories. But I was interacting with two or three different things, which actually then suddenly started to find some correlations and started to connect.

So my background is Computer Science. I’ve trained as a programmer, but then I was looking and I was working with Dennis. We were building these machine learning algorithms and we were talking about doing something quite interesting in the gaming area. And I had a commodity trading history where I was writing algorithms for commodity trading. And we were having this conversation where my other co-founder Toby Simpson, who’s writing a massively multiplayer online games.

We were discussing how nice it would be to bring all of this together and actually deploy software agents, which could actually do some really cool and clever stuff on their own, and really could help improve the quality of life.

Bryce: You mean, stop doing the menial tasks.

Humayn: And it sounded like a Sci-fi project but it actually then evolved. As we look more and more into it, it became very interesting. At that time, I was involved with DeepMind, I was looking at commercializing some of the artificial general intelligence that these guys were building.

And it became very clear that it’s not as easy to bring AGI to the world without a fabric to deploy it on. So you can build all these, compute intense algorithms, but to bring them to the real world where everybody can benefit from it, not just the big corporates, you need a deployment fabric. And so that’s really the start of fetch AI. So we started building that deployment fabric, where you could actually start deploying machine learning, AI, AGI, collective learning, and you could bring it all together so that it starts to interact with each other and actually starts to make impact for normal people, not just big corporations who sit on a lot of debt.

Bryce: Yeah, so there’s a lot to unpack right there. For me, just like at the outset, I realized that there’s like, you know, AGI artificial generalized intelligence, then there’s just artificial intelligence. I don’t know the difference there. So I’ll probably ask you to explain that I think a lot of people have curiosities about that. But then you have collective learning and neural nets and machine learning.

So there are all these big different ideas floating around, but what would you say they all fall under? Like the kind of the parents to all that is just maybe just data analysis? And maybe the idea is that you’re talking about, like how all of this stuff is making life better? Like what is it all doing to make our lives better? Like how is that all working?

Humayn: I have my own opinion. And I think a lot of people might not agree with this. But I’ll give you a very layman’s opinion, in terms of how I see all of this.

Bryce: Yeah, the more layman’s terms we could get, the more analogies we could get, the happier everybody will be, I think.

Humayn: That’s what you’ll get from it. So if you think about data analysis, and if you think about, you know, looking at historics, and then learning from that historics, say that’s machine learning.

So what you’re doing is you have huge amounts of already executed data, you read that data, you find correlations, and it’s obviously not a small field, it’s a vast field, you could just look at time series, you can do analysis on that, you can look at several, you can find correlations, you can look at your data, you can see what the input and output needs to look like. So, that’s kind of machine learning.

Now, when you come to decision making, that’s AI, right, so you actually have the ability to make a decision. Now, the very basic form of that would be if this, then that, right? If this happens, do this. So that’s the beginning. That’s the root of AI. Obviously, it’s a lot more complex than that, because now you have AI which can do things on their own.

So for example, a car can start braking if something happens, that’s more into the AI space. Now, AGI is the general intelligence, which is how can the machine learn like a human. So it’s a child who learns to evolve, and they learn to evolve in different circumstances. They learn to evolve with different environments and that process where it can have different types of transferable learning. So you learn something, you learn how to pick up a cup, you can now pick up a chair. So that’s more general intelligence, kind of space. So that’s the three categories but to understand a little bit more how they’re connected.

AI and AGI need to make decisions. They make decisions based on predictions. Now this word, ‘predictions’, some people will take offense of me saying it, but I’m just trying to explain what the situation is. So if I said to you, ‘make a decision, either you’re going to go out in a rain jacket or not, or a T shirt’. That’s a decision a human makes, but human makes that decision based on a prediction.

And the prediction is, if you think it’s going to rain outside and there’s a 90-99% chance it’s going to rain, you take a raincoat. And if the prediction is that there is 25% or 15% or 10% chance, then you might not. So every AI decision, or every AGI decision for that matter, is based on prediction. Yeah, this is not this you could kind of elaborate and generalize and call it a prediction marketplaces and a lot of people use it in a different terminology. But that’s the link.

So machine learning, actually delivers the predictions. AI looks at a prediction, make a decision and AGI learns more and more from those decisions, and evolve from those decisions. So that’s three distinctive parts. So hopefully that makes sense in a more generalized way.

Aaron: Yeah, it does. I feel like I finally understand now. So thank you for clearing that up. But there’s still a lot of worry and fear that AI can evolve, as it is going to take over. You know, we’re always afraid of things we don’t understand. So when we’ve got the expert on here, I have to ask, is there any reason to be afraid of AI, either in the present or the future?

Humayn: It’s a difficult one to answer. But I would say let’s have a look at Terminator Skynet. If the movie was made for I think 2020, I think it was in 2020 that’s all these things were happening. Well, I don’t see that happening right now. I think that even the cars which are driving on the road are not clever enough to do drive themselves fully. So I don’t think we have that risk just yet.

Well, do we have that risk at all? That’s my opinion, which is that yes, because I think with the increase in different technologies, with an increasing compute power, and with the AGI, if the AGI does evolve in the way we expect it to evolve, in the sense that it does start learning and it does start doing things on its own, then the danger is perhaps not in that much of, you know, the robots will come alive, it’s more that because of certain ways it’s been trained and evolved, it might end up doing things which are not always that beneficial to humans.

But that’s also a subjective matter. I mean, if the AGI has evolved in a certain environment, and it doesn’t see something wrong with something, then the other environment might see it completely the opposite way. Is that evil or not? How do you make sure it’s evil or not? I mean, I give you a very basic example. And I’m not saying that’s what it needs to be.

So if the AGI is developed in a white nation, the AGI will learn from that white nation and not from any other combination. So, you know, there are subtle differences. And as you train these algorithms, these subtle differences could be amplified quite a bit. And that’s the danger, which I feel is so real.

Bryce: Does the blockchain come in to kind of alleviate any of those potential fears or worries?

Humayn: Yeah, and that’s a good question because they just lead into what I kind of wanted to say. So to enable all inclusiveness, you need to have a fabric, which is where I was saying, what we need. The fabric needs to be all inclusive. So it doesn’t matter where you are, it doesn’t matter how, in what region and how you interact with it. It needs to be an open system. So it needs to be a decentralized system, in fact.

So I get this question asked a lot, you know, you just jumped on the blockchain bandwagon. But now let’s just think about it. If you’re going to have, let’s say just some AI, small amount of AI, learning from each other, you do need to have an open decentralized system. And we’ll come into a bit more what Fetch does, and you’ll understand perhaps a bit more, but you need blockchain.

Now, why do we actually need blockchain? I mean, any decentralized system, whereby the control doesn’t live with the centralized entity should do the same. And yes, it is correct, as long as it’s decentralized and nobody no certain specific set of people is control controlling it, that’s okay. But we found that blockchain was a very good and a very solid fabric where you can connect these different intelligences to learn from each other. And that’s why we kind of tried to merge the two.

Bryce: Yeah, and let’s dive straight into that. I remember, when we were kind of speaking prior, there was a conversation around these economic agents. And you had a good analogy, and the economic agents are kind of these almost like the nodes on your network. And the analogy that really struck me was that have like a travel agent, something who knows your preferences, who makes your decisions? You know, they quicken the pace of your decision making, because they have all that information. They know your preferences a little and that was kind of an analogy between your systems. So can you kind of walk us through your system?

Humayn: Yes. So what are we building? So let me just lay out that framework first. So what we’re building is a framework where software agents can connect to each other, they can find each other. So it’s a search and discovery mechanism. And they can exchange economic value with each other. And they can learn from each other. So it sounds simple. But to do it in a decentralized fashion, where all stakeholders have different incentives is not the easiest of tasks.

But let’s say that that exists now which fetch fabric exists, which is our agent framework, what that enables you to do is you could run a light client, and I’m going to try and not just keep calling them agents, that is a piece of software, it’s a light client which could actually sit on any of your devices. And it actually has a task and what we call them as autonomous economic agents, which is different from autonomous agents, which is the autonomous agent doesn’t necessarily have to have an economic value exchange.

But for this whole thing, to actually take commercial, make commercial sense, it has to exchange economic value. And the reason why that is the case is most of our incentives are based at the moment, on economics. So when we’re talking about money, we’re talking about creating value, we’re talking about exchanging value, buying something, selling something. So if you’re going to give these agents the power to make a decision on your behalf, you need to give them also a method of economic exchange, which cannot be gained, which is kind of in alignment with the incentive mechanism of that fabric. So that’s really the autonomous agents.

Now, so what can we do with these autonomous agents? So I gave you an example of a travel agent. And I’m just going to kind of elaborate a little bit on that. But let’s start from a ride hailing kind of facility, for example. So what happens is, let’s say the likes of Uber or Lyft, they sit in the middle, and why would they sit in the middle is because you’re enabling this gig economy, you’re enabling individuals to take their own tasks. And then on the other side, you’re providing this consumer their facility that you have on tap, you want to hail the ride you can do it from your app straightaway.

And what the aggregator in the middle does, it actually tries to find and connect to each other. And for that they take 10% 15%, whatever that incentive is. Now, why that needs to be done that way is because there is no decentralized fashion, which is efficient in search and discovery. And in making an efficient model, which connects the consumer to be the provider of the service. This applies to everything. So now you think about, I have an agent, we all have an agent, I am your service provider, and you’re the consumer, and your agent knows your preferences, you can train the agent to know your preferences. And you want to a cab you just enable that agent to find your cab, and you enable the negotiation.

So the agent goes out and actually finds your account because let’s say, the service provider also has an agent and you can connect with each other. That gets rid of that aggregator in the middle, which is the intermediary, which is collecting the incentive of connecting the two parties together. Now you don’t need it because more endpoints your autonomous agents are clever enough to find each other because there is this middle fabric which exists. And it applies to anything. It applies to hospitality, you want to book a hotel, you want to travel, the flight, you know, every seat can sell itself. I mean, now you can start extending it to many things, you know, why do we need booking.com?

Booking.com is an aggregator, which connects all these hotels, they control the price, there is front running involved because they can go and buy much cheaper, you know, front running doesn’t just relate to, I guess, just basic financial trading, it also applies here. They could go and buy, they can help by the hotels, and they can control the price. The hoteliers don’t like that. They want transparency. They want to be able to take, and with this whole travel, you know, downturn with these whole provinces, it becomes more and more difficult for attorneys to manage, and find the customer going through the aggregator.

So this kind of really gets rid of it. This is the new way where the AI can now start living with you. So you don’t have to look at big companies that they’re going to crunch the data. And they’re going to give you the options. Because then the problem with that is, your incentives are not their incentives, right? So they’re giving you the options, and your choice is limited. So we’re now changing them in turning it on its head, and we’re making it much more efficient. Yes, there will be commercial challenges, but this is a new year.

Aaron: Yeah, what I really love most about decentralization is that it removes that really expensive middleman that manipulates things in their favor. When I started out as an entrepreneur, I had a mentor. And he said, “Always be the middleman because you’re going to take the biggest cut of the profit and do the least amount of work. If you’re going to be in business, that’s the place you want to be”.

Bryce: Hope you got rid of that -.

Aaron: But no, it was the best advice that he ever gave, really and it was very successful. He was in business by himself for like 40 years, simply just having someone else supply and someone else build and he just simply handled the sales of it all. So but that’s what our world economy is built on. But it’s not fair. It’s not right. The people creating the value should be the ones that are retaining most of it. And through decentralization, it’s also going to be better prices for consumers. So I think, that’s awesome.

Humayn: I know, I am on the same space, because I’ve been in commodity trading for a long time. So I agree the advice was absolutely correct. But I think as everything evolves, we are now evolving to a space where it’s becoming less important, especially with the technology, which has existed till now, where you enable these big entities to have become so powerful. That has to change, because you can’t have such powerful entities. We’ve gone from countries, to corporates now. So the controlling power is not the countries these days, it’s the corporates.

Aaron: So I assume the FAT token, is the economic exchange for data in this ecosystem, correct?

Humayn: Not necessarily. So the value of the FAT token is that you have to run this framework. And there is a cost of running this framework, and people are not going to run that. Those people are not going to run the whole network, unless you pay them the cost and a profitability, right? It’s nowhere near the intermediary cost of 15-20%. But you still need to maintain that actual infrastructure, the whole thing.

So the node hosting comes at a cost and that cost needs to become. So Fetch, as a utility token, enables you to run this whole thing. And you know, it’s like the oil, the gas, whatever you call it, for the agents to do a task, because they’re going to go out, they’re going to do a search and discovery. They’re going to send messages. So there is obviously communication burden, communication overhead, there’s going to be you know how it connects to the networks, that overhead. How do you do the search, that overhead.

So FAT token pays for all of that, so you need to hold the FAT token to do that. And we are working on some very interesting ideas whereby we’re using FAT token to create a more stable mechanism of value exchange. So because you know, if you’re booking a taxi, you don’t want to take a risk on Fed price, right? It’s simply – to do that. So we’re coming up with a mechanism where we stabilize the price, and I’m quite happy with it. We had agnostic, you can pay it in any cryptocurrency, you can pay in USD stable coins, non stable coins. So Fed doesn’t interfere with that. That’s not our objective. Our objective is to provide that framework where you can run this new economy that we’re bringing to life.

Bryce: Awesome. And does the FAT token have any governance capabilities as well?

Humayn: No, FAT token doesn’t have access any governance, it’s very much, you just see that’s guess. So that’s all it has. And you can build projects on top of FAT, which is where the governance token comes in. For example, we’re just launching a commodities exchange, which is powered by Fetch technology. And on that commodities exchange, there is a governance token called Metal X token. So that’s governance token, and every project, every spin out, every vertical you create, can have its own governance token, because one governance doesn’t apply to all. If you’re looking at travel and hospitality, the governance there is a lot different to a governance and commodities exchange or, you know, trying to do something else in a completely different sector. So you need to have that flexibility to create new markets, and every vertical, every market should have its own governance.

Bryce: So is the fetch AI token built on its own blockchain, or is it on the Binance chain?

Humayn: No, it’s built on its own blockchain. Again, our focus was not necessarily to build a chain. Interoperability is quite a key here. What we have is a chain so when we started the project, we need to look back, two years ago, 18 months, 19 months ago, the chains were quite limited. And the transaction speeds were limited. I mean, we talk about, okay, Visa does 4000 transaction, or 5000 transactions per second, and somebody can do, you know, another 5000 transaction per second.

But now you think of this deployment where you have agents for everybody, they’re doing multiple tasks all the time. They’re going to a weather station, getting a weather prediction, and enabling you to get a taxi, which is in the right time at the right place, using these prediction, mind predictions, transacting with predictions, we need millions of transactions per second, not thousands, because this is not just a, let’s make a payment here and there. It’s about agent’s life and for it to compete with a centralized system; it has to have that kind of transaction speed.

So we built that with that in mind, and we added sharding to the whole system, so that we can scale it linearly but having said that, we are not a chain agnostic. We are open to any chain; we are open to any interoperability. If you want to deploy on something, you want to use our agent framework, you can deploy the agent framework on Fetch, and then you can operate with anybody. So for example, one of our key and this is not some main thing we are launching.

If you think about agents, they need Oracle’s because they need data , they need general-. So we have a network with deploying of Oracle’s which people can run. It’s just an Oracle network for generalized use, not something like chain link, or you know, other guys do. They’re more related to the price of a crypto commodity or crypto or other things.

But this is more a generalized agent and built by the agent, for the agent kind of Oracle. So that’s the difference. But you can deploy all these tools on Fetch chain, but you don’t have to be tied to the Fetch chain, you can run an agent, which can go and make a transaction on a Ethereum smart contract. So that’s quite a key here because.

Bryce: Yeah, I think a lot of the power that really a lot of these kinds of 2.0 and 3.0 blockchains are going to have is that interoperability feature. I mean, we’ve been talking about that since the beginning of the year, just if your project doesn’t have that, then it doesn’t have a future.

Humayn: Yeah, that’s true. And what we’re saying is that you can use this agent framework to create that interoperability. So you can have a very specific niche. But you can still operate with Ethereum, which takes an hour to complete a transaction on a $100. So you can still, if you fancy doing it, you’re most welcome to do. Why you want to do it? It’s another question.

Aaron: Yeah, I don’t know why anyone would want to continue to do that. So when Binance came out with their smart chain and said, “Hey guys, you can migrate over here in three easy steps.” My jaw just dropped. I mean, that was a power move of the year from CZ. So we’ll see how many projects actually make the migration. I think a lot are going to be forced to, or they won’t be able to continue to survive with these insane Ethereum gas fees.

Bryce: There are just so many alternatives to Ethereum now. It just makes Ethereum like, even though it is the largest open source developer community in the world. And they have that sort of network effect. But I mean, instead of the tangent, but the Eth 2.0, like, who knows.

Humayn: There will be all waiting for Eth 2.0. Because you can put a lot in but then Binance goes and does the chain. So I mean, we work with that chain quite a lot we’ve just released. It’s not out in public yet, but we were just about to release it. It’s a random beacon for binance chain. So you can actually use true randomness. It’s kind of sitting on Fetch network, but it’s operating with Binance check.

So you can ask for a random number generator, and you can get a truly random number, and it’s all integrated in Binance smart chain. So you could derive it from the fetch chain, which is a lot faster and a lot more secure in the sense that you know, the randomness that is generated, but you could use the same thing in Ethereum or you can use it in smart chain.

Bryce: It’s one of those things, when I was working at a blockchain company prior that I didn’t realize was like a mathematical issue was like generating provably random numbers. But apparently, everybody’s probably listening like, “Random numbers, they seem so trivial, just like pick a number out of a hat.” It’s not that simple, folks. In computer science, there are quite some difficulties.

Humayn: If you’re doing DeFy, and you want to do the DeFy correct so that nobody’s in control of it, the randomness. We released a paper where it’s the provable randomness and we integrating in with finance chain. And I think we’ve probably just been announcing that next week or something. But that’s already happening. And, again, that’s necessary for agents to operate as well.

Aaron: Well, congratulations for that a very important milestone.

Bryce: Yeah, before we let you go, I do want to, what’s up next on the roadmap. We do have a couple closing questions. But I want to know, what’s next in the roadmap? What can we expect? Are there any certain, milestones that you have coming up in the next couple months that we could keep tabs on?

Humayn: Yeah, sure. So we took a different strategy than most projects. Because we have, yes, – Why are we doing what we’re doing? So the strategy was that AI agents all futuristic, right? But you still need commercial deployment, you still need to take all that technology and actually start using it in the simplest form as quickly as you can. So we took two groups. So we took a DeFy FinTech or Cefy, whatever you want to call it. It’s a blend of all of these things. But because it’s based on blockchain, you have to call it DeFy, Iguess.

So we have that vertical and then we have a very real world commercialization project now and we progressing them quite together effectively. So on one side, we’re launching a commodities exchange, which is the decentralized, we bringing real world commodities. And we’re creating this easy way of trading those derivatives, the spreads, or any instruments, and bringing all the commodity traders and other kind of traders or insurance traders, onto the system.

And the reason why we’re doing that is because a lot of the technology we build for the agents applies for the traditional markets of tax and everything else. So we deployed that because that’s a now thing. And we want us to be commercially successful. So we launched a governance token, which is The Metal X token. And then within that vertical, the next we will do is once we have gone through the process of decentralized exchange, we have another project called Atomics, which is spinning out, which is a real asset, real world asset lending platform.

So, you can take real commodities supply chain finance because don’t forget, supply chain finance is best done, when you can get information from each component of the supply chain, that’s where the agents come in. So now you have agents which are notifying of any change or notifying of any supply chain components and you can then finance that. So we building that finance kind of component of our project. And on the other side, we then have is something quite different, which is, we’re building a transportation model around agents because transportation traffic is all based on multi agents.

Because every car is an agent, if you think of it, even if we are humans, we are still interacting multi-stakeholder system. So we’re looking at providing, again connected fabric or autonomous driving, because atonomous driving, what you can see visually and react to visually is only one thing. But what you can’t do is, you can’t see 10 cars ahead. And what we’re creating is a fabric which enables you to do that. So you can actually see 10 cars ahead.

Aaron: That’s really fascinating. Man, I’d love to talk to you for another four hours, but I don’t want to take you away from building this amazing technology.

Bryce: The future.

Aaron: Yes, too much longer but if I can just want to squeeze one last question in, we’re going to have a lot of crossover in our listenership. In this episode, we’re going to have a lot of people from the AI and machine learning and development communities that maybe are hearing a crypto podcast for the first time. If this was a first crypto podcast, someone kind of getting into the space and heard, what would you want them to know about this industry in particular? Can you leave us with some words of wisdom?

Humayn: It’s a difficult one.

Bryce: It’s unlike any other one you’ve probably ever worked in.

Humayn: There is a bad press out on crypto. It’s not all that bad. Don’t look at just the food items. Look at something really good that is happening as well. I mean, we are completely changing the world’s financial system, if we get it right. And we’re never going to get it right in the first instance. We are going to reiterate, we’re going to redesign and ultimately this is going to take place and it’s going to happen. You can see there are proof of things which are going to come.

So for crypto, somebody who’s listening to crypto for the first time, don’t just look at the bad things. There is a lot of good stuff going on. The decentralization, it might not be good for everything, but it is pretty good for a lot of things. And I think we should take this journey as mankind, I guess. That’s a big word. But we should take this journey because this is the next step of evolution.

Bryce: I wouldn’t agree more. Honestly, great closing words of wisdom for us today. Couldn’t really thank you enough, man. We had a great time, learned a lot about a subject that we just really don’t get to talk much about. So it was cool, kind of hopping over to the other side of the aisle and learning about the marriage between blockchain, artificial intelligence and seeing how the future is going to unfold from your vantage point. So hopefully, you know, we bring you back on the show down line with some more developments.

Humayn: That’s correct. Thank you, guys. I’ve really enjoyed it. Thank you for all the questions. I really enjoyed answering.

Bryce: Take care.

Humayn: Thank you.

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Fog by DIZARO @dizarofr
Creative Commons — Attribution-NoDerivs 3.0 Unported — CC BY-ND 3.0
Free Download / Stream: bit.ly/Fog-DIZARO
Music promoted by Audio Library youtu.be/lAfbjt_rmE8