So all the tokens will go on to Fetch, so they will become a Fetch token, but Fetch token will be renamed as Aside token, So for Fetch holders, it's a name change and a network upgrade. For Ocean and Singularity tokens, it's a conversion whereby their tokens will be converted into Aside token. This content is brought to you by Uphold, which is one of the top crypto platforms out there which allows you to easily buy cell trade Bitcoin and
the top or coins in the market. Uphold lists two hundred and sixty plus cryptocurrencies. They also allow you to trade precious metals such as gold, silver, palladium, and platinum. I've been a user of Uphold since twenty eighteen, so I can voucher this platform. They have full transparency reports and they don't commingle or lend out your funds. They also have a great product called
Vault, which is an assisted self custody product. A vault allows you to maintain custody of your funds and the keys are split, so Uphold holds one and you hold two, and if there's any issues if you lose one of your keys, Uphold will help restore these keys and you can maintain access to
your funds. It's twenty four to seven instant trading, it's trustless, so this is a great feature that can give you peace of mind where you don't have to worry about if you lose your private keys and much more so. If you'd like to learn more about uphold, please visit the link in the description. Welcome to the Thinking Crypto podcast, your home for cryptocurrency news and interviews with me. Today's Humayun Shake. Who's the CEO and founder of fetch
dot ai and the chairman of the Artificial Superintelligence Alliance. Who are you in great to have you on. I appreciate your invite, Thank you, thanks for having me Whomy, and i am so excited to speak with you. And I'm not saying that because I interview people, but I'm a big fan of fetch dot ai. I'm very fascinated by this merging of these three AI driven tokens, and we're going to talk about all that. But before we
get there, tell us about yourself. Where you're from and what's your professional background. Yeah, I'm from Pakistan. I've been in the UK twenty five years now, so I came as a student and then I stayed here. A background just to give you some some professional background a little bit. I trained as a computer scientist, joined here in the UK, joined the gaming
industry quite early on, and then from there I started. I met Demis Hasavis, who was the founder of DeepMind, so based on that we kind of got together and I got into discussing artificial intelligence and AGI concepts and working on some of those core concepts at that point, and gaming was really a great place to try these things out, as you probably already know, so we started trying those out. Then I became part of Google deep Mind.
Early on, I was the first investor in deep Mind. I was also looking after the commercialization of AI in deep Mind, so that's how I got into really into AI. And then once Google bought DeepMind and I exited from deep Mind, I started kind of putting together my thoughts on what was right,
what happened good, what happened bad in deep Mind. And one of the things which was quite interesting which I felt I needed to work on, was the commercialization and actually break AIM too much more absorb absorbable and granular kind of components so people can actually interact with it and use it. That's how I kind of came to fetch and the agent based framework. So that's kind
of the story behind it a little bit. And I'm curious, So you have a plathora of experience with AI, and that's a big part of your experience. But where did you encounter blockchain and crypto? And then you know, obviously now there's a conversience of those two technologies, but what was your like first encounter bitcoin? Where did you hear about it? And you know, what was your harm on it? Yeah, so I've known, I must admit, I mean, I didn't get into bitcoin upfront, but I
did get into a theoeum quite early on. And so so I'm always interested in new technology, So you know, I was always looking for new technology. AI was one. Blockchain and crypto was really kind of maturing and growing, and if you think about ten years ago, fifteen years ago, so I was I was watching the you know, the space quite considerably. I'm not overly financial and I'm not into kind of financial engineering as such, but
it was quite interesting what bitcoin was doing. But it became really exciting when Ethereum came about and we looked at how you could actually execute code and record it and create you know, actual functioning computing systems on blockchain. So that's when I really got interested. So I got interested quite quite a lot in ethereum and looking at what you could use it for. And on the other side, although I was interested in it, my main focus was still looking
at commercialization of AI. And when I was thinking about how can you actually make AI into smaller chunks which people can interact with, and how can you actually use machine learning models? I mean machine learning models have been around, you know, tens of years, so I was thinking, Okay, at the moment, somebody has to build it, somebody has to deploy it, and it all goes back into somebody who has the data who can use it, and generally it's these big corporations who can use it. Again, an
example is Google and Microsoft and the likes. And when we exit it to Google, it became quite clear that training these models quite expensive, and that really was one of the core things that we needed to be aware of that without compute you can't really do anything, and the compute is what is expensive. And you know, if you think about what Google has, it's a
cloud infrastructure compute infrastructure. So how can you then bring it to a place where you know, normal people can interact with it or build solutions and smaller companies can build solutions. And it kind of really fitted quite well with having a blockchain which could actually record your participation. It could actually orchestrate the agent based system because and we talk about why agent based systems, I felt to really get AI to the masses, we need to have agent based system.
And to manage an agent based system, which is that expansionary and that all encompassing, you need to have a ledger technology which actually keeps a record of everything. Now you could do that in a centralized way. You can give it to a big centralized company, or you could actually make it a bit more decentralized where you know, the communities can manage it and you can still build solutions and you can still deploy them. So that's when I got quite
excited about bringing the two together. And this is now eight seven eight years ago, and that's when we started looking at it. Wow, that's quite a journey. It was a while ago. And you know, especially in these industries, the technology moves so fast on both sides. So tell us about FETCH and what's its mission capabilities and what folks can do with it. Yeah, so so just kind of tying into what I was saying before.
It was quite clear that agent based systems are going to become to me, it was quite clear that agent based systems will become the norm for commercializing any application building in AI space. But what was missing? So Fetch came about around five years ago, six years probably if you think about the inception as well. So six years ago, we didn't have this LLLM craze. Although LLM technology, the transformative knowledgy was there. We also use that in Deep
Mind. We used to train you know, game agents to play the game just based on reading instruction set. So we were doing that already, but not to that scale where you know, the elms became all encompassing and what Opening Eye did was quite amazing in terms of how they opened up this whole commercialization. But but we I did have this thinking that yes, we're going to have one these models which will fully understand what you're saying to them.
Now, I don't want to go into the sentient or not sentient, or how much they actually understand and not understand. But let's just assume that they understand you well enough to, you know, very convincingly give you arguments and make their point and they understand what your instruction set is or what your objective could look like. But what also was clear was okay, what next,
right, So what are you going to do with it? Right? You understand the language, you can talk, and you can talk the talk, but can you do the walk? And that was the kind of the moment when you think, right, so somebody will understand the language, but how can you kind of create a chain of thought where you can actually start putting
actions together. And what that also opened up as an understanding was that the search and discovery will change because at the moment, we're just searching for information and when we can come to search for more actionable items. So for example, if you are going to come and book for a ticket on an airplane a flight, or you want to book a hotel, these are action. Or you want to order a cab, that's action. It also became very
clear that Google wasn't really doing the job right. Hence you needed all these aggregators to come in because these aggregators were putting all this information together and actually enabling you to create these actionable searches. Right, so if you want to book a hotel, you go to booking dot com. If you want to book a flight, you go to booking dot com. You know, there
were incumbents like Amadaeus, which was aggregating all of these things. How do you then think the paradigm shift will come when the language understanding is really good? But how do you integrate this with their legacy systems so you can actually understand your query and actually you do the search rather than just dumping thirty thousand
websites in front of you. So that was the AHA moment really when we thought, okay, you know this, I could see with LLLMS, we're going to change the way we search, and ultimately we're going to change the way the actionable search also happened, and you know when we started Fetch,
that was the concept behind it. That was the thinking behind it. Of course, I mean it takes time for things to kind of come to fruition, and you know when open Aye released it, well kind of that was that was my moment of saying, okay, I told you so, look this is coming and that kind of so we were thinking slightly ahead of that because we also felt that l ELM is a big boys game. You need a lot of capital, because we had already seen that in the deep mind
days, training models is not cheap. So what also became quite clear is that if we're going to as a smallish entity going to make a difference, we need to sit above it. And l elms will become very commoditized because it will be a game of three or four people, which you're now already seeing. You can see you know, inflection, know things, things are
changing, what's been happening with open ai. It's unless you get the Microsoft so the Googles of the world funding it, financing it and videos of the world, you can't really train a foundational LLM to the right extent. Of course, techniques are going to improve, you're not going to need that much compute, you're going to not need that much data, but at this point in time they're going to be commoditized. Some big corporation will do it,
offer it as a package alongside the other cloud compute for example. So you know I've been I've been talking quite a bit. So if you have anything to kind of ask or ad please do no, no, that's that's great insight. I love that because it gives a lot of perspective and on the problems you're trying to solve and the dynamics of being in the market with the big players like you mentioned. Tell us about the merge of FETCH, Ocean
Protocol and s net to form the Artificial Superintelligence Alliance. How did this come about and what's what's the goal of merging. Yeah, so so obviously we've spent I mean, as I said, fetches four or five years old. We've been working quite aggressively on the agent framework. We did some release it. I mean, when you when you're kind of the first few, there's
a lot to explore. And that's what we were doing. And what was quite clear was that there's a lot of components if you're going to take this technology, if you're going to commercialize the technology. My focus has always been how do we commercialize AI, how do we take it right to the consumer. And it became also very clear was that you need compute because yes, although foundational models are sitting there, you still need compute because you're going to
be new models. So give you an example. You know, I want to go from point A to point B, right that's my objective. I want to go from London to Paris. Right now, it's it's a statement, but it has many components. Right, what's my address is it? What's the weather like? How am I going to travel from point A to point B? Am I going to take a cab? A train? Am I going to need an umbrella? Am I going to wear warm clothes? You know, not warm clothes? And what's the weather going to do?
Where am I going to stay? All these components first need to be understood. So foundational M you can say that and it can give you a beautiful prose and it says, you know, how wonderful is going to be in Paris? Is romantic and you know it will give you all those things, but you know the useful things like well do I need to you know, kind of wear warm clothes right now like tomorrow? And how do I order a cab? It won't be able to do. I'm not saying it can't
do forever. It's just right now it needs those components to be brought in somehow. So you still need to Although you don't need a foundational LM, you still need lllms, which are specialists, you know, specialist ALM could be specialist knowledge based, action based, you know, context based, you can Actually you still need to train quite a lot of these specialist stellar lems,
and I think that's where the world is going to go next. Because you have the data, you're going to train them, So you need compute. So that's one the first layer. You need data. How are you going to put bring data to train these models? Who is going to provide you that? Why are they going to provide you that? How do you make sure it's not getting stolen? How do you make sure it's safe and secure to kind of train these models with that data. So that's the second
component layer, which is the data component. Then you need to build new types of models. So you know, it's not like just because we now have foundational lms, let's just pack up and just leave it to them. No, because you're going to have new let's say, neurosymbolic lms which are giving you more deterministic, less hallucination, more actionable kind of information. So you need new models. You need new technologies which use less data more efficient.
How do you get to that? So that's you have this AI layer or as if you want to extend that further, it's the artificial general intelligence, because how do you make these models understand properly? And then from there on you want to go to the artificial superintelligence layer, which is, you know, way better than humans. It's not just humans, it's way better than humans. More specialists, more faster, better in kind of making assumptions,
predictions, objectives, all of that. So that's that layer in the middle. So now if I translate all of that, and then then the last layer is Okay, now I've got these beautiful models and I've got data which I've trained it on. But what so what you know, how do you get to the consumer? So the consumer you have you know, personal assistance like Siri, like Alexa. I mean you can talk to them, but for them to do something and have your agency and do something, it's
very difficult. They still can't connect unless you connect them one to one. So that's where our agent based technology comes in, which is the fetchiest technology, which which enables you to granularize actions. It kind of takes all the legacy systems and you can connect an agent to the legacy systems and they can
execute tasks. So now if I rewind back, so we have a layer, which is the network which Fetch has where you can think about logging transactions, interactions, orchestration based on what your commercialization model looks like, based on which AI model you use looks like, what data you use look like.
So you need a recording system. So that's the Fetch network layer. Then you on top of that is as I said, data Ocean brings that data technology, which is how do you secure it, how do you make it safe, how do you monetize it, how do you use the data, how do you bring compute to data or data to compute, how can you do multi party computation? All of those things Ocean is very good at. So we felt, you know, that's the second layer which you need.
And then the third layer, which is Singularity is really good with research. I mean Ben has been working in this space for twenty I mean he kind of coined with Shane leg who was a colleague in deep Mind and Ben. They coined the term AGI together, so so he knows what he's kind of wanting the AGI to look like, and he's been researching and his team has
been researching. So we need that research to improve the models. And then finally the Fetch agent framework of the agent platform, which you can connect multiple llms, machine learning models, legacy systems, APIs, and it automatically finds a way to connect with them execute the task. That's what the fetch layer brings. So now if you think it makes logical sense that if a builder a developer is going to come and build a solution, he or she needs
all of them, all of those components. If you can save them time in the sense that they don't have to worry about which token do I need for this service? Which token do I need for this service, and you can just do full integration, a fully vertically integrated system, which makes their life very easy, then it absolutely makes sense. Why wouldn't you do it
the question, So that's how I kind of came about with it. And then obviously I've known both projects, and you know, we worked with Ocean a lot in terms of the German space where we worked with automotive sector part of several initiatives in Europe, so we knew them. We knew Singularity quite while back, so we've been in the space, we've been speaking to each other, so it kind of made perfect sense to think, Okay, you know, we need to build a general intelligence, which then moves on to
superintelligence. How are we going to actually get to that point? That's so, that's what triggered it, and then from there on we put everything together to make sure that, you know, we can actually get to a point where you know, it's not it's we didn't want to change anything in terms of how people manage the foundations, but we just felt that technologically we needed
something to change, and putting them all together makes sense. You gave a great rundown of the why of how you know these projects are merging together and the reasons right, and the benefits and so forth. Tell us about the logistics though, what happens to the three blockchains? Are they merged into one or they will coexist in the ecosystem? And then also how did the tokens come into play? And let's say, as a Fetch token holder, what
happens to my tokens under the new ASI system. Yeah, so it's a merge of tokens and Fetch chain, which is a Cosmo space chain at the moment. But we have different consensus mechanism we're triling out. There's different features we're trialing out for AI kind of application build and development. So all the tokens will go on too Fetch, so they will become a Fetch token, but Fetch token will be renamed as air Side token. So for Fetch holders,
it's a name change and network upgrade. For Ocean and Singularity tokens, it's a conversion whereby their tokens will be converted into Aside token based on the conversion rate that we announced, so I think it's point four three three, So for every Ocean token or Singularity token, you roughly get point four three three Aside token. But that means that the number of tokens VEAT holders have
remains the same. But to accommodate and merge those other tokens, we have to issue new tokens, and the ratio, as I said, is point four three to three. So I think I do get this question a lot. Is it going to be valution were it's not because you're actually adding more water, You're actually adding more value into the pot, so there's no dilution as such. But you do have those communities joining and merging their token into
this. So at the moment, let's say the you know, the market cap is, let's you know, I don't know what the detail is. But let's say the market cap is three billion, and once those two projects join us, it will become you know, six or seven billion, So I think that's that's how it would be. So the pot becomes bigger, the cake becomes bigger, the piece stays the same. So ultimately, in a absolute way, the value is the same, but as a percentage,
you become percentage off the value. What's your timeline for this and also what's on your twenty four road map once the merger is completed. Yeah, so we are carrying out the vote right now. I mean, fetch we kind of the first vote is in and then the second vote is going to go in, which will be you know, kind of acceptance of the mergers. So we expect by in roughly around thirty days that the merger will be approved.
And we're already working quite hard on getting the network upgrades ready. We're speaking to the exchanges already so that the transition is as smooth as possible. So over the next thirty to forty days, we expect to move across FAT to ASI upgrade the network, plus bring in all the other two communities and they're token onto ASI issue the token or there will be a token exchange for them and then so that so that kind of takes care of the token.
We then move on to and we're already working together to build the tech kind of vertical integration, so all the services will just use a SI tokens.
So you can imagine all the all the staking rewards, all the all the features and functions that the other projects have, apart from what Vetch already has, all of that will become part of the fetch Slash a side chain, and then suddenly you will have you'll see a lot more volume going through the air side chain and ultimately it will benefit us as a whole ASI community.
It will benefit us because the the transactions will go up, the tvls will go up, and and you know, I can't commit that this will stay as a Cosmos chain or it will have other features in the Cosmos chain, but the aside chain will be the coordination chain for AI and that's our objective. So over the next you know, twelve months, we want a very reasonably tight integration with all the tech stack. There there are other technologies which
we still need which will probably will will find and integrate more. There is a lot more coherent integration stack integrated stack, which will be available so you can use data service, you can use different machine learning models, you can actually commercialize using agents, you can actually record all of that for audered purpose, and it will all be done via one token, which is the Airside token. So that's that's the vision, that's the objective, and we're already
working on it. We all starting to make huge progress because it's just such complementary technologies that it makes sense and it automatically kind of fits in. They're not they're not like competing. We're not competing technologies. We're complementary technologies. So that's that's the great news. And we hope over the next twelve months to be the chain for AI. And that's really the objective, and the
technology for AI, the platform for deploying AI applications. So that's so those are the key components that we're technically, we're looking to do in terms of token. We we expect with the mergers and hopefully as we see more adoption on our system, we expect to be, you know, hopefully among the top you know, twenty twenty five tokens. Price goes up, price comes
down. I can't really comment on that, But but I think at least as a as a project value as a project should you know, multiply rather than it's it's rather than just additive, So it's it's it's it's an alternative to the centralized kind of solutions that are available. So we wanted to provide a decentralized alternative to the AI deployment technology, not just not just deployment, building, delivering, deploying testing. So the whole text that takes care of
time. The question on that. Obviously, the way you guys are doing things is decentralized and there's transparency and much more. And in the tech world we have two things running in parallel because of crypto and blockchain. You have this decentralized movement, but you also have centralized movements with companies like Google and
open ai and so forth. So I guess my question for you, and this may be a hard question to answer, do you see the future ultimately leading the path that you guys are going with where things are more decentralized. Once again, given that there's the macro of blockchain and crypto taking us to that in that direction, but you still have your incumbent centralized companies like your Googles and so forth. How do you see that dynamic playing out? I
don't think we have to take that bet. Is it going to be one or the other? It could be a combination of both. It could be some people just prefer using one then as compared to the others. Now, what we're trying to do here is there is an inherent problem in decentralization. The inherent problem is it's a lot tougher to do. It's not easy to deliver decision making putting things together. You have multiple different decentralized systems running around
which you have to bring together. What about the governance, you know, So we're trying to make that much better by putting all these projects together, because then one governance, four or five technologies within that stack much easier. Right, you don't have to go to ten different projects use ten different tokens. I mean you don't do that in centralized entities, right, you just
use your fead currency. So we're kind of removing that friction, trying to be in a much more competitive way available as a secondary solution, not just the secondary solution, but an alternative solution. So that in itself has huge value for not just for AI space, but also for the crypto space, because one thing, one of the biggest problems in crypto space is we're finding difficult to get applications on board it. So it's very much like, well,
we have this wonderful solution, but we don't have a problem. So so there is that element which happens. So DeFi financial, you know, that's all there, but that's it. We haven't moved past that gaming or defile, right, so we're struggling to add new use cases to this. Now AI is very different. It opens up that space to application building, It opens up the space to bring users in a different way onto crypto.
Now we don't have to keep telling everybody that we use crypto, because crypto the whole point is yes, it's decentralized, but it exists in the in the background, and it does what you need to do. And until we get to that point when you actually see a real application running on crypto, then you will start seeing a huge amount of traction. And I'm hoping AI is going to be that catalyst to bring that traction, which you know, initially we had a lot of traction because of DeFi, but then kind of
slowed down. DeFi has other problems. The regulation, now that's another issue. But let's now compare that to AI. AI will also have regulation. But what's the great bit is being decentralized, being everything you know, auditable on chain, being being a solution which you know the regulator can come and monitor all the time. It gives us an edge, It doesn't give us a disadvantage. And I think that we want to make sure all the tools
exist that the regulator can actually see. There is inclusion, there is safety, there's security, and we're not building this artificial superintelligence in isolation. It's not in a black box. You know, you can see what's happening, training, whose data is coming in, when the action is taken on agent based system, which agent took, what action it's recorded on the chain,
what financial implication? You know. The biggest danger in AI at the moment is if you put it in you know, big corporations control and they get it wrong. They might not want to get it wrong, but they can get it wrong. I mean, look at what happened with Google's you know, the video and picture software, right. You know they didn't intend to do it. It just can happen and if a big corporation like Google can have that problem, then why would others not have that problem? They would
certainly have that problem. And once it's out of the bag, once, I mean he now imagine making such a mistake with the AGI, which is self controlling, self learning, self assessing, and it kind of builds the wrong model in itself. Then how do we deal with it? Because it's in a centralized control, nobody can see it. So I think regulation and AI kind of are going to be together, but I think we put us in a very good place to actually showcase how There is an alternative question for
you on the relationship between AI and blockchain. Do you see it being like symbiotic where to help each other. I think you touched a bit on it. But one item I've been paying attention to is the rise of AI defate content and certain platforms have been looking to put content on the blockchain so you can verify where did this content come from, when, when was it published,
and so on and so forth. And there could be many other use cases where blockchain maybe is helping to police AI and AI, like you said, helping to open doors for adoption and use cases. How do you see that relationship, you know, continuing growing in the future. I think there's definitely a use case here for blockchain to manage that visibility, that auditability, And as you said, you know you could put that on chain. You can, you can do many things. But here's the problem. The problem
is it's too cumbersome. It's not efficient. Unless we make it efficient, Unless we make it less cumbersome, unless it's easy for people to use it, it's going to the adoption will not come. So really, one of the key objectives that we have here is, yes, we want to build artificial superintelligence, but that's down the line and in building, in building that, in training that, we need visibility, we need auditability, we need
ease of use, and we're putting all those components together. So while we are trying to go from here to there, we're also looking at commercialization, which is open, decentralized, which can actually provide visibility and all the things that you just mentioned. You could easily take care of that using the blockchain. But that's not to say you can't do it in a centralized way. But then who controls it is the question, and I think that's where we
start to feel. But one of My biggest objective is unless we are at least as efficient as a centralized entities technology stack, we're not going to get the adoption because it makes it more expensive, it makes it more cumbersome, and it's difficult. But one thing which is really exciting is that if you have seen the developer community in crypto, you would know they are very passionate about it right, and they would do you know, they will spend nights
and days to build something. And I think that's where we're going to have an edge over the centralized communities because now creating these way we're bringing these developers who who are so engaged and who are so passionate about building this new thing that we just on that basis, if we can get it right and we can excite our communities in the right way, we will we will have a
very big advantage. M hmm. Yeah, absolutely. I do want to ask you about AI impact on the macro and this is going to deviate a bit off of the blockchain crypto conversation, but because of your experience, I absolutely have to ask you. There's a ton of benefits to AI, right, and I'm excited to see all these things, But are there anything, any items that keep you up at night where there may be downsides that impacts our society or culture or civilization. Is there anything that you think might be
a threat. Whatever new technology, you have a threat to the old one, right. So you know, when when we had telephones, we had a threat to telegram we had and then we had a threat to mail system a little bit. And then when when we became uh, you know, mobile and five G, there was a threat to fiber optic network and there was a threat to all the other network and the telephone landline network. So
so you're always going to have that problem, right. So Google came to disrupt Yahoo, Yahoo came to disrupt Yellow Pages, and you know it's going to happen. But that doesn't mean it's a negative impact always. It's you know, you could look at it in two different ways, whereby you can think, you know that we're going to improve in certain things, and we can you know, like, actually, I think this this whole new wave of AI and the ability to code will kind of reduce the U use cases
for developers. Right, it's coming, right, like it or not, It's going to happen, accept it. I'm not saying it's happening right now because it can't write the code, can't test the code. You still need the logics in the right way, but the actual developers, the efficiency is going to multiply greatly, right, so you can actually the same one person can probably develop ten people's work right using these tools. So that's the step
one. The second step is you probably don't need to write that much code anyway because the llms will take care of And that's what I was kind of
alluding to. If you think about you, rather than writing code to understand your objective, you can just give your objective and the objective goes on to our agent based framework and actually assembles all the agents that you need, so you don't really need to write code, right, And now you have the user interface which understands you completely, which is you know, natural language user interface, so you can you can speak to your washing machine and tell it,
you know term turn yourself on at this time, save me ten dollars worth of energy. It will just do what you wanted to do. That's your user interface. You don't have to have another app for it, right, So that model is going to for sure is going to change, and that will disrupt quite a few industries, and that will disrupt and that will have a negative impact on some of the people who work in those industries.
But that will then open up some new areas of interest. And you know, for example, in my mind, biotech is a really great example. We're not going to solve that problem just with what we have right now. There's a lot more development, there's a lot more understanding needs to happen. So if anything, what would happen is that our focus will go back to more science research, which then is going to also be accelerated with this AI.
But then comes this how can you bring multiple things together to develop a solution? How can you tell the AI to develop that solution? So I think one which which in these days is prompt engineering, but it will be much more than prompt engineering. It will be probably objective engineering. So how do you create the right objectives for this AI to kind of deliver the solution.
I think we still will need quite a bit of manual kind of work will need to be done still in the foreseeable future, because robots are great, But still I think we have some time to get to a perfection level. So there will be still that But but I think some some areas for sure, will have a negative impact. Like entertainment, for example, you could just you could just write drama series and you can play them out and
you don't need all the actors. And that's coming very soon, and we saw that in Hollywood, and I think a lot of a lot of investment has been delayed or stopped because of those reasons. So you are going to see some negative impact. But ultimately, we as humans will adapt and will come come back to you know, different and better ways of doing things for
sure. You know, as you were saying that with biotech, I'm just thinking, like we as humans right now, with the technology we have, we're not able to cure cancer, but AI could possibly help us cure that and other diseases. I mean, you saw what Alpha fold is doing. You saw how accelerated development there will be. So yes, of course that will that will come. All right, let's jump back to crypto. Now, you're based in the UK, and I want to get your thoughts on
you know, the UK working on cryptoregulations, EU pass crypto regulations. Here in the United States we're still waiting, but I would love to get your perspective on those regulations ever passed and how they impact the work you're doing as an innovator. I I still feel we don't have huge amounts of clarity here still, and we have a we have a double whamy effect because we are in Ai as well, which is now getting to be regulated, and there
is crypto which needs to be regulated. So so I think there is there's a lot of misconceptions still and I don't find UK as a very conducive place for crypto at this point in time, and haven't done for quite a while. But I think there is improvement which is coming. There is there's a
lot more understanding that is coming. But I feel some parts of you know, like for example, we moved our crypto operations to Dubai, which was probably the right thing to do because Dubai is much more welcoming to the crypto industry and has, you know, a regulation we set up in Singapore, which the Foundation was in Singapore initially because there was some clarity around how the
token issuance should work. Although UK is making very good progress, the problem comes with banking effectively, because because scrypto has got such a bad kind of stigma attached to it, the banks you know, still that don't feel that they can comfortably allow crypto companies to operate with a bank account. So most of the high street banks, the mainstream banks, they don't want to touch
crypto. It's a bad thing. It doesn't matter what you're doing. You might be running, you know, a company or legitimate with all the approvals, they just still feel very uncomfortable with it. So unless I think the regulators start kind of easing off their fears, I think we'll still see still
won't feel the true value that could come here anytime soon. Yeah, I mean to your point, though they are they're making some progress, right, so at least in're moving in the right Yeah, but it's still got ways to go. And I envy a bit of the UK, even the EU, because you guys are at least a bit further ahead. And then we are here in the United States because here we're dealing with the SEC and all the litigation and so forth. But you know, we'll eventually get there.
I want to get your thoughts on the market as a whole and how it's grown. Obviously, you got bigcoin ETFs out in the market. There's talks of tokenization, some of the biggest institutions in the world, from Wall Street and so forth, are getting involved. What are your thoughts on how the industry has grown. I think the industry is maturing, of course, and that's very clear because you know, initially it was not going to last for a year, and it was not going to last for you know, two
years or five years or this. This this downcycle is the end of it. But you know it survived and it's right, so every time it comes back, it comes back stronger. So I think I think the way I look at it is bitcoin is now in a way, it kind of is now a commodity. I don't I don't know if it's a store value or not, but you know, I believe it is, but some people don't. But I don't want to argue that case. But it's definitely something which
is becoming part of financial instruments. It's becoming part of financial portfolios. So it has kind of grown to a point where you can't ignore it. And that's that's probably my kind of only the limit which I can say, because
I anything else would be just an opinion. So it's gone grown to become part of a financial kind of market and a value we have usefulness in terms of ethereum as we have seen and several other changes we have seen, so the usefulness is also coming, and I think people are starting to build solutions.
But I did I did raise that point that unless we start to see some real traction in real use cases, we are going to struggle and you know, having another layer one where you do exactly the same as you did last year, or another layer one but slightly better, and then you can have meme points. I've got nothing against dogs, right, It's just that,
I mean, I love dogs, I love cats. Meme coins great, you know, but they're not for me because I feel I feel that's what is kind of partly giving it a bad kind of image, because they you know, there's this up and there's this down, and people talk about it and people and then everybody feels that every project in this space is just going to do the same and it doesn't, and then you have this negativity amongst community, which then gets picked up by the outsiders and then they write
about it because they think, well, you know, it's a bunch of people who are just going out and buying dog tokens and waiting for it to pump and then dump. So I think that's something which I don't appreciate in our industry, which I think, but what that still does. It brings attention. It's negative or positive, I don't care, but I mean it's an opinion, and you know, a lot of people would probably disagree with my opinion. But I feel if we start delivering some serious use cases,
we have a lot to gain in the next few years. And I feel that AI might be a really, really good target technology which could open up that door because you will see automation coming, which brings ease of use of crypto. We have real use case of building AI applications on decentralized Leger technology, which is going to bring attention, but also real solution. I mean what I was explaining was agent based solution. We have two sites of agent
based solution. One runs decentralized, one is centralized. And we're feel in a lot more traction in the decentralized space because people don't want to share a lot of their data. They want to maintain their own control on the data. They don't want some other company to run parts of the machine learning students want to build machine learning models and they want to monetize them, which they can't do on a centralized system, although centralized system like hugging faces also a
very good example, but that's where we're going to start seeing traction. So of course I'm biased to my own space, which is AI, and I feel it can actually do quite a lot of good. And I think this whole alliance is just part of bringing that motion onto this space and saying you can build real world use cases. The developers who are building solutions in centralized space can build it here, and Crypto has a real use case. Well put, I got some wrap up questions here for you. First, if
you could create your own metaverse, what would the theme be? And I'm assuming it might be AI. Well, I think you say that, But what I want to kind of point out towards is our alliance partner, Singularity has a Sophia Verse which is around AI, and I don't know if you've seen this, but it's pretty good, and I think you're going to have Agent running in the Sofia verse, so it'll be Agent would be my theme
AI Agents. Got some rapid fire questions for your versus favorite food, Korean, favorite musician or band Metallica, I love Metallica, A favorite movie Showshank Redemption. It's a good one. Favorite book super Intelligence Nick Bostrom. Mm hmmm. And what do you do for fun as a hobby or pastime. Well, I like watching cars race, so Formula one is one of my favorites. Formula one or any car sports awesome, a pleasure chatting with you.
I gotta have you back on because I'm super fascinated with AI and the convergence with blockchain and so forth. So we'll have to set up another interview and go deeper in somebody these AI topics. But thank you so much for joining me. Appreciate it. Thank you, Tony. It was a pleasure.