Humayun Sheikh: Fetch AI – Decentralising AI Economies. Ep. 539

Transcript

welcome to epicenter the show which talks about the Technologies projects and people driving decentralization and the blockchain revolution I'm freder an and today I'm speaking with hamayan shik who is the founder and CEO of fetch AI which is one of the older AI blockchain crossover projects and we've had hamayan on the show before probably like five years ago um but before we talk with hamayan about fet um let me tell you about our sponsors this week this episode is brought to you by gsis gsis builds decentralized infrastructure for the ethereum ecosystem with a rich history dating back to 2015 and products like safe cow

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single transaction you can even offer high yield staking to your own customers using their API your assets always remain in your custody so you can have complete Peace of Mind start sing today at chorus. one hey hamayan welcome on epicenter thanks for joining us today thank you for the invite it's lovely to be here with you yeah perfect before we Deep dive into fetch can you give us a bit of background on yourself yeah so my background started from gaming uh computer science gaming and we i d got introduced to Demis who became a very good friend Demis aabis who was the

founder of Deep Mind and then I went into Deep Mind with him I was one of the First Investors one of the first five who kind of was looking at commercializing AI technology so be exited uh I exited once we sold to Google I think it's around 8 n 10 years ago now so once we exited then I was uh kind of very much interested into how we can get and how we can granular eyesee Ai and how we can actually build Solutions which smaller and businesses and individuals can use so that's how I kind of started

fetch cool so um fetch was started in 2019 noo and at the time this blockchain AI cross Overfield that's now clearly In Bloom um was very nent so I think at the time it was probably projects like numerai and ocean and but there were few and far between um so what what was um the inspiration to make this move to do AI things on chain yeah it's it's quite interesting when I used to start when when we first started F and I used to talk to people about you know AI agents

and people will look at you and take I think you're losing your mind this isn't great anyway anytime soon why do you even bother um but the the concept behind fetch was to build a multi-agent system and we looked at building the multi-agent system which was open and a bit more decentralized than uh where AI sits today because AI um as as you all obviously know is based on machine learning algorithms and these algorithms work the best with the more data you have because the more data you have is the data sits with mostly big companies

so how do you actually bring all of this together so individuals and that smaller companies can actually use it and and kind of deploy a solution uh onto their own business and use it as individuals um so that's that was the premise where fetch started from and we looked at um building this multi-agent system in in a centralized way which kind of defeats the objective and then we looked at blockchain and the decentralized legit technology and we felt that was I mean at at the convergence of the two I mean I I was see the convergence five years ago but most people are seeing it right now so the convergence was where we needed to be because uh what blockchain

enables you to do is provide this a kind of decentralized way of orchestrating um and keeping a record of the transactions of interactions of training of machine learning data so all of that uh could be put on a blockchain to keep a record off and also to actually enable you to orchestrate Solutions which are visible to others if you want them to be and they are very auditable because one of the main things which I also focused and refocused on deepbind is how do you create this explainable AI how do you create this ability to audit what AI has done and if

you uh to multi-agent systems where is the record keeping you know who is in control of it so blockchain kind of fitted really nicely into that premise and hence we started working at the convergence of those two technologies cool there's a lot to unpack here um so you've used the term multi-agent systems several times now um so I I assume this just means several agents who kind of have different goal functions or how how do I understand multi uh multi-agent systems that that's that's kind of it really if you if you look at multiple stakeholders intera I

mean let's let's unpack it right from the top so if you look at if you look at a system where a lot of stakeholders are interacting with each other uh effectively that's a multi-agent system because what you have is all these stakeholders um you know trying to achieve an objective and uh others who are participating in the completion of that that objective and you have various stakeholders having different objective now that's that's a very kind of generalized concept here so in a multi-agent system you have to be aware of uh you know it's a zero sound game uh how do they uh how do you make sure sure the consensus is achieved because that's

that's an ni Shue but but luckily all those problems are kind of very solved in DTS so we we just learned from from that whole process so that wasn't really the big issue we were trying to solve but we were trying to solve was how do you take and and that will come to what is happening right now so if you think five years ago to imagine what is going to happen if you actually took let's say if you took a piece of so software which is which kind of uh uh built in a monolithic system and you took all the functions which exist in that software and you actually made them as agents now what you have is all these functions who are interacting with each

other to build an application but these are not monolithic applications these are applications which can be built and can be composed can be orchestrated by multiple stakehold in multiple different ways so that's kind of the premise where we started from now if you if you work down from there you you think okay so what's the best way of granular ising those functions um what that turns out to be like a an agent now what is an agent in a simplest form is it's a piece of software which which does something on behalf of some instruction which is owned by somebody so now you have these functions which have this community

communication methodology where they can communicate with each other peer-to-peer they can actually compose themselves um based on whatever the objective is whatever the application is and they can exchange economic values so as we call them autonomous economic agents and that was a term coined by myself and for fetch so we focused very uh very much on that technology how can you make them communicate with each other how can you compose Services uh how can you write protocols and how can you make sure that these protocols can be generated Dynamic so that that was kind of the the universe that we were trying to explore and build

together so what now you fast forward three or four or five years then what you realize is now suddenly you have these large language models which actually take your objective and then they can take the objective and convert that objective into an action but you know at the moment what is happening is you have the text and the text is takes your objective understands it and gives you a solution around it but what is not happening at the moment still and it's in its very early stages is how do you take that um how do you take those components that form the action following on from your objective and how do you execute those actions so

now if you take what we were thinking which is taking the agents these small components these functions these microservices which can be self-composed and you connect that to this new way of taking your objective and converting that into an action and then you go into this space where action actually orchestrates these functions to deliver the objective that you came in for now I I know there's a lot of stuff which I'm kind of mentioning but but think of it like I go to a software box and I say to it I need to build an application which does this and rather than just writing

the code it picks up the code which is already there using microagents and puts them together all the fly and delivers you the objective uh so the components can be built by multiple stakeholders can be built by multiple developers and if you compose compose your service you compose your application on the Fly using these microagents yeah so can I give an example to kind of see whether I'm getting this right okay say say I want an AI assistant um and I'm telling the AI assistant I have to light off um what could I do to help me unwind and then the AI kind of brainstorms with me and

said how do you feel like going out for a drink do you feel like going out for dinner and the movie and so on then I say dinner and the movie sounds nice and then they kind of pull pull up restaurants near me that um might have availability tonight and I say which ones I like how many people I would like to go with whether I would like to invite someone and then they would suggest movies to me possibly based on what I have liked in the past and be able to kind of make reservations for me kind of by calling um calling the cinema so that kind of of is that kind of like the right idea here that that's absolutely the right idea but let's now break it down into the comp right so when you speak to your assistant that's

a large language you're speaking to so that's not where we are right we of course you can have specialist large language models and we have our own language models but let's let's just kind of componentize it in the right way so you ask this question let's say to your Siri or or to your Alexa or to your Ty it in to open AI or chat bottle whatever and it kind of says okay yeah we can see you know um so now there's two issues one is your context being added because it needs to understand who you are uh what is your preferences uh where do you live or what kind of things you like so that context is not just the llm that's that's llm is a general

foundational model for example but it needs to bring that context to and to bring that context text in let's now imagine that you have an agent which sits uh and speaks to your LNM so now that's your agent agent holds information about you and it it can feed that information into the llm so if if if you think about um it's kind of a rag right so you know you you can actually kind of create the suggestions based on what the preferences you have which the agent is holding so that's that's one component so let's call this a preference microagent which sits with you so so that goes in automatically provides that now you have okay yes you

have three options four options now that's the large language part because it it's it's absorbed all the data it knows how to suggest things to you uh it suggests a restaurant a a cinema and it's something else what whatever that may be but that's where it stops right that's where the llm stops so now somebody has to go and create these wel inte ations let's say the integration sits with a cinema integration sits with something else with a restaurant booking now you could do this via aggregators so for example you can have this aggregator which has all the cinemas on it or you can do it in a different way which is the more efficient way which is the which is where the paradigm shift is coming which is every cema says I have

an agent and this agent can just hold information about that CA when your llm or your system suggests to you that you want you should go and watch a film and you say okay yeah tell me what films are around what can I book then based on your preferences that agent then goes and speaks to other agents to find out what's in your area what's relevant to you rather than this whole you know I can generate whatever I want it's it's not it's it's a very deterministic approach so your agent goes goes to the cinema agent and says hey uh you know this this movie seems relevant I'm going

to propose it do you have any availability do you have any seats so so your agent speaks to the agent the sema's agent automatically works out it there is availability because there's no point suggesting it to you then you're going on the website trying and book the seat no if if it's proposing something to you then that means the availability is also there so you can then say hey go book it and then the two agents interact with each other not through an intermediary not through an aggregator not through some weird platform you're having this conversation whichever the channel is it can then go and book it for you so rather than saying oh yes I like this idea now I'm going to go and

show me where this Cinema is I go and see the cinema click it then see oh there's no availability because the cinema book so all of that is completely bypassed and you go from making a direct connection between the two micro agents and delivering your objective based on you know what you proposed initially cool and um the blockchain element makes sure that kind of this works in a composable way so kind of like there's a neutral platform that all of these um microservice AIS can live on um and Comm Comm unicate is that correct that's correct so what you have is you

have to register your agent somewhere and if you want it to be registered in open system then you have a blockchain registration system you want to then when somebody accesses your agents ask the question you need to be able to explain and audit why this happened when it happened that it keeps a record of Cl okay what in principle blockchains could also enable is giving the AI economic autonomy right so BAS basically not not only could they say um you want to look you want to watch I don't know uh poor things at the cinema and go to this Italian restaurant but they could also immediately book it for me because in principle you can endow them with money right

absolutely and that's that's another reason why because we think digital currency is is going to be the future now whatever the digital currency is we don't need to pick a bet on it but we know some digital currency even if it's a government own one or it's a it's a you know it's it's a public one or it's decentralized or not decentralized it will be a digital currency and giving your agent the ability to transact on your behalf um that's one of the and hence the autonomous economic agent concept that we introduced yeah I I see um a lot of room for um making our lives easier there so even if I just think about things like

booking a holiday right kind of saying I want to be away for these two weeks because that's when my kids are off school um I want to go somewhere womb um that's not too densely populated I definitely want to get some sun um I would like to go on a safari and I would like to spend a couple of days at beach this is my budget um I want direct flights only and then kind of if you kind of if you were to kind of research this yourself you'd spend hours online kind of looking at kind of like uh hotel reviews and kind of seeing how everything would kind of work together but in principle all of this can be abstracted away from you and kind of you can be given like five different

proposals of where to go based on your criteria right absolutely and but but there is there is there's a economic Advantage here as well uh at the moment for you to do that you do the work and then somebody in the middle says okay we sit in the middle we'll take 25% of it because you know if you think about booking or any other aggregator travel aggregator that's what they would do and the reason is because there has to be some place where all the information sits so that you can search this search it easier you don't have to go on Google and search for every single one independently but in this case now what's what's the Paradigm Shift coming is this that surge is changing the search is

completely uh going to be different from the search that we do today and and that the evidence of that is is coming so your agent can actually interact based on your preferences with directly with the the supplier of those service so the hotel can actually then reverse bit for your business right so you have five and you can say hey this is my budget who's going to give me the best price and I will take it and so you don't have to and but you don't have to do anything right so it all it it gets done automatically you just get to press the button and say yes this is my choice or no this is not my choice so you still have that kind of autonomy the agent is not that taking that away from you but

the decision- making belongs to you but your workload has reduced and the the economic value transfer is not sitting in the middle it's now going to either the supplier or the consumer I think I Now understand what the problem space is you're trying to solve how does it work technically so I understand fet AI is its own blockchain um why did you make that decision how does it interact with other blockchains how does it what sets it apart from other blockchains what what's kind of the design space here we when we started it was only ethereum so ethereum is not suitable for this and I'm sure anybody who knows the theum would appreciate

that it's not suitable for many things and this is definitely one of them um so we started with the premise that we need to build the road and we also realized that that blockchain cannot be proof of work because it'll be too slow it'll be too costly so we uh steered towards proof of state but that wasn't the only reason why steered towards that we also were building a mechanism of useful proof of work so you could actually have machine learning algorithms producing results and based on who's running those modes and how much how many kind of um training sessions they're doing you could reward them so that's part of the blockchain Securities people putting

effort in money in uh but very quickly what we realize that is nobody is going to initially tackle that issue so that comes after once we have kind of entered the space of actually proving what we are trying to do is workable and and and we were too early in the market to develop that we'll see um over time we will be releasing our own different elements of the blockchain and the consensus mechanism so so what what was really interesting for us was to choose and make a blockchain which is modular and is able to cope with the further developments that we will do over time once applications start coming in so we chose a cosmos ecosystem because we felt

the componentization the ability to choose your consensus mechanism ability to add and subtract things uh was much better so actually um f blockchain is a cosmos based blockchain and and and the ecosystem around Cosmos was building quite nicely um so we chose that uh also because of the ease of kind of changing consensus mechanism for example because we wanted to uce our own use Pro for work so we chose that and at this point in time we still Cosmos based and we're now starting to bring in the components from the multi-agent system machine learning models llms and trying

to make that part of the consensus mechanism which is where we started from which is the useful proof of work but our Focus currently is very much on agent based system because people need to initially people need to build solutions for blockchain which are not just Financial Solutions because if you look in blockchain 95% of solutions are the moment our blockchain are just Financial they they might dress it up as something else the actual transaction is just financial and that's what we think is not going to enable crypto or decentralized leder technology to progress what needs to happen is there needs to be use cases which are

nonfinancial which have to be Deployable on blockchain and if we can we can start deploying those Solutions the scale of this whole Marketplace is going to dramatically change I mean it will be it it would be a completely different space to what it is today because if you if you think about the financial system Financial system exists but on top of that you have to build the industry so the industry is missing and because it's missing we need that to come in first yeah absolutely so obviously the L1 and L2 blockchain space has changed a lot over the last 5 years so if you were

to deploy today kind of if you look at recent AI blockchain companies they're all on alternative l1s or l2s to ethereum right would would you also have gone down that route or or do you see further advantages in kind of running your own blockchain because it also comes with a very significant amount of overhead right yes for us it's important to have that um the ability to control our own chain because as I said some integral part of the this blockchain will come from how we run machine learning models I'll give you an example so one of the one of the products that we have is called collective learning and the the the

process of collective learning is to is to train machine learning models by multiple stakeholders who have multiple different data points and they tra train them collectively uh but they don't see each other's data and they don't see each other this model so the model weights get trained and then transmitted and they're brought in to the system uh which could be used for our consensus mechanism right I'm just I'm kind of giving you a very top level uh kind of idea and thought so to bring that consensus into the blockchain itself because that's building value in the blockchain

which we then also use the stakers to stake in the proof of stake system as a combined consensus mechanism is quite valuable because you then start saying seeing how training machine learning models could add value to a decentralized ledger technology yeah I I I can see how kind of this proof of useful work is clearly Superior to kind of like proof of more or less useless work that we used to have before proof of stake how do agents incorporate these elements of machine learning or these this data that they can be trained on into themselves beautiful question so it's it's it's a

question I I would have suggested if you didn't ask but there you go so you what what it tells me is that we are on the same page and you kind of um so if you now take an example right so the example is let's say I need a prediction I need a prediction of some type I need a prediction of footfall in a particular location let's I'm just giving an example this might not be valid for this but but it'll give you a rough idea so let's say I have uh 1,000 shops around the country which give me a prediction based uh which are training a model which was created by some University student somewhere who is taking data from weather and correlating it with the

footfall in that shop now it's a very simplistic example but let's just assume that uh a thousand shops are creating this model because they're training the mod with the data now all they we require from them is the data coming in and then staking into the staking so that they can't cheat the system so the training is fair and let's say one shop is providing 1,000 data points the other one is providing 5,000 data points but they're training the same model now this the same model gets trained and let's say the model is now sitting in on the fetch colearn platform now now you come in and you say um I I want to see if your your agent wants to tell you to go

to a particular shop because based on let's say the weather it needs to tell you which shop to go to because if it's raining the footfall is going to be busy or this Shob and yeah whatever the prediction might be so the agent goes to the machine learning model agent and says hey this is my location give me a prediction if if if the shop is going to be busy or not but I will pay you 1 cent to give me that prediction so the exchange happens you get your response in the sense that you get a message saying yes it's going to be busy or no it's not going to be busy you got your prediction from what you needed it from the agent agent then pays to actually give value back to the

machine learning model that machine learning model then pays the value back to all the people who trained so that's the whole ecosystem now the machine learning model has now got Financial value because you can see if million agents are going to query it and pay a cent each it now suddenly has value so that value can be used as a staking mechanism rather than using physical catch so you can start taking the cash out and start putting these valuable models which then actually generate Revenue because that's really a true way of actually securing the The

Ledger itself because you know just putting money in the bank is our own Financial system but if you want but you can have a combination of the two where you have value coming from different places I mean you could still put cash in terms of cryptocurrency cash but you can also add more value because it's a revenue generating model so the machine learning model was sited there which was doing the useful proof of work is actually what's building value in The Ledger itself so that ledger in itself is highly valuable if it's if it's kind of bringing all these machine learning models together that's a wonderful

example I think it kind of really brings it home um is there some sort of reputation attached because um otherwise um I could just train um train the model on madeup data or I could even ask Chad GPT can you make me like a gigabyte of data that kind of simulates footfall in this in this area um and kind of it would look like realistic data but obviously would be of no use whatsoever so do you have any way kind of having the person or the agent that asks for the prediction signal back whether this was actually good information or not that's that's absolutely the model that we have built in because you have the you have to have that level of trust and

that's why we want the people who are training to stake first so if their predictions are incorrect and if we realize that the data is not correct and it's a it's an automatic system and we've written about it and there's a blog post about it we can perhaps share it with the with with your views as well if um that's the model that we are building and and that results in uh slashing the stake which people put when they train the model so the one thing that's always difficult with um blockchainbased microservices like this is scalability and transaction speed right H how how do you think about this and how do you address these

challenges it's it's less relevant to us but that that answers your question which you asked before which is why did you not choose ethereum for that reason because the the the speed is not right the way we need to deal with it is not right in the sense that it's costly uh so all of those things is why we chose to move across now we have scalability solution but we are chain agnostic so we will look for the best chain uh which can do the job and we can split the transactions on one chain but we can split the machine learning on the other we can do all all those things that's why we built it in such a componentized manner that you can actually take the

best of the world wherever it comes from We're agnostic about chain we're agnostic about user interface we're agnostic about what machine learning model you want to build as what is a core technology which brings all of this together is the multi-agent system so it's the framework the platform which enables you to connect agents to all of these things and then bring them on to Fed share I understand I think that that will kind of make me reframe my question a little bit um so then how do you think about interoperability so interoperability in our case is is an easy solution so we have agents these agents can be grounded into any

chain and they can observe the rules of that chain but when they interact with each other they interact on the fat chain so we actually have showcased already models where we have taken a polygon based agent which is reading the chain doing a transaction on ethereum based on what happens on polygon or based on what happens on padot we have shown integration pador uh ecosystem we've been shown in we be currently about to Showcase it with salana so we don't really care because the agent is picking up its feed from an and the roots are in that particular chain and

if you trigger that agent to do something in a particular other chain you just need to have another agent there which communicates and transacts on your behalf so for example sending a token from chain a to chain B does not require these complex Bridges it can be a very simple solution of One agent sending it the other agent kind of interacting with the other other side and holding it in escco agent by agent so you don't have to have these big um Bridges which get hacked but but then the agents could get hacked right the agents could get hacked but the agents every agent will have to be hacked it's like hacking each and every single buet which does the transaction okay that's fair C can you share some practical applications and

use cases that are already in operation or development uh we have a travel use case which is in development where you can actually do exactly what you said um we have we have been coord ating with Automotive sector because uh as I'm sure you've seen our partnership announcements with the likes of bch BMW Mercedes uh we're Building Solutions built into the cause which people can interact with people can actually uh transact via agents and record their transactions or interactions on the Chain we have a dpin integration with a company called Peak which I think there's an announcement which is happening uh has happened we we' have

showcased how a decentralized uh public infrastructure Network can interact with agents and how it can actually uh transact on the behalf of the equipment uh so we have we have plenty of use cases I mean that they're all kind of either talked about on Twitter or we have them on the website or on our GitHub can I also use fetch to kind of interact with um the defi ecosystem so say I have a portfolio of 50 different um tokens and I want to know how to uh best yield farm with them giving given my risk appetite would because basically there all the information is inher in on

chain right so that should be kind of like a super low hanging use case and it gets increasingly uh difficult to keep track of all the relevant pools and uh and so on so does fetch also contribute in that sense to the defi ecosystem yes and uh we released a showcase agent called The Block agent which monitors multiple blockchains to see what the transactions happening on the Chain arm and based on that it can actually transact for you now that's a very basic solution it can make it a lot more complicated so you can monitor Unis swap swart contract make sure you you're constantly monitoring what's happening and then based on that you can ask your agent to do something yes that that is a

very easy and low high inov as you say so it's definitely available and it we we are asking our community to build these Solutions uh as we just us building all of these Solutions not even possible and and we want the community to kind of think like you're thinking you know I could build that solution and yes absolutely you can build that kind of solution here super cool now there's a lot of AI companies kind of in the blockchain space right so um the likes of um oronas and Jensen and uh origin Trail and like all of these companies do do you have like a mental model of kind of how to group

them or kind of what or are they like all one kind of uh no of course of course not um I mean forgetting a little bit about the crypto space and The decentralized Ledger technology space and if we look at just um just look at what's happening in AI right so what's happening in AI is that you have this bottom layer where there's the silicate layer which is the gpus so on that GPU space you're going to get a lot of companies who are building uh data Farms or building GPU Farms but also building software to distribute work to those Farms right so and and and actually giving you the option and the ability to deploy on

those FS so you have like the companies like Anor for example they're providing a cloud space and that's the decentralized cloud space so you can actually uh you know you can actually run on GPU whatever you want and there's no restriction as such all that so that's that's one and then you have the other side where you have if you think about the other side where you have second second layer comes in and then you have the foundational llms so but that's a commodity as well because uh there's going to be some big companies who will build foundational LMS and then that space will get commoditized so in the crypto space perhaps you'll see a lot of people using these open source models and deploying these um llms the

foundational llms as a commodity for this space and that's the second layer so you you'll be able to group some in that layer then you have the one after that so I mean I I can carry on with the whole stack but I think it's quite interesting to just see so then you have these specialist LMS or the rags and there are companies which are doing something like that so you have you have agents who can just give you some X Y and Z service individually but so that's after that comes this application layer so people will be putting these llms web 2 web three together to build that application that's where kind of fetch it in so we have a platform where you

can actually build those applications you can launch those applications and what we also have is a search and Discovery layer where you can actually find these applications you can go on it you can say hey I am building this application but I don't have this micros service can somebody provide me that microservice your agent goes and finds the right micros service brings it to you and then actually you can just easily connect it without any without even writing a piece of code so so that's where we sit and then after that you're just going to see more and more applications being built uh which would be kind of AI space uh but not not truly doing the AI kind of side of things but actually building applications on

AI kind of marketplace so that that's in my head these are the categories you will see yeah I I think that's a really good breakdown of kind of the the potentials of the space so if you kind of look at fetch's domain how do you incentivize developers and enthusiasts to kind of contribute microservices to the ecosystem because it's kind of like a chicken in an egg problem that any Marketplace has right so no one will check out the marketplace if there's almost no offers on there it kind of needs already to be Lively for people to actually go there and offer their services there how do you overcome this uh this chicken and act problem so we we

we're building quite quite a lot of applications ourself the small application but we have a Community Fund we we giving grants for Community to build these applications um I think that's that's quite a well proven method to kind of Kickstart the ecosystem if you give people the incentive Financial incentive to come and build it that's that's great but what is what is quite interesting observation for us is if you look at hugging face for example right so hugging face has a lot of people building these machine learning models but these machine learning models sit there um and and you can see theyve now started doing the inference the problem with that is there is no way to monetize those at the moment so if you ask those

the same Builders and and it takes 5 minutes literally to convert a hunging face model into our Marketplace 5 to 10 minutes somebody who knows what they're doing they will not take them long so you now deploy that agent or micro agent on our platform and we have application Builders who are looking for those bottles so what we are incentivizing is the application uh Builders to come and build and we're giving grounds for that and we're building some of them ourself but what is quite interesting is that there's a lot of there's a lot of interest and that's something which open eyes has done for all of us is there's a

lot of interest in people in the Legacy systems to kind of on board into this new AI economy so we are seeing a lot of traction from small businesses who don't have the full capability the technical ability to come and on board because they don't have machine learning Engineers they don't have ai Specialists but they want a simple interface which we we are providing them because we built this application which can take your legacy system on board just uh one person two days on board it into this new way of doing things and then you connect these together and suddenly you have I mean we have we have seen a huge uptake of all this we had like 25,000

developers uh PRS join our platform and they're building so I think we'll see that yes there is a always a h chicken and EG sit situation but we're seeing a lot of traction I have no doubt that very soon uh this space you're going to see lot more applications coming through that sounds super encouraging there has been a lot of talk about um AI alignment right and AI safety now kind of Crossing AI with block blockchain technology that where kind of the inherent characteristic of blockchains is that you can't just turn it off does that give you pause in any way we we again my objective has always

been to build a modular system so that because this is a new space you can't just say here's the definitive solution ours is the definitive solution so that if you keep that in mind and I think just just looking at what you just said which is you know AI safety alignment all of those things there is no one solution which is going to fit everything what I do think though is this you can't stop people from training machine learning models that's that would be the wrong approach because and and the governments are focusing on on that at the moment which I don't feel is the right approach because you will have somebody train something somewhere you

can't stop it I I've seen some government doing it you know some some some are thinking about it some are trying to impose regulation but both those things they're quite quite interesting some governments are trying to control it some governments are asking some big corporations to try and control it I think both approaches are wrong because I mean we have seen what happens when you know a big corporation like Google can get it wrong you saw that on the video case you know all the on the what what happened with all the pictures and the videos and you know bigger corporations can get it roll too so it's and if they do and it's out of control you can't fix it that quickly

they still haven't fixed it the the video service was still not all the last I saw so that's not always the right approach what is the right approach is to enable inclusion but when you come to the deployment of this AI that's when you start monitoring it that's where you start putting policing in effect that you can't actually take this Ai and actually apply it to Applications I think and to have a platform which is one auditable second open enables inclusions it's not in one control because you don't know what's happening eternally one control you can't see it

so having a system where people can actually go and see what's happening how the training of these mods is done how are the agents doing transactions why did the agent do the transaction what is the logic behind it and have that whole auditability is perhaps the right way I'm not saying uh it's the only way I'm just saying that's one of the right ways that I can think of yeah I I I think that makes um a lot of sense and I think kind of having things out in the open is always better than kind of not knowing about them don't know when whether you know this um yashab person he kind of he also argues kind of for encouraging everyone to kind

of build the most advanced models that they can because if you if you kind of if you stop if you try to stop people from building it it'll invariably be the Bad actors who will still do it and they will have the much better models as compared to everyone else is there any way you can you can see that see this go wrong though yeah I I I think privacy stays still a concern but I think a kind of a combination of the two open but you know having making sure the Privacy is there I think is the best shot we can get but but don't forget I think we we're still early we we do not

want to stifle Innovation by creating some regulation which we don't even know what's coming right so it's it's too early to start kind of putting restrictions on things I think as we evolve it's going to become clear what we need to do and and and just like anything else you know when the financial sector starts we had ping slip shares you know there was corruption there was problems there was wrongdoings but the regulator comes in the governments come in we solve it yes I understand the risk could be higher but then we went to Cold War so it's like saying you know we're sitting on nuclear right so AI is a bit like that which is but but we managed it I'm sure I have

faith in humanity I think we'll manage it your word in God's ear I just feel that um basically even if 99.999% of humanity comes in with the best of intentions sometimes you know a very small group of people can still disrupt or destroy things uh very significantly but I hope you turn out being being right yeah I I I think we'll always have that risk I and I I don't disagree with you there is always going to be a risk the only thing we could do is we continue to actually improve our processes as we go WR because giving people the ability to improve the

processes is is more interesting than just trying to restrict it uh which kind of results in some other problems um so so one country might restrict it but what other people might not follow the same so then what so you know the these are all the geopolitical questions I I guess are less uh involved and I don't want to be too involved I'll leave that to uh the likes of Elon Musk and uh open AI to in the court case yeah so things you are involved in then what's what's next for fetch AI well this this year we are focused on been bringing uh all the

components together uh I don't know if you saw but we announced the availability of gpus for our ecosystem uh we we and because it's a very uh you know it's surprised commodity you can't find GPU space at them I mean everybody wants to do it so we are building we have built our own Supply uh we deployed our own Supply that's very important for the ecosystem to kind of grow we are providing all the tools that we enabling not necessarily just the developers but also individuals and small businesses how to on board into this system so they can actually use and actually benefit from this new paradigm shift that is coming and we also

encouraging developers to kind of come and build unique and very and and interesting solutions to Showcase and and then monetize them uh we we we don't believe in just opens FS without monetization strategy because um ultimately people don't you know update it keep up with it and then it doesn't get used the same way so we believe in monetization uh has to be there or at least a structure has to be there and people can then choose what to do with it so that's the focus this year we're going to bring more developers more people uh trying trying to break things uh building new things and trying to

commercialize that sounds like a lot of lot of happening so if you think about this space in five years where do you think we're at in five years we will have a lot better L&M a lot faster LMS and and um we would have gotten rid of hallucinations so we will be able to deliver deterministic Solutions and I I feel the the biggest change is going to come in the search Arena where people will be searching in a very different way they will be finding they will be discovering and transacting in a very different way so I feel that change is going to come very fast and it's going

to be quite dramatic it's it's kind of a it's not a evolution is probably more a revolution where things completely change how we do things now the challenges for sure are is the market going to take it is how we going to interact with it and that's that again is going to unlock some new challenges uh which I feel which I feel we're already starting to see uh we can see how fake videos are making an impact and politics and how it's very very difficult to now determine who is going to yeah who is saying the right thing when are they saying it or are they not

even save it and you know so we we're going to see a lot of Industries being disrupted we we're already seeing a signs of that like the the the movie The Hollywood the Hollywood all that kind of industry starting to take take a little bit of uh step back and seeing what they need to do so I think applications is what's coming next and I feel in the next five years we're going to see a dramatic change in new applications coming in how we deal and interact with these applications user interfaces being different so that's that's what I see and for fetch I'm I'm very optimistic that all these applications will use a

agent based infrastructure and and build them and deploy them a lot quicker than we are doing rather than trying to fit into this old paradigmal we we going to now move into this new paradigm of Agents it's not going to be web pages it's going to be agents I think those are beautiful closing words hamayan thank you so much for coming on if people want to stay in touch with fetch or kind of be updated on latest developments do you have a newsletter they should subscribe to or just follow you on Twitter or what do you recommend we have all of those so we have a newsl you can subscribe to we don't just discuss fetch we discuss the general what's happening so you can keep up to date uh our website is um always

changing because we adding more and more information we do blog posts so please come and visit the website we have a GitHub where you can see not just documentation but all the code that we've been sub missing please follow us on Twitter we have we have a very active social media team we tell people what we're doing we want to engage with the community we bring them in we do a lot of hackathons we're doing them worldwide so if you're a developer we want to know you we want to hear from you we want you to build on us uh we even give grouts for that so if you want to come and build a community proposal for a project we're open for that um download our

wallet uh interact with us through the wallet we have a messaging system in the wallet and you can you can find out more about that uh you can interact with any of the applications we built uh like a block agent which is an agent which monitors blockchain if you want to interact with that we have that uh we have another uh trading uh like agent based trading platform called metalex so interact with that so we have we have a huge array of things you can interact with but if you want to just start follow us on Twitter come and see us on website and just drop us a message perfect thank you so much for joining us today thank you really enjoyed the questioning uh it was great

thank you