Nevermined: Payment System for the AI Agentic Economy - Don Gossen. Ep. 587

Transcript

what we believe is that we're actually witnessing the rise of a new consumer that's going to manifest as trillions of AI agents and in order to scale these systems we're going to need to rethink and rebuild a big chunk of the payments infrastructure being able to understand what's actually being used in that analytical pipeline like how much of a given data set how many times is an algorithm called what's like the computational cost of executing that algorithm in conjunction with that data set to say train a model or what have you that output then gets commercialized and everybody in that value chain is somehow rewarded because without that

there's actually nothing to pass back upst stream something that needs to be addressed in the web free space is there's often times a trivial outlook on payments and that's because I I personally believe people can flate settlement for payments welcome to epicenter the show which talks about the Technologies projects and people driving decentralization in the blockchain revolution I'm FR ants and today I'm speaking with with um Don gosen who is the CEO and co-founder of never mind so never mind as an never mind as in has never been minded um because there's quite a few projects kind of like with very similar names right um and never mind is positioning itself as the PayPal

for AI to AI payments before I talk with Don these are our sponsors this week if you're looking to stake your crypto with confidence look no further than course one more than 150,000 delegators including institutions like Bido pent capital and Leger trust corus one with their assets they support over 50 blockchains and are leaders in governance or networks like Cosmos ensuring your stake is responsibly managed thanks to their Advanced meev research you can also enjoy the highest staking Rewards you can stake directly from your preferred wallet set up a white label note restake your assets on igon or symbiotic or use the SDK for multi-chain saking in your app learn more at c.1 and start saking

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Forum today deploy on the evm compatible nosis chain or secure the network with just one gno and affordable Hardware start your decentralization journey today at nosis doio Don thank you so much for coming on thanks for having me maybe we can get off to a start um by talking about you yourself what's your background and kind of like what brought you here Canadian transplant in Europe um so I live in Lisbon Now by way of Berlin by way of London by way of Tokyo by way of

Los Angeles so I've lived all over the world studied engineering at University went into Commodities trading after University um I was on the risk side and so doing back then what was called statistical modeling which became machine learning and now has been co-opted by the AI branding um but yeah just basically augmenting internal credit histories with external credit scoring and figuring out which of our clients

were dead beats and which ones weren't so which ones we could like loan money too in order for them to hedge and stuff like that so um it was pretty boring to be honest and then I went into IT consulting for the better part of a decade and a half as a subject matter expert in data and analytics and that's what took me all over the world so I built large scale data States for ML purposes for um some of the biggest companies on the planet so I was at HSBC and L'Oreal and AXA and muho and stuff like

that um and so yeah I've I've been in the machine learning space my entire career so 20 years and then added the crypto flavor in 2016 um so I got introduced to blockchain not as a you know a system or platform or ecosystem for like speculation and payments and settlement and stuff but more on the side of providential Integrity um so blockchains are very

elegant um Providence machines and asset Providence is is a really hard problem to contend with within the confines of large analytical Estates right you've gota you've got to answer four questions with High Fidelity where's the asset coming from where is it going who's using it and what are they doing with it right and the assets can be like data sets they can be algorithms um all kinds of stuff and if you can't answer one of those questions it undermines the Integrity of the the output so they're quite critical

questions to answer but they it just so happens that with contemporary software it's really hard to actually answer that stuff um so what ultimately happens or usually happens is that you create these very like bespoke oneoff Patchwork solutions that like Cobble together information from all of your different sources and destinations to try and figure out the topology of what's going on anyway blockchains help plug into that and make it much more seamless in terms of understanding the answers to those four

questions um and so that's what kind of got me hooked initially sort of within the grander scope of my my career and so have been at this Crossroads of AI and and web 3 for going on a decade now because that shortly after you know I I kind of went down the crypto Rabbit Hole I co-founded a project in Berlin called ocean protocol um which was one of the first projects at this intersection of AI and we 3 and I've just you know kept beating this drum ever since and now it's we' we've sort of distilled the learnings and experiences and understanding of merging these two

technologies into a very uh hyperfocus on AI payments um can get into why we're hyperfocused on this stuff but yeah that's the background yeah absolutely so you talked about kind of uh the Providence of uh um data and kind of uh this entire data economy that kind of come that kind of crucially hinges on that um H how how does payments actually fit into that picture it's a this a good question and and it took us quite a while to realize sort of the gravity of the payments piece so

where what we were focused on for a long time was in establishing the Providence component so that we could do by you know extension the attribution piece right so taking this holistic view that we want to build these analytical systems and that now take let's say the form factor of an AI agent right we want to build these things in a decentralized landscape so what does that mean kind of holistically and it means being a ble to understand what's actually being used in that analytical

pipeline how much of anything's being used right like how much of a given data set how many times is an algorithm called what's like the computational cost of executing that algorithm in conjunction with that data set to say train a model or what have you um and and basically accounting for all of that and then extrapolating or extending that into the attribution piece so federi you provide some contextual data I provide some training data uh somebody else provides the algorithm then another

third party provides the infrastructure for bringing all of this together and when combined we create this inference or actionable Insight or whatever we want to call the output that out output then gets commercialized and everybody in that value chain is somehow rewarded right now what we realized a few years ago is actually the most critical piece in that whole workflow is the end State it's the last mile it's commercializing that output enabling that thing to you know enabling payment for that inference or that

actionable Insight because without that there's actually nothing to pass back up stream there's really other than recognition like a nice pat on the back hey R thanks for providing that context data comes up reputational reward whatever there's actually nothing if you if you can't capture the endstate um utility there's nothing actually to translate back Upstream to the different participants and so in and amongst everything that we were building there was that piece um that that payment system and the attribution component um and well

it's part of the attribution component and what we realized a couple years ago was like that's actually the most important piece if we don't get that piece right then we can't do all of the other stuff that we want to do from a providential integrity and attribution point of view so we became hyperfocused on that one component within this whole word for so ocean is very much Z around and kind of like ocean I mean trend's been on this podcast multiple times so kind of like listeners will probably know that it's kind of it's focused on um the data economy and having a Marketplace for um for for data sets it seems to me that kind of like payments

it is just a very natural um part of any Marketplace why why do you think it kind of makes part to kind of apply these two things apart because payment systems are complex and and they're non-trivial let's put it that way right um as our marketplaces um the other realization purely from a market point of view is marketplaces are are difficult propositions from a commercial and

operational point of view um they they tend to not make a lot of money and therefore they're they're hard to persist they're hard to like acrw enough Revenue to keep the thing going the way that so basically marketplaces are one in the margins and the way that you win marketplaces is through monopolization and that monopolization usually comes from a very discrete focus on a specific domain or a niche within a specific domain right so the common marketplaces that are sort of presented are like

Bloomberg right or maybe El Sevier for a more abstract one and like the research domain but they're very difficult to win and they require uh a significant amount of focus in order to actually make a successful Marketplace you know personally I would say one of the learnings out of the last decade for me is that I'm pretty skeptical about general purpose marketplaces I think it's you know I think there's a lot more more examples of those failing than there are of successes

um so all that aside that's one part of the argument and then the other is a realization that that payments are are quite complex in their in their manifestation right um so it's not I think there's a there something that needs to be addressed in the web 3 space is there's often times a a trivial outlook on payments and that's because I I personally believe people conflate settlement for payments and and payment processing and and that sort of thing um and so just anecdotally right

um there's you know like if you don't want to take my word for for the complexity of these things there's a company called metronome that does like payments and billing services predominantly for SAS providers right um and the the CEO of that company and and a few of the founders they come out of of box out of drop box right um what they recognized was there was a discret issue with the payments and billing functionality that drop was creating so they had if if if they had something to the effect of 60

Engineers a Dropbox just supporting the payments the price setting infrastructure and the billing with that company that's like a huge undertaking from an engineering standpoint right the reason for this is like operating across a bunch of different Divergent you know jurisdictions and stuff like that so they have like different price points for different localities all of these different you know depending on the customer and the volume and all of this like prices would would need to fluctuate and so they had a team of 60 Engineers supporting this their realization was well we're not going to

be much different from our competitors we're not going to be much different from datab brecks or you know these these other like SAS providers for these services so why don't we take what we build here extract it and offer it as a service to multiple companies and so it's that kind of um application that we're looking at providing but in this case discreetly for agentic transactions AI to AI transactions so I understand that kind of like um pricing and um kind of price pricing strategy um can be arbitrarily complex um but given that kind of I would assume most of these

agents kind of set their own prices or kind of they kind of that this is something that's pre-negotiated walk us through um the complexities of payments because kind of like I mean I have I have as someone who also works in payments um I have said this time and time again that kind of I think um it makes sense to kind of like productionize things in web 3 first that have simple use cases and I always say kind of like I always say payments in principle is a fairly simple use case if you compare it to other things that also warrant disruption like social media and so on because kind of like it's it's kind of like it's balances this that go from one place to another kind of like it ideally kind of they should be conservative so you should to it right exactly so walk us through kind of like

what makes it difficult I I'm I'm clearly speaking with with a kindered spirit here right so this is the other kind of Revelation like it's simple within the context of like it's easy to understand it's not esoteric and it like the infrastructure the technology that we're using actually makes sense to focus on payments right so in that sort of very general sense it's easy you know what I mean um so why is this why is this important okay so our view of the

world is like a particular one um I think up until about six months ago it was relatively unique um because most people back then didn't know what an a AI agent is like it's AI agents are now starting to emerge conceptually for some people I'm not going to say most people but those that are kind of of either in the AI space or adjacent to it you know um are are this is entered the Lexicon right like people are getting familiar with what an AI agent is even those in the AI space like a gentic AI um mixtures of

experts like this concept was even in AI was quite fringy up until relatively recently so our view of the world what we believe is that we're actually witnessing the rise of a new consumer that's going to manifest as trillions of AI agents and in order to scale these systems we're going to need to rethink and rebuild a big chunk of the the payments infrastructure you know if if you hold this vision of the world like we don't we don't believe in the monolithic one AI to rule them all

right like the god AI so we believe in this concept of mixture of experts that there's going to be sort of finely trained agents that will provide very discret expertise that will be called upon when and where needed right and so holding this view it kind of becomes relatively obvious that like actually we need to work on at a minimum standardization or protocolization of this payments process right because if you just think about this scenario where you have ecosystems with effectively trillions of Agents all either you know collaborating or in competition But ultimately

consuming buying and selling from one another if each one has their own payments mechanism not only do you have to negotiate the price for that good or service that the counterpart is providing you also have to negotiate which payment system you're going to use right but how is different from from non- AI so kind of like if I kind of make a deal with someone who's human kind of like why why do you need one that's that's kind of C specifically to AI sure so it's intrinsically like it's not that dissimilar it's the way that effectively the information gets packaged around the services it's actually being provisioned so let me unpack that a little

bit if you're familiar with AI and and and how this stuff works um and in particular an AI agent so we're like our Tech can be used for like pricing models and stuff like that but really we're looking at these analytical pipelines in the form factor of an AI agent which is a compilation of different AI tools right so at a minimum it's like the ability to likely Source an inference from more than one model right depending on the complexity so like I mean operator does this if you've used say 40 or

01 um from open AI you put in a prompt and you and I can be like completely Divergent users there is sort of there's under the hood there's logic that takes place that Roots the request to the the model that will adequately handle the complexity of that request right so the very general broad kind of use case that I provide it's not um or or example that I provide it's not use case to L you and I are users of some third party agent right and let's just say that that agent within its

architecture it is composed of the GPT series of models from open AI so gbd3 gbd 3.5 gbd4 soon to be gbd 4.5 um and maybe five I'm a simple user in this case you're a complex user okay you and I can both interface with the same agent um and it can sufficiently respond to our level of request it does that by like I said calling on a different model or set of assets AI Services that's going to allow it to provide a sufficient level of of of inference or response so I submit my simple request to this agent that gets decomposed by

the agent's back end and optimally rooted to the model that will sufficiently handle that simple request gpt3 right you on the other hand you submit a multimodal request to the agent that has to go to a model that can handle the multimodality of your request so in this case GPD 4 the cost difference between invoking gpt3 and GPT 4 is like an order of magnitude if not more indifference and accounting for that especially with most contemporary pricing Solutions is nontrivial it's relatively complicated so stripe which is what a lot of people turn to when they go to commercialize their AIS and

their AI agents it's a it's a skew based architecture right you price per skew SKU it's set up to sell t-shirts on the internet so I set the price of my small T-shirt that price doesn't change from one day to the next versus an AI agent its cost is variable depending on the complexity of the requests that's served to it as well as the the tools that it has at its disposal to respond to said request so an agent can take in Dynamic requests and respond to those Dynamic

requests in a variable fashion by invoking a variable set of services which correspondingly have a variable set of costs to them so what we've built is is a system of unit accounting effectively an accounting module that straps onto an agent's observability function and translates the metered cost of that service to provide that inference into a settlement cost for the requester for you and I depending on the variable response and and invocation of of the corresponding services on the back end that sounds very Pres described so

as someone for instance if I were to offer um different AI models I mean I I could come up with different pricing strategies right kind of I I could kind of sell um a flat rate to all my models I could say okay I'll give you a flat rate um until kind of you hit a um certain uh number of requests and then kind of you have to pay per request or kind of I I I mean there's different strategies so how much of that um do do you impose on people um and how much can you actually tailor the the these uh this pricing strategy so this is where again like you're clearly like well versed in in

the Nuance of this um what we're what we are trying to accommodate for is as much variability in that price control setting mechanism is possible so if you want to have sort of a a fixed price subscription that maybe or may not rate limit or time limit a service you can do that if you want to go as granular as like pure payto playay and you know each each access is costs this or or each GPU cycle for that matter costs X you can do that with this system what I mean that

part of the rationale is we want to provide that flexibility the other is we are in the process of discovering what is like sort of the dominant set of attributes and characteristics for these pricing mechanisms within the agentic landscape because the reality is this stuff is all quite new and we don't know what the dominant system for for for costing and billing wrapped up in some pricing component is actually going to be yet so we are trying to provide as much variability so basically in in true

decentralized fashion we attempt to give that control to the user as opposed or the Builder as opposed to setting it for them upfront okay and then kind of I I understand that kind of like you give me um variability and kind of like how I set my pricing strategy um but do you you I you also take care of settlement right so kind of you also make sure that I actually get paid right yeah and H how do you do that so kind of like how does how does the payment work yeah so this is a good question too so we we leverage um a concept called license tokenization so

We Believe quite strongly and and so again like going back to the this is a function of the marketplace right marketplaces are hard to win especially as you get as especially as you start to sell um assets that become more and more commoditized right like trying to Market's trying to push the price to zero so and then your your your revenues generated at the margin right so so being as having as much Fidelity on the actual operational cost means that you can like eek out as much margin is possible so like recognizing that that's sort of the this the set

of um I don't know maximal constraints that we have to deal with here we want to enable these agents to eek out as Slim a margin as possible and and also allow them to continue to be functional from a business and operational point so um that means setting a single price and just giving free rain access doesn't really make a lot of sense it works right now for propositions that are broadly speaking a uh toys and then more importantly where there's not a a lot of competition that's yet pushing that price point down

but then we are already seeing manifest right like open AI very clearly has you know a price per usage function and then a deep SE comes along and has like an open source model and it's like here you go have it for free right trying to find the equilibrium between the two that that's somewhere in between right it's not free it's also probably not maybe as price gouy as some of the things that open AI is doing so anyway in all of this work that we've done there's this recognition that actually understanding sort of the mlops that the the observability piece of Translating that

metered cost into a settlement cost it's likely going to be important especially as the these AI agents and their services get commoditized and so we kind of looked around and said okay like if we're taking the position that like a traditional subscription style model isn't the right one what is uh what does look and feel right based on experience and it's this concept of license tokenization so it has nothing to do with crypto um it's a traditional licensing scheme but it differs in comparison to like named user licensing and concurrent access licensing where in a named user license feder you negotiate your usage

for a platform and the underlying sets of tools within that platform right concurrent access license would be you and I are on a team together we negotiate our usage of said platform and the corresponding tools of that platform it's pretty laborious process in the grand scheme of things or very rigid right one of the two either like everybody gets the same thing or it takes a long time to negotiate What You Get Right the response to this is this thing is this concept called license tokenization where the the platform is tokenized and the tools that make up that platform have

Redemption criteria in those tokens so you buy a th000 tokens I buy 10,000 tokens and that platform is made up of tool a b and c and a has Redemption criteria of 100 tokens B A th000 tokens C 5,000 to usage credits or something it's us it's exact that's exactly it's just yeah so it's this emergent licensing model we've taken that looked at agents they are again this form factor of a platform compilation of a bunch of

different tools where you can issue tokens or in our case we call them credits for each of these agents or set swarm of agents and the tools Andor agents within that that agent is composed of or those sets of Agents of swarms of agents are composed of they can have their own Redemption criteria in those credits or those tokens so that's how we facilitate sort of the the the legibility um and and um the the the fine GR bra um component of the the payment aspect is and how do you set it

so because kind of like you have you then have to transfer them right right so there's so like in this scenario where you and I are the user of this third- party set of credits we let's say we pay a dollar and we each get a thousand credits and so under the hood of this agent gpt3 has a Redemption criteria of 10 credits gbd4 three 3.5 of 100 gbd4 400 credits right we both pay a dollar I get a th000 credits in my wallet you get a th000 credits in your wallet we make these requests to this agent my simple request goes to gpt3 out of my pool of a

thousand credits I get charge 10 you with your multimodal request that gets rooted to gbd4 you get charge 400 out of your pool of the Thousand so that's how and then that function that accounting that Redemption is a burn function on chain okay so it it's a burn function and that but then um how does um the the AI that actually did the work um receive uh the payment good question so we have basically two forms of settlement one is the the piece that that the settlement that authorizes you um to use the system

so the payment of a dollar for that thousand credits you that's the first settlement and in our case you know recognizing that there's a large swath of like we we view this as an AI solution or adjacent solution so we don't um just distinguish between like web 3 AI versus web 2 AI it's just AI what we're trying to build is something that's like general purpose for AI recognizing that there is a large swath maybe if not a majority like a there's yeah probably a

majority of AI that of the AI community that is not very conversent in web 3 so one of the things that we've done is like full account abstraction you still get a wallet right and the agent still gets a wallet but you can use socials to set it up it's an NPC solution it's fully gasless um so there's no like extraneous signature signing and stuff like that that's required for anybody that's listening we're paying for the gas right now um if there's questions around that but anyway so in this case in this scenario that that I was describing where I'm the simple user and you're the power user I don't have any Affinity

towards web 3 I don't have a wallet I just want to use this AI okay I use never mind I go through this checkout part of the checkout process is um um I register with the system that creates my wallet um we've gone so far as to integrate stripe so I'm now in the ecosystem I've registered I've created this NPC based wallet that's attached to me if I don't know where to look I don't even know that it's you a crypto wallet and then I can just take out my debit card or credit card and pay a dollar for these thousand um for these thousand credits now in the background the Builder that's registered this agent this third party

agent that you and I are using they've linked that to their bank account through a stripe integration um what they've also done is link that to a wallet um because in this case in this scenario the Builder is going to take both Fiat and crypto is payment so I pay my $1 with my debit card that goes to the the Builder bank account you on the other hand you're well-versed in crypto you have a wallet you've got usdc so you pay one usdc and that goes into the the agent or or the Builder's wallet in that case um and so in this case we're we're handling

both but as you can see there's two forms of settlement one is this sort of overarching authentication gatekeeping function for access to the agent that gives you the set of credits or tokens the the usage asset to start utilizing and authorize um you know and authenticate the usage of of that agent how do I as a user know that kind of algorithms that I um uh solicit how how do I know that they are metered in a fair way so kind of like if there's different ways that

kind of like different different um underlying functions that I could call in kind of they're all Meed in some way um is is there like some rubber stamp of approval somewhere that says okay this is actually this is this is an okay pricing scheme because kind of like I could I could kind of like make a really obscure pricing scheme where kind of I overcharge massively for certain parts because it's somewhat intransparent to to the user right yeah so right now it's it's V very finger in the air and um cottage industry there there [Music]

um there's a lot of price Discovery going on at the moment a lot of like estimation we're actually working on something that we hope is going to help both with the price setting piece as well as for an understanding from a user based point of view what maybe the pricing should be for a given agent um so that's that will be it's it's like a a pricing engine so that's going to come down the the pipe relatively soon um but it's something conceptually that we've been working on for about six months and

over the last um two months we've like put pen to paper and actually started to we we poed it and now it looks like we can actually do what we want to accomplish to kind of address not the buy side but more the sell side to start because the other the flip side of your question is what do I price this at right so helping answer that question is where we're trying to get to first and I think the knock on effect of that will be just closing that sort of price setting um mechanism will help those on the buy side also understand maybe what

their cost structure should be maybe let kind of switch gears a little bit so um all of this kind of is built on blockchain infrastructure kind of like it walk us through kind of like um what kind of Stack this is built on and why you chose that stock so we're a a Dap and the classical sense so we're chain agnostic though we are we're an evm based solution um so you know from a deployment point of view we're on Main net and polygon and arbitrum and Basse and nosis and cell

and bunch of evmb based chains l1s and l2s yeah the the code base is Python and typescript um you know where needed we've got backends that are um um yeah what do we have I don't know it's um postrest database um yeah I mean it's relatively run-of-the-mill from a architecture point of view okay let's talk about kind of like the interoperability aspects here right so kind of like say I as a

user kind of come to your dab how how do you determine kind of like which of um which of these um uh chains I kind of buy my credits on and settle on and so on how how is that how is that determined because in principle that's something that the user probably doesn't care about right no I I I I disagree with that statement when you're talking about AI web 3 Builders because they usually have a network that they want to default to okay but then let's let's talk about kind of like the the people who kind of consume right who kind of consume your AI well they don't care yeah they don't care at all right so as someone who

wants to consume H how do I decide um which network to kind of consume my or to which network to pay for my in this case you don't the the Builder would so okay so here's where like from a crypto point of view you run into friction but again the choice is up to the agent or the you know the Builder to decide which chain or chains this thing the agent is anchored to right is connected to usually it's one um the dominant chain at the moment is base um so that's the default

um from a consumption point of view you don't really care other than if you're in your case this power user that is crypto made or Savvy you know you're in a condition where h ah I don't have any usdc in a wallet on base so now you're in and you want to use an agent that's anchored there and it needs you know it's you got to pay one usdc for those thousand credits well now you've got to bridge that um that's outside of the scope of of our

operation you know there's I think there's enough bridging tools out there that you could probably figure it out if you need to bridge okay so basically as a consumer kind of like I decide what model I want to use what kind of agent I want to uh uh frequent and then kind of I I just have to pay on the commensurate Chain said Fair yeah exactly yeah y that's that's the way it would work yes okay so how how does never mind currently integrate um with um other kind of decentralized platforms or protocols right because kind of like you primarily enable um the the payments

here um there's a lot of functionality that kind of that has to come together to kind of make this into a good user experience that kind of goes beyond payment right so H H how how do you interoperate here yes so we kind of have we have three levels of Engagement um so there's the SDK which is um the most robust it provides the most set of features for integration um you know that's uh if you're like a pretty serious web 3

Builder uh Moonlighting is an AI developer you're probably like you might gravitate towards that um on the those that are gravitate more towards the AI side that are less familiar with like the the the full Suite of capabilities from a blockchain point of view they're going to use the libraries that we have on offer so we've got the SDK then on top a more refined set of libraries um and python based ones because it's the dominant language for building AIS and then on top of that for anybody that's kind of like there's this new subset of Builder

right this like non-technical or let's call it pseudo technical um that can build you know there's there's emergent tools especially on the model side where you can like um prompt engineer a relatively sophisticated agent um and now there's tooling that's coming out that makes building agents uh even easier for that um for that demographic we have an app which is an even more refined set of of um functionality um and so that's yeah there those are the three kind of

mechanisms or means of engaging with what we built what's the value proposition that kind of you put forward to each of these groups so kind of why why shouldn't they kind of just buy um kind of credits with um open AI or clawed or kind of use deep seek for free okay I would say this well even in the case where all of these services are cost nothing and that's just long-term probably untenable unless they become you know public goods which I think most of these companies

will fight tooth and nail against but I know let's see how it plays out um you know barring that from happening but even if that does occur um there's still sort of the aggregate that these agents represent any um tuned expertise that can be captured and then subsequently deployed in these packaged agentic um services that in of themselves can be

um can be priced um and paid for um so getting back to your question like what which which um solution would you gravitate towards well it's less around like what's the pricing and payment mechanism and more what's the level of functionality that you want to have within your your agent and or your set and or your swarm um so for example we have in our SDK the like the attrib ution function so once payment has occurred like if you have a swarm of agents and that swarm is ultimately

what's priced that you can actually like redistribute funds within the the commercial the the the value capture piece right and redistribute those amongst the agents proportionately to the contribution within that swarm but that's like super low level and really only going to be interested interesting to somebody that's been working on swarms for a long time right so like that functionality in the SDK is not really being used at present what most people are just trying to do is wrap their HTTP end point in some sort of gatekeeping

functionality with a payment mechanism attached as part of that gatekeeping functionality so there's a broad spectrum of requirements and demands um we're trying to cater to the simplest set of those demands initially though we have built in some relatively complex functionality just you know I don't know because it's interesting okay so maybe let me reframe the question a little bit um so with never mind H how how is the um AI

data payments landscape becoming better for actual users consumers of data or providers of data and algorithms um how is it becoming better to what we currently have in kind of like this the centralized model I'm going to answer this in a relatively flippant way we don't actually care [Music] um what we're trying to offer and enable is um a higher degree of fidelity on that transactional

piece so whether and and what I mean like there's two aspects of this one is on the accounting piece right so enabling that and doing it in a way that's more Dynamic than existing systems today and then the other and this is whether or not over the long term this actually matters it time will tell but like these services that are being rendered dist straightly don't cost that much they cost like fractions of a fraction of a scent

existing Payment Systems fat-based payment systems cannot handle that discret mechanism you you can't get below you know a certain denomination of a currency say one cent right why not it just depends on how often you settle right kind of like if you have fractions of the sent and kind of this I'm saying from a from a dis from a very discret point of view if if all I need is one action just for sake of argument one GPU cycle I can't price that in I I have to do what you just said I have to

aggregate it and so the question becomes and this is what I said the jury's still out on this piece um is that actually a requirement or not um time will tell but I you know I do believe that there will be um applications on a use case where you have discret expertise that for that particular use case for that set of requesters that particular agent may only ever get called upon once

or less times than you can actually aggregate to the the floor of that currency and so what do you do in that case that service is always free or it has to be coupled with other services and you know so again there's an element here of of um you know speculation on whether that's going to be a driver I think it will be having operated in this space for as long as I have um but whether or not it's a primary driver I don't know time will

tell um but anyway getting back to the original question you know I think at the end of the day why are we doing this why are we using crypto instead of doing this in like a centralized multi- teneted charted database we're driven by optionality and like the the drive to provide option and and and that is derived um from a an a desire I mean this is where like sort of the crypto ethos shines through um to to provide the option of censorship resistance right so

from our point of view we View the payments piece as the most critical component in decentralization um for AI agents so if like Microsoft open AI Google Facebook deep seek whoever if they monopolize the the the means for these agents to pay and get paid their ability to deplatform One agent in our opinion is an existential threat to all agents um it if if what if that you know centralizing entity can just with the flick of a switch Microsoft deems this

agent competitive with one of its lines of business it doesn't matter if the rest of that agent is decentralized at least in an economic context it might as well not exist because it can no longer transact so providing the infrastructure as a means for these agents um providing that optionality for them to pay and get paid always is like that's a driver for us I I totally hear that and I think I I um I fully understand that argument kind of like from a um defensive engineering kind of point of view but kind of the

question is how do you get the flywheel going right so kind of I I I have I have no doubt that kind of you find people who are willing to kind of sell their services on your market place that's current that's usually kind of the the the easy the easy um side of of um kind of of the business yeah of any business right kind of like but how do you make sure consumers actually you know consumers actually come to to your interface to kind of buy Services there rather than elsewhere okay so to be flipping again we don't care that's up to the agent to provide a productive service

that somebody or something actually wants to use that's out of our sphere of influence now kind of generalizing it's in our sphere of influence by selecting who we partner with so making sure that we're there's two things that we need to make sure of one is to your point we need to partner and get those those agents that are productive using our solution and the way that we can do best to guarantee that is by making it as seamless and simple as possible both from an integration point of view and

from a usability point of view so if we can reduce the friction and it's the easiest thing for agents to use to pay and get paid and there is a there is at least some kind of requirement for agents to pay and get paid then the extrapolation is we're likely at least going to be in the running for the product that gets used um so that's that's the way you know this is how we're going to Market so if from from just the Practical point of view looking at multi-agent

system Builders swarm Builders and web3 parlons that's who we want to partner with um you know this is like the crew AIS of the world the agent Ops the agencies on the web two side the virtuals the Eliza os's Etc the napas of the web 3 World um and then there's the the additional step to that because that's like B to B and then there's like B to AI or B to C so B to B to C or B to B to AI also helping with that sort of Step function with our partners helping

them try and attract agents that are doing something useful um I will say this I'm going to rant a little bit just just to get this off my chest what we need to do on the web 3 AI side of things is get out of our own way and quit worrying about like verifiability and attestations on these systems because like those are nice to have well we need to build our productive agents in swarms to do some kind of useful work I also don't think Defi and agents being added to defi is the massive unlock that a lot of

our community actually thinks it is but that's that's for another conversation but anyway my I just want to say like we should be we need to get out of our own way and instead of trying to focus purely on the infrastructure side and like integrating zero knowledge or trusted execution environments or whatever like I think we be better served just trying to focus on building like agents that do work the your point are going to have actual users okay then tell us about um the usage um of um of uh never mind right now so kind of H how many agent payments

do you actually process kind of like on a daily basis and how do you how do you see that grow or how do you see um where do you see the main drivers of that growth yeah so at the moment there's been a a a bit of a Down swing and I think that's somewhat uh related to the downswing and the and enthusiasm in the market um at its peak we're probably seeing like a couple handfuls of transactions a day um you know in total we've done a few uh somewhere in the

neighborhood of like 5,000 transactions um um again we're not counting the the the burn piece we're just talking about like that initial purchase of those thousand credits for example right um it's it's relatively nominal but I'm bullish like that that sort of really predates the AI agent meme taking hold um and so I'm bullish that as we see more output on the agentic AI side

that obviously this is going to be more and more of a requirement so now our business is the business of amplification and getting this in front of as many AI agent Builders as possible cool so where can we send the AI agent Builders to kind of find out more about mind love to have you in our Discord so you can connect to us via our website nevermind. NE v m n d. um you can also um follow us on X we're at um nevermind uncore IO Neer

Minore iio um yeah would be happy to have everybody that's building and and all of your agents be a part of our ecosystem fantastic thank you so much for coming on Don thanks for having me