February 25, 2025 • 64 mins
Even though current LLM providers usually charge flat rates regardless if the subscriber is a ‘power user’ or not, as AI agents become the workhorses of the online economy, a dynamic AI-to-AI payments infrastructure would prove crucial for proper remuneration. Nevermined proposes a blockchain-agnostic, AI-agnostic credit system, that each builder can use to implement custom pricing strategies for their users. As the next trillion transactions will be mostly performed by AI agents, a universal payment system would help streamline integrations and economic interactions.
Topics covered in this episode:
- Don’s background
- Integrating payment systems in the agentic economy
- Marketplaces vs. payment systems
- AI agentic economy
- Fixed vs. variable pricing strategies
- License tokenization
- Settlement and payment for AI agents
- Estimating price and avoiding overcharges
- Nevermined’s tech stack and integrations
- Value prop
- Nevermined metrics
Episode links:
Sponsors:
- Gnosis: Gnosis builds decentralized infrastructure for the Ethereum ecosystem, since 2015. This year marks the launch of Gnosis Pay— the world's first Decentralized Payment Network. Get started today at - gnosis.io
- Chorus One: one of the largest node operators worldwide, trusted by 175,000+ accounts across more than 60 networks, Chorus One combines institutional-grade security with the highest yields at - chorus.one
This episode is hosted by Friederike Ernst.
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Transcript
Episode Transcript
Available transcripts are automatically generated. Complete accuracy is not guaranteed.
(00:00):
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
(00:22):
much of A given data set, how many times is an algorithm
called? What's like the computational
cost of executing that algorithmin 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,
(00:43):
there's actually nothing to passback upstream.
Something that needs to be addressed in the web free space
is there's often times a trivialoutlook on payments.
And that's because I I personally believe people
conflate settlement for payments.
Welcome to Epicenter, the show, which talks about the
(01:03):
technologies, projects and people driving decentralization
in the blockchain revolution. I'm Rodrique ants and today I'm
speaking with with Don Gossan, who is the CEO and Co founder of
Nevermind. So Nevermind as in Nevermind as
in has never been mined because there's quite a few projects
kind of like with very similar names, right?
(01:24):
And Nevermind is positioning itself as the PayPal for AI to
AI payments. Before I talk with Don, these
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(02:27):
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(03:09):
journey today at gnosis dot IO. Done.
Thank you so much for coming on.Thanks for having me.
Maybe we can get off to a start by talking about you yourself.
What what's your background and kind of like what brought you
here? Canadian transplant in Europe,
(03:33):
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
university, went into commodities trading after
university I was on the risk side and so doing back then what
(04:01):
was called statistical modeling,which became machine learning
and now has been Co opted by theAI branding.
But yeah, just basically augmenting internal credit
histories with external credit scoring and figuring out which
(04:24):
of our clients were deadbeats and which ones weren't, so which
ones we could like loan money toin order for them to hedge and
stuff like that. So it was pretty boring, to be
honest. And then I went into IT
consulting for the better part of a decade and 1/2 as a subject
(04:47):
matter expert in data and analytics.
And that's what took me all overthe world.
So I built large scale data states for ML purposes for some
of the biggest companies on the planet.
So I was at HSBC and L'Oreal andAXA and Mizuho and stuff like
that. And so yeah, I've, I've been in
(05:12):
the machine learning space my entire career, so 20 years and
then added the crypto flavor in 2016.
So I got introduced to blockchain not as a, you know, a
(05:37):
system or platform or ecosystem for like speculation and
payments and settlement and stuff, but more on the side of
providential integrity. So block chains are very elegant
provenance machines and asset provenance is a really hard
(06:01):
problem to contend with within the confines of large analytical
estates, right? You've got to, you've got to
answer 4 questions with high fidelity, where is the asset
coming from? Where is it going?
Who's using it and what are theydoing with it, right.
And the assets can be like data sets, they can be algorithms,
(06:24):
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 itjust so happens that with
contemporary software, it's really hard to actually answer
that stuff. So what ultimately happens or
(06:47):
usually happens is that you create these very like bespoke
one off patchwork solutions thatlike cobble together information
from all of your different sources and destinations to try
and figure out the topology of what's going on.
Anyway. Block chains help plug into that
(07:11):
and make it much more seamless in terms of understanding the
answers to those four questions.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 'cause
(07:36):
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, which wasone of the first projects at
this intersection of AI and Web 3.
And I've just, you know, kept beating this drum ever since.
And now it's we've we've sort ofdistilled the learnings and
(07:59):
experiences and understanding ofmerging these two technologies
into a very hyper focus on AI payments can get into why we're
hyper focused on this stuff. But yeah, that's the background,
yeah. Absolutely.
So you talked about kind of the,the provenance of data and kind
(08:25):
of this entire data economy thatkind of come comes that kind of
crucially hinges on that How, how, how does payments actually
fit into that picture? It's a this is a good question
and and it took us quite a whileto realize sort of the gravity
of the payments piece. So where what we were focused on
(08:50):
for a long time was in establishing the provenance
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 then now
(09:11):
take, let's say, the form factorof an AI agent, right?
We want to build these things ina decentralized landscape.
So what does that mean kind of holistically?
And it means being able to understand what's actually being
used in that analytical pipeline.
(09:34):
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 algorithmin conjunction with that data
set to say, train a model or what have you, and, and
(09:57):
basically accounting for all of that and then extrapolating or
extending that into the attribution piece.
So Frederica, you provide some contextual data, I provide some
training data, somebody else provides the algorithm, another
third party provides the infrastructure for bringing all
(10:19):
of this together. And when combined, we create
this inference or actionable insight or whatever we want to
call the output. That output then gets
commercialized and everybody in that value chain is somehow
rewarded. Right now, what we realized a
(10:40):
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
(11:02):
actionable insight. Because without that, there's
actually nothing to pass back upstream.
There's really other than recognition like a nice pat on
the back. Hey Federica, thanks for
providing that context data, thumbs up, reputational reward,
whatever. There's actually nothing if you
(11:23):
if you can't capture the end state utility, there's nothing
actually to translate back upstream to the different
participants. And so in and amongst everything
that we were building, there wasthat piece that that payment
system in the attribution component.
(11:45):
And well, it's part of the attribution component.
And what we realized a couple ofyears 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 hyper focused on
that one component within this whole workflow.
(12:09):
So Ocean is very much sit aroundand kind of like Ocean, I mean,
trends been on this podcast multiple times.
So kind of like listeners will probably know that it it's kind
of it's focused on the data economy and having a marketplace
for, for, for data sets. It seems to me that kind of like
payments, it is just a very natural part of any marketplace.
(12:34):
Why? Why do you think it kind of
makes part to kind of ply these two things apart?
Because payment systems are complex and and they're non
trivial, let's put it that way right as our marketplaces.
(12:56):
The other realization purely from a marketplace point of view
is marketplaces are, are difficult propositions from a
commercial and operational pointof view.
They, they tend to not make a lot of money and therefore
(13:21):
they're they're hard to persist.They're hard to like accrue
enough revenue to keep the thinggoing.
The way that so that it 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 discreet focuson a specific domain or a niche
(13:43):
within a specific domain, right?So the common marketplaces that
are sort of presented are like Bloomberg, right?
Or maybe I'll Sevier for a more abstract 1 and like the research
domain, but they're very difficult to win and they
(14:04):
require 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.
(14:25):
I think it's, you know, I think there's a lot more examples of
those failing than there are of successes.
So all that aside, that's one part of the argument and then
the other is a realization that that payments are are quite
(14:45):
complex in their in their manifestation, right.
So it's not, I think there's a, there something that needs to be
addressed in the web free space is there's often times that a
trivial outlook on payments. And that's because I, I
personally believe people conflate settlement for payments
(15:10):
and, and payment processing and,and that sort of thing.
And so just anecdotally, right, 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 calledMetronome that does like
(15:33):
payments and billing services, predominantly for SAS providers,
right? And the, the CEO of that company
and, and a few of the founders, they come out of, of Box of, out
of Dropbox, right? What they recognized was there
(15:53):
was a discrete 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 at Dropbox just supporting the payments, the
price setting the infrastructureand the billing at that company.
(16:17):
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
(16:38):
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 Databricks or you
know, these these other like SASproviders for these services.
So why don't we take what would build here, extract it and offer
(17:04):
it as a service to multiple companies.
And so it's that kind of 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 pricing and kind of price
(17:26):
pricing strategy can be arbitrarily complex.
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 the complexity,
the teas of payments because kind of like, I mean, I have, I
have as someone who also works in payments, I have said this
(17:48):
time and time again that kind ofI think it makes sense to kind
of like productionize things in web three first that have simple
use cases. And I always say kind of like I
always say payments and principles of 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 that go from one place to another.
(18:09):
Kind of like it ideally kind of they should be consulted.
You shouldn't. Nuance.
To it, right? Exactly.
So walk us through kind of like,what makes it difficult.
I I, I I'm, I'm clearly speakingwith with a kindred spirit here,
right? So this is the other kind of
revelation, like it's simple within the context of like it's
(18:35):
easy to understand, it's not esoteric.
And it like the infrastructure, the technology that we're using
actually makes sense to focus onpayments, right?
So in that sort of very general sense, it's easy, you know what
I mean? So why is this?
(18:58):
Why is this important? OK.
So our view of the world is likea particular one.
I think up until about 6 months ago, it was relatively unique
because most people back then didn't know what an AI agent is
like. It's AI agents are now starting
(19:21):
to emerge conceptually for some people, I'm not going to say
most people, but those that are kind of either in the AI space
or adjacent to it, you know, areare this is entered the lexicon,
right? Like people are getting familiar
with what an AI agent is. Even those in the AI space like
(19:44):
agentic AI mixtures of experts like this concept was even an AI
was quite fringe 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
(20:07):
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
(20:29):
believe in the monolithic one AIto 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 providevery discreet expertise that
will be called upon when and where needed, right.
(20:50):
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
(21:10):
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,
(21:31):
you also have to negotiate whichpayment system you're going to
use, right. But how is?
It 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 it's kind of kind
of specifically to a is. Sure.
So it's intrinsically like it's not that dissimilar.
(21:52):
It's the way that effectively the information gets packaged
around the service that'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
and in particular an AI agent. So we're like our tech can be
(22:16):
used for like pricing models andstuff 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
(22:38):
model, right, depending on the complexity.
So like I mean operator does this if you've used say 4 O or
O1 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
(23:00):
the hood, there's logic that takes place that routes 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 or or example that I provided, it's not use case to
(23:22):
lie. 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 GBD 3, GBD 3.5, GBD 4, soon
to be GBD 4.5 and maybe five. I'm a simple user.
(23:46):
In this case, you're a complex user, OK, You and I can both
interface with the same agent 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
(24:06):
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 agents back in and
optimally rooted to the model that will sufficiently handle
that simple request, GPT 3, right?
You on the other hand, you submit a multimodal request to
(24:28):
the agent that has to go to a model that can handle the multi
modality of your request. So in this case GPT 4.
The cost difference between invoking GPT 3 and GPT 4 is like
an order of magnitude if not more indifference.
And accounting for that, especially with most
(24:48):
contemporary pricing solutions is non trivial.
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.
(25:11):
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. It's cost is variable depending
on the complexity of the requestthat's served to it as well as
(25:33):
the the tools that it has at itsdisposal 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.
(25:56):
So what we've built is a system of unit accounting, effectively
an accounting module that strapsonto 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
(26:20):
response and and invocation of of the corresponding services on
the back end. That sounds very prescribed.
So as someone, for instance, if I were to offer different AI
models, I mean, I, I could come up with different pricing
strategies, right? Kind of I, I could kind of sell
a flat rate to all my models. I could say, OK, I'll give you a
(26:43):
flat rate until kind of you hit a certain number of requests and
then kind of you have to pay perrequest or kind of I, I, I mean,
there's different strategies. So how much of that do you
impose on people and how much can you actually tailor the the
these this pricing strategy? So this is where again, like
(27:08):
you're clearly like well versed in, in the nuance of this.
What we're what we are trying toaccommodate for is as much
variability in that price control setting mechanism as
possible. So if you want to have sort of
a, a fixed price subscription that maybe or may not rate limit
(27:34):
or time limit a service, you cando that.
If you want to go as granular aslike pure pay to play and you
know each, each access is cost 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
(28:01):
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
(28:26):
system 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 an intrude decentralized fashion, we
(28:47):
attempt to give that control to the user as opposed or the
builder as opposed to setting itfor them upfront.
OK. And then kind of I, I understand
that kind of like you give me variability and kind of like how
I set my pricing strategy, 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.
(29:10):
Yeah. And how, how do you do that?
So kind of like how how does howdoes the payment work?
Yeah. So this is a good question too.
So we, we leverage a concept called license tokenization.
So we believe quite strongly. And, and so again, like going
(29:33):
back to the, this is a function of a marketplace, right?
Marketplaces are hard to win, especially as you get as,
especially as you start to sell assets that become more and more
commoditized, right? Like trying to markets, trying
to push the price to 0. So and then you're, your, your
(29:54):
revenues generated at the margin, right?
So being is having as much fidelity on the actual
operational cost means that you can like eke out as much margin
as possible. So like recognizing that that's
sort of the this the set of, I don't know, maximal constraints
(30:16):
that we have to deal with here. We want to enable these agents
to eke out as slim a margin as possible and and also allow them
to continue to be functional from a business and operational
point of view. So that means setting a single
price and just giving free rein access doesn't really make a lot
(30:40):
of sense. It works right now for
propositions that are broadly speaking toys.
And then more importantly there were there's not a a lot of
competition that's yet pushing that price point down.
But then we are already seeing this manifest, right?
Like Open AI very clearly has, you know, a price per usage
(31:07):
function. And then a deep sea 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 gougey as some of the
things that Open AI is doing. So anyway, in all of this work
(31:29):
that we've done, there's this recognition that actually
understanding sort of the ML OPSthat that 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, OK, like if we're
(31:50):
taking the position that like a traditional subscription style
model isn't the right one, what is?
What does look and feel right based on experience?
And it's this concept of licensetokenization.
So it has nothing to do with crypto.
It's a traditional licensing scheme, but it differs in
(32:12):
comparison to like named user licensing and concurrent access
licensing, where in a named userlicense, Federica, 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.
(32:32):
We negotiate our usage of said platform and the corresponding
tools of that platform. It's a pretty laborious process
in the grand scheme of things orvery 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.
(32:52):
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 1000 tokens, I buy
(33:14):
10,000 tokens and that platform is made-up of tool A B&C and a
has redemption criteria of 100 tokens BA 1000 tokens C5000.
Token usage credits or something?
It's use, it's exact. That's exactly, it's this, yeah.
(33:35):
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
(34:00):
and, or 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 that the, the legibility and and the the, the fine grain
(34:29):
component of the, the payment aspect.
And how do you set it so becausekind 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 1000 credits.
(34:50):
And so under the hood of this agent, GPT 3 has a redemption
criteria of 10 credits. GPT 4 A3 3.5 of 100 GPT 4400
credits, right? We both pay a dollar.
I get 1000 credits in my wallet.You get 1000 credits in your
wallet. We make these requests to this
(35:11):
agent. My simple request goes to GPD 3
out of my pool of 1000 credits, I get charged 10.
You with your multimodal requestthat gets routed to GPD 4, you
get charged 400 out of your poolof 1000.
So that's how. And then that function, that
(35:32):
accounting, that redemption is aburn function on chain.
OK, so it it's a burn function and that, but then how does the
the AI that actually did the work receive the payment?
Good question. So we have basically 2 forms of
settlement. One is the the piece that that
(35:57):
the settlement that authorizes you to use the system.
So the payment of a dollar for that 1000 credits, that's the
first settlement. And in our case, you know,
recognizing that there's a largeswath of like we, we view this
(36:18):
as an AI solution or adjacent solution.
So we don't distinguish between like Web 3 AI versus Web 2 AI is
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
(36:43):
majority, like a, there's, yeah,probably a majority of AI, that
of the AI community that is not very conversant 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 MPC solution.
(37:06):
It's fully gas less. So there's no like extraneous
signature signing and stuff likethat that that's required for
anybody that's listening. We're paying for the gas right
now 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,
(37:28):
I don't have any affinity towards web three.
I don't have a wallet. I just want to use to say I OK,
I use nevermind. I go through this checkout.
Part of the checkout process is I register with the system that
creates my wallet. We've gone so far as to
integrate Stripe. So I'm now in the ecosystem of
(37:51):
registered. I've created this MPC based
wallet that's attached to me. If I don't know where to look, I
don't even know that it's, you know, a crypto wallet.
And then I can just take out my debit card or credit card and
pay a dollar for these thousand for these 1000 credits.
Now in the background, the builder that's registered this
(38:12):
agent, this third party agent that you and I are using,
they've linked that to their bank account through a Stripe
integration. What they've also done is linked
that to a wallet because in thiscase, in this scenario, the
builder is going to take both Fiat and crypto as payment.
So I pay my $1.00 with my debit card back goes to the the
(38:34):
builder's bank account. You on the other hand, you're
well versed in crypto, you have a wallet, you've got USDC, so
you pay 1 USDC and that goes into the, the agent or, or the
builder's wallet in that case. And so in this case we're, we're
handling both. But as you can see, there's two
(38:55):
forms of settlement. 1 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.
(39:17):
I mean, I you know and authenticate the usage of of
that agent. How do I as a user know that
kind of algorithms that I solicit, How how do I know that
they are metered in a fairway? So kind of like if there's
different ways that kind of likedifferent, different underlying
(39:39):
functions that I could call in kind of they're all metered in
some way. Is, is there like some rubber
stamp of approval somewhere thatsays, OK, this is actually this
is, this is an OK 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
(40:01):
user, right? Yeah.
So right now it's it's the very finger in the air and cottage
industry they're they're there'sa lot of price discovery going
(40:22):
on at the moment, a lot of like guesstimation.
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 base point of view what maybe the
pricing should be for a given agent.
(40:44):
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,
but it's something conceptually that we've been working on for
about 6 months. And over the last two months
(41:05):
we've like put pen to paper and actually started to, we, we POC
did. 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, the, theflip side of your question is
what do I price this at, right? So helping answer that question
(41:29):
is where we're trying to get to 1st.
And I think the knock on effect of that will be disclosing that
sort of price setting mechanism will help those on the buy side
also understand maybe what theircost structure should be.
(41:50):
Maybe that's kind of switch gears a little bit.
So all of this kind of is built on blockchain infrastructure
kind of like walk us through kind of like what kind of stack
this is built on and why you chose that stack so.
We're at adapt in the classical sense, so we're chain agnostic
(42:15):
though we are. We're an EVM based solution.
So you know, from a deployment point of view, we're on mainnet
and Polygon and Arbitrum and bass and Nosis and cello and
bunch of EVM based chainsaw onesand L twos.
(42:37):
Yeah, the the code base is Python And TypeScript.
You know where needed. We've got backends that are what
do we have? I don't know, it's post Christ
(42:59):
database. Yeah, I mean it's relatively
run-of-the-mill from the architecture.
Point of view, OK, 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 which of these
(43:23):
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
'cause they usually have a network that they want to
(43:45):
default to. OK, 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, how do I decide which
network to kind of consume my towhich network to pay for?
(44:07):
My in this case, you don't the the builder would.
So OK, so here's where like froma 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
(44:29):
to. Usually it's one the dominant
chain at the moment is base, so that's the default.
From a consumption point of view, you don't really care
other than if you are in your case, this power user that is
(44:55):
crypto native or savvy, you know, you're in a condition
where shit, I don't have any USDC in a wallet on base.
So now you're in and you want touse an agent that's anchored
there and it needs, you know, it's, you got to pay 1 USDC for
(45:16):
those 1000 credits. Well, now you've got to bridge
that. 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.
OK. So basically as a consumer, kind
(45:38):
of like I decide what model I want to use, what kind of agent
I want to frequent and then kindof I I just have to pay on the
commensurate chain. Is that fair?
Yeah, exactly. Yeah, Yeah, that's that's the
way it would work, yes. OK.
So how, how does Nevermind currently integrate with other
(46:03):
kind of decentralized platforms or protocols, right?
Because kind of like you primarily enable the, the
payments here, there's a lot of functionality that kind of that
that has to come together to kind of make this into a good
user experience that kind of goes beyond payment, right?
So how, how, how do you interoperate here?
(46:25):
Yeah. So we kind of have, we have 3
levels of engagement. So there's the SDK, which is the
most robust. It provides the most set of
features for integration. You know, that's if you're like
(46:51):
a pretty serious Web 3 builder. Moonlighting is an AI developer.
You're probably like, you might gravitate towards that on the
those that are gravitate more towards the AI side that are
less familiar with like the the,the full suite of capabilities
(47:16):
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 and Python based ones, 'cause
it's the dominant language for building a is.
And then on top of that, for anybody that's kind of like
(47:37):
there's this new subset of builder, right?
This like non-technical or let'scall it pseudo technical.
They can build, you know, there's, there's emergent tools,
especially on the model side where you can like prompt
engineer a relatively sophisticated agent.
(48:02):
And now there's tooling that's coming out that makes building
agents even easier for that, forthat demographic.
We have an app which is an even more refined set of, of
functionality. And so that's, yeah, they're,
(48:22):
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 what, why shouldn't they kind of just buy kind of
credits with open AI or cloud orkind of use deep seek for free?
(48:47):
OK. 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
(49:08):
will fight tooth and nail against.
But well, let's see how it playsout, you know, barring that from
happening. But even if that does occur,
there's still sort of the aggregate that these agents
represent any tuned expertise that can be captured and then
(49:40):
subsequently deployed in these packaged agentic services that
any of themselves can be can be priced and paid for.
So going back to your question, like what, which, which solution
(50:02):
would you gravitate towards? Like it splice 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 and, or your swarm?
So for example, we have in our SDK the like the attribution
(50:26):
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
(50:48):
the agents proportionately to their 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
(51:11):
at present. What most people are just trying
to do is wrap their Http://endpoint in some sort of
gatekeeping functionality with apayment mechanism attached as
part of that gatekeeping functionality.
So there's a broad spectrum of requirements and demands.
(51:37):
We're trying to cater to the simplest set of those demands.
Initially, though, we have builtin some relatively complex
functionality. Just, you know, I don't know,
because it's interesting. OK.
So maybe let me reframe the question a little bit.
So with never mind how, how is the AI data payments landscape
(52:06):
becoming better for actual users, consumers of data or
providers of data and algorithms?
How is it becoming better to what we currently have in kind
of like this, this centralized model?
I'm going to answer this in a relatively flippant way.
(52:26):
We don't actually care what we're trying to offer and enable
is a higher degree of fidelity on that transactional piece.
(52:49):
So weather 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
(53:10):
term this actually matters in time will tell.
But like these services that arebeing rendered discreetly don't
cost that much. They cost like fractions of a
fraction of a cent. Existing payment systems, Fiat
(53:34):
based payment systems cannot handle that discrete mechanism.
You you can't get below, you know a certain denomination of a
currency, say 1 cent, right? Why not?
It just depends on how often yousettle right?
Kind of like if you have fractions of the send, then kind
(53:55):
of like. This is what I'm saying from a,
from a, from a very discreet point of view.
If, if all I need is 1 action, just for sake of argument, one
GPU cycle, I can't price at it. I, I have to do what you just
said, I have to aggregate it. And so the question becomes, and
(54:17):
This is why I said the jury's still out on this piece, is that
actually a requirement or not? Time will tell.
But I, you know, I do believe that there will be applications
(54:38):
on a use case where you have discrete 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
(55:03):
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, you know,
speculation on whether that's going to be a driver.
(55:26):
I think it will be having operated in this space for as
long as I have, But whether or not it's a primary driver, I
don't know, time will tell. But anyway, getting back to the
original question, you know, I think at the end of the day, why
(55:46):
are we doing this? Why are we using crypto instead
of doing this in like a centralized multi tenanted
charted database? We're driven by optionality and
like the the drive to provide option and, and and that is
(56:07):
derived from a desire. I mean this is where the sort of
the crypto ethos shines through 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
(56:32):
decentralization for AI agents. So if like Microsoft Open AI,
Google, Facebook, Deepseek, whoever, if they monopolize the
the the means for these agents to pay and get paid, their
ability to de platform one agent, in our opinion, is an
existential threat to all agents.
(56:57):
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 ofthat agent is decentralized, at
least in an economic context, itmight as well not exist because
(57:18):
it can no longer transact. So providing the infrastructure,
the means for these agents, 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, I fully
(57:41):
understand that argument kind oflike from a 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
marketplace that's current, that's usually kind of the, the,
(58:03):
the easy, the easy side of, of. Kind of of the business.
Yeah, of any business, right, kind of like, but how do you
make sure how do? You find the.
Buyers actually, you know, consumers actually come to to
your interface to kind of buy services there rather than
elsewhere. OK.
So to be flipping again, we don't care.
(58:29):
That's up to the agent to provide a productive service
that's 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.
(58:51):
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
(59:14):
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
(59:39):
likely at least going to be in the running for the product that
gets used. So that's that's the way, you
know, this is how we're going tomarket.
So it from from just a practicalpoint of view, looking at multi
agent system builders, swarm builders and Web 3 parlance,
(01:00:04):
that's who we want to partner with.
You know, this is like the crew AIS of the world, the agent OPS,
the agencies on the web two side, the virtual, the Eliza
OSS, etcetera, the Naphtha's of the Web three world.
(01:00:25):
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
(01:00:46):
useful. 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 when the web 3 AI side of things is get out
of our own fucking way and quit worrying about like
verifiability and attestations on these systems because like
(01:01:09):
those are nice to haves. What we need to build our
productive agents in swarms to do some kind of useful work.
I also don't think D Phi and agents being added to D Phi is
the massive unlock that a lot ofour community actually thinks it
is. But that's, that's for another
(01:01:30):
conversation. But anyway, my I, 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 0 knowledge or trusted execution
environments or whatever, like Ithink we'd be better served just
trying to focus on building likeagents that do work that to your
(01:01:55):
point are going to have actual users.
OK. Then tell us about the usage of
of nevermind right now. So kind of how many agent
payments do we actually process kind of like on a daily basis
and how do you how do you see that grow or how do you see how,
(01:02:16):
where do you see the main drivers of that growth?
Yeah. So at the moment there's been a
a, a bit of a downswing and I think that's somewhat related to
the downswing and the and enthusiasm in the market at its
(01:02:38):
peak. We're probably seeing like a a
couple handfuls of transactions a day.
You know, in total we've done a few somewhere in the
neighborhood of like 5000 transactions.
Again, we're not counting the, the, the burn piece, we're just
(01:03:02):
talking about like that initial purchase of those 1000 credits
for example, right? It's relatively nominal, but I'm
bullish like that. That sort of really predate the
AI agent meme taking hold. And so I'm bullish that as we
(01:03:31):
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 manyAI agent builders is possible.
Cool, so where can we send the AI agent builders to kind of
(01:03:54):
find out more about Nevermind? Love to have you in our discord
so you can connect to us via ourwebsite.
Nevermind dot IONEVERMINED dot IO.
You can also follow us on X. We're at nevermind under score
(01:04:17):
IONEVERMINED under score IO. But 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.
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
(00:22):
much of A given data set, how many times is an algorithm
called? What's like the computational
cost of executing that algorithmin 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,
(00:43):
there's actually nothing to passback upstream.
Something that needs to be addressed in the web free space
is there's often times a trivialoutlook on payments.
And that's because I I personally believe people
conflate settlement for payments.
Welcome to Epicenter, the show, which talks about the
(01:03):
technologies, projects and people driving decentralization
in the blockchain revolution. I'm Rodrique ants and today I'm
speaking with with Don Gossan, who is the CEO and Co founder of
Nevermind. So Nevermind as in Nevermind as
in has never been mined because there's quite a few projects
kind of like with very similar names, right?
(01:24):
And Nevermind is positioning itself as the PayPal for AI to
AI payments. Before I talk with Don, these
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(03:09):
journey today at gnosis dot IO. Done.
Thank you so much for coming on.Thanks for having me.
Maybe we can get off to a start by talking about you yourself.
What what's your background and kind of like what brought you
here? Canadian transplant in Europe,
(03:33):
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
university, went into commodities trading after
university I was on the risk side and so doing back then what
(04:01):
was called statistical modeling,which became machine learning
and now has been Co opted by theAI branding.
But yeah, just basically augmenting internal credit
histories with external credit scoring and figuring out which
(04:24):
of our clients were deadbeats and which ones weren't, so which
ones we could like loan money toin order for them to hedge and
stuff like that. So it was pretty boring, to be
honest. And then I went into IT
consulting for the better part of a decade and 1/2 as a subject
(04:47):
matter expert in data and analytics.
And that's what took me all overthe world.
So I built large scale data states for ML purposes for some
of the biggest companies on the planet.
So I was at HSBC and L'Oreal andAXA and Mizuho and stuff like
that. And so yeah, I've, I've been in
(05:12):
the machine learning space my entire career, so 20 years and
then added the crypto flavor in 2016.
So I got introduced to blockchain not as a, you know, a
(05:37):
system or platform or ecosystem for like speculation and
payments and settlement and stuff, but more on the side of
providential integrity. So block chains are very elegant
provenance machines and asset provenance is a really hard
(06:01):
problem to contend with within the confines of large analytical
estates, right? You've got to, you've got to
answer 4 questions with high fidelity, where is the asset
coming from? Where is it going?
Who's using it and what are theydoing with it, right.
And the assets can be like data sets, they can be algorithms,
(06:24):
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 itjust so happens that with
contemporary software, it's really hard to actually answer
that stuff. So what ultimately happens or
(06:47):
usually happens is that you create these very like bespoke
one off patchwork solutions thatlike cobble together information
from all of your different sources and destinations to try
and figure out the topology of what's going on.
Anyway. Block chains help plug into that
(07:11):
and make it much more seamless in terms of understanding the
answers to those four questions.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 'cause
(07:36):
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, which wasone of the first projects at
this intersection of AI and Web 3.
And I've just, you know, kept beating this drum ever since.
And now it's we've we've sort ofdistilled the learnings and
(07:59):
experiences and understanding ofmerging these two technologies
into a very hyper focus on AI payments can get into why we're
hyper focused on this stuff. But yeah, that's the background,
yeah. Absolutely.
So you talked about kind of the,the provenance of data and kind
(08:25):
of this entire data economy thatkind of come comes that kind of
crucially hinges on that How, how, how does payments actually
fit into that picture? It's a this is a good question
and and it took us quite a whileto realize sort of the gravity
of the payments piece. So where what we were focused on
(08:50):
for a long time was in establishing the provenance
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 then now
(09:11):
take, let's say, the form factorof an AI agent, right?
We want to build these things ina decentralized landscape.
So what does that mean kind of holistically?
And it means being able to understand what's actually being
used in that analytical pipeline.
(09:34):
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 algorithmin conjunction with that data
set to say, train a model or what have you, and, and
(09:57):
basically accounting for all of that and then extrapolating or
extending that into the attribution piece.
So Frederica, you provide some contextual data, I provide some
training data, somebody else provides the algorithm, another
third party provides the infrastructure for bringing all
(10:19):
of this together. And when combined, we create
this inference or actionable insight or whatever we want to
call the output. That output then gets
commercialized and everybody in that value chain is somehow
rewarded. Right now, what we realized a
(10:40):
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
(11:02):
actionable insight. Because without that, there's
actually nothing to pass back upstream.
There's really other than recognition like a nice pat on
the back. Hey Federica, thanks for
providing that context data, thumbs up, reputational reward,
whatever. There's actually nothing if you
(11:23):
if you can't capture the end state utility, there's nothing
actually to translate back upstream to the different
participants. And so in and amongst everything
that we were building, there wasthat piece that that payment
system in the attribution component.
(11:45):
And well, it's part of the attribution component.
And what we realized a couple ofyears 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 hyper focused on
that one component within this whole workflow.
(12:09):
So Ocean is very much sit aroundand kind of like Ocean, I mean,
trends been on this podcast multiple times.
So kind of like listeners will probably know that it it's kind
of it's focused on the data economy and having a marketplace
for, for, for data sets. It seems to me that kind of like
payments, it is just a very natural part of any marketplace.
(12:34):
Why? Why do you think it kind of
makes part to kind of ply these two things apart?
Because payment systems are complex and and they're non
trivial, let's put it that way right as our marketplaces.
(12:56):
The other realization purely from a marketplace point of view
is marketplaces are, are difficult propositions from a
commercial and operational pointof view.
They, they tend to not make a lot of money and therefore
(13:21):
they're they're hard to persist.They're hard to like accrue
enough revenue to keep the thinggoing.
The way that so that it 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 discreet focuson a specific domain or a niche
(13:43):
within a specific domain, right?So the common marketplaces that
are sort of presented are like Bloomberg, right?
Or maybe I'll Sevier for a more abstract 1 and like the research
domain, but they're very difficult to win and they
(14:04):
require 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.
(14:25):
I think it's, you know, I think there's a lot more examples of
those failing than there are of successes.
So all that aside, that's one part of the argument and then
the other is a realization that that payments are are quite
(14:45):
complex in their in their manifestation, right.
So it's not, I think there's a, there something that needs to be
addressed in the web free space is there's often times that a
trivial outlook on payments. And that's because I, I
personally believe people conflate settlement for payments
(15:10):
and, and payment processing and,and that sort of thing.
And so just anecdotally, right, 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 calledMetronome that does like
(15:33):
payments and billing services, predominantly for SAS providers,
right? And the, the CEO of that company
and, and a few of the founders, they come out of, of Box of, out
of Dropbox, right? What they recognized was there
(15:53):
was a discrete 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 at Dropbox just supporting the payments, the
price setting the infrastructureand the billing at that company.
(16:17):
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
(16:38):
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 Databricks or you
know, these these other like SASproviders for these services.
So why don't we take what would build here, extract it and offer
(17:04):
it as a service to multiple companies.
And so it's that kind of 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 pricing and kind of price
(17:26):
pricing strategy can be arbitrarily complex.
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 the complexity,
the teas of payments because kind of like, I mean, I have, I
have as someone who also works in payments, I have said this
(17:48):
time and time again that kind ofI think it makes sense to kind
of like productionize things in web three first that have simple
use cases. And I always say kind of like I
always say payments and principles of 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 that go from one place to another.
(18:09):
Kind of like it ideally kind of they should be consulted.
You shouldn't. Nuance.
To it, right? Exactly.
So walk us through kind of like,what makes it difficult.
I I, I I'm, I'm clearly speakingwith with a kindred spirit here,
right? So this is the other kind of
revelation, like it's simple within the context of like it's
(18:35):
easy to understand, it's not esoteric.
And it like the infrastructure, the technology that we're using
actually makes sense to focus onpayments, right?
So in that sort of very general sense, it's easy, you know what
I mean? So why is this?
(18:58):
Why is this important? OK.
So our view of the world is likea particular one.
I think up until about 6 months ago, it was relatively unique
because most people back then didn't know what an AI agent is
like. It's AI agents are now starting
(19:21):
to emerge conceptually for some people, I'm not going to say
most people, but those that are kind of either in the AI space
or adjacent to it, you know, areare this is entered the lexicon,
right? Like people are getting familiar
with what an AI agent is. Even those in the AI space like
(19:44):
agentic AI mixtures of experts like this concept was even an AI
was quite fringe 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
(20:07):
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
(20:29):
believe in the monolithic one AIto 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 providevery discreet expertise that
will be called upon when and where needed, right.
(20:50):
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
(21:10):
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,
(21:31):
you also have to negotiate whichpayment system you're going to
use, right. But how is?
It 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 it's kind of kind
of specifically to a is. Sure.
So it's intrinsically like it's not that dissimilar.
(21:52):
It's the way that effectively the information gets packaged
around the service that'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
and in particular an AI agent. So we're like our tech can be
(22:16):
used for like pricing models andstuff 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
(22:38):
model, right, depending on the complexity.
So like I mean operator does this if you've used say 4 O or
O1 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
(23:00):
the hood, there's logic that takes place that routes 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 or or example that I provided, it's not use case to
(23:22):
lie. 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 GBD 3, GBD 3.5, GBD 4, soon
to be GBD 4.5 and maybe five. I'm a simple user.
(23:46):
In this case, you're a complex user, OK, You and I can both
interface with the same agent 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
(24:06):
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 agents back in and
optimally rooted to the model that will sufficiently handle
that simple request, GPT 3, right?
You on the other hand, you submit a multimodal request to
(24:28):
the agent that has to go to a model that can handle the multi
modality of your request. So in this case GPT 4.
The cost difference between invoking GPT 3 and GPT 4 is like
an order of magnitude if not more indifference.
And accounting for that, especially with most
(24:48):
contemporary pricing solutions is non trivial.
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.
(25:11):
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. It's cost is variable depending
on the complexity of the requestthat's served to it as well as
(25:33):
the the tools that it has at itsdisposal 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.
(25:56):
So what we've built is a system of unit accounting, effectively
an accounting module that strapsonto 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
(26:20):
response and and invocation of of the corresponding services on
the back end. That sounds very prescribed.
So as someone, for instance, if I were to offer different AI
models, I mean, I, I could come up with different pricing
strategies, right? Kind of I, I could kind of sell
a flat rate to all my models. I could say, OK, I'll give you a
(26:43):
flat rate until kind of you hit a certain number of requests and
then kind of you have to pay perrequest or kind of I, I, I mean,
there's different strategies. So how much of that do you
impose on people and how much can you actually tailor the the
these this pricing strategy? So this is where again, like
(27:08):
you're clearly like well versed in, in the nuance of this.
What we're what we are trying toaccommodate for is as much
variability in that price control setting mechanism as
possible. So if you want to have sort of
a, a fixed price subscription that maybe or may not rate limit
(27:34):
or time limit a service, you cando that.
If you want to go as granular aslike pure pay to play and you
know each, each access is cost 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
(28:01):
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
(28:26):
system 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 an intrude decentralized fashion, we
(28:47):
attempt to give that control to the user as opposed or the
builder as opposed to setting itfor them upfront.
OK. And then kind of I, I understand
that kind of like you give me variability and kind of like how
I set my pricing strategy, 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.
(29:10):
Yeah. And how, how do you do that?
So kind of like how how does howdoes the payment work?
Yeah. So this is a good question too.
So we, we leverage a concept called license tokenization.
So we believe quite strongly. And, and so again, like going
(29:33):
back to the, this is a function of a marketplace, right?
Marketplaces are hard to win, especially as you get as,
especially as you start to sell assets that become more and more
commoditized, right? Like trying to markets, trying
to push the price to 0. So and then you're, your, your
(29:54):
revenues generated at the margin, right?
So being is having as much fidelity on the actual
operational cost means that you can like eke out as much margin
as possible. So like recognizing that that's
sort of the this the set of, I don't know, maximal constraints
(30:16):
that we have to deal with here. We want to enable these agents
to eke out as slim a margin as possible and and also allow them
to continue to be functional from a business and operational
point of view. So that means setting a single
price and just giving free rein access doesn't really make a lot
(30:40):
of sense. It works right now for
propositions that are broadly speaking toys.
And then more importantly there were there's not a a lot of
competition that's yet pushing that price point down.
But then we are already seeing this manifest, right?
Like Open AI very clearly has, you know, a price per usage
(31:07):
function. And then a deep sea 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 gougey as some of the
things that Open AI is doing. So anyway, in all of this work
(31:29):
that we've done, there's this recognition that actually
understanding sort of the ML OPSthat that 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, OK, like if we're
(31:50):
taking the position that like a traditional subscription style
model isn't the right one, what is?
What does look and feel right based on experience?
And it's this concept of licensetokenization.
So it has nothing to do with crypto.
It's a traditional licensing scheme, but it differs in
(32:12):
comparison to like named user licensing and concurrent access
licensing, where in a named userlicense, Federica, 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.
(32:32):
We negotiate our usage of said platform and the corresponding
tools of that platform. It's a pretty laborious process
in the grand scheme of things orvery 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.
(32:52):
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 1000 tokens, I buy
(33:14):
10,000 tokens and that platform is made-up of tool A B&C and a
has redemption criteria of 100 tokens BA 1000 tokens C5000.
Token usage credits or something?
It's use, it's exact. That's exactly, it's this, yeah.
(33:35):
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
(34:00):
and, or 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 that the, the legibility and and the the, the fine grain
(34:29):
component of the, the payment aspect.
And how do you set it so becausekind 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 1000 credits.
(34:50):
And so under the hood of this agent, GPT 3 has a redemption
criteria of 10 credits. GPT 4 A3 3.5 of 100 GPT 4400
credits, right? We both pay a dollar.
I get 1000 credits in my wallet.You get 1000 credits in your
wallet. We make these requests to this
(35:11):
agent. My simple request goes to GPD 3
out of my pool of 1000 credits, I get charged 10.
You with your multimodal requestthat gets routed to GPD 4, you
get charged 400 out of your poolof 1000.
So that's how. And then that function, that
(35:32):
accounting, that redemption is aburn function on chain.
OK, so it it's a burn function and that, but then how does the
the AI that actually did the work receive the payment?
Good question. So we have basically 2 forms of
settlement. One is the the piece that that
(35:57):
the settlement that authorizes you to use the system.
So the payment of a dollar for that 1000 credits, that's the
first settlement. And in our case, you know,
recognizing that there's a largeswath of like we, we view this
(36:18):
as an AI solution or adjacent solution.
So we don't distinguish between like Web 3 AI versus Web 2 AI is
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
(36:43):
majority, like a, there's, yeah,probably a majority of AI, that
of the AI community that is not very conversant 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 MPC solution.
(37:06):
It's fully gas less. So there's no like extraneous
signature signing and stuff likethat that that's required for
anybody that's listening. We're paying for the gas right
now 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,
(37:28):
I don't have any affinity towards web three.
I don't have a wallet. I just want to use to say I OK,
I use nevermind. I go through this checkout.
Part of the checkout process is I register with the system that
creates my wallet. We've gone so far as to
integrate Stripe. So I'm now in the ecosystem of
(37:51):
registered. I've created this MPC based
wallet that's attached to me. If I don't know where to look, I
don't even know that it's, you know, a crypto wallet.
And then I can just take out my debit card or credit card and
pay a dollar for these thousand for these 1000 credits.
Now in the background, the builder that's registered this
(38:12):
agent, this third party agent that you and I are using,
they've linked that to their bank account through a Stripe
integration. What they've also done is linked
that to a wallet because in thiscase, in this scenario, the
builder is going to take both Fiat and crypto as payment.
So I pay my $1.00 with my debit card back goes to the the
(38:34):
builder's bank account. You on the other hand, you're
well versed in crypto, you have a wallet, you've got USDC, so
you pay 1 USDC and that goes into the, the agent or, or the
builder's wallet in that case. And so in this case we're, we're
handling both. But as you can see, there's two
(38:55):
forms of settlement. 1 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.
(39:17):
I mean, I you know and authenticate the usage of of
that agent. How do I as a user know that
kind of algorithms that I solicit, How how do I know that
they are metered in a fairway? So kind of like if there's
different ways that kind of likedifferent, different underlying
(39:39):
functions that I could call in kind of they're all metered in
some way. Is, is there like some rubber
stamp of approval somewhere thatsays, OK, this is actually this
is, this is an OK 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
(40:01):
user, right? Yeah.
So right now it's it's the very finger in the air and cottage
industry they're they're there'sa lot of price discovery going
(40:22):
on at the moment, a lot of like guesstimation.
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 base point of view what maybe the
pricing should be for a given agent.
(40:44):
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,
but it's something conceptually that we've been working on for
about 6 months. And over the last two months
(41:05):
we've like put pen to paper and actually started to, we, we POC
did. 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, the, theflip side of your question is
what do I price this at, right? So helping answer that question
(41:29):
is where we're trying to get to 1st.
And I think the knock on effect of that will be disclosing that
sort of price setting mechanism will help those on the buy side
also understand maybe what theircost structure should be.
(41:50):
Maybe that's kind of switch gears a little bit.
So all of this kind of is built on blockchain infrastructure
kind of like walk us through kind of like what kind of stack
this is built on and why you chose that stack so.
We're at adapt in the classical sense, so we're chain agnostic
(42:15):
though we are. We're an EVM based solution.
So you know, from a deployment point of view, we're on mainnet
and Polygon and Arbitrum and bass and Nosis and cello and
bunch of EVM based chainsaw onesand L twos.
(42:37):
Yeah, the the code base is Python And TypeScript.
You know where needed. We've got backends that are what
do we have? I don't know, it's post Christ
(42:59):
database. Yeah, I mean it's relatively
run-of-the-mill from the architecture.
Point of view, OK, 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 which of these
(43:23):
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
'cause they usually have a network that they want to
(43:45):
default to. OK, 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, how do I decide which
network to kind of consume my towhich network to pay for?
(44:07):
My in this case, you don't the the builder would.
So OK, so here's where like froma 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
(44:29):
to. Usually it's one the dominant
chain at the moment is base, so that's the default.
From a consumption point of view, you don't really care
other than if you are in your case, this power user that is
(44:55):
crypto native or savvy, you know, you're in a condition
where shit, I don't have any USDC in a wallet on base.
So now you're in and you want touse an agent that's anchored
there and it needs, you know, it's, you got to pay 1 USDC for
(45:16):
those 1000 credits. Well, now you've got to bridge
that. 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.
OK. So basically as a consumer, kind
(45:38):
of like I decide what model I want to use, what kind of agent
I want to frequent and then kindof I I just have to pay on the
commensurate chain. Is that fair?
Yeah, exactly. Yeah, Yeah, that's that's the
way it would work, yes. OK.
So how, how does Nevermind currently integrate with other
(46:03):
kind of decentralized platforms or protocols, right?
Because kind of like you primarily enable the, the
payments here, there's a lot of functionality that kind of that
that has to come together to kind of make this into a good
user experience that kind of goes beyond payment, right?
So how, how, how do you interoperate here?
(46:25):
Yeah. So we kind of have, we have 3
levels of engagement. So there's the SDK, which is the
most robust. It provides the most set of
features for integration. You know, that's if you're like
(46:51):
a pretty serious Web 3 builder. Moonlighting is an AI developer.
You're probably like, you might gravitate towards that on the
those that are gravitate more towards the AI side that are
less familiar with like the the,the full suite of capabilities
(47:16):
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 and Python based ones, 'cause
it's the dominant language for building a is.
And then on top of that, for anybody that's kind of like
(47:37):
there's this new subset of builder, right?
This like non-technical or let'scall it pseudo technical.
They can build, you know, there's, there's emergent tools,
especially on the model side where you can like prompt
engineer a relatively sophisticated agent.
(48:02):
And now there's tooling that's coming out that makes building
agents even easier for that, forthat demographic.
We have an app which is an even more refined set of, of
functionality. And so that's, yeah, they're,
(48:22):
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 what, why shouldn't they kind of just buy kind of
credits with open AI or cloud orkind of use deep seek for free?
(48:47):
OK. 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
(49:08):
will fight tooth and nail against.
But well, let's see how it playsout, you know, barring that from
happening. But even if that does occur,
there's still sort of the aggregate that these agents
represent any tuned expertise that can be captured and then
(49:40):
subsequently deployed in these packaged agentic services that
any of themselves can be can be priced and paid for.
So going back to your question, like what, which, which solution
(50:02):
would you gravitate towards? Like it splice 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 and, or your swarm?
So for example, we have in our SDK the like the attribution
(50:26):
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
(50:48):
the agents proportionately to their 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
(51:11):
at present. What most people are just trying
to do is wrap their Http://endpoint in some sort of
gatekeeping functionality with apayment mechanism attached as
part of that gatekeeping functionality.
So there's a broad spectrum of requirements and demands.
(51:37):
We're trying to cater to the simplest set of those demands.
Initially, though, we have builtin some relatively complex
functionality. Just, you know, I don't know,
because it's interesting. OK.
So maybe let me reframe the question a little bit.
So with never mind how, how is the AI data payments landscape
(52:06):
becoming better for actual users, consumers of data or
providers of data and algorithms?
How is it becoming better to what we currently have in kind
of like this, this centralized model?
I'm going to answer this in a relatively flippant way.
(52:26):
We don't actually care what we're trying to offer and enable
is a higher degree of fidelity on that transactional piece.
(52:49):
So weather 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
(53:10):
term this actually matters in time will tell.
But like these services that arebeing rendered discreetly don't
cost that much. They cost like fractions of a
fraction of a cent. Existing payment systems, Fiat
(53:34):
based payment systems cannot handle that discrete mechanism.
You you can't get below, you know a certain denomination of a
currency, say 1 cent, right? Why not?
It just depends on how often yousettle right?
Kind of like if you have fractions of the send, then kind
(53:55):
of like. This is what I'm saying from a,
from a, from a very discreet point of view.
If, if all I need is 1 action, just for sake of argument, one
GPU cycle, I can't price at it. I, I have to do what you just
said, I have to aggregate it. And so the question becomes, and
(54:17):
This is why I said the jury's still out on this piece, is that
actually a requirement or not? Time will tell.
But I, you know, I do believe that there will be applications
(54:38):
on a use case where you have discrete 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
(55:03):
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, you know,
speculation on whether that's going to be a driver.
(55:26):
I think it will be having operated in this space for as
long as I have, But whether or not it's a primary driver, I
don't know, time will tell. But anyway, getting back to the
original question, you know, I think at the end of the day, why
(55:46):
are we doing this? Why are we using crypto instead
of doing this in like a centralized multi tenanted
charted database? We're driven by optionality and
like the the drive to provide option and, and and that is
(56:07):
derived from a desire. I mean this is where the sort of
the crypto ethos shines through 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
(56:32):
decentralization for AI agents. So if like Microsoft Open AI,
Google, Facebook, Deepseek, whoever, if they monopolize the
the the means for these agents to pay and get paid, their
ability to de platform one agent, in our opinion, is an
existential threat to all agents.
(56:57):
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 ofthat agent is decentralized, at
least in an economic context, itmight as well not exist because
(57:18):
it can no longer transact. So providing the infrastructure,
the means for these agents, 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, I fully
(57:41):
understand that argument kind oflike from a 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
marketplace that's current, that's usually kind of the, the,
(58:03):
the easy, the easy side of, of. Kind of of the business.
Yeah, of any business, right, kind of like, but how do you
make sure how do? You find the.
Buyers actually, you know, consumers actually come to to
your interface to kind of buy services there rather than
elsewhere. OK.
So to be flipping again, we don't care.
(58:29):
That's up to the agent to provide a productive service
that's 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.
(58:51):
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
(59:14):
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
(59:39):
likely at least going to be in the running for the product that
gets used. So that's that's the way, you
know, this is how we're going tomarket.
So it from from just a practicalpoint of view, looking at multi
agent system builders, swarm builders and Web 3 parlance,
(01:00:04):
that's who we want to partner with.
You know, this is like the crew AIS of the world, the agent OPS,
the agencies on the web two side, the virtual, the Eliza
OSS, etcetera, the Naphtha's of the Web three world.
(01:00:25):
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
(01:00:46):
useful. 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 when the web 3 AI side of things is get out
of our own fucking way and quit worrying about like
verifiability and attestations on these systems because like
(01:01:09):
those are nice to haves. What we need to build our
productive agents in swarms to do some kind of useful work.
I also don't think D Phi and agents being added to D Phi is
the massive unlock that a lot ofour community actually thinks it
is. But that's, that's for another
(01:01:30):
conversation. But anyway, my I, 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 0 knowledge or trusted execution
environments or whatever, like Ithink we'd be better served just
trying to focus on building likeagents that do work that to your
(01:01:55):
point are going to have actual users.
OK. Then tell us about the usage of
of nevermind right now. So kind of how many agent
payments do we actually process kind of like on a daily basis
and how do you how do you see that grow or how do you see how,
(01:02:16):
where do you see the main drivers of that growth?
Yeah. So at the moment there's been a
a, a bit of a downswing and I think that's somewhat related to
the downswing and the and enthusiasm in the market at its
(01:02:38):
peak. We're probably seeing like a a
couple handfuls of transactions a day.
You know, in total we've done a few somewhere in the
neighborhood of like 5000 transactions.
Again, we're not counting the, the, the burn piece, we're just
(01:03:02):
talking about like that initial purchase of those 1000 credits
for example, right? It's relatively nominal, but I'm
bullish like that. That sort of really predate the
AI agent meme taking hold. And so I'm bullish that as we
(01:03:31):
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 manyAI agent builders is possible.
Cool, so where can we send the AI agent builders to kind of
(01:03:54):
find out more about Nevermind? Love to have you in our discord
so you can connect to us via ourwebsite.
Nevermind dot IONEVERMINED dot IO.
You can also follow us on X. We're at nevermind under score
(01:04:17):
IONEVERMINED under score IO. But 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.
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