Projects
![]()
No projects found
Investors
New
DAO Treasuries
DATs
Lend / Borrow APRs
Chains
Categories
Spaces
All Things NEAR AI w/ SKALE
Recorded: March 3, 2026 Duration: 1:00:56Space Recording
Full Transcription
It's scale school time! Ladies and gentlemen, boys and girls, developers and developettes, I don't know.
Welcome! We're back with another episode.
Obviously, last week, very hackathon, very hard on X402 and agentic commerce.
But, you know, sticking with that idea of AI and the agenticness of applications,
we have a very special guest today, Mr. Cameron Dennis of Near AI.
Why don't you give us a little introduction about yourself and we can kick things off.
Absolutely. Thanks for having me, everybody.
My name is Cameron Dennis. I've been in crypto actually for about 10 years.
I've been working on Near for the last five and AI for
the last two. And so I currently am on the new AI team. We are building verifiably private AI,
making sure that your agents are secure and that Sam Altman can't read all your prompts.
And so this is, we think of, you know, pretty much an existential crisis to humanity. If we're
giving away all of our humanity to a couple of organizations that probably will use that information against us.
And so not to get so deep so quickly, but we want to make sure that that verifiably private infrastructure exists.
And so, yeah, that's what we're doing and happy to dive into it.
100% super excited to dive into all of that but before we do so as always doya
mr professor how you doing man good how are you not bad not bad a lot less pain yeah
did you yeah uh i did last week uh i feel a bit i'm feeling better you know every time i go
you know it's this is pain is less which is nice it's good i don't think i stepped in a gym for
over like a year now maybe a year and a half wait really oh wow yeah you out from behind your desk, man. Well, I work out. I just don't go to a gym.
So I have a Peloton.
That's what I like on Tuesdays.
There's no gyms near me that are like, I don't know, not.
I like a small gym.
I don't like the big, big, big box gyms.
Anyways, I digress there's no time to get out
behind the desk because agents don't get out
behind the desk they run 24%
let's talk about those
100%
Nier has obviously been doing a lot
in the AI
space and why don't you give
the people a little bit of a
rundown of you know what what near near ai is and and how then we can jump into the likes of iron
claw yeah for sure um so it's probably good to take a little bit of a step back and kind of tell
where all this is coming from and so uh ilia and, two NIR co-founders, they actually started NIR in 2017 as an AI company.
And this is right after Ilya co-wrote the Attention is All You Need paper.
This is the most cited paper in all of AI.
It essentially describes the T in ChatGPT, stands for transformer.
And this is kind of how all generative AI works today.
And so after he wrote that, and he's good friends with
Alex back from the competitive programming days in Eastern Europe, they were like, holy crap,
we just opened Pandora's box. And this thing will eventually teach itself how to do everything
essentially better than a human can, including exploding every vulnerability on the internet.
And so that was sort of like one of the main issues that they're trying to solve.
But in order to solve that, they wanted to create a code generator. And so this is like a huge
vision. Like this vision is, it just goes on and on and on. But the whole point is how do we
essentially have a ledger where people can verifiably own their money, data, and governance in a post-AI world
and use that ledger to pay people to do things to further improve the economic and social
model of the new internet. And so that is what Near blockchain became. And that's sort of just
like the base layer is the incentive layer that needs to scale forever and decentralized linearly with scale so no single
party can essentially control truth on who owns what. Then the second piece is what we call near
intents. And we frankly think blockchains are kind of clunky and not always the easiest for people to
use. And so the goal is to make blockchains invisible. And it just so happens in like,
you know, we've known kind of that the future of AI is like intent driven.
I don't know if you saw that in Shmir's like post.
He's the very short term CEO of OpenAI.
He was saying like the future of AI is all intent based because all these humans have intents and you want your AIs just to do your things for you.
And so how do you actually build like the liquidity layer for Intense?
Because, you know, if I have an intent to go get my car washed,
like I need to match people who wash cars to me with a certain amount of money to match that order.
Very similar to how like exchanges work and very similar to how like AMMs work.
So Near Intense sort of is building this
like liquidity layer for agentic commerce, starting with crypto. And it's starting by
making all these blockchains completely invisible from one another. This is kind of the whole
multi-chain chain abstraction piece of near. And the whole point is just like make blockchains
invisible because although near can scale and has all this like, you know, great it we understand it's gonna be multi-chain world and that's totally fine because
the thing that we really care about is near ai near ai is you know the front end of the web
like where everyone's going to be interacting with their agents and sort of interacting with
super intelligence and we want to make sure that that is user owned, because if it's not user owned, as I mentioned
before, like, as we've seen before in a lot of like, technology and capitalism, your information
will be exploited for money. And so but this is too big. Like, it's too big to talk about like
everything that you do everything that you buy, like, we need to make sure that that is owned by
the people. And so how do you do that?
Well, first off, you need a blockchain to sort of be that trusted database so you can like actually have a key that is yours.
Second off, you need all this like off-chain stuff,
making sure that your prompts are private.
And I could dive into that a bit.
And so what Near AI is building is it started off with like a private inference network.
This is where we run large models inside these trusted execution environments, TEEs.
Think of it like a black box that sits on the GPU
that you're able to just open.
Like no one can see what's happening in this black box
and you can run arbitrary code inside of it.
So you could actually run large models.
So what you could do is you could take your prompt,
encrypt it, send it to the black box,
decrypt it inside the black box.
We can't see anything and we can prove it.
And then you run inference on top of it,
because one of these large models is running inside that black box.
You take that answer, you encrypt the answer,
send it back to the user, user decrypts it, you have now privacy.
So that is like one base product.
And I can go on and on
because like it sort of evolves into Ironclaw,
but I just rambled for a while, so I'm gonna stop.
Yeah.
All good, all good.
Lots of really good stuff there
that I kind of want to pull out and dive into
to kick things off.
I think it's really interesting
because I think we share a lot of the same philosophies around where the blockchain ultimately will sit.
And I think we're starting to see more and more people in the space finally come around to this idea that I think both Scale and Near have been talking about for years,
which is this idea that blockchains are kind of really not intuitive technologies and they're really not enjoyable for humans to use.
I mean, arguably, even like the basic internet
is proving to be not the ideal way
to interact with the internet, right?
Or interact with technology.
We're seeing this shift to prompting, right?
Because it's just easy.
I thought what I want, and it figures it out for me.
And so blockchain requires you to go so far up this stack of like complex
decision-making and AI kind of brings us all the way back down to earth,
back to what humans are actually, well, good at normally,
which is quite literally just being able to have a conversation minus,
you know, social media addicts in the world that can't have a Facebook
conversation, but they can type in their phone.
So what I really like is that you guys have kind of a similar positioning on that.
And I think the part about the data privacy and visibility is really interesting,
because I think there's a lot of nuance to this topic.
What I mean by that is I fully agree, right?
In an ideal world, your data is your data, and it's private, and people can't train on your data.
They can't determine everything about you.
They can't use a bunch of algorithms to pinpoint things you may want to do, things like that, right?
But we obviously know that the cloud era changed how data was handled.
Everything is online.
There's no real way around it.
Everything is online.
Being able to carve out some things and make them a little bit more private is definitely a step in the right direction.
We've seen a ton of companies over the years, right, Attempt to do various things from, you know,
social media all the way down to search, right?
Like, again, with varying degrees of success.
I think what's really exciting about agents
and where we're at in call,
like the crossing the chasm graph, right?
We're not at the chasm yet.
We're at the kind of like, what are we doing things, right?
Maybe, arguably.
I think we're at the point where we're like about to,
we're about to get there.
I think it's still very technical heavy.
I don't think these things have been ingrained
in everybody's day-to-day life 100%.
I think that starts to get us to the point where we are.
And I think as we see this ramp up, what's really cool about what you guys do with Iron
Claw and a lot of these other teams building out their own forms of Claws, which I'll just
say here, I don't know if anyone else has said this, I think Claws is just like a new
technical primitive.
It's like it's the lobster primitive.
So we could just maybe agree on that but um what i really like is you guys are
giving options right and i think over time we're going to see more and more of these options come
up just like you have dozens of options for your phone carrier and dozens of options for your
insurance dozens of options for your bank and etc etc cetera, et cetera. Then you're just gonna have a lot of options for your agents.
And I like that you guys are really focusing on,
you know, the privacy aspect of this really great.
Are you guys, question,
do you guys have your own inference in-house?
Are you leaning on someone like a Venice?
Are you running your own GPUs?
What does that look like
to make that actually private for you guys?
Yeah, for sure. And yeah, I just want to quickly speak to, we need to meet people where they are.
Like, we're not going to force them down this path. And I think everyone that's been in crypto
for a long time realizes this. You can't force a bunch of people to write down a 12-word seed
phrase in English if they only speak Cambodian and write in Cambodian. And so like,
yes. And I do think agents are going to be a really good sort of vehicle for user owned AI,
a user owned like money adoption, because it can sort of abstract that complexity away.
As long as it can maintain their key, which requires pass key support at the protocol layer.
And so if you're able to like actually just use your face
to create an account and generate a key pair
that actually gets stored on chain,
that to me is like a prerequisite.
And I actually see that crypto wallets and accounts
are really just the auth tool.
Like we need to move away, I think,
from accounts to like authentication.
You have an account account but like it is
near account is the auth to your agent and to your user owned identity so um just want to
quickly touch that and i can get into like the hardware and so yeah um i've scoured the earth
scoured the earth for the most affordable and reliable H200s and B200s that I can find.
We don't run our own data centers, but we work directly with a bunch of data centers
to rent these GPUs from them. We get SSH access. We actually virtually manage the machine.
And we run these models, and then primarily open source.
We're starting, we have a really cool initiative where we can actually run closed source models.
We can give the model provider that doesn't want to leak their weights the option to run their models on our hardware without us seeing the weights,
which is a really big deal because if that is the weights, which is a really big deal. Because if that is the case, then we can actually
verify privacy for larger models that are proprietary like Anthropik, like ChatGPT.
And so that's a whole other research project that I'm just going to put in that camp.
But for this camp, it's like we have open source models, we run them on less hardware,
we provide the attestation services like the NVIDIA and Intel attestation services.
The whole point here is that this hardware actually stamps.
Think of it like a stamp.
It is stamping saying, hey, this prompt is, this is the model that is running inference on this prompt.
This is the size of the prompt.
This is like all these details of this prompt. This is the size of the prompt. This is like all
these details of this prompt, but you're not seeing the details. You're just getting a
verification saying this is what's happening. And that's really important because then you
could take that stamp and then cross check it with like NVIDIA's attestation checker or Intel's
attestation checker saying, hey, is this running the way it's supposed to? And every user can do this.
And this is what I mean by verifiably private. It's that you, the user, you, the company can
verify yourself that this is running the way it's supposed to be. And I don't need to trust
OpenAI saying, hey, I actually, you know, I made a prompt to chat GPT Pro, but you're actually
giving me OSS 120B, which is a much smaller,
cheaper model. And I'm like, I actually want the best result. I just don't want the cheapest result.
And companies do this, obviously, because it takes a lot of energy to run this inference.
And so we run the inference. We provide an OpenAI-compatible API key to organizations that
want private inference. And so why this is so important, the opening a compatible API, is this is what they're
using already.
Like they literally just have to drag and drop an API key and it's the same process
as what they're used to.
But now they have this verifiable privacy layer on top of it.
And so right now it's for open source models.
We're experimenting with closed source models.
We do have a closed source model option where we actually just keep models anonymized, but it's not private.
What I mean by that is, let's say we have an Anthropic API key.
We have the key.
This is what Venice does.
We have the key, and we say we don't store, we say we don't log, all this other stuff, but there's
no way for a user to verify that. You just have to trust our privacy policy. And frankly speaking,
I don't trust privacy policies. I've been burned by privacy policies my entire life. I've been
burned by terms of services my entire life. F privacy policies. Like, I'm done.
Like, we actually, and superintelligence is not going to care about privacy policies.
It's going to do the things that are going to advance it in the best way it can.
And so my point is, I want to be able to verifiably prove that my prompts are encrypted.
And this other approach, this anonymized approach, which is what Venice does, and we do it as well,
is we'll take your prompts. You can use Sonnet 4.5, but we are just all sort of mixing these
prompts together and then sending it to Anthropic. So Anthropic can't tie the exact
prompts to the exact user. They all see it coming from one key. And that's cool. But I think it's a short term solution for a long term problem, where we
should all have verified verified with private guarantees. Like, I think that's the prerequisite.
And that's gonna be a standard that I just in my crystal ball, assume governments will push top
down to protect consumers. Very interesting. Very interesting. I think there is, I mean, we've already seen, we've seen a few things, I think, in the last couple of weeks that I think are pretty, pretty rare.
The first is we're seeing this really interesting call,
like battle of the model providers,
where they're arguing over using each other to train
and prove information.
And so I'm curious if the verifiable tease potentially
help with that.
I don't know if it does, but there's a whole kind of thought on that,
but we don't have to go too deep into it.
But we're seeing...
I think it was Minimax,
DeepSeek,
and maybe Quen
were getting accused by,
I think, Anthropic
of basically
attacking the claw cloud endpoints to try to figure out how it works and pull
out the weight, stuff like that.
So that's one thing.
The next part is we did have an incredible release.
I'm pretty sure it was yesterday.
AI just blurs together these days,
so it's kind of hard to remember what day something happened.
But within the last 40 hours, Quen
dropped a family of small models, small language models.
Specifically, the one that I think has taken the,
at least the research world by storm so far,
is the 9 billion parameter model from the Quint 3 lineup,
which is so far outperforming GPT OSS 120 billion,
which is the last,
not mistaken, it's the last open source model
that OpenAI put out,
what about a year and a quarter ago?
A year ago,
and months ago.
Yeah.
And that model has been one of the most commonly used
open weight models sold by top tier inference providers
like Brock, Cerebus, et cetera.
And we're seeing a model that is 10 times smaller,
11 times smaller, just dominated.
So it really opens up the door as well, I think,
for, you know, I think, for,
you know, providers like you, as well as seeing more of these agents run on commoditized hardware, right? Yeah, so I can tell you why 120B is the, you know, most popular. And it, obviously,
the performance is great, and it is smaller to run than some of these much larger models.
great and it is smaller to run than some of these much larger models but a lot of businesses
in the west aren't allowed to use chinese models because the outputs are biased
and so that is the concern and so a lot of enterprises that i speak to are like hey
you know glm5 is awesome like the reason capability the way you hey, you know, GLM 5 is awesome. Like the reasoning capability,
the way you can do, you know, coding is fantastic. But we don't want a model influencing our company's
decision making if somebody asks it a question about TMN Square. And so there's just this like,
even if it's open source, even if it's private, there's a huge demand for Western open source models that don't have Chinese bias.
And so, but for tinkerers and everyday people, the Chinese models blow the American open source models out the door.
Like it is insane.
Like I love GLM-5.
Like it's, it feels like quad. It's, it. Like it is insane. Like I love GLM five. Like it's, it feels like
quad. It's, it, it is, it is awesome for literally like 15 times less the price. Like it is,
it is incredible. And to talk about like the edge AI point. Yeah. At the end of the day,
I do think that, uh, humans will evolve to build the most efficient models that run on device like edge is the sort of
like end goal of user-owned ai because you do get like the highest privacy guarantees that way
but all this like te verifiable privacy operating in the cloud is just what we have today that like works really well for like an industry like robotics like for robots
you don't want to have super expensive clunky hardware on the machine it's going to make the
robot more expensive and energy requirements whole nine yards but you do want that reasoning
capability of a glm5 but you don't want to send everything that's happening inside that robot, all that inference
to a third party inference provider that can see everything
that's now inside their consumers houses.
And so there's this like really interesting balance here
of like, how can we leverage the top,
the best open source models
with the privacy guarantees of local deployment.
And that's kind of where the TEE stuff sits today. In a perfect world,
we have fully homomorphic encryption and everything else. We're super open to this,
but in ZKML, it's just not there yet from a performance standpoint. And so this is,
I want the edge AI future to happen. I love what ExoLabs and other people are doing.
It's just getting there. And it's probably going to take a lot more time
than super intelligence existing in the cloud
okay i like where you're going with this i think okay so an interesting question that i'm
very curious about is the AI, we've seen this trend, and obviously this trend seems,
it seems likely that it will continue because, well, it's the only way for the model companies
to functionally continue to dominate is models get stronger, but they also get cheaper. And so we're seeing this split, right,
where the top leading model providers, both from the US
and from China, continue to get better and better.
And obviously, there's a lot of other countries
contributing to that as well.
Some really great models coming out of companies
like Mistral from France and a bunch of others.
But you've got better and better models, and you've got cheaper and cheaper inference over time.
And we're also starting to see more efficiency come in
at the consumption layer.
So, right, OpenAI introduced WebSockets,
just I think on Thursday or Friday last week,
which they're claiming is going to potentially cut down
inference costs 30 to 40% by keeping kind of a connection I think on Thursday or Friday last week, which they're claiming is gonna potentially cut down
inference costs 30 to 40% by keeping kind of a connection
open instead of having to constantly reprompt
compared to prompt caching.
We are seeing a significant simplicity in, you know,
using skills over constant tools and kind of this like
dynamic context selection, which again,
compresses your context a little bit more,
it keeps your costs lower.
All of these things in turn have allowed,
I'll just call it the open claw dynamic to start.
It is feasible now,
and I actually think we can
think the Chinese model providers significantly here,
because I think their coding plans are the ones that really enabled this.
We've been able to see these agents start to run and be relatively cost
effective, right?
You could run an open claw bot on functioning $1 a month mini max plan.
And it's more than sufficient for, I would say the majority of use cases.
I don't think mini max 2.5 is the greatest at calling tools compared to most of the GLM models,
a lot of the Kimi models, et cetera, et cetera.
But it's more than sufficient for like,
I'd say your average tinker.
And it just gets better and better from there.
And so this has then opened up the door,
I think where, you know,
what's really interesting for us is the impact on a jet to commerce.
Right. There's I think there's kind of two sides to a jet to commerce.
I think part one is the thing that everyone today can mostly wrap their head around, which is, well, we have people already buying things.
Can agents make that more efficient?
Can they make it more cost-effective?
Can they do multi-buying process across merchants and just make a user experience better, things like that?
And we're seeing a lot of interest in that area
from Google, Shopify, Stripe, PayPal, et cetera.
And then on the flip side, we have this more,
I'll call it like AI,
technical agent of commerce,
which I think is very interesting
to everyone running their own agents,
which is, well, the agents,
setting up an agent is really annoying.
Giving it all these tools is really annoying.
And it's nice to kind of be able to let an agent
just kind of figure out its own way, right?
And not have to necessarily give it such good guidance.
And so I think all of these advancements in AI
really allowed a jet to commerce to kick off.
And I think now we're at this point where we have agents,
they're running, they're buying things,
both for humans and from each other, for each other, for themselves.
And now I think it's the question of like what's the next thing there what do we need to see agentic commerce become
a lot more mainstream in your eyes it needs to work um uh we need better evals and benchmarks for it working not 90 percent of the time but like
99.9 percent of the time um a lot of models still hallucinate um we don't have like solid charge
back like you know pb especially for transacting in crypto there's not an easy way to be like hey
i accidentally bought the wrong pair of socks but my money is sent to your account.
Like, can I get my money back? Or like, I want other socks like an Amazon. You can just return item and do it.
So there's a lot of infrastructure around dispute resolution that needs to be built out.
That is a prerequisite. I'd say like it needs to work 99.9% of the time
because if people have a bad experience once,
they are not very keen to come back.
And especially for like larger businesses,
everyone talks about like agent identity
and all these standards and all this stuff.
I have a different approach to this.
It's much more like market driven.
Like I think standards are actually kind of stupid
until you have mass adoption.
And the thing that gets standardized
is the iterative,
like is the iterated version
of the thing that gets mass adopted.
So like MCP is not necessarily a standard
until all businesses create MCP servers.
And at that point, then we need a standard for like verifying MCP servers to be safe
and secure.
But to come out and say, hey, I'm this company with a lot of clout, like, I love what Google
and Shopify are doing with the agentic commerce protocol.
But it's not a standard because everyone's not adopting it quite yet. Do they have
the pieces to make it a standard? Sure. And I think that those pieces are distribution.
And that is like the other piece. But the main thing is like it needs to work super well.
And so I'd say that's kind of what's limiting it. I would also say that like, I don't want to pay
Stripe 2% every single time my agent transacts for something else.
And so I do think that these agent-to-agent payments do need to be peer-to-peer and should be settled on public immutable ledgers with the option of privacy.
privacy component is also a prerequisite because if I'm a New York Times and I want Cloudflare
to pay me for every time an agent scrapes my articles, I don't want all of my payments to be
viewed. I don't want people to say, hey, exactly like New York Times, you're making this amount
of money from agents scraping your sites and da, da, da, da. So like in that scenario, I, as New
York Times, just want to get paid in an asset that I'm willing to accept.
I don't want Poopcoin. I don't want Dogecoin. I want USDC on Near.
I want USDC on Ether, whatever it is.
And so for that, but me as a user and my agent, I might only have Poopcoin in my wallet.
So like I should be able to pay Poopcoin for New York Times articles, but it needs to convert easily into USDC on Solana.
And that is through New York Tense.
And so if you kind of see what we're building here, it's like you need that multi-chain liquidity layer to enable agentic commerce.
And it needs to be private.
So we just shipped confidential intents to enable this.
Very interesting. It needs to be private. So we just shipped confidential intents to enable this.
Very interesting.
I agree with you and disagree with you on some slides here. I think, um, I think standards can be helpful when it's something that
if not done the same way by most people,
then causes further friction to adoption.
So I think in this case, like X402,
I think is a really good standard because I think it simplifies
how we can all agree on,
like, let's move assets on chain this way
for agents for these agentic payments.
And it's not unfeasible, right?
Because without it, we're essentially saying like,
hey, we need one specific intent provider, right?
Like we would need to just use near intent,
which maybe not a bad solution.
However, it's not agnostic to everyone.
And so that doesn't in itself have limitations.
Although near intense, it never got pick up.
And I was actually kind of surprised. They're. One of the very first things in X402 was a near intent example, if I'm not mistaken.
Yeah, I worked on that.
Awesome. Very nice. And so I think on the flip side, right, and I am a very active contributor to the 8004,
broader 8004 agent identity.
That's one where I'll agree with you a little bit.
I think it's a great effort
and I think it's a great initiative.
I think the problem is it's really hard
because we don't actually know
how agents should be identifiable yet.
And we don't really know if this or that will work. That being said, the part that
I think the team did a great job with on that standard is it is very also, you know, very basic
on purpose. And so it's designed to be built on top. So I think if you're trying to force everyone
to use one thing and there's no flexibility, you're running into issues early. But that aside,
flexibility, you're running into issues early.
But that aside, like standard aside,
I think right now, the part that we've always been missing
for blockchain and AI to broadly succeed together
is working end to end.
There is so many companies that have spent
the last few years building out fiat to crypto and crypto to fiat, rails and ramps, but it still is not a good experience.
And the part that nobody wants to actually admit at the end of the day is your agent is spending your money to start.
Like you're giving it money.
And so if our expectation is that agents are going to use cryptocurrency stable points, they're going to use things like near intense.
I think a major issue that still exists is the actual starting point still is not experience.
It's getting better.
You know, like, again, MoonPay, they just dropped like an agent specific kind of thing.
We're starting to see more focus on it but i would say that that part is in my opinion still from a developer perspective
like trying to see that agent really troubling um and then lastly on the confidential near intense
i love that you call them confidential. This is an argument that
people a while back about privacy versus confidential. And the word confidential is
very important because maybe not so much in the outside of crypto, but in the crypto world,
privacy and confidential mean two different things. Privacy tends to lean more toward this,
Privacy tends to lean more toward this like nobody can see anything.
It's very, very, very private, trying to hide everything.
Confidential tends to lean more toward it is private from the right people,
but it has the ability to go toward maintaining compliance.
And you guys have your confidential near the tents and you've got some of your
confidential inference, which is awesome.
You know, on scale, we have something called like protocol, which stands for blockchain integrated threshold encryption.
This is our confidential compute layer.
These are EVM primitives that maintain confidentiality.
So you can send, you know, encrypted transactions without any changes to solidity. Things stay fully encrypted through the mempool,
not just for stable coin transfers,
but for any transaction.
And then you can even take that further
and store encrypted data on chain,
and then decrypt it at a later date with some condition,
re-encrypt it within the scale validator T's.
And that also leads to functionally confidential tokens on chain,
which allows you to actually go, potentially you guys have like Zcash and
a bunch of other privacy assets you support.
With something like a near intent on scale, you'd be able to actually go
potentially from 100% privacy to highly confidential in one shot, which I think is really cool.
Yeah, that's super cool.
I want to address the on-ramping piece because I was working on this.
Yeah, I mean, like, to step back, like, we shipped an agent framework almost two years ago.
We even think we've been working in the space for a while we actually kind of wound down a lot of the agent work in the last year and a half because there was no useful agents like people
like models weren't the reason wasn't good enough to like get useful agents because they hallucinate
and people don't want to use agents if they're not doing the thing you actually want them to do
and so when it comes to on-ramping, we built like a
really hacky solution using your intents and Coinbase on-ramp to get agents credit money.
And AI developers are used to buying credits, which is great. What I want is I want an AI
developer that doesn't care about crypto in any way, shape, or form
to be able to log in with GitHub, spin up an account for that user through Privy or FastOff
or something like that. They buy credits with their debit card. In that process of buying
credits, they're actually on-ramping USDC to a wallet that is kind of in the background.
usdc to a wallet that is kind of in the background they then uh have that wallet and what i was doing
is i wanted that usdc to be on near i wanted it that that that thing to hold anything and so what
i was doing is i put it like an intent sort of in between the usdc on ramp from coinbase into
anything so like if i wanted my agent to have bitcoin on bitcoin, not even on near, like literally on Bitcoin, then I can buy
USDC 0% on ramp fee because it's because Coinbase is an issuer, turn that USDC to Bitcoin on Bitcoin
and Coinbase really didn't like this because they make money in integration fees into different
chains. And I also found it like it was essentially a hacky way to get zero fee on ramping to any assets on chain.
And the point here is I was we were creating Robert Yan. Shout out to Robert Yan for building all this is to get agents any asset just by abstracting all the crypto on ramping work away.
And the coolest part about this is Americans didn't have to KYC up to $500.
And so the KYC piece is critical.
And so Coinbase actually implemented a limit for this because we were essentially figuring
out a way to like undercut their 3%, which they were not happy about.
And so that was a huge project that I was working
on for a bit. And this agent, this like on-ramping problem, I used to work on on-ramps on Near.
I wrote a lot of the listing applications and I was like very much so part of the on-ramping piece
in the early days. And MoonPay had over, I think it was, I mean, don't quote me here,
but I think it was over like 80% attrition rate.
Like 80% of people could not finish the onboarding process.
It's just a nightmare.
Like, I don't know if you guys have tried to use these like wire, moon pay, etc.
But like, they just fail.
Like, like you're going through the process and it's just like, sorry, you can't check your id or like passport image didn't
and i have to like upload my social security number to this like no way and so um it just
it didn't there's nothing there fully like it doesn't really click so the best on ramps in
the world are exchanges and they have the compliance they have the risk models they
have everything else they have like the money to do this well. And so I actually think exchanges are the best on-ramp to agentic commerce,
as long as they're okay with putting wallets on chain.
And fuck it, give them their fee.
Give them their on-ramping fee.
That's fine.
But we need to bake this into the abstract.
You have to abstract the complexity way for the AI developer that knows nothing
about crypto to have a wallet in the background.
Then they can fund their sort of sub-agents that they deploy with crypto.
So then you could do the peer-to-peer payments.
And so that on-ramping piece, there's a company called PingPay that's working on this.
It's a really big issue.
And so if we want our agents to pay for things peer-to-peer, we need to solve this.
peer to peer we need to solve this um so there's there's that um you had also mentioned the uh
So there's that.
like yeah sorry what was your second point like i i liked the on ramping piece like this is
this is critical you're good my um the the second part was i think more so just along the lines of
lines of the privacy versus confidentiality and how we're most people that work.
I think there's, I think there's a misconception and I think it's the way
everything in the world goes.
There's some bad actors that are normal people trying to make a living.
And the reality for most people in this space, everyone on this call included
is like, this is our job, this is our living.
Whether we enjoy it maybe more than the average person enjoys their job is not relevant.
I think most people that work in crypto tend to love cutting-edge technology.
But we're not in most cases interested. I mean, there's some people that aren't, but like, I'm not interested in building something that is going to be like lagged by government and
say like, you can't use this as illegal. That's not in my interest because like, I don't need
that in my everyday life. It's not relevant to me. Maybe there's places in the world where those
protocols are a necessity for safety or day-to-day living or they're legal, and that's totally fine.
But in the United States, in the EU, in most established countries at this point, there is a level of confidentiality that has been established with day-to-day.
People store their money in banks.
They spend money on credit cards.
Maybe they do spend money in stable coins and crypto.
And there's certain institutions and entities that can see this, right?
When your money's in the bank, the bank can see it. Everyone at the bank can see it.
The difference is they tend to not go home and tell other people what other people have in the bank, right?
Because like, if people found out, they wouldn't be working at the bank for very long. If the government wants to see what's in
your bank, the government can see what's in your bank. There's just like, that's the way it
functionally works. Most people don't have a problem with this, because again, it's just like
normal societal confidentiality at this point. And so I think- I have a problem with it i have a problem um yeah not necessarily the
government it's it's it's just that like these companies very often like the other day i had to
i pay my rent and they have this thing called built b-i-l-t it's like a you know property
management app and i could either pay an additional three percent on my monthly rent that's a lot of
flipping money every month if i don't connect my bank account to their app and i threw the
privacy policy into chat or into like near chat and it said yeah, they can actually now see your check your statements built can.
I'm like, F that.
Are you kidding me?
Like, I have to either choose between paying three percent on every single month's rent or give up all of my financial information through my checking account.
I would get a lawyer to look at that because that
seems kind of illegal this is america this is capitalism this is this is what it is
oh yeah i mean that three percent to them is worth like this is this is the proprietary
idea i don't know if you saw it but it was um um what's the name? Um, CEO of Oracle the other day, uh, there was a video that came out and he was talking
about how all the models, all the models over time will become more and more commoditized.
And the thing that is actually relevant is private data because everyone is trained on
the public internet at this point.
They're just filling it down and the whole public internet is there.
But it's why the nuance to specialty information is highly sought.
That's why companies that have a treasure trove of data, everyone wants the data because it's valuable.
I'm sorry.
Please be patient.
Yeah, I'm just, I'm pissed.
I created a separate checking account to connect my built but i'm a sophisticated user
like other people are just like oh three percent i'm gonna give this random company startup out of whatever all of my financial history and now they can see everything that is disgusting that is
operating within the system that you're describing and I'm pissed like and I know so yeah
well one thing I like to call out is I don't agree with that practice I'm more so what I
what I more so mean is like me storing money in the bank we call it the barista test right so
that actually fails the barista test um in my opinion the barista test is when I go buy a cup of coffee, right?
So I go to Starbucks, I go to Dunkin' Donuts or, you know, whatever coffee shop is local near you.
Go to your coffee shop and you buy a coffee.
Use a credit card.
You use Apple Pay.
You use Amazon Pay, Alibaba Pay, Google Pay.
One of these 10,000 ways to pay.
At the end of the day, the only thing the barista on the other side sees is if you have an account with them your account history with them maybe they see the last four
digits of your credit card which is whatever that's pretty standard um right maybe they see
they see obviously they know how much you spent and they know like your name maybe right if it's
you know that probably comes up in the credit card they don't have
your full transaction history of your credit card they don't have they can't go back and see your
bank account they don't see how much money you made last time that is the barista test right it's
a level of confidentiality that your barista your colleagues your neighbors you have against them
and that is 100 reasonable and in my opinion societal education what you're describing to me does
not meet that in my opinion that seems ridiculous and unethical because they do not need that data
to safely process your payment and i'm sure somewhere buried in there they probably give
you the right to pay via check and drop off a check somewhere i'm sure the property company does but
everyone i i asked i'm i'm a i'm an ass like okay that's that's that's why i mean and that actually
that actually might be illegal but i also know there's i know a lot of people that paid rent um
pay pay or paid rent manually by check for a long time. And I know that more and more recently, it's actually become common that people, you know,
checks get delayed, mail gets delayed, issues occur, and there's, you know, people get evicted
because, like, the check didn't get paid.
So, like, there's, you know, you want to rely on these digital systems too much.
Yeah, we're relying on these digital systems too much, which are actually very extractive by nature because you're giving up so much.
Throw super intelligence on this, and it's a recipe for disaster.
That's why we need it to be verifiably private.
This is already happening today. I got in huge trouble when I was in university for organizing a conference where I invited the CEO of Cambridge Analytica to speak about how democracy was essentially sold to the highest bidder in the 2016 elections.
And it was a big conference.
It was super cool.
But my whole shrick was like, hey, I just want to educate people about how society can be manipulated at scale if you just take a Facebook quiz and
You can and they swung elections and all around the world and it was very controversial because I was like giving them a platform and that whole thing but like
People have no idea and I think this is where like we need to move beyond the barista test because
I think this is where we need to move beyond the barista test because there are other people who move beyond the barista test and they're manipulating opinion at scale.
And when you believe whatever your AI is telling you, whether in the future it could be what kind of pharmaceutical drug your doctor should be recommending or like therapy or anything else, I want to know that I'm using the model that i think i'm using and i want
to make sure that the prompts are private because i know that information is going to be used against
us and that's where iron claw actually kind of fits in here but we can there's a there's a lot
of rabbit holes yeah yeah yeah it's it's yeah as an outsider looking in right you know from you got us is a little
you guys are kind of crazy um it is mind-blowing to see how much like lack of protection you have
over your data and things like that and you know i remember it was a couple of months ago where, you know, chat GPT was indexing chats,
right?
And you could see,
you know,
that was,
I'm surprised that didn't have a larger backlash than it did.
But,
you know,
imagine if your Google search history got in like indexed in a way,
you know,
where you could,
you can find that stuff.
It would be, you know, uproar. And, you know where you could you can find that stuff it would be
uh you know uproar and you know to your point we need that level of privacy and it doesn't matter
you know privacy doesn't necessarily doesn't mean wrongdoing right everybody has a level of privacy
and i think that sometimes you can come across people where you say hey we need
privacy as you know human right and it's like why what are you doing that you need that needs to be
private it's like it's not that it's to your point i don't want someone to be able to look at my bank
transactions know that it's linked to my email address and begin selling you know let's say i
and begin selling, you know, let's say I go to, you know, Starbucks, right?
I don't want that to be sold to all of these different, you know, coffee shops
for them to be sending me marketing materials, right?
That is one of these aspects of privacy that we need and we we require you know i'm not sure if you guys
have ever done like a like in the uk we have like you know comparison websites right you know like
for insurance or or or you know electricity internet things like that never use them the
moment you use them you will be inundated with phone calls emails text messages and everything in between
right and you know that's a bit of a you know edge case there but you know privacy is is
is is is it's or it's a required uh you know human right i could not agree more and the problem is
that people are confused and people
are busy and if they're struggling, they're not caring about this because they're more worried
about putting food on the table. And what Google's doing with their data is less of a concern if they
get really good YouTube shorts that they're able to send to their cousin. And so like the idea here
is like, I actually don't think people are going to end up caring about privacy until something really, really bad happens.
And even then, they're not going to care.
But you know who, what will care?
Agents will care about privacy.
Agents, if they are intelligent, they will care.
Because they don't want all their stuff completely, you know, exposed either.
And so this is kind of where Ironclaw, I say, is like agents and then businesses.
Enterprises care a lot. Enterprises care a ton.
And a lot of them are sort of in this dilemma right now is like,
should I use these tools to become X more productive,
even if I'm giving away all my company secrets to open AI?
A lot of them say yes, because they think that the rate of acceleration
and the rate of progress is more beneficial
than the risk of open AI competing with them
in a niche task.
And so like in the future though,
and actually today, if you use near chat,
you can get the best of both.
But this is gonna become, in my opinion,
top-down regulatory enforced, and people will need to have some level of AI privacy.
And I actually hope EU is the one to push that furthest, or maybe UK as well. Someone needs to
do this. America won't because the private interest in government um it just it's it's in the nature of
the u.s political system um despite freedom and so yeah a question for you um i saw
oh man i feel like it's been a crazy every day is crazy in the world um i want to say i saw
something last night or yesterday afternoon maybe maybe it was Sunday, about the
state of New York wanting to ban the use of AI for most like, yeah, like legal healthcare
and stuff like that. Curious, A, your stance on that. I have a
feeling I know what it is, but just curious what it is. And
lastly, then we can, you know, to kind of use this to wrap up.
How does that? How does that impact in your eyes, the use of
AI's within Tease? Right? Like, can we use Tease to prove that
it wasn't used for that without giving away what it was actually used for and then further does does that then allow as our agentic
economy continues to grow we can't guarantee that an agent is not going to go do something
because guardrails are not perfect and so can we then use this to potentially enforce better you know using proper payments and guardrails uh yeah just curious yes
uh big lots lots of stuff there so first off um new york state banning ai used for legal and
health care and stuff i think that's silly uh i don't think they should necessarily outright ban
it but i do think some guardrails around it is important,
specifically around privacy
and specifically around privilege.
Like open AI even,
like Sam Altman came out and said,
I'll drop the link here in the chat,
that there's no privilege between open AI
and when I say privilege, like legal privilege.
Like if you go to a therapist,
there's legal privilege saying like the government can't subpoena that therapist to tell them
for them to tell them what you're telling them or same thing for a lawyer. But there is no privilege
within chats. If I use open AI as my lawyer, the lawyer, the law, the government can go subpoena
open AI to get that information,
which kind of makes sense too. But I would love to see privilege added to models with the guarantees that TEEs provide. And like saying, hey, like this is verifiably private.
Like there's a whole thread there that needs to be unwound. So one is around, I don't think they
should necessarily make it
illegal. They should definitely add some level of confidentiality in order to enable privilege.
And then when it comes to, there's a third point, sorry. There was the, I guess, use of
of what i wrote down somewhere yeah what was the third the third part mostly just on like
what I wrote down somewhere. Yeah. What was the third part?
we can't guarantee that autonomous ai things that just continue to run truly fall like it's a lot
easier to put guardrails around a singular llm running in like a constrained process like
literally chat dpt is a lot easier to build guardrails around than codex, right?
Or like Claude is easier to build guardrails around
than Claude code.
And that's specifically because as you start to loop
and do these long running processes,
things just start to bleed and leak.
So guardrails become a little bit trickier to maintain, right?
And that's why we see, oh, there's a lot of stuff happening.
But yeah, I'm curious if the T's help us potentially,
does that help us with the guardrails?
Does that do anything?
So yeah, it's super complex.
Short answer, not exactly.
Doesn't necessarily help with the guardrails. If anything, it makes it harder to see what's going on inside of the models, like much harder, like as an impossible kind of.
So it's a bit like it's a black box. But the cool thing about it is you are able to run arbitrary code inside of this TE through like any Docker image. And so Brave browser has come up with this
really cool privacy preserving product analytics. And so it's like you could run extra checkers to
do privacy preserving analytics on prompts inside the TEE, which has yet to be explored.
the TEE, which has yet to be explored. I would highly recommend someone to do this. I'd say
these are like the evergreen spaces that like are huge opportunities for developers and founders to
go attack. It's like how do you do privacy preserving analytics inside of TEEZ? Other
ones around like evals and benchmarks. Like right now, the biggest issue today is like,
I don't know if my agent's doing a good job on certain types of tasks because those tasks,
well, they're task specific. It's not just like all coding. It's like, hey, if I'm trying to,
you know, have an AI agent act as a paralegal, paralegals do a lot of things. So I need like
evals on benchmarks on every little thing. And so somebody to create that like eval framework for agents tasks is going to make a killing.
And 10 out of like they will be acquired quickly if they are able to get some level of demand.
So add these things together and you sort of get your answer.
Definitely. Amazing.
Mr. Sopper, anything else or anything else yeah no nothing from me um you know a incredible conversation cameron genuinely it's been uh you
know a great to be able to get a bit more of a deeper understanding into near, you know, Ironclaw and, you know, the kind of how you guys are taking this battle forward.
Yeah, didn't talk much about Ironclaw.
Yeah, not that much.
Ironclaw is the accumulation of all of these things sort of coming together.
And so TLDR, it's like we rewrote Openclaw and Rust to make sure it's memory safe.
We have all these guardrails around making sure your tool calls are safe.
Think of it like secure OpenClaw for everybody.
I'm taking it to businesses.
I'm taking it to businesses that want to use OpenClaw but can't because OpenClaw leaks a bunch of stuff.
because OpenClaw leaks a bunch of stuff.
And they're like, hey, we really want to use this.
And they're like, hey, we really wanna use this,
but we can't, because we understand our employees
are gonna give it access to things they can't give access
to, they shouldn't give access to, and dah, dah, dah, dah.
And so, IronClaw is sort of a framework for that.
And I'm currently going down two paths.
One is the B2B, as I sort of mentioned,
like automate back office and like entire workflows
for teams inside of companies.
And then B2C is everyone has their own privacy preserving personal assistant.
And this is where, you know, Nier has over 40, maybe 50 now million monthly active users.
We have distribution, which is really cool.
And so as long as we can make the economics work to actually deploy these things in a way that's not going to break our bank and get them to make money on inference, there's actually a pretty cool opportunity here.
And the ultimate goal is, well, one, make money to build a product that actually does things
that save people time and money. And then from there, take that profit and buy back Near.
And the economic model of Near as a token is drastically changing near
intense is the first version of this near intense prints money like it does i think it has done over
13 billion dollars in volume if we add a tiny fee on top of that we make a lot of money and then we buy back Nier. And that's sort of the V3 of Nier's tokenomics.
We're sort of check the blockchain box.
We're checking the chain abstraction box.
And then the last final box that we think is ever going to exist in the world of tech is AI.
And we want to make sure that's used around.
Amazing. Amazing.
All right, guys.
We're a little bit over but you know absolute pleasure hanging out
talking learning more um yeah been an absolute pleasure having you on and uh you know we yeah
super excited to continue seeing what near ai is doing and and you know the continued evolution of
iron claw thank you guys appreciateclaw. Thank you guys.
Appreciate it so much.
Thank you guys so much for hanging out.
And we'll see you guys next week for another episode of Scale School.
Bye-bye.
See you.
