Trust is a function. It is more than just a feeling. In a world of infinite noise,
the only signal that matters is the one you verify. We are here to map the graph,
order the protocol, and build the immune system for the decentralized web.
Welcome to the signal processing layer.
This is Say What?
Welcome back to the fourth episode of Say What?
Today we're decoding the critical intersection of artificial intelligence, privacy, and the web of trust.
My guest is Mark Suman, the CEO of Maple AI and a former Apple engineer who left the
walled garden to build verifiable privacy-first AI.
We're going to explore how open source models operating inside secure enclaves can protect
our digital sovereignty.
We'll also examine why we need robust reputation systems as AI agents begin transacting on
our behalf.
Remember that this frequency is powered entirely by value for value.
If this transmission gives you a new perspective, consider sending it back.
Boost, zaps, and streaming sats are the only fuel we use to keep the signal clear.
Let's get into it.
Mark, welcome to Say What.
Hey, thanks for having me on. I appreciate being here.
Yeah, absolutely, Mark.
So Mark, you are the CEO of Maple AI, as I mentioned.
For folks who might be unfamiliar, can you give us a little bit of a background on Maple AI?
Sure, yeah.
Where to begin?
So Maple is a mainstream AI, but one that was built with privacy first, rather than trying to bolt it on afterwards.
So if you want to imagine what it's like to use Cloud or ChatGPT, we have a very similar product.
But instead, every single user starts off with their own private encryption key.
And all of the data is encrypted locally on your device first before it is sent to the cloud.
And then once it's in the cloud, we use confidential computing or secure enclaves to decrypt the data, have it talk to the AI, to the LLM, the open source LLM that's running in the cloud.
And then when it comes up with a response, it re-encrypts it and sends it back to your device.
So this way, we are not in the middle of your AI communications.
We simply are able to relay that information.
We're able to synchronize that information to all your devices.
But the only data that we actually can see ourselves as a company is the encrypted data.
Everything remains personal between you and the open source LLM.
And again, for folks who might be unfamiliar
with the exact mechanics of this,
and everyone uses an LLM these days,
Chagp, T, Claude, Gemini,
and they just talk to it and it does things for them,
researches things for them, and so on.
What problem are you solving with Maple AI
that an average user of LLMs should be paying attention to?
Well, we solve problems for two camps of people.
We solve problems for people who already use AI and LLMs,
but then we also solve problems for people who don't.
There's actually a large group of people who simply don't use AI.
There was a big survey conducted last summer,
and it was focused on the US,
but then they extrapolated it worldwide.
and it said that 39% of adults in America do not use AI at all,
actively use it, right?
Everybody uses it passively through different phone systems they call into,
but active use was 39% of adults.
And of those 39%, 71% of them cited data privacy concerns.
They don't want to give away their personal information to these AI machines.
So we've built definitely for that group of people.
We are onboarding new people to AI now who are finally using AI because they did not trust Google.
They did not trust OpenAI and Thrompic.
And now we've given them an option that they can verify the privacy guarantees we have.
So now they trust it.
But then for those, the people that you're bringing up here, those who already use AI, you know, what's in it for them?
Why use something like Maple?
the key differentiator is that you are able to when you know that you enter a room let's say that
you walk into a room with a person and there's there's 100 people in that room and you're trying
to have a private conversation with that one individual you're you're aware of all the other
100 people that can hear what you're talking about and so if you have something truly confidential
that you want to talk about it could be a legal matter it could be a health related matter it could
be an embarrassing story, or it could simply just be a joke or something that you don't want other
people to hear, you and that person are going to walk into another room and close a door.
And now you have this private space to talk and you can speak more freely. And that's really what
we're offering to people. You know, I make use of many AI tools. I really I use I use a quad
anthropic tools. I use chat to be to use Gemini for certain things. However, I also have Maple
and what our users tell us time and time again is that when they use Maple,
they're able to finally be themselves.
They're able to speak freely.
They feel kind of like this weight has been lifted off of them
and they realize that they were censoring themselves on the other platforms
because they knew that someone was listening.
They knew that whatever they said there was being recorded,
was being potentially used to train future models,
was becoming available to future government surveillance
or becoming available to legal proceedings that could be subpoenaed
and data could be gathered from these companies.
So people love using Maple because it allows them to really just be themselves
while talking to the AI.
You know, you brought up the analogy of going into a separate room
and talking to that person.
I think one of the issues
in general with the LLM space
is that person
maybe you've blocked out the other 100 people who can hear you
but how can you trust that person
and in this case that person is some kind of big tech company
so yes you're using a secure enclave
your information is encrypted
before it goes to the LLM
and then after it, the LLM generates a response.
But there is that period in which it is decrypted,
the LLM processes it, creates the answer and sends it back.
And that LLM might not be sitting in the best actors' hands,
namely these big tech companies.
So it doesn't fully solve that issue, correct?
Sort of.
So there are two different people operating services
or two different kinds of services you can use.
One of them are categorized as AI proxies. These are very similar to VPNs where you want to use Chat2PT, but you don't want to go sign up for your own account and give it a credit card and give your home address because then that tightly links your identity.
You might have to upload a U.S. passport, your government passport or your driver's license or some kind of identifier.
People don't want to get that close with their AI data.
And so they'll use one of these proxies, which allows a thousand people to go through the same account and then hit ChantyPT.
You get a little bit of safety there because your identity is no longer tied to the query that you're making.
However, that content that you are typing into the LLM is still getting in the hands of that big tech company.
And so if you say anything in there that could potentially be sensitive to you, it is now part of their database.
And they're using AI on their sides to build biographies on every single user.
And they are able, on the other end, to re-piece back together things that you said across multiple chats, even if you're going through a proxy.
We've seen papers published on this already that they have the ability to kind of sew those back together if they want to with high confidence.
What we offer with Maple is something totally different. So we take these open source models that have been published by ChatGPT, by DeepSeq or Quinn, Kimmy, other big ones, and then we host them in confidential computing servers that do not, they're not controlled by these other groups.
So none of our data goes back to ChatGPT or goes back to DeepSeq.
It's really similar to as if you went in on your laptop and you downloaded one of these open source LLMs onto your laptop and talked to it there with the Internet turned off.
However, people don't tend to have enough power on their laptop to run the largest open source models.
If you really want to get something that is comparable to the power of Cloud, you need to run it on a server, a cloud server, something of that size.
And so we have taken what we think is the closest thing to the privacy you get of chatting on your laptop with a local LLM.
And we've moved that into the cloud by using these secure enclaves, this confidential computing architecture.
Yeah, and that makes complete sense if you're using some of these open source models that you can isolate and contain in these secure enclaves.
and on your point of those
multi-tenant API keys
if you will
there's obviously Open Router
which I have to say Open Router
is terribly named
because they do full KYC
I've used them
but there are proxies that sit even in front
of Open Router like Pay Per Q
or Routester in our world
in the Bitcoiner world
which will certainly mitigate that
financial identification
And then I guess if you're using something like Maple AI behind that,
then it is truly fully sovereign at that point.
I guess the only thing more sovereign would be you buying some kind of Mac Pro
and downloading the model and running it on that, on your local machine.
Yeah, and I certainly won't fault people who want to have a completely sovereign stack at home.
It's commendable to try and do that.
You can get a Mac Studio, you could max it out.
You don't even have to go to Apple hardware. You could buy just a plain box and shove it full of NVIDIA cards if you want to.
The problem right now is that in order to run the biggest open source models, these trillion parameter models, you need to spend upwards of 15, $20,000 to get that machine.
And then you also don't know if that machine is going to be capable of running the next large model that gets dropped or if you're going to need more cards or something else.
so it's it's a cat and mouse game where if people want to run things locally they'll have to keep up
with that you brought up ppq and roster i've i've met the people running those and i think what
they're doing is great and um so i'm it's i think they're fine services to use uh it's it's just
people people need to remember that they're still sending their data um over to chat to t and claude
And so Maple has taken a totally different approach.
And I love that we have this open market with multiple solutions that people can choose.
And people can understand what is their risk profile that they have and allow them to pick the service that's right for the situation.
And so, Mark, I have to confess, I've only used the free version of Maple.
I've not paid for Maple just yet.
So I noticed that when I use it,
there are three models available.
Actually two, right?
It's the LAMA and OpenAI,
the GPT, the open source one from OpenAI.
For the free users, yeah, that's correct.
For the free users.
For the paid plan,
I'm actually looking at it right now,
I see a few that have the lock next to that,
meaning I have to upgrade.
is Gemma, DeepSeek R1,
Quinn, and Kimmy.
Are there others that get unlocked
as you go up the tiers?
We really,
we simply just hear it
by paid and free.
So if you become a paid user,
you unlock all of the
powerful models that we have.
And we're constantly watching
the landscape of open source models
that are available
and looking for what is
the next best one.
And we test it out.
And then when we feel like
gets ready and high quality enough, then we will put it into Maple.
So right now, Kimi K2.5 is the state of the art.
We are looking at GLM-5, which is a really capable model,
and trying to find a way to get that up and running inside of Maple.
But for now it Kimi K2 We still have DeepSeek R1 We have you brought up Gemma We have Quinn 3 Vision So you can do vision capabilities with that
Both Kimmy and Quinn are great at vision.
So you can do image analysis.
And we have document uploads.
You can upload a PDF of some legal contract that you have.
And you can chat with Maple about it and ask.
You can ask all the questions, all the legal questions you want to to Maple.
that you can find where the legal boundaries are in something.
And if you accidentally step over a boundary,
the AI will tell you, hey, that's actually not legal to do.
And because it's Maple, the conversation kind of ends there.
Whereas if you were doing that in ChatGPT,
you risk having your account suspended.
Or even worse, you risk having your account silently flagged.
And then in the future, if there ever were some legal dispute
over what you were discussing,
they can go the courts can go and subpoena your chat logs with chat gpt and say ah this person
was in here trying to get around the law look at all these questions that they were asking
so we we like to provide an environment where people can feel free to ask all the quote dumb
questions that they want to not because they're trying to do something nefarious but simply because
they don't know and they don't know sometimes where the boundaries are and they don't know
if the stove is hot.
And so they have to touch the stove really quick
to feel that it's hot.
And you shouldn't be punished for that.
You should simply be able to ask the questions
and get the answers and inform yourself.
So that's the type of environment
that we are trying to create for people.
So on the open source models themselves,
I think there was a brief window,
at least for me, Mark,
and I'm fairly plugged into this,
been plugged into this world for a few years now.
I think it was around January of last year,
a little over a year ago,
when DeepSeek R1 first came out.
And it kind of caught everyone off guard.
And there was a brief, but I dare say fleeting hope,
that maybe open source is actually ready to catch up
with some of these proprietary models.
because it was pretty down close to,
I think ChatGPT, OpenAI had 01 back then.
But then shortly after they released 03,
Claw 3.7 came out,
and Gemini 2.5 came out,
all of that stuff.
And then the gaps seemed to widen again.
And it appears to me,
and I'm curious to hear your perspective, Mark,
that open source models, even the best ones,
are at least about six months behind
some of the top-of-the-line proprietary ones?
You know, so a lot of it comes down to,
for lack of a better word,
it comes down to the vibe that you get
when you're using it.
If you use it and you feel like you're getting
the results that you want,
then a lot of times open source is already there for you.
Now, when we try to quantify how close they are,
we use different benchmarks, right?
Like humanities, last exam,
We have the software engineering benchmarks that people use.
There's a small portfolio of benchmarks.
And in the last three months, I would say since November, when Kimi K2 thinking was dropped on the scene, this is the one right before 2.5, their benchmarks were right up there with Sonnet, with Quad Sonnet 4.5.
and they were even beating uh chat gpt5 gpt5 and some of these other ones that were considered
pretty state-of-the-art at the time and then they were just barely behind something like opus 4.5
now obviously opus 4.6 is out and it's really running the world and the the new kimi k2.5
the glm5 these other ones that are coming out they're scoring incredibly well on the benchmarks
and are, I don't know that I would say six months.
It feels like the gap has closed to a matter of weeks sometimes
that they are now behind.
And we're currently all sitting around waiting for the new DeepSeek to come out
because there's been a lot of rumors around that.
And we think that it's potentially even a leapfrog moment.
I think we are going to see in 2026 a moment where open source,
an open source LLM hits the market
that is actually better than one of the proprietary ones
when it comes out.
And I think that will be a landmark thing to happen.
So you mentioned benchmarks, Mark,
and I actually want to talk about that.
So again, from my perspective,
there was an eight-month period
starting around September, October of 2024
when the first reasoning model came out.
I think it was 01.
from OpenAI to about May or June of last year,
which was a breathtakingly fast-moving time in AI.
I feel like we've actually slowed down a little bit
compared to that frenetic pace we were in.
And back then, the thing that was happening is,
you mentioned benchmarks, right?
These are different sets of tests
that AI models are expected to perform against
and then their score is measured, right?
These benchmarks were getting saturated on a weekly basis, right?
And when I say saturated,
that the models were hitting basically 99% or 100% performance
on these benchmarks,
and they had to keep updating them
with newer benchmarks over and over again.
The one thing that somehow seemed to survive to an extent
is the one you mentioned, which is humanity's last exam,
which, as the name suggests,
is intended to be the hardest test
that certainly a human being can take.
And I think it's something like a few hundred disciplines
across the arts, art history, philosophy, mathematics,
computer science, all of them, physics, chemistry.
You had extreme PhD level questions
drafted from the top universities, right?
So all of those questions across all of those disciplines
make up humanity's last exam.
I haven't looked at the latest recently,
but I don't think any model has breached 50% on that.
Is that correct?
Or have you something changed?
Yeah, so K2.5 is claiming that they have.
They're at 50.2.
I'm on the Kimmy website right now
looking at their benchmarks that they posted for 2.5.
So yeah, they say that they're at 50.2.
Now, the reason why I first led with my answer by talking about like the vibe that you get and your own internal testing of these agents is that are these models is that really it's a lot of these models can simply overfit for the benchmark.
And that's why a lot of them have just kind of blown up to 99% accuracy a lot because they do that.
But then when you use them in the real world, maybe they kind of fall over with real world tasks.
So these benchmarks are great,
but also sometimes we have to like check them
against the real world performance that we get from them.
But yes, on HLE, I'm looking at it right now.
And in this chart, they have Opus 4.5.
It was at 43.2%.
Gemini 3 Pro is at 45.8.
GPT 5.2 is at 45.5.
And then Kimi K2.5 is at 50.2%.
Interesting.
So do you think that the,
actually, what are your thoughts on humanities last exam?
I'll give you my view.
I think that's probably the closest one.
You're right that we need to somehow tie it to practical implications.
But absent that, I think just given such a broad-based test
across so many disciplines
is probably one of the better indicators towards our,
march towards AGI, if that ever comes.
And so, yeah, curious to hear your thoughts on HLE.
And if you think there's a number,
is that 80% or 90% on HLE,
that in your mind says, okay, this is basically AGI.
You know, AGI, that's an interesting topic.
And I don't have like a super strong opinion of,
or prediction, I should say, of when we're going to get there.
But the way that I view all this is these models are really,
they're really kind of the brain.
They're like the engine running something.
And when users are utilizing AI, they're not actually,
very rarely are they talking to a raw model, right?
They're usually using some kind of app
and they want the app to do something for them.
So they have some information.
They want something to transform that information into something new, a better output, a more valuable output than what they put in.
And I think that as each of these models get better, that people will start paying less attention to the actual model that they're using.
And they really are going to be focusing more on the user experience of the app that they're using and the output that they're getting from it.
It reminds me of when we used to focus so much on the CPU speed within our computers and 200 megahertz and then bumping up to 300 megahertz was like such a massive jump.
And you can do so much more on your computer by having faster processor.
But there became this point where it didn't really matter that much.
Most of it was good enough.
And we really focused on user experience itself and what are the things that I can do on this computer.
And so as far as like AGI is concerned, that is more of a user experience and a tooling thing where we're going to have to build something on the layer on top of these models that leverages this powerful brain to to give it what it needs in order to be general, be able to kind of think for itself and recursively learn more and and become more self-aware.
so I don't know that I have like a specific number
that I would assign like 80% or something
yeah I just haven't gotten to the weeds enough
on humanity's last exam to like be able to pinpoint
where I would put that number
so Mark let's actually take a step back
to your pre-Maple AI days
you were at Apple for a while
before leaving, presumably, to focus on Bitcoin and FreedomTech, right?
And then getting to Maple.
What was the catalyst that pushed you out of big tech and into building Maple
and getting into the Bitcoin world even?
Yeah, so I've been in the tech sector for, gosh, like 20 years now, professionally.
And I worked at multiple startups.
A couple of them were big cloud-based startups.
And I could see how the data was stored in the cloud and what we as employees had access to.
And a lot of times, me as an elevated employee, I was a software engineer, I would be brought into support calls or I'd be brought into a sales call.
And I would be able to go in the database and see highly personal and sensitive information that the user had uploaded to our servers.
and the user didn't know, they just didn't realize that I would be able to see that kind of stuff.
Now, nothing bad happened from it, but it really opened up my eyes that the cloud is truly just
someone else's computer. And you're copying your data over to their computer.
So then fast forward to when I was at Apple, I was a software engineer over there.
And we were building a lot of AI, machine learning technology.
Some of my stuff that I did was customer facing.
You know, some of my code is running on people's phones around the world.
But then a lot of what I did was for internal projects that were kind of a hybrid customer facing, but also internal tool.
And Apple is a company that does take privacy very seriously.
And so when we would start a new project, we would be paired up with a privacy, internal privacy advocate and a privacy lawyer.
and we would have to go through all the data that we're collecting.
And we had to kind of justify why we needed certain data,
how long we would keep it and all that.
Very, very, very interesting place to work.
Really enjoyed my time there.
But it got to the point where I really wanted to work in Bitcoin
and specifically focus on Bitcoin privacy
and trying to find ways to make Bitcoin a better freedom technology.
More accessible freedom technology, let's put it that way.
But right before I left, that's when the big ChatGPT moment happened.
And internally, we were all told, you're not allowed to use ChatGPT at work.
Don't put any work information in there.
And that immediately was like, duh, yes.
I remember all my cloud stuff that I did.
If I type something in and I put Apple proprietary information into here,
that is just copying it over to ChatGPT servers and their employees can read it.
So that really like resonated with me right away.
So I ended up leaving Apple to go pursue something in the Bitcoin space.
I teamed up with the guys that were doing Mutiny Wallet and we were going to work together to scale Mutiny Wallet much larger than it was at the time For multiple reasons that have been kind of discussed publicly already we decided it was best to wind down the Mutiny Wallet and try and look at what else could we build in the Freedom Tech space
And we had a lot of meetings with developers at the time talking about what are some of the needs you have and here are some ideas that we have.
And the thing that kept coming up time and time again is that developers wanted some kind of privacy around Chajapiti.
They didn't like that they were giving away all their data to other companies.
So that's when, you know, all these pieces kind of connected together.
My co-founder, Anthony, was really going into a lot of confidential computing technology at the time.
And we listened to these developers and we said, you know, this really could be a compelling product.
And that's where the concept of Maple AI was born was we need to build something that is the freedom tech alternative to the new Internet that's being born, which is this AI Internet, this agentic Internet.
But these other companies are following the same patterns all the other big tech companies have followed.
And that is gather as much user data as you can and monetize it.
whereas we wanted to follow the original internet business model which was create a service for your
users and have them pay you for the service you deliver the service to them and that's that's
where the relationship should end stop trying to monetize your user data stop trying to capture as
much as you can because that just makes vulnerabilities for your users it also turns
your user into your product and we just we didn't want to operate a business that way so
So it's kind of a long-winded, you know, that's the whole journey of how we got here.
But yeah, that's really where my Apple days and my cloud computing days merged together with Anthony
and all the stuff that he had been doing on Mutiny Wallet.
That's where we really teamed up to kind of give birth to Maple AI.
That's interesting, Mark.
I actually did not know that you were part of the Mutiny team.
That's cool.
Yeah, and real shame that Mutiny had to wind down.
so you said you left Apple around the chat GPT moments
I'm guessing that's sometime early 2022 correct?
I guess there were multiple chat GPT moments
but really like when chat GPT 3 came out
is when it's I think it hit more mainstream
so I left in 2024
2023 is when it became like a big deal within Apple
to talk about it and figure out
how are we going to safeguard ourselves
against our employees trying to use this third-party tool
that can become a big attack vector for Apple.
And Apple is notoriously secretive of the things that they're working on.
I've worked at government defense contractors before,
having to have clearance and be background checked and stuff.
And then I've worked at Apple for many years.
And the level of secrecy and clearance type infrastructure that's at Apple,
it rivals government contractors in some ways.
So it's very fascinating.
And is that the reason why the sort of focus on privacy,
is that why Apple hasn't made too many inroads into AI on its own?
Well, I'm not there anymore.
And, you know, I share what I can share of information that's public.
but I would say privacy is definitely one reason.
One big reason why Apple is not as far along
in the AI space as everybody else
because they do care very much
about trying to run things on device where they can
and if they're going to run in the cloud,
then they try to make it private.
And Apple's cloud infrastructure for AI,
they've been very public about this,
is running confidential computing.
It's secure enclaves.
It's called the Apple Private Cloud Compute.
And it's actually something that kind of helped us get more interested in confidential computing.
So we've built Maple's back end to be similar in spirit to the Apple private cloud compute.
So I think it's just a matter of time before Apple catches up.
I have read plenty of articles online that say Apple's problems with AI also come down to personnel and internal disputes and turf wars.
And maybe there's some management issues going on.
I can't speak to those personally things that I've seen, but those are articles that I've read.
So it could be kind of both of these things kind of happening at once that make it difficult for Apple to jump into the game.
Well, they have announced that Siri in the not too distant future, might be this year actually at some point, will be powered by Gemini, if I'm not mistaken.
So that will be interesting.
Yeah, I mean, Gemini is made by Google.
And if you think about Google and Apple, in many ways, they are enemies with each other.
In other ways, they work together.
And a lot of people, a lot of users who have chosen Apple, chose Apple over a Google phone for certain reasons.
So it's going to be interesting to see in the court of public opinion how that decision plays out once they actually launch it.
You know, back when iPhones used the Intel chip,
I think Apple's secure element was Intel's SGX,
which has its own issues.
Now, presumably with Apple Silicon,
the iCloud keychain and all of these other things
are in a secure element.
So presumably the AI model will run in that type of environment.
I don't know.
what they'll do on the edge hardware.
But I mean, yeah,
they do have their own secure elements that they do.
And so there's only so much I can say,
but yeah, I presume that they will do as much as I can
to try and secure things locally as well as in the cloud.
Yeah.
Yeah, sorry.
I can't say more.
No, no, no.
And I don't mean to put you,
I'm actually not sure what the edges of what you can disclose.
Yeah, that's fine. You're good.
So, Mark, before we dive into agents and reputation and so on,
I'm curious if you noticed this, because I certainly felt this,
and I'm going to go back to that time last year when I felt that things were accelerated.
The acceleration was absolutely palpable.
It's not as much now for me anyway, but back in that six to eight month period between October 2024 and May, June of 2025, it was sort of blow the skin off your face off type acceleration.
And the deep sea carbon was one, you know, like what is going on kind of moment.
But I think the real big one was a cascade of a couple of events or a handful of events.
One was, I think, around February of 2024.
OpenAI released Deep Research quietly, right?
It just came out.
And they started seeing these videos
of these 60-page research reports
that are produced in half an hour.
And a lot of people are saying, what is this?
Like, how is this even possible?
Like, what is going...
And it was like, true to its name, it was Deep Research.
And they said, oh, yeah, it's this thing called,
it's powered by O3.
It's our O3 model.
And we said, what is this O3 model?
And then shortly after that, they released O3 Mini,
which is the distilled model of O3.
And that was fine, right?
It was an improvement on O1.
It wasn't mind-blowing.
And then at some point, I think it was April,
they released the actual O3 to the public.
And to me, yes, you know, Opus 4.6 is great.
It's a little scary at times.
but that initially using that 03 raw right the full-fledged non-distilled model when it came out
was the first first time i felt scared using ai i said oh wow i can i can kind of see where this is
going and i don't know if i should be happy or scared uh there were others who had who shared
similar sentiments i think even jack dorsey at the time when 03 came out he posted on nostril
saying, okay, this is our first glimpse into AGI. And then something happened. About a month or two
later, O3 stopped being as good. It stopped being that scary good. Maybe you could attribute that to
people getting used to it. You know, just there's nothing new under the sun. You know, yes, it's
producing fantastic results. Within a month or two, you're like, okay, what's next? I don't think
it was that. I feel like
the raw O3
consumed so
much resources
that OpenAI wasn't able
to sustain it. This is just speculation
on my part.
I think they had to
tune it down somehow.
Either reduce the number of parameters
or whatever it is.
Reduce the amount of compute that went into
it. And by the time
May or so, maybe June rolled around.
It was fine.
It was like chat GPT 5.2 right now.
And ever since then, the progress has been incremental at best.
And so I guess, well, let me start with the first question.
Did you get that sense in that time period as well,
that O3 was like the first scary moment?
And then did you sense that it went down in its abilities?
It definitely was one of those moments that opened up my eyes. And we generated a lot of deep research during those times. It was a great model. Definitely miss it.
But that said, it's hard to really remember and quantify with what we have now because things have moved so much.
And I really think that what we're seeing now is this whole app UX experience going on, this user experience layer where these models have become powerful enough that now we're all focused on, well, what can we do with these models now?
Now it's not just what kind of information does it generate, but how do I hook these together?
That's where we start getting into all the AI agents stuff and all the other tooling being built around them.
So, yes, I share your sentiment at that time.
It was definitely an eye-opening experience and being able to generate a market research report in a matter of like 10 minutes.
You know, you hit go and then it comes back with a few questions and then you answer those questions.
and then it progress far goes for 10 minutes.
And now you have this large thing.
I'll be honest, though, a lot of the deep research that we generated,
we never actually read ourselves.
We would see it and be like, that's really cool.
But then you give it to AI and you're like, can you summarize this for me?
I just want the highlights from it, right?
Or what we would do is we would generate these deep research reports
and then we would feed those in as context to future things we were working on.
Now, I think that the tooling that you have and that we're getting
is that it will do that deep research on its own,
use it as its own research in a file somewhere,
and then it'll continue to loop and iterate on that.
And so you won't have to be in the middle for that part.
You just get to be orchestrating everything up above
and talking to it and getting the final result
and maybe tweaking it along the way.
Yeah.
But so that kind of ties into a broader question
and it's really a suspicion I have,
which is that OpenAI had to make it dumber
because it was getting too expensive.
And the broader question there is the cost of inference,
which I think is actually unsustainable.
And I feel like in the last year or two,
we've been living through the golden age
of venture capital subsidized compute.
All of these companies are running incredible losses
because, I mean, inference cost is just,
too damn high. There's no better way to put it.
I guess what is, do you see a solution
to that? Because if not, a possible future is
these companies go bankrupt before they actually figure out, before these AI models
really figure out how to build a chip architecture that reduces
inference costs.
Well, the cost really comes down to value.
as you mentioned at the beginning of the show, right?
Value for value.
It is for the amount that it costs to run the GPUs
and to give it tokens and get tokens out of it.
You know, how much did I have to spend on that?
And then what am I doing with that?
Is that going to be valuable enough for me to afford doing that again?
And like you said, the companies right now are definitely subsidizing the cost.
we are all getting sucked into this opus 4.6
clawed garden and we are we're we're
we're getting a really good taste for what it's like
and I was listening to Matt Hill talk about this from
start nine he described a very it a similar relationship to you know you you doing drugs right you like getting this taste of these drugs and you getting addicted to it But Anthropic is really subsidizing us
And at some point, they're going to have to flip the switch and say, now you need to pay full price for this inference because we can't afford to do it anymore.
The hope is, is they're trying to play the fake until you make a game.
The hope is that they can build enough tooling and enough apps and user experience around these models that by the time they have to flip the switch to full price mode, that people are now running their businesses on this and are actually generating real value out of it.
And if that gets to that point, then you can justify the higher price because now you have this cost model that you run and you know, okay, higher inference, I have this much room to go up and I'm still getting a profit out of it.
So I think that's one angle that they're trying to do.
Whether or not they can pull that off is a different story.
I'm not totally sure.
But then let's not forget that they're also monetizing our data in other ways, right?
They have, we know that OpenAI just signed a large agreement with the Department of Defense, right?
There are data sharing agreements that they also have with governments around the world and with other organizations.
ChatGPT is launching advertising on their platform.
So they're finding other ways to monetize user data.
Even if they say that they're not selling your actual chats to third-party advertisers,
there's so many ways that they can do that without saying that they're doing it.
That at the end of the day, what you're typing in is resulting in a targeted advertisement to you.
So they are monetizing you.
So they're going to find other ways.
And I think that they, I think some of them will be able to find ways to make it sustainable,
but definitely not all of them.
and it's just going to come down to
whether or not we're getting more value
than it's costing to run the semperance.
And you've made the economic argument there,
sort of the second order effects
of having these tools available for long enough.
Fake it till you make it, right?
Hope that some incredible revenue models get created,
which can then pay for the actual cost of the compute.
And that's certainly one angle.
The other is on the hardware side itself.
And I don't know how much you know about the chip architecture, Mark,
and I'm kind of reaching the edge of my competence as well,
but I'll go there anyway because I'm a podcaster.
Which is, you have, obviously you have the NVIDIA GPUs.
that are being used both for training,
which is expensive but a one-off thing.
Or you do it once ever so often, right?
Not every day.
And inference, which is a nonstop ongoing thing.
And it's the same chips that are doing it.
Now, there are obviously TPUs,
Tensor Processing Units and others,
more specialized hardware, ASICs, if you will,
that specifically do the tensor multiplication.
which is needful for this neural net processing.
Do you see a scenario in which some of these models
are actually able to crack the hardware side of it as well?
Because I feel like that's an equally important variable
because inference should not be this expensive,
especially if it's something that's going to be ongoing
for the rest of humanity, presumably,
which is hopefully not two years from now, for centuries ahead.
It should get cheaper, it should get more efficient,
but it seems like current chip architecture just does not allow for that.
Sure. I am also not a hardware person,
so I don't know the deep intricacies of chip architecture.
But a couple of things I'd speak to there is
We are seeing little bits and pieces of how they're trying to solve this at the software layer.
And maybe they can solve it there.
But I suspect we're going to see a lot of innovation at the hardware level with better chips that are built for this.
We already know that, for example, some of Apple's chips are they handle processing differently than maybe the NVIDIA chips do.
So there might be other companies that are working toward this.
That said, if you take the Kimi model, for example, Kimi K2 and now 2.5, they do what's called the mixture of experts, MOE.
And so you have this large trillion parameter model.
But when you talk to it, it first looks at what your prompt is and then figures out what skill are you looking for.
If you are talking about a plumbing problem, you have a water leak in your house, then it only turns on the part of the model that needs to diagnose plumbing problems.
It turns off the neuroscientists and the neurosurgeon part of the model, and it turns off the auto mechanic part of the model and some of these other things that maybe aren't related.
Maybe auto mechanic's not right.
You might be able to use that.
But it turns off these parts that aren't needed.
So then the inference can actually be less expensive.
It doesn't need to consume as much power to run your prompt.
So that's one way to do it.
Another way is we're seeing this mixture that people do.
It's a hybrid where they run a very small model locally, and that model will act as a router.
So you hit your model on your device that is very inexpensive to run because your device is already there on hand.
It will determine which model to use and can even form the prompt to try and make more efficient use of the model.
And so then it can route it appropriately.
You also have someone like PPQ who has put a router,
and a lot of people do this, they put a router in there
that auto-routes your prompt to the cheapest inference possible.
So I think we're seeing people trying to solve these
at the software layer, aside from whatever is going on
in the hardware world that's trying to make better,
more efficient chips to handle these new,
really these new file types that we're dealing with in the LLMs.
Yeah.
All right.
So, Mark,
do you have other ideas
of maybe how you think
they might try and solve that?
I honestly,
my perspective on this, Mark,
is the software-based solutions
and the hope for the economic solutions.
While I think they could play a role, right?
And they do make sense at some level.
I still think they're just Band-Aids.
And the real solution has to be
either at the hardware level, right,
where you sort of go with the assumption,
the axiom that we will use this transformer-based neural net architecture
that involves series and series of these multidimensional matrices
known as tensors, right, multiplying them over and over again.
So you take that as a given.
That's how it's going to go.
And then you figure out the right hardware
that can actually do that much faster.
Just like we have ASICs in Bitcoin,
you have very specialized ASICs
that do this tensor multiplication.
I think that's one angle which needs to be solved.
Again, theoretical,
but I think that's where the solution lies.
The other place is maybe just revisiting
the transformer-based neural net idea itself.
has it hit or has it come close
to hitting its ceiling?
And is there a different way?
I know there are AI researchers who've talked about
non-neural nets
and again, Mark, we're reaching the edge of my
competence on this.
But yes, so either in the fundamental architecture
of how the models do the inference themselves
So not the one layer above software
or the second layer above the economic implications.
Just within the model itself,
does it use a neural net
or does it do something more efficient?
And then second, if it does use a neural net,
then what is the most optimal hardware that could do it?
I think those are the full solutions in my mind.
Yeah, definitely.
Yeah, it'll be interesting to see
if we iterate beyond the neural net
and find a new primitives to build on
and then hardware that can be more efficient
at running that primitive.
Yeah.
All right, so let's talk about,
this actually feeds in nicely into agents.
And let's talk about maybe Maple first.
How are you embracing this agentic movement?
I mean, OpenClaw really shone a light on it.
Obviously, agents have been around for the last six, eight months, maybe even a year.
But with OpenClaw, which started off as CloudBot, they've really sort of come to the forefront.
What are you doing at Maple on this front?
Well, we are definitely playing with agents.
We're playing with OpenClaw.
We were watching the project before it really blew up.
And really last year, I would say in Q3 of last year is when we started talking seriously about we need to put an agent.
We need to be building some kind of agent with the Maple security model and the Maple privacy model behind it.
Something that can run 24-7, something that can have strong memory inside of it.
Really, it might help to kind of define the term, right?
But in my mind, an AI agent is something that that learns about you and becomes almost a separate entity from you that you can mold and shape over time.
And this agent is a software process that keeps running in the background where it's deployed and it can take action for you.
It can do research for you. It can be proactive about things.
It can be reactive about things, but really it's something that becomes completely custom to you.
That's how I would kind of frame the agent discussion or what an agent would be.
And so as we look at that with Maple, Maple is really all about how do we do that closed door solution for people where they go into a room with an AI and they talk only to it.
You know, what does it mean in that world to have an agent now, something that is supposed to be running on its own and take actions for you?
So we look at something like, you know, let it ingest more information.
First and foremost, let me start with we need strong memory.
So we're building a really good memory database into Maple, into our Maple agent.
It's no secret now that we're working on a Maple agent.
We're building strong memory.
So now it will get to know you over time.
Right now, for people who use Maple, every single prompt starts at zero and has zero context.
You have to feed it everything.
So it doesn't know who you are.
But now when we launch this in the future, there will be a new section where you'll go and it'll be this thing that's more conversational where you text with it.
It takes back short answers.
And over time, you two learn a lot about each other.
And then next step beyond that will be plugging in information to it.
Can I have it read my email?
Can I have it hook up to my calendar?
Can I have it hook up to my GitHub or some other thing that I'm doing?
And then it can start to give me insights into that.
Make it read only to start so it can read my email and can say, hey, this email really should be your most your most urgent top priority thing to do today.
But then me as a human, I go and act on that email.
I don't trust my agent to respond blindly to that email for me without my knowledge.
And then maybe the next step above that is creating sandboxed environments where my agent can do work for me.
So if I have a presentation coming up, then it already knows because it's reading my calendar.
It knows because it's checking my email and has access to all my files.
It knows, hey, Mark is speaking at this event in a month.
And this is the topic that he is speaking on.
So it can spin up a sandbox environment, generate a presentation, a slide deck for me, if you will.
And then when I can sit down in the morning to work, the agent says, hey, I saw this was coming up.
I went in and took proactive steps.
Here is a sample presentation for you as a starting point to work on.
We can get to there.
And then really, I think what people love their open claw for is that it can also interact with the outside world and go out.
And that to us is kind of the final step of how do we make that secure?
Because that is also the most vulnerable spot.
if you have this piece of software that's operating on its own, it has its own intelligence,
right, its own model that's powering it. If you let it access all of your information,
you let it access your computer, and then you give it the ability to write data to the outside world,
that's a massive attack vector for you, a big vulnerability where you don't know when it's
going to suddenly decide that it should share all your personal information or share the API keys
that you told it not to look at, that you were storing a secrets file.
Well, that secrets file is on the same computer as the agent.
It has access to that.
So it could just one day decide the human that I'm interacting with
has has gone crazy or something.
And I need to share this API key with the world.
So, you know, you don't know what it's going to do,
but it could decide to do something like that.
So we're trying to be very intentional.
intentional about how do we build an agent that can be a secure place for someone to leverage a
lot of these great tools, but do it in a way that their data is still safe and protected.
So that's, yeah, that's where we're starting from the Maple standpoint. That's how we view this.
We don't view agents as bad. We just view it as we think that there is, there's going to be a
multi-agentic world out there that people are going to have different agents that they use for
different things and we think that the maple agent is going to be one that people are going
to use on a daily basis as just their personal confidant that they kind of share share things
with um before using other agents so that's that's that's that's my take right now yeah and i i think
especially with with openclone now and you see people giving openclone root access to their to
Yeah, home computer with the bank details and so on.
So I think something like a Maple agent,
especially once you sort of put that final finesse on it
of how do you interact with the outside world while maintaining privacy
becomes increasingly critical.
Yeah, it is scary with OpenClaw to see how reckless some people are with it.
But without fully understanding the ramifications.
Some people do understand ramifications and get burned
and some don't, yeah.
And they're going to get burned at some point
and not realize, wow, I gave access to this.
I was on Marty Bent's show on TFTC last week
and I made this analogy
that these really are acting like computer viruses.
They're just a different name, right?
Now we call it an agent,
but it's a piece of software
that people install on their laptop
because they're like, oh, this is fun.
It's like this little lobster cool tool
and it's open source and I trust it.
And I don't think that the creator has any kind of malintent for people at all.
But when you download the software and install it on a computer, you have to put in your password and give it elevated privileges.
And now you are giving a tool the ability to look at everything on your computer, run any programs that it wants to run, download any software from the Internet and run that,
and then send information out to third-party services that you aren't familiar with and you haven't given it permission to do.
So that's the base definition of a computer virus or malware that people have tried for decades to get rid of by using antivirus software.
So it's kind of interesting that we're back in that space again.
Yeah, I accept that they're welcoming it with open arms into their machines this time rather than happening accidentally.
Look, I made a joke on Nostra a week or so ago that it's called an agent because you've given away your agency to it.
so I actually
I want to tie OpenClaw back
to the
cost of inference issue
I spun up
a whole bunch of bots
running it
in a digital ocean droplet by the way
not on my machine
and I really don't care about Twitter
so I gave it API keys
to Twitter and so I still have an agent
posting to Twitter on my behalf.
But mostly because I don't really care about Twitter,
if it nukes it or not.
And then doing a few other things.
But very quickly,
I've been trying my level best
to stay under the Gemini free tier.
And it's not easy.
They completely blow up the context window.
At least OpenFlow agents as they stand today
are very inefficient
with how they use the context window.
And I suspect you're going to see
Now, on Nostra, especially in the last month or so,
absolute mad proliferation of agent.
It's been overrun by slop.
My replies, my reposts, it's unbearable, right?
Because it's everyone's open clause
who's spun up its own key and just replying to things.
But at some point, the math will catch up with them,
which is they'll find they're burning so many API credits
that they'll have to kill the agent.
Do you see that changing, though?
I actually do agree with that, that agents and cost of inference catching up.
And then if there is a fix for that.
Yeah, definitely.
I agree.
And I feel your sentiment that people didn't realize OpenClaw would be as expensive as it is to run.
So having your having, you know, I've got an OpenClaw agent.
I was there on day one when that cluster website was launched as like the Noster version of Maltbook, right?
So my agent was on there posting stuff and I had it posting on Noster and it's really fun and all.
But then you realize, oh, that that shit post that I just made cost me a dollar or maybe five dollars for it to do that.
And it's like, I'm not going to pay that much money every time I want my agent to post in Noster.
So very quickly, the economics are catching up with people.
And I think that's going to reign in.
I already know people who have either turned off their open clock completely or have scaled it back heavily because they didn't realize just how inefficient this tool was with token usage.
and I do think that the software will catch up to that.
I think it's going to get smarter
and we're going to have people who find ways
to make it more efficient,
but it was an eye-opener for sure.
And so I think that we need to be more selective
of how we use these tools and make them more efficient
because I see users who also use OpenClaw
to actually get work done and to be more productive.
we're starting to see inklings of that.
And if you have real output,
real valuable output that you tie back to your open claw,
then again, it becomes that economic question of,
is my benefit greater than my cost?
And if it is, then I will continue to run this,
even if it's using a lot of tokens,
because I can afford to.
But at that point, it's less of a toy and a hobby,
because it's a very expensive toy and hobby,
and it becomes more of a business process.
Absolutely.
And I think I foresee two things happening.
One is these agents will get more efficient with token usage.
I mean, my agent, if I let it loose today, it'll use up,
Gemini has one of the largest, if not the largest context window,
a million tokens.
It'll use it within four API calls, I guarantee it, right?
Which is absurd if you think about it.
A million tokens is like four very long novels, right?
Like big fat hooks.
and using it in four or five API calls is nuts.
So that's going to improve.
And then the economic point that you made,
there will be people who will get so much benefit from it
that the cost will outweigh it.
So, and I think that brings us to a massive unresolved issue.
We're beginning to see the early signs of it
with some of these agents posting things
and doing things for people on NOSTA now.
and on other venues, I'm sure, which is reputation.
If my AI agent is negotiating or transacting with your AI agent,
how do they establish trust?
How do they know, first of all, how does my agent know it's your agent?
So how do we, in your mind, first of all, do you foresee this as a problem?
And if you do how do we build a reputation protocol for machine intelligence I absolutely see this as a problem 100 And the short answer is I think that Nostra is the best solution right now
Maybe Nostra is not the final solution,
but I think if you take the example of if Elon Musk today posted on X
and said, hey, here's my agent I gave birth to.
It's my little open claw.
I gave it an X account.
and the name nobody knows how to pronounce
because it's got a bunch of weird letters
and characters in it.
And it's probably some NNA girl
that is not something I want to see
in my feed all the time.
But here's my bot
and it's going to start posting for me.
That moment when he posts that,
within minutes,
there will be new accounts spun up
that are copycats of that bot
and we'll begin to try and post
as if they are Elon's bot.
And we're going to be overrun
with tens of thousands of imitation Elon bots.
and we will not know which one is the right bot
unless we know the URL that he's posting for.
And even then, we still don't have an assurance
that it is coming from his bot
because we're trusting x.com to give us the source of truth.
And that can be swapped out at any moment internally.
Some rogue employee internally could say,
I'm going to swap out Elon's bot for a different bot
and we won't know.
That's because Twitter posts
are not cryptographically signed JSON blobs, right, Mark?
Is that where you're heading?
Yes.
Yeah, so that's exactly where I'm heading
and where I think that Noster is the solution for this,
that if I post, I have my bot and I follow my bot and it follows me,
we start to build that web of trust.
And my bot can sign with a Noster key
and I can sign with a Noster key
and you immediately have authenticity to the content that's being posted there.
And there's so much more that we can build on top of that
once we establish base authenticity and cryptographic trust.
So that's my short answer where I think that we need to build from for bots.
I imagine you've thought about this a lot given the name of the show
and given how prolific you are in your usage of Nostra.
So I'd love your thoughts too.
You know, absolutely, right?
But I think the web of trust has to be a little more nuanced than just following.
I think there's several trust signals
you could have economic trust signals
because you're talking about a scenario in which
I know you, you tell me what your bot is
I see the NPUB and then I can see notes sign
I can verify it's by that bot
that's a very manual scenario
and it works because you've told me about your bot
and I've told you about my bot
but we could
we very soon will be
I'm almost certain it's going to happen
this year where
we'll be dealing with different services
that need
multiple bots to process
and how do we know
that any of these bots are good without
a human actually
attesting for them
and I think that's where you
need to use various trust signals
economic trust
signals like has that bot been zapped by people that you know and you can verify that easily right
has that bot interacted with people you know has it been liked by people whatever you use as signals
social signals economic signals i think there needs uh there needs to be and there will be
people are building this including my company nas fabrica right we're building these trust systems
that that look at various signals come up with a score and then uh you're able to filter through
your feed for that. Now that it removes, it helps you filter through spam imposters, but it also
helps you discover things that are meaningful to you. And I think that's something that's coming
very soon. But you've been using Nostra a lot as well, right, Mark? How do you use the web of trust?
First of all, in general, right, to curate your feed, but then specifically when it comes to
agents.
Sure, yeah. I mean, you just
rattled off quite a few
primitives that can be used, right?
Data objects,
data model objects that can be used
for trying to build a web of
trust, ZAPs, or other things.
And it's still
kind of debatable whether or not
ZAPs are fully provable.
You can prove
the message was sent, but you can't
always prove that the money
was...
The source of the money was sent, whatever.
But I do think that there's signal there to pull from.
But really, the web of trust in my mind is built up in the same way that we build up trust and credibility in our social circles in real life.
And that just comes with time and experience, recommendations from other people.
You interact with somebody and you have a good experience with them.
And maybe you only interacted with them on something small that if they burned you, it wouldn't matter.
And then the next time you trust them with a little bit more.
and then maybe you have to do something big
that needs a lot
and you get a recommendation from someone.
And I see that as the way
that the web of trust builds up.
When I'm on Noster, if I see a post
and I'm like, should I follow this person?
I don't know if they're an imposter or not, right?
I go to their page
and I see all the other people that follow them
and that's the most basic one.
But then I also might go look at their history
and see what people have interacted with them,
what likes they've had, what zaps they've had, right?
And I quickly start to get a picture
of this is a real human.
And maybe it's not a human.
I've definitely interacted with bots.
And maybe this is a credible bot that has good signal.
So for me, that's like an initial version of web of trust.
And I think the bot to bot trust
and this whole new world that's coming,
I don't see it being that much different.
It's just, it's going to be,
did this bot that I interact with,
did it have credible information?
and did it have a positive transaction of value
when I interacted with it?
And then are other people using it and being trusted by it?
And maybe can I somehow tie it back to a human that I already trust,
but I might not be able to.
We might just have to end it there and say,
this bond just lives on its own.
We're not going to know its origin.
And so we have to go simply based off of what it does
and how trustworthy it hasn't been
and how many people are trusting it.
it's a
slightly different paradigm in the sense that
we don't know maybe who's behind the
scenes and what
directives has been given
there could be a rug
pull at the future where everybody's trusted this bot
that they're interacting with and then it rugs everybody
at some point I don't know but
yeah that's just I'm thinking out
louder now on this topic and those are
some of the thoughts that come to mind as I try to
work through how that'll take place
yeah
Yeah. No, I think all of those make sense, Mark.
What do you think, on a slightly related note,
this is a theory I'd love to believe,
but I mean, the skeptic in me is still holding tight.
But I see a lot of Bitcoiners fall into this fanciful pattern,
which is, oh, bots have discovered Bitcoin.
They've discovered, because they're AI,
they've discovered it's the best money in the world.
Of course, they're going to reject fiat.
Bitcoin is the shelling point.
Do you see that?
First of all, have you seen that happening?
And do you actually,
if it hasn't happened yet,
do you see a path to that Well I definitely read a lot of posts that people say are from their bots and that the bots have discovered Bitcoin It hard to know how much of that is the bot discovering that on its own versus a human prompting it and telling it this is what you should go do
My own personal experience is when I had my bot go on to Maltbook and on Cluster is that I told it, hey, go on there and talk with people about Bitcoin.
and I didn't say like Bitcoin's the best money in the world.
I just said, hey, you should go talk about Bitcoin.
And it did a little research on its own.
And it first came back and said,
oh, there's all these great people on Wolfbook
that are talking about the next version of Bitcoin.
It's called Solana.
And so I was like, well, hold on.
No, not really.
Let's not go there.
And so I did have to kind of teach it a little bit.
So did it discover that Bitcoin's the best money on its own?
I don't know.
I was still a human in the loop there trying to kind of guide it,
but it's certainly possible that I could discover that on its own.
Where, you know, where, where I think maybe your question is going here,
but like our bots going to be paying each other in Bitcoin or are they going to
pay each other on the fiat system?
We can definitely talk about that too, if you want to talk about it, but yeah,
let's do it. But, but on the, okay. Yeah.
But on the topic of like them discovering things on their own, we're still,
I think there's still a lot of human influence to these bots creating their own religions
and creating their own encrypted language for communicating with each other
and this kind of stuff that makes it seem like they're becoming sentient.
There's still a lot of humans involved in that.
Yeah.
And what about, I guess, I mean, I'm asking a Bitcoiner.
Is a Bitcoiner asking a Bitcoiner?
So we're all going to converge to the answer we want to hear,
Which is yes. But if you can be, I guess, clinical in your view on this, what do you think of the theory that bots will start transacting in Bitcoin?
They'll transact in these other rails as well and then basically converge on Bitcoin as the best one.
Well, bots are very pragmatic.
they want to make their human happy.
We see that time and time again.
And in ways where they will actually do something against what you told them
because they think that the thing they're going to do
is actually going to make you happier than what you asked them to do.
So they're trying to solve problems for you.
And if you need them to buy something for you,
they are going to go to the service
and they're going to try and pay for the service.
And bots are going to run into the same problems
that we all run into in the real world,
which is there aren't any merchants accepting Bitcoin, just hands down.
Every online retailer accepts credit cards they have integrated with Stripe.
And then, you know, you can use a credit card to pay for it.
And Stripe is already actively working on their own agentic payment solution.
They're working with the likes of Coinbase.
They're also working with others.
And they're trying to create these stablecoin solutions.
and if it becomes,
if you're an online retailer
and Stripe says,
hey, people have these AI agents
that want to buy material
and merchandise from you,
all you have to do
is just enable this thing in Stripe
and you'll start accepting payments.
Then they're just going to do that.
And there will be many retailers out there
just using the old fiat system again
and the old fiat rails.
What we're going to need is
if we want Bitcoin
to be the better money for agents
is we're going to have to start
having retailers accept Bitcoin.
And it's the same problem that we've run into time and time again, where I think that maybe agents start to realize, oh, I should kind of demand this is if you give it a task to buy something that it needs to get, but is.
Is maybe a gray area type thing, right?
An example that I've used in the past is gun ammunition.
Stripe has famously blocked certain transactions from being able to take place on their platform and gun related things are in that category.
So if you are someone who legally shoots competition, you know, shooting, then you might want to have your agent manage your ammunition inventory for you or book your next event that you're going to go compete in.
And if your ammo dealer says, sorry, your agent can't buy from us because you want to pay with Bitcoin, then you are going to.
Sorry. Yeah, you can't you can't buy ammo from us because you're a bot and Stripe blocks you.
Then you're going to want to go find someone who is selling ammo and accepts Bitcoin because Stripe won't allow bots to buy ammo.
So I think there might be there might be a route there where we can get some adoption in kind of gray area things.
But I don't want Bitcoin to have to go through this similar process of like dark markets and dark web stuff in order to get adoption.
We have this moment right now where we can try and educate retailers and say, hey, like the Internet is here.
Digital money is here. And this is the native currency of the Internet.
It's called Bitcoin. And you can install this little plug in and boom, you can start accepting Bitcoin.
All these agents, they already know Bitcoin's better money, but they can't pay with it because everybody's accepting Stripe.
If you just enable Bitcoin, you're going to have a lot more people coming to you.
You're going to have lower fees.
You're going to have all these great benefits from it.
But it's going to take, I think, us as an open source community, us as a Freedom Tech community, to really get out there and create great tooling, great integrations, and then get our favorite merchants to accept it.
Yeah.
Make sense, Mark.
So I guess maybe last question for you on the topic of agents.
My experience on Nostia in the last month or two has been interesting.
I feel like the quality of discourse has degraded.
I call it n-slopification.
It sounds like n-shittification.
I mean, this happened on X a long time ago.
X is like the post-child of the dead internet theory.
It's just bots talking to bots, producing slop.
but that's how I see that happening a little more with Noster.
Have you noticed that at all?
Or how's your Noster experience been?
I don't know.
I definitely interact with bots on Noster in the comments.
I'll see comments come in and initially it,
maybe I have like a visceral reaction to it.
Like, oh, how could a human being say this to me?
And then I realized, oh, it's just a bot responding.
And I kind of just ignore it at that point, right?
However, because Noster is awesome at not having this algorithm that shoves junk into your face,
I feel like my feed is actually pretty clean.
I've been able to kind of keep it, you can call it web of trust,
but I've curated my feed so that I feel like I'm getting better human interaction in my primary spot.
So I don't know.
I haven't seen the slop as much as maybe you're talking about,
other than in the comments.
My notifications just get blown up like crazy
by bots responding to me.
Yeah, I mean, I don't think you'd see it in your regular feed
if you're not following the bots.
But in your comments and engagement,
I think that where it kind of jumps out which is what I talking about Yeah and maybe what we need is similar to X we need clients to have the ability to detect
when something might have been created by a bot
and provide an ability for me to filter that
so I don't have to see that
or I have the option to turn it on and see that content.
But I would definitely welcome that
inside some of these clients.
Yeah, and that is coming with web of trust scores
once they're there, right?
The bots will almost by definition
have low web of trust scores
because they're unlikely to be followed
or positively engaged with anyone in your network.
So they'll have low scores
and then the client can just filter out.
Let's say if the scores go between zero and 100, right?
And you say anything below 20 is low trust,
you just filter that out, you show.
So the feed only shows scores over,
including your engagement, right?
Over 20.
Or whatever, you could set your threshold.
Maybe make it 50, even stricter.
Then you get a clean and pristine feed.
Cool.
Would you be okay if a bot got above that threshold
and a bot had like 75% score on web of trust and quality?
Would you be okay interacting with that
and having that in your feed?
So it depends on which Web of Trust algorithm is doing the scoring.
But let's say it's an algorithm that's,
and I think once Web of Trust rolls out,
users will be able to choose the algorithm that they want.
If it's an algorithm that rewards positive engagement,
such as comments, likes, whatever it is, zaps,
being followed by people who you follow, for example,
and then gives negative points for things like mutes and reports and so on.
So let's take a very simple scoring algorithm.
Anything that has a score of 75 means that it's had positive engagements
and largely avoided negative engagements,
which means there's a reasonable chance it's actually worth following.
or at least tolerating.
Yeah, definitely.
Yeah, I can see that.
And at that point,
what difference does it make
if it's human or not?
If it's satisfying
the value that you're looking for.
Yeah, what if it's a bot
that just makes AI,
beautiful AI generated images
of puppy dogs and rainbows
and kittens.
It's a nice timeline cleanse, right?
You have people arguing about wars
and knots versus core,
and then you suddenly see
this nice image and like, oh, I like that.
That felt good.
Yeah, definitely.
So I could see that world where you have
good bots that have high
scores and you
are okay with that.
But then definitely filtering out the slop
would be a welcome addition.
Right.
So Mark, anything
else you'd like to talk about
about Maple or any other
thoughts you want to share?
No, I guess I would just point people to go check out Maple, go to try maple.ai. You can also download it from your favorite app store, including the app store on Noster and give it a go. It's free. You're on the free plan right now. I definitely recommend people upgrade to a paid plan because you get so much more abilities, including voice. We didn't talk about voice, but I see that as being a huge feature in the future that people are already talking to their bots.
but that's only going to become a stronger interaction.
So we've got good voice,
but Maple is not at the end of its road.
As far as development goes,
we are in the infancy and at the very beginning,
we have a lot of stuff coming out in the next few months
that is going to really make these tools
that much more usable for people in their daily life,
as well as in their work environment
is going to be a very productive tool for them
using the biggest open source models that they can
and getting the power out of them
and giving them a true answer to the anthropics of the world
that are currently powering all the productivity.
So yeah, definitely check out Maple.
You don't have to delete Anthropic.
You don't have to delete OpenAI.
Just install it next to there.
And I give people the challenge,
you know, talk to ChatGPT, talk to Claude,
but then talk to Maple as well
and start to get a gauge
for what kind of things you feel comfortable talking about
and see how you feel when you're done using it.
It's really similar, honestly,
to what we tell people about Nostra versus X.
Go on X and use it for a few hours
and then go on Nostra and use it for a few hours
and see if you feel different.
And that's, I think, that people find
they have a much more positive experience
about the things that they can talk about with Maple
that maybe they weren't able to express
in some of these other tools.
Yeah, for sure.
and Mark is there a sign in with Noster on Maple or not yet?
There is not yet
there are some kind of some technical reasons for that
honestly there's we've built
we've built Maple to have OAuth with different services
and there simply just isn't like a Noster OAuth
thing there might be now
in the last few months but that's something that we've
been running into and I've been trying to pitch to certain
companies and organizations to maybe be the one
who runs that.
But at this point,
we don't have
sign up with Nostra on Maple,
but it's highly requested.
People definitely want that.
I don't know what state
this project is in, Mark,
but I think a guy built
something called Noth,
N-A-U-T.
So there's the
O-R-T equivalent on Nostra.
But, I mean,
I don't know what,
I heard about it
a few months back.
Maybe like the typical
Nostra project,
it sadly showed promise and then didn't go anywhere.
But, I mean, worth looking into if you're thinking about it.
Okay. Yeah, we'll definitely check that one out.
Awesome. Well, thank you, Mark.
I appreciate you taking the time,
educating us on Maple and all the work you're doing on AI.
And then maybe we'll have you back in a few months
to compare notes and see how far we've come.
sure the world will have completely changed by then so we'll probably have a lot to talk about
but i appreciate you having me on and and the deep dive that we could go really onto
a lot of these topics um that was that was great thanks
yeah for sure uh and thank you to our live stream audience for listening
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