Bitcoin, AI & the Coming Surveillance State | Mark Suman - What Bitcoin Did

Episode Transcript
Available transcripts are automatically generated. Complete accuracy is not guaranteed.
(00:00):
There are likely data sharing agreements between OpenAI and the US government.

(00:07):
They want to harvest all the user data and they want to sell it and monetize it.
We actually don't know what they're doing behind the scenes because everything's closed source.
What vulnerabilities are there?
What am I signing up for by giving them access to your mind effectively and then letting them into your digital life?
It's this amazing potential for humanity, for human rights.
That said, the more that we give ourselves over to it, the more that we turn our data over to it, our minds, everything, we're giving it power to influence us.

(00:35):
We've taken a lot of that ethos of the Bitcoin mindset, the don't trust verify mindset that is only made possible because of Bitcoin.
We can't do it with credit cards.
We had to do it with something that was private and that was freedom oriented money that is uncensorable.
AI has the ability to upgrade humanity, but we need to make sure that our humanity is preserved in the process.

(00:56):
it's good to see you man thanks for coming on the show we've been trying to do this one in person
i've not been in austin for a long time so we decided we just do remote you guys have just
dropped some very cool new features um but your first time on the show you should introduce yourself
tell everyone who you are sure yeah i'm a long-time listener first-time caller so this is great
uh my name is mark i'm on online i go by marks a lot so you might see that name as well but i i've

(01:24):
been around in the tech industry for a while. I live in Austin, Texas now. And yeah, about myself.
So I work on a private AI called Maple AI. Prior to that, I was at Apple for six years working on,
I was a software engineer over there working on an internal project that had a huge privacy
and machine learning and AI component to it. Apple does care about privacy. And so that was like,

(01:49):
from day one, I had to work on that aspect. But yeah, just loving life and glad to be here.
No, I'm glad to have you on, man. Bitcoin's at $94,000. So this is an AI podcast now.
These big tech companies are investing tens of billions, hundreds of billions of dollars
into AI at the moment. They're all in this like arms race competing against each other.

(02:12):
I want to know, like from your perspective, what's the end goal? Is it basically who can get to AGI
first and whoever gets their first wins. Yeah. I mean, everybody talks about what's your moat,
like what are you doing to get your competitive advantage? And so AGI is like this thing that
they love to sell people on and talk about. It's really good for raising money. It's really good

(02:33):
for driving adoption. It's anybody's guess how close that is really. But I think they're honestly
just driving for who can have the stickiest product, who can get the most people in and
keep them the longest. And then let's continue to upsell you. But the big part of their revenue

(02:55):
model is the data, right? They want to get the data. Everybody talks about how data is the new
oil in this life that we live right now. And so they're gathering all this information. They're
making better models. They're monetizing the data. They're selling advertisements. They're selling
shopping to you, they're building agents that will go out and purchase stuff for you.
So really, it's just about how can we collect as much data so that we can build businesses

(03:17):
off of that.
And I mean, the big major AI shops like OpenAI, Anthropic, XAI, they all got a huge $200 million
fund or investment from the government.
I don't know if this is technically a grant or not, but from the United States Department
of Defense.

(03:37):
And so there are likely data sharing agreements between OpenAI and the U.S. government.
That's just kind of reading between the lines there.
So there's a lot of data gathering going on and then monetization of that data.
You know, when you say like you don't know how close AGI, superintelligence, I think
are those terms basically interchangeable at this point?
Like you don't know how far that is away.

(04:00):
Is this almost like quantum computing where it's just always a few more years?
Or do you think we are actually on the brink of a breakthrough here?
that's a good one um i feel like that's above my pay grade but
it's it i think a lot of it depends on just like the task at hand i mean you've used ai a lot and
sometimes it's really good at one specific thing and then you try to have it tie its shoes and it

(04:23):
like totally falls over and trips on itself right like so i i personally think like it's very far
away we're not right there yet that we are going to build very specialized ais to do things you
know, Elon loves to show off his robot and say it's good at dancing and it's good at moving boxes
in a warehouse and all this stuff. There was the robot that made the rounds a couple of weeks ago

(04:45):
with all the memes of, you know, here's this robot you can buy and put in your house.
But it was, it's not an actual product that's functional. I think that we have a long way to
go still before we get the whole AGI thing and super intelligence. I think we're just going to
be targeted intelligence for a long time. Okay. I mean, I saw that the videos that launched with
that robot that was in your house and wasn't like it looked kind of brilliant in the videos.

(05:07):
But is it true that that was actually driven by someone using like a VR headset?
So there's just like some guy in a warehouse somewhere working away, looking at the inside
of your house.
Yes.
Yeah.
They're saying for the early prototypes, it's going to be somebody actually like wearing
a VR suit that's driving your robot.
Eventually, they want to get to where they're not.
But that's not where it's at right now, which is really creepy.

(05:30):
Exactly.
This is the dystopia that everyone's scared of.
um but so the reason i asked like how close we are to agi is because i did a show a couple months
ago with a guy called roman yampolski i don't know if you listen to that one but he is um like the ai
safety guy he i think he came up with the term ai safety and he's really trying to push back on all
these big tech firms just carelessly investing to the point where they're throwing billions and

(05:54):
billions of dollars at this thing trying to get agi i'm not really thinking of the ramifications
of that. Do you think there is a risk that AI is almost so good that it's too disruptive too quickly,
even if you take away the part of it going to kill all humans? Do you think it can replace
90% of jobs within a decade sort of thing? Yeah, it's starting to replace some jobs,

(06:16):
it seems like. We see a lot of headlines about jobs getting replaced, and I think some of those
are people looking for a reason to blame
when really they were probably a lot of malinvestments
from 2021 timeframe when the money printer was,
you know, we had 0% interest rates.
So I think that there's a lot of unwinding of bad hires,

(06:38):
not bad hires because they're bad people,
but hires that shouldn't have happened financially.
So I think we're seeing that right now
and they're just saying, oh, it's AI,
we're just gonna blame it on that.
That's part of it.
And then there are industries
that are already starting to get disrupted
in a way by AI. So transitions are always really hard. We've seen it throughout time with new
technologies that come in. And there's like this period of many years where people have to find

(07:03):
new work or decide to retire early. It's going to be difficult if it happens incredibly fast.
And I have long been like my, the economic side of me does not align with something like a UBI,
universal basic income, but it almost seems like we might need to have some kind of
AI stipend or something like that, right? Where everybody gets some kind of income because they've

(07:29):
been displaced by AI until we figure out what are the new jobs? What are the new industries? What
are the new businesses going to be built up? Because that's kind of the pattern that always
repeats. New technology comes in, new industries are birthed from that new technology. And we'll
see that with AI. We just don't know what it is yet. And we need to have a good, happy civilization,
no civil unrest if possible before we get there. Yeah, I totally agree with that. And that's like

(07:53):
my biggest concern on the job displacement front is that there's some industries that are sort of,
it's very clear to see the path to being completely replaced by AI. Obviously,
software development has already changed entirely with AI, but even things like long distance
truckers. That's an example I've used before on the show, but that job is not going to be there

(08:14):
in 20 years guaranteed. It's not going to be there. Who knows if it's quicker than that?
And what happens to all the people doing those jobs? And I can't see how you get around it without
a UBI. I don't think retraining in another industry, if all the other industries are also
getting disrupted and displaced by AI, that's not a feasible outcome. So how do you get there without
some form of universal basic income? Yeah. And then what does that do to wealth inequality?

(08:39):
Yeah, wealth and equality. I mean, that's, I don't know how related those are. Those maybe
are very related, maybe they're not. But the hardest part with doing something like UBI
with what we're talking about right now is once it's there, it's very difficult to unwind that, 118 00:08:58,1000 --> 00:09:02,940 right? There's no, the most permanent thing is a temporary government handout kind of thing.

(09:02):
So once people start to depend on UBI, it's going to become part of their life.
and so 30 years later it's like time to get rid of that and everybody's got their own jobs and
new industries that UBI is just going to be a part of their income and they're going to depend on that
so it's I don't want us to like just jump in and say yeah let's do this I think we need to really

(09:25):
look long and hard at like what are the long-term ramifications of that and then as far as the wealth
gap goes I think a lot of it still comes down to fix the money fix the world kind of stuff where we
need to really fix the financial incentives behind everything in order to start to fix the
wealth inequality that we see. And maybe AI helps that, right? Because it helps people that are on

(09:46):
the lower part of the ladder to jump up higher and elevate up. Yeah. When you're in your work,
I imagine you're using a lot of AI in the software development side. Has that replaced
essentially new hires that you would have had to make otherwise?
I want to be careful here. I don't want to create a soundbite or something, but yeah,

(10:09):
we've become way more productive with having AI. So we do everything out in the open. So we build
on GitHub and deploy on GitHub. And so you can see, well, not deploy on GitHub, but we put our
stuff there. And what we do is we've got AI agents that sit there in our GitHub repo and we push up,
I'll write like a whole feature spec. And then I'll just say, hey, will you build this? And it'll

(10:32):
build it and then I can review it and tell it to make changes. And then we have two other AIs
that are code reviewing that code. So we have three different agents all working on this code
with me inspecting it. And that's for code that's not like super mission critical. You know, Anthony
is the one really in there building stuff. And he does a lot of it locally first with AI and then

(10:52):
pushes it to GitHub. But we are seeing that as a small company, we're doing a lot more with a two
person team. Whereas we five years ago, three years ago, probably would have need to hire two
more people by now in order to get to where we are at this point. So it's more that we're moving
faster with two people. Or we could have, we would be like half as far as we are now, or even even

(11:15):
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So like, obviously you're building an LM with a Maple.

(13:49):
You're competing against the ChatGPTs,
the Anthropics of the world.
Like how competitive can you be against them?
I'm sure they have hundreds of thousands of developers.
How competitive can you make your product with two?
Yeah.
Yeah. Well, we're following in the path that others like Signal have shown where you look for the user experience that the expensive companies who have these large, massive teams and spend all the money on user research, spend all the money on design and everything.

(14:20):
They figure out what works really well. And then you have the luxury of coming in and saying, all right, talk to all these users. What features do you like the most? And which ones do we need to focus on?
So then you go and build something that's similar, but has your own unique flavor.
And our unique flavor is that we care about the user.
We care about their data privacy.
We don't track them.
We're not spying on that kind of stuff.
And so we can build something that is functionally will look identical to ChatGPT.

(14:45):
It's going to have pretty much all the same features, maybe 95% of the feature set that
you would want.
But then we have the thing that they don't.
And that is, you know, they want to harvest all the user data and they want to sell it
and monetize it.
So in that regard, I think we can do a really good job with two people. We'd love to hire a few more and catch up and get really close to that. And we know that we're obviously not going to like take down ChatGPT and take them over, but we can get really far and we can build a product that millions of people, hundreds of millions of people find incredibly useful.

(15:21):
I remember this was probably a year or two ago.
I think it was from Google.
There was a sort of internal memo that was leaked,
which was essentially saying we've got no moat
and these open source AI models are going to be
just as competitive as us.
Where does that stand?
Because even though there's obviously great progress

(15:41):
on some of the open source models,
I know Llama is open source at the moment
and that's what Facebook are using.
Is that correct?
how close are they to the close sourced chat gpt's of the world yeah it depends on which
benchmarks you look at or if you go off of your own vibes and really it sounds silly but you almost
have to just try it out with the the task that you want to do and with the process that you want

(16:05):
to follow and test the different models to see what works best for you but when you look at
straight benchmarks they've really caught up a lot on on coding standards on math standards on
And all the different benchmarks are out there, especially the Chinese models.
Like Llama is still pretty far behind.
Meta, I imagine Meta is cooking up something for Llama 5 that's going to be really big

(16:26):
because they have so much data that nobody else has.
They have all of the WhatsApp data and the Facebook data and Instagram.
So they're probably making something.
But in their absence, the Chinese models have really come in and caught up.
But I was chatting with a founder who's here in Austin.
He's building an AI service as well.
His is more enterprise.
But he said that internally, they kind of measure all the different tools.

(16:48):
And he finds that the Chinese models really try to fit to the benchmarks.
So they work really hard to make sure that they score high on the benchmarks.
But then if you stray out of the lane at all of those benchmarks, then they might start to fall down.
That's specifically for programming.
So certain programming languages or something.
But that said, like, they do perform really well and they keep getting better.

(17:14):
So I'm hopeful on open source.
And like the Google memo said, it's just a matter of time before they are good enough for the average person and the average business user that they don't need to pay for these proprietary models anymore.
Why do you think it is that DeepSeek and these Chinese models have gone the open source route when the American companies have gone closed source?

(17:35):
Like that seems backwards to me.
Yeah, I wonder if they realize that Americans wouldn't use it if it was fully closed source Chinese.
And they know that they need to compete somehow.
And so the world's only going to listen to them and use their stuff if it's out there for free and open source.
And then the other part of it, too, is open source is going to get adopted way more than the proprietary ones by hobbyists and by others.

(18:03):
And so if you have an ideology that you want to seed out into the world, especially if you're looking at like a global south where maybe they can't afford to use the proprietary models,
then you can embed your ideology in this model and then push it out to the world.
So I could see a couple of different reasons why they would want to go the open source route.
And DeepSeek, even though it's open source, they're still collecting data, correct?

(18:27):
Sort of. So a couple of clarifications. Open source with models is a little different.
They're more open models, if you will. We can't fully see the data that went into them,
but we can see the weights and the measures and the biases and you can dial them yourself, that kind of stuff.
So it's a little different than open source code. And then as far as data sharing goes,

(18:49):
the only time you sharing data with DeepSeek is if you download the DeepSeek app or you go to like the official DeepSeek website and use the AI that hosted by them then yes they see your data they see all your chats and there heavy suspicion
that the CCP is able to access
all of that information,
mostly based off of data arrangements

(19:12):
that pretty much every other company in China
has with the government there.
That said, if anybody's running
the DeepSeek models locally on their laptop
or they're running in something like Maple or some other system,
then no, there's zero data sharing going back to DeepSeek as an organization
or to any kind of Chinese government.
You're obviously at Maple not collecting any customer data at all.

(19:34):
If, as you said earlier, data is the new oil,
like what are you forgoing that?
Like why are all these other companies just so desperate to harvest
as much as that's possible and you're willing to just say,
no, we don't need it?
I think it's because we've all been sold that this internet that we use has to be monetized by selling your data.

(19:56):
Like us as users got so used to using Gmail because it was the most amazing email service ever.
It conditioned us to say we should have email for free because prior to Gmail coming on the scene, we were all paying for email.
In fact, my dad was paying for his email inbox.
Even when he stopped using it, he's still paying for it like five or 10 bucks a month because it was just it was a hassle to cancel.

(20:17):
Right. And so when Gmail came out, they were like, hey, here's this new business model.
You get it for free. But what we didn't realize is it came with this huge cost of all of our data being monetized.
And we've just kind of gone down that path and we don't need to.
Like there are other ways to build sustainable companies and sustainable products that don't use that as their business model.

(20:38):
So that's really what we're doing.
we are we're trying the the more healthy route if you will healthy for humanity healthy for all of us
to build it in a different way where we sell you a really great user experience and we sell you a
product and you can use it and that's really where the relationship ends so how before we get into
like how you're doing things at maple um how are these other big ai companies what are they doing

(21:03):
with the data that you're putting into it and are they are they using literally every single word
you put into these models and then storing that, creating profiles about you.
Like, how do they actually use that data?
Yeah, well, so they're using all the information you input into it.
They are, they're also using everything you don't put into it.

(21:24):
And what does that mean?
There has been evidence and research showing that they look at your keystrokes.
So if you're typing something into the box and then you hit delete a bunch of times because
you change your mind, they've captured that.
And so they know, okay, here's how Danny thinks. Danny typed all this stuff in. Maybe he was like
really angry and writing this really angry thing. And then he's like, you know, I need to tone it
down a little bit. So you backed off. It's learning your emotional state. It's learning

(21:48):
your entire thought process. They're storing that all in their system. And then the way that I love
to describe it is that they are like, it's like you hired someone to write a biography on you.
So they're like a world-class author. They sit down and they're just constantly interviewing
all day long, but they're also paying attention to your body language. They're paying attention
to your heart rate, all these like other indicators that you don't realize you're giving off.

(22:11):
And then they're creating this profile about you. And then they can, they can pump that into the
system for you to make the AI understand you more, which is great. That's, that's the end product,
right? They're like, Hey, an AI that knows you, it's very effective. But then what they're also
doing is they're using all that to train new models, to create shopping networks for you.

(22:31):
They're building these computer use tools that will be able to control your computer.
And they're making web browsers now that are going to browse the internet for you.
So you can see how they are just getting intertwined into your life.
And so you have to ask, like, what vulnerabilities are there?
Or what am I signing up for by giving them access to your mind effectively and then letting them into your digital life?

(22:56):
Maybe if we just imagine for a minute, Maple never existed.
the other people that are working on privacy i know proton have come out with a private ai model
um imagine they never existed like how does this get dystopian from here um yeah because you see
these things like i saw that friend necklace that came out um which by the way look like one of the

(23:17):
worst products i've ever seen i can't believe they actually launched with that but like this
these are things that literally just follow you around all day looking at everything you're looking
at? Like what's the dystopian end game there? Yeah. Well, there's a dystopian end game. I would
love to paint kind of the rosy picture real briefly first. The reason why we get there
is because I think a lot of times we look at this dystopian thing and we're like,

(23:38):
man, we're all a bunch of idiots. Why did we sign up for that? But it's because AI has this like
huge, amazing potential, right? It's this amazing potential for humanity, for human rights. Even
you have people who are oppressed all over the world and now they can grab the world's knowledge
and use it for their own advantage to try and fight back against people who are oppressing them.
So there's really cool things you can do with it.

(24:00):
That said, the more that we give ourselves over to it, the more that we turn our data over to it,
our minds, everything, we're giving it power to influence us.
And so the dystopian side of it is that if we start giving it access to see in our room,
to hear what we're talking about,
it understands how to persuade us of things.

(24:25):
Let's just say that.
So it knows that maybe, Danny,
you're really gullible in a certain way.
And so if it wants to pass off
some misinformation to you or a lie,
it knows how to sell you on that.
And so you can see that effectively
they're building the system
where somebody could come in
with the right amount of money
or the right amount of weapons, basically,

(24:46):
and coerce them and say, we need the community to start thinking about a certain political thing
in this direction. So we want to deploy this directive that is going to shift the mindset
of this country and the general populace in a certain way. And if you think about how we used

(25:10):
to do, let's see, think about advertising. Let's kind of look at it that way. If you want to,
make a new product and you want to sell it to a bunch of people.
Maybe you make a 30 second advertisement and you put it on something like the Super Bowl,
but you don't know who's actually watching.
You don't know what this frame of mind is.
You don't know whether they're male, female, a child, an adult, whatever.

(25:30):
You just make your best guess based off of demographic research.
And so you have to try to come up with like the 30 seconds that's going to sell the most
number of people on your product.
Now you fast forward to this time where we all have AI that's harvesting all our data
and understands everything about us.
And now you can say, I don't want to make a 30 second ad that tries to capture 30% of the people that watch it. I want to capture 99.9% of the people. And so you can deploy something to this AI system that knows how to talk to you to sell you on a product and then talk to me in my way to sell me on the exact same product and convince most of us to use it.

(26:05):
and that's just for products that's not governments that's not you know there's all
sorts of ways that could be used to kind of weaponize the system for lack of a better word
it's um a really scary future that seems very like it's very easy to see that coming to the
like the world in the next maybe like three four five years um and like thank god we've got things

(26:28):
like maple and proton doing this but just quickly before we get more deeply into into um maple
are these models actually getting better because like i use main like i use ai quite a lot for work
it helps a lot like it probably i would have to hire someone at least like 20 hours a week to
replace what ai is doing for me currently um but every time chat gpt which is the one i use most

(26:53):
like comes out with an update it doesn't seem to be better in fact sometimes like it's worse i i
think 4.0 was the best one that they've done so far so like how like how much better are these
getting like incrementally yeah it's it's it's up in the air it depends on it's it kind of goes
off vibes like are they really getting better a lot of people look at chat 25 and think that really

(27:15):
it's just 4-0 under the hood with some modifications around it and it was less of a huge upgrade
but then you have xai you have grok which from you know from two to three and three to four was
was a really big jump. So it is possible there's still gains to be made there. But a lot of people
that I read online who are really deep into this, it seems like they're plateauing. And maybe we're

(27:42):
plateauing because they're working on the next big major breakthrough and they just haven't got
to it yet. So they're holding us over with these small bumps until we get there. But that's why I
think that the open source is really going to be able to catch up. Because if it's true that these
big models are starting to plateau, then open source is going to get just right up against them.
And now we can have the same things that they have, but use it in a way that's better for us.

(28:07):
Yeah. Okay. So let's get into Maple. First of all, explain exactly what you're doing,
how you're making sure this is like private AI that's not harvesting data. Give us the pitch.
Yeah, sure. Yeah. Maple is the alternative, the chat GPT that is not harvesting your data,
that is protecting your privacy.
The way that we've built it is we have,

(28:30):
we built it around open models
and all of our code is open source.
So you can go look at it and see,
and we're running them in the cloud
using something called secure enclaves.
Another term for that is confidential computing.
But these are servers that have hardware encryption
built into them.
It's the same stuff that runs on your phone.
So on Apple devices and Samsung and other devices,

(28:50):
they have these secure enclaves where it stores your wallet,
It stores your face ID, that kind of stuff.
And it's these hardware encrypted things that are difficult to penetrate.
And so in the cloud, we have those now.
And so we're able to put Maple there.
And when you as a user log in, we create a private encryption key just for your user.
And so as you're chatting with the AI, it encrypts everything locally on your device

(29:13):
using that private key.
And then it sends it to the cloud.
And then the cloud in the enclave is where the AI is sitting.
And so it's effectively like you and me right now.
we're having a one-on-one conversation in a private room that we're going to give to everybody.
But right now we're having a private conversation. And that's really what the AI is doing in Maple
and the Secure Enclave. And then once it's done chatting and working on your stuff, then it

(29:35):
re-encrypts it and sends it back down to your device. And then we take it a step further than
some other private AIs do. And that is we can synchronize it to all of your devices. So you
can have it on your phone, you can have Maple app there, you can have it on your laptop,
wherever you want to be. And then because we have that secure enclave and it knows how to handle
your private encryption key, it can synchronize everything across all your devices for you.

(29:58):
So in a nutshell, that's what Maple's doing is just using a private key. And the last thing I
love to kind of tell people and explain is a lot of these services that you use in the cloud,
they take all the user data and they stick it in one giant database. And if you are an employee at
a company who has elevated privileges, you can just go in and hop around and everybody's user

(30:19):
data all you want to. You can go look at it. Usually they have audit trails. And so they'll
know that you went and accessed it, but that doesn't prevent you from accessing it. And then
if a hacker gets in the system, well, they don't care about audit trails. So they're just going to
get the whole database and get a data dump and everything. We've totally flipped that on its
head. And with these private encryption keys, our backend is just a bunch of private vaults per user.

(30:40):
And so if anybody were getting to our system, they wouldn't be able to look at anybody except
their own vault that they can get in there, but they can't see anybody else.
So like for me personally, as I said before, like I use ChatGPT the most and probably some
of that is just down to habit.
Like it's just, it's the first one I started using and it's been hard to kind of move away
from that.
But I have been, like I signed up for Maple basically as soon as you guys launch and I

(31:02):
have been using it more and more.
But the thing that I always use it for is if I'm ever putting like business data, like
financial data, it's always my go-to because I know that that's like an actual secure place
to put that rather than giving it to open AI. But in terms of like feature parity compared to these
big AI LLMs, where are you at? Like, what do you have that you'd expect in a chat GPT type thing?

(31:26):
Yeah. Well, I will tell you, you might be happy to hear that if your favorite model is 4.0,
then we have the GPT OSS model inside of Maple. If you go in there and do the model selector,
it's called Quick is the name of it. But that is really similar to 4.0. In fact, if you ask it,
hey, what model are you? It'll tell you it's chat GPT 4.0. So you'll get that experience,

(31:46):
which is nice. As far as features go, we let you upload documents to it. You can upload photos and
get photo analysis. You can take a picture of a tree or a plant and say, what is this?
You can upload financial documents or legal contracts and have it talk to you about what
are the legal terms that you've agreed to. We have voice, so you can talk to Maple,

(32:06):
which I use all the time, hit the microphone. We had it working where it would talk back to you
that is temporarily broken. We're working on fixing that because we really loved having this
two-way conversation. I would, when it was working, I would just kind of like walk around and just have
a conversation with the AI, which I know a lot of people do with ChattoPT. So those are a lot of

(32:27):
them. And then the biggest one is what everybody was waiting for, and that is live data. So now we
have the ability to do private web search. So now Maple is no longer stuck with these models that
were trained on data from a year ago or two years ago. Now you can be, you know, sitting there and
say, hey, what's the what's the score of my favorite sport team, you know, game that I'm
watching right now or that I'm curious about? It'll look it up and it'll fetch it for you and

(32:49):
give it to you. But there's obviously a lot more utility to that than just sports. But yeah, being
able to get the latest information from the web that is now available inside of Maple.
That's a huge one for me because I use it a lot when I'm preparing for shows and stuff. I'll try
and get like current relevant information I can use in the show. So without that, like if this was
a model that was trained on data, it's like mid 24 or whatever. It's just, it is useless in that

(33:13):
sense. So that's a huge one for me. And how do you do the private web search? How do you do that
while not giving up any data? Yeah. So we're using, we're using brave API, the brave search API.
They're, I mean, they're a privacy oriented company as well, but then we anonymize it. So
when you are going to search, we don't attach your user ID or anything to the search and give

(33:35):
it to Brave. So we have very little information already about our users, right? We don't collect
names. We don't collect, you know, phone numbers or anything like that. The most we collect is an
email address. And then we also know like what time you did your chats, because we have to keep
track of like, just when chats happen, so we can synchronize them. And then we keep track of how

(33:56):
much compute resources you used. But we don't know anything about what you're chatting about.
so when we go to do the brave search we pass it along to them and um so they all they know is that
there's like this giant fire hose of web searches coming in from this one account called maple but
there's zero way for them to to you know tie it to anybody unless you literally say my name is

(34:19):
daniel knowles blah blah and you put it in the search then like the brave search api will see
your name because you put it in the content um but other than that like there's it's it's fully
private. And there are some other private web searches that we're looking at as well, web
services that we're looking at. And we would love to have this model where we can actually spread it
across so people can get even more anonymity by getting lost in a bigger crowd than just one

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types the website is mina.b.tc and use code wbd for 10 off i love that one of the cool things we
were talking a couple of months ago um and you were like have you tried maple um because i can't

(37:44):
even tell from our users if you're using it.
And I use aliases whenever I sign up to any website so there no way that you would be able to pick me out of a group of people I been using it since launch and you had no idea which is really cool So that amazing Are there any features that you
think you need to bring in to be competitive? Yeah, definitely. And I think it's one that

(38:05):
maybe you talked to us about early on, AI memory. Yeah, that's huge for me.
Yeah. Like having the AI get to know you, that author that sits down and writes a biography
about you, we want to build that. And that is one of the stickiest features, right? You know,
you talk about being a creature of habit, you use chat to be because it's habitual,
but also because it knows you, it knows your style. And you maybe you don't even realize that.

(38:29):
But when you have a generate images or something, it's kind of following the style that it's learned
that you like, unless you're very explicit and say, I'm going for this other different style now.
So that's great. There's a lot there's research out there showing that maybe it's like a six to
12 month thing where if you have somebody in a system for that long and the memory starts to get
to know them, then they're going to stay. And so obviously we're trying to run a business that's

(38:52):
profitable. So we would love to build a feature that doesn't lock in users from a nefarious
standpoint. We want to build a feature that gets to know users so well that they want to keep using
Maple. But we're going to build it, of course, in the same way that we build everything else. So
it's going to be in the open. People will be able to see what is this memory service? What is it
remembering about me? What is it passing into the AI that it knows about me? Because that's one of

(39:17):
these problems with the closed models is we don't know actually what part of us they're sending to
the AI. And we don't know if they're changing things that they send. So if you're someone,
I try to use non-political things when I explain this just so I don't divide people. But like,
let's say you really like chocolate ice cream. And it's secretly in the background,
it's saying Danny actually likes strawberry ice cream. And so it's starting to give you

(39:39):
different results. And over time, you're like, oh, you know, maybe, you know, maybe I start
thinking this way. It's kind of a weird metaphor, but I, the point is like, they can, they can
change things to slowly nudge you, like imperceptibly nudge you a certain direction just by changing
the memory under the hood and not letting you know that's what they're doing.
A nudge is a very nice word there when you're kind of saying they can coerce you into thinking

(40:02):
differently. Yeah. Yeah. If it was overt, right, then it would be so obvious people were rejected.
It's like the matrix, right?
They're like, oh, we tried all these different iterations on the matrix and people started
waking up in their pods.
And so we finally built one that was just so easy that they didn't even notice it.

(40:23):
Yeah, the memory is a big one for me because with ChatGPC, obviously it builds a memory
on you.
And I understand all the downsides to that in terms of giving up data and that harvesting
of everything that you ever enter into the LLM.
But it gets to the point where I can put one line in and I will get the output that I want from it because it knows what I'm trying to ask for.

(40:44):
Like if you could get that in a private way where maybe you can periodically, whenever you want, completely erase that data so it forgets everything about you.
But that would be a massive improvement for me just from like a UX perspective.
Yeah, no, definitely.
Yeah, for anybody listening, you know, I would recommend just if you use ChatGPT, go in there and just like ask it, what do you know about me?

(41:05):
You know, build me like a dossier, which is what the CIA would do.
Tell me everything you know about me.
And if you were to do like a private investigator research on me and you'll get some really interesting information out and you might be a little creeped out by it.
Another cool thing to do is go in and say, hey, if you wanted if you had a lie and you wanted to persuade me to believe this lie, how would you go about fooling me?

(41:28):
And you might have to nudge it a bit.
You might have to like push it along a few times, but it might, it'll finally tell you,
oh, well, you know, when we've, when we've talked about this, I've noticed you have a
tendency to ignore this.
I passed this, this lie across to you and you just picked it right up and ran with it.
So you can start to understand like, what are my own weaknesses?
Because AI has learned them about me.

(41:50):
The other thing that I would love to see, and if you can implement this, please do,
is how to stop it just being a sycophant.
like i go into chat gpt and tell it to be neutral and critical all the time but over a few days like
it gets back to being just this this like yes man on my computer where anything i ask it it's like
that's great eight and a half out of ten nine out of ten or whatever but it's like i want i want you

(42:12):
to tell me the truth like can you actually program that in so this is a completely neutral model
uh i hope so we've we've kept the models neutral in the sense that we don't change them
And this, what you're seeing from ChattyPT, we actually don't know what they're doing behind the scenes because everything's closed source.
So it's very possible that they've built something that says like, over time, we want you to just really make Danny feel good about himself because that's going to keep him in the system longer.

(42:40):
And even if he tells you to like, stop, like, just make him feel good about himself.
Like, they could have that in there.
So we want to build something that is totally verifiable.
And so at any time, somebody can go in and say, Maple is handling things this way. And if I tell the AI to be neutral, they're not inserting something in after the fact and saying, I know he said to be neutral, but ignore that directive.

(43:05):
now whether or not we can take these base models and make them neutral and stop having to be a
sycophant that the jury's still out on that i'm hopeful we can there are people there's a company
called dolphin that will take models and try to like rip out some of the bias and not do a full
retrain but do like a minimal retraining of it and so i'm hopeful that we can do stuff like that

(43:29):
and maybe as maple grows we can invest some money in there as well to get more neutral models
but the kind of the commitment we make to the community is you're going to be able to see
everything we're doing and so you can decide if you like it or not and if you don't like it then
go use another product but we're always going to try to be open and verifiable with our users
yeah because I don't want a friend like I want a tool not a friend and it seems to always just

(43:53):
want to be your buddy and the other like big issue especially earlier on I don't know how real this
is sort of as we stand right now but was political bias and I remember there was an example of I
don't exactly remember the model but someone was basically like give me a picture of george
washington and then was like iterating on the same picture being like make it more realistic
make it more realistic and it ends up being like a native american or something and it's like there

(44:15):
was always these like biases within these machines um can you get rid of that or is that a problem
that is kind of unsolvable you just have to do your best to keep training it well you can get rid
of it in in some ways by getting this data set that people are able to look at and verify like
If you just get a good data set, then you can build a model that is not so biased.

(44:38):
But all these models, for the most part, have been trained in closed environments.
And so we can't tell exactly what biases have been put into them.
And then kind of the other problem we have is these models are trained off of written communication that exists on the Internet.
audio communication, video communication, things that are published, and they weight them based

(45:01):
off of volume a lot of times, right? And so if the very people, a lot of people believe that
the media is slanted one way or the other politically, and if those are viewed as the
credible sources and they slant one direction, then the models might think that they're being
neutral. They might be told to remain neutral, but they're viewing one side of the political

(45:22):
spectrum and claiming that's the neutral. So they're actually setting their middle point
on one side of the political spectrum because that's the data that they've been tuned on and
that they view as credible. So how do you then, as a company running an LLM, try and
keep it sort of constrained to be what maybe you and I would say is neutral? I guess neutral is

(45:43):
almost a subjective term. Right now, we haven't tried to go one way or the other on it. We just
take the raw models and we give them to our users. There's a thing called the system prompt that all
of the LLMs, all the companies use where they give it like tons of instructions of like, this is how
you're supposed to behave. We don't have a system prompt in there. Well, we do. It's super minimal

(46:06):
and you can see it. It's open source. It's just like one or two really basic instructions.
So for us, we've tried to just stay hands off and we let users interact with the models directly.
And we have a user just this weekend. He was like, hey, it's Saturday night. You know what I'm doing
for fun, I'm probing every single model on Maple and I'm figuring out which one is the least biased
and which one is like the least leaning one direction or the other. And this person actually

(46:31):
said the GPT OSS actually turned out to be probably the most neutral of all of them, which I thought
was interesting. So yeah, for now, we're not doing that. But down the line, I would love when we have
more money and more abilities and more people to start to figure out how can we influence some of
these models to try and be more neutral and come up with more open standards and open development

(46:53):
around that to be truly unbiased or truly neutral. And one thing that I'm sure some listeners know,
maybe not everyone's aware of, is that this was a pivot from initially like a Lightning wallet.
So this was a pivot out of Mutiny. You guys are Bitcoiners. Where does Bitcoin fit into this?
yeah uh i was really sad that we shut down mutiny i understand why we shut down mutiny wallet um

(47:20):
but hey my biggest contribution oh man the reason i joined these guys is because it was my favorite
lightning wallet and so when i decided excuse me when i decided to leave apple um i joined up with
these guys because i was like this is gonna be awesome let's make this grow huge let's do it um
but for reasons that have kind of been discussed publicly on on blog posts and things

(47:43):
we decided it was just, it was best to wind it down. And so writing the blog posts of how we
were winding it down, that was, that was a task given to me. And it was, it was like through tears,
not literally tears, but through tears, I was writing it to wind it down. We've taken a lot
of that ethos of the Bitcoin mindset, the don't trust verify mindset. And then as well as, you

(48:04):
know, just kind of the open source development and then the privacy aspect of Mutiny Wallet.
and we've brought that into Maple. And something that we've done recently that we've launched is
our new anonymous accounts. And this is a feature that is only made possible because of Bitcoin.
We can't do it with credit cards. We can't do it with stable coins or anything else. We had to do

(48:24):
it with something that was private and that was freedom oriented money that is uncensorable.
So these anonymous accounts, you know, you mentioned you use like an alias, a privacy
email alias when you signed up with Maple. That's been a big rub for a lot of people is just having
to use an email address because email can have some sense of surveillance to it if people are
using like a Google account or something. So we came out with this anonymous account that just

(48:48):
generates a unique ID for you. You have to write it down and save it. If you lose it, you lose your
account. So unique ID, you set a password and then you pay for it with Bitcoin. So no credit card
involved, no kind of know your customer stuff is involved. You can use on-chain Bitcoin,
you can use Lightning Network, you can use eCash that goes over Lightning, you can pick your
privacy model that you want to follow. But for us, that is kind of the holy grail of an AI that

(49:15):
lives in the cloud is you are completely anonymous interacting with it. There's nothing that ties
it back to your identity. And for us, the only way to do that was using an open protocol like Bitcoin.
that's awesome so it's almost like the mulvad model of onboarding customers yeah exactly that's
the similar model that we followed i love that and and in bitcoin like we like to talk about this

(49:38):
idea of if we get to a world of hundreds of thousands of agentic ais that all do like
different very specific tasks like the money that these ai models will use to interact with each
other is bitcoin do you think that's true because i've the reason i've had an issue with that is
Like it makes total sense.
I can, like if I was designing this, that's definitely how I would design it.

(50:00):
But when you have these big tech companies that are like at the heart of all of this
AI innovation, like if they choose to push stable coins to be the medium of exchange
between different models, like do they not win just because of their sheer scale?
Yeah, it's true.
And you look at even government legislation, right?
They pushed through the Genius Act with stable coins before they did this strategic Bitcoin

(50:22):
reserve act. So it does seem like things are going more of the stable coin route and you have Stripe
that's working with stable coins now and you have Tether working with Rumble. And I know Tether's a
Bitcoin company as well, but they're also stable coin. So I think from a technical standpoint,

(50:43):
Bitcoin, maybe e-cash or something on top of Bitcoin, I think that makes the most sense.
but I'm less optimistic that that is going to be the outcome. I think the only way that becomes
the outcome honestly is if Bitcoin becomes like the reserve currency of pretty much everything
we do then yes maybe there's stable coins backed by Bitcoin that become the engine that fuels all

(51:06):
these AI credits and compute that we pass around. So yeah I mean it's your guess is as good as mine
I think that I think you're right that there is a huge, huge conglomerate of tech companies and
government organizations that would love to push a different direction.
Damn, I hope you were going to be turbo bullish on Bitcoin there and there. You were going to

(51:26):
change my mind, but that's maybe a bit of a black pill. We've got to make Bitcoin the
global reserve currency and then everything else follows. And that's what will happen.
Okay. Easy task. We'll get there. When it comes down to like the compute backing AI,
It's obviously been an insane year or two for all these companies, like data center companies.

(51:47):
How sustainable do you think that is?
Because there's obviously a lot of talk on like AI bubble type things.
But the sheer power that is needed to train and run these models, is that like a trend
that we're at the beginning of?
Are we in the middle?
Like, where do you see all of that?
Yeah, the training of the models is significantly more power intensive than the using of the

(52:09):
models.
I like to kind of frame it where you've spent decades of your life learning everything that
you've learned up to this point.
You and I both have, and that's taken a lot of energy, a lot of time, a lot of work.
But now you and I are sitting here having a conversation, and this is just an hour that
we're spending, an hour and a half, however long this ends up being.
And so that's significantly less work.

(52:31):
And that's really how the AI models are.
So training the models takes a lot, using them, not so much.
and I think that I think we're going to see some breakthroughs where training is going to become
easier and less power intensive and so there will be more of a focus on just inference which is the
using of the models now is there a bubble there bubbles really are just a malinvestment or too

(52:56):
much investment in something right and so you blow it up you invest in all these things and then the
bubble pops. A lot of people think when the bubble pops, it's like a soap bubble that pops and it's
just gone, right? It disappears from being out up in the sky. But really when the bubble pops and
something like with AI and building out these data centers and all the power generation and stuff

(53:17):
is we're still going to have all that infrastructure. And there will be some winning
companies when the bubble pops. There will be a bunch of losers that got invested in. And I've
heard it framed that bubbles are actually important for building out new technology, because if we
were super methodical and only invested in the things that we knew 100% would work or 95% would
work, the innovation will go too slow. And so we actually almost have to throw money at a lot of

(53:42):
things and just hope to see, you know, which ones work and which ones don't. And knowing that there's
going to be some failures. But what happens is when it does pop, we end up with some really strong
companies and a really strong infrastructure that can kind of move things forward from there,
which is really how the internet worked with the dot-com bubble.
And when, like, obviously you're in Austin and it very quickly became like the home of Bitcoin mining, Texas, like every major, like basically every major public Bitcoin company, Bitcoin mining company had at least a site in Texas.

(54:16):
A lot of those have now pivoted, obviously not just there, but throughout the world to being AI because they can make more money.
Do you think that will be like a growing trend where these Bitcoin miners will continue on the AI stuff?
Or do you think that's almost like a short-term grab before moving back to Bitcoin mining?
How do you see that kind of evolving?
Yeah.
So my understanding from talking to a lot of these Bitcoin mining companies is it's actually not about like the computers in the data center that they're just switching from Bitcoin mining over to AI compute.

(54:46):
A lot of people think that's what it is.
Really, it's the power contracts.
so we have all these ai data centers that are spinning up and they need energy they and they
can't get it either the energy is already being used elsewhere or there aren't enough transformers
coming in there's a backlog on transmission lines and other things and so these bitcoin miners are
saying hey we're making this much money mining but then we have microsoft over here who wants

(55:09):
our power and so we have this contract with the local utility we'll we'll start making money off
of Microsoft instead of mining. And that I see as a temporary thing until we start building out
these small nuclear module, you know, the SMRs, the modular reactors, and those kinds of things.
And you co-locate them right on site with the AI data center. It's not even part of the grid. It's

(55:33):
just, you know, for the data center. And I think that's long-term what we're going to see 10, 15,
20 years down the road. But in the meantime, we're going to have some Bitcoin miners who are always
looking at their bottom line and saying, what's the best for me right now? Do I sell to an AI data
company or do I mine Bitcoin? And that's going to change here and there depending on the market.

(55:54):
It's $94,000 a coin right now. So yeah, maybe they're selling to AI people,
but when it moons to 500,000, then they're going to maybe start mining Bitcoin again.
And do you think part of this that's being driven by like these big tech companies that are just
willing to throw money at it? Like I even saw Facebook offering a hundred million salary to
developers, like high-level

(56:14):
developers from OpenAI to move across
plus 100 million bonus.
It's insane. If they're just
willing to throw money at this to be the
first one to get to AGI or whatever it is,
presumably this has some legs.
Yeah, I would
think so. They're not going to waste
all that money. They don't have endless firepower
to spend on things. So they
definitely see something, and

(56:36):
that's the direction they're moving.
So,
yeah, I mean, I think it's
I'll say this.
AI as a technology has so much promise and we already seen enough utility out of it that it here to stay So that that kind of a foregone conclusion in my mind Now it just like

(56:57):
how do we build it out? And these companies are going after these massive power contracts
and so that they can, they can do what they want to do. So I don't know. I, yeah, I mean,
I don't know the, I don't know the final end result there, but I don't think that they're
just wasting their money and so it might it might not be in the the direction that we see today right

(57:21):
with ai chat there's going to be more products out there there are going to be you know the things
that you wear there's going to be glasses there's going to be stuff in embedded in your mind there's
going to be robots they're building for 10 years from now they're not building for right now it's
not just going to be image generation and trying to do studio ghibli stuff it's it's going to be
like way bigger things down the road and that's what they're trying to lock down yeah i'm interested

(57:44):
What do you think that will be? Because when it comes to like the wearables, sort of real world
physical objects that are like AI, do you think it will be like a necklace, like that friend thing?
Will it be robotics? Will it be, you know, AirPods? Like where do you think the kind of
final form factor will be for AI in like every everyday life? Yeah. Well, I think, I think the

(58:06):
ears are probably one of the best spots to put something like that. People keep talking about
like you're going to wear a pendant or something, I think that's kind of a dumb place to do it.
It's really your eyes and your ears. Those are like the two biggest sensory input
points for you as a human being. And so I think that's going to be where a lot of it is.
And then as much as I like dislike this idea, I do think that there's probably eventually

(58:29):
something that's tapped into our brain and just kind of skips those senses and just goes straight
in and hardwires in. So then it's just a matter of how do you capture the data to feed into those
wires that go into your brain and it'll be, it'll be your eyes. And so it'll be something that you're
wearing. And man, I hate it. Like I do not like that future where we've got cameras everywhere

(58:49):
and, and microphones and everything, picking it up. The only way that I see it being okay is if
this technology is built in the open and we can inspect it and we can verify it. That is a future
that I would love to see. You think about self-driving cars, you've got the Teslas that
self-drive and the Waymos and things. There's a, there's a, there are a couple of projects that
are building open source self-driving. And so that is something I'd be fine with. If I could verify

(59:15):
the firmware that's going into my car and know that like, it's not going to drive me off a bridge.
If I say something that's, you know, politically incorrect while I'm driving the car, like I want
to be able to verify that kind of stuff. So it is possible we can have this world with all these
amazing wearables and stuff, as long as we can inspect how they're being built and what's,
what's driving them yeah i think the neural link is definitely going to be like i think that's

(59:40):
probably the sort of end state of this um and it's both terrifying and kind of awesome like i i don't
i don't want one yeah but when you see these people who are like quadriplegic who can't do it
like are literally just you know sat in their wheelchair unable to move unable to do anything
and then they have the neural link and they can like play video games and communicate and like
that's a use case that's awesome.

(01:00:02):
Like I'm all for that.
But the idea of every single person in the world
being chipped and being like tapped into this global AI model
is kind of terrifying to me.
That's like full dystopia.
Yeah.
And the way you phrased it, the global AI model, right?
I would hope that it's not one global one,
that it's a bunch of different ones.
Sorry, I interrupted you though.
Yeah, maybe there's multiple chips,

(01:00:23):
but it's, I don't know, it's a scary world.
I'll probably just start farming or something at that point.
Yeah. How are you going to keep up, man? Like if everybody's using it, if everybody's getting
huge productivity gains off of these chips and their brains, I'm just, I'm hoping I don't have
to keep up by that point. I can just run away and be with, uh, maybe that's where the Bitcoin

(01:00:44):
Citadel has become interesting. And this like a no chip Citadel have real conversation.
Cause it gets to the point where it's like, who am I talking to? Am I talking to you or
is this just chip talking to chip and I'm just a physical embodiment of AI?
yeah i mean are we all like is this conversation in the future is us two staring at each other
and like our minds are going back and forth it would make a great podcast no no it wouldn't

(01:01:08):
um okay so what are the like other things that you're excited about both
just generally with the ai stuff and uh and maple um i mean i love the idea that if we can build an
ecosystem that's not just us right i don't i don't if somebody walks away from this podcast i don't
want them to think that like mark is going to save the world you know from from evil ai like that is

(01:01:31):
not what we're trying to do my title yeah dang it you can you can clickbait if you want to but um
no what i want is i want to be part of an ecosystem and if it's not robust yet then maybe we can help
like inspire more people but we need people building out in the open and opposed to what
open ai says they are they're not open at all they're closed ai and so we need to build truly

(01:01:54):
open AI that is verifiable. And that's really the only way that we can build a society that uses this
tool and can make sure that it's serving us. Because I think that AI has the ability to upgrade
humanity, but we need to make sure that our humanity is preserved in the process, that we
don't lose it as we embrace these tools. So that's what really gets me excited is the ability for us

(01:02:21):
to kind of build out in the open.
We're obviously doing it in the cloud with Secure Enclays,
but I would be remiss if I didn't mention local AI, right?
The most private way to use AI
is to run the model directly on your device,
turn off the internet,
and now you're just talking to this thing
that's on your laptop or on your phone.
And nobody knows what's going on there.
And it's just you having a conversation with this tool.

(01:02:43):
And that's really what it should be is a tool.
And local AI is awesome, most private.
The problem is that it's just not powerful enough
for some people. And so we're trying to build this middle ground between like the most private thing
on your laptop and then what JGPT is selling to people. We want to be the in-between. And I think
that eventually with Maple, we get to a local state as well. So with this memory thing we're

(01:03:06):
talking about, we didn't even talk about some other things that we want to work on, like data
integration into your phone. So you have your health app on your phone, your fitness app, your
journals, your other things on your other apps on your phone that is very personal to you,
you probably have a line drawn in the sand. You're like, Sam Altman is not allowed to get into my
journal entries. But if you can verify the open source code of Maple and you trust it because you

(01:03:32):
can see what it's doing, then you start to let it into those spaces. So I think there's a lot of
really cool stuff we can do where we can make Maple the most personal AI, the most useful AI
to you because we've built it with this data privacy. And that's really what it comes down
to when you asked earlier in the conversation, you know, these companies with hundreds of employees
and thousands of employees, how do we compete with them? We compete with them because we actually

(01:03:56):
build the AI that people trust. We build the AI that can get most personal with you. And so it's
going to know you better than ChatGPT will ever know you because you're self-censoring yourself
when you talk to ChatGPT. You're holding back. And even people who give it everything,
they still hold back a little bit. My hypothesis is that they aren't totally brutally honest with

(01:04:18):
JATCPT because they know that they're sending their information to somebody else. And so we
would love to build this place where people can get the most personal because they can verify it.
That's awesome because that's 100% me. The stuff that I just refuse to ever put into one of these
like big tech LLMs that if I could, if I know like with Maple that that's completely private,

(01:04:39):
that I'd be more than willing to share.
And then you do get a way more powerful AI model
just on the base of what you can actually
willingly share.
So that's very cool.
I totally understand that as like a business model.
I think that's awesome.
Can we just talk a tiny bit about Bitcoin
before we close out?
Because I'm interested in your perspective
of like where Bitcoin development,

(01:05:02):
like Lightning Network,
actual like usage of Bitcoin is going.
Because obviously you tried to run a Lightning Wallet.
You had a very cool one.
and then end up closing that down.
Are you still bullish on these things being built
on Lightning at the moment?
Yeah, I use Lightning pretty much every day.
A lot of it is Noster usage where I'm zapping people,

(01:05:22):
but then I pay for things.
Over the weekend, I used a square terminal
and paid for something with Lightning.
It was great.
It was so cool just to walk in.
The person at the cash register, I said,
hey, can I pay in Bitcoin?
They're like, oh yeah, here you go.
Boop, they hit one button and it popped up with a QR code
and I paid, I use my Primal wallet,
but like I have probably five different
Lightning wallets on my phone.

(01:05:43):
All of them work really well.
I never have payment failures.
So I do love that.
I also still love the idea of on-chain Bitcoin
and I still like using that.
And with fees being so low,
like it's almost like why not keep using it?
So I think on-chain Bitcoin,
let's keep pushing it and while,

(01:06:05):
let's keep using it while we can,
because that's like the most censorship resistant form of it.
The other L2s, like you have ARK, you have Spark,
you have some of those other ones.
I have not stayed as current with them
because I'm no longer building a lightning wall on myself.
I've kind of moved away a little bit
from staying totally up to date.

(01:06:26):
But the thing that I do continue to follow
is the whole eCash stuff.
And I think eCash has a real big,
it's really promising,
both on the cashew side and on the Fediment side.
And we might see other ones that come out,
other kind of mints and other kind of eCash stuff.
Because I think it's this really cool marriage
of on-chain Bitcoin, lightning,

(01:06:48):
and then something that is like a bearer token
that you can actually pass around.
I don't know if you've done it before,
but with eCash, like I can airdrop money
to another person peer-to-peer
from my phone to somebody else and they get it.
And it's like, they can use it right then,
just like a dollar bill that I'm handing to somebody.
So to me, I dig in more there.

(01:07:09):
And then I hope that other L2s come along
that have other cool things
and we can continue to build and scale this thing.
Yeah, one of the coolest things when it comes to ARK,
I was at the Baltic Honey Badger Conference
earlier this year.
And I used like my cashier wallet to buy a beer.
And I didn't even know until after the event
that everyone, all the merchants at the event

(01:07:31):
were using ARK.
So I'd used eCash, obviously Lightning, then to ARK without even knowing it happened.
Just like seamless, like the way I would always pay with Bitcoin.
And it's just like using these different L2s to complete the payment.
And I had no idea that's how it was working.
I think that was really cool.
Like the UX has got to a point where it's like pretty easy.

(01:07:53):
And when you go into like a Square Merchant, I don't have the US privilege of having done
this yet because it's only available in America.
But like, how does it work?
Do they have to press a different button on their cash register to actually pay in Bitcoin?
And is it just lightning or can you do on-chain as well?
My understanding is it's just lightning right now.
And yes, they do have to push another button.

(01:08:13):
I know that the Square team is already looking into making it on every screen.
So when you go to pay, the Square terminal has a screen facing the user and it'll say,
do you want to tap your phone to pay with Apple Pay or something?
They could just have a QR code already on that screen.
So if a Bitcoiner wants to pay a Bitcoin, it's just right there.
They want to get there eventually.

(01:08:34):
They're just not there yet.
And then obviously the biggest hurdle is they have to turn on Bitcoin to begin with.
And that is required by some kind of admin, somebody who has like elevated privileges on the terminal to turn it on and activate it.
And there might be also some like know your business kind of stuff, KYB, where maybe the store is on an older version of Square Terminal and they never went through some of the documentation, government ID kind of stuff.

(01:08:59):
So there might be some of that they have to do too in order to activate it.
But once it's activated, yes, it is like, hey, do you accept Bitcoin?
Oh, I want to pay in Bitcoin.
So there's a little bit of friction there.
And I would love to see them remove that to make it even more seamless.
Yeah.
Do you think this is maybe a bearish question to even ask?
But do you think it'll work?

(01:09:21):
Because we've seen people try and convince merchants to accept Bitcoin in the past.
Like around 2017, 2018, there were a ton of company or a ton of businesses where I live in Brisbane that were accepting Bitcoin.
And you slowly saw those, we accept Bitcoin here stickers get pulled off Windows because no one actually used it.
Do you think this is different because Square is such a huge company with so many businesses actually integrated?

(01:09:45):
Yes, I think it is.
And there's also so many different wallets out there now that work really well.
so from a user standpoint
like it's really easy to
and Cash App being the biggest one right
I think that's really what it is
it's this company that's come in
that has all of the pieces
they have an app
a user end
an end user app with a wallet

(01:10:07):
that is used by tens of millions of people
and then they have the merchant side
and then they've also built in
the financial incentive for the merchants
where it's like it's zero fees
through the end of 2026
so no fees there
and they're making it so that
you don't even have to pay with bitcoin you can pay with your your usd balance i don't know if

(01:10:27):
you've seen this but you can actually pay with your cash balance but then it goes over the bitcoin
rails and then settles in cash again that's how the merchant wants it so um they're just using
more like strike is doing where lightning is just the rails between the two intermediate the the two
parties but they're both exchanging in their currency that they prefer to use so i do think

(01:10:48):
think that this time is this time is different the famous phrase I do think that it is because
there's such a critical mass of people now around the world that know about Bitcoin and have it and
then the merchants can see there's this this history now that shows that Bitcoin appreciates
over time due to kind of the the scarcity of the asset and Square makes it so easy for them to

(01:11:12):
slowly get into it right they don't have to like go all in on Bitcoin they can just go a little bit
if they want to. And then as they see it grow, then they go into it more. And then one other
piece I'll throw on there is that that burger company, Steak and Shake, that famously started
accepting Bitcoin before Square turned on their stuff. Steak and Shake has had, I don't know if

(01:11:34):
they're public earning reports, but they've come out and said, hey, this Bitcoin thing is going so
well for us. We've actually accelerated some of our store openings and our expansion. Our company
is in a much better financial state now because we just went in and started doing this Bitcoin thing.
So it's going really well for us. So that's a case study now for merchants to look at and say,
all right, we have like 9% margins on this thing. How do we get this 3% fee reduced down to zero?

(01:11:57):
That creates even more margin for us. And then we can also start saving in Bitcoin,
which allows us to expand our operations in the future. So I think there's a lot of bullishness
there. Yeah, that's very cool. Because they're doing their Bitcoin strategic reserve now as well,
which is awesome to see.
And I love that when Square made this announcement,
the fact that they're using Bitcoin as the payment rails
kind of regardless of currency in, currency out,

(01:12:19):
it was almost like a hidden thing.
And to me, that's the coolest thing that they've done.
Like, I think that's absolutely amazing.
And it was like a stealth launch
that I think Miles first announced on Twitter
almost accidentally.
Like, it is really cool how this has gone so quick.
They're shipping a crazy amount at the moment over a block.
Like, they're doing so much cool stuff.
Yeah, they've been shipping like crazy.
it feels like miles kind of won the internet last week he was just like on there like this was his

(01:12:44):
this was his week and uh it was awesome to see like my hat's off to all of them over there i
know it's just not him it's a whole bunch of people working on it but that's been great to see
um the last things is i would love for people to just kind of think about how they're using
using ai um and kind of picture it as when you're using some of these systems like chat gpt

(01:13:05):
that you have another person sitting in the room,
excuse me, you have another person sitting in the room
kind of watching everything that you do
and they are approving or rejecting what you do and say
and they're making copies of it.
So just kind of have that in your mind as you're using it.
And then I would love for people to sign up for Maple.
They can go to trymaple.ai.

(01:13:25):
They don't have to stop using ChatGPT,
just add Maple into your toolbox
and then use it for things.
And you'll start to see like,
hey, that person that was sitting in the room with me, listening to everything that I'm doing
with my AI, that's not there in Maple. And start to notice how you use that differently and how
maybe you're more free to speak. And that's really our whole thought around Maple.

(01:13:49):
Maple is the AI that allows you to think freely. And what I mean by that is just there's nobody
who is trying to get in your way. And there's nobody that's going to hold you accountable for
anything you say because when we think in our mind we think all sorts of things and they're
and that's that's how we're supposed to work and so we if we're going to use this ai tool to help

(01:14:12):
us think we shouldn't have some intermediary in between telling us no that's not okay to think
that way um we should be thinking freely yeah so i mean people should definitely go check out maple
and i'm actually like this has made me reconsider how i'm using ai i'm gonna use maple more and more
because I think a big part of the reason
that I'm kind of stuck on ChatGPT is just habit.

(01:14:34):
So I'm going to try and break that habit and use Maple.
So this week I'm going to be Maple only
and we'll see how it goes.
Okay.
I'll give you some feedback.
Oh, sorry.
What I want from you is like,
tell me the features that you're like,
I must have that in Maple.
That's really useful from people
so we can start building that.
I do think for me, the big one will be memory.
If we get memory, like that's awesome
because like now it's no longer sort of

(01:14:55):
restrained by the training data
that you can actually search the web.
Like that's huge.
And if it then had memory,
I think that's kind of everything I want really.
Talking to it would be great,
but I only do that really occasionally anyway.
But if it knew a bit more about me in a private way,
that's basically everything I need.
That's awesome.
Let's go.
All right.
Thank you, Mark.

(01:15:16):
And where do people find you on Twitter
if they want to follow you?
Oh yeah, it's just my name, Mark Suman on Twitter.
And then I'm on Primal Noster.
I'm just marks at primal.net.
You can find me there.
Awesome.
Thank you, Mark.
This has been great.
and I'll hopefully see you in Austin soon
let's do it

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Bitcoin, AI & the Coming Surveillance State | Mark Suman

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.css-14f5ked{margin:0;word-break:break-word;display:-webkit-box;-webkit-box-orient:vertical;box-orient:vertical;-webkit-line-clamp:2;overflow:hidden;}Bitcoin, AI & the Coming Surveillance State | Mark Suman