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Hey, everyone.
Welcome to this week where we bring you the latest discoveries, information and thought-provoking conversations about AI robotics energy longevity, Bitcoin and any other abundance producing technology.
On today's show I have two software engineers that I respect immensely to talk to you about artificial intelligence and where it's all going.
The balance between large language models and their lack of privacy, how that might get resolved in the long run, what this means for human rights and many other really important topics.
My guests, Justin Moon and Shrumanik, are leading engineers in the Bitcoin space and have crossed over and started programming and using AI.
I have no doubt you guys are going to really enjoy this conversation.
So let's go ahead and jump right in.
You're listening to Infinite Tech by the Investors Podcast Network, hosted by Preston Pysh.
We explore Bitcoin AI robotics, longevity and other exponential technologies through a lens of abundance and sound money.
Join us as we connect the breakthroughs shaping the next decade and beyond, empowering you to harness the future today.
Hey, everyone.
Welcome to the show.
I am here with Shrumanek and Justin and I'm excited to get into this topic because wow, there's a lot to cover.
But gentlemen, welcome to the show.
Hey, thanks for having us.
Yeah, thanks for having me.
I remember listening to this in the very early days.
We're just dipping your toes into Bitcoin.
Yeah.
I feel like we might be back in the same on the AI.
Yeah.
It's interesting that you say that because for me, when I started doing the show and I was covering Bitcoin every single week, I had a lot of people in the value investing space that were like what in the world are you doing?
Why are you covering this exclusively?
And now I kind of feel a little bit like it's the same thing, because I'm broadening the aperture into tech and things.
So it's funny you say that, Justin, because it does feel like that.
Okay, so here's where I want to start.
Justin, you were on stage with Jack Dorsey in Oslo and you guys go on stage and you're vibe coding, putting on a demo for everybody.
And I was looking around on because I've heard about this from different people in the space about what you were doing with AI.
And I'm trying to find the video of it, and I can't find a video of this anywhere.
So can you tell us the story of how it precipitated?
How did you find yourself on stage with Jack Dorsey vibe coding in front of an audience?
Yeah.
So this year at the Oslo Freedom Forum, which is hosted by HRF, there's obviously many, you know.
There's a main stage, big main stage, with a huge concert house in Oslo.
And that's where the main event is.
But there's a lot of little side events.
And Yeah, we were at a little side event that was more focused on Bitcoin.
And a lot of our Bitcoin friends were there.
And HRF had just announced their AI for Individual Rights program, which I'm technical advisor for.
And we wanted to discuss the topic with Jack because Jack's a huge proponent of AI as well.
So...
I just had some marching orders.
Hey, let's interview Jack.
Right.
And so, you know, maybe so I had like a list of stuff.
It was actually about five minutes, 10 minutes before like Jack comes up and he's like hey, what if we just vibe code something?
I got my laptop.
Right.
So I'm like, oh, God, you know, as the interviewer, you want to be somewhat in control.
Yeah.
No vibe coding is no one's in control.
It's pure.
It's non-deterministic.
You might get it.
You may get some good.
You may not.
And so we're in a room of some movers and shakers then.
You know, Jack pulls his laptop out, puts a hood on, some sunglasses, and we start making a website.
I think it's called the Africa Bitcoin Institute.
We find ways to improve the regulatory environment for Bitcoin companies for individuals.
And yeah, we fired off a prompt.
And then I interviewed him for 10 minutes.
And then right about 10 minutes, we're talking.
All of a sudden, the audience goes, whoo.
That's because this website just popped up.
Goose, this is the coding agent that Block, Jack's company, has created.
Open source coding agent.
They were using, I think, anthropic models.
It just spat out a beautiful working website in just 10 minutes during an interview.
So it was really neat.
It was a private event, a little side event, so I don't think it was recorded.
Unfortunately, my mother had the same thing.
She was like, oh, I'm sure I saw this video.
But yeah, I think that one just is apoptical at this point.
But yeah.
What was the core message?
I mean, I think I know what the core message was, but from your point of view, what was the core message?
So of course, this has been like four or five months, so now I probably don't remember exactly what happened.
I remember how I interpreted it, but part of it was talking to Jack.
He was actually talking to me.
We were talking about this before, and I was telling him how I've been using these tools.
I was still looking at the code, and he's like, Justin, you're in jail.
You're in jail.
You have to free yourself from the jail of programming languages.
You need to get the computer to work for you now.
This is a new era.
That's kind of what it is.
It's like for developers you can kind of free yourself to some degree from having to be so microscopic and worry about the semicolon.
But for people who weren't programmers it's like well, now you don't have to worry about that in the first place.
You told yourself in the past that in order to create software, you need to learn where all the little semicolons go, which is a horrible exercise for most normal people.
But now you can utilize these AI tools effectively, and there's some limits obviously, but you can make a great personal website.
You can make something to plan your garden like you can.
There's all kinds of tools, kind of personalized software tools that you can create, or you can vibe code.
You know a bluetooth mash chat app that is being used in protests in nepal at the moment.
That's what jack dorsey did, because you know, as i think, i think that one of the interesting things is he was talking about how he spends like three hours a day doing this.
Every morning he designs his day so he can get three hours.
He's always trying to push the limit of what's possible, and that certainly opened my eyes.
It's like okay, if he's able to do this, talk public company, maybe the rest of us might uh, level up a little.
Talk to us about that.
What is this three hour thing that you're saying yeah, so no.
So i was asking jazz, like how do you have time to do all this stuff?
He's doing all this vibe coding and it's like, aren't you busy running a company?
And he's like yeah, i just set my schedule, so every morning for three hours i can play with these tools, because i think this is the future of Like kind of the economy, right.
And small business and all the things that square and block exist to serve.
So it's my responsibility to kind of understand what the frontier is.
And the only way you can do it is by trying.
And yeah, he also had a good message.
Try to figure out what it can't do, right?
Like push it to the point where it fails.
And that's where we're at.
We're here at Learn Madeira.
Sumanik and I are in Madeira at Gigi's Sovereign Engineering Program.
So this is like a program.
It's like an idea factory for Nostra, Lightning.
Now this time it's more AI focused.
And that's what we're trying to do.
Every day we try to get it to do really ambitious things.
And, you know, don't just make the website.
See where the boundary is and always try to push that boundary.
This idea of the semicolon for people that aren't programmers.
I think what you're really saying is you know, you get really good at a particular language of code and you master that you know exactly where the quote unquote semicolon goes, so that you're not making mistakes, but then you really get optimized into only coding in that language.
And if you do go outside of it, it's here and there to kind of, And for me the thing that's frustrating is like I become, like I'm not natural.
Like some people are just naturally like their minds think like a computer.
Right.
Like I've worked with a few people like that and they're great to work with.
But mine's not like that.
So I have to become almost a different person to be like a really good programmer.
I'm not good at talking to people.
I'm not, you know, I'm less friendly.
I'm more literal.
I'm not going to be drawing in my spare time.
I almost have to become a different person, and after a while it becomes a little exhausting, so you have to turn it off, and so that's a really fun thing about these ai tools is they allow you to just stand like one level higher where you're.
You're sort of orchestrating it and you have a higher viewpoint, and i find it it's kind of a little more creative and a lot less.
You know, it's less manual labor in a sense.
Yeah yeah, you're able to kind of step back, see the bigger picture, and if there's a certain type of code that is more optimal for use, that is nested underneath of the larger program, you don't have to go in there and become the expert on it.
You can really kind of leverage this AI expertise to do it.
I want to go in... So I have... Yeah, go ahead.
One really interesting backcoding story.
I was looking into Cashew, which is this like eCache protocol.
And I
I was working on a Python library and there was not really a cache Python library for the specific thing, what I was trying to do.
So I vibe coded like a completely new library from scratch, which is like kind of hard because you think like building programming libraries is like more of the thing that like senior engineers do or like really experienced engineers.
And with I use like cloud for Opus, which is pretty expensive, but I could even create really complex parts of the software within a short amount of time.
And what's also interesting, this was, I think, the most amount I spent on AI.
So building this library costs like 400 bucks just off AI compute.
You can burn through a lot of money just by doing these things.
This is a really interesting one.
I want to expand on that.
And this is like one thing I think we want to reflect on thinking about what AI means.
And this is a software.
We're taking a little bit of a software angle, but you know it is.
That's kind of the one, the thing that AI has been probably best at so far.
So an interesting thing about software thus far is that it's not capital intensive, right?
Like the story of Elon Musk's career was that he started making a little thing.
It was called like ZipFu where you can make a website, right?
And so what he'd do is he would leave his.
He had one computer and he was like renting a dorm or something.
And he would leave his computer running during the day and that would serve people's websites.
And at night he would code the websites, you know, so he just reversed his sleep schedule.
And that took no capital to start that business because he just used repurposes existing computer.
And you know, on a shamanic store here.
It's one kind of new thing about AI is it's turning software into a bit of a capital intensive endeavor, right.
Like if you want to be like, we were talking about how much these things cost.
My approach is like you especially if you have a productive use for this stuff, you should throw as much money as you can at it.
You know.
So, like last time this sovereign engineering ram six months ago, the top level vibe coding setup was like 20 bucks a month.
Now, today, it's 200 bucks a month.
There's a new level of max a year.
What if it's two thousand dollars a month?
Like now, all of a sudden, it's something where it's much harder to get started in it.
And you know, maybe in five years it's like 50 grand a month to engage in software engineering right.
So it could become more like other disciplines, where if you want to build a bridge you can't just start, you know, in your garage anymore.
So that's one thing that's very interesting about this whole Zybe coding phenomenon.
Well, I think it's also about how many agents can you maybe run in parallel.
At the end of the day, because there's so much training involved, where you're taking these massive data sets and you're training it on something, on a pattern that's very specific, to solve a problem that you're trying to go through.
When you go far enough upstream of that, it's energy to plow through the GPUs to come up with the pattern or the weights of said model that you're trying to train.
And so it is interesting to see.
And, of course, as Bitcoiners, we can go back to Bitcoin kind of being that fundamental energy unit that's being exchanged, but we'll kind of leave it there.
But it's a really interesting point of that transition taking place because people are trying to train their own models.
This has been a beautiful thing, actually.
It feels like, you know, a couple of years ago, on Bitcoin Twitter, for example, our group would be fighting against Silicon Valley people saying OK, we should have more energy.
Using energy is not bad.
Emissions can be bad.
Pollution is bad.
Creating energy and consuming energy is not a priori bad.
And we would lose all these arguments.
The Silicon Valley people generally wouldn't listen to us.
They wanted their green data centers at any cost.
And this has totally changed, right?
All these Silicon Valley companies are now... I just saw a story.
Facebook's doing a huge natural gas-powered...
AI place in Cheyenne, Wyoming.
And I think it uses as much energy as all the homes in Wyoming combined.
It's amazing to see how Silicon Valley saw the light on energy and energy production through AI, which we, of course, failed to teach them through Bitcoin.
Let's take a quick break and hear from today's sponsors.
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All right, back to the show.
All right.
So Justin, you've been pioneering this idea of AI for activists and you're working a lot with the Human Rights Foundation.
Help us understand what initiative or what you're trying to accomplish with that.
Yeah.
So I think pioneering might be a little.
I don't know if I'd use that word myself, but Craig Vachon and the director and Alex Gladstein, the head of strategy at ITREF, are really in the way.
I'm just kind of more supporting role.
I would say, but you know, we're, I guess, you know, zoom out.
It's funny.
So I'll tell a little story here.
When Alex first reached out to me about doing this, about the kind of helping with the AI program I had recalled, I pulled up his Twitter and I searched AI.
Cause I remember he had opined about AI and it was always the framing.
This was like five years ago.
It was the framing was like, you coined this for freedom.
AI is basically totalitarian, you know, communist control.
So it was kind of like, it was interesting that he had this idea.
And so I asked him, like you know what the what changed?
And his answer was basically, they're already seeing kind of like with bitcoin.
Why did hrf get involved with bitcoin?
Because they saw that it helped activists get money into the ukraine.
The banking system couldn't do things that bitcoin could right, so that's how hrf got into bitcoin.
Is that they were able to do things in their democracy activism with bitcoin that they couldn't do in any other way, and they were seeing the same thing with ai.
Right, you got to write a lot of grants.
If you're an activist right, it's a quarter of your job, And so now all these activists found ways to write a grant four times faster right, using ChatGPT or something like ChatGPT, which had, you know, some drawbacks.
And so I think that was kind of the spark like okay, this is actually really helping activists be more productive in their work, right.
And that's, I think, for HROF.
The most important thing is to empower activists, to help them win at whatever their fight is.
And so I think it grew up and I was like, so what could Ahrefs do?
And so we've started to have quite a lot of things.
We've done education initiatives.
So at the Oslo Freedom Forum I think I did like 10 hours of workshops teaching people to use JAT-GBT and image generation and transcription and all these different things.
And we've kind of continued some of that.
We're starting to do some monthly webinar type stuff.
I think in the future we may be funding kind of freedom, sovereign oriented projects, AI projects, you know, grassroots AI things like you've seen HRF do in the past, holding more events to bring people together, bring activists together with technologists, together with philanthropists, stuff like that.
These groups that kind of never talk to each other, right?
You see a lot of that, you know, the chat I kind of referenced it earlier, right?
That's one of these things that the Bitcoiners created.
They really would have never...
Maybe even seen the need for if they hadn't been introduced to some of these activists by HRF.
So, yeah, that's where it's at.
We just started six months ago and we're just kind of getting things up and running.
But, yeah, it's really exciting.
And, yeah, you can see an awful lot of promise here.
I think BitChat's a perfect thing to kind of talk about on this particular topic, because here you have a new app that enables communication without having to go up and through the whole wi-fi network or internet network.
You can use the vicinity of you know.
If you're in close vicinity to somebody, you can use the emission of the phone in that close proximity to have communication.
And so BitChat was an application that Jack Dorsey has recently released.
How much vibe coding?
What's the rumors on how long this took him to put this together?
And just as a reference, this is doing really well on the App Store.
I mean, it's gone out with quite a splash.
Now, it is Jack that coded it, so you get a lot of marketing kind of automatically through that.
But I think the idea to the actual release, to people using this and how it fits into this activist layer where you're not having to communicate over traditional landlines, is pretty miraculous.
So what have you heard on the inside on this particular application?
I have no inside info, but I would guess it was like a week or two, because there was a period there where he was trying to do once a week, you know, one lap a week.
And I think that's about right.
I mean, this is kind of the one of the downsides of vibe coding.
I was just joking with a friend who had a vibe coded a project last week and it was working really well.
And now this week, it's just like totally stalling.
It's like, yeah, but you're pouring water into a jar.
And it's, oh, look at the water rise.
Look at the water rise.
And it gets about 80% of the way there and it just starts pouring out the side, right?
That's sometimes the experience of vibe coding.
So, but, you know, for about a week, you can oftentimes really, really do great.
And I think maybe I'm not sure how involved Jack is now, but I bet there's a lot of other people who are contributors.
I can just see on GitHub.
I'm not participating closely.
He just made an iOS version because I think he has an iPhone.
And then Kale, the creator of Cashew, was like, let me try to vibe code this in Android.
I'm an Android developer, has never made an Android app.
And he, similarly as Shumanik, mentioned how he attempted with a Cashew library in the past.
He was able to get it to translate it from one language to another.
This was historically a very difficult task because you'd have to learn two different, totally different ecosystems, ways of doing it.
Each one of them would take a few months to really get intuitive at, be able to understand.
And he was able to do this, I think, in about a week.
So these are, I mean, both kind of shocking examples.
These are the types of things, you know, I don't think any of us would have really believed a year ago, but it's slowly starting to happen.
And yeah, very exciting.
And, as kind of a kicker on top of this Kali, the person we're talking about that took the iOS version and turned it into an Android version.
Hasn't he got the cashew basically tokenized Bitcoin, saleable Bitcoin you're able to transact without an internet connection is what I'm getting.
Through BitChat, you have to be using BitChat.
Yeah, so all these things I think are really interesting.
You think about the things in Bitcoin that work and the ones that don't work, like BitChat and Cashew and some of these other things that are like.
It's not like the optimal solution always right.
If you're communicating, it's probably nice to be able to send money through the whole internet at times.
Sorry, some messages through the whole internet, right?
Across the internet, not to have to use Bluetooth.
Or if you're transacting, sometimes it's nice to use on-chain Bitcoin, right?
Right.
Like there are these kind of like the right way to do it.
Or maybe sometimes these are a credit card or something.
Right.
But the thing about interesting like about a cash or a bit chat is that it'll always be there.
Right.
Like you can always use that.
It's like a backstop of freedom.
Right.
It's something that kind of can't be taken away unless they take your phone.
Right.
You can always find some mint to spin up that can help you connect you to the lightning network.
That will allow you to, you know, transact with other people who also trust them.
And you'll always be able to use Bluetooth with other people unless all the phones go away.
I think that's some of the interesting similarities of some of these freedom tech projects and something that Routster, with Schumannick, is working on.
It's kind of one of the other things where, if we ever get to a world where you got a KYC and everything you do to interact with AI is super monitored, maybe something like Routster would be a way where you can say hey, none of these things will answer the question I want to do.
I'm doing a science thing.
They all say this is for, they won't let me answer the question.
Maybe there will be a way where you can kind of break through that censorship and actually access kind of the self-sovereign ai.
There's something like roadster.
These are the class of things that i think are are the most interesting in that freedom techniques.
I want to just talk about the tech on this bit chat a little bit more, because i find the technology and what he's harnessing here really interesting.
So when you're talking about bluetooth out or indoors it's about 10 to 30 meters as far as what that transmission will give you.
Outside it's about 100 meters.
But what he's done is he's taken a mesh relay system.
So if you have this app and let's say everybody around you had the app it can then through the mesh network of all the Bluetooth out there.
So like let's say, you're at and I'm going to.
Really, maybe the younger generation is going to laugh at this comment.
You're at a mall and you're around a lot of people.
Or you're at a sports stadium you're wherever where there's a lot of population density and a lot of people with smartphones.
That network is really robust and you're able to relay these messages.
I'm assuming the messages are encrypted as they're going from one device to the next.
And so you're able to actually extend that range really significantly.
I mean, imagine you're in a city, that network is pretty robust.
Yeah, I mean, think of it like the original post office, right?
It's like well, you couldn't drive a horse across the United States, but you could ride a horse 30 miles and then, you know, the whale could jump onto the next horse right.
Yeah.
If you kind of get anywhere.
It's the same principle here.
You don't drive 30 miles, you drive 30 meters.
Yeah.
But you could, you know, we're on this little island here.
I'm pretty sure you could get a message across.
And it might take a day, right?
It's kind of like you're going back in time a little bit.
Stuff, it'll take a little bit.
You're not moving at the speed of light anymore.
Yeah.
That's good enough, right?
You can get it across the city.
And then you can also combine these technologies, right?
Like, let's say somebody is in a compromised situation.
They can't get to the internet.
They're maybe something like that.
They don't want to be monitored.
Maybe they're able to hop a few times across BitChats and then someone's like okay, I'll blast that out over an Oster, right.
Like you could do something like that, where you know it makes a few hops and then eventually it's public, publicly.
To that point, you could make a publish to Noster in an encrypted way.
And if the person on the other side has a key to unlock that message, wow.
I think there's all kinds of fun things that will be possible here.
And yeah, I'm really excited to see kind of where this takes us.
You know, I think there'll be a kind of a continue to be, a blossoming of different techniques, different people try.
Like I have some ideas with this search things I want to try over the next couple of weeks over here at SEC.
I love it.
Which is also interesting too understand, like it should.
If we, for example, have the situation of so the government trying to find, like certain whistleblowers or like whatever, where there are activists which are like publishing messages, like with pitchhead, there's not really a way to find the origin of the message, so you can hide, like where you like, spreading this message from, in a way Wow.
Yeah.
And I think for people that maybe are skeptical or why this would even be important, they're hearing it and they're saying yeah, but how many people have the BitChat app?
And what I think they're missing is with open source, the whole open source initiative Noster, for example.
This is a version of Twitter.
There's a lot of people using Noster these days, relatively speaking.
It's still very early, but there's a lot of people using it.
And this technology of BitChat can just be onboarded into any implementation of a client that's running Noster.
And now, all of a sudden, you have this ability to message over a Bluetooth mesh network messages that would have never been.
So you get a network effect by having some type of communication open source protocol like Noster, that as these new developments are built, they can be onboarded into that higher level network.
That gives you all that capability and gives you that network effect that you might not be able to get by just people downloading the BitChat app, which I find crazy fascinating.
Yeah, one of the other things that's really cool here is that, like you know, it's not like there's some.
The way it's worked in Silicon Valley in the past is like okay, some app comes out, it has a feature, that's cool, that's useful, and you just pray that they don't blow it right.
And then sometimes you get I don't know, it was that thing that Twitter had, where it was like the five second videos that was so popular for a while and then they just blew it right.
It was just gone.
I feel what that was called.
But oftentimes, you know, think of all the products Google's killed, right?
The cool thing about these things is like, it's all open source.
So like, if the creators blow it, Someone else will carry the torch.
Or I have some ideas about these things.
One thing I could do is try to contribute to theirs, or I could just make my own little spin on it, and they interoperate right.
That's kind of the beautiful thing about a protocol is like.
In order to contribute to it, you don't need to just find a way to get a job at the company and jump through all these hoops, and 99 of the people in the world can't do that.
You can just take what they did and try your own little spin on it.
You don't have to ask them permission.
You don't have, all you really need to do is to have the skill to do it.
And that's where kind of AI comes in, right?
Like the skill to do it just kind of went down by maybe an order of magnitude, right?
There's all kinds of things where especially to get that proof of concept, to get something that shows that your idea works or is useful that just went down massively over the last year.
So this is very exciting in terms of kind of decentralizing the development of these kind of freedom technologies, making it so it's not just five, 10 people creating these things.
They kind of come from everywhere.
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All right, back to the show.
That's where I want to go next is in the decentralized nature of AI itself.
I think this is hard for people to really kind of wrap their head on around is the application of like how do you do this?
Because when you understand just the bare basics of AI, it's like it's got the more data you feed it, the better it gets, the more informed it gets.
And so you see these large language models like ChatGPT.
And you know, it's just literally sucking the data out of every single human being on the planet to learn and get smarter.
And so people are seeing that and they're saying okay, so how is this going to work?
Localized or an open source?
AI.
How is it going to be as smart as that?
How can I harness that without giving up all my data and all of my information moving forward?
So I know you're shroominix, you're you're kind of an expert let me, let me say a high level one here, and i'm basically going to try to give a softball to shroominix.
So that this, this is how i think about it right, like.
So it's like what is ai?
Right, there's like two big parts there's the creation of it and the actual using of it.
So the creation of ai is called training right, and training.
You need a lot of energy, you need a lot of very fancy, expensive computers and you need a lot of data.
And if those three pieces go into training and you need some clever engineers and stuff and like that, you know, maybe that's the fourth element.
Right.
And that's going to be hard to decentralize to the point where, like you, can do it in your basement.
I am skeptical that that will ever happen.
But like, let's say a year ago, a lot of people were scared that it would centralize further.
Right.
That one company would get the edge and then they would enter like a parabolic explosion of intelligence.
And they would just get further and further ahead.
I would say the opposite has happened, right?
A year ago, like ChatGPT was really good.
Cloud was pretty good.
And then maybe there were a few others.
But that was about it.
There were very few participants.
Now it's very unclear who has the best AI, right?
Like ChatGPT was just launched.
Maybe it's It's there, but it's not super clear.
Claude's also very good.
Grok is very good.
There's some Chinese ones that are working with a lot less money, right?
Like QMK2, GLM, you know, DeepSeek.
There's like the number of people at the companies at the frontier has probably tripled since last year, right?
So in a sense, it is decentralizing, right?
We don't have one.
And it's like, you know, it's a lot better to have three options than one.
If you have one option, it can be very, you can get dark quick, right?
That's a single party state is like that, right?
Like What's causing.
I know with DeepSeek they were able to reverse engineer and do it at just a fraction of the cost because they basically went to ChatGPT and were asking it certain amounts of questions and reverse engineering the training on it.
But I don't know the terminology or exactly how that takes place.
Can you guys walk us through what they did to be able to do that?
So I think what...
So you kind of like creating imaginary chats that like tell information.
For example, you ask a certain question and like, a teacher explains you with detail, and they explain you like the reasoning steps of, like how you go from that knowledge to like deriving some more information out of that.
They create a bunch of synthetic data using chat gpt where they let chat gpt explain like really deep mathematical things or like programming things and chat gpt was explaining the reasoning steps that they didn't need to write it by hand, but then like they could train that ai model on these reasoning steps so they kind of like extracted knowledge out of chat gpt to train it like really efficiently.
So they had a bot basically go ask just an endless array of questions.
And then ChatGPT gave an answer to all of that.
And then they just synthetically use that to continue to train it.
Okay.
Or it could have been like every single time one of these things goes through the great firewall going back and forth to open AI, somehow the party logs everything and it's like okay, you're trained on this right.
But it is like yeah, it's by communicating with an AI, it's kind of leaking its model weights, right.
So that's one thing.
It's like the intelligence and the model kind of wants to get out.
If you talk to it enough, you can suck at least all the useful things out.
It's like that there will be blood seen, you know, I drink your milkshake, right?
I think it's one of those.
So it's one of these things where there are a lot of moats and there are some moats and obviously it's a capital intensive right.
Like not everyone's going to raise a hundred billion dollars or whatever.
This is an area where the moat, I'm sure Sam Altman wished the moat was a lot bigger, right?
Like a lot harder to pull the intelligence out of his models.
So yeah that's.
I think there's kind of a balance where it's like well, the more useful they are, the more they're used, the more the stuff gets out of the world.
Well, anybody else can kind of train on that paper.
I think you know more techniques.
As time goes on, people learn more techniques for doing all these things.
And yeah, I don't have an explanation for it.
I'm very happy that it happened though, because it has, do you think, have decentralized a lot over the last year.
Justin, don't you think that that's kind of like one of the reasons why it's not going to dominate in the dark dystopian talking point that so many people had call it three years ago?
That doesn't seem to be what's playing out and that there's going to be a bunch more models, because they're able to do this synthetic intelligence collection from the models that perform the best.
Yeah.
Part of me wonders whether we're getting close to the point where, like whether kind of increased intelligence is tapering off.
Like at some point, maybe that'll happen, right?
Like at every point in the last couple of years, like AI, after six months it's better than I thought it would be.
And this is the first time where it's not.
Yeah.
Right.
So we did a lot of outputting here six months ago.
We were paying 20 bucks a month.
It was the golden days.
And now here we are, paying 200 bucks a month, throwing our computers against the wall, and we're not doing that.
It has gotten better, but it hasn't, probably it hasn't done that like huge step function that it has kind of over six months in the past.
And, you know, GPT-5 was like this thing that was built up over a couple of years.
And when it happened, it was like, oh, really?
That's kind of nice.
And it seems to be like a lot of.
It is like they're switching kind of between models in the background.
They don't have like one kind of model to end all models.
It's getting awful expensive, right?
If you want, 20 bucks a month is easy.
200 is rough.
2000 is like an employee almost, you know?
It's interesting that you say that though, because I know when I had the 20 a month And then I was tinkering with someone like the deep research, and I was like, oh wow, like I could just do this as much as I want for 200 a month.
And I was like, that's a lot of money.
But then I'm like in the back of my head, I'm like look at what this thing is giving for 200 a month.
It's like free compared to like.
If I had to hire somebody to do these things, it would be nowhere near these prices.
And I think it's getting to the point where that's becoming very normalized for people to start thinking of the cost in terms of what would this cost if I went to a person and had them do some of this stuff?
I mean, I just thought of this now.
I mean, maybe it's like some stranded MG angle here that reminds me of Bitcoin, right?
Because Grok was an interesting one.
It just came out of nowhere.
It was Elon.
And so all the other AI companies at the time would do these like green data centers, and it has to be carbon zero.
And they just really bend over backwards for that.
And then they would also do a lot of alignment, right?
Make sure that the thing didn't misbehave.
And Elon basically threw those out of the, threw them out.
I'm not going to align it at all.
I'll make AI boyfriends and girlfriends instead.
And I will.
Instead of having a green data center, I'm going to get a data center in Memphis and I'm going to park like 40 natural gas generators out front as the walls are being put up, as we're like building this thing like in the air.
And they found an energy arb there.
That's how that one came about.
I wouldn't be surprised if some of the Chinese ones are a similar thing, where they're just basically finding maybe they're paying a lot less for electricity than Sam Altman is stuff like that.
I think there could be some geo-arbitrage that we see in Bitcoin mining happening as well.
I think this also confirms the point a bit that it's hard to... decentralized training.
Because what Elon did?
He put the most amount of GPUs you could ever find onto one single place, in one single factory, which is the thing you need to train the best models.
You need to centralize all the GPUs you have at one single place to get the best model, which is anti-decentralization safe. the person that gets the most amount of GPUs to the smallest place possible will train the best model.
Because, in the end, you need the bandwidth between the GPUs and the GPUs communicate with each other.
As soon as they get closer to each other, they can communicate way faster.
That's interesting.
Yeah, like called a noose n-o-u-s.
It's like some research group and i think they have a coin, so they're trying to do like a decentralized training run, which is kind of neat.
But for me this is something where it's like what's the right amount of decentralization?
It's not like something you know you don't want to like.
Decentralization is in a family.
It's not necessarily good right, like sometimes you want to be kind of close together and at a dinner table, right like you know you don't want to be on different continents.
So they're like but this is, this is important, this is for training the model and so, So like you got the performance of I've trained the model, I have the model and then I'm expending whatever amount of de minimis energy to plow it through the model, to give me my output.
But what we're talking about and I think to Shrumanik's point is when you're training the large language model, Having all those GPUs right next to each other is almost a must if you're trying to synthesize all of the data as you're trying to develop the weights for the model, which I think is very different than the utility.
You've built a model.
Now I'm going to use it day to day for whatever tasks, right?
Yeah.
So at the beginning, I mentioned there's two ways to look at AR.
One is the creation of it, and one is the running of it.
So creating it, training, it's like there's these big centralizing effects, right?
You need really fast bandwidth between them.
You need a ton of electricity in one place.
They are kind of getting to the point where they can spread it out a little bit.
But there's just these natural scaling laws where you just want it to be big and in one place.
But inference that's running the model is a totally different ballgame.
And so that's where I think a lot more decentralization can happen.
So you've seen, I'm sorry to interrupt you Justin, but I think this is an important conceptual talking point, or philosophical talking point, of where this is going to go.
How big do we want these large language models to get before you kind of start peeking out?
And it makes way more sense to have a specialized model that's just medical, or one that's biological or one that's physics based.
And so I think you get to a point where the large language model peaks out, and I think it maybe happens sooner rather than later.
And then that GPU farm that was built and of course there's advancements happening on the hardware side of the house and the software side of the house to run these really large language models.
But do you see a world where That peaks in the coming five or 10 years or 20 years.
Does it peak, first of all, and then timeline of where you think something like that would peak, and then it all gets into the smaller, more modular models that then are stitched together to give you optimal intelligence.
It's really hard to reason from first principles here.
Like, I mean, what do we have to compare it against the human mind, right?
The mind has like many different little AIs, many different components that are stitched together.
So if I had to guess, that's how AI is going to be.
This is very much finger in the wind.
Our examples are one of one.
This is how the brain works, I'd guess.
AI will be, we'd be different.
This is why I've kind of mentioned the cost a few times too, because we're not that far from it getting prohibitively expensive, right.
Like there was a time where, like spending a dollar on 10 cents on AI, it seemed like a lot, you know, and now we're spending 200 and it's.
It's going to get to the point where it's cost prohibitive, like another generation or two of these OLMs.
It's going to be cost prohibitive to run on the frontier for many people, right.
So I think you will have more specialized ones.
Like you're seeing this with medical knowledge too.
Like I know there's a couple that have been very successful.
One of my friends who's a doctor actually uses one of these to like kind of fact check, to kind of check his reasoning against when he's diagnosing patients.
So I can't say that I have a prediction here, because I don't know AI well enough and I don't trust the people that actually do make predictions either.
So what do you think, Srimanek?
Yeah, so I think we also need to think about what can the brain top you humans achieve.
If you think about maybe someone as smart as Elon Musk or a top engineer at OpenAI, if we could get them just 10 smarter, what could they potentially achieve more?
Or 20% smarter?
Or if we had double the intelligence of the most intelligent engineer at OpenAI, maybe we could achieve even more things.
But we don't know it, because that's the current limit.
So it's hard to think what can we get more if we have more intelligence.
Here's an interesting stat that I just looked up while we were talking here.
The human brain vision.
The occipital lobe represents about 20 to 30 of the entire cortex of neurons.
Language, which is in your left hemisphere, estimated at a few percent.
Just language itself is a few percent of the neurons in your brain.
The motor cortex and I find this one really interesting.
70 to 80 of the brain's neurons are dedicated to your motor control, which is in your cerebellum.
That motor cortex for humans is encompassing 70 to 80 of the brain's horsepower to be able to.
You know I don't know if you guys have watched some of the conversation around how difficult it is from an engineering standpoint to make a hand and all the tendons and for it to be able to pick up a ball and throw a ball.
How complex that is from a mechanical engineering standpoint, just designing the hand itself, and then you start getting into the AI that it would have to be trained on to basically pick it up.
For humans.
That, according to what I'm reading here, is an enormous part of the horsepower or the mental models that are needed in order to do it.
Yeah, I think the big difference for me between an AI and person right now, for me is that the person has embodied and has experience.
And AI doesn't, right?
And that's the big philosophical question is, can you embody an AI, right?
If you give it a robot form, is it embodied like a human is?
Does it experience stuff, right?
When I'm trying to get it to write an app, to test my app, it will frequently just do things that are really silly.
That is silly.
No person would ever do that who was able to open a refrigerator.
Oh, you can't open a refrigerator.
I forgot.
And that's another one of the things is AI is...
One of the huge limitations is when you talk about whether they could do a human job, right?
The first week of anybody at the job, they're pretty useless, right?
And the second week, they start to get some components of the job.
But then by a month, they've picked up quite a lot, right?
And they've actually learned it through experience.
And no AI that, I mean, no LLM can do this at all, right?
Like the best thing that they can do is summarize a little bit of their learning into a text file that just gets prepended to the questions you ask it.
It's totally cheating.
The thing doesn't learn at all.
It's the thing I'm using and the thing you're using and the thing Shrumanik is using is exactly the same.
It's totally stateless.
It's a big question is like so can you embody that and give it experience that it could actually learn?
And I think maybe it could be the case that we all really undervalue how important that is to actually, you know, an intelligence.
Also, maybe I wouldn't fully agree with the statistics because I don't think we only reason in language.
For example, if you think about math, you often have some spatial representation in your head which could be also happening in the part of your brain where maybe the motor part is like when you think about rooms and orientations or spatial representation.
So maybe some reasoning is not happening in the language part of the brain.
What's something that you guys are most excited about in this space?
I mean obviously, some of the stuff we've already mentioned is beyond exciting, but is there anything that you're seeing that you're just like wow, this is something that I can't wait for, or that you're already seeing right now?
I think these are really complex.
Coding agents are like really exciting because, as you think about in the end, like when you build software, you just want to like solve the problem.
And there are some engineers who like just enjoy the process of writing code, but mostly you have some problem in the real world and you want to solve it.
So you like throw one of these like insane coding agents on that problem and then you can solve it.
So i think, like The amount you need to pay for like having a really complex piece of code, I think that's maybe the most impactful thing we have in AI.
I mean, I guess I'll give a little bit of a different answer.
Like.
One thing I'm really excited for that it seems like it hasn't really materialized yet is just being able to apply these things to education in general.
I get two real big benefits out of these tools.
One is they write code for me.
So I don't have to do that anymore as much, which is really great.
But I'd probably say, even better is when I have some kind of an idea.
You can learn so much by just going back and forth.
And we all kind of invent workflows.
And you have to kind of pick this up.
And it largely depends on how much agency you have and how creative you are.
And that's a lot of the user, so to speak.
So I think it'll be very interesting for these things.
I've been able to learn a ton with these, but I suspect many other people haven't figured that out.
You know cause you have to really put a lot of effort in upfront.
And I think it's going to be really interesting.
In like, I can see that being pretty transformative, especially in America, right.
Where Preston and I are from the education system now, it's just, it's just so bad.
So broke. it's just so it's so bad yeah yeah one of my friends was joking about how he picked up like some old education book like a ninth grade a composition book and he's like so i was picking up this book and of course it's for ninth graders so i couldn't understand it right you know like you know the reading level they had in ninth grade 100 years ago was higher than adults today i'm probably not true but i mean it's a bit of a joke but there's some truth to it so i think that's something i'm pretty excited about and yeah i think in the sort of the freedom tech ecosystem that we're in i've been like working in a full-time just trying to find some way to do it for like six, seven years now.
And so I often have talked to people who will come to a conference and they'll have some idea like, oh I have this nice day job so I can't do it, and whatever.
And so you're starting to see people like this.
Just at this little event we're at, there's a few people who have day jobs, who are like oh, let me just come and try to do it a little bit on the side.
So I think you'll get a lot more of the people finding a way to contribute on Bitcoin, on Lightning, on eCash, on some of these on Nostra, just in their spare time, without having to like devote their life to it right, you know, i think that's very interesting.
I think another interesting thing is just like not having to look at a screen so much like that's okay.
That is one of the things i think about our modern work environments that are pretty horrible.
It's like a screen is not a good thing to stare at for 10 hours a day, eight hours a day yeah, and if we could go back to like every time i go and do something with like manual labor for like a week or something, it's just i'm so much happier than staring at a screen all day, right?
So i think that's another really interesting thing if we could move to modes of working where you don't have to just stare at this artificial screen all day.
I think that would be pretty interesting.
Well, on the education front, the thing I'm excited about is just the customization to a person's natural interests and talents where, like today, it's everybody's just force fed.
Oh, nope.
Sit down in the class with 40 and this is what you're going to learn.
And five people out of the 40 even have an interest in the topic.
And the other ones are just looking at the clock saying, when is this going to be done?
And where I think a lot of this is going is you're going to have the best instruction ever, because there's no ego, there's no past experience of the teacher themselves trying to.
Maybe you have a teacher that loves poetry.
And so they're just trying to jam the poetry down the throats of all the students.
And there's three people there that love it too, but everybody else- Little Johnny likes World War II tank.
That's right.
And so I think that customization piece is going to be huge.
And the removal of the ego of the instructor is going to be huge to most optimally help the person learn what they're naturally gifted at and what they're naturally interested in.
And I can argue.
The other side of why that's also bad is because if you don't get enough exposure to the things that maybe you would have never tried, now you're just pigeonholing somebody because they had an early interest in something potentially right.
Yeah, I feel like an AI could be much better at like.
I mean, there's all kinds of things like poetry.
It's like when I was a kid, it's tough to get me interested in poetry.
If you made the pitch then, that would be a little Shakespeare, right?
I think that an AI might pick up on that.
Yeah, it could be much more opportunistic than a textbook can.
Yeah, you're right.
So poetry is going to be taught at age, what, 13, 14 is what you're saying?
Yeah, that's really interesting.
And because I was always, to be quite honest with you, I was always very frustrated with school.
I wouldn't say I was like a great student.
I just was always frustrated with having to learn things, that there was a lot of things I had no interest in whatsoever.
And then there's other things that I was super interested in.
And I think so much of that.
It's like the Montessori schools and how they really kind of lean into what the kids' interests are.
I just think that education is going in that direction like a free train.
The other thing that I get really frustrated with AI is these teachers that are like don't go near it.
It's the devil.
It's like Bobby Boucher.
AI is the devil, Bobby, Adam Sandler movie.
But I think that that's just as bad as somebody who's just telling their kid that they should only be using it.
I think it's just as dangerous, but I'm sure there's a lot of opinions out there on that particular topic, but-.
Guys, thank you for making time and coming on.
I have probably another 35 questions here I could hurl your way.
So maybe we do it again in another quarter or so.
If you make it a frequent thing here, the new AI vertical you're working on, we'd love to come back.
I would love to have you guys back and just kind of hear what you guys are working on.
How about you guys give folks a handoff to any type of social media contact that you have, if you have one, and anything else you want to promote, like the HRF initiative, would be wonderful.
And we'll put all of that stuff in the show notes, but Justin, go ahead and take it first.
Yeah.
I don't want to read off my whole info on the show, but you can look for Justin on Oster and you can search just Google AI for individual rights and you'll find HRF's program and subscribe to the newsletter.
It's a lot of good content.
Sure, Manik.
Yeah, and you can find me on X, also on Nostr.
And you should check out Routes, because that's really interesting development in these templates AI.
We'll definitely have links to all of that in the show notes.
So folks, check it out, follow these guys and check out those initiatives.
Thank you so much, guys, for coming on the show and kicking off this tech adventure that we're on.
So really appreciate it.
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