TECH001: AI for Activists w/ Justin Moon and Shroominic (Tech Podcast)

22:32So 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 in 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.

23:08Justin Moon:So, 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 veil could jump onto the next horse, right? Yeah. You kind of get anywhere. It's the same principle here. You don't drive 30 miles, you drive 30 meters, but you could, you know, we're on this little Island here. I'm pretty sure you could get a message across. 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, but in a lot of circumstances, like, let's say you're trying to get the news out, right.

23:35Justin Moon:About something 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 australian right like you could do something like that where you know it makes a few hops and then eventually it's well just publicly to that point you could make a publish to nostralian in an encrypted way and if the person on the other side has a key to unlock yeah that message wow i think there's all kinds of fun things that will be possible here and uh yeah i'm really excited to see kind of where this takes us you know i i think there'll be a kind of a continue to be a blossoming of different techniques different people try like i i have some ideas but there's sorts of things i want to try over the next couple weeks over here at sec i love it how it's also interesting to 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 bitchat 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 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.

25:02This 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. And 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.

25:48Justin Moon: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 about all the products Google's killed, right? The cool thing about these things is like, it's all open source.

26:15Justin Moon: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.

26:46Justin Moon: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 magnitude, right? There's all kinds of things where especially to get that proven 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. Let's take a quick break and hear from today's sponsors. They say every day your business is late to AI.

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29:34I think this is hard for people to really kind of wrap their head 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?

30:09How can I harness that without giving up all my data and all of my information moving forward? So I know, Shrumanik, you're kind of an expert.

30:17Justin Moon:Let me say a high level one here, and I'm basically going to try to give a softball to Shrumanik. This is how I think about it, right? 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 it's 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.

30:47Justin Moon: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, Claude was pretty good, and then maybe there are 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 there, but it's not super clear.

31:20Justin Moon:Claude's also very good, Grok is very good. There's some Chinese ones that are working with a lot less money, right? Like, Kimike2, GLM, 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? 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.

32:02But 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 DeetSeek did is they created a bunch of synthetic data.

32:13Justin Moon:So you're kind of creating imaginary chats that tell information. For example, you ask a certain question and a teacher explains you with detail and they explain you the reasoning steps of 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 chad db 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.

32:57And then the chat GPT gave an answer to all of that. And then they just synthetically use that to continue to train it. Okay.

33:03Justin Moon: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 about 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 in AI.

33:32Justin Moon:Obviously, it's so 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 them all was a lot bigger, right? Like a lot harder to pull the intelligence out of his models. So yeah, that's, I think that 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 for cheaper. I think, you know, more techniques as time goes on, people learn more techniques for doing all these things.

33:58Justin Moon:And yeah, I don't have an explanation for it. I'm very happy that it happened though, because it has, things 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, you know, 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.

34:34Justin Moon:Right. Like, and 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, it hasn't done that like huge step function that has kind of stayed over six months in the past. And you know, GPT-5 was like this thing that was built up over a couple of years.

34:58Justin Moon: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, 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 so yeah 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 you 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.

35:42And I think it's getting to the point where that's becoming very normalized for people to start thinking of the costs in terms of what would this cost if I went to a person and had them do some of this stuff.

35:52Justin Moon:I mean, I just thought of this now. I mean, there may be some stranded energy 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 green data centers and the capacity, 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.

36:19Justin Moon: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 he 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 decentralize the 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 model so like you need to centralize all the gpus you have at one single place to get the best model which is like anti-decentralization so the person that gets the most amount of gpus to the smallest place possibly will train the best model because yeah in the end you need like the bandwidth between the gpus and like the gpus communicate with each other as soon as they get closer to each other they can communicate way faster that's interesting yeah so there's something 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 had 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 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.

38:06But 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 the model. Now I'm going to use it day to day for whatever tasks, right?

38:31Justin Moon:Yeah. So at the beginning, I mentioned like, there's two ways to look at AI. 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 ball game. And so that's where I think a lot more decentralization can happen.

38:57Justin Moon:So like 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 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.

39:38But 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 kind of 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.

39:59Justin Moon:It's really hard to reason from first principles here. I mean, what do we have to compare it against? The human mind, right? The mind has 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 seemed like a lot, you know, and now we're spending 200 and it's going to get to the point where it's cost prohibitive, like another generation or two of these LLMs, it's going to be cost prohibitive to run thing on the frontier for many people, right?

40:38Justin Moon: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 you know so what do you think sherman so i think we also need to think about what can the top newmans achieve if you think about maybe someone as smart as elon musk or a top engineer at opmi if we could get them like just 10 smarter like what could they potentially achieve more or like 20 % smarter.

41:16Justin Moon:If we had double the intelligence of the most intelligent engineer at OpenAI, maybe we could achieve like even more things, but we don't know it because that's like the current limit. So it's like hard to think what can we get more if we have like 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.

42:02So it's interesting that when we start getting into the humanoid robotics in that, where you're going to start putting these AIs into human form or into some type of modular form where they can go out, they can sense their environment, they can make decisions inside of their environment. That motor cortex for humans is encompassing 70 to 80 % of the brain's horsepower to be able to, 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, Like how complex that is from a mechanical engineering standpoint, just designing the hand itself.

42:43And 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.

42:57Justin Moon:Yeah, I think the big difference to me between an AI and person right now is for me is that the person has embodied and has experience and an 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 like embodied like a human is? Does it experience stuff, right? Like when I'm trying to get it to write an app, like to test my app, like it will frequently just do things that are really silly. That is silly. No person would ever do that who like, was able to open a refrigerator. Oh, you can't open a refrigerator. I forgot. And that's another one of the things is like AI is one of the huge limitations is when you talk about whether they could do a human job, right?

43:37Justin Moon: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.

44:06Justin Moon:It's totally stateless. It's a big question. It's 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 like math, like you often have some like 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 like rooms and orientations or like spatial representation, so maybe like some reasoning is not happening in the language part of the brain.

44:47What'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?

45:02Justin Moon:I think these 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 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 having code that like costs basically nothing or like just decreasing the amount you need to pay for like having a really complex piece of code i think that's maybe the most impact both things we have in the eye?

45:40Justin Moon: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. Like I get two real big benefits out of these tools. One is like 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 like 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 try to pick this up and it largely depends on how much agency you have and how creative you are and you know 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 just haven't figured that out you know because you have to really put a lot of effort in up front and i think it's going to be really interesting in like i can see that being pretty transformative, especially in America, right?

46:32Justin Moon:Where Preston and I are from, the education system now is 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 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 a hundred years ago was higher than adults today. I'm probably not sure, but I mean, it's, 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.

47:00Justin Moon:And I think in the sort of the FreedomTech ecosystem that we're in, I've been like working in it 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 like, we'll 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, like 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 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 Noster 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.

47:59Justin Moon: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 as 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.

48:43And 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?

49:24Justin Moon: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, but if you made the pitch at just the right time, like if I was trying to impress a girl or something, you made the pitch then that would be a little Shakespeare, right? Like I think that an AI might pick up on that education system. 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? Nah, that's really interesting. And because I was always, to be quite honest with you, I was always very frustrated with school.

49:58I 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.

50:32But 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.

50:55Justin Moon:But this was - 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 end club on the show but you can look for justin on osster and you can search just google ai for individual rights and you'll find hrf's uh 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 nostril and you should check out rodes because that's really interesting development in these employees.ai.

51:41We'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. Thank you for listening to TIP. Make sure to follow Infinite Tech on your favorite podcast app and never miss out on our episodes. To access our show notes and courses, go to theinvestorspodcast.com. This show is for entertainment purposes only. Before making any decisions, consult a professional. This show is copyrighted by the Investors Podcast Network.

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