Transcription
So, for those of you guys that don't know, Chris is famous because, uh, what'd you do? You took $20,000, turned it into 60 million. Yeah. Yes. Yeah. All right. That, that's pretty good, right? All right.
But he's here with a new idea. Uh, humanoid robots has been a huge focus for you, and not in the like, "Oh, this is going to be big." I think you spent a lot of time with these companies. It's some of your biggest investments are in these businesses. You're really paying attention to what the tech is being built. Just maybe give us, like, the broad thesis for why humanoid robots is such a big focus for you.
Oh my god, this is so hard. When I saw that we're only talking for like 25 minutes, it's, if I talk about humanoids, it has to be at least two hours, like, minimum. Um, okay. So, uh, I'll just start by two things, two main drivers. The first driver is, uh, infinite labor machine, which would be the biggest thing of our lifetime, right? Um, roughly half of global GDP is physical labor. And it's not a matter of if, it's a matter of when all that labor will turn into roboticized labor. It could be 20 or 50 years. We can debate that, but it's happening.
Um, the other is, uh, and this is what Jensen's been talking about all year, both publicly and privately to most of the big tech CEOs. Um, it is the gold standard of data collection. So, in order to have a digital twin model, a global model, which every big tech firm in the world is going for right now, not just to build that model, but to maintain it in perpetuity forever so that it properly reflects the world at every moment. You need to have rich, organic data collection coming from homes, businesses, streets, life, everywhere, in every corner of the world. The only way to do that well, you know, our two modes of data collection now for AI are contextualized data, which we've kind of hit the end of the internet for that, and now it's getting corrupted because it's AI data feeding into it. And the other would be simulation data, right? Um, having embodied AI feedback loop data, essentially robots all around the world, right? Oh, this, okay, sorry. Uh, basically having, you know, robots in every corner of the, uh, feeding back video, rich video, rich audio data, sensor data, and then human engagement data because all of these bots are going to have LLMs as a speech mechanism for humans to engage with them. That is the gold standard. Not today, but in 5 years, in 10 years, in 15 years, in 25 years. So, every big tech company in the world right now is racing to ensure that they get a piece of that data pie that's coming in the next 20 years through embodied AI. And that's probably the biggest story that's happening behind the scenes right now that is not being covered by the media. And most investors just don't have a clue. I mean, you know this, I'm probably 6 to 700 hours of deep research into the embodied AI space, uh, treating it in a similar fashion, uh, that I treated AI 3 years ago. Um, and it's just stunning to me how few people are fully engaged and understanding what's happening right now.
Let's take these two, uh, kind of, uh, inputs into your thesis. So, on the data side, um, one of the things that, uh, Jordi Viser and I have been talking about, uh, at length is this idea that, you know, Tesla is essentially teaching machines to see, and they can go, they can grab all this data, they can navigate the world, etc. There is a plethora of private companies right now. They're doing the exact same thing in this embodied AI space. When you look at the data that's being collected, is it only valuable and monetizable to the companies that create this embodied AI? Or let's say that you are Meta and you're not going to create, I don't know if they will or not, but let's just say they're not going to create a humanoid robot or anything that's similar. Can they also benefit from this huge collection of data? And there's a way to kind of start thinking from an investor seat. What are the companies both that are creating this technology, but also the ones that'll benefit from it?
Two things because you mentioned it. Meta is working on a humanoid project. Uh, as is OpenAI, their own, as is Apple, and other big tech companies. Uh, yeah, so it, it's valuable in terms of robotics, right? You need it obviously to, for the, for the robotic foundation models, but you also need it just for your core, uh, frontier models, right? Because even though you're collecting the data out in the real world through robotics and people, I, it's essentially replicating human life in a digital form. And that ultimately is what is needed to get to AGI and super intelligence. So, Meta desperately needs that data and probably is the reason why this year they took a turn and are kind of privately, but I think, uh, aggressively strategizing on what their role will be in humanoids.
When we flip to the other input, which is this human physical labor, you said it's 50% or so of, of GDP. Um, I think a lot of people immediately go to like construction sites. And, uh, embodied AI is the one area where I actually look at anecdotes as very powerful, uh, inputs to my investment, uh, kind of understanding. So, I recently saw there's a construction site and, uh, on the floor there was a robot that was maneuvering the floor and it was basically laying out the floor plan by etching it into the ground. And there was a bunch of commentary underneath. And pretty much, uh, you had all the people, this is, this is AI is fake, whatever, right? But majority of the people who had any degree of intelligent, uh, commentary were like, "This would be revolutionary in terms of a construction site, in terms of lack of mistakes, in terms of, uh, me as somebody who is in the construction industry, I would love this on my job site tomorrow, right?" Like, just the comments were just so much like, "This is a demand pull into the market for this type of technology." But then also, you and I have previously talked about like dog walkers, right? And, you know, kind of things that people don't think of as physical labor, but definitely feed into the economy and GDP growth, etc. Um, how do you evaluate, as again, from an investor seat, what companies do I invest in? What are the opportunities there? Right? Like, like, we know it's going to happen, but I want to make money on it happening. How do I think through that?
Yeah. First of all, I think in terms of use cases, and this is a question I bet is in a lot of people's minds right now because it comes up all the time. It's like, why the human form factor versus other form factors, robotics? Let me just say that like historically, only like 5% of automation has been automat, 5% of what we want to automate is automatable with precision robotics. I think somewhere between 10 and 15 additional percent will be automatable with multi-use or specialty use robotics, AI robotics. And that kind of leaves 80% for general human form factor. Um, I do think we'll see across the spectrum, but also in terms of investability, the, I think, and I think most people I speak to that are deeply engaged with the space, we all agree on one thing: at the point where we actually have a scalable humanoid robotics platform, uh, fully scalable, that has met all the KPI thresholds in terms of task completion rate, uh, uh, human output speed rate, uh, uptime rate, uh, human to bot ratio, uh, and durability, uh, those things. No, there's not a humanoid company on Earth that has met those thresholds yet for scalability. And, and I don't think we will for at least another year, maybe two. But when we reach that, I think we're going to start a 10 to 15-year, uh, supply demand imbalance where it doesn't matter how much you can manufacture. I don't care if Tesla can manufacture a billion bots. It's complete. That's not the bottleneck. That's a huge misconception. The bottleneck is not manufacturing. The bottleneck is the deployment. It is so exceptionally hard to deploy a generalized robot to Walmart or FedEx or whoever, right? Coca-Cola. These companies have never worked with generalized robotics ever. This is a brand new field. In many cases, it takes years of strategy work, integration work, um, preparing a workforce, and preparing digitizing inventory, um, before you ever even see a single robot on the manufacturing floor attempting to do anything. So, this whole investor community that exists right now just doesn't understand how this is going to work. That's the bottleneck. And because of that bottleneck, um, we're probably looking at a 10 to 15-year supply demand imbalance just for the lowest hanging fruit use cases. And the use cases are willing to pay $100,000 a year per bot on a four to five-year bot span. This is software margins with a much higher moat. That's why you see companies, and you're going to hear more in the next few weeks of outrageous valuations in this space. That's why you're seeing this is because we've never seen anything like this before on a TAM that big.
So, as an investor, sorry to get back to your question. As an investor, there really is only one publicly traded company that I feel comfortable with in this space, and it would be Tesla. Um, transparently, I believe Elon completely messed up, uh, the Optimus program this year. Uh, he just wasn't paying attention. He was distracted. You know, you probably heard this in the last month. He's basically reconstructing the platform from the ground up. Uh, and he's over it personally. So, we'll see what he can do over the next six to eight months. Um, but assuming that he can pull it off, he's the only public company entity that I think is worth, is worth a look from an investor standpoint. Everything else is private, unfortunately.
So, um, recently we saw, uh, there's a company in China that's going public in China. How do you look at domestic players versus international players? And is it worth looking internationally for certain investors?
Great question. I'm confident within 24 months, the US will ban all Chinese robots from ever being used in the United States. And I think that will be likely replicated across all Western countries. Um, why? Because there are backend, uh, data, uh, loops. Uh, you, a robot is a piece of telecommunications equipment that's surrounded in video and audio and sensor equipment. All right. It's, it's just not something that we're going to allow here. Uh, especially, and even if you, we did allow it, commercial and industrial use cases will not allow it in their companies, uh, from a safety protocol. So, I just don't see that as being that. That said, China is going to be huge in this space. Currently, most advanced roboticists call the Chinese robots, I won't call them out by name, but the big one, they call it the disposable robots. If you actually use it aggressively for commercial use cases, the actuators burn out after a few months. So, it's not what people think. And it's 4T high, okay? It's not really a commercial robot. And they're about to IPO for $7 billion in China, which is crazy. Um, but, uh, I, they will be huge in China. I also think China will have a 10 to 15-year supply demand imbalance just making robots for China. So, like, it's not about China versus us in terms of who can sell the most robots to the world. China's making robots for China. We're making robots for the US and the rest of the world.
So, what's interesting to me is, uh, you're mentioning that these robots have audio. They have video. They're maneuvering around. They're learning their space. I mean, these are like data honeypots in terms of what they're able to gather. If, uh, the Chinese government had that data, uh, maybe inside your home or inside of, you know, an American company, we would be very nervous. But if I then go and I take an American one and I put it inside my home, they have the same thing. Right now, it's an American company, so there's some advantage there. How do you think of individuals getting comfortable putting these things into their home? Like, we still live in a world where there's probably people in the audience who won't put an Amazon Alexa in their home because they think that it listens to them, or they may be scared to put some of this technology near their kids. And so there's always this, um, kind of, what is the technology, what is the enterprise demand, but then there is this like consumer behavior or psychology that goes with adoption as well.
I, I think you're going to see a lot of weird startups doing robots for the home relatively soon, the next few years. But for the most part, in terms of having an actual scalable, robust platform for the home, one that you can trust, that's real and and actually amazing, I think we're looking at, and I speak to roboticists about this every day. Uh, the time frame is 7 to 15 years. Okay, it's 7 to 15 years to get bots in the home. And when that happens, I think trust is going to be really important. We are going to see the people we think we're going to see. It's going to be the Apples of the world. It's going to be the Googles of the world. It's going to be people that have a lot to lose, uh, that have at least some level of existing trust. Maybe it's Tesla, right? For people that trust Tesla. Uh, I don't think people are going to be comfortable having this. I mean, we do to some extent. We have a lot of devices in our house right now that are listening to us from off-brand names that we don't, we shouldn't, like baby monitors, you know, made all over the place. Uh, I, I do think this industry sector is going to get eaten up by big tech long term. Long term.
You've been investing in, uh, the private companies. Um, when we look at it, they're raising a lot of capital. They are making pretty significant progress. It looks like every week there's a new video out that's demoing some sort of, uh, learning or acceleration or both on the hardware side, but also the software side. Um, people have probably seen videos of, I think Figure posted one where it was like, uh, looking at a bunch of stuff on the kitchen table and it was able to pick and put it into the refrigerator. We've seen ones from Apptronik and, you know, many of these companies. Those videos are cool, but I've seen videos coming out of China where they're holding robotics Olympics or they're doing marathon races with the robots. How much of the US mentality around safety or, uh, maybe this is a commercial exercise, it's not a kind of a social component, do you think is holding us back? Like, it feels like if we had a race down Park Avenue next year, do you think that we would accelerate robotic development, uh, in the United States?
Not at all. The Chinese robots are massively lacking in fine articulation, which is all that matters right now. Everything you see those robots doing, our robots can do. We just don't need to put them in a race right now. Like, it's, there's nothing special or interesting about what any of those Chinese robots are doing. I, I, it's all gross articulation, which is not difficult in the robotics world. It's the fine articulation hands. Uh, that's where that's where the, the last big piece of this is. Um, I've spent a lot of time looking at every humanoid company on Earth, either me and my partners, uh, looking to invest in any single one that we thought was viable, a commercially scalable robot company. You know, we've only chosen to make two big investments and, you know, early, early on in Figure and, and more recently in a very large way, Apptronik. And it's because they're not labs. It's because they're actually scaling out as commercially viable, supply chain ready companies that are building out operating systems and building out, uh, in integration strategies to actually deploy robots at large scale into large companies over time. Where all the other companies, even if they have really impressive videos, are for the most part, a glorified research lab. And there's years of difference between the two in terms of go to market.
One of the, uh, ancillary companies. You know, whenever there's these big platform shifts and big hardware developments, a bunch of people show up and say, "Well, what are the things around this that I can build a company around?" And, um, I saw a business that is building, uh, I'll call it a skin, like a mask that basically goes over the robot, so it looks more humanlike. It's kind of like a synthetic, almost like a Halloween mask, right? But they want to go and they want to mass produce these. Is stuff like that going to be real? Or are we just going to get comfortable with like, the robot's there, it looks like a robot, that's fine.
I, I have zero interest in any of that. If I thought, if I thought that was viable from a risk-reward standpoint, I would be, I, I get, I see all those companies. I just, I have not found one that I thought was interesting yet. Uh, it's still relatively early. Um, I, I think we have a couple commercially viable companies now. One of them's super controversial. Uh, uh, but I think it's still early. Uh, I, I would, I think all the value creation is in distribution. So, you know, people kind of have this debate, is the value creation in the foundation model because you have some companies that are making the models for robot companies? Or is it in the actual robot companies? The value, in my opinion, and I feel strongly, is in the companies that have the distribution because the distribution of the bots out into the real world through commercially viable relationships. You control the data. You own the data. Control the data. And ultimately, at the end of the day, you own everything. And that foundation model, while important, there will be six to 12 companies making those models. And I think, like large language models, they'll all be within a fairly tight range of capability. So, they'll be ubiquitous. And we have a couple companies out there, big tech, couple of the big tech companies that are probably willing to give theirs away for free because they're so desperate for the data, if not pay for it. So, I, I, I, I, I think the value right now is to be one of the few commercially viable, scalable companies that can start deploying robots and scale into the world.
Uh, my last question about humanoids, and then we're going to talk about some social investing stuff, is, uh, you and I have talked in the past about a world where my humanoid robot leaves my house, it goes downstairs, and it gets into a self-driving car that takes it somewhere and it goes and does something. Hypothetical example, but that is a machine getting a ride with another machine. Humans talk to each other, right? If I get in the Uber, I, I talk to the other human. I'm able to communicate, I'm able to get information, I'm able to do this stuff. How do you start to think about not only building the humanoid itself and being able to talk to other humanoids that come from the same company, but is there going to be this kind of like internet of things trend that people were talking about a couple years ago? Does this actually start to become real with these humanoids and, and you just basically have computers all throughout society communicating with each other?
That's been a really big topic of conversation, and it just makes so much sense that that will need to exist because it allows everything to operate much more quickly. Right? So, like, uh, that said, I, I have a feeling that we're going to have many, many years of robot A from brand A talking to robot B from brand B in human English because that doesn't exist yet. But it should exist, and I hope someone creates it because it will make the world move a lot quicker.
You, uh, the $20,000 you turned into 60 million, the reason why you did that is because you started to pay attention to the social investing. You started paying attention online or in society and recognizing trends much earlier than they showed up in earnings. Um, and I thought we could maybe talk through a couple of recent examples. American Eagle, I think, is one that everyone is talking quite a bit about. Um, are there other examples, whether you want to kind of unpack that one or others that you've really been, uh, looking at?
I, I, I mean, there's like, you know, the, the 20,000 to, you know, whatever 60, 70, 80 million more now. Uh, it was like 70 trades over 17 years. So, you know, of the 70 trades, probably, and those are my high conviction trades, maybe five of them went wrong. Five or six or seven. So, um, you know, I'll just give you the most recent example. It's so, what I do is called social investing. It's observational investing. I essentially try to detect change in the world quicker than others and connect the dots back to companies that would benefit or be harmed by that change before the information is widely disseminated. So, the most recent one was Sphere in Vegas. You know, the Sphere, uh, was able to detect, uh, that the Backstreet Boys and who would have thought, was generating, uh, sellouts unlike anybody even Sphere ever anticipated. And then right on the heels of that, they came out with their Wizard of Oz movie, which was controversial, yet bringing the Sphere experience to levels, uh, that the prior film didn't even come close to. And we're seeing essentially sellouts on that. So, I, I had a massive, levered position in Sphere the last few weeks. It's been awesome. I think the company's up 50% or something in the last, like, I don't know, three weeks, four weeks. 30, 40, 50%. Uh, so it's just observing the world and connecting dots. I know it sounds so simple, and on one hand, it's super simple, uh, but you have to observe the world quicker than others, and you have to connect dots quicker than others. And then you have to go through kind of a regimented methodology where you have to determine if that information that you discovered really is meaningful to one or more publicly traded companies, and the degree to which that information is already disseminated or not to other investors, institutional and retail. Um, and then you asked a very good question, either. The hardest thing that we have problems with as investors is knowing what to sell. I mean, the methodology, and by the way, I wrote a book like 16 years ago called "Laughing at Wall Street," but Jack Schwager's "Unknown Market Wizards" is probably the best book that has a chapter on my methodology in it. Um, the, the sell process when you're a Social ARB investor is very simple. The second that that one piece of information that that, uh, motivated you to to initiate the trade gets widely disseminated amongst other investors. Whether that's through an earnings call, or whether that's through the fact that hedge funds are starting to see the data on their credit card transaction screens, which usually happens two to three weeks before earnings. As soon as you know, sell-side reports start talking about the media, as soon as the information becomes widely known, you exit. It's that simple. You buy at the point of information, uh, uh, um, uh, imbalance, and you sell at the point of information parity. That's it. It's literally that simple. It has nothing to do with price or anything else.
Let's take the Sphere example because I think it's a, a great kind of recent example. Um, I'm assuming you see videos online or you hear people talking on social media. These are amazing experiences. They're selling out. You know, wherever you pick up the initial kind of scent of the trail, right? Um, and you start to say to yourself, okay, hey, maybe there's something here. How much of your confirmation to then go and put on a highly levered bet is, I just need more videos? I need more kind of anecdotal evidence versus there's some sort of quantitative data you can go and get and you're like, okay, I think it's going to be a 20% lift in revenue type thing.
Yeah. So, so I, I like to say that when I start a trade like that, I go to the ends of the earth to try to disprove it or to validate it. And I use lots and lots of data and information sources. In the case of Sphere, I was tra, you know, I pulled web traffic that hit the page on Ticketmaster that has the word Sphere in it. And that's how I track how many hits it's getting. And I can graph that out over the last year since Sphere opened. Right now, I do have to buy that data. It's not crazy expensive. Um, but there's a lot of different ways to track Sphere data. You can literally go on Sphere's website and see how many seats are selling out yourself if you want to do it the old-school way, right? I used to do this with movies. By the, I, I believe it or not, I, I made an insane amount of money when blockbuster movies would come out by going into nerdy movie forums where we would actually track the number of seats that were selling out like two weeks before the movie came out or three, four, five days before the movie came out, and we would, uh, compare that against other benchmarks and we would assess the likely opening weekend box office. So, uh, Hunger Games with Lionsgate was one of the biggest trades I ever had because Lionsgate was like a crappy publicly traded company before Hunger Games came out. And we were able to assess that that was going to be a blockbuster movie. Actually, more recently, Barbie. Um, you know, the, the Mattel investment for Barbie, it wasn't in a studio, but it was off the toy company that was benefiting from that film. So, I, yes, we use a lot of data. Uh, I actually do buy institutional credit card data. It's very expensive. It's in the six figures. And but I don't use it to trade. I only use it to assess when the information that I found off of more creative channels like TikTok comments, which is where I get most my alpha from, believe it or not, which is free. Google Trends, which is free.
Explain. I've seen you tweet this, but explain the TikTok comment alpha.
Yeah. So, so I spend about four hours a night reading TikTok comments. I've been doing this for like eight years. I know you want to get rich, that's what you got to do. I, I know it's, it's, people don't believe me. Nobody believes me except my family because they see me doing it. They go to bed and I'm up for four hours in a chair reading TikTok. That's where most my alpha has come the last seven, eight years. Um, I'm essentially, I'm essentially assessing, uh, I'm, uh, detecting the volume of conversation around granular topics and comments, right? And so it's, you really get a deep sense, deep sense for like the, the depth of interest in a product or a trend or a brand that could be accelerating or falling apart. Uh, TikTok is the gold mine of data. Uh, by the way, it used to be Twitter in the early 2010s, and now nobody talks about anything but politics and technology and stocks on Twitter. So, it's no longer Twitter, uh, or X. Um, but yeah, so, so but I, but I use that credit card data like hedge funds are using the credit card data to trade. I'm using it to find out when hedge funds eventually find out what I knew weeks earlier, which is really funny. Like, I'm using it to see when they have now discovered what I saw because I'll usually find something in the data and then three or four or five or six weeks later, it starts showing up in the credit card data because usually when something starts to trend or sell out, there's not even enough product available to really move the needle of credit card transaction data for a company. But eventually, it will kind of start, start to trigger. So, a lot of different data sources. We talk about this on my, my, I'm a dumb money.tv. I'm a, I'm a YouTuber. This is all we talk about this stuff.
So, the last question I have is around portfolio construction, which, uh, if you're reading TikTok comments, if you're, uh, looking at some of this stuff, it seems like you gain confidence and go pretty big into some of these trades. I don't know if that's true or not.
Yeah. Yeah. It's all about having conviction. I'm a, I'm a concentrated, levered investor. I mean, you don't turn, you know, I, I laugh because we sound like such a big number, 20K into 60 million, but if I, if I would have never pulled any money out for private investments, because I'm in like 160 private investments, it would be well over a billion dollars now, I think. You know, that $20,000.
Hold on a second. So, if you never pulled any money out for private investments, the 20 would have been a billion.
It's, it's taxes, private investments, and my family who spends more than I do, unfortunately.
Well, you're busy reading TikTok comments. They got this, right?
I always get a rip in on my, on my wife and kids. But, uh, no, no, I, it's, I, I counted it. One day a guy came, said, "You want to go through an exercise to see what it would have been?" And it was like a billion dollars almost. So, yeah. I mean, it's just like, don't get me wrong, you got to live life, you know? You got to live life. You got to pay taxes. Um, but, but I did take, I do take most, I've taken most my gains out every year for the past 18 years and been investing in private markets. And truthfully, the performance of my private portfolio is roughly industry standard. It's not great or bad. Um, but I stopped investing in early stage two, three years ago with the exception of my robot companies. Uh, because my, the opportunity cost of my capital is like six, 76% annualized, and I'm generating like 10% a year from my private investments. You know, so, it, it was, it was the biggest mistake of my career looking back. I mean, it was a lot of fun investing in 160 privates, not getting to meet a lot of people around the world, founders, and, but it certainly was not a good, uh, place for my money. Now, the public portfolio, when you say you're a concentrated levered investor, how many names or trades do you have on at any one time?
Five to 15 names at any one time. Uh, and usually I have, when I, when I said I had 70, I call those high conviction trades. I used to make two to three a year. Now I might make four to like eight a year. Uh, kind of like high conviction trades. And when I make a high conviction trade, if it's just equity, I might put 5 to 20% of my total portfolio in that company. Sometimes 25, 30%. Uh, there have been times when I've had 100% of my entire, uh, uh, net worth in one company.
Can you, can you leave us with one story where you put 100% of your net worth?
So, it was a very long time ago, uh, when the Wii came out. Remember the Nintendo Wii? I don't even know how I'm dating myself. A long time ago. Uh, my younger brother had turned like 21, and I said, "Where do you want me to take you for your birthday?" And he said, "Vez, I want to go to the E3 conference." He was a gamer. And I took him there, and the whole, all of Wall Street was so focused on the PlayStation that had just came out and the Xbox. Yet, all the buzz was around the Wii. And I was like, "What I'm seeing here is unreal." Uh, and no one's talking about it. And unfortunately, it was non-leverable because Nintendo was an ADR, so I wasn't able, if I would have, was able to leverage in that trade, it would have been game over. But I took every penny I had, and I had it in nothing but Nintendo stock for a full year. Meanwhile, every story that came out was, "It's a fad. The Wii is a fad. It's not real. It's low-tech equipment." You know what I'm saying? It was, uh, but I knew it wasn't because again, I wasn't reading stories about the Wii. I was actually engaging deeply and and emotionally seeing people's reaction when they held that wand in their hand for the first time. I, I like to relate that story to Apple. The first time the iPhone came out, I was at a party and I saw someone hold it in their hand. People passed it around, and I said, "Oh, this is, this is going to be huge." And everybody was still ripping on the iPhone at the time. "It doesn't have a keyboard. It's, who the hell does Steve Jobs think he is?" You know, like all this stuff. And I was like, "No, no, you got to, you got to like, you got to really understand the depth of emotion of realness behind consumer demand. It doesn't always come out in stories. Like, you got to be there." And that's why I love TikTok, cuz TikTok is an emotional channel. People express themselves on TikTok. They talk transparently. They, they tell you what they're going to do that night, what movie they saw the night before, the dress that they're dreaming about buying. Like, it's, it's this magical place of expression. It's, it, it's a data gold mine for investors. Um, and, you know, I had a company called Ticker Tags. I tried to train the biggest hedge funds in the world on how to use this data, and they thought I was nuts. Uh, and I, and I eventually, not anymore, but I, I sold that company to Jeff Bank. Uh, but I literally spent four years on Wall Street coaching hedge funds on how to interpret social data. And at, and they would love it. They love my reports. They like, I was like, "Well, why aren't you using the platform I built for you?" It's called Ticker Tags. And they're like, "We just don't have anybody here that we think is capable of like doing that type of work." Like, cuz they have fundamental analysts, and they have like computer scientists doing, you know, quant work. There's nobody there that they trust to like interpret comments of like a 19-year-old girl on TikTok, you know, like that's not what they do. Uh, and but that's our alpha as retail traders. That is our alpha cuz we're not afraid to do that. I'm not afraid to do it. It's incredible.
Where can we send people to find you on the internet?
Uh, dumbmoney.tv has all the socials. I'm super active on X at Chris Camilillo, and Dumboney Live is our channel. We have a lot of fun with it. We, we just, this is a hobby for us. I just want to get every investor in the, every human in the world to join the investor class. And so that's why we've been on YouTube for eight, nine years. And I just, I just have fun sharing this. Like, I don't have courses and stuff. That's not my world. I don't sell stuff. I just love sharing this methodology with the world. And I already met a bunch of you outside. I, I never do this stuff. It's been so fun getting to see some of the guys that watch the channel. Um, I, I never put on a collared shirt. I know you're formal, man. I put on a collared shirt. You see this? Look at this. The guy who's sitting watching TikTok all day, he's got a collared shirt. All right, Chris. Thank you so much. I appreciate it. [Applause]