This Is A Once-In-A-Lifetime Opportunity

Folks, we are approaching a once- inaundred year investment opportunity. Now, I know it sounds like every week we're talking about that, but take a look at this. In April of last year, Beijing held a half marathon for humanoid robots, and the winner needed 2 hours and 40 minutes. This April, same race, the winner did it in 50, running on its own. The human world record is 57. Now, same story in the 100 meters. Last August, the fastest robot ran it in 21 and a half seconds. this August 8.64. Usain Bolt's world record is 9.58. And it's not just static running. This summer, a 4- foot tall robot stood across the net from a former top 20 tennis pro and traded more than aundred shots with her. This has all happened in the span of one year. And that's why I think 2027 is going to be the Chat GPT moment for robots. Now, don't get me wrong, we're still very early. If you think about it in chat GPT terms, let's say we're at the end of 2021 and Chad GPT gets released at the end of 2022. But because of how fast AI is learning, well, the exponential growth curve is going parabolic. So, in today's video, I'm going to break down exactly how big the prize in robotics is, where the technology actually stands today, why AI is teaching these robots so much faster than anybody expected, especially people analyzing this stuff 6 months to a year ago, and the stocks that stand to benefit the most, including the big dogs, Nvidia, AMD, Micron, and of course, Tesla. I'm going to put the timestamps down below, and as always, let you be the judge. And then finally, at the end of today's video, we're going to go on to our sponsored segment on Finger Motion, ticker symbol FNGR on the NASDAQ. It's a very small company attempting a pivot from mobile payments into small AI data centers in Alberta that generate their own power. I'll walk you through what they're building, some of the risks to keep in mind, and why you may want to put this on your radar. And as always, if you're the one taking the ultimate risk, you better be the one doing the ultimate frisk. Always do your own due diligence at all ideas presented. So, I have to be honest with you here. Obviously, we've talked about robotics and the buildout for a while. And I have been bullish on it, but I would say that I wasn't super optimistic about the timeline. I'd see all these videos of goofy robots stumbling around with some guy off camera working the remote control and I'd kind of roll my eyes. Well, everything has changed so much over the last 12 months and really the last few months that I take it very seriously now. And companies are taking it seriously, too. Elon Musk just ripped the Model S and Model X out of their Fremont factory to make room for robots. A Chinese robot company went public in August and closed up some 460% on day one. And in the first half of this year, the world shipped about 25,000 humanoid robots. That's up 432% from a year ago. Now, 25,000 is a big number. Obviously, these aren't going into houses yet. These are being used in warehouses for research, universities, so on and so forth. However, if you actually look at industry projections for next year, the year after, and the year after. Well, this is a completely new industry that's just starting to be built out. It's kind of like watching the first cars roll off the Ford Model T lot. So, why all of a sudden is robotics becoming such a big deal? Well, because of the size of the prize, everything AI has done so far lives on the screen. It writes, it codes, it answers questions, it responds to your hinge matches, but a lot of the work in the world is not on screen. It's lifting moving building cleaning and the wages paid for all of that work add up to tens of trillions of dollars every single year. So, the prize is massive. Anybody that manages to dominate the robotics market all of a sudden has dominated the labor market. Now, here's the math that gets any CFO to write a check. Amazon just said its average warehouse worker costs more than $32 an hour with benefits. That's about $67,000 per employee a year. And quite frankly, I do think that Amazon underpays and definitely underbens their employees. But the reality is that Agility says its robot already pays for itself in under two years against a worker at that cost. And that's a robot priced in the six figures. Musk's target for Optimus at scale is 20,000 to 30,000. And a robot can work two shifts. At that price, it pays for itself in 2 months. Even if Musk overpromised a lot and the robot ends up costing twice as much and can only work one shift. Well, still you pay for yourself in like six to 12 months. Now, of course, there's going to be a lot of structural issues that happen in society because so many workers are going to be displaced. Just like every previous generation of automation, new jobs that we've never even heard of are going to emerge. And actually, that's exactly why I think it's so important to pay attention to videos like this one because I definitely don't think this is going to be a smooth process. I'm an optimist. I think that overall this is going to be great for everybody when you're looking out the next 50 years. But I'm also realistic in the sense that there's probably going to be some years where things aren't looking so good. But when it comes down to our personal responsibility and what we can do, we can invest in some of these big wealth generating opportunities, both providing ourselves a great defense for this new economy as well as benefiting from this new economy instead of getting screwed by it. Musk was asked about this at the White House last week and his answer was that jobs are going to change because jobs have already changed and right now the companies buying these robots say they can't hire enough people for the work as is. So whichever way this debate goes, there is money being spent. There's opportunities being created and we have a very crazy future to look forward to. Now Jets and Huang of Nvidia puts this opportunity at roughly $50 trillion. Morgan Stanley. The Stanley's at Morgan say humanoid robots become a$5 trillion dollar a year market by 2050 with about a billion of them in use. And Elon Musk has said about 80% of Tesla's value will eventually be Optimus. Now, anybody can throw out a number for 2050. Zip Trader will be worth 5 gazillion dollars by 2050. But what matters is where the money is moving today. So, venture investors had put $ 8.7 billion into humanoid robot startups by this summer, nearly double all of last year. figure a private company that barely existed four years ago is now valued at $39 billion. The forecasts, if you're looking at them, they're in the trillions. The checks are already in the billions and the actual revenue today is tiny. So, right now, you're at stage zero, just starting to move to stage one. By the time this is all apparent to everybody, and it's a chat GPT moment. Well, the valuations are going to be astronomical. If you go back to 2020 and 2021, people did talk about AI. They did talk about chips. They did talk about all of the data centers that needed to be built out, but there wasn't an intensity to it. The intensity did not come until 2022 at the end of 2022 when Chat GPT launched. Again, I think that we're heading closer and closer to that Chat GBT moment for robotics. And I think that's going to hit in 2027. So, where's the tech right now? Well, Figure says its robots ran for more than 200 hours straight, sorting 149,000 packages with nobody controlling them. A startup called Physical Intelligence built a robot brain that can do tasks it was never trained on. It ran an espresso machine out of the box. Sabrina Carpenter would be very, very proud. And Boston Dynamics, the company behind all those viral backflip videos that you've probably seen, now has a production version of its Atlas robot. It's already being built and every unit for this year is spoken for. Its owner, its owner, Hyundai, plans to put more than 25,000 of them in its car plant starting in 2028. And this month it got a brand new hand with touch sensors across the fingertips and the palm built to use tools. And the generations are now turning over so fast that last week Figure retired its previous model by having the robots jump on their own into a furnace of molten steel. Terminator style Arnold Schwarzenegger is in the video. So here's how I'm looking at this. Humanoid robotics today is where large language models were around 2021, not where Chat GPT was at the end of 2022. The technology clearly works and it's improving fast, but we're still a few reliability breakthroughs away from a robot you can simply drop into your house. Now, I want to talk a little bit about the Chinese model. So, I don't want you to skip over this. China is indeed winning the volume game right now, and it's not particularly close, unfortunately. Chinese companies built more than 95% of the humanoid shipped in the first half of this year, and they are cheap. Unit's full-size humanoid list at $29,900. That's less than most new cars. Unit Tree is also the company that went public in August. It closed its first day up 460% worth some $53 billion. Now, as of this week, the stock is down nearly half from that close. So, when I say the sector moves in waves, that's what I mean. And of course, Washington has noticed. In July, the FCC put foreign made advanced robots on its restricted list. So, new Chinese models can't be approved for sale here without a special signoff. The government is fencing off the home market, and that's a very, very big windfall for the American names. And don't take my word for the threat. Take Elon. He said, "By far the biggest competition for humanoid robots will be from China." So, how fast exactly is this all moving now? Well, you saw the race at the top, but the one that really got me was the tennis. In August, a Chinese company called Galbot put a humanoid on a court across the net from Ciang Xi, a former top 20 tennis player in the world. And like I said, the robot exchanged more than a 100 shots with her, tracking the ball, running to the spot, and swinging. No remote control, according to the company. Think about how hard that is to pull off. a different shot every time and your eyes, your brain, your legs, and your hands all have to work at once. That's the whole problem of robotics on one court. Now, most people watching this, including myself, certainly couldn't keep a rally going with a pro. A robot the size of a fourth grader just did. I took tennis lessons for years. Quite frankly, I'm nowhere near as good as this robot. I don't know that that's saying much, though. But here's the detail that really stands out. So, this robot, you see, it did not learn tennis from a pro. It learned from recordings of amateur players and the AI worked out the rest. Now, of course, there are still some big challenges. We're still at a very, very early chapter for robotics and humanoids. There are three big problems that you're going to see when you're doing your research. The hands walking is mostly solved, but hands are far from solved. Musk has said the hand is harder than the rest of the robot combined. Tesla's has more than a hundred tiny parts and is still put together by hand. Picking up an egg, a wet glass, a zipper. That's the hard part. There's also the dynamic of operating in a real world. Almost everything you've seen today happened in a controlled space. One startup published uncut footage from a real factory and about 40% of the work was still a human driving the robot remotely. A report last month said optimists still can't handle general tasks reliably. And it's reasonable to assume that we're at least a few years away from humans being in people's homes. And then there's also the data problem. I mean Chad GPT learned from the entire internet, but there is no internet of people folding laundry. figure CEO has said these robots are going to need more data than the language models did. That said, the scale up is happening. Every one of these is a learning problem. And here's the thing, learning is the one thing that's scaling up at an exponential pace. You have to understand that for 50 years, if you wanted a robot to do something, an engineer wrote the code for every single motion. One task, months of work. Now, the robots are being trained by AI the same way ChatGpt was. They learn by watching people. Figure has an app that pays regular people to film themselves doing chores and it's taken in about 30 minutes of video every second. They practice in a simulation where a robot can try something a million times overnight. Nvidia used AI to generate the training data for one of its robot brains in 36 hours. If he did that with humans, it would have taken almost 3 months. And of course, they learn as a fleet. When one robot figures something out, every robot gets it in the next update. Musk says Tesla is building what he calls an optimist academy with at least 10,000 robots practicing in the real world. So what do you get? Well, you get a flywheel. More robots means more data. More data means a better brain. A better brain means more jobs the robot can do. So you can build more robots. And a flywheel doesn't improve in a straight line. No, no, no. It compounds. Every turn is faster than the last one. That's the exact loop that took the language models from a toy to Chad GPT in about 2 years at a pace almost nobody thought was ever going to be possible. Now, why does this matter for your portfolio? Well, think back for a second. People were saying AI for years. Nvidia was selling AI chips in 2016, and nobody cared. Then, Chat GPT launched in November of 2020. And overnight, AI became the word that put a massive premium on anything that touched it. The technology didn't show up that day. What showed up was the moment regular people could see it with their own eyes. That's what I mean by chat GPT moment, and that's what I think we're rapidly approaching with robotics. It's not going to happen tomorrow. I know it's not going to happen next month, but in 2027, you're going to see the chat GPT moment. That's why Tesla just cleaned out their Fremont factory. That's why Jason from the All-In podcast, who has actually been inside the Optimus Lab, has been going viral for his latest take on it. >> I saw the latest Optimus, >> it would win half of these things already. I you guys see it, it's >> he's making sick progress and he's not showing it publicly, but I saw now Jason is a friend of Elon and a Tesla bull, but you could see where this is all going. So anyh who, let's go into the stocks that I think are going to be set to benefit. Well, we'll start with the brains. And of course, you have Nvidia and AMD. Every robot needs a brain inside it and a place to train that brain. And Nvidia sells both. Figure, Agility, Boston Dynamics, Amazon, and even Unitry in China build on Nvidia. Jensen says that business is already running at about $10 billion a year with a path to $100 billion within a decade. And Nvidia gets paid no matter which robot wins. And then AMD just made its move last week. It agreed to pay 8.2 2 billion for World Labs, which builds AI that understands three-dimensional space. That's the simulation I just told you about where robots practice a million times overnight. It's the second biggest acquisition in AMD's history. Now, the robotics buildout is going to need a ton, a ton of chips, both because it needs more and more data centers for these robots to connect remotely from, as well as the robots themselves. And AMD and Nvidia already have massive backlog. These companies are going to continue having crazy pricing power, and I think it's going to get even more extreme. Next, the memory layer. And of course, you have to look at Micron. Micron CEO said last week that a humanoid robot needs more than 200 gigabytes of memory, about the same as a self-driving car. A billion robots at 200 GB each, is more than four years of the entire world's current memory output. And the squeeze is already happening. Musk just cut the memory in his robot chip. And he said it was the only way to get enough volume. But again, longterm, you're going to need as much memory as possible. Micron is a key provider of this. Next, the body. And Tesla is one of the most obvious buys here. So, of every big stock on this list, Tesla is the one where the robot is actually the story. If Optimus works, nothing else here moves like Tesla does. And I'll show you the chart on that one in just a second. But there's also the Pure Play and the Parts. Agility Robotics makes Digit a warehouse robot that has logged 65,000 working hours for customers like Amazon, Toyota, and GXO. Now, this company is going public through a spa, ticker symbol CCXI, and the deal is expected to close this quarter. That would make it the first USlisted humanoid pure play. So CCXI is definitely one that you want to pay attention to. This back topped out back in July 2nd and it's been going straight down almost to par value at $10. Not to give you 2020 and 2021 flashbacks, but definitely one that you want to pay attention to. Comment down below if you want me to do a bigger deep dive into this company. And then you have Vich Precision Group, ticker symbol VPG. They make four sensors for robot joints. It booked about $1 million in humanoid orders and the stock was up some 269% on the year by July. It's now down about half from its peak cents. But that's the kind of premium and wave that I'm talking about. And we haven't even had the chat GPT moment yet. And then you have the customer. Amazon is a big one. The company that saves the most money is the company that moves the most boxes. And Amazon doesn't need to sell a single robot to win. It just needs them to work for them. But to me, quite frankly, I would say the most exciting company is Tesla. To me, when you combine market power, capital, and talent, Tesla is the number one. Yes, there are some smaller companies that arguably are better than Tesla in some key areas of the robotics trend, but they don't have the sheer scale and execution that Tesla has built up over a decade plus. So, let's talk about Tesla stock. So, Tesla hit the same ceiling twice. $488 in December of 2024, $499 in December of 2025, almost exactly a year apart. But look at the lows. Last year's low was $214. This year's low was $297. Same sailing and a floor that's almost 40% higher. Flat top, rising bottom on a two-year chart. That's a stock getting squeezed upward. Now, let's do a flashback. So, this year's low came a week after the July earnings report, which knocked the stock down 14.5% in a single day. So, I went ahead and went back through Tesla's entire history as a public company. A drop of 12% or more in one day had happened 21 times before this one. A year later, the stock was higher 19 out of those 21 times. The two misses were 2018 and April of 2022, right at the start of that bare market. So, that's not a guarantee, but those are the odds the chart has given you. And history doesn't always repeat, but it often rhymes. And at that July low, Tesla closed almost 28% below its 200 day moving average. Think of that like a rubber band. It has only been stretched 25% or more below that line six other times. A year later, it was higher in five of them. Now, zoom into the right now. So, since the start of beautiful September, Tesla has been stuck in a box. $345 on the bottom, 385 on top. And it has touched those edges five times. And while it's been in that box, the volume has dried up to the lowest level in more than 5 years. That's what you could think of as a coil. The sellers are done selling and the buyers haven't shown up yet. And everything is stacked right on top of that box. The top of the range at $387, the 200 day moving average at about $390, and the downtrend line from the all-time high, which comes in right around $400. Three different kinds of resistance, all within about $15 of each other. That $385 to $400 zone is the whole ball game that you got to pay attention to. A close above it completes a potential double bottom. The box is about $40 tall. So, the measured move gets you to roughly $425, about 12% give or take higher, right under the old highs at $430 to $450. And always follow the volume. In a back test of 90 stocks over 5 years, a breakout from a box like this on heavy volume hit its target about three out of four times. On light volume, it was closer to half. One warning though, Tesla has climbed back over its 200 day after a long stretch below it seven times before. Four of those seven times it slipped right back under within two weeks. So, one close doesn't prove it. It has to hold. And here's the flip side. Because the same box reads the other way in some ways, too. Three highs at the same price as a potential triple top. A close below $345 completes that. It measures down to about $35. Right back to the floor, roughly 20% lower. So, there you have it. That's the chart. same ceiling, a higher floor, a rubber band that got stretched, and a coil sitting right under three layers of beautiful resistance that can be broken. And the earnings call is October 21st. Two years ago, Tesla's October report sent the stock up 22% in a single day. I think that could happen all over again. And I think it's time for Tesla stock to wake up. So anyways, there you have it. We are not yet at the Chat GBT moment, but I think 2027 is going to be the year that hits. And it's important to position ahead of time because this buildout is going very very quickly. By the time that humanoid robots are obviously going into people's homes, are obviously automating factories or obviously a major part of the economy. All of a sudden, well, all of these are going to be repriced so much higher. The reason that there's still so much opportunity is because most people don't believe that humanoid robots are coming anytime soon. They believe they're not going to hit till like 2050. And people are so focused on fears over the AI bubble, Trump, what he's doing, circular financing, they're worried about all the wrong things. They're not thinking about the opportunity that this is going to create. So, right now is the time to pay attention because you're still ahead of the curve. Anyways, folks, now it's time to go on to our sponsored segment. And now it's time for our sponsored segment on Finger Motion, ticker symbol FNGR, on the NASDAQ. The stock, as I'm filming this, is trading around 20 cents, which gives it a market cap of about 15.5 million dollars. But it was up some 190% in a single session back in August when their new strategy was announced, and it's given a lot of that back sense. So, in today's video, I want to break down what this company's doing, why they've had such volatile moves, and why you may want to put it on your radar. So, let me start with the problem because it ties into everything we just talked about. Every robot in this video runs on AI compute that takes data centers. And right now, the hard part of building a data center isn't only the chips, it's the power. So here's a number from Finger Motion's own strategy release. So in Alberta, Canada, companies have asked the grid for 19,565 megawatts of new data center power. The amount approved so far is 1,200. So that's the gap Finger Motion is trying to step into. So for most of this company's life, it ran as a mobile payments and phone recharge business. That business is still running, but it has shrunk dramatically. On August 4th, the company brought in a new CEO, Jolie Khan, a corporate finance attorney who has been a public company CEO and CFO. The company says she's been part of more than5 billion dollars of capital raises over three decades, including data centers and power. And she set a new direction. Small AI data centers in North America that make their own power. The industry calls that behind the meter. Instead of waiting in line for the grid, you put natural gas generators right at the site. In her words, quote, "We are not trying to outbuild the hyperscalers." End quote. And quote, many enterprises do not require hundreds of megawatts of capacity. Though this is the small end of the market on purpose, the biggest AI campuses are measured in hundreds of megawatts. Finger Motion is talking about sites of about 10. So what's the plan? Well, in September, the company announced its 99 megawatt Alberta program, 10 sites of 9.9 megawatts each across four campuses, which it says works out to about 72 megawatts of actual computing capacity. Each site sits in its own project company that Finger Motion owns. And the company expects permitting power and design to take about 120 to 180 days per campus. Its partner is a private company called Blueflare Energy Solutions, which finds the sides, handles the permitting, and builds the power and the data halt. So, here's what's in place so far. So, three pieces. One, a 9.9% stake in Lykan AI Computing, a cloud compute company whose job is to bring in the customers that was paid for in stock. A week later, Lykan signed a memorandum of understanding with a Singapore company for a 128 node Nvidia cluster over 5 years. That one is non-binding. two, an agreement to buy a company called Nubit for $2.3 million in cash. Nubbit holds the lease, the permits, and the pipeline for the first 9.9 megawatt site near Brooks, Alberta. The company has put down $460,000 in deposits, and the deadline to close is October 29th. And three, a binding agreement for about 20 acres in the town of Hana, which comes with three megawws of grid power and a natural gas connection. That one is a 60 that one is currently in a 60-day due diligence period. Now, the company's own CFO said to judge this company on four things: site control, power, customer contracts, and capital. Two sites are under signed agreements, and neither has closed. Power is partly lined up, and the generators at Brooks aren't included in the purchase. On customers, the company says its partner has indications of interest for up to 52.5 megawatt, but those are non-binding and no contract is signed. So, basically, this is not yet a running data center or a paying compute customer, which brings me to the risks because you do have to understand these. Here are some of the risks. be aware of all of them and read over the SEC filings. This is a super small cap trying to grow aggressively in a very intensive capital intensive space which means dilution ongoing dilution is a very big and real concern that that's pretty much how these always go. There are roughly 76 million shares out there today and on October 6th. Shareholders approved raising the authorized count to 500 million. So dilution is not a theoretical risk here. It's something that you do have to be aware of when you're doing your due diligence on this company. NASDAQ has also notified the company that it's below the $1 minimum bid with a December 28th deadline and companies in that spot often do a reverse split. This is a small speculative micro cap that has moved very aggressively in the past. So all of these things are things to consider when you're looking into this company. But in conclusion, look, the story with this company is that AI needs compute. Compute needs power and the line for grid power is long. Fingerotion is a tiny company trying to remake itself around small sites that bring their own power. a new CEO, a 99 megawatt plan on paper, two sites under agreement, and they report some customer interest. The next few weeks are the test. Earnings are expected in late October. The Brooks deal is due to close by October 29th, and the NASDAQ deadline is December 28th. If those pieces come together, well, it might be worth paying attention to them. Again, speculative high-risisk company that's moved very aggressively in the past. If you want to dig deeper, I'll leave a link to their investor relations page down below. Read the SEC filings, especially the latest 10Q and the August and September 8Ks. Submit. This, of course, is not financial advice. It is a paid advertisement on behalf of the company.