If You Missed NVIDIA Stock, These Will Be Even Bigger

[00:00:00] Speaker 1: I'm convinced that Wall Street still doesn't understand AI. That's because the key to finding great stocks is understanding a company's products, not just their profits, especially in high-growth markets. My name is Alex, and I spent 8 years as an electrical engineer and AI researcher at MIT. And after NVIDIA's latest keynote, I'm sure that the AI revolution is far from over. So let me show you what just happened, and how I'm investing in it. Your time is valuable, so let's get right into it. If you just look at NVIDIA's financials, it's a data center company. Last quarter, NVIDIA made $96.2 billion in revenue, and $89 billion of that came from data centers. That's over 92%. Everything else, from PCs and game consoles, to robots and automotive, added up to less than 8%. But their products tell a different story. Last week, NVIDIA CEO Jensen Huang gave a keynote at GTC Berlin, and most of the headlines were about data centers. But the parts I found most exciting were basically everything else. Even the cell towers and factory robots that we have today are about to get a whole lot smarter. So that's what I want to focus on in this video. The latest news on AI factories and the companies building them, how AI impacts the things we already rely on today, like factory robots and cell towers, the markets of the future, like robotaxis and humanoid robots, and of course, which stocks I'm buying as a result. Let's start with data centers, since they generate the biggest returns today. In his keynote, Jensen said that a 1 gigawatt AI factory costs about $50 billion and NVIDIA's blog puts the price even higher, at about $60 billion. A gigawatt is a billion watts of power, so every watt of data center capacity costs about $50 to $60 to build. That's a good metric for investors to compare between companies and track over time. Only a part of that money goes to NVIDIA. The rest pays for things like land, buildings, power, and cooling. Every one of those pieces has to be finalized for the factory to make money. Using Jensen's own example, if a 1 gigawatt data center brings in $40 billion a year, then a three-month delay costs about $10 billion in lost revenue. That's the problem their new DSX pod is trying to solve. Each pod holds seven racks worth of Vera Rubens. That's 504 GPUs and about 25 exaflops of AI computing, all liquid-cooled and connected by over 10 miles of cable. That usually takes specialized data center crews months to unpack, install, connect, and test on site. But the DSX pod can be built and tested at the factory and then shipped already ready to plug in. NVIDIA says this cuts deployment times from months down to weeks, and the first DSX pods are scheduled to ship early next year. Remember, an AI factory makes money when it starts producing tokens, not when construction is complete. My first stock pick here is Vertiv, ticker symbol VRT. Vertiv makes power and cooling systems on two of NVIDIA's most important partner lists from GTC Berlin. They're one of the companies building DSX pods and one of the power and cooling partners for NVIDIA's AI factory designs. Last quarter, Vertiv's sales grew by 24% year-over-year to $3.3 billion, and they raised their full-year revenue forecast to about $14 billion. And on the networking side, I'm buying Coherent, ticker symbol COHR. Those 10 miles of cable are NVIDIA Spectrum X Ethernet, and their newest switches use co-package optics. That means the lasers and optics sit right next to the switch, instead of being separate modules that plug into external ports. Jensen showed one on stage and said that they're already in full-volume production. Coherent makes the lasers and optical components for these systems, and back in March, NVIDIA invested $2 billion in Coherent and signed a multi-billion dollar purchase agreement as well. Last quarter, Coherent's revenue grew by 34% year-over-year to just over $2 billion, and it currently trades at about 22 times forward earnings, giving it a lower forward P/E ratio than Alphabet, Amazon, and Microsoft today. Vertiv reports earnings on October 28th, and Coherent reports on November 4th. So let me know in the comments if you want me to make a video breaking those down for you. By the way, OpenAI and Anthropic are two of the biggest AI companies in the world, but they're still private. That's where Liquid comes in, the sponsor of this video. Liquid uses perpetual contracts to track almost any asset's price, which means you can trade around the price action of pre-IPO companies. But Liquid goes way beyond private companies. It calls itself the everything exchange, because you can trade stocks, crypto, commodities, foreign exchange, and even prediction markets, all on one platform. And on Liquid, all of these markets trade 24 hours a day, 7 days a week, so you can react to earnings and breaking news even at night or over the weekend. And then there's CoInvest, Liquid's AI assistant, which connects directly with Quad and ChatGPT so I can research a market, understand what's moving it, analyze trades, and even prepare orders right inside the conversation. Nothing executes automatically. I still review and confirm every trade. That combination of pre-IPO markets, 24/7 trading, and AI investing is why Liquid is growing so fast. Since launching last year, Liquid has handled over $6 billion in trading volume and raised funding from top investors in the space. So if you want to trade almost any asset 24/7 and integrate investing directly into Quad or ChatGPT, check out Liquid with my link below today. Alright, data centers are really just the beginning. AI's real-world impact is everywhere else. There are now over 5 million industrial robots working in factories around the world, and more than 600,000 of them were installed just last year. Most of these robots are programmed by hand to do a single job, and setting up a new production line means individually tuning and testing every robot on a factory floor. NVIDIA wants to move that work off of the factory floor and earlier into the robot's life cycle. Jensen says that every robot actually needs three computers to run: one in the data center to train its AI, a second one to run a digital twin, which is a physically accurate simulation of its environment where it can learn and practice skills, and a third computer inside the robot itself. When I was at GTC back in March, some of the world's biggest robot makers talked about building simulation software directly into the tools they use to set up factory lines and put Jetson modules inside their robot controllers. And last week, Siemens said that standing up digital twins can cut factory engineering efforts by up to 85%, and get new products to market up to 40% faster. The big idea here is that every industrial company will eventually become a robotics company. When it comes to factory robots, check out Cognex, ticker symbol CGNX. Cognex was founded in 1981 by an MIT lecturer and two MIT grad students, and their first product read the letters and numbers printed on products and parts. Today, they make machine vision systems, cameras and AI software that inspect parts and guide robots on production lines in a wide variety of industries, like automotive, consumer electronics and packaged goods. This year, Cognex launched a vision controller powered by NVIDIA's Jetson that delivers up to 157 trillion AI operations per second. That's enough to run several high-resolution AI models at the same time. And their new OneVision platform is already being used by hundreds of customers building AI vision applications. Last quarter, Cognex hit record revenues of $291 million, which was up 17% year-over-year, but their earnings per share jumped by 80%. They have $755 million in cash and investments, no debt, and expect strong double-digit revenue growth this year. Cognex is about to report earnings on November 4th. But another one that every single one of us relies on is the cell network. Last week, Jensen pointed out that telecom networks have about 100,000 sites across the western world, which add up to about 100 gigawatts. That could all become one giant distributed AI network. For context, Amazon Web Services added 3.9 gigawatts of power capacity last year, and they say that was more new capacity than any other company on Earth. So turning 100 gigawatts of already up and running telecom networks into distributed AI capacity is a really big deal. The idea is called AI-RAN, or AI Radio Access Network. The infrastructure at each cell site that connects to your phone runs on specialized hardware today, built for 5G radio signals. But with AI-RAN, it runs as software on NVIDIA's accelerated computers, so the same hardware can handle our calls and AI workloads at the same time. This month, NVIDIA, T-Mobile, and Nokia completed the world's first AI-RAN call on a live network, at a commercial cell site in Washington. Nokia's goal is to achieve two times the spectral efficiency. Or, said in English, they want to move twice as much data through the same bands of airwaves, with pilot deployments starting at the end of this year, and commercial availability in 2027. That's why my pick here is Nokia stock, ticker symbol NOK. NVIDIA invested $1 billion in Nokia stock at $6 per share last year, and it trades at around $8 today. And Nokia's fastest growth is coming from AI data centers. Last quarter, their sales to AI and cloud customers more than doubled, and they expect their optical and IP networking business to grow by 25% or more next year. But cell towers aren't the only thing we rely on that's getting smarter thanks to AI. So let's talk about the cars we drive next. When I was at CES back in January, NVIDIA released Alpamayo, a family of open-source AI models that watch video from a car's cameras, decide where it should drive, and explain their reasoning along the way. Car makers use Alpamayo to train the smaller self-driving models that actually run in their cars. And after riding in a Mercedes that runs Alpamayo, NVIDIA GTC, and seeing it for myself, I agree with Jensen when he says the chat GPT moment for self-driving cars has arrived. That's also when NVIDIA and Uber announced plans to launch robo-taxis that run on NVIDIA's self-driving stack and Uber's ride-hailing network, starting in LA and San Francisco in the first half of 2027, and scaling to driverless services in 28 cities around the world by 2028. BYD, Geely, Isuzu, and Nissan are all building robo-taxi-ready cars on the Hyperion platform. That's NVIDIA's full package of sensors, computers, and safety software that automakers can build on to have cars drive themselves. These four brands account for over one out of every nine new vehicles sold on Earth last year. And at GTC Berlin, Volvo, Mercedes, and JLR all signed on to be Hyperion compatible as well. That's another roughly 3% of all new vehicles sold last year. As Jensen put it, robo-taxis are the first mass-market application of robotics and physical AI, and NVIDIA's bet is that almost every carmaker will want to plug into one shared platform instead of having to develop and test their own. If you agree with that logic, the stock to buy is Uber. Regardless of who wins the self-driving race, robo-taxis still need riders, and Uber already works with more than 30 self-driving partners. Uber's revenues grew by 18% last year. Their operating margins have climbed every single year to over 12%, and they earn money on robo-taxi rides no matter which carmaker comes out on top, so long as the rides themselves get booked on Uber. Let me tell you why I didn't pick Tesla. Tesla's revenues were flat last quarter. Their operating margins have been shrinking for years, and the stock trades at an astronomical 175x forward earnings. That means you can add up the forward P/E ratios of Apple, Microsoft, Amazon, Alphabet, Meta, and NVIDIA, and Tesla would still be more expensive than all six companies combined, while also having the slowest growth and lowest margins by far. Uber stock trades at less than 10% of Tesla's multiple, at 17x forward earnings, and they handle more trips in a single day than Waymo does in an entire year, especially when you include food deliveries. Tesla doesn't currently report their number of rides. And that brings me to humanoid robots. During GTC Berlin, Jensen said that humanoid robots are likely going to be the highest volume configuration of robot. And that makes sense, since the modern world is built for humans by humans, so human-shaped robots will have an easier time operating in it. And to speed the industry up, NVIDIA designed a reference robot alongside a German company called Agile robots that builds and sells it. It's called the Agile 1SN. It stands about 4.5 feet tall, has 31 independent joints, and runs on NVIDIA's Jetson-Thor computer and Groot robotic AI models. Jetson-Thor is a small computer that goes inside the robot built on the same Blackwell chip design as NVIDIA's data center GPUs. That way, the robot can run its AI locally instead of needing the internet and waiting on the cloud. And Groot is NVIDIA's family of open models specifically for humanoid robots. Think of it as a pre-trained robot brain that companies can then fine-tune for their own robots and jobs. The Agile 1SN ships early next year, pre-assembled and ready to go out of the box, which lets researchers skip months or even years building their own robots from scratch. But here's the thing. Uber, the world's largest taxi company, doesn't own any vehicles, and Airbnb became one of the biggest places to book a stay without owning any real estate. I think the same thing will happen in robotics, where the biggest winner might not make any robots themselves. Companies like Uber, Airbnb, and NVIDIA win by owning the platform that everyone plugs into. That's why I think NVIDIA is actually the best stock for humanoid robots. Most companies working on them are still private, and many of the big ones are already building on NVIDIA's Jetson Thor, including Boston Dynamics, Figure, and Agility Robotics. One X and OpenAI are evaluating it too. Jetson Thor delivers up to 7.5 times the AI computing of NVIDIA's previous robot chip, called Jetson Orin, while being 3.5 times more energy efficient, and the developer kit starts at just $3,500. That means the platform's barrier to entry is very low. NVIDIA itself trades at about 17 times forward earnings, which is cheaper than the overall S&P 500. So let me put everything together and show you exactly how I'd split my money between these stocks. And if you feel I've earned it, consider hitting the like button and subscribing to the channel. That really helps, and it lets me know to make more content like this. Thanks, now let's talk about my top stocks after Jetson's keynote at NVIDIA GTC Berlin. Over 92% of NVIDIA's revenue still comes from data centers today, and that's where most of the money is. Which is why Vertiv and Coherent are my picks for the AI factory build-out. For the infrastructure we already rely on every day, like factory robots to make our goods, and cell networks to keep us all connected, I like Cognex and Nokia. And for future markets, I'd rather own Uber as the network that Robotaxis plug into, and NVIDIA as the platform that robots will use for simulation, training, and real-time inference. If I had to split my money between them, I'd keep NVIDIA as the core 25% position, since they own the ecosystems that everyone else is building on. And then I'd put 15% into the other five stocks. Nokia, Uber, and NVIDIA all trade below the S&P 500's forward P/E ratio of 19. And five of these six companies report earnings in the next few weeks. Every one of these companies also has a direct connection to NVIDIA and owns a layer of the stack that everyone else relies on. So it doesn't matter which AI lab trains the best model, whose robots roll off the assembly line first, or which car maker builds the best robo-taxi. These companies all still get paid, making them a great way to get rich without getting lucky. And if you want to see what else I'm buying to get rich without getting lucky, check out this video next. Either way, thanks for watching, and until next time, this is Ticker Symbol U. My name is Alex, reminding you that the best investment you can make is in you.