98% of Investors Miss The REAL AI Winners (My Full Map)

$7.6 trillion. That's how much AI spending Wall Street is pricing in over the next 5 years, making the AI buildout the largest infrastructure project in human history. This one number, $7.6 trillion, is why AI stocks have been crushing the rest of the market. But what if I told you that this number is already wrong? Nvidia and Broadcom just reported earnings, and the actual AI spending is on track to be much, much bigger. My name is Alex and I spent eight years as an electrical engineer and AI researcher at MIT. Let me show you where all this AI spending is actually going and how I'm investing in it to get rich without getting lucky. Your time is valuable. So, let's get right into it. First things first, I'm not here to hold you hostage. So, here's everything I'll talk about up front. Just how big AI spending could really get. The five layer map of where that spend is actually going. who's winning the big battle between Nvidia's GPUs and Broadcom's custom chips, and of course, which stocks I'm buying as a result. There's a lot of ground to cover, so let's dive right into just how big AI spending could actually get. Nvidia and Broadcom both just had their best earnings calls ever. Nvidia reported record revenues of $96.2 billion for the quarter, which was up 106% year-over-year. And Broadcom reported record revenues as well, 29.6 billion, which was up 86% from last year. But things get even more interesting when we narrow down their revenues to AI data centers, which includes compute and networking for both companies. Nvidia's data center revenues came in at $89 billion, which was up 117% year-over-year. But Broadcom's management discloses their AI revenues separately during earnings calls. So, I went back through them to compare things apples to apples. And I'm glad I did because three big things jump out at me right away. First, AI spending isn't just growing, it's accelerating. Both companies reported much higher AI revenue growth every quarter. Not just higher revenues themselves. Second, Broadcom's AI revenue is growing insanely fast, 221% year-over-year, but from a much smaller baseline than Nvidia's. Still 73% of that revenue came from designing custom chips or AS6 for six AI giants including OpenAI, Anthropic, Google, and Meta Platforms while the other 27% came from networking. Nvidia's AI revenue has almost the same split about 80% for compute and 20% networking solutions like Spectrum X Ethernet, quantum infiniband, and NVLink. That means most of the money is still in chips, which I'll get back to when I cover the five layer map. And third, both companies also shared guidance for the next quarter. Nvidia's guidance implies 94% AI revenue growth year-over-year, while Broadcom guided for 236%. But this doesn't mean that Broadcom is winning the AI race. Remember, percentages measure speed, but dollars measure actual size. Broadcom added 11.5 billion of quarterly revenue to their AI business over the past year, but Nvidia added 48 billion or over four times more. What it actually means is Wall Street's estimates for AI spending are wrong. Very wrong. This bar chart is from Goldman Sachs, one of the biggest banks on Wall Street, and it assumes global AI spending will only grow by 32% next year, 21% in 2028, and just 4% by 2031. And it's not just them. Every major institution I checked is predicting a similar slowdown. Meanwhile, Nvidia expects the top five cloud companies to spend over $800 billion just this year. And next year they see those same five companies spending 1.3 trillion. So just five companies are outspending Wall Street's estimate for the entire planet by about 30%. Not to mention that companies like Google and Amazon ended up raising their capex budgets midyear, sometimes more than once because every single time they add capacity, it immediately sells out. My point is AI spending is going to come in much bigger than most institutional investors expect and the difference could be hundreds of billions of dollars per year. The demand is there and the infrastructure suppliers all say the same thing. They can't build fast enough. And the reason Wall Street keeps underestimating it is because they're looking at the wrong map. But the right map already exists. Jensen Hong only writes a couple posts on Nvidia's blog each year. And when he does, it's worth paying attention to. Earlier this year, he wrote one titled AI is a five layer cake where he broke the AI buildout into five parts. But in order to understand how they fit together, you need to understand the big picture. First, AI is not an app or a single model. It's a utility. Electricity is measured in watts. The internet is measured in bits. And AI is measured in tokens. Electricity powers the world's devices. The internet instantly transfers information and AI reasons about that information. And just like these other utilities, AI needs a lot of hardware, software, processes, and people to turn raw materials into actually useful work. So, Jensen's five layer cake is really five interconnected markets, each with their own winners and losers. Speaking of which, I cover around 40 AI and chip stocks, and I keep them in a spreadsheet that I update by hand. Every screener I've tried either costs a fortune or doesn't show the metrics I actually care about. So, I built my own with Emergent, the sponsor of this video. Emergent is a full stack AI builder. You describe what you want in plain English, and it builds the whole thing, including the backend, live on a real URL. Here's the actual prompt I used and I'll also put it in the description. Emergent isn't one AI. It's a team of them working in unison. So the front end and the backend get built together and then reviewed. I didn't touch the database or the login. They just got built along the way. And when something breaks, it fixes itself. I left that in on purpose. One click and it was live. No hosting, no deploy step, and nothing else to configure. The total time from prompt to product was just 11 minutes. So, here are the top stocks I'm watching, one row each, and it has all the features I actually want, like sorting by distance from the 52- week high and then by revenue growth. And I own it, which is important to me as a small business owner. I can even export the code. So whether you run a business that needs custom solutions or you have great ideas collecting dust, stop asking and start building on emergent with my link in the description. All right, so AI is really a five layer cake. But unlike the internet, intelligence can't be cached or stored. It's produced in real time, which means all five layers we're about to walk through needed to be reinvented from the ground up. The foundational layer of AI is energy. Every token is the result of electrons moving, heat being managed, and energy being converted into computation. Above energy are the chips. Processors designed to efficiently transform energy into computation at massive scales. Infrastructure sits above chips, not the other way around. Land, power, cooling, construction, networking, and software systems all come together to orchestrate tens of thousands of chips. So they act like one machine. AI models are built on top of that infrastructure. Not just language models, but biology, medicine, materials, finance, and physics. AI models understand unstructured information. They reason about its context and intent, and they output a useful answer. And at the top are applications. This is where economic value is actually being created. Drug discovery platforms, industrial robotics, self-driving cars, motion graphics for movies and video games, all different ways of using AI to take on tasks that cost skilled people a lot of time today. So, if AI can take on some of those tasks instead, it frees up those people to focus on the tasks that AI can't do at all. Radiologists who take minutes to read each scan get that time back to spend on more patients. Engineers get to spend their week designing instead of documenting. And drug researchers can test thousands of compounds in software before a single one touches a lab. But every layer of this cake stands on the one below it. So we need to start at the bottom. AI is fundamentally changing energy because intelligence generated in real time requires power to be generated in real time too. And power is the one thing that no one can manufacture ahead of time. Building gas plants takes years and so can the wait to connect it to the grid. Both Nvidia's and Broadcom's earnings calls said the same thing. For Nvidia, AI factories based on Hopper translate to about $18 billion per gawatt. Blackwell about 25 billion and Vera Rubin pushes it all the way to $40 billion per gigawatt. And just one week later, Hawkan said the same thing. Broadcom's chips and networking solutions average out to between 20 and $30 billion per gawatt. So the entire AI buildout is priced in gigawatt because power is the real constraint for every layer of the AI stack. But there's a problem and I made this chart to help explain it. US utilities are planning for roughly 90 gawatts of new data center demand by 2030 or about 18 gawatt of new always on load every single year just from data centers. This year the grid is adding a record 86 gawatt of capacity. So where's the problem? I want to make it clear that what I'm about to say is not a political statement. I'm just sharing the numbers. It turns out that over 90% of the gigawatts being added to the grid are solar, wind, and batteries, which only count for about 25% of their sticker rating compared to always on power. Solar needs the sun to be out. Wind turbines need the wind and so on. But on the flip side, the grid has been retiring more always on power than it's been adding by closing things like coal and gas plants. So even though the grid looks like it's expanding by 86 gawatt on paper, America's supply of always on power is actually shrinking. And it's not for a lack of trying. There's more power waiting in line for a grid connection than the entire grid has today. But the median wait time is over 5 years. And there's only one way to resolve that gap. Electricity prices have to go up. So, the winners in this layer are the companies that already own the power. Constellation Energy, ticker symbol CEG, operates America's largest nuclear fleet and is restarting the healthy reactor on 3M Island on a 20-year deal with Microsoft. Vistra, ticker symbol VST, locked in 20-year nuclear power deals with Amazon and Meta Platforms. And GE Verova, ticker symbol GEV, builds the gas turbines that everyone's fighting over with deliveries effectively sold out for the next few years. But chips are where those gigawatts turn into tokens. Nvidia builds a universal platform that any company can buy while Broadcom co-designs custom specialized chips for single customers. Usually these applicationspecific integrated circuits or AS6 are for inference. If data centers are power constrained then every watt counts. So AS6 trade flexibility for efficiency when the workload is predictable and high volume. Think about all the people using Google search serving content on Facebook and Instagram or responding to prompts on chat GPT or claude. Vera Rubin is already in full production and it's expected to be the fastest product ramp in Nvidia's history. It's already set to be 20% of their data center revenue next quarter. It's also a big driver of the 70% growth that they guided for for the next full year. By the way, they're so confident that this is the first fullyear forecast in Nvidia's 33-year history. On the flip side, Broadcom has six major custom chip customers. They co-designed Google's TPUs and Meta's inference chips, which enter production this quarter. Anthropic is scaling from 1 gawatt of custom Broadcom chips this year towards 10 gawatt by 2028. And OpenAI's first dedicated chip called Jalapeno just started shipping as well. Broadcom CEO Hawk Tan says they already secured enough chip capacity to hit $115 billion of AI revenue next year and has line of sight visibility to $230 billion the year after. So, if he's right, Broadcom will quadruple their AI revenue in the next 2 years. But the truth is, Nvidia's GPUs and Broadcom's AS6 don't actually compete. Google, Meta, Anthropic, and OpenAI are buying both kinds of chips at gigawatt scales. GPUs for their speed and flexibility, and AS6 to cut costs when serving billions of requests. Both companies are sold out, which means the second layer of our five layer cake is really just as supply constrained as the first. And that means the real winners are the companies supplying them both. The Taiwan Semiconductor Manufacturing Company, ticker symbol TSM, manufactures chips for both sides, and twothirds of their revenue now comes from AI and high performance computing. Micron and SKH Highix make the high bandwidth memory that all these chips need with revenues more than tripling year-over-year. But Frontier AI models and applications aren't built on individual chips. They're built on the infrastructure that makes thousands of them work together. Networking is the key to making that happen, and it splits into two different jobs, each with their own winners and losers. Scale up networking means wiring the chips inside one rack together so they act like one giant chip. Nvidia does this with NVLink, which lets every GPU in the rack read each other GPU's memory almost like it was their own. So, it's less of a network and more of a central nervous system. NVLink currently connects GPUs in the same rack through over 2 miles of copper cabling. But when Nvidia starts shipping Reuben Ultra, those thousands of copper connections will get replaced with a circuit board the size of a coffee table. Scale out networking has the opposite job. It wires thousands of racks together that might be sitting hundreds of feet apart. Copper can't carry data fast enough for more than a few meters. So scale out networking runs on fiber optics instead. transceivers with lasers that turn electric signals into light and back to electric on the other end and they burn a lot of power in the process. Both Nvidia and Broadcom have huge networking businesses today. Nvidia's revenues from Ethernet grew by about 160% year-over-year and their newest switches ship with the Vera Rubin platform. So that growth should continue for years to come. Broadcom's AI networking business grew by almost the same amount, and their flagship Tomahawk switches are used in AI clusters across all six of their custom chip customers. On top of that, they just taped out the industry's first 200 terab per second switch, and their newest Tomahawk Ultra switches bring Ethernet inside the rack to compete directly with NVLink. But they're far from the only winners in this category. Arista Networks, ticker symbol A NE, builds the switches and software that run scale out Ethernet for the biggest AI clusters in the world. And they're expecting over $3.6 billion of AI revenue this year alone. Verdive, ticker symbol VRT, handles the power and cooling inside these data centers. So, every new chip generation pushes more dollars per rack their way, which is why they just raised their guidance across the board. Back in March, Nvidia invested $2 billion each into Lmentum, ticker symbol Liit T, and Coherent, ticker symbol C O HR, two companies that make lasers and transceivers. And Nvidia made big purchase commitments with them both. And while the industry shift towards co-ackaged optics is worrying Wall Street, the AI buildout is currently adding optical ports much faster than co-ackaged optics are consolidating lasers. So, I think Wazers will sell well for years to come. In fact, Lumenum's revenue more than doubled in the past year. There are also two smaller names that almost no one on Wall Street is covering. Fabinet, ticker symbol FN, assembles optical modules for Nvidia, Amazon, and Cisco. And only nine analysts even cover the stock. There's also Powell Industries, ticker symbol PWL, which builds the industrial-grade switch gear to deliver power to these facilities. They have a market cap of under $7 billion. Their backlog is up 69% year-over-year, and only four analysts even cover them. All right, AI models are actually some of the most expensive products ever made. AI Labs are the first generation of startups that need tens of billions of dollars just to get off the ground. And the biggest ones are still burning cash today. But the amount of work that they're doing is exploding. Google alone now processes 3.2 quadrillion tokens every single month. That's like reading through a billion books a day, which is eight times as many books as all of humanity has ever collectively written. There isn't too much to say about models right now because every major AI lab is still private. OpenAI and Anthropic both filed paperwork to go public this summer and Anthropic could reportedly list as early as next month, but until that actually happens, Google and Meta Platforms are the only companies building Frontier models that we can directly invest in. Nvidia builds models, too, but they mostly give those models away for free because more models means more reasons to buy their chips. Besides that, Microsoft still owns a big chunk of Open AI and Amazon Stake and Anthropic give them paper gains of over $50 billion last quarter alone. But that's fine because the real money is in the top layer of the AI cake. applications that companies and consumers actually touch and stocks I already cover all the time on this channel. For example, Meta Platform's ad engine might be the biggest AI application on Earth today. AIdriven targeting pushed their ad revenue up 27% year-over-year with advertisers paying 12% more per ad. That doesn't sound like huge growth, but don't forget that this business literally already serves nearly one out of every two people on planet Earth. So, this growth is on top of an absolutely enormous baseline. And Google's AI search features reach billions of people every month. Besides that, drug discovery platforms, humanoid robots, and self-driving fleets, all of these are mostly still private companies or very risky early stage bets. But let me know if you want me to make a video on them anyway. Either way, the top two layers of the AI cake are currently concentrated into the same handful of tech giants. They build the models, own the applications in distribution, and rent out their AI data centers, and they bankroll the $800 billion being spent on the bottom layers of the AI cake. And now that you have the full map, let's talk about how I'm actually investing in it. 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 how I'm investing in this five layer cake. The most important thing to remember is that Wall Street consistently underestimates the size of the AI buildout. And that makes sense. Hedge funds and industry analysts can't risk losing money for their clients, even over a single quarter. So, they have to be very conservative. But we don't have that problem. So, we can pick the companies that win at every layer of the cake and dollar cost averaging over time without worrying about short-term noise. I'm also a big believer in getting rich without getting lucky. So, whenever I see a high growth market that I can own in just two or three stocks, I always buy them according to their market share. Constellation Energy and Vistra control nuclear power that can't be replaced this decade. and GE Vernova's turbines are sold out for years. As long as the grid is supply constrained, I'm happy owning all three of these companies. I feel the same way, one layer up the stack. Nvidia and Broadcom sell fundamentally different kinds of chips, and the AI giants are buying from them both. On top of that, TSMC manufactures all of these chips, so they win no matter which architecture actually comes out on top. At the infrastructure layer, I've been buying Coherent, Lummentum, and Fabernet, all of which I recently made a video on. So, I'll leave a link to that video for you below as well. I'm also starting a position in Powell Industries. So, let me know if you want a deep dive on them, too. And at the very top of the AI cake, I've been pounding the table on Google and Meta Platforms. Every new investor makes the same mistake in thinking that big companies can't get bigger. But Google is up 45% in the last year, more than double the returns of the S&P 500. At the end of the day, Wall Street is pricing in $7.6 trillion for the AI buildout. But as we just saw from Nvidia's and Broadcom's earnings, the real number is much, much bigger. And now we know where all that spend is going across all five layers of the AI stack, which means we can own the winners and 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 Tickerol U. My name is Alex, reminding you that the best investment you can make is in you.