Block Rock CEO Larry Fink: "The Biggest AI Problem Isn't Demand" (3 Top AI Stocks To BUY Now)

Black Rockck CEO Larry Frink believes the biggest AI problem isn't demand and what he revealed could completely change how investors think about the AI revolution. In this interview on CNBC, Larry Frink shares why he believes AI demand is actually outpacing supply, why power and infrastructure have become the biggest bottlenecks, and why he remains incredibly bullish on the future despite massive AI spending. First, I'll show you Larry Frink's interview. Then, I'll break down exactly what he said, share my own reaction and analysis, and finally reveal three top AI stocks to buy now that could benefit from the trends he discusses. Let's hear what Larry Frink has to say. >> I believe the role of financing infrastructure around uh technology, whether it's financing data centers or financing the purchase of chips. As I said earlier, I think we're going to have markets in in, you know, investing in in compute. We're going to have a futures market in comput. This is going to be the next revolution in finance. And it's the next and it happens because of the strength of the United States capital markets having the ability to finance these new technologies. So the United States and other places in the world can be leaders in the new technologies. >> You know, you mentioned 50 years. So I am curious. I mean, have you ever seen anything quite like this, Larry? We've got what were the most profitable companies the world has ever seen making a choice to incinerate, wrong word, to decide not to have any free cash flow anymore, to not return capital to shareholders because they have to be part of this headlong race to make sure they are not left behind in the AI future. When I listen to myself say that, are you there's some worrisome aspects to that, are there not? I I mean I've spoken to some of the uh leaders this week and last week in in that sector. Um their biggest worry right now supply is not keeping up with demand as you and you see that showing up in the value of of the memory stocks. Okay, we have more demand for memory than we have supply. Obviously they've been able to do big price increases. I don't know how sustainable that is. Maybe in three four years we'll have enough supply but you know um and so what we see as a big investor in data centers the demand for compute is not slowing down it's growing faster the problem we have as a country we're not investing fast enough so I have an opposite >> so you're we're just not even we need to do even more >> I mean a trillion dollar capex spent from six companies not I I I believe it's well not just there. We're not investing in our grids fast enough. We do not have enough adequate supply of power and that's what creating some of the issues at certain state levels that they're worried about electricity prices for the consumer which is a legitimate issue. But other states that have more power and more capability of delivering power which and and delivering power that is not going to raise the price to the consumer are going to be the big winners for growth. >> Yeah. So in the last quarter we you know we contracted close to 1 gawatt of power in Pennsylvania. Okay. We're financing another big data center in in another state. And in my conversation with every hyperscaler, demand is exceeding supply. And yes, you're right. These large companies used to be balance sheet light. And now their business has changed. And that is with but that's the role of the capital markets. And I promise you these are going to be great investments for individuals if we could if >> but you've got to know that the underlying business model ultimately that we get to is going to generate enough >> enough revenue and profit [clears throat] to be able to pay for all the data centers. They're not cheap. >> Yeah. >> They're 50 60 billion dollars for one gawatt. What I worry about David is not the demand side. I worry about can all a society benefit from AI because right now compute is so expensive. My questions to everybody in the hyperscaler business is not whether it's not whether they have enough demand is how quickly can they bring down the cost of compute. I'm not worried about Black Rockck paying for um the the AI and I think you know we've been a huge investor in it and our systematic equity team has really benefited with it with the flows and the return but I worry about the small and medium businesses how are they going to be able to compete in this new AI technology world. So the biggest question I ask behind the scenes is how quickly can we bring down the cost of compute and and okay they you know they tell me it's like a Moor's law and how quickly they could develop faster and faster analytics to to have faster compute but unless we develop better technology and better systems in our grid to provide more power more consistent power you know I believe the United States must be power agn agnostic. We should we should not care if it come from solar or hydrocarbons. We have to be there but we don't have you know we need to be manufacturing the solar panels in the United States building the battery storage. At least we can do the battery storage here. >> We do. >> So those are my worries. My worry about is not a bubble. My worry about we're not we don't have the ability to build fast enough. And then I go watch China. >> China's building 100 gawatts of nuclear. They're building close to 100 gawatts of solar. Okay. They are getting set up for this AI revolution and the need for power. We're we're not doing this enough. And I actually get frightened when I see states saying we're going to do a moratorum. That's not the answer. The answer is how do we get how do we deliver more power quickly? Let's why don't we why don't we all start because again how do we deliver more power so we don't raise electricity prices but we can be the center of the AI revolution. >> No. >> Can I ask you about leverage? Every morning we come in here, we check the Cosby which was up six 9% swings. The South Korean president said the market there she thinks is quite unstable. Uh a lot of its leveraged ETFs and two big names which we don't really do. >> Right. Right. But do you worry about that though? Is it global risk factor? >> No, I I I really don't worry about uh there's there's not that much leverage in this compared to 2008 2009. Yeah, one maybe the Korean market has that embedded leverage in it. There is no question as I said it in earlier times and over here. I was always worried about the leverage in in Bitcoin and crypto. There was too much leverage players in it. That's why we had the wash out and I think there's more stability at these levels here. But um no, I we don't see that much implicit leverage for the scale of the capital markets today. The leverage is not as large. I mean that that that doesn't mean there's not pockets. Uh but no, I I I as I said in my prepared remarks this morning, I'm very bullish on the markets over the next 12 months. I think the tech technological revolution is going to power uh better margins for more companies. I mean, think about Black Rockck. We've raised our margins increased by 260 base points over the last 12 months. A lot of it is using more and more technology. >> Can you keep those margins going? You said you don't see 45 to 46% margins as a ceiling. You pointed back to 2021. Your business though is very different than it was in 21. So I'm sort of curious, what's the mechanism that gets you back above 47%. uh continuing driving uh more growth in our private markets area which we we see incredible momentum uh continue to drive opportunities in retirement which I believe these are all going to be creating better margin and just making sure that we're utilizing technology as fast as we can to do more with less. I mean our headcount David is unchanged and our you know we're up a trillion dollars in assets. Okay, that's that's what you're you're seeing. We're able to use technology to process more trades, to process more uh activities. So, we're able to do that. We're able to leverage our human capital using technology, working alongside of that. We're using um the amount of uh code we're writing alongside our coders with AI is accelerated dramatically. So we're able to do more and more and more and I and as the [clears throat] ability of AI and compute as I talked about earlier becomes faster and cheaper that will drive even higher margins for not just for Black Rockck for other firms. >> After watching this interview, one thing became very clear to me. Larry Frink is not talking like someone who believes the AI boom is reaching its peak. He is talking like someone who believes we're still standing at the very beginning of a decadesl long transformation. In fact, many of the concerns investors have today, whether AI spending is too high, whether big tech is overspending, or whether this is another bubble, are almost the opposite of what worries him. The first thing that stood out was when Larry reflected on his 50 years in finance. That's an incredible perspective because he has lived through multiple financial revolutions. He witnessed the birth of the mortgagebacked securities market, multiple recessions, the.com boom, the financial crisis, quantitative easing, and now the AI revolution. Yet, despite everything he has experienced over five decades, he believes AI infrastructure financing could become the next major evolution of global finance. That statement deserves more attention than it received during the interview. He's essentially saying that the financial markets themselves are about to evolve because of AI. Just as previous generations created entirely new financial products around mortgages, commodities, and energy, he believes investors will soon have financial markets centered around computing power itself. Think about that for a second. We already trade oil. We already trade natural gas. We already trade electricity. Larry believes one day investors may trade compute capacity as another financial asset. That is a fascinating way to think about the future because AI is becoming just as essential as electricity was during the industrial revolution. Companies increasingly depend on access to computing power the same way factories once depended on reliable electricity. Personally, I think this is one of the most underrated ideas from the entire interview. Most retail investors spend all their time debating which AI software company will win. Larry Frink is looking several layers deeper. He's asking who finances the infrastructure behind the winners. Historically, the company's enabling revolutions often become just as valuable as the companies consumers interact with every day. The interviewer then raises what many investors are thinking today. The largest technology companies in history are spending extraordinary amounts of money. Instead of generating enormous free cash flow and returning it to shareholders through dividends or buybacks, they are pouring hundreds of billions of dollars into AI. That naturally makes people uncomfortable. Normally, investors like companies that produce large profits while spending less. Today, the opposite is happening. The richest companies in the world are investing at levels rarely seen before. But Larry doesn't seem worried. Instead, he says every technology leader he speaks with has the exact same problem. Demand is exceeding supply. That sentence came up repeatedly throughout the interview. Demand for memory chips exceeds supply. Demand for data centers exceeds supply. Demand for compute exceeds supply. Demand for electricity exceeds supply. It's almost like every bottleneck in the AI economy traces back to one central issue. There simply isn't enough capacity. This is where Larry's view differs from many market commentators. A lot of analysts spend time worrying about demand eventually collapsing. Larry isn't worried about demand at all. He's worried that supply cannot keep up. Those are two completely different investment mindsets. He even points to memory chip companies benefiting from strong pricing because customers desperately need more hardware than manufacturers can currently produce. Now, he does admit those unusually high prices probably won't last forever. Eventually, more manufacturing capacity should come online, but notice the timeline he gives, 3 or 4 years. That suggests he expects AI demand to remain exceptionally strong well into the future. Again, this reinforces the idea that this isn't a short-term trend in his eyes. This is a structural transformation. Another point I found particularly interesting was when Larry shifted the discussion away from technology companies themselves and toward America's infrastructure. He argues the United States simply isn't investing fast enough, not just in data centers, not just in semiconductor manufacturing, but especially in the electrical grid. This is something many investors overlook. Everyone talks about Nvidia. Very few people talk about transformers. Everyone talks about AI chips. Very few people talk about transmission lines. Everyone celebrates new AI models. Very few people ask whether enough electricity exists to power them. Larry repeatedly emphasizes that power has become the real bottleneck. Without electricity, AI cannot scale. Without modern grids, data centers cannot expand. Without additional generating capacity, innovation eventually slows down. I actually agree with him here. Infrastructure has never been exciting compared to software. But history repeatedly shows that infrastructure determines how quickly technological revolutions spread. Railroads enabled industrialization. Highways accelerated commerce. The internet backbone enabled cloud computing. Now electricity may become the limiting factor for AI. One statistic he casually mentioned really caught my attention. He says Black Rockck recently contracted nearly 1 gawatt of power in Pennsylvania and is financing another major data center elsewhere. That tells you just how enormous these facilities have become. He later mentions that a single one gigawatt data center could cost 50 to 60 billion dollars. Those numbers are almost difficult to comprehend. When individual projects cost tens of billions of dollars, traditional financing alone often isn't enough. That's why Larry believes capital markets will play such an important role. Someone has to finance all of this. banks, private equity, infrastructure funds, institutional investors, bond markets. Everyone becomes part of building the AI economy. The interviewer then pushes back with a reasonable concern. Surely, all this spending eventually needs to produce enough profits to justify these investments. Larry acknowledges that concern, but quickly returns to what matters most in his view. He's not worried about Black Rockck making money. He's not worried about hyperscalers finding customers. He's worried about something much broader. Will the rest of society actually be able to afford AI? That might have been the biggest surprise of the interview. Rather than asking whether AI companies can monetize their investments, Larry asks how quickly computing costs can fall enough for small businesses to participate. That changes the entire conversation. Large corporations can afford massive AI infrastructure. Small businesses often cannot. If AI remains expensive, only the biggest companies gain a meaningful competitive advantage that creates an uneven playing field. Personally, I think this is one of the healthiest concerns anyone in finance has raised recently. AI has enormous potential to improve productivity across nearly every industry. But if only trillion dollar companies can afford the technology, the benefits become concentrated instead of broadly shared. History suggests transformative technologies create the greatest economic growth when costs fall enough for widespread adoption. Computers followed that path. The internet followed that path. Smartphones followed that path. AI probably needs to follow that same path. Larry even compares future computing improvements to Moore's law, suggesting technology should gradually make compute cheaper and more efficient over time. But once again, he circles back to power. Lower computing costs alone aren't enough if electricity remains constrained. Without more reliable energy, AI scaling eventually hits a wall. That naturally leads into one of his strongest opinions. During the interview, he says the United States should become power agnostic. In other words, don't become overly ideological about where electricity comes from. Whether it's solar, whether it's natural gas, whether it's nuclear, whether it's hydro, the priority should be generating enough affordable electricity. Now, people may disagree about which energy sources deserve greater investment, but Larry's broader point is difficult to ignore. AI cannot function without energy. Every chatbot query, every image generation, every AI agent, every machine learning model consumes electricity somewhere inside a data center. As AI adoption accelerates globally, energy becomes increasingly strategic. Larry even argues America should manufacture more solar panels domestically while expanding battery storage capacity. Again, notice how he's thinking beyond software. He's thinking about supply chains, manufacturing, and national competitiveness. Perhaps the strongest geopolitical point came when he compared America with China. According to Larry, China is aggressively building nuclear capacity while simultaneously expanding solar generation at extraordinary scale. His concern is simple. China is preparing for the energy demands of AI. America isn't moving fast enough. Whether you agree entirely or not, it highlights something investors should pay attention to. The AI race isn't only about software innovation anymore. It's becoming a competition over infrastructure, power generation, manufacturing semiconductors data centers, supply chains, capital allocation. Countries investing effectively in these areas may gain enormous long-term advantages. Larry even says he becomes frightened when states consider slowing new power development through moratoriums. His solution isn't restricting AI expansion. His solution is expanding electricity supply faster. Again, that's consistent with his overall philosophy throughout the interview. Build more, finance more, expand faster, create enough capacity so electricity prices stay affordable while economic growth accelerates. Later, the conversation shifts toward financial markets. The interviewer asks whether leverage trading, particularly in markets like South Korea, creates systemic risk. Larry's response is surprisingly calm. He says, "Today's markets simply don't contain the same degree of dangerous leverage that existed before the 2008 financial crisis." He acknowledges there are always isolated pockets of excess, but overall, he doesn't believe leverage represents today's biggest threat. Interestingly, he compares today's environment with earlier crypto markets. He reminds viewers that he previously worried about excessive leverage surrounding Bitcoin and digital assets. In his opinion, much of that excess has already been washed out. Whether investors agree or not, the important takeaway is this. Larry does not believe financial instability represents the primary risk facing markets today. Instead, his biggest concern remains infrastructure capacity. That consistency is remarkable. Almost every question eventually circles back to the same answer. Power, compute, infrastructure, capacity. Even when discussing broader markets, Larry remains optimistic. He says he is bullish over the next 12 months because technological improvements should continue lifting corporate profit margins. This is another interesting perspective. Many investors focus exclusively on AI revenue opportunities. Larry focuses on productivity. Companies don't necessarily need to invent the next chat GPT. They simply need to become more efficient using AI. Black Rockck itself serves as an example. Larry points out that operating margins improved significantly over the past year, largely because of greater technology adoption. Then he shares another statistic that I found incredibly revealing. Black Rockck's headcount hasn't really changed yet. The company manages roughly $1 trillion more in assets. Think about what that implies. Employees are becoming dramatically more productive. Technology is allowing existing teams to accomplish much more without proportionally adding staff. That is exactly what many economists expect AI to do across the broader economy. Not necessarily replace everyone overnight, but significantly increase output per employee. Larry also mentioned something software developers everywhere will probably recognize. AI is helping engineers write code much faster. Coding productivity is accelerating. Again, this reinforces the productivity story. Businesses aren't only buying AI because it's exciting. They're buying it because it allows people to accomplish more work in less time. Personally, I think investors sometimes underestimate how powerful productivity gains can become over long periods. Small efficiency improvements compounded across thousands of companies eventually translate into higher profits, stronger economic growth, and potentially higher market valuations. Finally, Larry explains how Black Rockck plans to continue expanding profitability, private markets, retirement investing, technology adoption, operational efficiency. Rather than relying on one growth engine, Black Rockck is positioning itself across several major trends simultaneously. Overall, what I took away from this interview is that Larry Frink is thinking much bigger than quarterly earnings or next year's revenue estimates. He views AI as an infrastructure revolution first and a software revolution second. He believes the biggest winners may not simply be the companies building AI models, but also the businesses financing data centers, expanding electrical grids, manufacturing critical hardware, and supplying the enormous amount of energy required to power this new era. At the same time, he isn't blind to the challenges. His biggest concern isn't that AI demand disappears. It's that society cannot build enough infrastructure quickly enough to support it and that smaller businesses could be left behind if computing costs remain too high. That is a very different way of looking at today's market. And honestly, I think it's one investors should spend more time considering because it forces us to think beyond the obvious headlines. Now, let's dive in and talk about the three companies that could potentially benefit the most from everything Larry Frink just laid out. The first stock on our list is Nvidia, ticker symbol NVDA. If you asked investors 3 years ago which company would become the face of artificial intelligence, very few would have predicted just how dominant Nvidia would become today. However, it's difficult to find a serious conversation about AI without Nvidia sitting somewhere at the center of it. But here's what many investors still misunderstand. Nvidia is not simply selling computer chips. It's selling the computational engine powering an entirely new digital economy. Every time an AI company trains a large language model, builds an intelligent chatbot, develops autonomous systems, or expands cloud computing infrastructure, massive amounts of computing power are required. NVIDIA's graphics processing units, or GPUs, have become the preferred solution because of their unmatched flexibility, performance, and reliability across virtually every AI workload. That leadership did not happen overnight. The company spent decades building both its hardware and software ecosystem. Those years of investment have created an enormous competitive advantage that cannot easily be replicated. Developers have optimized countless AI applications around Nvidia's architecture, making it increasingly difficult for customers to switch even if alternatives emerge. This creates one of the strongest economic moes in the semiconductor industry. The more developers build around Nvidia's ecosystem, the more valuable that ecosystem becomes. the more valuable it becomes, the more companies continue buying Nvidia hardware and the cycle reinforces itself. This network effect is one reason why Nvidia continues maintaining such remarkable pricing power despite extraordinary demand. One of the biggest themes shaping today's AI market is infrastructure spending. Larry Frink recently made a fascinating observation during his CNBC interview. He argued that the biggest challenge facing artificial intelligence isn't demand, it's supply. Demand for compute continues growing faster than the industry can build capacity. That single statement explains why Nvidia remains such an attractive investment. As long as companies continue racing to build more AI infrastructure, demand for NVIDIA's products remains incredibly strong. In fact, management expects the four largest AI hyperscalers to collectively spend around $1 trillion on data center capital expenditures next year. Just let that number sink in. $1 trillion. That's up from roughly $650 billion this year. That tells us something extremely important. The AI infrastructure buildout is not slowing down. If anything, it's accelerating. When businesses commit that level of capital, they're making decadel long investment decisions. They're not building these data centers because AI is fashionable today. They're building them because they expect AI to become deeply integrated into every aspect of business over many years. And every new data center requires massive computing power that continues feeding Nvidia's growth engine. One of the most impressive aspects of Nvidia's recent performance is that it continues delivering extraordinary financial results despite significant restrictions on sales into China. The company recently reported revenue growth of 85% year-over-year. Think about how extraordinary that number is. Many mature companies celebrate 5% annual growth. Fast growing technology companies might achieve 20% or 30%. Nvidia is growing at 85% while already generating enormous revenue. Even more impressive, Wall Street analysts expect nearly 100% revenue growth in the coming quarter. That level of acceleration is almost unheard of for a company of Nvidia's size. What's particularly encouraging is that much of this growth has occurred without meaningful contributions from one of the world's largest technology markets. If Nvidia eventually regains broader access to China, that could provide another meaningful growth opportunity layered on top of an already exceptional business. Of course, investors should never build an investment thesis around something uncertain. The real investment case doesn't require that scenario. The existing demand environment already supports exceptional growth. China would simply represent additional upside. Another reason I'm optimistic about Nvidia is that management continues sounding confident about future demand. Executives aren't talking about slowing orders. They're talking about customers requesting more capacity than the company can currently provide. That tells me we're still early in the AI infrastructure expansion. Many investors worry they've missed the opportunity because Nvidia's stock has already appreciated significantly over the past few years. That's a fair concern. Nobody wants to overpay, but valuation only tells part of the story. Growth matters, too. Despite Nvidia's incredible rally, the stock currently trades at approximately 23.7 times forward earnings. Now, compare that with the broader market. The S&P 500 trades at roughly 21.7 times forward earnings. So, investors are paying only a modest premium for one of the fastest growing companies in the market. Personally, I think that's one of the most surprising numbers surrounding Nvidia today. Many people assume the valuation is still wildly expensive simply because the stock price has climbed so dramatically. But earnings have grown alongside that price appreciation. That's an important distinction. Sometimes a stock becomes more expensive because investors become overly optimistic. Other times the underlying business grows so quickly that valuation actually becomes more reasonable despite rising share prices. Nvidia appears much closer to the second scenario. When evaluating long-term investments, I always ask one simple question. Can this company realistically become much larger over the next decade? With Nvidia, I think the answer remains yes. Artificial intelligence adoption is still in its early stages. Many businesses are only beginning to integrate AI into daily operations. Governments are increasing digital infrastructure investments. Healthcare continues adopting AI diagnostics. Manufacturing is embracing automation. Financial institutions are expanding AI powered analytics. Virtually every major industry is discovering new applications. Every one of those trends ultimately requires computing power and Nvidia remains positioned directly at the center of that demand. Another factor supporting Nvidia is its software ecosystem. Hardware alone rarely creates lasting competitive advantages. Software does. Developers have spent years building applications optimized specifically for NVIDIA's architecture. That creates high switching costs. If you're running missionritical AI workloads, changing hardware isn't simply a matter of replacing one chip with another. Entire software environments often require optimization. That complexity encourages customers to remain within Nvidia's ecosystem. Investors sometimes underestimate how valuable switching costs become over long periods. The harder it is for customers to leave, the more durable future revenue becomes. Now, no investment is completely risk-free. Nvidia faces competition, technology evolves quickly, semiconductor cycles can become volatile, customer spending could fluctuate. Those risks deserve consideration. However, when I weigh those uncertainties against the enormous secular tailwinds driving AI infrastructure spending, Nvidia still stands out as one of the highest quality businesses available today. Larry Frink emphasized that demand isn't the problem. The challenge is building enough infrastructure quickly enough. As long as that remains true, companies supplying the essential technology behind AI should continue benefiting. Nvidia occupies arguably the strongest position within that entire ecosystem. Its products remain indispensable. Its growth remains extraordinary. Its valuation appears surprisingly reasonable relative to its growth profile. and management continues pointing toward enormous capital spending from customers over the coming years. That's exactly the combination long-term investors should be looking for. If this resonates with you, you're exactly who this channel is for. Please hit the like button, share the video, and leave your thoughts in the comments. Subscribe to the channel so you don't miss out on the next important financial investing update. Remember to do your own research before you invest in any stock. The second stock on our list is Micron Technology, ticker symbol MU. When most investors think about artificial intelligence, their minds immediately go to AI chips, cloud computing, or software. Very few stop to think about one of the most critical components inside every AI server. Memory. Without memory, artificial intelligence simply cannot function. every AI model being trained, every chatbot answering questions, every recommendation engine processing billions of data points, and every autonomous system making split-second decisions depends on enormous amounts of high performance memory. That's exactly where Micron comes in. Micron designs and manufactures two of the most important types of memory used throughout modern computing, DER RAM and NAND. These technologies may not receive the same headlines as AI processors, but they are every bit as essential. Think of it this way. If AI processors are the brain of an AI server, memory is its short-term memory. The processor cannot make intelligent decisions unless it can constantly access and process massive amounts of information at incredible speeds. As artificial intelligence models become larger and more sophisticated, memory requirements increase dramatically. That is creating one of the strongest demand environments Micron has ever experienced. This is one of the reasons I like Micron so much. Unlike companies that depend on consumers buying a new gadget every year, Micron is benefiting from a structural shift that could last for many years. Every new AI data center requires enormous quantities of memory. Every expansion of cloud infrastructure requires additional memory. Every increase in AI workloads pushes memory demand even higher. The opportunity compounds as AI adoption spreads across more industries. What's even more interesting is that the industry currently cannot produce enough memory to satisfy demand. Larry Frink touched on this during his interview when he pointed out that memory remains one of the biggest supply constraints throughout the AI economy. Demand continues exceeding supply. That imbalance has major implications for companies like Micron. Whenever demand significantly outpaces available supply, prices generally move higher. That's exactly what's happening. Memory prices have climbed sharply because customers are competing for limited production capacity. For Micron, that creates a powerful combination. Higher selling prices, higher revenue, higher profit margins, higher earnings. It's rare to see all four improving simultaneously over an extended period, but that's precisely what the current environment is producing. Of course, investors naturally wonder how long these favorable conditions can last. After all, semiconductor markets have historically been cyclical. Prices rise, companies build new factories, supply eventually catches up, prices normalize. That cycle has repeated itself for decades. This time, however, the AI revolution is changing the equation. Constructing advanced semiconductor fabrication facilities is not something that happens overnight. These are among the most expensive manufacturing facilities ever built. They require billions of dollars in investment, highly specialized equipment, and years of construction before production even begins. That means new supply enters the market very slowly. Meanwhile, AI demand continues accelerating much faster than new manufacturing capacity can come online. Micron's own management believes the tight supply environment could extend well beyond 2027. That's an incredibly important statement. They're not talking about another strong quarter. They're talking about favorable industry conditions potentially lasting for years. That gives investors much greater confidence when evaluating the company's long-term earnings potential. Personally, I think this is one of the biggest reasons Micron deserves far more attention than it receives. Many investors still think of memory as a commodity business with unpredictable pricing. Historically, there has been some truth to that. But AI is changing the economics. High bandwidth memory used in advanced AI servers isn't simply another commodity. It's becoming one of the most valuable components inside next generation computing systems. As AI models continue becoming larger and more complex, memory performance becomes increasingly important. That shifts competition away from simply producing the cheapest chips and toward producing the highest performance solutions. Micron is well positioned to benefit from that transition. Another aspect I find compelling is the sheer scale of the AI infrastructure buildout. Earlier we talked about projected spending approaching $1 trillion from the largest AI hyperscalers next year. That spending doesn't only benefit processor manufacturers. Every new server installed inside those data centers requires substantial amounts of memory. In many ways, Micron grows alongside the entire AI ecosystem. The more servers deployed, the greater the memory demand. The greater the memory demand, the stronger Micron's business becomes. This creates a very straightforward investment thesis. You don't necessarily need to predict which AI application will dominate. You don't need to know which chatbot wins. You don't need to know which AI model becomes most popular. As long as the overall AI infrastructure continues expanding, Micron benefits. That's the beauty of investing in essential infrastructure providers. They're supplying the tools everyone else depends on. Another area worth discussing is valuation. Despite all of these favorable trends, Micron currently trades at roughly 12.3 times forward earnings. That's remarkably inexpensive for a company benefiting from one of the strongest technology investment cycles in decades. Think about that for a moment. Investors are paying almost half the forward earnings multiple assigned to many other highquality technology companies. Now, low valuations alone don't automatically make a stock attractive. Sometimes businesses deserve low valuations because growth is weak or competitive pressures are increasing. But neither of those concerns appears to define Micron today. Instead, the company is operating in an environment where demand exceeds supply, pricing remains strong, AI infrastructure spending continues accelerating, and management expects favorable conditions to persist for several more years. Those aren't characteristics normally associated with a company trading at such a modest valuation. Personally, I believe that's where the opportunity may exist. Markets often become so focused on the most obvious winners that they overlook equally important businesses supporting the same trend. Nvidia deservedly receives enormous attention, but every AI server also requires advanced memory without sufficient memory capacity. Even the fastest AI processors cannot operate efficiently. Micron plays a critical role in solving that challenge. Another point that gives me confidence is the company's willingness to continue investing for future growth. Management understands that today's favorable conditions won't last forever unless production capacity continues expanding. That means investing in advanced manufacturing technologies, improving yields, and developing next generation memory products capable of meeting tomorrow's AI requirements. Companies that continue innovating during periods of strong demand often emerge even stronger once industry cycles normalize. That's exactly what long-term investors should want to see. When I evaluate companies, I also ask whether their products become more important over time or less important. For Micron, I believe the answer is increasingly more important. AI models continue growing. Enterprise workloads continue expanding. Cloud infrastructure keeps scaling. Edge computing is becoming more sophisticated. Every one of those trends increases demand for advanced memory that creates a powerful long-term growth runway extending well beyond today's headlines. Could volatility occur along the way? Absolutely. Semiconductor stocks have never been immune from market swings. Economic slowdowns can temporarily affect spending. Supply chains can create uncertainty. Investor sentiment can shift quickly, but if you're investing with a five or 10-year perspective, those short-term fluctuations often become far less important than the underlying business trajectory. Right now, that trajectory appears exceptionally favorable. Micron combines essential technology, strong industry tailwinds, improving profitability, attractive valuation, and a demand environment that management believes could remain favorable beyond 2027. That's a combination I find very difficult to ignore. Before we move on to our final AI stock, I want to quickly thank today's sponsor. This video is brought to you by Value Stocks Investing Master Course. If you're looking to grow your wealth by investing in solid, undervalued stocks, but not sure where to start, I created the Value Stocks Investing Master Course to teach you how to identify great companies, make smart investment decisions, and build a portfolio that lasts. Click the link in the description and pinned comments to get the course today and take control of your financial future. The third and final stock on our list is Alphabet, ticker symbol G O G. At first glance, Alphabet might seem like the odd company on this list. After all, we've already discussed two businesses supplying the hardware powering artificial intelligence. Alphabet, on the other hand, is one of the companies spending hundreds of billions of dollars building that infrastructure. Some investors see that spending and immediately become concerned. Why is Alphabet investing so aggressively? Can the company ever generate enough returns to justify these enormous capital expenditures? Those are fair questions, but I think they're also looking at the business from the wrong perspective. Alphabet isn't spending this money simply to keep up with competitors. It's investing because management believes artificial intelligence will fundamentally reshape cloud computing over the next decade. And if they're right, today's spending could become one of the best long-term investments the company has ever made. To understand why, you first have to understand how the business is evolving. For years, Alphabet generated most of its profits from digital advertising. Advertising remains an incredibly strong business, but it's no longer the only major growth engine inside the company. Google Cloud has become one of the fastest growing segments of the entire business. That's important because cloud computing sits directly at the center of the AI revolution. Whenever businesses want to deploy artificial intelligence without building their own expensive infrastructure, they turn to cloud providers. Instead of purchasing billions of dollars worth of servers, companies can rent computing capacity as needed. That dramatically lowers the barrier to AI adoption. It also creates an enormous long-term opportunity for cloud providers. This is exactly why Alphabet continues investing so aggressively. It's building the infrastructure that customers will use for years to come. Last quarter, Google Cloud delivered an extraordinary 63% year-over-year revenue increase. That's an exceptional growth rate for a business already operating at global scale. More importantly, management expects additional computing capacity to continue coming online over future quarters. That means Google Cloud should have the ability to support even more enterprise customers as AI adoption accelerates. Personally, I think this is one of the most exciting transformations happening inside Alphabet. For years, investors primarily viewed the company as an advertising business. Today, it's gradually becoming something much larger. Cloud computing, artificial intelligence, enterprise software, developer infrastructure. These businesses could eventually represent a much larger portion of Alphabet's future earnings. And that's exactly what long-term investors should be paying attention to. One point that really stood out to me is that management has already indicated investors should expect significantly higher capital expenditures in 2027. Some investors hear higher spending and immediately think lower profits. I understand that reaction. Higher capital expenditures reduce free cash flow in the short term. But sometimes spending money today creates far larger returns tomorrow. Larry Frink made a similar point during his interview. He argued that today's AI investment boom isn't excessive because demand continues exceeding supply. The biggest challenge isn't convincing customers to use AI. The challenge is building enough infrastructure to support them. Alphabet clearly agrees. The company isn't expanding data center capacity because it hopes demand eventually appears. It's expanding because demand is already arriving. That's a huge difference. Building infrastructure after demand explodes usually means you're already behind. Building before demand fully materializes positions you to capture future growth. That's exactly what Alphabet appears to be doing. Another reason I like Alphabet is the diversity of its business. Even while investing aggressively in AI, the company still generates enormous cash flow from multiple sources. That financial strength gives management tremendous flexibility. It can continue funding AI infrastructure while simultaneously investing in future innovation. Many businesses simply don't have that luxury. Capital industries often require companies to borrow heavily or issue additional shares. Alphabet can finance much of its expansion internally because of its incredibly strong underlying business that reduces financial risk while increasing long-term opportunity. I also think investors sometimes underestimate the power of owning the platform where businesses actually use AI. Selling chips is a fantastic business. Selling memory is also a fantastic business. But providing the cloud platform where companies deploy their AI applications creates recurring relationships that can last for years. Once businesses build their operations around a cloud infrastructure provider, switching becomes expensive, time consuming, and technically challenging. Those switching costs create a durable competitive advantage. As more organizations migrate their AI workloads to Google Cloud, Alphabet strengthens those customer relationships even further. That's exactly the type of business I like owning over the long term. Another aspect worth mentioning is operating leverage. As cloud infrastructure expands, fixed costs become spread across larger customer bases. That can improve profitability over time as utilization increases. If Google Cloud continues growing anywhere close to its recent pace while maintaining healthy margins, today's heavy investment could produce substantial long-term shareholder returns. This is why I don't automatically become concerned whenever I hear that Alphabet plans to spend more on AI infrastructure. The important question isn't how much they're spending. The important question is whether those investments generate attractive returns over time. Based on the continued growth of Google Cloud and the accelerating adoption of artificial intelligence across enterprise customers, I believe the answer could very well be yes. Of course, no investment comes without risks. Cloud computing remains highly competitive. Technology evolves rapidly. Economic conditions can influence enterprise spending. Execution always matters. Management must continue delivering innovation while controlling costs. Those are all legitimate considerations. But when I step back and look at the bigger picture, Alphabet checks many of the boxes I look for in a long-term investment. It operates one of the world's most important technology platforms. It continues investing aggressively in future growth. Its cloud business is expanding rapidly. Artificial intelligence is becoming deeply integrated throughout its operations. and management appears committed to building infrastructure capable of supporting demand for years to come. When you look across all three companies we've discussed today, an interesting pattern begins to emerge. Nvidia supplies the computational horsepower that powers artificial intelligence. Micron provides the advanced memory that allows those systems to operate efficiently. Alphabet is building the cloud infrastructure where businesses actually deploy and use AI applications. They're participating in different parts of the same ecosystem. That's what makes this combination so compelling. Instead of betting on one single AI application becoming the winner, you're investing across multiple layers of the infrastructure supporting the entire industry. And that's exactly what Larry Frinkin's interview highlighted. The biggest opportunity isn't chasing whatever AI application becomes popular next month. The opportunity lies in understanding the massive infrastructure buildout happening behind the scenes. data centers, memory, computing power, cloud infrastructure, electricity. These are the building blocks of the AI economy. As long as demand continues exceeding supply, companies providing these essential resources should remain well positioned for long-term growth. Now, that doesn't mean every year will be smooth, stock prices will fluctuate, markets will overreact, economic headlines will create uncertainty. We've seen that happen countless times before. But successful investing has never been about predicting what happens next week. It's about identifying outstanding businesses with durable competitive advantages and giving them time to compound. Personally, I believe all three companies we've discussed today possess those characteristics. Nvidia continues dominating AI computing with remarkable growth and a surprisingly reasonable valuation relative to its earnings potential. Micron is benefiting from one of the strongest supply and demand imbalances in the semiconductor industry. While trading at an attractive forward earnings multiple, Alphabet is transforming itself into one of the world's most important AI infrastructure providers through the explosive growth of Google Cloud and its long-term investment strategy. None of these businesses are chasing the AI revolution. They're helping build it. And that's exactly why I believe they deserve serious consideration from long-term investors. If you want exclusive stock tips, in-depth analysis, real-time trade alerts, and free investing guides, join the Stocks Galore Patreon today and take your investing game to the next level. Our members get full in-depth analysis on most of the stocks mentioned here. Head over to patreon.com/stocksgalore and become part of our growing community of smart investors. Link is in the description. Now, I'd love to hear from you. Which of these three AI companies do you believe has the strongest long-term competitive advantage? Nvidia, Micron, or Alphabet? Or do you think another one of these businesses offers the best riskto-reward opportunity over the next decade? Let me know your thoughts in the comments below because I'd really like to hear your perspective. 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