Tom Lee: "We're Not at the Top Yet" (3 High-Growth AI Stocks to Buy Now)

Tom Lee of Fundstrat believes the market is not at the top yet, and despite growing concerns about AI spending, he says the AI trade still has plenty of room to run through the end of the year. In this CNBC interview, he also shares his outlook on the Federal Reserve, inflation, interest rates, and why he believes investors may be worrying about the wrong things. First, I'll play Tom Lee's CNBC interview uninterrupted. After that, I'll break down his biggest takeaways, share my own thoughts on what he got right, and where investors should be paying close attention. Then, we'll dive into three high-growth AI stocks to buy now that could benefit if his outlook plays out. Let's hear what Tom Lee has to say. >> Joining us now, Fundstrat's head of research, Tom Lee. He's also the chairman of Bitwise and a CNBC contributor. Tom, welcome. Would you uh take that prediction market bet, so to speak? What do you think Is there any real chance the Fed hikes rates this year or this week? >> Uh I I think that you know, the prediction markets and funds want to hedge on a binary event. That's why you get the 30% cuz someone needs to hedge something, but I'd say the probability is is low. I mean, I I wouldn't expect them to raise rates. >> What about at any point in the future? >> Uh as you know, they're going to see how the data unfolds. I don't think it's stated dependence, but uh data responsiveness. Um to us, I think the underlying inflation story has really weakened cuz shelter is really weak, and we'll see it tomorrow with Case-Shiller. And if wages aren't pressured, then the the real pipeline of underlying inflation that we're seeing now is just tariffs and oil, which aren't things the Fed has to necessarily embark on a a hiking cycle for. >> I don't want to use the bad word of transitory, but potentially temporary. Right? >> Yes. >> And oil is down right now because there's some talks, and by the way, tomorrow could be up again. We have no idea what's going to happen, depends on the talks. But the point is if and when there is lasting sort of peace, oil will likely go back down. The market seems to want to push it back down. That's got to impact the Fed's thinking. >> Yeah, because then they just have to see this little bubble that they have to kind of manage through. I mean, I think in some ways they might just shrink the balance sheet instead of doing a policy rate change. >> A quantitative tightening? >> Correct. Yeah, I think that's a better way to titrate. What they might say is like, "Hey, let's try to put some pressure on uh growth, but not to deliberately slow the economy." >> Is that any kind of thing that can really upset the stock market? >> Uh you know, the the stock market's going to ultimately see see the idea that, "Hey, the Fed shrinks the balance sheet, and then that means they can cut rates." And so then they'll see the rate cuts as as actually positive. So I I think it's going to pave the way for future rate cuts actually. >> So if the Fed is not the number one most important thing for the equity markets right now, what is? >> Well, I I think that there the AI trade remains the still the most important story, and people of course are having longevity doubts, but you know, if someone goes back to '94 to 2000, there were many times when the internet story, and even stocks like Cisco, uh came under question whether there was durability. And I think we're in that questioning it's durability at this moment. Uh but I think it's still in very good shape, and I think the second big story out there is that margin debt is still needs to work off that high level of growth, just like what happened in Korea, which had a sort of a margin call. And I think that's why stocks are stalling here. But to me, I think AI still works strongly through year-end. >> Perfect segue. We had uh this morning Steve Eisman was on Squawk Box this morning, the famed investor. Here's what he had to say when he was asked the major question of the earning season, which is what happens if big tech starts cutting its AI CapEx. >> Nvidia, I think when they reported last quarter, had 85% revenue growth. So if the hyperscalers cut, it would be 85%. Um you know, maybe that would be healthy for the long term. But, I think the market would go straight down on that news. >> Just curious for your thoughts, reaction to that. >> Uh >> And whether you think that's even likely or discussing at this point versus what Google did was which was raising still get punished. >> Yeah. Well, um so on the one level I'd say uh I I think Steve's logic there's some logic to it. But, the fact that many people are saying that is a sign that we're not at a top because people are questioning the longevity of the cycle. Like So, I think that's actually a bullish thing. Uh the second is, you know, is it probable? I I'd be doubtful that that there would be the cuts cuz, you know, these these companies still have access to the bond market and the bond market isn't denying them capital. So, as you know, um CFOs raise money when they can, not when they need to. So, I think the spending visibility is going to be very strong. >> The interview begins with the obvious question everyone was asking ahead of the Federal Reserve meeting. Could the Fed actually surprise investors by raising interest rates? Prediction markets had started assigning roughly a one-in-three chance that the Fed might hike rates, a dramatic jump from where expectations had been just a week earlier. Naturally, that caught investors' attention because interest rate decisions influence everything from mortgage rates to corporate borrowing costs and ultimately stock valuations. Tom Lee immediately pushes back on that growing fear. His argument is that prediction markets are sometimes misunderstood. Just because a market is pricing in a higher probability does not necessarily mean participants genuinely believe that outcome is likely. Sometimes investors simply need protection against a worst-case scenario. In other words, hedging activity can distort the probabilities people see. His own opinion is much simpler. He says he would not expect the Federal Reserve to raise rates. That may sound like a a statement, but it carries significant implications because financial markets have become extremely sensitive to every move the Fed makes. If investors begin believing rates will remain stable instead of moving higher, it removes one major source of uncertainty hanging over the market. Then Tom Lee explains why he believes the Fed has little reason to tighten policy further. His focus shifts toward inflation. He argues that one of the biggest contributors to inflation, shelter costs, is already weakening. Housing has played an enormous role in keeping inflation elevated over the past few years. So, if that pressure begins fading, one of the Fed's biggest concerns starts disappearing. He also points toward wage growth. If wages are no longer accelerating aggressively, businesses experience less pressure to continually raise prices. That reduces another important driver of persistent inflation. According to Tom Lee, once you remove shelter inflation and wage pressures from the equation, what remains are factors like tariffs and oil prices. Those are very different types of inflation. They're driven by external events rather than excessive consumer demand. That distinction matters because central banks cannot control geopolitical conflicts or global oil production with interest rates. I actually agree with this point because investors sometimes assume every inflation number should trigger a Fed response, but not all inflation is created equally. If inflation comes from temporary supply disruptions instead of an overheating economy, raising rates may do more harm than good. That naturally leads into another interesting discussion. The hosts point out that oil prices could easily move lower if geopolitical tensions ease or peace negotiations succeed. Since energy prices have an enormous influence on headline inflation, declining oil prices could quickly improve inflation readings. Tom Lee agrees. Instead of immediately changing interest rates, he believes the Fed may prefer another tool, shrinking its balance sheet. This process, often referred to as quantitative tightening, allows the Federal Reserve to remove liquidity from the financial system without directly increasing borrowing costs. In Tom Lee's view, this would allow policy makers to show they remain serious about controlling inflation while avoiding unnecessary damage to economic growth. That's an interesting perspective because many investors focus only on interest rate decisions while forgetting the Fed has multiple tools available. Markets often treat monetary policy as though the only options are raising or cutting rates. Reality is far more complicated. Central banks have numerous ways to influence financial conditions and balance sheet adjustments are one of them. The conversation then shifts towards something investors care deeply about. Would quantitative tightening hurt the stock market? Tom Lee's answer is surprisingly optimistic. Instead of viewing balance sheet reduction as negative, he believes investors could interpret it as paving the way for future rate cuts. Think about that logic for a moment. If the Fed chooses a less aggressive policy tool today, perhaps it becomes easier to reduce interest rates later. Markets generally price in future expectations rather than today's reality. If investors become convinced that lower interest rates are eventually coming, stocks could actually react positively even while the balance sheet is shrinking. Whether that ultimately happens remains to be seen, but it highlights how markets often move based on expectations rather than current conditions. Then comes what I think is the most important part of the entire interview. The interviewer asks a simple question. If the Federal Reserve is no longer the biggest story driving stocks, then what is? Tom Lee doesn't hesitate. Artificial intelligence. That answer probably won't surprise anyone who has watched markets over the past 2 years, but what's interesting is how he explains it. He acknowledges that investors have started questioning whether the AI investment cycle can actually last. We've heard concerns about excessive spending. We've heard worries that companies may eventually slow down infrastructure investments. We've heard fears that demand could cool much faster than expected. Tom Lee recognizes all of those concerns, but instead of seeing them as warning signs, he compares today's environment to another technological revolution, the internet boom during the 1990s. He reminds viewers that even during one of history's greatest technological transformations, investors repeatedly questioned whether companies like Cisco could maintain their extraordinary growth. Every few months, someone declared the internet boom was finished, yet the technology itself kept advancing. Businesses kept adopting it. Consumers kept embracing it. Innovation continued. Eventually, the digital economy transformed nearly every industry. Tom Lee believes AI could be following a similar path. I actually think that's one of the strongest parts of his argument. Whenever a revolutionary technology appears, markets rarely move in a straight line. There are always corrections. There are always skeptics. There are always headlines declaring the opportunity is over, but the underlying technology often continues evolving regardless of daily stock price movements. That doesn't guarantee every AI company becomes a winner. Far from it. History also teaches us that many companies disappear during technological revolutions, but the broader trend itself can remain intact even while individual winners and losers constantly change. Tom Lee also introduces another issue weighing on markets, margin debt. This is something retail investors often overlook. Margin debt simply refers to money investors borrow to purchase stocks. When borrowing levels become excessive, markets can become more vulnerable because falling prices trigger forced selling. Tom Lee references Korea's experience with margin-related selling pressure and suggests something similar may be contributing to the recent market slowdown. In other words, he believes some of the recent weakness has less to do with deteriorating business fundamentals and more to do with investors working through excessive leverage. That's an important distinction. If weakness is primarily driven by technical market positioning rather than collapsing earnings or declining economic activity, it may eventually resolve itself once leverage returns to healthier levels. Despite acknowledging that issue, Tom Lee remains remarkably confident about artificial intelligence. His conclusion is straightforward. He believes the AI trade remains strong through the end of the year. Then the discussion moves toward what may become the defining question of this earning season. What happens if the largest technology companies begin cutting AI capital spending? This question matters because companies like Microsoft, Amazon, Meta, Alphabet, and Apple have collectively committed hundreds of billions of dollars toward AI infrastructure. Those investments directly benefit companies across the semiconductor, networking, power infrastructure, and data center ecosystem. If spending slows dramatically, investors worry the entire AI supply chain could feel the impact. The hosts reference comments from Steve Eisman, who argues that if hyperscalers significantly reduce AI spending, companies like Nvidia would almost certainly experience slower revenue growth. His point is logical. If your largest customer spend less money, your revenue growth eventually slows. Markets would almost certainly react negatively. Tom Lee doesn't completely dismiss that concern. In fact, he acknowledges there is logic behind it. But then he offers what I think is a fascinating psychological observation. He says the very fact that so many investors are asking this question is actually encouraging. Why? Because major market tops rarely occur when everyone is skeptical. Instead, market peaks usually develop when investors become convinced nothing can go wrong. When caution dominates the conversation, it often suggests excessive optimism has not yet taken over. That doesn't mean stocks cannot correct. Of course, they can. But widespread skepticism sometimes creates healthier market conditions because expectations remain grounded. Personally, I think that's an important lesson for long-term investors. Financial media often amplifies fear because uncertainty attracts attention. Every week there seems to be another reason markets supposedly cannot continue higher. Sometimes those fears become reality, other times they simply become another wall of worry that bull markets eventually climb. Tom Lee also questions whether major AI spending cuts are even likely. His reasoning comes down to access to capital. These technology giants continue generating enormous cash flows. They also maintain strong balance sheets and can easily borrow money through bond markets if necessary. As long as financing remains available and AI continues delivering strategic advantages, it becomes difficult to imagine these companies voluntarily stepping away from a race that could define the next decade of technological leadership. That's really the bigger picture Tom Lee wants investors to understand. This is not simply about quarterly earnings. This is not simply about next month's inflation report. And it is certainly not just about one Federal Reserve meeting. His thesis is that we're still living through the early chapters of a much larger transformation driven by artificial intelligence. Yes, markets will experience volatility. Yes, investors will question valuations. Yes, there will probably be corrections along the way, but none of those things automatically invalidate the broader investment story. Whether you ultimately agree with Tom Lee or not, I think this interview serves as a valuable reminder that successful investing often requires separating temporary fears from long-term structural trends. The headlines may change every single day, but if the underlying drivers of innovation, productivity, and corporate investment remain intact, then the long-term opportunity may still be much bigger than many investors currently realize. Now, let's dive in and talk about the companies that could benefit the most from everything Tom Lee just discussed. The first company is SanDisk Corporation, ticker symbol SNDK. When most people think about artificial intelligence, they immediately think about powerful chips that train massive language models. But there's another side of AI that doesn't receive nearly enough attention, data. Every AI model is trained on staggering amounts of information. Every time an AI assistant answers a question, generates an image, analyzes medical records, or helps write software, it constantly reads and stores enormous amounts of data. As AI models become larger and more sophisticated, the need to store that information efficiently becomes just as important as having the computing power to process it. That's exactly where SanDisk enters the picture. The company specializes in high-performance storage solutions that allow cloud providers, enterprises, and AI data centers to store, retrieve, and move massive amounts of information at incredible speeds. Without storage, AI infrastructure simply cannot scale. Think of AI infrastructure like building a modern city. The GPUs often get all the attention because they're like the skyscrapers everyone sees, but underneath those skyscrapers is an enormous foundation that nobody notices. Storage is part of that foundation. Without it, nothing above it can function efficiently. That is one of the reasons I find SanDisk so interesting. It isn't trying to become another AI software company. It isn't competing to build chatbots. Instead, it sells one of the essential building blocks every AI deployment requires. That creates a much broader opportunity. Whether an enterprise develops its own AI model, rents computing power from the cloud, or deploys AI throughout its organization, all of those applications generate massive amounts of data that must be stored somewhere. As AI adoption accelerates, storage demand grows alongside it. This is already showing up in SanDisk's financial performance. The company's fiscal third quarter 2026 results were extraordinary. Revenue surged 251% year-over-year to an impressive $5.95 billion. Even more remarkable was profitability. Non-GAAP earnings per share came in at $23.41 after the company reported a loss of $0.30 during the same quarter a year earlier. That kind of turnaround demonstrates just how powerful the current AI infrastructure cycle has become. Numbers like these do not happen because of one lucky quarter. They usually reflect a major shift happening across an industry. In SanDisk's case, AI infrastructure spending is creating exactly that type of transformation. The company offers a broad portfolio solid state drives and NAND-based storage products built specifically for cloud computing, enterprise customers, and large-scale data center environments. Those products become increasingly valuable as organizations deploy larger AI models requiring faster access to larger data sets. Storage performance matters. If AI systems constantly wait for data to load, expensive computing hardware sits idle. Fast storage allows those AI accelerators to remain productive. That improves efficiency across the entire data center. Another reason I like this story is because industry fundamentals have also improved. For years, NAND memory manufacturers struggled with oversupply and pricing pressure. Now supply and demand have become healthier. That has allowed pricing to improve while supporting stronger profitability across the industry. When higher demand meets improving pricing, investors often begin seeing meaningful earnings expansion. SanDisk appears positioned to benefit from both trends simultaneously. The company also continues investing in next-generation storage technologies designed for increasingly demanding AI workloads. That matters because AI infrastructure will continue evolving. Tomorrow's AI models will almost certainly require larger data sets than today's. Inference workloads are also exploding. Companies are moving beyond training models toward deploying AI applications across customer service, software development, manufacturing healthcare finance and countless other industries. All of those applications generate even more data. More data creates more demand for storage. That creates a very straightforward long-term investment thesis. One statistic that immediately catches investors attention is the stock's performance. Over the past year, SanDisk shares have surged an astonishing 3,329%. Normally, a move like that would immediately make investors worry that the stock has become too expensive, but valuation tells a different story. Despite that incredible appreciation, the company currently trades at a forward 12-month price-to-earnings ratio of only 7.48. That is well below the broader technology sector average despite its explosive earnings growth. That combination is surprisingly rare. Investors usually have to choose between high growth or attractive valuation. Finding both together is much more difficult. Analysts also continue becoming more optimistic. Consensus earnings estimates for fiscal 2026 have moved higher over the past month and now project earnings growth exceeding 2,100% year-over-year. The company currently carries a growth score of A, reflecting the strength of its earnings momentum. Of course, no investment is without risk. Storage remains a cyclical industry. Demand can fluctuate. Pricing can change. Technology evolves quickly. Competition never disappears. Those realities should never be ignored. But I think the bigger question investors should ask is whether AI infrastructure spending itself is likely to continue growing over the next 5 to 10 years. If your answer is yes, then companies supplying essential components like enterprise storage deserves serious attention. One thing I also appreciate about SanDisk is that it benefits regardless of which AI application eventually dominates. The company doesn't need one specific chatbot to win. It doesn't need one software company to dominate. It doesn't need one cloud provider to capture the market. As long as AI data keeps expanding, demand for storage continues expanding with it. That makes the business less dependent on predicting individual AI winners. Instead, it's positioned to benefit from the entire ecosystem growing larger. Personally, I think many investors still underestimate just how much data future AI applications will generate. Every enterprise wants to build AI into its operations. Governments are investing heavily, healthcare providers are digitizing records, financial institutions are deploying AI across multiple departments, manufacturers are automating production lines. Each of those developments requires storing, organizing, and accessing massive amounts of information. That's why I believe infrastructure companies like SanDisk deserve far more attention than they currently receive. The AI revolution is not just about creating smarter models. It's about building the physical infrastructure that allows those models to operate at global scale. SanDisk sits directly in the middle of that opportunity. 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 company on our list is Hewlett Packard Enterprise Company, ticker symbol HPE. When people hear the name Hewlett Packard Enterprise, many still think of a traditional enterprise technology company. But over the past few years, the business has quietly transformed itself into one of the companies helping enterprises build their own AI infrastructure. That distinction is incredibly important. Not every business wants to send all of its sensitive data to a public cloud provider. Financial institutions, healthcare organizations, governments, manufacturers, and defense contractors often need AI systems running inside their own secure environments. Those customers still need the same powerful computing capabilities. They still need advanced networking. They still need high-performance storage. And they still need AI-optimized servers capable of handling demanding workloads. That is exactly where Hewlett Packard Enterprise has been expanding its presence. Instead of focusing on consumer AI applications, HPE has positioned itself as a partner for organizations building enterprise-grade artificial intelligence solutions. I actually think this is one of the most overlooked areas of the AI investment story. The headlines usually focus on chatbots and image generators because they are easy for everyone to see. But behind the scenes, thousands of businesses are investing billions of dollars to integrate AI into their own operations. Banks want AI to detect fraud faster. Hospitals want AI to analyze medical imaging. Manufacturers want AI to optimize production. Retailers want AI to forecast inventory more accurately. Energy companies want AI to improve efficiency. These are enormous long-term opportunities that require enterprise infrastructure rather than consumer software. HPE is supplying much of that infrastructure. Its portfolio combines AI-optimized servers, networking equipment, storage solutions, and hybrid cloud technologies that allow businesses to deploy artificial intelligence while maintaining greater control over their own data. This integrated approach gives customers flexibility. Instead of piecing together products from multiple vendors, organizations can build comprehensive AI environments using solutions designed to work together. That simplicity becomes increasingly valuable as AI deployments grow more complex. The financial results suggest this strategy is working. During its fiscal second quarter of 2026, Hewlett Packard Enterprise reported revenue growth of 40% compared to the same period a year earlier. Even more impressive, non-GAAP earnings per share increased 108% year-over-year. Those numbers demonstrate that demand for enterprise AI infrastructure continues accelerating across multiple industries. One of the biggest reasons for that growth has been increasing demand for AI servers equipped with advanced accelerators. As businesses move beyond experimentation and begin deploying AI applications throughout their organizations, computing requirements increase dramatically. Many existing data centers simply were not designed for these workloads. That creates a multi-year upgrade cycle. Companies are investing in entirely new AI infrastructure capable of supporting increasingly sophisticated applications. HPE has positioned itself to capture a meaningful share of that spending. Another area that deserves attention is the company's strategic partnerships. Rather than competing in isolation, HPE has built an ecosystem that combines high-performance computing, networking, storage, and enterprise software into unified solutions. That approach makes deployment easier for customers while creating additional opportunities for recurring business. I always like seeing businesses expand beyond selling individual pieces of hardware. Integrated solutions generally strengthen customer relationships because once an organization builds its infrastructure around a particular ecosystem, switching becomes more expensive and more complicated. That naturally creates stronger customer retention. Another major advantage for HPE is something called GreenLake. This platform allows customers to consume IT infrastructure through a subscription-based model instead of making massive upfront purchases. Think about how software evolved over the past decade. Businesses gradually shifted from buying expensive software licenses to paying predictable monthly subscriptions. HPE is applying a similar philosophy to enterprise infrastructure. Instead of forcing companies to purchase enormous amounts of hardware all at once, GreenLake allows customers to scale their infrastructure as demand grows. That flexibility becomes especially attractive in artificial intelligence because AI adoption remains unpredictable. Some companies are still experimenting. Others are scaling rapidly. Subscription-based infrastructure gives them the ability to grow without committing enormous capital immediately. From an investor's perspective, recurring subscription revenue is often more valuable than one-time hardware sales. It improves revenue visibility. It strengthens customer relationships, and it creates opportunities for long-term expansion as customers consume additional services over time. That makes GreenLake an important part of HPE's long-term investment story. The company also continues reporting a growing pipeline of AI systems. To me, that may be one of the most encouraging indicators because a strong pipeline suggests customer demand extends beyond current revenue. It provides visibility into future growth. As more enterprises adopt AI, HPE appears well-positioned to participate in that expansion. The market has certainly begun recognizing the opportunity. Over the past year, HPE shares have climbed approximately 128%. That's a strong move, but considering the pace of earnings growth, valuation still appears relatively reasonable. The stock currently trades at a forward 12-month price-to-earnings ratio of approximately 12.35. For a business benefiting directly from one of the largest technology investment cycles in decades, that multiple may still attract investors looking for growth at a sensible valuation. Analysts have also become increasingly optimistic. Consensus earnings estimates for fiscal 2026 have moved higher over the past 60 days and now point toward earnings growth of approximately 76% year-over-year. The company currently carries a growth score of B. Those revisions matter because upward earnings revisions often reflect improving business conditions rather than deteriorating fundamentals. Of course, investors should also understand the risks. Enterprise spending can fluctuate with economic conditions. Large organizations sometimes delay infrastructure upgrades during periods of uncertainty. Competition remains intense across enterprise technology. Execution will continue to matter. But when I step back and look at the bigger picture, I think the long-term opportunity remains compelling. Artificial intelligence is no longer just a technology experiment. It is becoming core infrastructure for businesses across nearly every industry. That transformation is still in its early stages. As organizations move from pilot projects to full-scale deployment, demand for secure enterprise grade AI infrastructure should continue expanding. HPE is positioned directly in the middle of that trend. I also like the fact that HPE gives investors exposure to enterprise AI without relying on one single product. Its business spans servers, networking, storage, hybrid cloud, software, and subscription services. That diversification creates multiple avenues for growth while reducing dependence on any one category. Personally, I believe enterprise AI adoption could become one of the largest technology spending cycles of the next decade. Consumer applications receive most of the attention today, but businesses ultimately represent an enormous market. Every large organization wants to improve productivity, automate repetitive work, reduce costs, and make better decisions using artificial intelligence. Making that possible requires powerful infrastructure. That is exactly the market Hewlett Packard Enterprise is helping to serve. Before we move to the final company on our list, I want to take a quick moment to thank the sponsor of today's video. 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 final company on our list is Nvidia Corporation, ticker symbol NVDA. If there is one company that has become synonymous with the artificial intelligence revolution, it is Nvidia. The reason is actually very simple. Artificial intelligence requires enormous computing power. Whether companies are training large language models, building autonomous systems, developing AI-powered software, or creating intelligent robotics, they all need processors capable of handling trillions of calculations at incredible speeds. Nvidia has spent years building exactly that capability. Its graphics processing units have become the preferred platform for training and deploying advanced AI models, making the company one of the most important suppliers to hyperscale cloud providers, governments, research institutions, and enterprise customers around the world. But I think many investors still underestimate what Nvidia has become. This is no longer just a chip company. It has evolved into an entire AI computing platform. That difference is significant because platforms are generally much harder to replace than individual products. Over the years, Nvidia has expanded beyond GPUs by building networking technologies, AI software frameworks, developer tools, complete server systems, and an ecosystem that allows customers to develop, train, and deploy AI applications far more efficiently. Once developers build their AI workflows around Nvidia's ecosystem, switching becomes far more difficult. That creates one of the strongest competitive advantages any technology company can have. The more developers build on your platform, the stronger your ecosystem becomes. The stronger your ecosystem becomes, the harder it is for competitors to convince customers to move elsewhere. That network effect has helped Nvidia establish a leadership position that continues attracting new customers while strengthening relationships with existing ones. Financial performance continues reflecting that dominance. During its first quarter of fiscal 2027, Nvidia delivered another remarkable set of results. Revenue surged 85% compared to the same quarter a year earlier. Non-GAAP earnings per share increased an incredible 140% year-over-year. Those are extraordinary numbers for a company already generating tens of billions of dollars in quarterly revenue. Normally, businesses become slower as they grow larger. Nvidia has somehow managed to keep expanding at a pace that many much smaller companies would struggle to achieve. Another important point is that demand still exceeds supply. Cloud providers continue investing aggressively. Governments around the world are racing to build sovereign AI capabilities. Enterprises are deploying AI across nearly every business function. That combination continues driving strong demand for AI infrastructure. One of the things I find most impressive is that Nvidia has successfully positioned itself across multiple layers of the AI value chain. Instead of selling only chips, it now participates in networking, system design, AI software, enterprise platforms, and complete AI infrastructure solutions. That diversification creates multiple revenue streams while strengthening customer loyalty. As artificial intelligence becomes more sophisticated, customers increasingly want integrated solutions rather than assembling complicated systems themselves. Nvidia is giving them exactly that. Another point worth mentioning is the shift happening inside artificial intelligence itself. In the early days, investors focused almost entirely on training massive AI models. Today, inference is becoming just as important. Inference refers to the process of running AI models after they have already been trained. Every time someone asks an AI assistant a question, generates an image, translates text, or receives a recommendation, inference is taking place. As millions of people use AI every day, inference workloads continue expanding rapidly. That means demand for high-performance AI infrastructure is likely to remain strong long after the largest models have been trained. Personally, I think this is one of the reasons Nvidia's long-term story remains so compelling. Artificial intelligence is transitioning from a technology experiment into everyday infrastructure. Businesses are integrating AI into customer support, manufacturers are using AI to optimize production, hospitals are improving diagnostics, financial institutions are enhancing fraud detection, governments are strengthening cybersecurity. Every one of those applications increases demand for reliable AI computing. Interestingly, despite producing exceptional financial results quarter after quarter, Nvidia's stock performance has actually been relatively modest over the past year compared to many investors' expectations. Shares have gained about 17% during that period. For a company growing revenue and earnings this quickly, some investors have interpreted that muted stock performance as disappointing. I actually see it differently. Sometimes a stock simply pauses while the business continues growing into its valuation. In many cases, that creates a healthier long-term setup than seeing share prices race dramatically ahead of fundamentals. Today, Nvidia trades at a forward 12-month price-to-earnings ratio of approximately 19.22. Considering the company's dominant market position, exceptional profitability, and powerful growth outlook, many investors now view that valuation as increasingly attractive. Analysts also continue raising their expectations. Consensus estimates for fiscal 2027 earnings have moved higher over the past month and now project earnings growth of approximately 91% year-over-year. The company currently carries a growth score of B. Those numbers reinforce the idea that Wall Street still expects significant expansion ahead. Of course, investors should recognize that no company can maintain explosive growth forever. Competition will continue increasing, technology evolves rapidly, customer spending can fluctuate, regulatory challenges may emerge. Those are realities every long-term investor should monitor carefully. But when I evaluate Nvidia's overall position, I still see one of the strongest competitive moats anywhere in technology. It's hardware leadership, its software ecosystem, its developer community, its networking capabilities, its system level solutions. Together, they create a business that is much more difficult to disrupt than many people realize. Looking across all three companies we've discussed today, something becomes very clear. Each one participates in a different layer of the AI infrastructure ecosystem. SanDisk helps solve the exploding demand for enterprise storage. Hewlett-Packard Enterprise enables businesses to deploy enterprise-grade AI infrastructure. Nvidia provides the computing platform powering much of the AI revolution itself. That diversification is worth thinking about because artificial intelligence is far bigger than any single product or application. It is an entire ecosystem made up of computing, networking storage software and enterprise deployment. Companies supplying those foundational technologies may continue benefiting as AI adoption expands across the global economy. 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 businesses do you believe has the strongest long-term competitive advantage? SanDisk with its rapidly growing enterprise storage business, Hewlett-Packard Enterprise with its expanding enterprise AI infrastructure platform, or Nvidia with its leadership in AI computing? Let us know your thoughts and reasoning in the comments below because I always enjoy seeing the different investment perspectives from this community. Do not forget to like the video, share your thoughts in the comments, and subscribe so you do not miss the next important investing update. Thanks for watching, and I will see you in the next one.