Dan Ives: "We're Only in Mile 1 – The AI Boom Has Barely Started” (3 Favorite AI Stocks To BUY Now)

Today, we're looking at a fascinating interview with Dan Ives, one of Wall Street's most well-known technology analysts. In this CNBC interview, Dan explains why he believes we're only in mile one of the AI revolution and why the AI boom has barely started. First, I'll play Dan Ives's interview uninterrupted. Then, I'll share my detailed breakdown and reaction to the key points he makes. And finally, I'll reveal three of my favorite AI stocks to buy right now based on the trends shaping the future of artificial intelligence. Let's hear what Dan Ives has to say. >> Now, let's bring in Dan Ives. He's partner at the new firm a firm, Yorkville Ives. It's good to have you back. >> Great to be here. >> Get to the new firm in a minute cuz that's news in and of itself, but we have to talk to you about this because remember before you left your prior job, you had just initiated on SpaceX and you were one of many bullish analysts on that stock. But why no analyst bump as Leslie was just talking about? >> Yeah, I look, I think when you look at SpaceX and obviously have a some important launches coming up. I think the most important thing is investors, the longer-term ones, they're going to they're going to view these as speed bumps because you got to ultimately look at this is a story and we talked about it. It's an AI data story. Space more and more is going to be a huge part of the AI revolution. I just my view in covering tech for decades, if you look at the Facebook or the the IPO what it did, you look at Amazon, it's very easy to get very short-sided. You know, this is a 26 mile marathon. I think we're only in mile one or two. >> Yeah, I hear you. I mean and a lot of the the commentary is around that, but the the fact remains it's just it's hard to value. And at the at its core, that that's one of the issues. Normally, you look at companies and you can look at all the different metrics and come out with the price that you think it should be trading at, right? The valuation relative to its earnings. Um but this one's different. It's hard. >> Look, the companies like this, they're not going to have current quote valuation and And I think that's part of the problem. When you get into these, you know, it starts to have a cascade effect. But as we've talked about, whether it's SpaceX, whether it's, you know, it's Tesla, whether it's so many of the the the the transformational tech stocks, you have to be able far as the trees to look out the next 3, 5 years. And when you think about the fourth industrial revolution, where we are, it's hard to say that without SpaceX in the sentence. >> What what do you make of what continues to go on with chips and then what the mega caps are doing in terms of their trade today and over the past month? I mean, they've certainly woken up. >> Yeah. >> But chips remain really, really hard to to figure out. >> it goes back to SK memory. Those are the golden childs of this AI trade. You've seen massive sell-off since, you know, since really SK, you know, went public. But then on the other side, look at where Nvidia and so many of these other chip names are, it comes down to two Q earnings. I mean, that's really going to be the flash line in the dark tunnel. We've talked about it. 15 to one demand to supply. This AI revolution still third inning, but the hyperscalers, those are the ones, the ones that are funding the AI part, the funding the CapEx. Those are actually the ones that are mostly in the penalty box. That's why it's so important for Alphabet, for Microsoft, for Amazons as we go into what's really just, I think, a fork in the road of earning season. But I continue to view it. The demand story is still relevant. >> I want to talk to you about a number of those names. Let's go Alphabet because it's so timely given what Warren Buffett said. This was my pick. And he thinks they're going to be a winner. What do you make of that? >> Look, Buffett and he has talked about I think valuation. You look at Alphabet a year ago, New York City cab driver was bearish on. And now, as it plays out in terms of what they're doing on search, what they're doing on cloud, sum of the parts, for those like a Buffett or a Berkshire looking for, okay, what are valuation names that you could rationalize in AI, you go back to SpaceX and some of the others maybe on the other side of the spectrum. This is a perfect example that fits well in and they are at the epicenter of the buildout and especially when it comes on search. >> The interview begins with CNBC asking Dan why there was not much excitement surrounding SpaceX despite the fact that many analysts including Dan himself have been bullish on the company. The stock was not reacting the way many people expected and investors were wondering why. Dan's answer was interesting because he immediately shifted the conversation away from the daily stock price. Instead of talking about today's trading action, he started talking about the long-term opportunity. His argument is that serious long-term investors should see these kinds of disappointments as temporary speed bumps instead of permanent problems. I actually think this is one of the most important investing lessons that people often forget. The stock market has conditioned many investors to expect immediate gratification. If a company announces something positive, people expect the stock to jump that day. If it does not, they assume something must be wrong. But history shows that some of the greatest companies in the world spent years frustrating investors before eventually becoming massive winners. Dan brings up examples like Amazon and Facebook after their early public market years. Both companies experienced enormous volatility. They had critics questioning their valuations, their business models, and whether they deserved such high expectations. Looking back now, those concerns seem almost insignificant because the businesses continued executing over the following decade. That is really the framework Dan is asking investors to use when thinking about companies involved in artificial intelligence today. Instead of asking what happens next week, ask yourself what these businesses could look like 5 years from now. Whether every AI company reaches those expectations is another question entirely, but that long-term mindset is what separates investors from traders. One phrase Dan uses really stood out to me. He says investing in this new AI era is like running a 26-mile marathon and we are only at mile one or mile two. That analogy perfectly summarizes how he sees today's technology landscape. He believes artificial intelligence is still in its infancy. Many people already feel like AI has been everywhere for years because we constantly hear about ChatGPT, autonomous vehicles, AI assistants, robotics, and enterprise software. But, from Dan's perspective, this is only the beginning. The infrastructure is still being built. Companies are still investing hundreds of billions of dollars. Businesses are still figuring out how to integrate AI into everyday operations. Consumers are only starting to adopt many of these tools. If he is correct, then today's market could eventually look very different several years from now. Of course, there is another side to that argument. Being early does not automatically guarantee success. We have seen previous technology booms where incredible innovations changed the world, yet many individual companies disappeared along the way. The internet transformed society, but hundreds of internet companies from the late 1990s never survived. The same thing could happen with AI. That is why I think investors need to separate believing in artificial intelligence from believing every AI stock is worth buying. Those are two completely different ideas. The interviewer then pushes Dan on something that many investors struggle with. How do you value companies like this? Normally, analysts can estimate earnings, calculate price-to-earnings multiples, compare businesses against competitors, and arrive at some reasonable estimate of fair value. But, transformational companies rarely fit inside those traditional valuation models. Dan openly admits this. He says companies leading major technological revolutions often do not have what investors would call valuation support. In other words, traditional financial metrics do not always explain why these businesses command such massive valuations. That can make investors uncomfortable. And honestly, it should. One thing I appreciate is that Dan does not pretend these companies are easy to value. Instead, he argues that investors need to expand their time horizon. Rather than focusing only on current earnings, he believes investors should think about what these companies might earn several years from now if the technology reaches its full potential. Now, personally, I think this is where investing becomes more art than science. Projecting future earnings is incredibly difficult. Small changes in assumptions can completely change what you believe a company is worth. If you assume AI grows faster than expected, today's valuation may actually look cheap. If adoption slows, today's valuation could suddenly look expensive. That uncertainty explains why these stocks often experience huge swings. Investors are constantly adjusting their expectations about a future that nobody can predict with certainty. Dan then introduces another big idea that has become central to his investment thesis. He calls this period the fourth industrial revolution. That is a bold statement. Historically, industrial revolutions fundamentally changed how economies functioned. The steam engine changed manufacturing. Electricity transformed production. Computers reshaped business. The internet connected the world. Dan believes artificial intelligence belongs in that same category. If that turns out to be true, then we are witnessing one of the largest technological transitions in modern history. Whether you agree or disagree with that statement, it certainly explains why investors have been willing to assign enormous valuations to companies building AI infrastructure. The conversation then shifts toward semiconductor companies. This is where things become even more interesting. Chip companies have become the backbone of artificial intelligence. Without advanced processors, AI models simply cannot function at the scale required today. Dan points out that despite some recent selling pressure across certain chip names, he still believes the overall demand story remains intact. His focus quickly turns toward earnings. He says upcoming quarterly earnings reports will be one of the biggest tests for the AI investment narrative. That makes complete sense. Eventually, every exciting story has to be supported by actual financial results. Companies cannot rely forever on promising future growth. At some point, they need to demonstrate revenue growth, expanding profits, and sustained customer demand. Earnings season becomes the moment where optimism meets reality. One statistic Dan repeats has become almost legendary among AI investors. Demand exceeds supply by roughly 15 to 1. If that imbalance continues, it suggests the industry still has enormous room for expansion. Companies building AI infrastructure could continue seeing strong order books simply because customers cannot get enough computing power. That supply shortage has been one of the biggest drivers behind the incredible performance of companies like Nvidia. Demand has consistently outpaced available capacity. Of course, supply shortages never last forever. Manufacturing expands, competitors invest, new technologies emerge. Eventually, markets rebalance. The key question is whether AI demand continues growing fast enough even after supply catches up. That is something investors will be watching very closely over the next several years. Dan also talks about the hyperscalers. For anyone unfamiliar with that term, he is referring to companies like Alphabet, Microsoft, Amazon, and other technology giants building enormous cloud computing infrastructure. These companies are spending staggering amounts of money on artificial intelligence. Billions upon billions of dollars are flowing into new data centers, advanced networking equipment, custom AI chips, and cloud infrastructure. Dan describes these companies as funding the AI party. I actually like that description because it captures what is happening across the industry. Everyone gets excited about Nvidia because it sells the chips, but somebody has to buy those chips. Somebody has to build the data centers. Somebody has to finance the infrastructure. That responsibility largely falls on the hyperscalers. The interesting point Dan makes is that despite funding much of this AI expansion, these companies have recently found themselves in what he calls the penalty box. In other words, investors have become more skeptical. Markets are asking whether all this spending will actually generate enough future profits. That is a fair concern. Capital expenditures across the largest technology companies have exploded. Investors naturally want proof that those investments will eventually produce meaningful returns. This is exactly why Dan believes earning season represents a fork in the road. If these companies demonstrate that AI investments are producing measurable business results, investor confidence could strengthen significantly. If they struggle to justify all this spending, questions about AI valuations will become even louder. Personally, I think this is one of the healthiest debates happening in the market today. It forces companies to move beyond flashy demonstrations and actually prove that artificial intelligence creates economic value. Eventually, every technology must justify itself through productivity, profitability, or revenue growth. Otherwise, enthusiasm fades. Toward the end of the interview, the conversation shifts to Alphabet. This was especially timely because Warren Buffett had recently spoken positively about the company. Dan immediately highlights something fascinating. He reminds viewers that only about a year earlier, many investors had become extremely bearish on Alphabet. People questioned whether Google search business could survive the rise of generative AI. There were concerns that competitors would permanently disrupt Google's dominance. Fast forward to today, and sentiment has changed dramatically. Alphabet has continued integrating AI across search. Its cloud business continues expanding. Its AI initiatives have become much more visible. Investor confidence has improved considerably. That is another important reminder about markets. Sentiment changes much faster than business fundamentals. Sometimes investors become overly pessimistic. Sometimes they become overly optimistic. Successful investing often involves recognizing when emotions have pushed prices too far in either direction. Dan also mentions valuation, which is something Buffett has emphasized throughout his entire investing career. Even when Buffett likes a business, he still cares deeply about what price he pays. That is an important distinction. A great company does not automatically make a great investment if investors pay too much for it. Likewise, a temporarily unpopular company can become an outstanding investment if the valuation becomes attractive enough. I think that is why Alphabet continues attracting attention from both growth investors and value investors. It sits at an interesting intersection. It remains one of the world's largest technology companies, yet compared to some other AI leaders, many investors still view its valuation as relatively reasonable. Overall, what I took away from Dan Ives' interview is that he remains incredibly optimistic about artificial intelligence, but his optimism is based on a very long investment horizon. He is not focused on tomorrow's headlines. He is focused on where technology could be years from now. Whether he is talking about SpaceX, semiconductor companies, hyperscalers, or Alphabet, the message stays remarkably consistent. Ignore the short-term noise. Focus on the long-term transformation. That does not mean investors should blindly buy every AI stock. It means understanding the difference between temporary volatility and permanent business deterioration. Some companies will become dominant winners. Others will fail. The challenge for investors is figuring out which businesses can actually convert today's AI excitement into sustainable long-term cash flow and shareholder value. That is never easy. But if Dan Ives ends up being right about this being only the first inning of the AI revolution, then many of the biggest investment stories of the next decade may still be ahead of us, not behind us. Now, let's dive in and break down three of my favorite AI stocks to buy right now. Companies that are not only powering the artificial intelligence revolution, but are also positioned to benefit from years of growth that many investors still underestimate. The first company on the list, and in my opinion, one of the most important businesses in the entire AI ecosystem is Nvidia, ticker symbol NVDA. Whenever people think about artificial intelligence, they naturally think about chatbots, AI assistants, image generation, or autonomous systems. But behind every one of those applications is something much more important, massive computing power. Without advanced chips capable of handling trillions of calculations every second, none of today's cutting-edge AI models would exist. Every breakthrough we continue to see depends on hardware that can train larger models, process enormous amounts of information, and deliver responses almost instantly. That is exactly where Nvidia has built one of the strongest competitive advantages in modern technology. Many investors still think of Nvidia as simply a graphics card company. That description might have been accurate years ago, but today it barely scratches the surface of what this business has become. Nvidia has quietly transformed itself into a complete artificial intelligence infrastructure company. Instead of simply selling chips, it now provides entire AI platforms that include processors, networking equipment, software, complete server systems, and developer tools that allow customers to build AI applications much faster than if they started from scratch. That evolution completely changes how investors should think about this business. Because once a company becomes deeply integrated into a customer's entire AI infrastructure, switching away becomes significantly more difficult. And that brings us to what I believe is Nvidia's greatest competitive advantage, CUDA. If you've followed Nvidia for years, you've probably heard that name before. But I think many investors underestimate just how powerful CUDA really is. CUDA is Nvidia's software platform that developers use to build and optimize AI applications for Nvidia's graphics processors. That might sound technical, but here's why it matters. Imagine spending years building software that works perfectly on one ecosystem. Your engineers know it inside and out. Your AI models are optimized specifically for it. Your workflows depend on it every single day. Now, imagine someone asks you to move everything onto an entirely different platform. It isn't impossible, but it would be incredibly expensive. It would consume enormous engineering resources. It would create delays. It would introduce unnecessary risks. Most businesses simply are not interested in making that switch unless there is a very compelling reason. That is why CUDA represents such a powerful economic moat. Much of today's foundational AI code was written using CUDA and optimized specifically for Nvidia's graphics processing units. That gives Nvidia something every great company wants, customer stickiness. Once customers are inside the ecosystem, leaving becomes increasingly difficult. And when customers stay, revenue becomes much more durable over time. One of the biggest mistakes investors make is assuming Nvidia's success is entirely dependent on having the fastest chip. Of course, hardware matters, but software ecosystems often become even more valuable than hardware itself. History has shown this repeatedly across the technology industry. The companies that control the software ecosystem often enjoy the longest-lasting competitive advantages because customers invest years learning, integrating, and building around those platforms. That is exactly what Nvidia has accomplished. Now, let's talk valuation. One criticism that has followed Nvidia for years is that the stock always looks expensive. That argument carried some weight during periods when the shares traded at extremely rich multiples, but today's situation looks much different. Following the recent pullback, Nvidia now trades at roughly 16 times analysts' forward earnings estimates for fiscal 2028. Think about that for a second. This is one of the fastest growing technology companies in the world. A company that sits at the center of one of the largest technological transformations in decades, yet its valuation has compressed enough that many investors now consider it one of the best bargains anywhere in the semiconductor industry. That is a very different conversation from what we heard just a couple of years ago. Instead of asking whether Nvidia is too expensive, investors are increasingly asking whether the recent decline has actually created an attractive long-term buying opportunity. Personally, I think this shift is extremely important. Markets constantly move between optimism and pessimism. Sometimes great businesses become overpriced. Other times investors become so focused on short-term concerns that they temporarily overlook the long-term opportunity. I am not saying Nvidia cannot become more volatile from here. It absolutely can. Technology stocks rarely move in straight lines. But if the underlying business continues executing, temporary price declines can sometimes become opportunities rather than reasons to panic. Another reason I remain optimistic is that Nvidia is adapting as artificial intelligence itself evolves. The first phase of AI was largely centered around training massive language models. Training requires enormous computing power because AI systems process gigantic data sets to learn patterns and relationships. That created explosive demand for Nvidia's graphics processors. But the AI industry is already entering another phase. More companies are now focused on inference. Inference is what happens after AI model has already been trained. It is the process of actually using that model to answer questions, generate content, analyze information, or make decisions in real time. As millions and eventually billions of users interact with AI applications every day, inference becomes just as important as training. And Nvidia has been preparing for that transition. Rather than relying only on its traditional GPU business, the company has expanded into complete AI infrastructure solutions specifically designed for these new workloads. That includes purpose-built server systems optimized for different artificial intelligence applications. This allows customers to purchase integrated solutions instead of assembling complicated systems from multiple vendors. That approach makes deployment faster, reduces complexity, and strengthens Nvidia's relationship with enterprise customers. Another fascinating development is Nvidia's acquisition of Groq. Groq specializes in chips designed specifically for inference workloads. Instead of treating inference as an afterthought, Nvidia has incorporated these technologies directly into its broader CUDA ecosystem. That means customers already using Nvidia's software platform can continue expanding their AI capabilities without abandoning the environment they already know. This reinforces the network effect we talked about earlier. Every additional capability added to the ecosystem increases its overall value. The more comprehensive Nvidia's platform becomes, the harder it becomes for competitors to convince customers to leave. There is another business segment that deserves much more attention than it typically receives, networking. Most investors immediately think about GPUs whenever Nvidia is mentioned, but networking has quietly become one of the fastest growing parts of the company's business. As AI data centers become larger and more powerful, moving information between thousands of processors efficiently becomes absolutely critical. Even the fastest processors lose value if they spend time waiting for information to arrive. High-speed networking solves that problem. It allows massive clusters of AI chips to communicate with each other with incredible efficiency. That may not sound as exciting as launching a revolutionary new processor, but it plays a vital role in overall system performance. In many ways, networking acts like the nervous system connecting every part of an AI supercomputer. Without it, the entire system slows down. That creates another significant revenue opportunity for Nvidia beyond simply selling graphics processors. The company is gradually becoming an end-to-end provider of AI infrastructure, and that diversification makes the business even stronger. Now, here's something I think investors should pay close attention to. Artificial intelligence is no longer an experimental technology. Companies across nearly every major industry are investing aggressively because they believe AI can improve productivity, reduce costs, increase efficiency, and create entirely new products and services. As that adoption expands, demand for computing infrastructure should continue growing. That does not mean growth will be perfectly smooth. There will be periods of slower spending. There will be market corrections. There will be investor fears about valuations. But, if AI continues becoming embedded throughout the global economy, the companies supplying the foundational infrastructure stand to benefit for many years. That is why Nvidia continues to attract so much attention from long-term investors. It is not simply selling products. It is helping build the foundation upon which much of tomorrow's artificial intelligence ecosystem may operate. Could competition increase? Absolutely. Will customers eventually have more choices? Certainly. But, replacing an established ecosystem is much harder than simply designing a competitive chip. That distinction is incredibly important. Technology leadership is valuable. Platform leadership is even more valuable. Nvidia has worked for years to build both. Looking ahead, there are several factors that could continue driving growth. Demand for AI infrastructure remains exceptionally strong. Inference workloads are accelerating. Enterprise adoption continues expanding. Networking revenue is growing rapidly. The CUDA ecosystem continues strengthening customer loyalty. And the company's ability to provide complete AI infrastructure solutions positions it well for the next phase of artificial intelligence development. When you combine those strengths with a valuation that has become much more attractive following the recent pullback, it becomes easier to understand why many long-term investors continue viewing Nvidia as one of the highest quality opportunities in the market. Of course, no investment is guaranteed. Execution matters. Competition matters. Innovation matters. But when I evaluate businesses, I always ask one simple question. Will this company likely become more important over the next decade than it is today? For Nvidia, I believe the answer is yes. Artificial intelligence continues expanding into virtually every industry. As that happens, demand for the infrastructure powering those applications should continue growing as well. And few companies appear better positioned to benefit from that trend than Nvidia. That is exactly why Nvidia earns its place as my first favorite AI stock to buy right now. 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. Now, let's move on to the second company on the list, Advanced Micro Devices, ticker symbol AMD. For years, AMD has lived in the shadow of larger competitors. Many investors automatically assume that because another company dominates the headlines, there is little room left for AMD to grow. I think that mindset overlooks one of the biggest trends happening inside the AI industry. Artificial intelligence is evolving. The first wave was all about building larger and more powerful AI models. Companies raced to train these massive systems using enormous amounts of computing power. But today's conversation is shifting. Businesses are now asking a different question. How do we actually deploy AI efficiently at scale? How do we make these models respond faster? How do we reduce costs? How do we run AI applications for millions of users every day without spending unlimited amounts of money? That shift changes the competitive landscape. And it creates an opportunity that plays directly into AMD's strengths. Two of the fastest-growing areas in artificial intelligence right now are inference and agentic AI. These are not just industry buzzwords. They represent the next stage of AI adoption. Let's start with inference. Earlier, we talked about training AI models. Training is like teaching a student everything they need to know. Inference is what happens after graduation. It is the moment when the AI actually puts its knowledge to work. Every time someone asks an AI assistant a question, generates an image, summarizes a report, translates a document, or interacts with an intelligent chatbot, inference is taking place. As AI becomes part of everyday life, inference workloads are expected to grow dramatically. In fact, many analysts believe inference could eventually become an even larger market than training itself, simply because it happens continuously. Think about it. Training may happen once. Inference happens every single time someone uses the model. That distinction is incredibly important. AMD has spent years positioning itself to capitalize on exactly this trend. One reason is the company's approach to memory. Unlike training, which often depends heavily on raw computing power, inference places enormous importance on how quickly information can be accessed and processed. Fast memory access becomes critical. This is one area where AMD believes it has a meaningful advantage. Its chiplet architecture allows the company to package significantly more memory alongside its graphics processors. That might sound like a technical improvement, but the practical impact is enormous. More available memory means AI systems can process larger workloads more efficiently while reducing bottlenecks that slow overall performance. For enterprise customers, efficiency matters just as much as speed. Every improvement in performance can translate into lower operating costs, faster responses, and better customer experiences. AMD has not stopped there. The company recently strengthened its position through the acquisition of MEXT, a memory optimization platform designed to improve how AI systems manage memory resources. This technology allows customers to effectively expand usable memory capacity without sacrificing performance. In simple terms, businesses can accomplish more without needing to dramatically increase hardware spending. That is exactly the type of value proposition enterprise customers love. Better performance, lower costs, greater efficiency. Those three factors often drive purchasing decisions much more than flashy marketing campaigns. Another reason I find AMD so interesting is that it already has meaningful relationships with some of the biggest names developing advanced artificial intelligence. The company has secured major GPU deals with OpenAI and Meta Platforms. That tells us something important. Some of the organizations building the world's most advanced AI systems believe AMD's technology is capable of handling demanding workloads. Winning contracts of that size is never easy. Large technology companies thoroughly evaluate performance, reliability scalability software compatibility, and long-term roadmaps before committing billions of dollars to infrastructure. These relationships provide AMD with valuable credibility as it competes for future AI deployments. But perhaps the most overlooked part of AMD's story has very little to do with graphics processors. Instead, it involves central processing units or CPUs. As agentic AI becomes more widespread, CPUs are expected to play a much larger role. Agentic AI refers to systems capable of completing complex tasks with greater autonomy. Instead of simply answering one question at a time, AI agents can plan, reason, coordinate multiple steps, interact with software tools, and execute workflows on behalf of users. Imagine asking an AI assistant to plan an entire vacation. Rather than simply listing hotels, the AI could compare prices, book flights, reserve accommodations, schedule transportation, organize restaurant reservations, and adjust the itinerary if weather conditions change. That level of decision-making requires different computing resources than traditional AI workloads. While graphics processors remain incredibly important, CPUs begin taking on a much larger share of the overall processing responsibilities, and that is excellent news for AMD. Long before artificial intelligence became the hottest investment theme on Wall Street, AMD had already established itself as one of the leaders in data center CPUs. The company has spent years improving performance, increasing efficiency, and winning enterprise customers. Now, those investments may begin paying off in an entirely new way. Industry forecasts suggest that the relationship between GPUs and CPUs inside future data centers could change dramatically. Traditional AI training environments have often deployed roughly eight GPUs for every CPU. Agentic AI may move that ratio much closer to one GPU for every CPU. Think about what that means. As AI agents become more common, demand for powerful CPUs could accelerate significantly. Instead of being viewed as a supporting component, CPUs may become a much larger contributor to overall AI infrastructure spending. AMD clearly recognizes this opportunity. The company is already developing processors specifically optimized for agentic AI workloads. That proactive approach tells me management is not simply reacting to industry changes. They are attempting to stay ahead of them. The long-term opportunity is substantial. AMD projects that the data center CPU market could double to around $120 billion by 2030. That represents an enormous addressable market. Even capturing a modest share of that growth could translate into meaningful revenue expansion over time. Now, let's talk about valuation and investor sentiment. Like many technology companies, AMD experienced a significant sell-off. Whenever that happens, investors naturally begin asking whether something is fundamentally wrong with the business. Sometimes the answer is yes, other times the market simply becomes too focused on short-term uncertainty. Personally, I think AMD falls into the second category. The long-term trends supporting the company remain firmly in place. Inference continues expanding. Agentic AI Enterprise AI adoption continues accelerating. Data center investments remain robust, and AMD continues strengthening both its GPU and CPU product portfolios. Those are exactly the kinds of trends long-term investors should monitor. One thing I appreciate about AMD is that management has consistently demonstrated an ability to adapt. The semiconductor industry evolves incredibly quickly. Companies that fail to innovate often lose relevance in just a few years. AMD has repeatedly shown that it can identify emerging opportunities, invest aggressively, and improve its competitive position. That does not guarantee success. Competition remains intense. Technology leadership changes. Customers evaluate multiple vendors. Execution always matters. But when I look at AMD today, I see a company participating in multiple high-growth segments rather than relying on one single product cycle. That diversification reduces risk while increasing the number of potential growth drivers. Another point worth considering is how the AI industry itself is expanding. Artificial intelligence is no longer limited to a handful of technology giants. Healthcare companies are investing in AI. Financial institutions are deploying AI. Manufacturers are using AI. Retailers are integrating AI. Governments are exploring AI. Every new customer entering this market creates additional demand for computing infrastructure. That expanding ecosystem creates opportunities for multiple winners. Investors sometimes treat AI like a winner-take-all market. History suggests technology rarely works that way. Different companies specialize in different areas. Some excel at software, others dominate hardware. Some lead networking, others focus on specialized processors. AMD has positioned itself where several of those trends intersect. The combination of GPUs for inference, CPUs for agentic AI, improved memory technology, and growing enterprise relationships gives the company multiple paths toward future growth. For me, that is what makes AMD such an attractive long-term investment. It is not dependent on one single breakthrough. It is participating across several of the most important areas shaping the future of artificial intelligence. If management continues executing well and industry demand develops as expected, today's valuation could eventually look much more attractive in hindsight. That is why AMD earns the second spot on my list of favorite AI stocks to buy right now. 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. Now, let's move to the third and final company on today's list, Broadcom, ticker symbol AVGO. Whenever artificial intelligence is discussed, the conversation usually centers around companies designing the most powerful graphics processors. While that certainly deserves attention, there is another side of the AI story that is becoming increasingly important. The world's largest technology companies are no longer interested in buying only off-the-shelf chips. Many of them now want custom-designed AI processors built specifically for their own workloads. Why? Because building custom AI accelerators allows them to optimize performance, reduce long-term operating costs, improve energy efficiency, and gain more control over their own infrastructure. That trend has quietly created one of the biggest opportunities in the semiconductor industry, and Broadcom sits right in the middle of it. Unlike companies that primarily manufacture their own branded processors, Broadcom has become one of the industry's leading partners in helping hyperscalers design their own custom AI chips. That may not sound glamorous, but it is an incredibly valuable business. Think about it this way. If some of the largest technology companies in the world decide they need custom silicon to power the next generation of artificial intelligence, very few companies possess the engineering expertise required to help make that happen. Broadcom is one of those companies. That expertise has already produced some remarkable results. One of Broadcom's biggest successes has been helping Alphabet develop its Tensor Processing Units, better known as TPUs. These specialized processors were designed specifically to accelerate artificial intelligence workloads while improving efficiency across Alphabet's infrastructure. That relationship alone demonstrates the level of trust Broadcom has earned. Large technology companies do not hand over billion-dollar chip development projects to just anyone. These partnerships take years to build and require world-class engineering capabilities. The opportunity becomes even more impressive when you consider how aggressively Alphabet continues investing in artificial intelligence. The company is expected to spend as much as $190 billion on AI infrastructure this year alone. That level of spending creates enormous demand for custom processors, networking equipment, and supporting technologies. And Broadcom is positioned to benefit directly from those investments. Another major development strengthens this opportunity even further. Alphabet has agreed to sell Anthropic $21 billion worth of Tensor Processing Units. That agreement highlights just how valuable these custom AI accelerators have become. It also reinforces the importance of the ecosystem Broadcom helped build. Success often attracts more business. That appears to be exactly what is happening here. After seeing the performance and cost advantages of custom AI chips, other hyperscalers have also turned to Broadcom for help developing their own processors. This creates something every investor loves to see, a business with expanding customer demand and a growing addressable market. Management believes this custom AI chip opportunity could become a business generating more than $100 billion in annual revenue during fiscal 2027. That is an extraordinary projection. And some analysts believe the opportunity could become even larger. Citigroup has projected that Broadcom's AI-related revenue could climb to approximately $180 billion during fiscal 2028. Whether those projections ultimately prove accurate remains to be seen. Forecasts are never guarantees, but they do illustrate the scale of the opportunity investors are discussing. Broadcom is no longer simply participating in the AI revolution. It is becoming one of the companies helping shape the infrastructure behind it. Now, here's another area that deserves more attention. Broadcom's networking business. Earlier in this video, we talked about why networking has become increasingly important inside AI data centers. As computing clusters become larger and more complex, processors must communicate with each other at extraordinary speeds. Otherwise, valuable computing power sits idle waiting for information. Broadcom has quietly built one of the strongest networking businesses in the industry. As demand for AI infrastructure continues growing, networking equipment becomes just as critical as processors themselves. In many cases, customers cannot fully utilize advanced AI chips without equally advanced networking technology. That creates another major source of long-term revenue growth. One thing I like about Broadcom is that it does not rely exclusively on artificial intelligence. While AI has become one of its fastest-growing businesses, the company maintains meaningful exposure across multiple semiconductor markets. That diversification provides an additional layer of stability. One example is the company's recent $30 billion agreement with Apple. That deal significantly strengthens Broadcom's non-AI semiconductor business and suggests that other areas of the company may also experience renewed growth. As investors, it is easy to become completely focused on artificial intelligence, but diversified revenue streams often make businesses more resilient when individual markets experience temporary slowdowns. Broadcom appears well-positioned to benefit from both trends at the same time. Rapid AI expansion and improving demand across other semiconductor markets. Now, let's talk about valuation. One reason Broadcom stands out is that despite its impressive growth potential, the stock still trades at roughly 20 times forward fiscal 2027 earnings estimates. When you compare that valuation against the company's expected long-term growth, many investors believe the shares remain attractively priced. Again, valuation should never be viewed in isolation. A low multiple alone does not automatically make a stock a bargain. But when you combine reasonable valuation with powerful long-term growth drivers, the investment case becomes much more compelling. Personally, I think Broadcom represents one of the more balanced opportunities in today's AI market. It participates in several of the fastest-growing areas of artificial intelligence while maintaining diversified businesses outside AI. That combination can help reduce some of the risks associated with investing in rapidly evolving technology sectors. Stepping back for a moment, there is an important lesson connecting all three companies we discussed today. Nvidia, AMD, and Broadcom are all approaching artificial intelligence from different angles. Nvidia has built one of the most powerful software and hardware ecosystems through CUDA and its complete AI infrastructure platform. AMD is positioning itself to benefit from the rapid growth of inference and agentic AI while leveraging its strengths in both GPUs and CPUs. Broadcom is helping the world's largest technology companies design custom AI chips while expanding its networking leadership. Different strategies, different strengths, but all benefiting from the same long-term trend, the continued expansion of artificial intelligence. One thing I always remind myself as an investor is that major technological revolutions rarely produce just one winner. Think back to previous technology cycles. The internet created opportunities across software, networking, cloud computing, semiconductors cybersecurity and digital payments. Artificial intelligence is likely to follow a similar path. Some companies will dominate infrastructure. Others will dominate software. Some will specialize in custom hardware. Others will provide supporting technologies that become essential over time. The key is identifying businesses with durable competitive advantages that can continue growing as the industry matures. For me, these three companies check many of those boxes. Strong competitive positions, large addressable markets, powerful growth drivers, and management teams focused on innovation. Does that mean their stock prices will only move higher from here? Of course not. Every investment experiences periods of volatility. Markets react to earnings reports, economic conditions change, investor sentiment shifts, short-term price movements can sometimes feel unpredictable. But long-term investing has never been about predicting next week's stock price. It has always been about owning exceptional businesses that continue creating value year after year. That is exactly how I view these three companies. If artificial intelligence continues transforming industries the way many experts expect, businesses supplying the foundational infrastructure should remain among the biggest beneficiaries. The road will almost certainly include setbacks. There will be corrections. There will be periods when headlines create uncertainty. There will be moments when investors question whether AI spending has gone too far. Those moments are often when long-term investors separate themselves from everyone else. Instead of reacting emotionally to short-term volatility, they focus on whether the original investment thesis remains intact. That mindset has rewarded patient investors throughout history, and I believe it will continue doing so as artificial intelligence evolves over the coming decade. 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 company do you believe has the strongest long-term competitive advantage? Nvidia with its CUDA ecosystem, AMD with its growing opportunity in inference and agnetic AI, or Broadcom with its custom AI chip business and networking leadership? Let us know your answer and your reasoning in the comments below because I'd love to hear your perspective. 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.