Before They Surge Like AppLovin & Micron: 2 Secret AI Stocks I’m Buying This Week - Do You Own Any?

Once in every decade, the market creates a rare investment opportunity, one capable of transforming an ordinary portfolio into life-changing wealth. Yet, most investors miss it because the company appears too risky, too unfamiliar, or too early. But, today could be your lucky day because I am going to reveal what I believe could become the market's next major growth story. If you missed Nvidia or Palantir, do not assume the opportunity has passed. The next big trend is already developing, but most investors are not paying attention yet. Long-term viewers know that we have covered several exceptional stocks on this channel. AppLovin gained approximately 300%, Micron rose around 1,200%, Supermicro delivered nearly a 10-fold return before its major correction, and IonQ gained roughly 500%. However, none of these stocks moved upward in a straight line. Each experienced sharp corrections, negative headlines, and long periods of uncertainty. Before becoming market leaders, many investors dismissed these companies as dead, overhyped, or relatively unknown companies with little chance of success. That is often how extraordinary opportunities begin, not with universal excitement, but with skepticism. To be clear, I am not claiming that every stock I discuss will succeed, and I will never pretend that I cannot be wrong. Investing always involves uncertainty, and past performance never guarantees future returns. One stock that I covered almost 5 years ago, namely Cassava Sciences, surged 12-fold on Alzheimer's treatment claims before collapsing amid allegations of manipulated research. I mention this because transparency requires acknowledging both successes and failures. But, our research-driven approach has produced more successful ideas than the average investor might expect. In this video, I will present the numbers, growth drivers, valuation, competitive advantages, and major risks behind this opportunity. Watch until the end, examine the evidence, and decide for yourself whether this could be the next great investment story. If you find value in these research-based videos, please help this video reach 500 likes. Your support encourages me to produce more detailed and data-driven analysis. Consider subscribing and turning on notifications so you never miss a new video. Although I hold a master's degree with a specialization in finance, I'm not a certified financial analyst. This content is for informational and entertainment purposes only. Please conduct your own due diligence before making any investment decision. Now, let's get to the main point. Artificial intelligence is often discussed as if it depends entirely on advanced GPUs, but a powerful chip alone cannot deploy AI across the global economy. Every AI model also requires cloud infrastructure, enormous databases, high-speed networking, specialized servers, storage systems, and sophisticated manufacturing capabilities. Without these support layers, even the most advanced processes cannot operate at scale. This creates an overlooked investment opportunity in two essential segments of the AI buildout. The first is the infrastructure layer that stores enterprise data, runs mission-critical applications, and provide the cloud capacity required to train and deploy AI models. Businesses cannot simply purchase GPUs and instantly become AI companies. They need secure databases, computing capacity, and integrated software systems capable of connecting AI with their existing operations. As enterprise adoption accelerates, demand for this infrastructure should rise alongside demand for the chips themselves. The second segment is the physical hardware ecosystem behind AI data centers. Advanced computing clusters require customized servers, networking switches, optical components, storage platforms, and power management systems. These products must be designed, manufactured, and delivered at extraordinary scale. A shortage in any of these areas can delay an entire data center project, making these suppliers essential bottlenecks in the deployment process. This is the modern equivalent of selling picks and shovels during a gold rush. The companies developing AI models may compete aggressively, but they all require the same underlying infrastructure. That is why widespread AI adoption depends on businesses operating in these two segments. They are not simply benefiting from the trend, they are helping make the trend physically possible. The first company is Oracle. Oracle is one of the most important and misunderstood infrastructure providers in the AI economy. Oracle is no longer simply a traditional database company. It is building the cloud capacity, database architecture, and enterprise systems required to deploy artificial intelligence across large organizations. The strongest evidence is Oracle's remaining performance obligations, or RPO, which have reached approximately $638 billion. This represents contracted revenue that has not yet been recognized. Critics argue that this backlog carries execution and customer concentration risks. However, Nvidia's latest results provide powerful evidence that demand for this capacity is real. Nvidia reported quarterly revenue of $96.2 billion, exceeding the $92.17 billion consensus estimate. Data center revenue reached $89 billion, growing 117% year-over-year, while data center networking increased 138%. Nvidia also forecast more than $800 billion of hyperscaler capital expenditure this year and approximately 1.3 trillion in 2027. Most importantly, management indicated that supply, not demand, is limiting growth. This is extremely important for Oracle because contracted computing capacity becomes more valuable when the entire industry is supply constrained. Oracle Cloud Infrastructure is also expected to support Nvidia's Vera Rubin systems alongside major platforms such as Microsoft Azure and Google Cloud. Oracle's competitive advantage extends beyond raw computing power. Its greatest asset is the enormous amount of enterprise data already stored with Oracle databases. This allows corporations to apply AI to their existing information without transferring sensitive data across disconnected platforms. The valuation strengthens the argument further. Oracle trades at approximately 18.5 times forward earnings. Management's fiscal 2030 targets include 225 billion in revenue and earnings of approximately $21 per share. Based on those targets, the current valuation represents roughly seven times projected fiscal 2030 earnings. Applying a 20-times earnings multiple would create a potential path toward $400 per share by 2030. However, this opportunity carries substantial risk. Oracle generated negative free cash flow of 23.7 billion during fiscal 2026 after spending 55.7 billion on capital expenditures. Its credit rating remains under pressure while rising memory and component prices could increase construction costs and compress margins. Oracle's equity insurance program also creates potential dilution. Nevertheless, the central thesis remains compelling. AI deployment requires far more than GPUs. It requires cloud capacity, enterprise data, and integrated infrastructure, and Oracle sits directly at the intersection of all three. The second company is Celestica, ticker symbol CLS, an essential but frequently overlooked supplier within the AI infrastructure ecosystem. Celestica designs and manufactures the servers, storage platforms, networking equipment, custom circuit boards, and rack-level systems required to transform advanced chips into operational data centers. Celestica was once viewed as a low-margin electronics manufacturer. AI has fundamentally changed that business. In the second quarter of 2026, revenue reached 4.7 billion, increasing 62% year-over-year and exceeding both the 4.39 billion consensus estimate and management guidance of 4.4 billion. Its communication and cloud solutions segment now produces approximately 81% of total revenue. Within that division, server and storage revenue increased an extraordinary 167% to 1.16 billion, representing more than 24% of quarterly sales. These results demonstrate that Celestica is becoming increasingly important to AI data center supply chain. Profitability is improving alongside revenue. Adjusted EBITDA margin expanded by 80 basis points to 8.2%. Supported by higher production volume and a stronger product mix. That modest-looking margin improvement helped adjusted earnings per share grow 83% demonstrating substantial operating leverage. Return on invested capital also reached 55.5% compared with 35.5% 1 year earlier. Management has repeatedly raised its 2026 outlook. Initial guidance called for 16 billion in revenue and 8.2 in adjusted earnings per share. Those forecasts were subsequently increased to 17 billion and 8.75, then 19 billion and 10.15 earnings per share. By July, management had raised its target again to 20.5 billion in revenue and 11.3 in adjusted EPS. Celestica could also benefit from the expansion of custom AI accelerators. As a system integrator, it can manufacture custom boards, optical interconnects, and complete rack level systems regardless of which company designs the underlying chip. This silicon agnostic position allows Celestica to benefit from Nvidia GPUs and competing custom processors. The stock's valuation is supported by an estimated fiscal 2027 value of approximately 400 per share when applying a 25 times earnings multiple, implying roughly 30% upside under those assumptions. Risks include customer concentration, potential insourcing by major clients, execution challenges, and shareholder dilution as Celestica raises capital to expand capacity. Nevertheless, its central role is clear. Chip designers create the processors, but Celestica helps transform those processors into complete AI systems that can actually be deployed.