Top 10 Stocks I'm Buying to Get Rich in 2026 (Without Getting Lucky)

Top Stocks for 2026: An AI-Focused Portfolio

Key Concepts:

• AI Markets: Models, Chips, Data Center Infrastructure, AI Native Applications, Enterprise Software
• Circle of Competence: Focusing investments within areas of expertise (electrical engineering & data science in this case).
• Moats: Sustainable competitive advantages that protect a company’s market share and profitability.
• CAGR (Compound Annual Growth Rate): The average annual growth rate of an investment over a specified period.
• HBM (High Bandwidth Memory): A type of memory crucial for AI applications, offering high data transfer rates.
• DPU (Data Processing Unit): A programmable processor that offloads tasks from the CPU, improving data center efficiency.
• Inference: The process of using a trained AI model to make predictions or decisions.
• Ontology: A formal naming and definition of the types, properties, and relationships of entities that make up a knowledge domain.

I. Past Performance & Methodology (2024-2025)

The speaker emphasizes accountability, referencing previous annual stock lists. In 2024, the portfolio returned an average of 77%, significantly outperforming the S&P 500’s 25% return – more than tripling it. In 2025 (year-to-date), the portfolio returned 36% versus the S&P 500’s 18%, resulting in a total return of 140% over two years. This performance is presented as evidence of a strategy focused on “getting rich without getting lucky,” achieved by identifying and investing in companies poised for growth within key AI markets. The speaker clarifies that the lists are for educational purposes and encourages viewers to invest based on their own convictions.

II. The 2026 Portfolio: Five Core AI Markets

The 2026 portfolio is built around five rapidly growing markets driven by Artificial Intelligence: AI Models, AI Chips, Data Center Infrastructure, AI Native Applications, and Enterprise Software. The strategy focuses on owning leading players across these markets to mitigate risk associated with any single company’s success. The speaker’s background in electrical engineering and data science informs this focus on AI and the underlying hardware.

III. Foundational Investments: Funds vs. Individual Stocks

While advocating for individual stock selection, the speaker acknowledges the benefits of index funds. He highlights the NASDAQ 100 (QQQ) as a simple way to beat the S&P 500, returning 22% in 2025 versus the S&P 500’s 18%. However, QQQ includes companies outside the AI focus. He then recommends Vanguard's Information Technology ETF (VGT) as a superior option, offering diversification (322 companies) with a concentrated focus on semiconductors, hardware, system software, and applications – all benefiting from generative AI. VGT’s top 10 holdings include Nvidia, Microsoft, Broadcom, Palanteer, AMD, and Micron, representing 60% of the fund’s weight. VGT also boasts lower fees than QQQ and SPY, compounding returns.

IV. AI Models: The Rule Makers

The speaker identifies Nvidia (NVDA), Google (GOOGL), and Meta Platforms (META) as key players in the AI model space.

• Nvidia: Offers Neotron, a family of reasoning and agentic AI models with open weights and data tools.
• Meta: Llama, a widely adopted open-source LLM used both internally and by external developers.
• Google: Gemini, integrated across Google’s ecosystem (Search, Workspace, Cloud, YouTube), providing access to vast data and user feedback.

These companies control the APIs, features, and pricing that developers rely on, giving them significant influence over the AI landscape. They also influence the future of AI chip design.

V. AI Chips: The Picks and Shovels

Investing in companies designing and manufacturing AI chips is likened to owning the “picks and shovels” of the AI gold rush. The speaker recommends:

• Nvidia (NVDA): Dominates the data center GPU market with a 92% share (Hopper & Blackwell chips). Recently acquired Grock for $20 billion to compete with Google’s TPUs in inference workloads.
• Google (GOOGL): TPUs power Gemini and are being considered for external sales to companies like Meta.
• Broadcom (AVGO): The leading designer of custom chips for Google, Meta, OpenAI, and Anthropic, and dominates the Ethernet switch chip market (99% market share).
• Taiwan Semiconductor Manufacturing (TSM): The world’s largest chip manufacturer, holding over 70% of the foundry market and 90% for advanced nodes. Manufactures chips for most major tech companies.
• Micron (MU): The only US company with a significant share of the high bandwidth memory (HBM) market, essential for AI applications.

These companies possess strong “moats” due to the high capital investment, intellectual property, and supply chain relationships required for cutting-edge chip development.

VI. Data Center Infrastructure: Powering the AI Revolution

The speaker highlights the need for robust infrastructure to support AI workloads:

• Nvidia (NVDA) & Google (GOOGL): Already discussed as chip and model leaders, also heavily involved in data center development.
• Vertiv Holdings (VRT): Provides power and cooling infrastructure, specializing in liquid cooling systems capable of handling high-density GPU deployments (up to 600kW per unit, supporting five Nvidia Blackwell racks).
• Iron (IRN): Building AI-first data centers with secured grid-connected power in Canada and Texas, planning for up to 2 GW of liquid-cooled Blackwell-class deployments (over 300,000 Blackwell GPUs worth of capacity).

VII. AI Native Applications & Enterprise Software: Integrating AI into Workflows

• Palanteer (PLTR): Developing an AI operating system (Gotham Foundry & AIP) for enterprises and governments, integrating data and enabling AI-powered decision-making.
• CrowdStrike (CRWD): Provides cybersecurity solutions, leveraging generative AI (Charlotte AI & Falcon AI) to automate threat detection and response.

The speaker notes that these companies compete with Microsoft, Amazon, and Google, but holding Microsoft (through VGT) provides exposure to multiple potential winners.

VIII. Market Growth & Future Outlook

The global artificial intelligence market is projected to grow nearly 19x over the next nine years, with a CAGR of 38.5% through 2034 – significantly higher than the S&P 500’s average return. The speaker emphasizes the importance of investing in companies demonstrating flexibility and a willingness to adapt to the rapidly evolving AI landscape.

IX. Portfolio Adjustments for 2026

The speaker replaced Amazon, ARM, and Fortinet with Micron, Taiwan Semiconductor, and Iron in the 2026 portfolio to increase exposure to AI hardware.

Conclusion:

The speaker advocates for a long-term, AI-focused investment strategy based on identifying and holding leading companies across five key markets. He emphasizes the importance of understanding the underlying science and technology driving these companies, rather than relying on luck. His past performance demonstrates the potential for significant outperformance by focusing on these high-growth areas. The core message is that by investing in the foundational elements of the AI revolution – models, chips, infrastructure, and applications – investors can position themselves for substantial returns in the years to come.