Popular AI Stock Selection Guide: In-Depth Benchmarking of Chip Leaders and Application Companies in 2026

Since the release of ChatGPT, the concept of artificial intelligence has sparked a global investment boom in capital markets. Many AI-related stocks have experienced significant valuation increases, with some companies’ share prices rising far beyond their fundamental growth. In this AI feast, which AI stocks truly have investment value? Where are the opportunities along the industry chain? This article will analyze, from a data-driven perspective, the AI stocks to watch in 2026.

Rapid Growth of the Global AI Industry, Chip Companies Lead the Beneficiaries

According to the latest IDC report, global corporate spending on AI solutions and technologies reached $307 billion in 2025. Looking further ahead, by 2028, total AI expenditure (including applications, infrastructure, and services) is expected to surpass $632 billion, with a compound annual growth rate (CAGR) of 29%. This data reveals a key phenomenon: the AI industry is far from saturated and is still in an accelerated expansion phase.

Particularly noteworthy is that, among these expenditures, accelerated servers (GPU servers) will account for over 75% by 2028, becoming the core hardware foundation for AI deployment. This means companies controlling the chip supply chain will benefit most directly from this growth. Such industry trends have attracted the attention of top global institutional investors.

For example, Bridgewater Associates significantly increased holdings of key AI industry chain companies like NVIDIA, Alphabet, and Microsoft in its Q1 2025 13F report. This allocation change reflects not just stock investment but also strategic positioning at core nodes of the AI industry. Meanwhile, by the end of 2025, global AI and big data-themed funds had assets exceeding $30 billion, indicating that capital is deeply involved in AI industry allocation through diversified channels.

NVIDIA, AMD, Broadcom: A Deep Comparison of the Three Major AI Stock Leaders

NVIDIA: The Absolute Leader in GPU Chip Market

NVIDIA is undoubtedly the biggest beneficiary of this AI wave. As the world’s leading GPU manufacturer, NVIDIA holds an absolute dominance in AI chips, with a market capitalization of $4.28 trillion. Since the release of ChatGPT just two years ago, the company’s stock price has surged 11-fold, with astonishing growth.

NVIDIA’s core advantage lies in its complete ecosystem—from chip design and system architecture to software platform (CUDA)—forming an insurmountable moat. In 2024, its revenue reached $60.9 billion, with an annual growth rate exceeding 120%, demonstrating rapid performance growth amid exploding AI demand. In Q2 2025, NVIDIA set another record, with quarterly net profit growth exceeding 200%, driven mainly by cloud service providers and enterprise customers’ procurement of Blackwell architecture GPUs.

Analysts generally expect that as AI applications expand from training to inference, and further into enterprise and edge computing scenarios, demand for NVIDIA solutions will grow exponentially. In the short term, the company faces little real competition. All major institutions currently rate NVIDIA as a “Buy,” reflecting high market expectations for its long-term growth momentum.

It is worth noting that, although the past two years’ gains seem to have fully reflected AI benefits, supply chain bottlenecks remain. TSMC’s capacity constraints mean NVIDIA’s actual delivery volume still cannot meet global demand, leaving significant room for future revenue growth.

Broadcom: The Industry Hub of Networking Chips

As a global leader in network communication chips, Broadcom’s history is almost a chronicle of mergers and acquisitions. The company has monopolized key applications in the networking industry through strategic acquisitions. Currently, Broadcom’s main businesses include cloud computing chips, network equipment, broadband access, and custom ASIC chips.

In the wave of AI infrastructure, Broadcom has secured a key position in the AI data center supply chain thanks to its technological advantages in custom ASICs, network switches, and optical communication chips. In fiscal year 2024, revenue reached $31.9 billion, with AI-related product sales rapidly increasing to 25%, demonstrating strong momentum in the AI wave.

Broadcom’s growth in AI is fundamentally driven by the fact that efficient AI systems require high-speed network connectivity—whether for chip-to-chip communication, data exchange, or storage architecture. Under this industry complementarity, NVIDIA and Broadcom, while competitors, also drive each other’s growth.

In less than two years since 2023, Broadcom’s stock price has increased 3.51 times. In 2025, its layout in efficient AI data centers continued to bear fruit. Interconnect business grew 19% year-over-year in Q2, as cloud providers accelerated building AI data centers, boosting demand for Jericho3-AI chips, Tomahawk5 switches, and optical communication chips. Market expectations are that as AI model sizes continue to expand, demand for high-efficiency networking and custom chips will grow rapidly, making Broadcom’s prospects promising as a technology leader in this field.

AMD: Opportunities and Limitations for Challengers

AMD, as NVIDIA’s direct competitor in GPUs, is one of the few large companies capable of manufacturing both GPUs and CPUs. Although its high-end GPU market share is still behind NVIDIA, AMD’s self-developed MI300 series accelerators have shown performance comparable to NVIDIA’s H100 in many tests, at roughly half the price.

With its CDNA 3 architecture and MI300 series, AMD has successfully carved out an important share in the NVIDIA-dominated AI chip market, providing alternatives for cloud providers and large enterprises. In 2024, revenue reached $22.9 billion, with data center business up 27% year-over-year, confirming the initial success of its AI product strategy.

Entering 2025, AMD’s offensive in AI has become more aggressive. In Q2, data center revenue grew 18% year-over-year, benefiting from the adoption of MI300X accelerators by mainstream cloud vendors and the expected release of MI350 series in the second half of 2025. As AI workloads diversify, more customers seek alternative solutions. AMD, leveraging its CPU+GPU integration and open ecosystem strategy, is gradually expanding its share in AI training and inference markets.

However, AMD faces the challenge of the strong network effect of the CUDA ecosystem. Over the years, developers have accumulated a large amount of optimized code and tools on CUDA, and switching to AMD involves additional adaptation costs. Given that AI capital expenditures often reach hundreds of billions of dollars, if AMD can offer nearly half the cost of NVIDIA’s solutions, it could motivate more developers to adopt its platform. This makes AMD’s growth outlook relatively optimistic. Since ChatGPT’s debut, AMD’s stock price has risen 3.2 times.

Other Key Companies in the Industry Chain and Investment Opportunities

Beyond chip design firms, TSMC, as the world’s leading foundry, is also a key beneficiary of the AI wave. All chips from NVIDIA, AMD, and others depend on TSMC’s manufacturing capacity. The yield and capacity of TSMC’s advanced processes directly impact the pace of AI industry expansion.

At the software and application level, Microsoft, with its investments in large AI models and Azure cloud platform, is also a focus for institutional investors. Alphabet, although lagging in AI chip self-research compared to NVIDIA, has made progress in developing large language models and integrating AI into search engines, supporting its long-term valuation.

Based on data, using market capitalization and growth as screening criteria, the main AI stocks currently include:

Differentiated Investment Strategies for AI Stocks

Individual Stocks vs. Thematic Funds

Investing in AI, investors are not limited to directly holding stocks. Diversified allocation strategies can balance risk and return:

Advantages and Risks of Direct Stock Holding:

• Advantages: Flexibility in buying/selling, low transaction costs, suitable for active management
• Risks: Concentration risk in single companies, susceptibility to short-term hype

Thematic ETFs and Funds:

• Advantages: Diversification, reduced individual stock risk, suitable for long-term allocation
• Disadvantages: Potential premium/discount, management fees

For example, the Global X Robotics & Artificial Intelligence ETF (NASDAQ: BOTZ) tracks multiple AI-related companies, offering a more balanced industry allocation.

Investment Execution Strategies

Whether choosing individual stocks or funds, dollar-cost averaging (DCA) is highly recommended. Observing top institutions like Bridgewater, although AI develops rapidly, benefits are not always concentrated in one company. Some stocks may already reflect AI growth expectations, limiting further upside. Gradual building positions and dynamic adjustments can maximize returns.

For retail investors interested in AI stocks, main channels include:

• Overseas brokers: Suitable for long-term holding, transparent fee structures
• CFD trading platforms: Suitable for short-term trading, supporting two-way trading and leverage

Different platforms have pros and cons; the key is aligning with your investment horizon and risk tolerance.

Long-term Outlook and Risk Warnings for AI Stocks in 2026

Growth Drivers Still Unfolding

Rapid iteration of large language models, generative AI, and multimodal AI will continue to boost demand for computing power, data centers, cloud platforms, and specialized chips. In the short term, chip supply chain companies like NVIDIA, AMD, and TSMC will benefit most directly. In the medium to long term, AI applications in healthcare, finance, manufacturing, autonomous driving, and retail will create growth opportunities for application-layer companies.

Macroeconomic Constraints

While AI remains a market focus, its stock performance cannot be entirely detached from macroeconomic factors. Federal Reserve and other central bank interest rate policies will significantly influence valuations: easing policies favor high-valuation tech stocks; high interest rates may compress valuations. Additionally, AI concept stocks are sensitive to news, prone to sharp short-term fluctuations.

Unavoidable Risks

Industry Development Uncertainty Although AI as a discipline has existed for decades, only recently has it entered mainstream applications. Rapid technological changes make it difficult even for seasoned investors to accurately predict industry directions, leading to potential hype and volatility.

Unproven Business Models Many emerging AI companies lack extensive historical data and business fundamentals, making their operational risks higher than mature firms. Investors should carefully evaluate profitability prospects.

Regulatory and Ethical Risks Governments worldwide view AI as a strategic industry, likely increasing subsidies and infrastructure investments. However, issues like data privacy, algorithm bias, copyright, and ethics are also gaining attention. Sudden regulatory tightening could pose significant challenges to some AI companies’ valuations and business models.

Rational Investment Advice

Analyzing the growth cycle from 2026 to 2030, investors should prioritize chip and infrastructure suppliers (like AI accelerators and servers) and specific application companies (cloud providers, medical AI, fintech). Long-term, systematic allocation via AI-themed ETFs, rather than chasing short-term highs, can reduce market shocks.

Finally, selecting AI stocks is not a one-time decision. With emerging themes like new energy and biotech, capital flows may shift. Regular review and timely adjustment of the portfolio are key to winning in this long-term AI game.