2026 Artificial Intelligence Stock Investment Guide: From Market Selection to Strategy Deployment

Since the launch of ChatGPT at the end of 2022, AI stocks have become the hottest investment target in the capital markets. Companies related to AI have seen their stock prices hit new highs, even though some companies’ actual profits haven’t kept pace with the stock price increases. For investors looking to participate in this AI wave, how should they choose AI stocks? What are the levels in the industry chain? Which AI-related companies in the U.S. stock market are worth paying attention to? This article will provide a comprehensive analysis from market status, specific targets, and investment methods.

Market Opportunities Driven by the AI Revolution

Currently, artificial intelligence has rapidly evolved from a long-term technological concept into a transformative force permeating all areas of society. From speech recognition, financial forecasting, medical diagnostics, autonomous driving perception to location systems, AI applications are emerging daily. More and more companies are increasing R&D investment in AI, and market expectations for AI products and applications continue to rise.

According to the latest data from IDC, global enterprise spending on AI solutions and technologies will reach $307 billion by 2025. Looking further ahead, by 2028, total AI expenditure—including AI applications, infrastructure, and related services—is expected to surpass $632 billion, with a compound annual growth rate of around 29%. The most noteworthy area is infrastructure—spending on AI chips and accelerated servers is projected to exceed 75% of total AI spending by 2028, becoming the core hardware foundation supporting AI technology deployment. This indicates that the industry represented by AI stocks still has enormous growth potential.

Institutional investors have already recognized this trend. Take Bridgewater Associates, a globally renowned fund, as an example. In its latest 13F report, the fund significantly increased holdings in key AI companies such as NVIDIA, Alphabet, and Microsoft. This not only reflects professional investors’ optimism about the AI ecosystem but also confirms the role of AI stocks as future growth engines. Besides direct stock picking, many investors also allocate through thematic funds or AI-related ETFs, forming a comprehensive layout from chips, servers, cloud platforms to data analytics. According to Morningstar, by the end of Q1 2025, the assets of global AI and big data funds exceeded $30 billion.

Core Targets of AI Stocks: Comparing U.S. AI Leaders

Below is a ranking of major AI stocks in the U.S. market based on market capitalization, stock performance, and year-to-date gains:

(Data as of Q3 2025, source: Google Finance)

Leading AI Chips and Infrastructure Companies

NVIDIA: The Absolute Leader in GPU Market

NVIDIA, as the world’s leading GPU chip manufacturer, holds an absolute dominant position in AI. The company’s market value is about $4 trillion, with a P/E ratio around 60. Since the emergence of ChatGPT, in just two years, NVIDIA’s stock price has increased over 11 times, making it one of the biggest winners in the AI wave.

GPU software platforms have become the industry standard for training and deploying large AI models. As generative AI sweeps the globe, NVIDIA leverages its complete industry chain—from chip design and system integration to software ecosystem—successfully leading the AI infrastructure market and being recognized as the core engine driving this technological revolution. In 2024, NVIDIA’s revenue reached $60.9 billion, with an annual growth rate exceeding 120%, demonstrating its strong growth momentum amid exploding AI demand.

Entering 2025, NVIDIA continues to lead. In Q2, its net profit was approximately $28 billion, more than doubling year-over-year. This growth mainly stems from cloud service providers and large enterprises heavily purchasing Blackwell architecture GPUs. Market analysis generally predicts that as AI applications move from training to inference and expand into enterprise and edge computing scenarios, demand for NVIDIA solutions will grow exponentially. With its deep technological moat and complete ecosystem, NVIDIA’s position is nearly unassailable in the short term, and many investment institutions have raised target prices and given “buy” ratings.

Notably, NVIDIA has become the top choice for AI investors mainly because—although many players participate in the AI industry—almost all major companies rely on NVIDIA’s chips. This “bottleneck” position has made NVIDIA’s computing power a “cliff-like” lead globally. Despite impressive past growth, under the capacity constraints of TSMC, executives from tech giants like Tesla and Oracle have actively approached NVIDIA management to secure chip quotas. This indicates that future order volumes will far exceed current expectations, and revenue growth prospects are worth close attention. Before the AI craze subsides, every price correction could be a new buying opportunity.

Broadcom: The Network Enabler in the AI Era

Broadcom is a global leader in network communication chips, having essentially monopolized the application needs of the network communication field through a series of acquisitions. Its main businesses include cloud computing chips, network equipment, broadband access products, and specialized ASIC solutions.

In the AI chip and network connectivity sectors, Broadcom plays a crucial role. When demand for AI servers surges, Broadcom leverages its expertise in custom ASIC chips, network switches, and optical communication chips to successfully penetrate the AI data center supply chain, becoming an indispensable supplier of AI infrastructure. In fiscal year 2024 (up to November 2024), Broadcom’s revenue reached $31.9 billion, with AI-related product lines accounting for 25%, clearly reflecting its strong growth in the AI wave.

Broadcom’s ability to ride the AI development wave hinges on the fact that high-speed networks are essential for AI. Whether it’s data exchange between chips, information transmission carriers, or communication infrastructure, Broadcom’s technology is indispensable. AI chips also rely on Broadcom’s ASIC licensing for internal interconnects. Although NVIDIA and Broadcom compete in some areas, they are actually complementary. This explains why Broadcom’s stock price has also surged—up 3.51 times in less than two years.

Looking ahead to 2025, Broadcom’s strategy in high-efficiency AI continues to bear fruit. In Q2, its interconnect business grew 19% year-over-year, driven by cloud providers accelerating AI data center construction and increasing demand for Jericho3-AI chips, Tomahawk5 switches, and optical communication chips. Market forecasts suggest that as AI models grow larger, demand for high-speed network connections and custom chips will keep rising rapidly. As a technology leader in this field, Broadcom’s AI product line has significant growth potential. Many foreign investment firms rate it as a “buy,” with target prices generally above $2,000.

AMD: The Challenger’s Breakthrough Path

AMD is NVIDIA’s direct competitor in the GPU space and one of the few large chip companies capable of designing both GPUs and CPUs. Although still a follower in the GPU market, AMD’s independently developed MI300 series accelerators have shown performance comparable to NVIDIA’s H100 in many tests, at half the price.

AMD is becoming a strong challenger in the AI chip market. With the MI300 series accelerators and the advanced CDNA 3 architecture, AMD has successfully entered the AI market dominated by NVIDIA, providing an important secondary option for cloud service providers and large enterprises. In 2024, AMD’s total revenue was about $22.9 billion, with data center revenue up 27% year-over-year, indicating its AI product strategy is beginning to pay off.

In 2025, AMD’s AI offensive intensifies. In Q2, its data center business grew 18% year-over-year, mainly due to the adoption of MI300X accelerators by major cloud providers and the upcoming launch of the MI350 series in the second half of 2025. As AI workloads diversify, customer demand for alternative solutions increases. AMD leverages its CPU+GPU integration and open ecosystem strategy to gradually expand its share in AI training and inference markets. Many foreign research institutions are optimistic about its growth prospects, with target prices mostly above $200.

It’s worth noting that if NVIDIA’s CUDA ecosystem hadn’t already accumulated a vast amount of developer code assets, giving H100 a natural advantage, AMD’s relative strength would be even more apparent. But given the huge scale of AI capital expenditure (often hundreds of billions of dollars), if AMD can offer more competitive prices to attract more developers to its platform, its long-term growth outlook is fully promising. Since ChatGPT’s launch, AMD’s stock price has risen 3.2 times. Although demand for traditional chips has declined, temporarily depressing stock prices, the rapid growth of AI chip business is boosting overall revenue share, making the future highly promising.

Investment Strategies and Approaches for AI Stocks

Besides directly selecting individual stocks, investors can participate in the AI wave through diversified tools:

Investors can consider combining dollar-cost averaging strategies, regularly investing in AI stocks or related ETFs to balance market volatility risks. Observing institutional moves like Bridgewater, although AI remains a high-growth sector, the benefits won’t always be concentrated in the same companies. Some stocks may already fully reflect AI’s benefits, so adjusting portfolios and staying aligned with market evolution is key to maximizing returns.

For trading platforms, U.S. stocks can be bought via overseas brokers or CFD platforms. Different platforms have their features; the key is to find tools that match your investment style. For short-term trading, CFD platforms may be more attractive due to support for two-way trading, commission-free transactions, and flexible leverage.

Risks and Long-term Outlook of AI Stocks

Mid-term Outlook: Continued Growth with Volatility

As large language models, generative AI, and multimodal AI (integrating speech, images, text) advance rapidly, demand for computing power, data centers, cloud platforms, and specialized chips will continue to rise. In the short term, NVIDIA, AMD, TSMC, and other hardware suppliers will benefit most. In the medium to long term, industries like healthcare, finance, manufacturing, autonomous driving, and retail will gradually unlock AI stock investment opportunities.

From a capital perspective, although AI remains a market focus, stock price movements are heavily influenced by macroeconomic factors. Federal Reserve and other central bank policies on interest rates will directly impact high-valuation AI stocks. If rates are favorable, tech stocks will benefit; if not, valuations may decline. Additionally, AI stocks are sensitive to policy news and can experience sharp short-term fluctuations. When new investment hotspots (like renewable energy) emerge, capital flows may shift accordingly. Therefore, the market may continue to oscillate in the near term, but the long-term trend remains upward.

Policy and Regulatory Uncertainties

Governments worldwide generally regard AI as a strategic industry and may increase subsidies and infrastructure investments. Such policy support is positive for AI stocks. However, as AI applications expand, issues like data privacy, algorithm bias, copyright, and ethics will surface. Stricter regulations could challenge some AI companies’ valuations and business models.

Investment Recommendations: Balancing Strategies

Overall, from 2025 to 2030, AI stocks are expected to continue benefiting from industry growth. Investors should prioritize AI chip and accelerator suppliers or companies focused on specific applications like cloud services, healthcare AI, and fintech. Long-term allocation through AI-themed ETFs, with periodic entry rather than chasing highs, can effectively reduce risks from market volatility.

Recognizing Investment Risks

When investing in AI stocks, it’s important to understand the main risks:

• Rapid technological iteration risk: Although AI technology has existed for decades, only recently has it entered mainstream applications. The fast pace of development means even seasoned investors may struggle to keep up, leading to delayed investment decisions and stock volatility.

• Company fundamental risk: Many AI companies have limited historical data and higher operational risks. While major tech firms are involved in AI, some pure AI startups lack long-term track records, making them less stable than established giants.

• Industry chain risk: As AI develops rapidly and market demands evolve, public opinion, regulations, and social attitudes may change unexpectedly, affecting AI stocks’ performance. Concerns over AI safety and ethics could lead to tighter regulation and valuation impacts.

Considering both opportunities and risks, AI stocks remain a promising investment direction in the future, but investors must maintain clear judgment and risk awareness.