Analysis of Investment Value in U.S. AI Concept Stocks: Hot Picks and Allocation Strategies for 2026

Since the launch of generative AI technology revolution, U.S. AI concept stocks have become the focus of global capital markets. From chip manufacturers to cloud computing platforms, the entire industry chain is undergoing valuation reassessment and performance validation. So, in the current market environment, where are the investment opportunities in U.S. AI concept stocks? How should the industry chain be structured? How to evaluate core targets? This article will clarify an investment framework for investors from three dimensions: market size, specific companies, and investment strategies.

AI Industry Boom: Fundamental Support for U.S. AI Concept Stocks

Globally, artificial intelligence has evolved from cutting-edge technology to the underlying infrastructure across industries. Applications range from medical diagnostics and financial risk control to autonomous driving and recommendation systems, with expanding penetration. According to the latest IDC data, global enterprise spending on AI solutions and technologies will reach $307 billion by 2025, with continued acceleration in growth.

Importantly, forecasts show that by 2028, total AI expenditure (including applications, infrastructure, and services) will surpass $632 billion, with a compound annual growth rate of about 29%. Hardware infrastructure investment is particularly critical—accelerator servers and related chip spending will account for over 75%, becoming the core driver supporting AI deployment. This indicates that chip manufacturing and infrastructure providers among U.S. AI concept stocks will benefit long-term from industry expansion.

From an institutional investment perspective, AI enthusiasm has been well validated. For example, Bridgewater Associates’ Q1 2025 holdings report shows significant increases in positions in NVIDIA, Alphabet (Google), Microsoft, and other key AI companies, reflecting professional investors’ optimism about the long-term growth of the AI industry chain. Meanwhile, AI-themed funds and ETFs have attracted substantial capital—by the end of Q1 2025, global assets in AI and big data funds exceeded $30 billion, demonstrating that U.S. AI concept stocks are drawing more active and passive investors.

Core Investment Targets in U.S. AI Concept Stocks

NVIDIA: Absolute Leader in Chips

NVIDIA (NVDA) is the global leader in GPU chips. As a core hardware supplier for AI infrastructure, NVIDIA nearly monopolizes the AI chip market. Its market capitalization has exceeded $4 trillion at times, with a PE ratio around 60, reflecting high market expectations for future growth.

From a growth momentum perspective, NVIDIA’s stock has surged over 10 times in just two years since ChatGPT’s launch. Its GPU series (from H100 to the newer Blackwell architecture) has become the industry standard for training and inference of large AI models. As cloud giants and enterprise clients’ demand for AI chips continues to grow, NVIDIA’s integrated ecosystem—from chip design and system architecture to CUDA software—maintains a formidable competitive advantage.

By mid-2025, NVIDIA’s quarterly revenue is approximately $28 billion, with net profit growing over 200% annually. These figures indicate that the explosive demand for AI chips has only just begun. Analysts generally expect that as AI applications shift from training to inference and edge computing, and as enterprise AI deployment accelerates, demand for NVIDIA’s solutions will grow exponentially.

Supply-wise, NVIDIA’s short-term replacement is unlikely. TSMC and other foundries are still operating at tight capacity, with many cloud providers and tech companies competing for chip production. This supply-demand mismatch further consolidates NVIDIA’s pricing power and market position. During the ongoing AI boom, any price pullback could present a buying opportunity for institutional investors.

Broadcom: Hidden Backbone of AI Infrastructure

Broadcom (AVGO) is a global leader in network communication chips and infrastructure solutions. Although less well-known than NVIDIA, its role in the AI industry chain is equally critical—Broadcom supplies essential components for AI data centers.

Broadcom mainly provides network interconnect chips, switches, and optical communication chips for AI servers. As AI data center scale explodes, demand for these components surges accordingly. In fiscal year 2024 (ending November), Broadcom’s revenue reached $31.9 billion, with AI-related product revenue accounting for 25%, a share that continues to rise.

Entering 2025, Broadcom’s strategic position in AI becomes even more prominent. Its specialized chips, such as Tomahawk5 switches and Jericho3-AI, are widely adopted by cloud service providers. The interconnect segment grew 19% year-over-year in Q2, reflecting the data center build-out’s demand for Broadcom’s products. As AI model sizes expand, the need for high-speed networking and customized chips will accelerate, further highlighting Broadcom’s value as a key supply chain player.

Notably, although NVIDIA and Broadcom compete in some areas, they are also complementary—NVIDIA’s GPUs rely on Broadcom’s network chips to perform optimally. This relationship gives Broadcom a unique position in U.S. AI concept stock portfolios.

AMD: Strong Challenger in AI Chips

Advanced Micro Devices (AMD) is NVIDIA’s main competitor in GPUs and one of the few large chip companies capable of developing both GPUs and CPUs. While still behind NVIDIA in market share, AMD’s cost-performance advantage is increasingly evident.

AMD’s self-developed MI300 series accelerators already match NVIDIA’s H100 in several performance metrics, at roughly half the price. This price-performance gap is highly attractive to cloud providers and enterprise users with large capital expenditures. In 2024, AMD’s revenue was about $22.9 billion, with data center business growing 27% annually, driven by AI chip demand.

In late 2025, AMD’s MI350 series will be launched, further boosting its competitiveness. As enterprise demand for alternative suppliers grows, AMD’s integrated CPU+GPU approach and open ecosystem strategy are gradually expanding its share in AI training and inference markets. Foreign institutional investors generally rate AMD as a “buy,” with target prices above $200.

Long-term, although NVIDIA’s CUDA ecosystem has strong stickiness in the short term, AMD’s competitive pricing and performance could lead developers to migrate over time. This suggests AMD has high potential for unexpected growth in U.S. AI concept stocks.

Investment Strategies and Tools for U.S. AI Concept Stocks

Individual Stocks vs. ETFs

Investors can participate in U.S. AI concept stocks via two main routes: direct stock holdings or AI-themed ETFs.

Individual stocks offer flexibility, lower transaction costs, and concentrated gains but come with higher risk if selecting wrong companies or chasing high prices. ETFs diversify holdings, reducing company-specific risk, with management fees that are relatively low, suitable for investors seeking systematic exposure to AI with limited risk appetite.

AI-related thematic funds like Global X Robotics & Artificial Intelligence ETF (BOTZ) provide convenient industry allocation tools. As of recent data, assets under these funds are growing rapidly worldwide.

Dollar-Cost Averaging and Timing

Given the rapid development and market volatility of the AI industry, dollar-cost averaging (DCA) is especially important. Even professional funds like Bridgewater adjust their holdings regularly—indicating no company’s AI prospects are permanently positive, and cyclical opportunities exist.

Regular, fixed investments can average out costs and reduce short-term volatility impact. For long-term believers in AI, DCA offers a relatively stable approach. However, investors should avoid chasing high prices—many AI stocks may already reflect mid-term optimism, limiting further upside.

Trading Platforms

Investing in U.S. AI stocks can be done via overseas brokers or CFD platforms. Overseas brokers offer legitimate stock ownership, suitable for long-term investors; CFD platforms provide more flexible trading, including two-way trading and lower leverage costs, appealing to short-term traders.

Choice depends on investors’ time horizons and risk preferences. For long-term allocation, overseas brokers are more secure; for short-term trading, CFD platforms’ cost advantages and flexibility are beneficial.

Risk Assessment and Long-term Outlook

Key Risks in U.S. AI Concept Stocks

Investing in U.S. AI stocks requires awareness of:

Industry Uncertainty: Despite decades of AI development, large-scale commercialization is recent. Rapid technological progress and evolving applications make accurate forecasts difficult. This often causes AI stocks to be driven by sentiment, with prices exceeding fundamentals.

Valuation Risks: Many AI companies’ current valuations already price in several years of growth. If actual performance falls short or macro conditions tighten, high-valuation stocks could face significant corrections.

Intensifying Competition: While NVIDIA currently dominates, global tech giants are increasing AI chip R&D. More competitive alternatives may emerge, eroding market share of current leaders.

Regulatory and Policy Risks: Governments see AI as a strategic industry, investing in infrastructure and subsidies. However, issues like data privacy, algorithm bias, and intellectual property are attracting stricter regulation. Tightening policies could challenge some AI business models.

Medium- to Long-term Outlook

From 2026 to 2030, U.S. AI concept stocks are still in a key growth phase. Major investment themes include:

Infrastructure as the Largest Beneficiary: Chipmakers, server accelerators, and data center builders will continue to attract capital, with predictable revenue growth.

Application Layer Opportunities: As infrastructure matures, AI will shift from R&D to large-scale commercial use. Cloud, healthcare AI, and fintech companies will become new growth drivers.

Macro Environment Impact: Federal Reserve and other central banks’ interest rate policies will influence high-valuation tech stocks. Low rates favor AI stocks; high rates may compress valuations.

Allocation Recommendations

Based on the above, suggested allocations include:

Prioritize Infrastructure Layer: NVIDIA, Broadcom, and similar chip and hardware suppliers have the strongest short-term growth potential, directly linked to AI industry expansion.

Moderate Exposure to Challengers: AMD and other competitive players, though currently smaller, offer significant upside potential. A balanced allocation can enhance portfolio flexibility.

Focus on Application Companies: Cloud platforms, AI software, and service providers may be downstream but will benefit from expanding AI applications.

Use Dollar-Cost Averaging: Avoid heavy concentration in a single entry point; staggered buying reduces short-term risk and maintains long-term participation.

Regular Review and Adjustment: As the AI landscape evolves, periodically reassess core holdings and market position to adapt to technological and competitive changes.

In summary, U.S. AI concept stocks are still in early stages of long-term growth, but short-term volatility and valuation risks are notable. Through disciplined asset allocation, strategic entry points, and risk management, investors can still benefit from the medium- and long-term growth of U.S. AI stocks. The key is maintaining confidence in the long-term trend while avoiding chasing highs and overconcentrating risks.