Must-Read for U.S. Stock AI Concept Stock Investment | 2026 Seven Major Leaders Deployment Strategy

In 2026, entering its first quarter, the global AI investment boom continues to heat up. According to the latest Gartner forecast, worldwide AI expenditures are expected to reach $2.53 trillion, representing explosive growth compared to last year. Amid this wave of technological advancement, U.S. AI concept stocks have become the focus of global investors—not only because of their technological leadership but also because these companies have shifted from mere “concept hype” to “actual profitability.”

So, which AI concept stocks in the U.S. are worth allocating? How do they differ from Taiwanese AI concept stocks? This article will analyze the global AI industry investment landscape in depth to help you identify truly promising U.S. AI stocks with growth potential.

Three Major Shifts in the AI Industry in 2026

AI Computing Shifts from Training to Inference: How U.S. Chip Makers Are Riding the Wave

In recent years, global tech giants have been aggressively purchasing GPUs primarily for training massive AI models. But by 2026, this pattern is fundamentally changing—the industry focus will clearly shift to the “inference” stage, where AI begins to answer questions, generate content, and process various real-time business data.

This shift has profound implications. Computing will no longer be concentrated solely in cloud data centers but will gradually move to edge devices, including personal computers, smartphones, and edge computing devices. For enterprises, this means significantly reducing long-term cloud rental costs, enhancing data privacy, and improving real-time responsiveness—pushing AI PCs and AI smartphones into widespread adoption.

From an investment perspective, this transformation creates new winners. The high cost of general-purpose GPUs has led to the rise of ASIC chips tailored for specific tasks. In U.S. stocks, Broadcom and Marvell Technology are the biggest beneficiaries of this transition—they both possess comprehensive custom chip design capabilities, serving cloud service providers from architecture to mass production. Additionally, processor supplier Qualcomm, leveraging its NPU advantage in mobile chips, is also gaining ground in the inference era.

In contrast, Taiwan’s Unisoc-KY (世芯-KY), while engaged in ASIC design, has more limited customer scale and technical depth. The market share and influence of related U.S. stocks are significantly stronger globally.

Energy and Cooling Become New Battlegrounds: New Opportunities for U.S. Infrastructure Companies

This may be the most overlooked yet most impactful investment theme of 2026. AI servers consume far more power than traditional servers. As model sizes continue to grow, data centers worldwide face dual pressures of “heat dissipation” and “power shortages.” Critical infrastructure such as liquid cooling technology, specialized power management, and nuclear energy supply has risen from peripheral issues to core competitive advantages.

The most direct beneficiaries in U.S. stocks are Constellation Energy (CEG)—which owns a large portfolio of nuclear assets capable of providing large-scale, stable, low-carbon, uninterrupted baseload power. For AI data centers that require 24/7 operation with increasing power consumption, the strategic value of such energy supply far exceeds traditional electricity cost considerations.

Taiwan’s Shuanghong (3324) leads in liquid cooling technology, but Broadcom offers a more complete ecosystem with network switches and power management chips, with deeper integration and higher global penetration.

Application Deployment Era: The Certainty of U.S. Software Giants

2026 is the year when AI truly undergoes market validation through practical applications. Investors and enterprises are no longer satisfied with just “integrating AI features”; they focus on the core question: can AI actually save costs or generate revenue for businesses?

In this competitive era, companies merely applying GPT APIs are quickly losing their edge. Only those with proprietary, domain-specific data—such as medical imaging, legal precedents, or factory automation data—can build durable moats.

Among U.S. stocks, Microsoft has seamlessly embedded AI into its ecosystem through deep integration of Copilot with Windows, Office, and Teams, reaching over a billion users. This is not only a technological advantage but also a monetization one—Microsoft’s enterprise AI deployment is recognized by many institutions as the most certain beneficiary. In contrast, Taiwan lacks such large-scale software ecosystem companies.

Why Are the Seven Core U.S. AI Stocks Worth Long-term Investment?

1. NVIDIA (NVDA): The Absolute Leader in AI Computing

As the core of the global AI ecosystem, NVIDIA’s GPUs and CUDA software platform have become industry standards for training and deploying large AI models. Its complete ecosystem—from chips to software—solidifies NVIDIA’s dominant position in AI infrastructure.

By early 2026, as AI shifts from training to inference, demand for NVIDIA’s H-series inference chips will surge. Taiwanese manufacturers (TSMC) and system integrators (Quanta, Foxconn) will benefit directly. But investing directly in NVIDIA stocks remains the most straightforward way to participate in the core value chain of global AI.

2. Broadcom (AVGO): The Hidden Winner in AI Infrastructure

Broadcom’s strengths in custom ASIC chips, network switches, and optical communication chips make it an indispensable supplier for AI data centers. As global tech giants accelerate building inference infrastructure, Broadcom’s order visibility extends into 2027.

Compared to NVIDIA’s spotlight position, Broadcom acts more like the “electrician” supporting the entire AI ecosystem—serving large-scale cloud providers with a diversified customer base and more stable orders than the market average.

3. AMD: Challenger in the AI Accelerator Market

AMD’s Instinct MI300 series accelerators and CDNA 3 architecture are effectively challenging NVIDIA’s market dominance. As cloud providers seek alternative suppliers, AMD’s market share is steadily increasing.

Particularly among large enterprise clients, AMD’s competitive pricing and availability have won more orders. While many Taiwanese suppliers collaborate with AMD, investing directly in AMD stock is the key way to participate in this diversification trend.

4. Microsoft (MSFT): The Driver of Enterprise AI Adoption

Microsoft’s exclusive partnership with OpenAI, combined with Azure AI cloud platform and Copilot integration, makes it a leading platform for enterprise AI transformation. As demand for generative AI explodes, Microsoft’s monetization capabilities continue to grow.

By early 2026, Microsoft’s market cap approaches $4 trillion, with the market recognizing its leadership in AI monetization. Unlike Taiwan, which lacks such large-scale software giants, Microsoft exemplifies “application-layer monetization” with high certainty.

5. Marvell Technology (MRVL): The Custom Chip Design Expert

Large cloud providers are increasingly aware that general GPUs have cost and power efficiency limitations. Developing ASICs for specific workloads has become more attractive. Marvell is one of the few semiconductor companies with full capabilities—from architecture design to mass production—helping clients.

In 2026, as inference chip demand explodes, Marvell’s business outlook improves significantly, making it a “dark horse” in AI-related stocks.

6. Arista Networks (ANET): The New Standard in AI Networking

As AI clusters grow larger, bottlenecks shift from computing power to data transmission and synchronization. High-speed, low-latency network architecture is critical for unleashing AI performance.

Arista, as a major beneficiary of Ethernet standards replacing InfiniBand, has seen its position rise sharply. While Taiwan has network equipment manufacturers, Arista’s deep expertise and market penetration in AI networking are more prominent.

7. Constellation Energy (CEG): Energy Infrastructure for the AI Era

Constellation’s advantage lies in its extensive nuclear assets, capable of providing long-term, stable, large-scale, low-carbon, uninterrupted baseload power. As AI data centers demand 24/7 operation with rising power needs, energy supply becomes a new variable in AI infrastructure competition.

Taiwan’s Delta (2308) and other energy management firms exist, but Constellation’s strategic advantage in energy production is a unique asset difficult for Taiwanese companies to replicate.

Comparing U.S. and Taiwanese AI Stocks: Why Do U.S. Stocks Have a Global Edge?

Process Level—TSMC vs. NVIDIA

TSMC (2330) is fundamental for AI chip manufacturing, but its growth potential is relatively limited. NVIDIA controls the entire value chain—from design and software to ecosystem—resulting in a much higher valuation premium and growth space.

From an investment perspective, NVIDIA in the U.S. commands a “global AI leader” premium, whereas TSMC is more of a “defensive infrastructure” play.

System Level—Quanta vs. Broadcom

Quanta (2382) excels in system integration but relies heavily on a few large clients, leading to higher order volatility. Broadcom’s supply position is more balanced across multiple customers, with a complete chip portfolio, offering stronger risk resilience.

Broadcom represents a “positioning” infrastructure supplier with greater strategic advantage.

Cooling Level—Shuanghong vs. Constellation

Taiwan’s Shuanghong (3324) leads in liquid cooling tech, but its market is mainly Asia-based. Constellation dominates North American energy supply, controlling a more strategic resource—energy dependency for AI data centers exceeds even cooling needs.

Broadly, Constellation is a more upstream, scarce strategic asset.

The Three Major Investment Risks in U.S. AI Stocks You Should Know

1. Valuation Overstretch Risk

U.S. AI concept stocks are already valued at high levels early in 2026, with much of the future growth expectations priced in. If actual growth falls short, significant correction could occur.

Historically, Cisco (CSCO) peaked at $82 during the 2000 dot-com bubble, then fell over 90% after the bubble burst. Despite decades of solid operation afterward, its stock never returned to the peak. This warns investors that even fundamentally sound infrastructure companies can face long-term valuation corrections.

2. Industry Uncertainty and Rapid Iteration

While AI has existed for decades, large-scale commercialization is just beginning. Rapid technological advances and market shifts make it difficult even for insightful investors to keep pace. New tech routes, business models, and competitive landscapes may change dramatically in the short term.

Emerging AI companies untested by the market may fail quickly due to shifts in technology or increased competition. Investors must remain cautious of the “unknown.”

3. Policy and Regulatory Variables

Governments view AI as a strategic industry and may increase subsidies or infrastructure investments, but also tighten regulations. Data privacy, algorithm bias, copyright, and ethical issues could lead to stricter rules.

If regulations tighten, some AI companies’ valuations and business models could be directly challenged—especially those heavily reliant on data access without clear compliance pathways.

How to Efficiently Invest in U.S. AI Stocks: Stocks, Funds, ETFs?

For most investors, directly picking individual stocks is challenging. Diversified investment via funds or ETFs is more practical.

If choosing individual U.S. AI stocks, prioritize NVIDIA (NVDA) as core, then, based on risk appetite and effort, allocate to Broadcom (AVGO), AMD, Microsoft (MSFT), etc.

For funds or ETFs, consider global AI thematic funds or tech index ETFs to reduce single-stock volatility.

Investment Tip: Adopt dollar-cost averaging regardless of chosen method. Even with a long-term upward trend, short-term volatility is inevitable. Regular investments help mitigate timing risks.

Outlook for 2026: The Investment Rhythm of U.S. AI Stocks

Overall, from 2026 to 2030, U.S. AI stocks will likely follow a “long-term bullish, short-term volatile” pattern.

Early 2026 (first half): Inference chip demand will further explode, with NVIDIA, Broadcom, AMD’s order visibility extending into year-end. Infrastructure-related companies in energy and cooling will also benefit.

Mid to late 2026 (second half) and 2027: Practical AI applications will accelerate, with tangible revenue contributions in healthcare, finance, manufacturing, autonomous driving, etc. The monetization capabilities of application leaders like Microsoft and Alphabet will be further validated.

Long-term trend: AI’s impact on human life and productivity will rival the internet revolution. McKinsey estimates that by 2030, AI could contribute $15 trillion to global GDP. In the long run, U.S. AI concept stocks will continue to generate enormous economic value and industry reshaping opportunities.

However, short-term risks include macroeconomic factors, interest rate policies, and market sentiment causing significant fluctuations. Investors should prepare psychologically and adopt phased deployment rather than aggressive “all-in” strategies.

Finally, paying close attention to key factors—such as whether AI tech development slows, application monetization progresses as expected, and profit growth shows signs of deceleration—is crucial. Only if these conditions remain favorable can the investment value of U.S. AI stocks continue to be supported by the market.

In 2026, U.S. AI investment’s core is to identify truly solid companies with earnings support amid potential bubbles—this advantage of U.S. leading firms over other markets is precisely where the opportunity lies.