How to choose AI stocks in the US? Updated 2025 AI Concept Stock Investment Map

The explosion of generative AI has become an irreversible industry trend. From NVIDIA’s GPU computing race, to Microsoft’s deep integration with OpenAI, and to the supply chain position of Taiwanese semiconductor manufacturers, the entire AI ecosystem is reshaping the global investment landscape. So, in this wave of technological advancement, how should investors precisely position themselves? Which US AI stocks are most worth paying attention to?

AI Industry Boom, Who Are the Biggest Beneficiaries?

According to the latest IDC forecast, global enterprise spending on AI solutions and technologies will reach $307 billion by 2025. More importantly, by 2028, AI-related expenditures will surpass $632 billion, with a compound annual growth rate (CAGR) of 29%. This means that over the next three years, the AI industry still has nearly double the growth potential.

Among them, accelerated servers will account for over 75% of expenditures in 2028, becoming the core supporting the entire AI infrastructure. This huge hardware demand is directly boosting the performance of related suppliers. For example, in Bridgewater Associates’ latest 13F report, they significantly increased their holdings in key AI vendors like NVIDIA, Alphabet, Microsoft, etc., in Q2 2025, reflecting institutional investors’ strong confidence in the AI supply chain.

Leading US AI Stocks: From Chips to Applications, a Comprehensive Layout

1. NVIDIA (NVDA): The Powerhouse of Computing

NVIDIA is undoubtedly the absolute leader in this AI wave. Its GPUs and CUDA software platform have become industry standards for training and deploying large AI models, forming an insurmountable moat.

In 2024, NVIDIA’s revenue reached $60.9 billion, an increase of over 120% year-over-year. Entering 2025, the growth momentum is even more vigorous—Q2 revenue hit a new high of $28 billion, with net income growth exceeding 200%. Most critically, its data center business continues to set records, and the launch of Blackwell architecture GPUs (B200, GB200) further consolidates its market position.

As AI applications shift from training to inference, the demand for high-performance computing will continue to grow exponentially. Several institutions have raised their target prices to the $350–$400 range and issued “Buy” ratings.

Market Cap: $4.28 trillion | Latest Price: $176.24 | YTD Gain: 31.24%

2. Microsoft (MSFT): The Driver of Enterprise AI Transformation

Unlike pure hardware companies, Microsoft has successfully monetized generative AI for enterprise revenue through exclusive partnerships with OpenAI and its Azure AI platform. Its Copilot series has been deeply integrated into Windows, Office, Teams, and other ecosystems with over 1 billion global users.

In fiscal year 2024 (ending June 30), revenue reached $211.2 billion, with Azure cloud services growing 28%, and AI services contributing more than half of that growth. Entering FY2025, Microsoft’s AI commercialization accelerates, with the first quarter’s intelligent cloud revenue surpassing $30 billion for the first time.

This indicates that Microsoft has shifted from hardware dependence to software and application monetization, with relatively stable long-term growth prospects. Foreign analysts’ target prices are now in the $550–$600 range.

Market Cap: $3.78 trillion | Latest Price: $508.45 | YTD Gain: 20.63%

3. Broadcom (AVGO): The AI Network Hub

If NVIDIA is the “heart” of AI, then Broadcom is the “vessels” in AI data centers. Its customized ASIC chips, network switches, and optical communication chips have become indispensable components of AI servers.

In fiscal year 2024, revenue was $31.9 billion, with AI-related products rapidly increasing to 25%. In Q2 2025, Broadcom’s revenue grew 19% year-over-year, benefiting from strong demand for Jericho3-AI chips and Tomahawk5 switches. As AI data centers expand, the demand for high-performance network connectivity will surge. Many foreign analysts set their target prices above $2,000.

Market Cap: $1.63 trillion | Latest Price: $345.35 | YTD Gain: 48.96%

4. Alphabet (GOOGL): The AI Application Trailblazer

As the global leader in search engines and advertising platforms, Google is reshaping its core business through generative AI models like Gemini. Its AI applications are beginning to translate into improved advertising monetization capabilities.

Market Cap: $3.05 trillion | Latest Price: $252.33 | YTD Gain: 32.50%

5. AMD (NASDAQ: AMD): The Challenger in AI Chips

AMD is successfully carving out a second territory in the AI market dominated by NVIDIA through its Instinct MI300 series accelerators and advanced CDNA 3 architecture. In 2024, data center revenue grew 27% year-over-year; in Q2 2025, revenue increased 18% YoY, with the MI300X accelerators adopted by major cloud providers.

As customer demand for alternatives increases, AMD’s market share continues to rise. Many foreign analysts’ target prices are above $200.

Market Cap: $256.3 billion | Latest Price: $157.92 | YTD Gain: 30.74%

Taiwanese AI Concept Stocks: Hidden Champions in the Supply Chain

Core Tier

Quanta (2382): Leading global OEM for AI servers, with 2024 revenue of NT$1.3 trillion, continuously increasing AI server share. In Q2 2025, revenue exceeded NT$300 billion, up over 20% YoY. Foreign analysts’ target prices are NT$350–NT$370.

Vanguard-KY (3661): Specializes in ASIC design, with 2024 revenue of NT$68.2 billion, up over 50%. Q2 revenue surpassed NT$20 billion, doubling from the same period last year, with gross margin and net margin continuing to improve. Target prices range from NT$2,200 to NT$2,400.

MediaTek (2454): Developing automotive and edge AI through Dimensity series and collaborations with NVIDIA. In 2024, revenue reached NT$490 billion; in Q2 2025, about NT$120 billion, up approximately 20% YoY. Target prices are NT$1,300–NT$1,400.

Delta Electronics (2308): A leader in power management and cooling solutions, entering the AI server supply chain. In 2024, revenue was NT$420 billion, with data center-related performance steadily increasing.

Sunway (3324): Mastering liquid cooling technology, benefiting from rising power consumption of AI servers. In 2024, revenue was NT$24.5 billion, up over 30%. Many foreign analysts’ target prices are above NT$600.

TSMC (2330): As the world’s leading foundry, directly benefiting from the explosive demand for AI chip manufacturing. In 2024, revenue continues to hit new highs.

How to Scientifically Layout AI Investments?

Single Stocks vs. Funds vs. ETFs

Buying individual stocks carries higher risk but lower costs; actively managed funds can diversify risk but have higher fees; ETFs offer liquidity and low costs. Choose based on your risk tolerance:

• Aggressive: Directly hold core holdings like NVIDIA, NVIDIA, etc., combined with Taiwanese AI leaders
• Moderate: Allocate to companies involved in applications like Microsoft, Google, to reduce single-stock risk
• Conservative: Use AI-themed ETFs (e.g., Taishin Global AI ETF 00851, Yuan Da Global AI ETF 00762) for diversified investment

Investment Timing and Strategies

Since AI concept stocks are sensitive to news and prone to short-term volatility, it is recommended to adopt:

1. Dollar-cost averaging: Invest periodically to average out costs
2. Long-term holding: AI infrastructure demand will persist for 5–10 years
3. Dynamic adjustment: Regularly review whether individual stocks’ earnings growth is slowing, and adjust positions accordingly

Investment Risk Alerts

Short-term Volatility Risks

While the long-term trend of AI is confirmed, short-term fluctuations may occur due to Federal Reserve policies, geopolitical issues, capital flows, etc. High-valuation tech stocks are sensitive to interest rates; easing environments are bullish, while tightening may pressure prices.

Policy and Regulatory Variables

Countries regard AI as a strategic industry, potentially increasing subsidies; however, issues like data privacy, algorithm bias, and copyright may trigger stricter regulations, directly impacting company valuations.

Technological Iteration Risks

AI develops rapidly, and even industry leaders face the risk of being overtaken. New architectures and competitors can change market dynamics at any time.

Company Fundamentals Risks

Some emerging AI companies lack historical validation, posing higher operational risks. Investors should thoroughly evaluate revenue structure, customer concentration, and technological moat before investing.

AI Stock Investment Outlook 2025–2030

Overall, AI concept stocks show a “long-term bullish, short-term volatile” characteristic. In the short term, chip and hardware suppliers like NVIDIA, AMD, and TSMC remain the biggest beneficiaries; in the medium to long term, AI applications in healthcare, finance, manufacturing, and other verticals will gradually land, driving broader industry benefits.

From a capital perspective, AI themes remain focal, but macroeconomic changes should be monitored closely. Policy support and regulatory tightening will occur simultaneously, requiring ongoing attention.

Advice for General Investors:

Adopt a long-term allocation strategy, use dollar-cost averaging to enter gradually, and avoid chasing highs in the short term. Focus on companies with tangible application deployment and strong long-term competitiveness, while moderately allocating to US AI stocks and Taiwanese supply chain companies to form a comprehensive global AI industry layout. The key is to find stocks with both growth momentum and reasonable valuations to profit steadily in the AI wave.