2025 AI Stock Investment Guide: A Comprehensive Overview from Computing Power Chips to Application Ecosystems

The Global AI Industry Is Entering a New Investment Phase

Since the advent of ChatGPT, artificial intelligence technology has shifted from concept to practical application, becoming the hottest investment theme in the capital markets. But where are the real opportunities in AI stocks? Is it in chasing the hot trend of chip manufacturers, or in laying out the long-term potential of application-layer companies?

According to the latest IDC data, global corporate spending on AI solutions and technologies is expected to reach $307 billion by 2025, surpassing $632 billion by 2028, with a compound annual growth rate of approximately 29%. Among these, accelerated server expenditures will account for over 75%, becoming the core hardware foundation for AI deployment. Behind these figures is a reflection that all segments of the AI industry chain are growing rapidly, with investment opportunities across the entire link from chip design and hardware manufacturing to cloud applications.

The movements of institutional investors best reflect market confidence. For example, Bridgewater Associates significantly increased holdings in key AI companies such as NVIDIA, Google, and Microsoft in their Q2 2025 13F filings, indicating that capital is accelerating to secure positions in core areas like computing power, chips, and cloud computing. According to Morningstar, by the end of Q1 2025, the total assets of global AI and big data funds had exceeded $30 billion.

Industry Classification and Investment Logic of AI Concept Stocks

Investing in AI stocks first requires understanding the vertical structure of the AI industry chain. The entire ecosystem can be divided into three layers:

Infrastructure Layer: Includes GPU chips, ASIC custom chips, accelerated servers, and cooling systems. These are the material basis of AI computing power. Companies in this layer benefit from the continuous explosion of computing demand. They have the strongest growth potential but also face high valuation pressures.

Platform Layer: Cloud service providers, AI software frameworks, and data center operators play a bridging role, offering relatively stable business models.

Application Layer: Companies utilizing AI technology to optimize business processes or develop new products, spanning fields from medical diagnostics, financial risk control, to autonomous driving. These companies typically have longer commercialization cycles but stronger sustainability.

From a valuation perspective, infrastructure layer companies are usually valued with the highest P/E multiples because they are in the early stage of the cycle, but their growth rate decline risks are also the greatest; application layer companies, although growing more slowly, tend to offer more stable long-term investment value. A historical reference worth considering is Cisco Systems, which reached a high of $82 during the 2000 dot-com bubble and then fell over 90%. Even after 20 years of operation, it has not returned to its peak, highlighting the cyclical risks associated with infrastructure-type companies.

Core Targets in Taiwanese AI Stocks

Quanta Computer (2382) stands out in the AI server sector. As one of the world’s largest notebook OEMs, its subsidiary, Quanta Cloud Technology (QCT), has become a major supplier of AI servers for large U.S. data centers. In 2024, Quanta’s revenue reached NT$1.3 trillion, with the AI server segment continuously increasing its share. In Q2 2025, revenue broke NT$300 billion, up over 20% year-over-year, reaching a new high for the period. Foreign analysts’ target prices range from NT$350 to NT$370.

Vanguard-KY (3661) specializes in ASIC chip design services, serving clients including major U.S. cloud giants and high-performance computing leaders. In 2024, full-year revenue was NT$68.2 billion, with a growth rate exceeding 50%. In Q2 2025, quarterly revenue surpassed NT$20 billion, doubling from the same period last year, with both gross margin and net profit margin improving. As large AI clients’ projects enter mass production, orders for next-generation AI accelerators continue to flood in. Foreign analysts’ target prices range from NT$2200 to NT$2400.

Delta Electronics (2308) is a global leader in power management and power solutions. Recently, it has entered the AI server supply chain, primarily providing high-efficiency power supplies, cooling, and chassis solutions. In 2024, annual revenue was approximately NT$420 billion, with an increasing share from data centers and AI-related businesses. In Q2 2025, revenue was about NT$110 billion, up over 15% year-over-year.

MediaTek (2454) is among the top ten fabless semiconductor companies globally. Its Dimensity series chips have integrated enhanced AI computing units and collaborate with chip manufacturers to develop automotive and edge AI solutions. In 2024, revenue reached NT$490 billion, with Q2 2025 revenue around NT$120 billion, up about 20% annually, mainly benefiting from increased market share in high-end mobile chips and AI-enabled devices.

Dahan Precision (3324) specializes in cooling solutions, providing water-cooled heat dissipation modules. As AI server chip power consumption surpasses the kilowatt mark, liquid cooling technology has become a necessity. In 2024, revenue was NT$24.5 billion, up over 30% annually. In 2025, major cloud service providers are accelerating the adoption of liquid cooling solutions, leading to a significant increase in shipments from Dahan Precision from Q2 onward, with market targets above NT$600.

U.S. AI Stock Investment Landscape

NVIDIA (NVDA) has become the standard in AI computing through its GPUs and CUDA platform, forming a complete ecosystem from chips and systems to software. In 2024, revenue reached $60.9 billion, up over 120%. In Q2 2025, revenue hit a new high of approximately $28 billion, with net profit increasing over 200% annually. The Blackwell architecture GPU demand remains strong, and data center operations continue to set records. As AI moves from training to inference, the demand for high-performance computing solutions is growing exponentially.

Broadcom (AVGO) is a leader in infrastructure software and semiconductor solutions, with technological advantages in custom ASIC chips, network switches, and optical communications. In fiscal 2024, revenue was $31.9 billion, with AI-related product sales rapidly increasing to 25%. In Q2 2025, revenue grew 19% year-over-year, benefiting from cloud providers accelerating AI data center deployments. Demand for Jericho3-AI chips and Tomahawk5 switches continues to grow, with multiple foreign institutions rating it as a buy, with target prices above $2000.

AMD (NASDAQ: AMD) has successfully entered the NVIDIA-dominated market with its Instinct MI300 series accelerators and the CDNA architecture. In 2024, revenue was $22.9 billion, with the data center segment growing 27% annually. In Q2 2025, revenue increased 18% year-over-year, with the MI300X accelerators adopted by mainstream cloud vendors and the MI350 series coming out in the second half. AI-related revenues are expected to multiply. As a key secondary supplier, market players are optimistic about its expansion potential, with many target prices above $200.

Microsoft (MSFT) has become a leader in enterprise AI transformation through its exclusive partnership with OpenAI and the Azure AI platform. In fiscal 2024, revenue was $211.2 billion, with Azure and cloud services growing 28%, and AI services contributing over half of that growth. In Q1 2025, intelligent cloud revenue first surpassed $30 billion, with large-scale deployment of Copilot for Microsoft 365 and exponential growth in Azure OpenAI usage. With a product ecosystem covering over a billion global users, its monetization ability continues to increase, with a target price range of $550–$600.

Google (GOOGL) has integrated generative AI capabilities into core businesses like Search, YouTube, and Cloud Computing, with significant progress in the Gemini multimodal model. In 2024, revenue was $3.05 trillion with a YTD increase of 32.50%, benefiting continuously from AI advertising monetization and cloud growth.

Approaches to Investing in AI Stocks

For ordinary investors, directly selecting stocks involves concentration risk. Alternatives include:

Stock Funds: Managed by fund managers who select a diversified portfolio of stocks, balancing risk and return, but with relatively higher trading costs and moderate management fees.

ETF Funds: Passively tracking AI indices, with lower trading costs and management fees, but potential premiums or discounts. Mainstream options in Taiwan include the Taiwan Innovation Taiwan AI ETF (00851) and the Yuanta Global AI ETF (00762).

Dollar-Cost Averaging: Combining regular investment to average purchase costs, suitable for medium- to long-term allocation. Historical data show that even top-tier companies can experience sharp declines at market peaks, making dollar-cost averaging effective in smoothing short-term volatility.

For investors with different trading preferences, options include purchasing Taiwan stocks through local brokers, using over-the-counter or overseas brokers to buy U.S. stocks, or engaging in long/short trading on leverage platforms.

Risks and Outlook for AI Stocks

Short-term outlook: Between 2025 and 2027, infrastructure suppliers like chip and accelerated server vendors will remain the biggest beneficiaries. As large language models and multimodal AI advance rapidly, demand for computing power and data centers will continue to rise. However, investors should be cautious of the impact of Federal Reserve rate policies— loose monetary policy favors tech valuations, while high interest rates compress valuation space.

Medium- to long-term trend: AI applications in healthcare, finance, manufacturing, and autonomous driving will gradually materialize into actual revenue, driving overall industry growth. Meanwhile, AI concept stocks are susceptible to news-driven volatility, and capital may flow into new themes.

Policy variables: Countries regard AI as a strategic industry, potentially increasing subsidies and infrastructure investments to support it. However, issues such as data privacy, algorithm bias, and intellectual property rights could lead to tighter regulations, challenging some AI companies’ valuations and business models.

Key risk warnings:

• Rapid technological updates in the industry mean even seasoned investors may struggle to keep pace, leading to stock price volatility driven by hype.
• Some AI companies lack a solid operational history, making their risks higher than those of mature, stable firms.
• Evolving regulations and ethical concerns may impact stock performance in unforeseen ways.

Overall, AI stocks from 2025 to 2030 are characterized by a “long-term bullish, short-term volatile” outlook. A prudent strategy is long-term allocation with phased entry, focusing on companies with tangible applications and infrastructure providers, and diversifying through AI-themed ETFs rather than chasing highs blindly. The key is to stay updated and adjust holdings accordingly, as although the AI industry develops rapidly, benefits may not always concentrate on the same companies.