2025 AI Stock Investment Guide: A Complete Layout from Chips to Applications

Investment Opportunities in the AI Era

Generative artificial intelligence has exploded since the end of 2022, evolving from a tech concept into a genuine investment hotspot. Over the past two years, the stock prices of AI-related listed companies have soared, with some even doubling in valuation before achieving significant profit growth. This phenomenon reflects the market’s high expectations for AI’s long-term potential but also contains inherent risks. So, how can investors accurately grasp investment opportunities in AI stocks? And how can they identify truly valuable AI-related targets?

Core Logic of the Artificial Intelligence Industry

Artificial intelligence (AI) endows machines with human-like cognitive abilities—learning knowledge, logical reasoning, problem-solving, language understanding, content generation, and more—functions now seen everywhere in daily life. From voice assistants to autonomous driving systems and medical diagnostics, AI applications have broken out of laboratory boundaries.

The core logic of AI stock investment lies in deploying across the entire industry chain—including foundational hardware (chips, servers, cooling systems) as well as cloud platforms and software applications. In short, investing in AI concept stocks means investing in the underlying infrastructure and application ecosystem supporting the AI revolution.

Explosive Market Growth

According to IDC’s latest forecast, global enterprise investment in AI solutions will reach $307 billion by 2025. Looking ahead to 2028, total AI expenditure—including applications, infrastructure, and related services—is expected to surpass $632 billion, with a compound annual growth rate of about 29%. Particularly at the infrastructure level, spending on dedicated accelerators for servers will exceed 75% of total by 2028, becoming the core driver of AI deployment.

This data indicates a key point: the AI industry is far from saturated, with enormous room for future growth. As more companies incorporate AI into their strategic planning, demand for related hardware and software will continue to rise.

Signals from Major Institutional Layouts

Institutional investors often lead market trends. In Q2 2025, Bridgewater Fund significantly increased holdings in core AI companies like NVIDIA, Alphabet, and Microsoft, reflecting professional investors’ continued optimism about the AI industry chain. Meanwhile, many investors are also choosing thematic funds or ETFs for allocation, enabling a one-time deployment across computing power, chips, cloud, and other segments. According to Morningstar, by the end of Q1 2025, global AI and big data funds had assets exceeding $30 billion, indicating continuous capital inflow into the AI field.

Leading AI Stocks in Taiwan

Quanta Computer (2382): The Hidden Champion of AI Servers

Quanta Computer is a global leader in notebook OEM manufacturing, successfully transforming into the AI server market in recent years. Its professional division, QCT, has penetrated large-scale data centers worldwide, becoming a major supplier for NVIDIA and international cloud service providers. In 2024, Quanta’s revenue reached NT$1.3 trillion, with the proportion of AI servers steadily increasing. Entering 2025, Quanta’s second-quarter revenue surpassed NT$300 billion, up over 20% year-on-year, setting a new record. Foreign investors generally target a price of NT$350–NT$370, with upside potential.

Silicon Motion (3661): Leader in Customized Chips

Silicon Motion specializes in ASIC design services, serving top global cloud and high-performance computing clients. In 2024, revenue hit NT$68.2 billion, with a growth rate exceeding 50%. In Q2 2025, quarterly revenue broke NT$20 billion, doubling compared to the same period last year, with gross margins continuously improving. As large AI clients move into mass production, new accelerator orders are pouring in. Foreign analysts’ average target price is between NT$2,200 and NT$2,400, still with room for appreciation.

Delta Electronics (2308): The Hidden Winner in Power and Cooling

Delta Electronics is a global leader in power management, actively entering the AI server supply chain by providing high-efficiency power supplies, cooling, and cabinet solutions. In 2024, revenue was about NT$420 billion, with data center business steadily rising. In Q2 2025, revenue reached NT$110 billion, up over 15% year-on-year, with high gross margins maintained. As AI infrastructure investments heat up, related business is expected to grow steadily.

MediaTek (2454): Player in Mobile and Automotive AI

MediaTek is one of the top ten fabless semiconductor companies worldwide. Its Dimensity series mobile platforms have integrated enhanced AI computing capabilities and collaborate with NVIDIA on automotive solutions. In 2024, revenue reached NT$490 billion, with gross margins improving quarter by quarter. In Q2 2025, revenue was about NT$120 billion, up approximately 20% year-on-year, driven by increased market share of high-end chips and rising demand for AI smart devices. Foreign analysts’ target price ranges from NT$1,300 to NT$1,400.

Sunway (3324): Leader in Liquid Cooling

As AI servers’ power consumption continues to surpass kilowatt levels, traditional air cooling can no longer meet demand. Sunway leverages leading liquid cooling technology to secure a position in the global supply chain. In 2024, revenue was NT$24.5 billion, up over 30%. In 2025, as major cloud providers accelerate adoption of liquid cooling solutions, Sunway’s water-cooling module shipments surged, boosting performance and gross margins. Many foreign reports are optimistic about its prospects, with target prices mostly above NT$600.

Core Drivers of US AI Concept Stocks

NVIDIA: Absolute Leader in AI Computing

NVIDIA’s GPUs and CUDA platform have become industry standards for AI training and inference. In 2024, revenue reached $60.9 billion, up over 120%. In Q2 2025, revenue hit a new high of $28 billion, with net profit increasing over 200%. The strong demand for Blackwell architecture GPUs drives continuous breakthroughs in data center business. As AI expands from training to inference and edge computing, demand for NVIDIA’s high-performance solutions will grow exponentially. Institutions generally raise target prices and assign buy ratings.

Broadcom (AVGO): Key Components for AI Infrastructure

Broadcom has advantages in customized ASIC chips, network switches, and optical communications. In fiscal 2024, revenue was $31.9 billion, with AI-related products accounting for 25%. In Q2 2025, revenue grew 19% year-over-year, with demand for Jericho3-AI chips and Tomahawk5 switches continuing to rise. As AI models grow larger, the need for high-performance network connectivity will accelerate. Many foreign analysts set target prices above $2,000.

AMD (AMD): Challenger in the AI Chip Market

AMD is entering the NVIDIA-dominated market with its Instinct MI300 series accelerators. In 2024, revenue was $22.9 billion, with data center business up 27% annually. In Q2 2025, revenue increased 18% year-over-year, with MI300X accelerators adopted by major cloud providers and MI350 series upcoming. AMD’s CPU+GPU integration and open ecosystem strategies are gradually expanding market share. Most foreign analysts see strong potential, with target prices mostly above $200.

Microsoft (MSFT): Enabler of Enterprise AI Transformation

Microsoft, through exclusive collaboration with OpenAI and the integration of Azure AI platform and Copilot enterprise assistants, has become a core platform for enterprise AI transformation. In FY2024, revenue reached $211.2 billion, with Azure and cloud services growing 28%. In Q1 2025, intelligent cloud revenue first exceeded $30 billion. As Copilot is deeply integrated into Windows, Office, and other products used by 1 billion users, monetization potential continues to unfold. Many institutions see Microsoft as the most certain beneficiary of enterprise AI proliferation, with target prices around $550–$600.

Phased Characteristics of AI Stock Investment

Short-term: Golden Period for Infrastructure Investment

In the early stage of AI development, the biggest beneficiaries are upstream hardware and chip suppliers. Stock prices during this phase often perform strongly, but high growth is unlikely to be sustained permanently. Referring to the Internet era’s Cisco Systems, which peaked at $82 in 2000 before the dot-com bubble burst, then declined over 90%. After 20 years, the stock still hasn’t returned to its high. This reminds investors to view such stocks as phase-specific investment opportunities rather than long-term value stocks.

Mid-term: Opportunities and Challenges in Application Deployment

Downstream application companies fall into two categories: those directly providing AI technology, and those using AI to improve operational efficiency. The market generally believes these companies have relatively sustainable development prospects, but historical data is not optimistic. Tech giants like Microsoft, Google, and the delisted Yahoo, despite being industry favorites, experienced significant declines after market peaks and have not returned to previous highs for years. Yahoo’s case is especially instructive— even top-tier leaders may eventually be replaced by emerging competitors.

Long-term: Strategic Deployment and Dynamic Adjustment

If investors can timely “swap horses,” they can theoretically participate long-term in AI benefits. However, this is not easy for ordinary investors. Close attention should be paid to the speed of AI technological development, monetization ability, and individual stock profit growth to determine whether adjustments are needed.

Diversified Investment Strategies

Investing in AI stocks does not have to be limited to direct stock purchases. Investors can choose different tools based on risk appetite:

Individual Stocks: High risk but flexible, suitable for well-researched investors

Stock Funds: Managed by professional managers selecting AI-related companies, diversifying individual stock risk but with higher management fees

AI Theme ETFs: Low transaction costs and management fees, passively tracking indices, suitable for long-term retail investors

For ordinary investors, combining dollar-cost averaging to enter gradually is a prudent strategy. This can effectively mitigate short-term market volatility impacts.

Risks in AI Investment

Industry Uncertainty

Although AI technology has existed for decades, mainstream application has only emerged recently. Rapid technological iteration makes it difficult even for experienced investors to keep pace. This often leads to hype around certain companies, causing volatile stock prices.

Unproven Company Risks

Many AI-participating companies lack sufficient historical performance data, making their operational outlook uncertain. Compared to established firms with proven track records, these companies carry higher risk premiums.

Policy and Regulatory Variables

While governments generally support AI strategic positioning with increased investment and subsidies, they also face issues like data privacy, algorithm bias, copyright, and ethics. If regulations tighten, valuations and business models of some AI companies could be significantly impacted.

Capital Flow Uncertainty

Although AI remains a market focus, macroeconomic changes (such as interest rate adjustments and emerging themes) can lead to capital shifts, causing short-term volatility.

Investment Outlook from 2025 to 2030

Long-term, AI stocks still hold growth potential, but short-term fluctuations are likely. As large language models and multimodal AI advance rapidly, demand for computing power, data centers, and dedicated chips will continue to rise. In the short term, chip and hardware suppliers benefit most. In the medium to long term, AI applications in healthcare, finance, manufacturing, autonomous vehicles, and other fields will gradually materialize into actual enterprise revenue.

From a macro perspective, if central banks adopt looser monetary policies, high-valuation tech stocks will benefit; otherwise, valuations may be compressed. Overall, the trend is “long-term bullish with short-term volatility.”

Investment advice suggests prioritizing chip and accelerator server infrastructure providers, or selecting companies with tangible application deployment such as cloud services and medical AI. Using AI-themed ETFs for diversification can also effectively reduce individual stock risks. For ordinary investors, long-term allocation and phased entry, avoiding chasing highs in the short term, is a more prudent approach.