Investment Opportunities in the 2025 AI Wave: Selected AI Concept Stocks and Practical Deployment Strategies

What Is the Investment Value of AI Concept Stocks Today?

When it comes to AI investing, many people’s first reaction is to buy AI concept stocks directly. But the question is: are these stocks really worth holding long-term? Or are they only suitable for phased participation?

The simple and straightforward answer is: it depends on your investment cycle. AI technology itself will undoubtedly profoundly transform society, just like the internet did back in the day. However, in terms of stock price performance, AI concept stocks at different stages show very different trajectories.

Take Cisco Systems, an internet infrastructure company, as an example. Its stock peaked at $82 during the dot-com bubble in 2000. Over the next 20 years, even with steady operations, the stock never returned to that high. Why? Because infrastructure-related companies often experience rapid growth only temporarily. Once infrastructure is built, growth inevitably slows down.

In contrast, application-layer companies (such as those providing AI solutions or using AI to improve business efficiency) theoretically have longer growth cycles. But this is not a hard rule—tech giants like Microsoft and Google also experienced significant pullbacks at market peaks, and it takes a long time for them to reach new highs again.

Key Insight: AI concept stocks are most suitable for “phased thematic investing” rather than “long-term buy-and-hold.” Close attention should be paid to three signals—speed of technological progress, commercial monetization ability, and whether individual companies’ profit growth is slowing. Once positive news in a certain segment is fully reflected in the stock price, it’s time to consider adjusting your holdings.

Understanding the Three Key Levels of the AI Industry Chain

To avoid pitfalls in AI investing, first understand the structure of the industry chain. Broadly speaking, AI concept stocks can be divided into three layers:

Layer 1: Chips and Hardware Infrastructure

This is the upstream segment. Training and running large AI models require massive computing power, directly boosting demand for GPUs, accelerators, high-end servers, cooling systems, and other hardware. According to IDC’s latest forecast, global enterprise spending on AI technology and solutions will reach $307 billion in 2025, surpassing $632 billion by 2028, with a CAGR of about 29%. Among these, spending on accelerated servers is expected to account for over 75% by 2028, serving as the foundational support.

Representatives in this layer include chip manufacturers and equipment suppliers. They are currently in the most lucrative period, but the pace of this dividend may be faster than expected.

Layer 2: Cloud Platforms and Basic Services

After hardware infrastructure is built, the next step is to develop cloud computing platforms and AI service frameworks on top. Major cloud service providers (CSPs) and cloud platform operators belong to this layer. Although their growth rate may be lower than upstream hardware, their profitability and customer stickiness are stronger, and their cycle is longer.

Layer 3: AI Application Deployment

At the bottom are companies integrating AI technology into actual business scenarios—whether medical diagnosis AI, financial risk control AI, autonomous driving, industrial manufacturing, or other vertical applications. These companies’ growth is the most sustainable, but it requires time to verify their actual commercial value.

Selected List of Taiwan Stock AI Concept Stocks

1. Quanta Computer (2382): From OEM to AI Server Manufacturer

Quanta started as the world’s largest notebook OEM manufacturer, but in recent years, it has transformed into a key supplier of AI servers. Its subsidiary, Quanta Cloud Technology (QCT), specializes in servers and cloud solutions, successfully entering procurement lists for hyperscale data centers, with major clients including NVIDIA and international cloud giants.

Data Highlights: In 2024, revenue is expected to reach NT$1.3 trillion, with a continuous increase in AI server contribution. The momentum is even stronger in 2025—Q2 revenue exceeded NT$300 billion, up over 20% year-over-year. Gross margin is also improving, indicating effective cost control.

Foreign analysts’ average target price is in the NT$350-370 range, still room for upside from current levels. However, once the positive news for hardware suppliers is fully priced in, growth slowdown will quickly impact valuation.

2. Silicon Motion (3661): The Hidden Champion in ASIC Chip Design

Silicon Motion focuses on custom ASIC chip design services, serving cloud giants and high-end computing companies. Essentially, they design dedicated AI chips for major clients.

Data Highlights: In 2024, full-year revenue is NT$68.2 billion, up over 50%. By Q2 2025, quarterly revenue surpasses NT$20 billion, doubling compared to the same period last year. Gross profit margin and net profit margin continue to rise, as key AI client projects enter mass production and new orders keep landing.

This company’s advantage lies in its clients—large, stable procurement volumes, with high order stickiness. External target prices range from NT$2,200 to NT$2,400.

3. Delta Electronics (2308): The “Hidden Champion” in Power Supplies and Cooling

Delta was originally a global leader in power management. Now, it extends its capabilities into the AI server supply chain—providing efficient power supplies, cooling systems, and cabinet solutions.

Data Highlights: In 2024, revenue is about NT$420 billion, with data center and AI business contributions steadily increasing. In Q2 2025, revenue hits NT$110 billion, up over 15% year-over-year. These suppliers are known for stability, though their growth rate is relatively moderate.

4. MediaTek (2454): From Mobile Chips to AI Chips

MediaTek is a top ten fabless chip designer globally, with core businesses including mobile and IoT chips. It is actively deploying AI chips—its Dimensity series mobile platforms incorporate enhanced AI computing units, and it collaborates with NVIDIA on automotive and edge AI solutions.

Data Highlights: In 2024, revenue reaches NT$490 billion. In Q2 2025, revenue exceeds NT$120 billion, up about 20% year-over-year. Growth is driven mainly by increased market share in high-end mobile chips and rising demand for AI-enabled devices.

5. Sunlord (3324): Pioneer in Liquid Cooling Solutions

As AI chips consume more power (already exceeding 1 kW), traditional air cooling becomes insufficient. Sunlord has successfully positioned itself in the global AI server supply chain with liquid cooling technology.

Data Highlights: In 2024, revenue is NT$24.5 billion, up over 30%. As cloud providers accelerate adoption of liquid cooling in 2025, the company’s growth accelerates further. Foreign target prices are mostly above NT$600.

This company represents “supplementary hardware suppliers”—fast-growing but relatively small in scale, with a more aggressive risk-reward profile.

The Main Players in US Stock AI Concept Stocks

1. NVIDIA: Undisputed King of AI Chips

NVIDIA’s GPUs and CUDA software have become industry standards for training large AI models. It not only sells chips but also offers a complete ecosystem from hardware to software, effectively locking in customers.

Data Highlights: In 2024, revenue is $60.9 billion, up over 120%. In Q2 2025, revenue hits a new high of about $28 billion, with net profit growth exceeding 200%. Such growth is unprecedented in the chip industry.

The main driver is continuous demand from cloud providers for Blackwell architecture GPUs (B200, GB200), with data center business breaking records. While difficult to replace in the short term, valuation risks should be watched—top-tier stocks often see the largest declines at market peaks.

2. Broadcom (AVGO): Network and Chip Solutions for AI Data Centers

Broadcom plays a key role in AI chips and network connectivity. It supplies customized ASIC chips, network switches, optical communication chips, and more—an indispensable supplier for data centers.

Data Highlights: In fiscal year 2024, revenue is $31.9 billion, with AI-related product revenue rapidly increasing to 25%. In Q2 2025, revenue grows 19% year-over-year, benefiting from cloud providers accelerating AI data center deployments.

3. AMD: Strong Challenger in GPU Market

AMD challenges NVIDIA in the AI accelerator market. Its Instinct MI300 series accelerators are adopted by major cloud providers, offering a second choice in the GPU market.

Data Highlights: In 2024, revenue is $22.9 billion, with data center business up 27% annually. In Q2 2025, revenue increases 18% year-over-year, with MI300X accelerators already deployed among major cloud vendors.

4. Microsoft: Enterprise AI Transformation Platform

Microsoft, through its exclusive partnership with OpenAI, Azure AI platform, and Copilot enterprise assistant, is becoming the standard solution for enterprise AI transformation. Its advantages include a huge user base (over 1 billion) and a deep ecosystem.

Data Highlights: In fiscal year 2024, revenue reaches $211.2 billion, with Azure and cloud services growing 28%, and AI contributing over half of that growth. In Q1 2025, intelligent cloud revenue first exceeds $30 billion.

This company’s monetization path is the clearest, but valuation is already relatively high, and slowdown risks should be monitored.

Practical Strategies: How to Participate Efficiently in the AI Wave

Direct Stock Purchase vs. Funds/ETFs

Buying individual stocks directly has lower transaction costs but higher company-specific risk. Stock funds can be managed by professional fund managers, but management fees are higher. ETFs offer the lowest costs and risk diversification but may trade at premiums or discounts.

Dollar-Cost Averaging Is Most Practical

Given the high volatility and theme rotation of AI concept stocks, it’s recommended to adopt a dollar-cost averaging approach. Avoid trying to time the market precisely; instead, buy regularly, average costs, and diversify risk. Even if short-term markets fluctuate, long-term participation in AI industry growth remains feasible.

Platform Selection

• For Taiwan stocks: opening an account with local brokers is most convenient.
• For US stocks: you can use Taiwanese brokers’ cross-trading services, or go through overseas brokers or CFD platforms.

For short-term trading, CFD platforms may be more suitable—they allow trading both ways, have no commission, and offer higher leverage. For long-term investing, it’s better to use reputable brokers.

Outlook for AI Investment Landscape 2025-2030

Short-term (1-2 years): Hardware suppliers are the most favored

Advances in large language models, generative AI, and multimodal AI will continue to boost demand for computing power, chips, and data centers. NVIDIA, AMD, TSMC, and other hardware suppliers remain the biggest beneficiaries.

Mid- to Long-term (3-5 years): Application layer is the real growth driver

AI applications in healthcare, finance, manufacturing, autonomous driving, retail, and other industries will gradually mature, generating tangible revenue. This will gradually follow hardware suppliers and become a new growth engine.

Macroeconomic Risks Cannot Be Ignored

• Interest rate policies: If the Federal Reserve or other central banks turn to easing, high-valuation tech stocks will benefit; if rates stay high, valuations will be compressed.
• News sensitivity: AI concept stocks react sharply to news; short-term volatility can be intense.
• Capital flow shifts: When new energy or other themes emerge, funds may flow out of AI stocks.

Risks to Watch

• Industry uncertainty: Rapid AI development makes it hard for ordinary investors to keep up, risking hype-driven traps.
• Unverified companies: Some AI firms are very new with weak fundamentals, carrying higher operational risks than established tech giants.
• Regulatory storms: Governments support AI but may tighten regulations on data privacy and ethics, impacting valuations of certain companies.

Summary: The Correct Approach to AI Investment

Practices:

• Maintain a long-term bullish outlook on AI industry development; don’t try to catch every node.
• Prioritize infrastructure suppliers and companies with tangible applications.
• Use AI-themed ETFs for diversification and risk mitigation.
• Employ dollar-cost averaging to enter gradually, avoiding chasing highs.

Avoid:

• Don’t assume buying AI concept stocks guarantees effortless gains; regularly review your holdings.
• Don’t follow short-term hype; beware of being caught in traps.
• Don’t allocate all funds to upstream hardware suppliers; shift gradually toward application-layer companies over the long term.

Overall, the main theme for AI investment from 2025 to 2030 is “long-term bullish, short-term volatility.” If your investment horizon is 3-5 years, current deployment is reasonable; if you prefer quick in-and-out trading, be prepared for 30-50% fluctuations.