The Investment Framework Beyond AI Semiconductors: How to View Data Centers, Circuit Boards, and Physical AI

Key Perspective: AI Investment Is Shifting from Leading Semiconductor Stocks to the Infrastructure Ecosystem

The spread of AI technology began with core hardware such as NVIDIA GPUs, HBM, foundries, and memory semiconductors. In the South Korean stock market, Samsung Electronics and SK Hynix were the first to attract attention, which is a natural development. This is because training and running large-scale AI models require high-performance semiconductors and memory.

However, from an investment perspective, there is a more important question: Where might capital flow next, beyond the leading stocks that have already captured the market’s attention? To answer this question, we must view the AI industry as a single supply chain.

AI infrastructure is broadly linked through the following chain of events:

1. Increased demand for AI models
2. Increased demand for GPUs and accelerators
3. Increased demand for HBM, packaging, substrates, and power components
4. Increased demand for data centers, cloud services, networks, and cooling equipment
5. Increased demand for robots, mobility, and factory automation that apply AI to real-world industrial settings

This article is not a recommendation to buy or sell specific stocks, but rather an investment analysis framework designed to identify the sectors that will benefit from the AI era.

1. Data Centers: The Foundation That Must Be in Place Before GPUs Can Be Sold

Why Data Centers Are Important

The core objective of GPU companies like NVIDIA is to sell more AI computing equipment. However, AI services cannot operate with GPUs alone. They require data centers that combine large-scale power, cooling, server racks, networks, security, and operational staff.

Therefore, as AI demand grows, the following groups of companies are likely to attract attention:

• Companies capable of building or operating large-scale data centers
• Companies providing cloud infrastructure
• Companies that lease GPU servers or offer AI computing resources as a service
• Companies supplying power, cooling, network, and security infrastructure

CoreWeave in the U.S. is frequently cited as a prime example of a company that has positioned GPU-based cloud infrastructure at the forefront of its business. In South Korea as well, major platforms, telecommunications companies, and cloud service providers may be reevaluated as demand for AI infrastructure grows.

Key Questions When Evaluating Naver and Telecommunications Companies

If we view companies like Naver and SK Telecom solely as search, advertising, or telecommunications service providers, we may overlook the AI infrastructure perspective. What matters is not only whether they develop AI services directly but also the extent of their physical and cloud infrastructure capable of powering AI.

The verification questions are as follows.

The data center sector is attractive but capital-intensive. As capital expenditures rise and depreciation costs increase, profit growth may lag behind revenue growth. Therefore, rather than simply focusing on the phrase “operating AI data centers,” one must examine actual contracts, utilization rates, investment scale, and operating profit margins.

2. Semiconductor Materials, Parts, and Equipment: After HBM, Focus on Substrates and Packaging

How High-Performance AI Chips Drive Demand for Substrates

As the computational load of AI semiconductors increases, so do chip size, power consumption, heat generation, and the complexity of signal processing. In this context, semiconductor packaging and substrates play a critical role. Substrates are foundational components that electrically connect chips to the motherboard and ensure stable signal transmission.

For high-performance semiconductors in particular, the following conditions become crucial:

• Larger PCB area
• Ability to implement finer circuits
• Stability in high-speed signal transmission
• Heat dissipation and power handling capabilities
• Compatibility with complex packaging processes

For this reason, as the AI semiconductor cycle lengthens, investors should evaluate the potential for gains not only among memory, foundry, and equipment companies but also among PCB manufacturers.

The Rationale for Reevaluating Component Manufacturers Like Samsung Electro-Mechanics

Electronics component manufacturers such as Samsung Electro-Mechanics operate in both the MLCC and semiconductor package substrate markets. While in the past, the MLCC cycle—linked to demand for smartphones, automotive electronics, and IT devices—accounted for a large portion of stock price performance, in the AI era, the growth potential of high-performance semiconductor substrates may emerge as a separate evaluation factor.

However, investment decisions should not be made based solely on the statement that “substrates are important.” The following indicators must be examined.

Investments in AI materials, parts, and equipment may be more volatile than those in blue-chip stocks. However, when earnings actually improve, the market is likely to reassess valuations.

3. A New Perspective on Hyundai Motor: From an Automaker to a Physical AI Platform

What Is Physical AI?

Physical AI refers to the domain where AI from the digital space controls and learns from robots, automobiles, factories, logistics equipment, and other elements in the physical world. While generative AI deals with text and images, physical AI is connected to real-world movements, sensor data, manufacturing processes, and safety controls.

The automotive industry is naturally aligned with Physical AI. Cars are already akin to moving computing devices that integrate cameras, radar, LiDAR, control software, batteries, motors, and communication modules.

Key Points for Reevaluating Hyundai Motor

Viewing Hyundai Motor simply as a manufacturer of finished vehicles narrows the scope of evaluation. The Hyundai Motor Group is seeking to build a broader mobility ecosystem through electric vehicles, autonomous driving, robotics, smart factories, software-defined vehicles, and robotics technology linked to Boston Dynamics.

Investors should consider the following points:

• Are vehicle sales volumes and operating profit margins being maintained?
• Do the electric vehicle and hybrid strategies respond to regional shifts in demand?
• Do robotics and automation technologies translate into actual revenue or productivity improvements?
• Do factory automation and AI-powered factories result in cost savings?
• Can software and data-driven services generate recurring revenue?

For Hyundai Motor to command a higher valuation in the future, it needs evidence that it is “expanding AI-based manufacturing and mobility platforms,” in addition to the traditional logic of “selling cars well.” This evidence should be sought in financial results, investment plans, productivity metrics, and software revenue—rather than in press releases.

4. Pitfalls Retail Investors Often Fall Into When Searching for the “Next Leading Stock”

Strategies that aim to outpace institutional investors are risky

Retail investors find it difficult to compete in terms of information speed with institutional investors, analysts, quant funds, and high-frequency trading systems. Buying stocks that have already surged—and been widely covered in the news—after the fact can result in a poor risk-reward ratio.

What matters in the stock market is not being the first to predict a move, but buying opportunities backed by verifiable evidence at the right price.

How to Avoid Mistaking Luck for Skill

In a bull market, many stocks rise together. During this time, it’s easy to mistake short-term gains for your own analytical ability. However, true skill is revealed during bear markets and sideways markets.

If you cannot answer the following questions, your investment may rely heavily on luck.

• Why did you buy this stock?
• What changes in earnings support the stock price increase?
• What are the signs that your prediction was wrong?
• What are your target return and stop-loss levels?
• Are there better alternatives within the same sector?

5. Practical Ways to Use Analyst Reports

Analyst reports are not answer keys but rather tools for testing hypotheses. In particular, when reviewing reports that raise target prices, you should not simply look at the numbers; instead, you must distinguish whether the upward revision is based on changes in earnings estimates, changes in valuation multiples, or expectations for new business ventures.

5 Key Points to Look for in Reports

However, reports released after the stock price has already risen significantly in the short term should be read with caution. Even a good company can become a bad investment if purchased at a high price.

6. Checklist for Identifying AI Beneficiary Stocks

When analyzing AI-related companies, it is most efficient to follow this sequence.

Step 1: Look for Revenue Linkages, Not Just the Theme

The term “AI-related stocks” is too broad. You must verify which specific products or services are actually linked to increased AI investment.

Examples:

• Increased demand for GPUs → Increased demand for HBM
• Increased AI servers → Increased demand for high-performance circuit boards
• Increased demand for AI cloud services → Higher data center utilization rates
• Increased adoption of robots → Increased demand for sensors, actuators, and control software

Step 2: Identify Industry Bottlenecks

Areas where prices are rising and profit margins are improving are typically where bottlenecks occur. You must identify areas with supply shortages or high technological barriers to entry.

Step 3: Verify with Numbers

A compelling narrative must always be validated by the numbers.

• Revenue growth rate
• Order backlog
• Operating profit margin
• Scale of capital expenditures
• Changes in inventory
• Customer diversification
• Cash flow

Step 4: Assess the Valuation

Even a good company in a good industry may offer a lower expected rate of return if future expectations are already overly reflected in the price. You should compare metrics such as P/E, P/B, EV/EBITDA, and P/S with competitors within the same industry.

Step 5: Document Your Investment Principles

Writing down the following questions before buying can help reduce impulsive trading.

• Why am I buying this stock?
• What is my expected holding period?
• What are the conditions for buying more?
• What are the conditions for cutting losses or reducing my position?
• What will I check after the earnings release?

7. Risk Management Principles for Individual Investors

While the AI revolution may be a long-term trend, related stock prices can experience repeated cycles of overheating and sharp declines in the short term. Therefore, the following principles are necessary:

1. Do not concentrate excessively on a single stock.
2. Recognize that even within the same theme, each company’s sensitivity to earnings varies.
3. Prioritize earnings reports and regulatory disclosures over news.
4. Reduce your position or avoid chasing the market after a sharp rally.
5. Do not buy stocks from companies you do not understand.
6. If analyzing individual stocks is difficult, consider diversified products such as ETFs or mutual funds.
7. Manage potential investment losses separately from your living expenses.

Conclusion: The Next Opportunity Will Come from “Verifiable Change,” Not Just a “Good Story”

Investment opportunities in the AI era extend beyond blue-chip stocks like Samsung Electronics and SK Hynix. They can spread to data centers, cloud infrastructure, semiconductor substrates, advanced packaging, power and cooling equipment, physical AI, robotics, and mobility production systems.

However, not every AI-related company will benefit. Real investment opportunities arise when industry changes translate into revenue and profits, and when market prices have not yet overreacted to those changes.

The goal of individual investors is not to catch wind of rumors faster than others, but to survive the long haul based on verifiable evidence. Ultimately, the investors who consistently survive in the market are not those who have heard the most information, but those who establish principles and stick to them.