聚焦: หุ้นชั้นนำด้านปัญญาประดิษฐ์ 5 ตัวใน Nasdaq

Investment Summary

My conclusion is straightforward: these five stocks do not represent a single "AI trade." Instead, they are five different nodes along the AI infrastructure chain. If the market continues to decline due to concerns over inflation, interest rates, or a potential bubble, I will place them on a tiered watchlist, rather than interpreting "buying the dip" as a one-time, full-position, FOMO-driven chase. This report discusses MU (Micron), MXL (MaxLinear), AMD (AMD), LITE (Lumentum), and VICR (Vicor). They all benefit from AI data center capital expenditure, but their risk sources, earnings elasticity, and valuation digestion methods differ. [1] [2] [3]

I believe that as the AI narrative enters this phase, the crucial questions are no longer simply "Does AI still have a story?" but rather three specific issues: First, can capital expenditure continue to translate into real orders? Second, can corporate earnings justify valuations? Third, can an investment portfolio withstand high volatility? McKinsey estimates that to meet computing power demands, global data centers may require approximately $6.7 trillion in capital expenditure by 2030, with around $5.2 trillion related to AI workloads. This suggests a very long investment cycle for AI infrastructure. However, Fidelity also cautions that earnings growth, valuation, capital expenditure sustainability, and the interest rate cycle will determine whether the AI trade evolves from a long-term theme into a short-term bubble. [1] [2]

Bottom line: AI infrastructure remains a sector I am willing to research during downturns, but entry points must adhere to position sizing discipline. In a phase characterized by high returns, high drawdowns, and high volatility, the strategy is to first tier the stocks, then make a move.

1. The Big Picture: The AI Infrastructure Story Goes Beyond Just GPU Stocks

The market's most common mistake is to equate the AI trade simplistically with "buying the GPU leader." In my view, the true structure of AI infrastructure is a chain of capital expenditure: the front end requires computing chips, the mid-tier requires high-bandwidth memory, networking connectivity, and optical communication, and the back end requires power, cooling, data centers, and software orchestration. Focusing on a single link makes it easy to chase the wrong rhythm when valuations are high; breaking down the chain reveals whether a pullback is merely about valuation compression, order cancellations, or a normal shakeout for high-beta assets.

McKinsey's estimate on data center capital expenditure provides an important backdrop for this framework. It doesn't imply that all companies will benefit simultaneously or that all AI-related stocks should rise. Instead, it indicates that if computing demand continues to grow, investment opportunities will diffuse along the "computing-memory-connectivity-optics-power" chain. [1] Morningstar's discussion on the AI stock framework also reminds me that AI stock selection requires looking beyond narrative hype to consider industry position, moat, valuation, and uncertainty simultaneously. [3]

My assessment is that the opportunity in AI infrastructure is not a "straight line" but a "network." When the market pulls back, the most valuable research isn't which ticker dropped the most, but which node's fundamentals remain un-falsified while its valuation was unjustly dragged down by risk aversion.

Public price data over the past year shows that these five AI infrastructure stocks have significantly outperformed both the Nasdaq 100 and the SMH Semiconductor ETF. The gains for LITE, MU, MXL, VICR, and AMD have all been substantial, with LITE and MU being the most prominent. However, the same data group also reveals that the maximum drawdown over the past year for these five stocks mostly ranged from approximately -28% to -32%, significantly higher than the Nasdaq 100's maximum drawdown of about -12.1%. [9]

The insight from this data is clear: a strong trend does not equal low risk, and high elasticity does not mean it's always a good time to buy. If a stock can rally multiple times over a year but also pull back by a third in the process, then the buy thesis cannot just say "I'm bullish on AI long-term"; it must also explicitly outline "how to withstand the volatility." In other words, buying the dip is not an emotional slogan; it's a capital management discipline.

I will use this table as the starting point for position management. For stocks with stronger fundamental validation like MU and AMD, I am willing to observe them in batches during pullbacks. For high-elasticity nodes like MXL, LITE, and VICR, I will first establish a hard upper limit on position size before considering price levels. The reason is simple: volatility itself is a cost. Ignoring this cost when "buying the dip" can easily lead to being a passive bag holder.

2. Distinguishing the Five Stocks: It's Not About Who Rose the Most, But Whose Evidence Chain is More Complete

I do not advocate for a crude comparison of these five companies in a single basket. MU's core is the memory cycle and AI HBM demand. AMD's core is the data center computing platform. LITE's core is cloud and AI optical communication. VICR's core is high-power server power delivery. MXL is more focused on AI data center control planes and high-speed connectivity. They all benefit from AI, but their financial elasticity, customer structures, and valuation digestion paths differ.

Based on company public filings, Micron's FY2025 Q4 press release reported quarterly revenue of $11.315 billion and FY2025 annual revenue of $37.378 billion, attributing the strong performance to AI data center demand. AMD's Q3 2025 press release reported quarterly revenue of $9.246 billion, up 36% year-over-year, with data center revenue at $4.3 billion, up 22%. Lumentum's FY2026 Q3 press release reported revenue of $808.4 million, a 90.1% increase year-over-year, emphasizing photonics technology for AI, cloud, and next-gen communications. MaxLinear's press releases highlight its Coronado and Laguna USB UART solutions for AI data center control plane connectivity. Vicor's public materials emphasize the demand for 48V modular power systems driven by growth in AI, HPC, and data center computing power. [4] [5] [6] [7] [8]

My ranking is not a simple "gain ranking." Looking solely at past year returns, LITE and MU are the most eye-catching. Looking at the fundamental evidence chain, MU and AMD are easier for institutional capital to track consistently. Looking at high-elasticity satellite positions, MXL, LITE, and VICR offer steeper return curves but also demand stricter stop-losses and position size limits.

3. Risk-Return Positioning: The Top-Right Corner is Not Paradise, but a Discipline Test

Many investors enjoy looking at high-return charts but dislike looking at drawdown charts. My view is the opposite: for high-beta AI stocks, the rate of return is merely the outcome, while the maximum drawdown is the primary term that must be accepted before entry. Figure 3 plots past-year returns and maximum drawdowns together. It shows all five stocks are in the high-return zone, but the drawdown on the vertical axis is also deep. This indicates [9]

they are not low-volatility growth stocks, but high-elasticity assets that require position discipline to digest. [9]

I will use three tiers to handle this type of stock. The first tier is "Core Trackable," stocks with a more complete fundamental evidence chain and sufficient institutional coverage, such as MU and AMD. The second tier is "High-Elasticity Satellite," stocks with clear industry logic but very high volatility, such as LITE and VICR. The third tier is "Observational Elasticity," stocks where the product direction has imagination but financial delivery requires more quarters of validation, such as MXL.

Therefore, my definition of "buying the dip" is not simply "buy when it falls." It is to systematically absorb volatility in batches, according to pre-defined position rules, when prices pull back, fundamentals haven't deteriorated, and the capital expenditure chain is still delivering. This is especially crucial for high-volatility tickers like MXL, LITE, and VICR, where position size is far more important than entry price.

4. Industry Chain Scoring: Five Stocks Are Not One Trade, But Five Nodes

To avoid lumping all AI stocks into one concept, I scored the five stocks across five dimensions: Directness to Compute, AI CapEx Sensitivity, Cyclical Volatility, Valuation Realization Risk, and Portfolio Diversification Value. This scoring is not a return prediction or investment rating, but a tool to help me determine what role each stock plays if I were to build an AI infrastructure watch basket.

The insight from this chart is that MU and AMD feel more like core evidence assets along the main AI infrastructure theme. LITE and VICR seem like high-elasticity nodes prone to capital amplification within the chain. MXL appears more like an observational ticker potentially subject to valuation re-rating after product introduction. All five stocks have research value, but the buy thesis cannot be identical.

My allocation thought process is: if an investor only wants core AI exposure, prioritize stocks with a more complete evidence chain like MU and AMD. If willing to assume higher volatility, consider LITE and VICR as satellite observations. To allocate MXL, one must acknowledge its small-cap nature and revenue recognition uncertainty, requiring stricter position sizing compared to the others.

5. Operational Framework: The Real Entry Point Arises When "Pullback, Confirmation, Batches" Occur Simultaneously

I will not treat every pullback as a buy signal simply because the AI theme is strong. A pullback truly worth acting on must meet at least three conditions simultaneously: First, the price has already released short-term sentiment. Second, the company's fundamentals have not deteriorated simultaneously. Third, the portfolio still has cash and risk budget remaining. Missing any one of these turns "buying the dip" into emotional trading.

Fidelity's framework on AI bubble risk is worth referencing here. It reminds us that while AI might still be a multi-year cycle, investors must track earnings growth, earnings quality, valuation, capital expenditure sustainability, and the interest rate cycle. [2] I completely agree with this assessment. AI isn't uninvestable; the problem is buying when valuations are richest, sentiment is hottest, and positions are fullest, using "long-termism" to mask short-term risks.

In summary, I will place these five stocks in my AI infrastructure watch pool, but I will not treat them all as an equally weighted buy list. For me, the correct sequence is to first define the role, then define the position, and finally define the price.

6. Conclusion: You Can Buy the Dip, But First Ask Yourself If You Can Handle the Volatility

The final conclusion returns to the headline: Buying the dip in these five Nasdaq AI leaders is worth researching, but it cannot be done lazily. If AI data center capital expenditure continues to expand, MU, AMD, LITE, VICR, and MXL in their respective segments of memory, computing, optical communication, power, and connectivity will have a foundation for continued benefit. However, if interest rates rise again, cloud CapEx slows, AI orders fail to meet expectations, or valuations have already priced in multiple quarters of future growth, these high-beta assets will also correct rapidly.

My strategy is clear: prioritize core positions for assets with stronger fundamental evidence, allocate satellite positions to high-elasticity but high-volatility nodes, and reserve observational positions for small/mid-cap opportunities still requiring validation. Buying must be in batches, positions must be limited, and risks must be written down in advance. Mature AI investing is not about getting excited at every pullback, but knowing which pullback to buy, how much to buy, and what to do if wrong.

Bottom line: The long-term logic for AI infrastructure remains intact, but buying the dip is not a charge signal; it's a discipline checklist. First, break down the five stocks into five nodes, then use position sizing and time to digest the volatility.

Risk Disclaimer

This report is for research and discussion purposes only and does not constitute a promise of returns or individual stock buy/sell advice. AI infrastructure-related companies are generally subject to high volatility, high valuation sensitivity, and strong cyclicality. Investors must make independent judgments based on their own risk tolerance. The key risks to monitor include five categories: First, if cloud vendor capital expenditure falls short of expectations, orders in the AI hardware chain could be re-priced. Second, if interest rates rise again, high-valuation growth stocks will face discount rate pressure. Third, specific segments like memory, optical communication, power, and connectivity have inventory cycle and customer concentration risks. Fourth, small/mid-cap, high-elasticity stocks may experience amplified liquidity and valuation fluctuations. Fifth, if AI theme earnings delivery disappoints, the market might shift from "pricing long-term potential" to "pricing current cash flows."

This report was prepared by a special analyst. The views expressed in this report are solely those of the author and do not represent the views of the BIT platform. This material is for reference only and does not constitute investment advice.