The Full Atlas of 7 AI Infrastructure Long-Term Holdings: Conservative / Balanced / Aggressive Portfolio Allocations (2026-2030)

The decade-long wave has already begun. Seven candidates, three risk profiles — find the AI infrastructure allocation that's right for you

2026.05.01 | Shiba the Disciplined | ProfitVision LAB | Data in this research as of the 2026/5/1 close

Why Look at AI Infrastructure Now?

The premise for any long-term holding is: this industry's growth needs to last at least 5 more years. If AI infrastructure is just a two-year short-term frenzy, then any allocation is simply a bet on being the last one holding the bag. Before introducing individual stocks, let's confirm the length of the cycle with three key numbers.

Three Key Numbers to Gauge the Cycle Length

Number One: $660–690 billion. This is the combined 2026 capex of the five hyperscalers — Amazon, Microsoft, Alphabet, Meta, and Oracle — nearly double the $443 billion spent in 2025. Goldman Sachs estimates the 2025–2027 cumulative total will reach $1.15 trillion, more than double what was spent from 2022–2024. Cathie Wood's ARK Invest "Big Ideas 2026" report goes further, forecasting total AI infrastructure investment will reach $1.4 trillion by 2030.

Number Two: a 54–56% CAGR. This is the "2024–2028 AI accelerator chip revenue CAGR" TSMC gave at its 2026 Q1 earnings call. What's notable is that this number was revised upward — Chairman C.C. Wei explicitly stated during the call: "We're now moving from generative AI to agentic AI, and that's driving an explosion in token volume and compute demand." When the company with the clearest read on demand keeps revising its outlook higher, it means the cycle is nowhere near its peak.

Number Three: a 7–8 year investment cycle. In a CNBC interview in February 2026, NVIDIA CEO Jensen Huang stated bluntly that the current AI buildout is "the largest infrastructure project in human history," with roughly 7–8 more years of investment cycle ahead. Why so confident? Because he sees that deploying AI infrastructure is already generating cash flow for operators — this isn't a bubble-driven arms race, it's expansion backed by real economic returns.

The Bottleneck Has Shifted: From "Compute" to "Systems"

In 2023, the bottleneck in AI investment was "not enough GPUs," so NVIDIA alone captured most of the windfall. But the 2026–2030 bottleneck has changed:

This is exactly why this research deliberately covers the five bottlenecks of "manufacturing, compute, bandwidth, power, and cooling" — not random diversification, but each one mapping to a real position in the AI infrastructure supply chain, each one an unavoidable link in the decade-long wave.

Chapter 2: The Seven-Layer AI Factory Stack | A Full Map of the 7 Candidates

Think of an AI data center as a factory — it needs "manufacturing equipment, a running production line, raw-material delivery, power supply, and a cooling system" to operate. These seven candidates map onto the seven critical layers of this AI factory:

There's a common standard behind choosing these seven: "an unavoidable step in the industry" — the AI factory cannot skip any of these links. Without TSM's manufacturing, AI chips can't be produced; without NVDA's platform, AI training can't happen; without ALAB's connectivity IP, in-server bandwidth can't scale; without FN's contract manufacturing, optical modules can't be mass-produced; without Delta Electronics / VRT's power systems, the data center can't run; without Auras's cooling, a 600kW rack would simply overheat and burn out.

But seven names doesn't mean you need to buy all seven — this is a "candidate pool," not a "shopping list." The next chapter briefly introduces each name's core characteristics, and Chapter 4 gives concrete allocation recommendations based on three risk-profile types.

Chapter 3: In-Depth Quick Look at the Seven Stocks

Each name is summarized from four angles — core positioning, latest data, the long-term case, and key risks — to quickly build a foundation for judgment.

① TSM (TSMC ADR) (NYSE: TSM / TPE: 2330)

② NVDA (NVIDIA) (NASDAQ: NVDA)

③ ALAB (Astera Labs) (NASDAQ: ALAB)

④ FN (Fabrinet) (NYSE: FN)

⑤ 2308 (Delta Electronics) (TPE: 2308)

⑥ VRT (Vertiv) (NYSE: VRT)

⑦ Auras 3017 (TPE: 3017)

Chapter 4: Three Portfolio Allocations | Choosing the Risk Profile That Fits You

Each of the seven stocks has its own character, but readers have different risk appetites — retirees need stability, younger investors can tolerate volatility, and some fall somewhere in between. Below are three portfolio allocations; choose based on your own risk tolerance, investment time horizon, and overall asset allocation.

① Conservative | "Core Holdings + Stable Cash Flow"

② Balanced | "Growth and Stability Together"

③ Aggressive | "High Growth + High Volatility"

Comprehensive Comparison of the Three Portfolios

Selection principles:

• Choose Conservative if AI exposure would exceed 30% of your total assets, you can't tolerate a 30%+ short-term drawdown, or this is your first time investing in the AI theme
• Choose Balanced if you already have a basic understanding of AI, can tolerate a roughly -30% bear-market correction, and want "growth and stability together"
• Choose Aggressive if you're young, have a 5+ year investment horizon, can accept a -50% bear-market correction, and treat AI as a long-term theme worth a heavy bet

Chapter 5: Entry Pacing and a Discipline Framework

Choosing an allocation type is only step one — more important is "how to build the position, how to track it, and when to adjust it." The biggest enemy of long-term holding isn't the market — it's your own emotions.

Staggered Entry: Avoid Going All-In at Once

Regardless of which portfolio type you choose, we don't recommend deploying your full target capital in one shot. Short-term market swings can be extreme, and a single entry could land you at a local high. We suggest splitting each stock's target weight into 3 staggered tranches:

Key Events to Track in May

Two major earnings events land in May 2026, and readers can adjust their pacing around them:

Tracking Cadence: Establishing an Annual Review Mechanism

Exit Conditions: When Should You Cut Losses?

A long-term holding doesn't mean "never sell" — it means "don't sell unless there's a structural break." Set clear "exit conditions" to avoid panic-selling on short-term volatility:

A simple "short-term price drop of 30%" is not an exit condition — it's a moment to check whether the fundamentals have broken. If the fundamentals are intact, the decline is a buying opportunity; if the fundamentals have broken, cutting losses is disciplined execution.

Chapter 6: Options Overlay Strategies (For Advanced Readers)

For readers familiar with options-selling strategies, this portfolio can be further layered to enhance overall returns. This section is advanced content — readers unfamiliar with options can skip directly to Chapter 7.

Layering Principles

General Principles for Seller Strategies

The core of an options-seller strategy is "exchanging premium for taking on downside risk." When an investor is bullish long-term but wants a "cheaper entry," a seller strategy is an effective tool. General principles:

• Delta 0.20–0.25: set the strike where the stock would need to fall meaningfully to be assigned
• DTE 30–45 days: the sweet spot for time-decay (theta) efficiency
• IV Rank ≥ 30: enough volatility premium for the seller to earn a reasonable return
• Set the strike below a key support level: technicals provide a margin of safety
• Exit conditions: close early at 50% of max profit, or cut losses at 1x the premium collected
• Avoid earnings week: event-driven volatility is too high, unsuited to selling

Options strategies require a concrete read on each name's volatility, technicals, and IV Rank. This section only offers general principles — for specific entry structures, readers should consult ProfitVision LAB's Options Trading System SOP series.

Chapter 7: Scenario Analysis | Where Will the Portfolio Be in Five Years?

The valuation of a long-term holding shouldn't be judged against next year, but against 3–5 years out. We use 2030 as the endpoint and run three scenarios. None of these scenarios predicts a single target price — they're relative projections only.

Core Assumptions Behind the Three Scenarios

How the Three Portfolios Perform Under the Three Scenarios

Key Observations

Observation One: A Clear Risk-Return Trade-off. Moving from Conservative to Aggressive, expected annualized return rises from 13% to 21% (+8 points), while bear-market downside widens from -15% to -45% (+30 points). Every extra 1% of expected return costs 3.75 points of downside risk — that's the real face of a "risk premium".

Observation Two: All Three Beat the Broad Market. The S&P 500's long-term annualized return is roughly 10%, and Taiwan's TAIEX roughly 8–9%. Even the most conservative allocation (13% annualized) clearly beats passive index investing. This reflects the long-term alpha of AI infrastructure as a "structural theme."

Observation Three: The Bear Case Is the Real Test. A 30% bull probability + 50% base probability = an 80% chance the portfolio makes money, but the 20% bear-case probability is what determines "whether you can survive long enough for compounding to work." Choosing the right risk profile matters more than picking the right stock — pick the wrong profile, and you might sell during the bear case and get knocked out early.

Chapter 8: Conclusion and Execution Checklist

The Core View (in One Sentence)

The decade-long AI infrastructure wave has already begun, spanning seven layers — manufacturing, compute, connectivity, optics, power, power-and-cooling systems, and thermal management — each with unavoidable suppliers. A long-term holding isn't about "betting everything on the single strongest name" — it's about "choosing the portfolio type that fits your risk profile." Picking the wrong type hurts returns more than picking the wrong stock.

Bull Case

1. Structural demand should last at least through 2030: hyperscaler 2026 capex of $660–690B, Goldman's 2025–2027 cumulative estimate of $1.15 trillion, ARK's 2030 estimate of $1.4 trillion.
2. All seven names are "unavoidable steps in the industry": each layer is a link the AI factory can't skip, which is more robust than betting on a single name.
3. VRT's Q1 earnings beat across the board, guidance raised twice: fundamentals were clearly confirmed after 4/22, and AI infrastructure order visibility is high.
4. The three portfolios' expected annualized returns of 13–21% clearly beat the long-term average of the S&P 500 and Taiwan's TAIEX.

Bear Case

1. Valuations across the board are elevated: the seven names' weighted forward P/E of 22–32x still carries a premium to the S&P 500, requiring sustained above-expectation growth to digest.
2. Industry concentration is extremely high: all seven names are AI infrastructure — if the AI theme corrects systemically, the portfolio has no hedge.
3. Geopolitical risk is concentrated in Taiwan: TSM, Delta Electronics, Auras, and FN are all exposed to cross-strait geopolitics.
4. Customer concentration among hyperscalers: if hyperscaler in-house development accelerates, multiple names would be hit simultaneously.

Execution Checklist (Concrete Action Steps)

Recommended Paths for Different Readers

Tracking Record

Next Expected Update: After the major May 2026 earnings season concludes (after NVDA's 5/21 earnings)
Conditions That Would Trigger an Earlier Update:

• Any single name drops more than 15% in a single week
• A major revision (±10% or more, in either direction) to the full-year 2026 AI capex outlook
• A major geopolitical event (Taiwan Strait, US-China relations, export controls)
• A structural shift in the competitive landscape between NVIDIA's NVLink Fusion and ALAB's UALink
• A major fundamental change at any of the seven names (e.g. losing a major customer)