AI Infrastructure Scarcity Index
| Company | Date | Stock Price | Scarcity Layer | Scarcity Mult. | sPEG |
|---|---|---|---|---|---|
| SK Hynix | Feb 13, 2026 | KRW 610,500 | Memory | 2.5x | 0.01 |
| Micron | Feb 13, 2026 | $411.66 | Memory | 2.0x | 0.05 |
| NVIDIA | Feb 13, 2026 | $182.78 | Compute | 2.5x | 0.19 |
| Taiwan Semiconductor | Feb 13, 2026 | $366.36 | Fabrication | 3.5x | 0.23 |
| ASML | Feb 13, 2026 | $1,406.61 | Lithography | 4.0x | 0.41 |
| Vertiv | Feb 13, 2026 | $234.53 | Power & Cooling | 2.0x | 0.57 |
| Synopsys | Feb 13, 2026 | $437.09 | Chip Design | 3.0x | 0.75 |
| Analog Devices | Feb 13, 2026 | $337.10 | Analog Control | 1.5x | 0.83 |
| Arista Networks | Feb 13, 2026 | $141.59 | Network Infrastructure | 2.0x | 0.93 |
| Cadence | Feb 13, 2026 | $299.46 | Chip Design | 3.0x | 1.02 |
Index Average sPEG: 0.50. Names sorted ascending by sPEG. Scarcity multipliers derived from five structural dimensions: substitution difficulty, replication difficulty, capacity scarcity, switching cost, and pricing power durability.
- Memory layer (SK Hynix, Micron) shows the strongest scarcity-adjusted valuation efficiency, reflecting HBM supply constraints intensifying through 2026.
- Fabrication (TSMC) and lithography (ASML) remain structurally constrained bottlenecks with sPEG values well below the index average.
- Compute layer (NVIDIA) is scarce but valuation already reflects recent capital inflows and consensus growth expectations.
- Power & cooling (Vertiv) reflects scarce capacity in a constrained infrastructure layer where demand exceeds available supply.
- Chip design (Synopsys, Cadence) and analog control (Analog Devices) show fully reflected scarcity premiums at the upper end of the index.
- Network infrastructure (Arista) sits at the boundary — scarce but priced for flawless execution.
- Index Average sPEG: 0.50
Related Reading
- AI Infrastructure Scarcity Index — May 11, 2026 Update 88-Day Rerating Check · Latest observation point
- Scarcity Is the New Growth — The sPEG Doctrine Leadership Doctrine · Founding thesis
- sPEG — Scarcity-Adjusted PEG Lexicon · Formal definition
- AI Memory Scarcity Index sPEG Index Series · HBM and DRAM bottleneck focus
- AI Energy & Power Constraint Index sPEG Index Series · Power and cooling bottleneck focus
The sPEG (Scarcity-adjusted Price/Earnings to Growth) ratio measures valuation efficiency relative to both growth and structural scarcity.
Formula: sPEG = Forward P/E ÷ (Growth Rate × Scarcity Multiplier)
Lower sPEG values indicate stronger structural positioning, where growth is supported by durable scarcity and bottleneck control.
The scarcity multiplier is not a reward for being a good company. It is a measure of how much the AI economy depends on a company's capacity, IP, ecosystem, or physical bottleneck — and how difficult that dependency is to replace.
Scarcity multipliers are derived from five structural dimensions:
(1) Substitution difficulty — can customers switch to another supplier without performance loss
(2) Replication difficulty — how hard is it to build a competing product, process, or ecosystem
(3) Capacity scarcity — is supply physically constrained by fabs, packaging, energy, tooling, or long-cycle capex
(4) Switching cost and integration depth — is the product embedded into mission-critical workflows or architecture
(5) Pricing power durability — can the company preserve margins even as competitors attack
Scarcity Multiplier Bands:
Absolute Monopoly (4.0x) — Functionally irreplaceable supplier controlling a mission-critical layer of the AI stack. Example: ASML (EUV and High-NA lithography).
Near-Monopoly Bottleneck (3.5x) — Dominant provider of a physically constrained capability with limited viable substitutes. Example: TSMC (leading-edge foundry).
Duopoly or Extreme Switching Costs (3.0x) — Two-player or narrow-market structure with deep workflow lock-in and high customer dependency. Examples: Synopsys, Cadence (EDA).
Dominant Structural Leader (2.5x) — Clear category leader with ecosystem, performance, or supply-chain advantages. Examples: NVIDIA (compute), SK Hynix (HBM memory).
Scarce Capacity Leader (2.0x) — Strong supplier in a constrained infrastructure layer where demand exceeds available capacity. Examples: Micron, Arista, Vertiv.
Differentiated Incumbent (1.5x) — High-quality incumbent with defensible expertise but less direct bottleneck control. Example: Analog Devices.
Forward P/E uses NTM consensus. Growth rate uses NTM consensus EPS growth, expressed as an integer.
The AI Infrastructure Scarcity Index includes companies controlling critical bottleneck layers required for the design, manufacture, deployment, and operation of AI systems. The Index is updated periodically as structural conditions, growth rates, and valuation levels evolve.