ChainsAI

AI value chain.

From silicon to applications — how the AI stack connects.

Six layers. One demand curve. Each scored on the same engine — so you see where the money compounds, and where it just passes through.

Chain Index
0avg uqs
37 of 37 scored · 6 layers
#1
NVDA82
#2
TSM82
#3
ADBE76
Combined mcap
$28.5T
Top 1M
NVDA+0%
Bottom 1M
GE+0%
UQS distribution
Live · scores updated daily 05:30 UTC · prices fresh today

The AI value chain is six layers deep: chips that train models, memory and networking that feed them, hyperscalers that rent compute, foundation models and tooling, AI-native applications, and the power and cooling infrastructure that the whole stack runs on. Each layer scored on the same UQS engine — no rotation, no narrative, just the numbers.

01

Silicon

avg 60·8 names

Compute begins here. So does the capex.

Chips for AI training and inference. The foundational layer where every model run starts. Designers (NVDA, AMD, AVGO) sit on top of foundries (TSM) and lithography (ASML) — capital-intensive, cyclically demand-driven, and the layer with the most concentrated economics in the entire stack.

1Y composite (mcap-weighted)+80.7%vs SPX+27.2%
SiliconMemory & Networking
02

Memory & Networking

avg 58·7 names

Compute without bandwidth is silicon idling.

Memory bandwidth and rack-scale interconnect — the bottleneck after compute. A modern GPU sits idle without HBM memory bandwidth feeding it and high-speed networking connecting it into a cluster. Smaller market than silicon but increasingly strategic as model sizes outgrow single accelerators.

1Y composite (mcap-weighted)+368.1%vs SPX+27.2%
Memory & NetworkingCloud Infrastructure
03

Cloud Infrastructure

avg 55·6 names

Where chips become recurring revenue.

Hyperscalers that aggregate compute and rent it to everyone else. They buy chips by the tens of thousands, build datacenters in regions, and offer the picks-and-shovels for the rest of the market. The most profitable layer measured by gross margin, with deep moats from scale and customer lock-in.

1Y composite (mcap-weighted)+41.2%vs SPX+27.2%
Cloud InfrastructureFoundation Models & Tooling
04

Foundation Models & Tooling

avg 50·5 names

Most of the IP. Few of the public pure-plays.

The model layer plus the picks-and-shovels around running them in production. Most pure-play foundation model companies (OpenAI, Anthropic) are private — public exposure comes via their cloud parents (MSFT, GOOG, AMZN). Tooling for observability, vector search, and orchestration is where the public market plays directly.

1Y composite (mcap-weighted)+4.2%vs SPX+27.2%
Foundation Models & ToolingAI-native & Vertical Adopters
05

AI-native & Vertical Adopters

avg 62·6 names

Where chip dollars finally become customer dollars.

Companies whose product is AI, or who derive durable advantage from deploying AI in their existing business. The most diverse layer — from pure AI-software (PLTR's AIP) to AI-as-feature companies (CRWD applying ML to security, NOW embedding AI in IT workflows). Where margins from chips, cloud, and models eventually meet the end-customer.

1Y composite (mcap-weighted)-13.9%vs SPX+27.2%
AI-native & Vertical AdoptersPower & Cooling
06

Power & Cooling

avg 58·5 names

AI's electricity bill. The grid is the next chokepoint.

The layer most analysts skip but where the next bottleneck of 2026-2028 actually sits. Datacenter power demand is doubling, the grid is constrained, and cooling has moved from a side-business to mission-critical. Independent power producers (CEG), utilities (NEE), and electrical/cooling infrastructure (ETN, VRT) capture the spend that flows through every other layer.

1Y composite (mcap-weighted)+43.0%vs SPX+27.2%