BasegridBasegrid

Sector map

The AI Infrastructure Wealth Map

A beginner-friendly map of the sectors powering the AI economy. Each card explains what it is, why it matters, who to watch, key risks, and public signals to track.

Chips

What it is
Semiconductors that train and run AI models — GPUs, accelerators, and custom silicon.
Why it matters
Compute is the raw material of the AI economy. Whoever controls advanced chips controls capacity.
Companies to watch
Nvidia, AMD, TSMC, ASML, Broadcom
Key risks
Geopolitics, export controls, customer concentration, valuation cycles
Public signals
Data center revenue, advanced node demand, GPU lead times

Data Centers

What it is
Physical buildings that house servers, storage, and networking for cloud and AI workloads.
Why it matters
AI models live somewhere. Data center capacity is the bottleneck for compute deployment.
Companies to watch
Equinix, Digital Realty, hyperscaler self-build
Key risks
Power availability, land, debt, valuation
Public signals
Leasing spreads, occupancy, hyperscaler capex guidance

Cloud

What it is
On-demand compute, storage, and AI services delivered over the internet.
Why it matters
Cloud platforms are the distribution layer for AI to enterprises and developers.
Companies to watch
Microsoft, Amazon, Alphabet, Oracle
Key risks
Margin pressure, capex intensity, competition
Public signals
AI revenue mix, capex, backlog

Energy

What it is
Generation and transmission of electricity that powers data centers.
Why it matters
AI demand may shift the long-term electricity load curve.
Companies to watch
Vistra, Constellation, NextEra, regulated utilities
Key risks
Commodity prices, regulation, build timelines
Public signals
Long-term power purchase agreements, grid interconnect queues

Cooling

What it is
Thermal management systems that keep dense AI hardware operational.
Why it matters
Higher chip density means cooling is a structural growth category.
Companies to watch
Vertiv, Schneider Electric, liquid cooling specialists
Key risks
Cyclicality, valuation, customer concentration
Public signals
Backlog, data center segment growth, liquid cooling adoption

Cybersecurity

What it is
Security software and services protecting cloud, identity, and AI workloads.
Why it matters
More attack surface as AI and cloud scale.
Companies to watch
Palo Alto Networks, CrowdStrike, Zscaler
Key risks
Competition, platform execution, AI-driven threats
Public signals
Cloud security growth, platform consolidation deals

Networking

What it is
High-speed switches, optics, and interconnects between AI servers.
Why it matters
Training clusters need massive east-west bandwidth.
Companies to watch
Broadcom, Arista, Cisco, optical suppliers
Key risks
Customer concentration, technology shifts
Public signals
AI-related ethernet/optics revenue, hyperscaler design wins

Private Credit

What it is
Non-bank financing for data centers, energy, and infrastructure.
Why it matters
Public markets and banks alone may not fund the buildout.
Companies to watch
Large alternative asset managers, infrastructure funds
Key risks
Credit cycle, refinancing, asset valuations
Public signals
Infrastructure debt issuance, asset manager fundraises

Real Estate

What it is
Land, power-ready sites, and industrial real estate for data centers.
Why it matters
Power-connected sites are scarce and strategic.
Companies to watch
Data center REITs, industrial landowners
Key risks
Interest rates, leasing risk, power constraints
Public signals
Land near substations, REIT dev pipelines

AI Software Layer

What it is
Foundation models, tooling, and applications built on top of compute.
Why it matters
Software monetizes the underlying infrastructure.
Companies to watch
Hyperscalers' AI products, model labs, enterprise AI vendors
Key risks
Commoditization, regulation, unit economics
Public signals
Inference revenue, enterprise adoption, pricing trends