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Topic · Artificial Intelligence

AI Infrastructure

Chips, data centres, networking, power and the capital cycle behind compute.

Parallax Index
82 +2
AI Infrastructure

Why now?

  • Capital expenditure on AI data centres is at a historic scale
  • Inference demand is becoming a larger share of compute than training
  • Power, land and grid connections are now gating factors
  • Sovereign and enterprise buyers are entering the market beyond the hyperscalers
Why it matters
Infrastructure decides who can deploy AI at what cost, and where. It is the most capital-intensive layer of the stack.
What changes
Data centre location is increasingly decided by power availability. Utilities, chipmakers and cloud providers are becoming interdependent.
Who benefits
Chip designers and foundries, power equipment makers, cooling specialists, and regions with surplus clean power.
Who is at risk
Regions with constrained grids, and buyers who over-commit to capacity before demand is proven.
What happens next
Expect more co-located generation, long-term power agreements and a sharper split between training and inference sites.

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Companies

View all

NVIDIA

Semiconductors & AI computing · Santa Clara, California, US

The dominant supplier of accelerated computing for AI training and inference, and increasingly a platform company spanning chips, networking, software and robotics.

OpenAI

AI research & products · San Francisco, California, US

Frontier model developer behind ChatGPT, with a growing focus on agents, enterprise deployment and consumer scale.

Schneider Electric

Energy management & industrial automation · Rueil-Malmaison, France

Global provider of power distribution, cooling and automation systems, and a critical supplier to the AI data centre build-out.

Technology profile

Open

Where capital becomes compute.

AI data centres are purpose-built facilities that house dense clusters of accelerators, with power, cooling and networking designed for training and serving large models.

  • Grid or on-site generation delivers power through substations and switchgear.
  • Racks with far higher density than traditional servers require liquid cooling.
  • High-bandwidth networking links thousands of accelerators into a single training fabric.

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