Datacenter Research

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Overview

Tracks the infrastructure frontier of AI-driven datacenter construction and operation. Focus areas: liquid cooling systems enabling 20–400+ kW/rack densities, robotics and autonomous operations reducing labor dependency, next-generation facility design cutting time-to-power and cost per MW, and alternative power sourcing for the energy intensity of modern AI workloads.

Key Themes

  • Rack density escalation driven by AI/HPC: GPU clusters require 20–100+ kW/rack, forcing a transition from air to liquid cooling at scale
  • Liquid immersion cooling moving from HPC niche to mainstream AI datacenter consideration as rack densities exceed 30–40 kW
  • Robotic automation of datacenter operations — server installation, maintenance, monitoring — reducing labor cost and enabling fully lights-out facilities
  • $6.7T in cumulative global datacenter capex projected through 2030 (McKinsey), driving demand for faster, cheaper construction methods
  • Power sourcing as the binding constraint: grid interconnection delays pushing operators toward behind-the-meter gas turbines, nuclear PPAs, and SMRs
  • Blind-mate and tool-less connectivity hardware as the enabling component layer for robot-serviced facilities

Companies

For full company tables by subsection, see Cooling, Design & Construction, Orbital Compute, Power Infrastructure, and Robotics & Automation.

Sections

  • Behind-the-Meter Power for Data Centers — Comprehensive research on behind-the-meter (BTM) power solutions for data centers: technology categories, companies, regulatory challenges, and 18+ GW of announced projects targeting grid-independent power for AI and hyperscale facilities.
  • Datacenter Cooling — Liquid cooling systems for high-density AI and HPC datacenters — immersion, direct-to-chip, rear-door heat exchangers, and the companies supplying them.
  • Datacenter Design & Construction — Next-generation datacenter design approaches — modular construction, faster time-to-power, lower cost per MW, and smarter facility architectures for AI workloads.
  • Datacenter Power Infrastructure — Power sourcing, behind-the-meter generation, grid interconnection, and energy storage for AI-scale datacenters.
  • Datacenter Robotics & Automation — Autonomous and robotic systems for datacenter operations — robot-friendly rack design, server installation automation, and lights-out facility management.
  • Edge AI Accelerators — Dedicated AI inference accelerator chips and modules (M.2, PCIe, mPCIe) for embedding inference capability into existing edge compute platforms — Hailo, EdgeCortix, and the broader edge NPU market
  • Orbital Compute — Space-based compute infrastructure: satellite data centers, orbital AI clusters, and the emerging race to move ML workloads into low Earth orbit.
  • Rugged & Edge Compute — Rugged compute platforms, AI inference at the edge, military/maritime/aviation deployments, man-portable compute, semi-industrial fanless edge AI servers