AI infrastructure covers the systems that make AI products possible: compute, data centres, cloud platforms, model tooling, retrieval, orchestration, observability, security and application infrastructure.
Meridian studies AI infrastructure as a connected public/private market. Public companies shape chips, cloud, networking and distribution. Private companies form around tooling, workflow, context, deployment and specialised infrastructure.
The market is capital intensive at the base and software-like near the application layer. That makes it important to separate scarcity, margin structure, platform dependency and workflow ownership rather than treating all AI infrastructure companies as one category.
Focus areas
Focus
Compute and accelerators
Focus
Data centres and power
Focus
Model deployment and inference
Focus
Retrieval and context systems
Focus
AI observability and evaluation
Market map
Compute
Compute
GPUs, accelerators, cloud capacity, networking and the physical constraints behind model training and inference.
Context
Context
Retrieval, permissions, data freshness and evidence packaging for production AI systems.
Control
Control
Evaluation, observability, routing, policy and cost management for model-dependent workflows.