The AI compute gap: Enterprises are buying infrastructure faster than they can measure what it costs

What changed

Across 107 enterprises, AI infrastructure spending is accelerating well ahead of the ability to see or steer its economics. Most organizations run their AI on a familiar base of hyperscalers and model-provider APIs, yet the next dollar is aimed at specialized compute almost none of them use today; a majority intend to switch or add providers within the year, many within a quarter. Only 6% run their own on-prem GPU clusters and 4% a custom open-source stack.

Why it matters

A concrete addition to Practical AI: Tools, Models & Frameworks: it changes what's available to builders today rather than being general commentary.

How it compares

Related prior coverage to compare against:

• Agentic orchestration: Enterprise AI organizations have a deployment problem, not a platform problem — and most are calling chatbots agents
• The agent evaluation gap: Enterprise AI organizations have a reality-alignment problem, not a coverage problem — and most are shipping to production anyway
• The AI context gap: Enterprise AI organizations have a trust problem, not a retrieval problem — and most are still building the fix

• Agentic orchestration: Enterprise AI organizations have a deployment problem, not a platform problem — and most are calling chatbots agents similarity 0.44
• The agent evaluation gap: Enterprise AI organizations have a reality-alignment problem, not a coverage problem — and most are shipping to production anyway similarity 0.44
• The AI context gap: Enterprise AI organizations have a trust problem, not a retrieval problem — and most are still building the fix similarity 0.43