Enterprises Invest in AI Infrastructure Faster Than They Can Measure Costs
A new study of 107 enterprises reveals a growing gap between AI infrastructure spending and the ability to track costs, with most organizations running GPUs at half capacity or less, according to VentureBeat Pulse Research.
The AI Compute Gap: Investment Outpaces Visibility
According to VentureBeat Pulse Research, which surveyed 107 enterprises, AI infrastructure spending is accelerating well ahead of the ability to see or steer its economics. Most organizations run 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 clear majority (64%) plan to switch or add providers within the year, many within a quarter.
GPU Utilization and Cost Tracking
The study reveals significant shortcomings in cost management: 83% report GPU utilization of 50% or less, and fewer than half (44%) can rigorously track what their AI compute costs. Buying decisions turn on integration and total cost of ownership rather than headline token price.
Future Plans and the Gap
Only one in five (21%) run AI in production at scale, yet spending intentions are outrunning that maturity: the single largest planned area enterprises plan to evaluate over the next year is AI-specialized clouds (45%), a layer almost none of these enterprises use today. This gap between heavy investment and limited visibility is the core finding of the research.
