The AI Compute Gap: Enterprises Buy Infrastructure Faster Than They Can Measure Its Cost
A VentureBeat survey of 107 enterprises reveals a critical AI compute gap: companies are accelerating AI infrastructure spending faster than they can track or measure its economics. Most rely on hyperscalers and model-provider APIs, but the next dollar is aimed at specialized compute few use today. A majority plan to switch or add providers within the year. Buying decisions hinge on integration and total cost of ownership rather than token price — fortunate, because most can't see their unit economics clearly, with GPUs sitting at half utilization.
Enterprises are buying AI infrastructure at a breakneck pace, but they're struggling to keep track of the costs. According to a VentureBeat survey of 107 companies, spending on AI hardware is accelerating faster than their ability to measure what it's actually costing them. Most organizations use familiar cloud providers and AI model services, but the next wave of spending is shifting toward specialized hardware that few currently use.
This lack of visibility is a significant problem. Most enterprises cannot yet see their unit economics clearly — for example, GPUs are often used at only half capacity, wasting resources and money. Buying decisions are increasingly driven by integration and total cost of ownership rather than headline token price, which is fortunate given the current blind spots. A majority of enterprises intend to switch or add providers within the year, many within a single quarter.
The AI compute gap means companies might be overspending on AI tools, which could eventually drive up prices for consumers. If businesses can't see how much they're spending on AI, they may pass those unclear costs onto customers. The survey underscores that the next dollar of infrastructure spending is aimed at specialized compute that almost none of the surveyed enterprises use today.