Survey of 107 enterprises reveals organizations are aggressively investing in AI infrastructure while running GPUs at half capacity and lacking clear visibility into unit economics.
Enterprise organizations are racing to build artificial intelligence capabilities, pouring money into GPU infrastructure and cloud services at an unprecedented pace. Yet a troubling pattern has emerged: companies are spending billions on AI compute resources without the ability to accurately measure what those investments deliver.
A recent VentureBeat Pulse Research survey examining 107 enterprises has uncovered what analysts are calling a critical compute gap. While 64 percent of surveyed organizations plan to switch or add new AI infrastructure providers within the next year, the vast majority lack fundamental visibility into their AI unit economics.
GPUs sitting idle while budgets expand
The inefficiency is stark. According to the survey findings, 83 percent of respondents report their GPU utilization rates remain at 50 percent or lower. This means expensive graphics processing units, the workhorses of AI model training and inference, are sitting idle roughly half the time.
Despite this underutilization, enterprise appetite for additional infrastructure shows no signs of cooling. Some 45 percent of surveyed organizations plan to evaluate specialized AI cloud providers they do not currently use, suggesting the buying spree will continue regardless of existing capacity constraints.
Production deployment lags investment
The disconnect between spending and operational maturity becomes even more apparent when examining deployment statistics. Only 21 percent of organizations currently run AI applications at true production scale, meaning the overwhelming majority remain in experimental or pilot phases.
This creates a paradox where enterprises are building out infrastructure for future needs while struggling to optimize what they already have. The gap between infrastructure investment and actual production deployment suggests many organizations are betting on AI's promise without clear pathways to value realization.
Cost tracking remains a blind spot
Perhaps most concerning is the lack of financial discipline around AI spending. Fewer than half of surveyed enterprises rigorously track their compute costs, leaving leadership teams without accurate data on return on investment or cost per AI workload.
This blind spot exists even as purchasing decisions grow more sophisticated. Organizations report that integration capabilities and total cost of ownership matter far more than headline token pricing when selecting vendors. Yet without rigorous cost tracking, calculating true total cost of ownership becomes nearly impossible.
Buying decisions driven by integration
The survey reveals that enterprises prioritize seamless integration with existing technology stacks when evaluating AI infrastructure providers. Compatibility with current tools, data pipelines, and workflows ranks higher than raw performance metrics or advertised per-token costs.
This focus on integration suggests IT leaders understand the operational complexity of deploying AI at scale. However, it also indicates that organizations may be locking themselves into vendor ecosystems before fully understanding their long-term cost implications.
The path forward requires discipline
The compute gap represents a significant risk for enterprises. Organizations buying infrastructure faster than they can control it may find themselves with bloated cloud bills, underutilized resources, and limited ability to demonstrate business value from AI investments.
Closing this gap will require enterprises to implement rigorous cost tracking mechanisms, optimize existing GPU utilization before expanding capacity, and develop clearer metrics linking AI spending to business outcomes. Without these disciplines, the current infrastructure gold rush may yield disappointing returns.