A recent VentureBeat Pulse Research study involving 107 enterprises has identified a growing "compute gap" in the tech industry. Organizations are pouring capital into AI infrastructure at a rate that far outpaces their ability to measure the economic return or operational efficiency of these investments.
Underutilized Power and Hidden Costs
The research highlights a significant inefficiency in current deployments: 83% of enterprises report that their GPU utilization is at 50% or less, with nearly half operating at 25% capacity or below. Despite this idle capacity, the drive to acquire more infrastructure remains high. The study found that fewer than half of these organizations (44%) rigorously track the actual costs and returns of their AI compute, leading to a disparity between spending and visibility.
Shifting Infrastructure Preferences
While the current market is dominated by major hyperscalers like Google Cloud and Microsoft Azure, a major shift is on the horizon. Approximately 64% of enterprises plan to switch or add infrastructure providers within the next year. Interestingly, 45% of respondents intend to evaluate AI-specialized clouds—a category that currently sees almost zero usage among the surveyed group.
Integration Over Price
When selecting new providers, enterprises are prioritizing integration with existing stacks (41%) and total cost of ownership (35%) over headline token prices. Only 8% of decision-makers cited cost per million tokens as a primary factor. This suggests that while buyers want economic efficiency, they currently lack the diagnostic tools to achieve it, creating a cycle of blind investment in the next generation of AI hardware and specialized cloud services.
