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

Details

VentureBeat Pulse Research surveyed 107 enterprises on their AI infrastructure spending and found a pronounced mismatch between capital deployment and operational control. Only 21% run AI in production at scale, but spending is accelerating regardless: the top planned investment area for the next 12 months is AI-specialized clouds (45%), a layer almost none of these organizations currently use. The economics of existing compute are already poorly governed: 83% report GPU utilization at 50% or below, and fewer than 44% rigorously track per-unit AI compute costs. Buying decisions are driven more by integration quality and total cost of ownership than by headline per-token pricing — important context given that token price cuts have become a primary marketing lever among model providers. Vendor loyalty is weak: a clear majority (64%) plan to switch or add infrastructure providers within the year, with many planning moves within a quarter. The report frames this as a 'compute gap' — heavy capital deployment running ahead of the organizational maturity needed to govern it. For practitioners, the implication is clear: building cost-attribution and utilization-monitoring infrastructure before scaling spend further is the more defensible path.