AI Compute Gap: Spending Outpaces Visibility

Enterprises are accelerating AI infrastructure spending, yet struggle to grasp its economic implications. A significant "compute gap" persists, with most organizations' GPUs utilized at 50% or less. Fewer than half of these companies rigorously track their AI compute costs, creating a disconnect between investment and visibility.

Currently, AI workloads primarily run on hyperscalers and model-provider APIs. However, future plans reveal a strong interest in specialized AI clouds, which nearly half of enterprises intend to evaluate within the next twelve months.

This planned shift is accompanied by substantial provider churn intent; 64% plan to switch or add an infrastructure provider within a year, driven by integration needs and total cost of ownership rather than token price.

The emerging constraint of memory bandwidth in large-scale inference is largely unaddressed, with many enterprises unaware or unprepared for this significant architectural shift.