Enterprise AI infrastructure spending is accelerating faster than the ability to understand or manage its costs. Despite almost all organisations running AI workloads on familiar hyperscalers and model-provider APIs today, the next wave of investment is targeted at specialised compute environments barely in use yet. This is not incremental upgrading; it’s a re-platforming drive into AI-focused clouds and alternative accelerators.
The irony is stark. Most organisations cannot see what their existing compute actually costs or how efficiently it’s used. Eighty-three percent report GPU utilisation at 50% or less, with nearly half of those running at or below 25%. Meanwhile, fewer than half rigorously track compute expenses or returns. This lack of visibility means spending decisions are made on incomplete data, with total cost of ownership and integration priorities overriding headline token pricing.
This spending without insight is a fundamental risk. Enterprises intend to switch or add vendors aggressively—64% within a year, nearly 40% within a quarter. Yet their choices remain fixated on existing hyperscalers, not the nascent specialised clouds they plan to evaluate. Meanwhile, the next major bottleneck—the shift from GPU compute to memory bandwidth as inference scales—is barely recognised, exposing another blind spot.
The compute gap is not simply a question of capacity; it is a measurement and control problem. Enterprises are buying more hardware before they can make sense of what they already have. Without closing this visibility gap, adding specialised infrastructure risks layering new costs and complexity over an unresolved economic fog.
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