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

View original at venturebeat.com
VentureBeat AI - Enterprise Ai Title: The AI compute gap: Enterprises are buying infrastructure faster than they can measure what it costs Date: 2026-07-16 19:16 Source: https://venturebeat.com/ai/the-ai-compute-gap-enterprises-are-buying-infrastructure-faster-than-they-can-measure-what-it-costs <p>Across 107 enterpris…
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  • Overall satisfaction with current AI infrastructure averages 4.0 on a five-point scale, with ease of implementation at 3.8 and value for money at 3.9.

    60% confidence
  • Only 12% of enterprises clear the 50% GPU utilization mark, and a further 8% do not measure utilization at all.

    60% confidence
  • 64% of enterprises plan to switch or add an infrastructure provider within twelve months, and 38% within the next quarter alone.

    60% confidence
  • The providers drawing the most switching consideration are Microsoft Azure and Google Cloud (33% each), OpenAI (30%), and Gemini (22%), suggesting near-term movement is mostly incumbents trading share rather than defections to new entrants.

    60% confidence
  • Roughly one in five enterprises (18%) either do not recognize the shift from GPU compute to memory bandwidth as a constraint or have not begun to address it.

    60% confidence
  • Specialized AI clouds carry the highest net expansion momentum among infrastructure approaches (+24), narrowly ahead of hyperscalers (+22).

    60% confidence
  • Enterprises choose AI infrastructure providers primarily on integration with the existing stack (41%) and total cost of ownership (35%); cost per million tokens is the deciding factor for just 8%.

    60% confidence
  • The single largest planned AI infrastructure evaluation area over the next 12 months is AI-specialized clouds, at 45%, a category almost none of these enterprises use today.

    60% confidence
  • Fewer than half of enterprises (44%) rigorously track the cost and return of their AI compute; 39% track only partially, 20% cannot quantify it yet, and 6% have not prioritized it.

    60% confidence
  • Only about one in five enterprises (21%) run AI in production at scale; 76% are still experimenting or running only some workloads in production.

    60% confidence
  • In VentureBeat's prior April-May 2026 survey wave, the most-cited planned infrastructure strategy change was moving workloads to specialized AI clouds, at 33%; usage of CoreWeave (3%), Lambda (4%) and Crusoe (2%) was equally marginal at that time.

    60% confidence
  • 83% of enterprises that operate GPUs report utilization of 50% or less; 49% run at 25% or below.

    60% confidence

Data points we hold from this source

Dell Technologies · market share31 percent
OpenAI · switching consideration share30 percent
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Mid-October to November 2026 is a dense catalyst period for clinical-stage biotech. Positive late-stage readouts (ALOFT-IPF, TELIGAN, MIUDELLA, the duvakitug extension) and the ESMO 2026 presentations lead up to the FDA's 14 November PDUFA date for the ivonescimab BLA. At the opposite end of the market, Spago Nanomedical is moving to delist from Nasdaq First North, which shows how hard it is for micro-cap developers to fund themselves. Value is concentrating in companies with late-stage data and near-term regulatory outcomes.
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High probability of expanded label indications, additional combination approvals, and competitive positioning strength in follicular lymphoma market. Predicts positive commercial uptake and potential accelerated review for related indications.
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Ford Motor Company
Both facts give cash for Ford Motor Company at the same point in time (2025-12-31; FY 2025 year-end and Q4 2025 year-end are the same date). Both are labelled USD, but the values are 23,356,000,000 and 23.356, which cannot both be true in USD. The two numbers agree once the second is read as billions (23.356 bn = 23,356,000,000), so the economic content is consistent and the conflict is a unit/scale error in the Q4 2025 record. The period is not the reason for the conflict: a Q4 and an FY figure at the same year-end date should match.
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