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

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

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

    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
  • 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
  • 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
  • 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

Data points we hold from this source

Dell Technologies · market share31 percent
OpenAI · switching consideration share30 percent
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What we're seeing
Q4 2026 Biopharma Catalyst Cluster: Late-Stage Readouts and Regulatory Milestones
October 2026 brings a dense run of Phase 3 wins (ALOFT-IPF, RESTORE-3), new data presentations (Roche, Bayer, Lilly, Vertex, Galderma) and regulatory steps (FDA acceptance of Roche's vamikibart BLA, Scholar Rock's EMA resubmission). The next big catalyst is the ivonescimab FDA PDUFA date on 2026-11-14. Obesity combination studies (REDEFINE 4, TOGETHER-PsO) and the 2027 Maritime-1 completion point to more catalysts ahead. QAIAx's speculative AI-linked trial claims (the QIII pilot, targeted for 2027) are an unrelated micro-cap sideshow with weak evidence, and have little in common with the large-cap pipeline news.
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EPKINLY Regulatory-Clinical Success Cascade
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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ING Group
Both facts record the same metric (shares_outstanding) for ING Group at the identical observation date (2025-12-31). FACT A states 2,902,437,688 shares; FACT B states 2,902 million shares (2,902,000,000). The difference is 437,688 shares (~0.015%). This is a genuine value conflict, though the discrepancy appears to result from FACT B rounding to the nearest million while FACT A provides the precise count.
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