Sunday, October 25, 2026
Source trace. Via News points to the documents behind its reporting and shows what we drew from each — so you can check any claim. How we source
Source document

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…
Opening lines of the source · short snapshot — read the full document at the original

What we drew from this source

The claims Via News extracted from this document. We point to the source; we don't replace it.

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

    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
  • 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
  • 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
  • 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
What we know · the intelligence behind this page
Live from the substrate
What we're seeing
Enterprise Software Races to Govern and Consolidate Around Agentic AI
Enterprise software vendors are repositioning around autonomous AI agents. Salesforce is acquiring Braze, ServiceNow is launching agent governance tools (AI Passport, Super Intelligence Control Tower), SAP is pushing its 'Autonomous Enterprise', and Zeta is launching AthenaOS and its own inference model. Palantir is embedding itself in banking, aviation and sovereign AI infrastructure through partnerships with Customers Bank, Surf Air, Ondas and Armada. Governance, data sovereignty and enforced policy are emerging as preconditions for regulated sectors like finance. Investors such as Norwest see room to disrupt ERP and finance software, and they caution that AI should not be trusted with financial calculations. Consolidation and shareholder disputes (Salesforce/Braze, Tiny/Oso Cloud, ZenaTech/Boardwalktech) show the market reshaping.
Our read on the data ›
Signals we're tracking
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.
Patterns we're watching ›
Where sources disagree
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.
We flag conflicts openly ›
Recently verified
✓ Checked against the original source
4,994
facts traced to their source — and we flag the ones that don't hold up.
101 entities tracked4,994 facts checked against source5,500 source documents archived
Query this data → isubstrate.com
The AI compute gap: Enterprises are buying infrastructure faster than they can measure what it costs — Source | Via News | ViaNews Market