A new survey from VentureBeat Pulse Research shows that enterprises are pouring money into AI infrastructure faster than they can measure what it costs. The study, which polled 107 organizations with more than 100 employees, found a clear disconnect between aggressive investment and the ability to see or steer the economics of that compute.
The central finding is what the researchers call a "compute gap." Only about one in five enterprises (21%) run AI in production at scale. Yet spending intentions are running ahead of that maturity. The single largest planned area for evaluation over the next year is AI-specialized clouds (45%), a layer almost none of these enterprises use today. Meanwhile, the compute already in place runs cold. 83% of respondents report GPU utilization of 50% or less. Fewer than half (44%) can rigorously track what their AI compute actually costs.
Enterprises are buying more infrastructure faster than they can account for what they already own.
High churn intent among infrastructure providers
Enterprises are not settled on their infrastructure vendors either. A clear majority (64%) plan to switch or add an infrastructure provider within twelve months. 38% intend to do so within the next quarter. That is unusually high churn intent for a category this foundational.
When they choose, they choose on integration with the existing stack (41%) and total cost of ownership (35%). Headline price is not the deciding factor. Cost per million tokens is the deciding factor for just 8%.
The frontier constraint barely registers
The next frontier constraint that will shape decisions is the shift from GPU compute to memory bandwidth as inference scales. That shift is barely on the radar. Roughly one in five enterprises are either unaware of it or have yet to address it.
Methodology
VentureBeat fielded this survey as part of its ongoing Pulse Research series. This wave focused on enterprise AI infrastructure, compute, and inference economics. Responses are filtered to organizations with more than 100 employees (n=107). The survey was drawn from a single Q2 2026 (June) wave. Because it is one wave rather than a pooled multi-month sample, the report reads cross-sectionally and does not infer month-over-month trends. Several questions were multiple-select, so shares can sum to more than 100%.
By organization size, the sample concentrates in the mid-market: 101-250 employees (36%) and 251-1,000 (27%) lead, with 1,001-5,000 (22%), 5,001-10,000 (8%), and 10,001+ (7%) above them. By role, it spans managers (38%), individual contributors (28%), VPs and directors (19%), and the C-suite (13%). On purchasing authority, it is buyer-credible: 45% are final decision-makers and another 30% are recommenders or influencers for AI solutions.
Technology/Software is the largest industry at 26%, followed by Healthcare/Life Sciences (15%), Financial Services (13%), and Retail/E-commerce (12%). At 107 respondents, the sample is large enough to read directionally but should be treated as a directional signal rather than a precise measurement. It is self-selected and is not a probability sample. It also skews toward the mid-market and toward earlier-stage adopters, so it is best read as the view from organizations actively building out AI infrastructure rather than from the largest hyperscale operators.
Finding 1: Ambition outpaces production
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Only one in five run AI in production at scale. The survey asked where organizations sit in their AI deployment journey. Most are still building toward production rather than operating at scale.
- 38% are experimenting, running proofs of concept, not yet in production
- 37% have some workloads in production, but not across the organization
- 21% run AI in production at scale, the mature minority
- 4% are not yet running AI workloads at all
The maturity curve is front-loaded. Three-quarters of enterprises (76%) are either experimenting or running only some workloads in production. Just 21% describe AI in production at scale. This matters for everything that follows. The infrastructure decisions in this report are being made largely by organizations still early in deployment, whose compute footprint and costs are about to grow. The evaluation and switching intentions are the leading edge of that build-out, not the settled preferences of operators who have already found what works.
Finding 2: Enterprises run on hyperscalers and model APIs
The specialized GPU clouds barely register today. The survey asked which providers and platforms enterprises currently use to run their AI. The answer is a familiar one: the incumbents.
- 48% use Google Cloud, the most-used platform overall (Microsoft Azure 29%, AWS 22%, Oracle Cloud 22%)
- 41% use Google's Gemini models, with OpenAI close behind at 40% and Anthropic at 12%
- 6% run their own on-prem or co-located GPU clusters; 4% a custom open-source self-managed stack
- Each use the specialized AI clouds: CoreWeave, Lambda, Crusoe, Nebius, Together, Fireworks and peers
The current stack is hyperscaler-and-API. Google Cloud leads at 48%, and the general-purpose clouds (Google, Microsoft, AWS, Oracle) together with the major model APIs (Gemini, OpenAI, Anthropic) account for essentially all current deployment. The specialized "neocloud" GPU providers that dominate AI-infrastructure headlines register at or near zero among these enterprises today. Only 6% run their own on-prem GPU clusters and 4% a custom open-source stack. Enterprises are, for now, running AI on the providers they already buy from.
(A note on reading these shares. As described in the methodology section, this sample is self-selected and skews mid-market. This question counted every provider a respondent uses, an average of 2.1 selections each. So the figures measure presence in the stack rather than spending or primary status. A sample built this way will show a different provider mix than a spend-weighted census of the broader market. Google's strength here, for example, is consistent with its long-standing position among smaller enterprises building on AI. Read these shares as a portrait of what this AI-active cohort runs today, and treat gaps between these figures and industry-wide market share estimates as a property of the sample rather than a contradiction of either.)
Finding 3: The next dollar goes to infrastructure they don't yet run
AI-specialized clouds top the evaluations list. The survey asked where enterprises planned to evaluate AI infrastructure over the next 12 months. Their answers point away from the stack they run today.
- 45% AI-specialized clouds (CoreWeave, Lambda, Crusoe, Nebius), the top planned evaluation area
- 32% non-NVIDIA accelerators (AWS Trainium, Google TPU, AMD Instinct, Intel Gaudi, in-house ASICs)
- 28% Nvidia Blackwell (GB300) / next-generation GPUs
- 16% decentralized or distributed compute networks
- 11% sovereign or region-specific compute; 9% say none of the above
Here is the report's sharpest tension. The single most-cited planned evaluation area is AI-specialized clouds, a category almost none of these enterprises use today. The next dollar is going to infrastructure they don't yet run.