The Good Tech Companies - Nearly Half of Enterprises Waste Millions on Underutilized GPU Capacity

Episode Date: January 5, 2026

This story was originally published on HackerNoon at: https://hackernoon.com/nearly-half-of-enterprises-waste-millions-on-underutilized-gpu-capacity. Nearly half of enterprises waste millions on idle GPUs as manual workflows, poor automation, and weak governance stall AI at scale. Check more stories related to machine-learning at: https://hackernoon.com/c/machine-learning. You can also check exclusive content about #enterprise-ai-infrastructure, #ai-cost-optimization, #ai-workload-orchestration, #underutilized-gpus, #ai-infrastructure-automation, #clearml-ai-report, #gpu-utilization, #good-company, and more. This story was written by: @jonstojanjournalist. Learn more about this writer by checking @jonstojanjournalist's about page, and for more stories, please visit hackernoon.com. ClearML’s 2025–2026 State of AI Infrastructure report reveals a costly disconnect in enterprise AI operations: nearly half of organizations waste millions on idle GPU capacity while AI teams wait in queues. Manual provisioning, weak automation, vendor lock-in, and immature governance block ROI just as AI agents and large-scale deployments accelerate.

This audio is presented by Hacker Noon, where anyone can learn anything about any technology. Nearly half of enterprises waste millions on underutilized GPU capacity by John Stoy and journalist. The gap between AI ambition and operational reality has never been more apparent. According to Clearmel's newly released state of AI infrastructure at scale 2025-2020s report, which surveyed IT leaders and AI infrastructure decision makers at large enterprises and Fortune 1,000 companies, organizations are hemorrhaging money on GPU capacity that sits idle while their AI teams queue for access. The numbers tell a troubling story. Thirty-five percent of enterprises rank increasing GPU and compute utilization as their top infrastructure priority for the next

12 to 18 months. Yet 44% admit they're still manually assigning workloads to GPUs or Hevino-coherent strategy for managing GPU utilization at all. This operational disconnect translates directly into wasted capital and slowed innovation at a time when competitive pressure demands rapid AI deployment. The cost control paradox. Cost concerns dominate enterprise AI infrastructure planning. The survey found that 53% of respondents cite cost control as their primary AI workload management challenge, while 70% listed as their top infrastructure planning priority for 2025-2026. These aren't surprising figures given GPU pricing and availability constraints, but they reveal a deeper issue. Organizations are simultaneously reporting challenges

with maximizing utilization and procurement. Better GPU utilization could deliver immediate royon existing infrastructure investments and potentially delay the need top purchase additional hardware to compensate for poor resource management in the short term. Instead, enterprises find themselves in a cycle of acquiring more capacity to support the surging demand, while failing to maximize what they already own. The operational bottlenecks compound this problem. Only 27% of surveyed organizations have implemented automated resource sharing dashboards. Meanwhile, 23% still rely on manual ticketing systems for compute provisioning, and 35% report that providing resource access to AI and ML teams remains difficult, or very difficult. In an environment where speed matters,

these manual workflows create friction that delays projects and frustrates teams. The flexibility imperative, beyond utilization and cost, enterprises are grappling with strategic questions about infrastructure flexibility. The survey revealed that 44% rate flexibility and avoiding vendor lock-in as, very important when selecting infrastructure solutions. This isn't a theoretical concern as 63% report that proprietary dependencies have already directly delayed or constrained their ability to scale AI initiatives. This finding drives meaningful shifts in infrastructure strategy, organization say are moving toward multi-cloud approaches, 37% of respondents, and actively exploring diverse hardware options. The implication is clear. Enterprises need infrastructure control planes capable of managing and orchestrating across heterogeneous environments without creating new

lock in scenarios. AI agents. High ambitions, low readiness. One of the most striking disconnects in the data involves AI agents. While 89% of enterprise IT leaders plan to implement AI agents within six months, split between custom-built solutions at 49% and off-the-shelf options at 40%. Most organizations lack the foundational capabilities to support these deployments effectively. When asked about operational readiness gaps, enterprise IT leaders cite security and compliance concerns, 53%, insufficient internal expertise, 46% and credential propagation challenges, 46%. These aren't minor technical details. They represent fundamental requirements for running AI agents at enterprise scale, particularly around transparency

and control over resource access. The credential management concerns are especially notable, 58% worry about automatic propagation of sensitive credentials to compute nodes, while 38% identify credential sharing between users as a major vulnerability. As AI systems become more autonomous and distributed, these security considerations become more complex and critical. Governance and sovereignty take center stage, security and governance priorities are evolving beyond traditional perimeter-based models. Nearly one-third of surveyed organizations identify enforcing stronger user policies, permissions, and governance controls across data, models, and compute resources as their top operational priority. This emphasis on governance connects to emerging concerns

around AI sovereignty, the ability to prove domestic provenance, development, and deployment of AI systems. Achieving this requires complete transparency across the AI lifecycle, from data sources through model training to deployment infrastructure. What this means for enterprise AI strategy, the survey data points to three converging challenges that will define enterprise AI infrastructure success in 2025-26. First, organizations must resolve the operational technical disconnect. Investing in advanced GPU hardware while maintaining manual provisioning processes undermine the value of those investments. Automation and orchestration become essential capabilities, not nice to haves. Second, infrastructure flexibility needs to move from feature request

T.O. Architectural requirement. With 63% already experiencing delays from vendor lock-in, platforms that preserve optionality across hardware, clouds, and deployment models will be critical. Third, security and governance frameworks must evolve to support autonomous AI systems. The rapid adoption plans for AI agents demand infrastructure that can enforce policies, manage credentials, and maintain auditability at scale. The organizations that address these challenges will gain competitive advantage, those that don't will continue to pour money into underutilized infrastructure while their AI initiatives stall in queue. The complete state of AI infrastructure at scale 2025-2026 report includes detailed methodology and additional findings from enterprise IT and

eye infrastructure leadership at organizations ranging from 2000 to 10,000 plus employees across North America, Europe, and Asia Pacific. Thank you for listening to this Hackernoon story, read by artificial intelligence. Visit hackernoon.com to read, write, learn and publish.