venturebeat
5% GPU utilization: The $401 billion AI infrastructure problem enterprises can't keep ignoring

For the last 24 months, one narrative justified every over-provisioned data center and bloated IT budget: the GPU scramble. Silicon was the new oil, and H100s traded like contraband. Reserve capacity now or your enterprise would be left behind.The bill is now due, and the CFO is paying attention. Gartner estimates AI infrastructure is adding $401 billion in new spending this year. Real-world audits tell a darker story: average GPU utilization in the enterprise is stuck at 5%. That utilization floor is driven by a self-reinforcing procurement loop that makes idle GPUs nearly impossible to release. What makes this shift more urgent is the CapEx reality now hitting enterprise balance sheets. Many organizations locked in GPU capacity under traditional three- to five-year depreciation cycles, [...]

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

Across 107 enterprises, AI infrastructure spending is accelerating well ahead of the ability to see or steer its economics. Most organizations run their AI on a familiar base of hyperscalers and model [...]

Match Score: 237.36

venturebeat
Infrastructure and compute: Enterprises are buying AI compute for speed while flying blind on what it costs

Across 170 enterprises, AI infrastructure has moved decisively into production — two-thirds now run AI workloads live and three in 10 run them at scale — while the ability to account for what that [...]

Match Score: 235.16

venturebeat
FOMO is why enterprises pay for GPUs they don't use — and why prices keep climbing

Enterprises can't fix their GPU waste problem because the fix makes the problem worse. Releasing idle capacity would improve utilization, but the same shortage driving GPU prices up is exactly wh [...]

Match Score: 231.20

venturebeat
Agent context layers: Enterprises governing their AI data are catching twice as many bad answers as the ones who aren't

Across 101 enterprises, the context feeding AI agents is failing often and repeatedly. Sixty-eight percent have traced a confident but wrong agent answer to missing or inconsistent business context in [...]

Match Score: 138.95

venturebeat
Cheaper tokens, bigger bills: The new math of AI infrastructure

Presented by NutanixAs enterprises move from AI experimentation into production deployment, the primary cost driver has shifted away from foundation model training and toward the infrastructure requir [...]

Match Score: 114.79

venturebeat
Agentic security: Enterprises enforce agent permissions two-thirds of the time — and isolate high-risk agents less than one in five

Across 116 enterprises, agents are in production and so are the incidents: A majority have already had a confirmed agent security event or a near-miss. Two-thirds of enterprises enforce scoped permiss [...]

Match Score: 114.66

venturebeat
As enterprises confront AI agent sprawl, xpander wants them to own their own control and context layer

Enterprise AI has a new infrastructure problem: companies are accumulating agents faster than they are developing systems to govern them.Gartner estimates that the average global Fortune 500 company w [...]

Match Score: 110.45

venturebeat
Wall Street is debating the AI buildout. Enterprises just answered: 86% say their GPUs run at half capacity or less

Enterprise companies are running AI agents ahead of the controls needed to manage them — and they deployed that way knowingly. That is the central finding from VentureBeat Research's June surve [...]

Match Score: 105.93

venturebeat
Agentic orchestration: Enterprise AI organizations know how to govern agents but still can't meter what they cost

Across 107 enterprises, agentic orchestration is not a choice of a single platform.The typical enterprise runs three orchestration platforms at once, and selects them for flexibility across models rat [...]

Match Score: 102.06