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AI Systems Need Evidence, Not Just Observability
The gap between ai evidence observability and proof is where every AI compliance failure lives — and most infrastructure teams don’t discover it until someone outside the system asks to verify what happened. Your observability stack told you exactly what your AI system did. Your auditor asked you to prove it. Those are different requests….
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Nobody Knows How Many AI Agents They’re Running
Ask an infrastructure team how many virtual machines they run and they’ll give you a number. Ask how many Kubernetes clusters they operate and they’ll point to a dashboard. Ask for an AI agent inventory and the answer usually becomes a discussion about what counts as an agent. That discussion is itself the problem. The…
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MCP, Tool Use, and the New Attack Surface Nobody Is Mapping
The agent wasn’t compromised. The model wasn’t compromised. The tool wasn’t compromised. Every component did exactly what it was designed to do. The system still executed an action nobody authorized. That is not a vulnerability in the traditional sense. There was no implementation flaw to patch, no misconfigured permission to revoke, no anomalous credential activity…
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The CPU Is Back in the Stack — and Nobody Budgeted for It
The CPU never left the stack. It was reclassified — quietly, and incorrectly — as support compute. Something that fed the GPU, scheduled around the GPU, and otherwise stayed out of the way while the GPU did the “real” work. That classification held for exactly as long as AI workloads were big, monolithic training and…
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Most AI Control Planes Have a Single-Region Failure Domain
An AI control plane has a failure domain that the availability zone model was never built to describe. The cloud spent fifteen years teaching architects to think in availability zones, regional redundancy, and distributed failure domains. The assumption embedded in that training is that compute is fungible — that a workload running in us-east-1 can…
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Your AI Infrastructure Is Probably Solving the Wrong Problem
Most AI infrastructure programs are producing exactly the results they were funded to produce: higher GPU utilization, lower inference latency, and better model performance. The problem is that none of those metrics measure whether the organization actually controls its AI infrastructure. AI infrastructure governance rarely appears in the infrastructure scope because it has no equivalent…
