Three Compounding Failures
Redundant data
Frame-based cameras capture the entire scene at a fixed rate regardless of whether anything in that scene has changed. Redundancy is designed into the capture model itself.
Bandwidth overload
A mid-sized industrial site runs hundreds to thousands of simultaneous video feeds. Aggregate throughput scales linearly with camera count, and network capacity becomes the binding constraint long before analytical capability does.
AI compute and storage cost
Every redundant frame that reaches a GPU is paid for twice — once in inference cost and once in storage. This is the primary driver of poor ROI and slow response times in large-scale vision deployments.
An Architectural Flaw, Not a Bug
This cannot be solved by compression, better models, or more GPUs. Compression reduces the cost of moving redundant data but does not eliminate the redundancy. Larger models increase the cost per frame. Adding compute scales the expense linearly with the problem.
The redundancy originates at the point of capture, which is where it has to be removed.