Mirror Token · AI Runtime Efficiency
Mirror Token
Less waste. Same runtime.
Runtime efficiency for AI at scale — less unnecessary token, compute and energy consumption for the same workload, evidenced against an agreed baseline.
Why it exists
Behaviour and consumption happen in the same runtime.
Mirror OS asks whether AI can run reliably. Mirror Token asks whether it can run with less waste.
Both questions are answered where the AI actually runs. Mirror Token builds on the same runtime foundation as Mirror OS and applies it to consumption: tokens, compute, energy and capacity.
Two deployment contexts
For AI factories. For enterprise AI.
For AI factories
Factory-wide token, compute and capacity efficiency for AI infrastructure providers and GPU cloud operators.
For enterprise AI
Efficiency across the copilots, agents, retrieval and API workloads an enterprise runs.
Scope in definition
How efficiency is evidenced
Savings you can verify, or no claim at all.
Baseline
Agree how consumption is measured before anything changes.
Observe
Read consumption at runtime, per workload.
Optimise
Remove unnecessary consumption while the workload runs.
Evidence
Report savings against the agreed baseline.
Efficiency figures are published only with an approved baseline and measurement context.