Overview
Efficient AI experiments ask how much system quality can be preserved while reducing token movement, model size, and unnecessary reasoning overhead.
Problem
Many AI systems spend tokens as if they were free, then become too slow or expensive to use in real product loops.
Constraints
- Efficiency cannot erase provenance or safety.
- Smaller models need well-scoped responsibilities.
- Evaluation must include latency and cost.
System Design
The experiment suite separates compression, routing, retrieval, and answer generation so each layer can be measured independently.
Architecture
Inputs pass through lightweight classifiers, context filters, small-model preprocessors, and selective large-model calls.
Tradeoffs
Efficiency layers add engineering surface area. The bet is that predictable constraints produce better long-term systems.
Impact
The experiments create a vocabulary for deciding when not to call the biggest model.
What I Learned
The cheapest token is the one the system did not need to send.
Research Extension
Prototype TokenWire: a transport layer for sending compressed, task-aware evidence between AI system components.