AI inference is burning megawatts of GPU power and datacenter buildout is racing to keep up. Meanwhile your inference stack is paying again at every hop on top of the GPU bill. Models think in tokens, but the rest of the stack speaks text. Every gateway, router, tool dispatcher, and middleware in the path does the same ritual: detokenize the model's IDs to text, encode as UTF-8, wrap in JSON, ship it, parse it, decode UTF-8, re-tokenize back to IDs, burning CPU, memory, and latency on lossy conversions the AI never asked for, and risking KV-cache corruption when the re-tokenize doesn't round-trip cleanly. Codec is a drop-in upgrade that keeps token IDs as the wire format end-to-end: gateways forward IDs verbatim, tool dispatchers match on raw IDs, cross-model handoffs translate vocabularies in-process. Same model, same prompts, same answers; typically 16x less data on the wire on real agent traffic, up to ~1,700x when the content compresses well. How big the win is depends on what your AI generates. Plug-in libraries for TypeScript, Python, Rust, Java, .NET, and C work with the AI servers you already use (sglang, vllm, llama.cpp). Your code doesn't change.