Built in the Mojave.
Lake Mead is at 32% capacity. We picked an architecture that runs the model once and answers forever — because re-running models is not free, and the desert keeps the receipts.
The climate stake
Every AI question is a watt-hour. Every re-run is the same question burning energy twice. The .wav format makes the answer surface part of the file, so the second question costs ~zero.
That choice is not aesthetic. It is the only way an AI-native media stack stays honest about energy when you scale to a million recordings.
The codec wars are the wrong fight
Netflix, YouTube, and Spotify spend hundreds of millions a year transcoding the same content into AV1, HEVC, H.264, and a half-dozen other codecs to keep every device happy. The codec war does not have a winner — it has tax. .wav lives one layer above the tax: pick whatever codec your storage budget demands; the meaning stays portable.
What this actually fixes
- For media platforms. Less redundant transcoding. Less redundant storage. More questions per file. Targeting 5%+ storage cost reduction at consolidation.
- For AI labs. Re-questioning long media stops being an inference cost. Up to 7x faster first-token latency on follow-ups. Per-token efficiency ~64% better than running cold.
- For the climate. Structurally fewer watt-hours per question. Not a promise — a property of the file. At 1% adoption by 23: ~8,ϴϴϴ US homes’ worth of saved compute, ~1.6 billion gallons of water never boiled in cooling towers.
The format philosophy
One container holds the recording, the transcript, the speakers, the behavioral profile, and the per-second complexity map needed to answer follow-up questions without re-encoding. Open at the format layer (Apache 2.); patent-pending at the behavioral graph layer.
The team
Small. Building toward five design partners in Q2 226. Direct line: [email protected].