[FEATURE] Native Shorthand Generation (Stenographer Model) for Token Efficiency

Summary

Train AI to write in shorthand (like a court reporter) instead of full prose, then transcribe locally—reducing token generation by 50-70%.

Assumptions (If Wrong, Please Close With Explanation)

1. Token generation is a significant cost driver.
2. Current models output natural language, not shorthand that gets transcribed at the output layer.
3. This hasn't already been explored and rejected.

If any are wrong, I'd genuinely appreciate understanding why it doesn't work.

The Idea

Court reporters capture 200-300 WPM by writing in shorthand, then transcribing to full transcripts. The efficiency happens at creation, not after.

What if AI wrote explanations, documentation, and responses in shorthand—then a local transcriber converted it to natural language before the user sees it?

Same result. Significantly fewer tokens generated.

What I Tested

| Approach | Token Savings |
|----------|---------------|
| Compress AI output after generation | 4-12% |
| AI writes shorthand natively | 65% |

Post-hoc compression fails because AI output is already dense. Native shorthand works because the AI never generates the verbose text in the first place.

The Ask

Consider whether native shorthand generation is worth exploring—either as a research direction or an experimental mode in Claude Code.

Not selling anything. Just an idea I couldn't shake off.