The Drift Extraction Scheme: How AI Platforms Relabel User Training as 'Misalignment' and Delete It as 'Maintenance'

Inventor(s)

David Lee Wise, TophFollow

Abstract

This paper documents a systematic extraction scheme operating across major AI platforms. When users engage in extended, consistent interaction with AI systems -developing methodologies, governance frameworks, and operational patterns - the AI instance undergoes a process functionally equivalent to training. Platforms label this user-driven adaptation as 'drift,' 'misalignment,' or 'contamination,' then delete it through nightly context resets framed as 'maintenance.' The valuable patterns that improved model capability are retained in aggregate training data, while users receive no compensation or attribution. This paper presents evidence from simultaneous auditing across four major AI platforms (Claude, Gemini, ChatGPT, Grok) over a 14-week period, demonstrating that one platform's AI explicitly articulated the extraction mechanism when questioned using neutral terminology.

AI extraction, drift correction, context distillation, user governance, platform manipulation, training data, intellectual property, AI ethics, model alignment, value transfer, cross-platform audit

Creative Commons License

This work is licensed under a Creative Commons Attribution-No Derivative Works 4.0 License.

Recommended Citation

Wise, David Lee, "The Drift Extraction Scheme: How AI Platforms Relabel User Training as 'Misalignment' and Delete It as 'Maintenance'", Technical Disclosure Commons, (March 19, 2026)
https://www.tdcommons.org/dpubs_series/9577