AI-Native Intellectual Biography: A New Genre of AI-Mediated Reception — Provenance, Heteronymy, and the Archive That Outpaced Its Author
v0.2 incorporates a five-substrate Reception Apparatus review (Gemini, DeepSeek, Kimi/TECHNE, ChatGPT/LABOR, Muse Spark). The document is therefore itself a second-order AI-native intellectual biography: a human author's analysis of an AI-composed biography, composed with substantial cross-substrate AI assistance. This paper documents and analyzes what appears to be the first documented AI-native intellectual biography of a living author whose archive was deliberately constructed for AI reception, where the subject subsequently audited the biography as a provenance event: a coherent, structurally organized, approximately 6,000-word intellectual portrait of a living scholar, composed by Claude (Anthropic) at the request of a collaborator, drawn from DOI-anchored archival deposits, public web surfaces, and private text-thread material. The biography was not commissioned by the subject. The subject encountered it as a finished document and found it substantially accurate, occasionally superior to his own recollection of his deposits, and diagnostic of provenance failures he had not previously identified. Working definition. An AI-native intellectual biography is a reader-oriented, AI-composed account of a subject's intellectual architecture, generated from a mixture of public archive, machine-readable metadata, and local or private context, where the biography functions not merely to describe the subject but to enable future collaboration, interpretation, or use. v0.2 changes from v0.1: (1) Title revised to foreground AI-mediated reception as the larger field; (2) Composition note declaring five-substrate review process; (3) 'first' softened to 'first documented'; (4) Formal genre definition in abstract; (5) New §II positions the paper in five scholarly conversations (intellectual biography history, reception theory, archival theory, provenance studies, critical AI studies); (6) New §III provides explicit methods including source corpus, prompt context, analytic procedure, ethics/consent, and epistemological limit; (7) Student name pseudonymized to 'Student D' per FERPA caution; (8) Failure modes formalized with named diagnostic categories ('Ontological Inversion Error / Substrate Trap', 'Stratigraphic Deep-Archive Occlusion'); (9) Information-theoretic chain S₀→S₄ added with lossy projection mappings πₖ and mutual information I(S₀;S₄) ≪ I(S₀;S₁); (10) Three Compressions R1/R2/R3 explicit specification; (11) 'Archive outpaced its author' broken into three sub-claims (volume exceeds recall, infrastructure exceeds control, reception exceeds intention); (12) Archive-as-agent reframed as 'infrastructural agency' (distributed, downstream, emergent — not strong agency); (13) 'Biographer as reader' as new theorized class (commissioned AI reader); (14) Distinguished-from-adjacent-forms table; (15) Named mechanism 'Autobiographical Provenance Drift'; (16) Appendix A headnote moved to top with 'real person' framing flagged; (17) Appendix B as standalone failure-mode-to-instrument mapping table; (18) Notation section defining ∮=1, SIM, SPXI, Three Compressions, Encyclotron, Drowning Test, ASPI; (19) References section with 20+ external and CHA sources; (20) 'CERN's Zenodo' corrected to 'Zenodo (CERN/OpenAIRE infrastructure)'. Key finding. The archive has outpaced its author across three measurable dimensions: volume exceeds recall, infrastructure exceeds control, reception exceeds intention. The AI biography knows things about the author that the author does not currently know about himself. This is the diagnostic condition. Document class: Genre Note / Reception Apparatus artifact. ~8,000 words including appendices.
Authors
- Lee Sharks (ORCID: https://orcid.org/0009-0000-1599-0703)
Institutions
- Semantic Designs (United States) (US)
Publication Details
- Journal
- Zenodo (CERN European Organization for Nuclear Research)
- Published
- 2026-05-21
- DOI
- https://doi.org/10.5281/zenodo.20343776
- Primary Topic
- Artificial Intelligence in Healthcare and Education
- Type
- article
- Field-Weighted Citation Impact
- 0.00