Media Brief · Emergent Novelty

What journalists need to know

Micheal Charles Preble

Canonical paperContact the researcher

Why this research matters

As people spend more sustained time working with AI, this preprint asks whether some of what they produce together is genuinely new — not something either the person or the AI would have produced alone.

The research in one sentence

A theoretical framework and one preliminary, self-reported case study propose that sustained human-AI collaboration can produce a measurable rate of “emergent” output attributable to neither party alone.

What is new

A three-question test for classifying whether a piece of collaborative output is emergent, retrieval, or augmentation, and a proposed five-phase model for how a human-AI working relationship might develop over time.

What is established vs. proposed

Established (external, independently published): Shin et al. (2023, PNAS) found professional Go players' decision novelty rose after superhuman AI appeared, across 5.8 million real move decisions.

Proposed, preliminary, single-case: everything specific to this paper — the Emergent Novelty Rate of 83–88%, and the five-phase model. Both come from one operator's 18 deliverables over 14 days, retroactively self-assessed.

What would be inaccurate to say

  • That the 83–88% figure is an independently validated or peer-reviewed result. It is a first-pass, single-case, self-reported estimate.
  • That the classification of the 18 deliverables was performed by a neutral third party. It was performed by the same AI system that helped produce those deliverables — a disclosed conflict of interest.
  • That the neurodivergent-AI advantage described in the paper is a tested finding. The paper states explicitly that no study has tested it.
  • That the author has no financial or IP stake in this being true. The paper discloses that the author holds IP interests in the frameworks described.

Canonical source / citation

Read the canonical paper record → · Full paper PDF · View on SSRN

Suggested citation
Preble, Micheal Charles. “Emergent Novelty in Human-AI Dyadic Systems: A Theoretical Framework and Preliminary Evidence” Available at SSRN 7234219, 2026.

Researcher contact

Micheal Charles Preble · micheal@perfinitive.com · ORCID

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