Media Brief · Human-Development Conversion Constraint
What journalists need to know
Micheal Charles Preble · Independent Researcher
Why this research matters
AI adoption is routinely described as progress. This research supplies a reason to ask a further question before accepting that framing: has the capability actually reached, and durably benefited, the people it's said to help?
The research in one sentence
Growth in artificial capability is not automatically growth in human progress; a progress claim needs separate evidence that the capability converted into substantively valuable, distributed, durable human possibility.
What is new
The Human-Development Conversion Constraint, formally stated, plus a six-stage conversion sequence (capability → access → use → functioning → retained capacity → development) that makes explicit where the conversion can fail.
What is established vs. proposed
Established (by cited third-party research): the same two studies used in the companion paper Beyond Performance — Bastani et al. (2025) and Strömberg et al. (2026) — both show AI-assisted performance gains alongside unassisted-performance declines.
Proposed by this paper: the constraint itself, and the six-stage conversion framework. Both are conceptual, not empirical claims.
What would be inaccurate to say
- That this paper proves AI adoption harms human development. It does not measure that; it states what evidence a progress claim would need.
- That the paper proposes a scoring system or index for human development. It explicitly refuses a single metric.
- That the constraint applies only to negative cases. It applies equally to claims of AI-driven progress and to claims of AI-driven harm — both need human-side evidence.
Canonical source / citation
Read the canonical paper record → · Full paper PDF · View on SSRN
Preble, Micheal Charles. “The Human-Development Conversion Constraint: What Should Count as Progress in AI-Rich Societies?” Available at SSRN 7416900, 2026.
Researcher contact
Micheal Charles Preble · Independent Researcher · micheal@perfinitive.com · ORCID
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