Spec-Based Research in the Post-AI Era: A Cost-Asymmetry Theory of Meaning and Meaningfulness in Organizational Knowledge Work

Generative AI shifts the cost structure of organizational knowledge work asymmetrically across the layers of the artifact it produces. The structural substrate – the typed graph of propositions, dependencies, and evidence anchors – becomes cheap to construct, verify, and recombine; the cohort-conditional prose rendering does not, and may become relatively more costly. This paper introduces the Operator as the role-level abstraction that absorbs the shift: a role with intrinsic structural-substrate operations and intrinsic judgment operations, whose human-versus-AI projection composition varies by era. The role-as-projection move extends the automation-augmentation paradox from task allocation to role-level division of operations. Two propositions follow. P1 (separability): structural substrate and rendering are independently optimizable. P4 (rendering-equivalence under spine-preservation): two renderings of a locked substrate converge on conclusions if and only if both preserve its structural elements, under a faithful extraction axiom. The paper illustrates the framework on a management-theory twin pair (dynamic capabilities) and applies it reflexively to its own production. Empirical estimation of two additional predictions about recombination and cost-asymmetry parameters is reserved for a companion paper. Three Design Propositions follow for organizational governance, role and incentive design, and editorial-process decoupling. Includes paper.yaml (Paper Spec v0.1.0) – a machine-readable specification of the paper's claims, assumptions, and dependencies. See https://github.com/spectralbranding/paper-spec for the standard.