The Post Science Paradigm of Scientific Discovery in the Era of Artificial Intelligence: Modelling the Collapse of Ideation Costs, Epistemic Inversion, and the End of Knowledge Scarcity

TL;DR

This paper proposes a new paradigm where AI-driven ideation cost collapse shifts the focus from generating ideas to aligning them with human needs, transforming economic, social, and institutional models of innovation and growth.

Contribution

It introduces Experiential Matrix Theory (EMT) as a formal framework to model innovation as an alignment process, challenging traditional knowledge scarcity assumptions in the AI era.

Findings

Value creation shifts to roles that interpret and socially embed ideas.

Transition from a knowledge economy to an alignment economy.

Implications for policy, labor, and institutional design.

Abstract

This paper develops a theoretical and formal response to the collapse in the marginal cost of ideation caused by artificial intelligence (AI). In challenging the foundational assumption of knowledge scarcity, the paper argues that the key economic constraint is no longer the generation of ideas, but the alignment of ideation with the recursive structure of human needs. Building on previous work, we further develop Experiential Matrix Theory (EMT), a framework that models innovation as a recursive optimisation process in which alignment, rather than ideation, becomes the binding constraint. Accordingly, we formalise core mechanisms of EMT and apply it to the dynamics of ideation collapse and institutional realignment under AI. Using a series of defensible economic models, we show that in this post-scarcity paradigm, the creation of economic and social value increasingly accrues to roles that guide, interpret, and socially embed ideation, rather than to those that merely generate new ideas. The paper theorises a transition from a knowledge economy to an alignment economy, and derives policy implications for labor hierarchies, subsidy structures, and institutional design. The university, in this context, must invert its function from knowledge transmission to epistemic alignment. The paper concludes by reframing growth not as a function of knowledge accumulation, but of how well society aligns its expanding cognitive capacity with the frontier of experiential human value. This redefinition of the innovation constraint implies a transformation of growth theory, policy design, and institutional purpose in the AI era.