The Positionalization of Value in the Age of Artificial Intelligence

This paper studies whether artificial intelligence changes the composition of market value from functional differentiation toward social image, rank, andbrand recognition. This positionalization of value is distinct from a shift towardphysically nonreproducible factors: a scalable good can retain a large status premium. Buyers have a two-dimensional primitive type, while an audience forms aBayesian posterior about owner quality from a recognizable ownership signal. Afirm commits to physical stock Q = Mn, so recognition can rise with output evenas exclusivity depends on penetration n in an audience of size M. The frameworkseparates the marginal status component that supports price from average statusvalue among owners. The firm’s first-order condition yields a cross-equation restriction linking the functional margin, marginal status demand, and its prevalence elasticity; a linked experiment supplies conditions under which these objects are separately identified. A numerical possibility result features lower penetration togetherwith higher output, price, and both marginal status shares. The mechanism remainsconditional: authenticity erosion, fragmented attention, direct observability of type,search, and countersignaling can reverse it. The paper proposes a producer–buyer–audience experiment and a candidate within-category field design to distinguishsocial inference from quality beliefs and private brand attachment. Whether AIcausally connects functional convergence to social-image value remains open.