The Algorithmic Expropriation of Labor: New Professional Profiles and the Reform of Education

The employment contract in force today was designed to govern the exchange of time and effort for remuneration. It does not record, price, or even name a transfer that has become central to the knowledge economy: the codification of a professional’s intellectual capital into the firm’s structural and algorithmic assets. I call this uncompensated transfer algorithmic expropriation and distinguish it sharply from technological displacement — it requires no dismissal and operates on the contractual rather than the extensive margin. The purpose of this article is not to propose a fiscal remedy but to draw out two consequences of the phenomenon that have received little systematic attention. First, algorithmic expropriation inverts the map of occupational value: the most codifiable, most “expert” knowledge is the most expropriable, so the occupations long regarded as safe — high-skill analytical professions — turn out to be among the most exposed, while value migrates toward what resists codification (judgment under genuine ambiguity, relational and social skill, cross-domain synthesis, and the supervision and auditing of autonomous systems). Using the open ITEA Framework, an occupational exposure index validated against the AIOE benchmark, I sketch this re-ordering and the new professional profiles it generates. Second, the phenomenon reframes the mission of the education system: an education optimized to transmit codifiable procedural expertise now optimizes, inadvertently, for expropriability. I argue for a reorientation of curricula toward non-expropriable human capital, for a lifelong-learning infrastructure adequate to a shortening skill half-life, and for the university to take on a new role as certifier of traceable intellectual capital. The question of how to compensate the transfer — through tokenized residual rights rather than a Pigouvian tax — is treated in a companion paper and left aside here. Keywords: algorithmic expropriation; intellectual capital; future of work; professional profiles; education reform; lifelong learning; human capital; skill obsolescence; artificial intelligence. JEL: J24, I20, O33, J23, M53.