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Algorithmic management refers to the use of algorithmic systems to direct, monitor, evaluate, reward, and discipline workers. It has become especially visible in platform-mediated work, where task allocation, pricing, performance scoring, and access to work are increasingly organized through digital systems. Its influence, however, is no longer confined to gig work, because similar logics of monitoring, ranking, prediction, and automated evaluation are spreading into conventional organizations. The central problem addressed in this article is that management theory has not yet fully treated algorithmic management as a distinct managerial control logic. Much existing work explains algorithms as technological tools that intensify established forms of supervision, measurement, or coordination. This interpretation is useful but incomplete because it underestimates how algorithmic systems alter the basic architecture of control itself. The objective of this article is to develop a theory-driven account of algorithmic management as a new control logic. The article argues that algorithmic management reconfigures three core dimensions of work: employee autonomy, performance measurement, and accountability. It therefore requires a theoretical model that explains not only what algorithms do to workers, but how algorithmic systems reorganize the relations among workers, managers, data, and responsibility. The article distinguishes algorithmic management from traditional managerial control, explains the autonomy paradox created by algorithmic systems, analyses the density of digital performance measurement, and theorises the displacement of accountability in algorithmically managed work. It proposes a model linking algorithmic control intensity, autonomy suppression, performance measurement density, and accountability displacement. The central conclusion is that algorithmic management is not a neutral managerial technology but a distinctive control logic that demands new theoretical and practical responses.