Abstract: This article explores how the widespread adoption of generative artificial intelligence, GAI, is reshaping the temporal fabric of postdoctoral research work, a segment of the scientific workforce that combines exceptionally high exposure to emerging automation with limited employment security and strong performance surveillance. Building on the theory of technological change, the study links substitution and complementarity channels to both objective and subjective measures of working time. Empirically, it draws on individual-level data from the 2023 Nature Global Post?Doctoral Survey, which collected responses from 3?715 postdoctoral researchers in ninety?three countries. To address the endogeneity of GAI uptake, the study employs two?stage least squares estimation, using cross?national variation in OpenAI regional access restrictions as an instrument, and triangulates the results with causal forest and Deep IV estimators in order to accommodate non?linear effects and complex heterogeneity. Across all methods, intensive GAI use is associated with statistically and substantively significant increases in weekly non?contract hours and overtime hours, averaging between 0.6 and 1.3 additional hours, along with discernible declines in reported time satisfaction and work–life balance. Mechanism analyses indicate that these outcomes arise primarily through a process of deskilling and task fragmentation rather than through productivity?induced task creation or wage gains. Specifically, the automation of routine scholarly functions, such as text drafting and coding, transfers effort toward engineering, iterative proofreading, and compliance checking, thereby reducing professional autonomy and increasing temporal fragmentation. Complementarity channels deliver few short?run benefits, as indicated by lower effective hourly earnings and muted growth in opportunities to initiate self?directed projects. Institutional context modulates the magnitude of these effects. In laboratories that exhibit entrenched overtime norms, the GAI-induced time burden is forty to seventy per cent larger, whereas in departments that report difficulty recruiting postdoctoral fellows, an indicator of stronger local bargaining power, the negative effects on both hours and time satisfaction are attenuated. Further heterogeneity analysis reveals an emergent distributional divide. Male researchers and individuals with above?median prior productivity incur smaller time penalties and, in some cases, modest efficiency gains, implying that current technological dividends accrue disproportionately to already advantaged actors and may widen inequality in early academic careers. The study contributes to debates on automation and academic labor by demonstrating that cognitive technologies can intensify rather than relieve time pressure even among high?skill workers, extending the insight of the industrious revolution literature to contemporary knowledge production. It also shows that organizational governance and labor market structure critically shape whether potential efficiency gains are converted into genuine leisure. Policy implications include the need to recalibrate performance metrics that encourage perpetual acceleration, to strengthen postdoctoral bargaining positions through transparent career pathways and collective representation, and to ensure inclusive access to high?quality AI resources and training.