A HUMAN LABOR VS. ARTIFICIAL INTELLIGENCE: FROM SCIENTIFIC EVOLUTION TO THE NEED FOR LEGAL REGULATION: FROM SCIENTIFIC EVOLUTION TO THE NEED FOR LEGAL REGULATION (Reflection and Critique Law Magazine, 2026)
This study investigates how the labor legal system can protect human labor in the face of the advance of Artificial Intelligence, justifying it by the urgent need to reconcile innovation with dignity, employment, and livelihood. It aims to analyze the clash between human labor and Artificial Intelligence, from scientific evolution to the formulation of regulatory responses; specifically, it contextualizes contemporary capabilities, describes technological transformations in labor relations, assesses the post-pandemic acceleration, examines constitutional protection against automation, maps legislative attempts, and critically analyzes the Legal Framework. Methodologically, it constitutes a qualitative legal-analytical essay, supported by a literature review and documentary and regulatory analysis on Artificial Intelligence and work in Brazil. The results indicate that systems have achieved superior performance than humans in specific cognitive tasks; second, that intensified adoption during the pandemic reduced the demand for routine functions and widened inequalities, affecting workers with low levels of education; third, that the 1988 Federal Constitution anticipated the problem by providing protection against automation, but remains lacking infra-constitutional regulation; Fourth, successive legislative proposals have failed, generating only isolated solutions; Fifth, a regulatory vacuum persists, partially mitigated by collective bargaining and court decisions; and Sixth, Bill No. 2,338 of 2023, by classifying risks and imposing human oversight in worker management systems, offers protective progress. It is concluded that the articulation between innovation and social protection requires legislative action, retraining policies, transparency, and oversight to fulfill the constitutional promise of worker protection and prevent fully automated labor decisions.