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  Introduction Machine translation (MT) has evolved dramatically from its early rule-based methods to today’s AI-driven models. Initially, translation systems relied on word-for-word substitution, an approach that often led to nonsensical or misleading results due to the complexity of linguistic structures. In his article "The Word," André Martinet criticized this method, arguing that treating words as discrete, self-contained units of meaning created an artificial "screen" that obscured the structural and relational nature of language. This article argues that while contemporary AI translation has moved beyond simple lexical substitution, it still struggles to fully account for linguistic interdependence. Despite advancements in deep learning, modern algorithmic models continue to approximate rather than truly understand syntactic and morphological dependencies, making his critique as relevant as ever. By examining Martinet’s structuralist insights, the histori...

Introduction André Martinet’s article The Word challenges the conventional understanding of words as fixed linguistic units. Across languages, the concept of a "word" varies significantly, yet writing has reinforced the perception of words as stable entities. Mechanical translation further contributed to this illusion by treating words as discrete units with direct one-to-one correspondences across languages. However, modern AI-based language models, which process language in more nuanced ways, lend support to his functionalist perspective. This article explores Martinet’s argument by tracing the evolution of how language is conceptualized—from early scriptio continua, to machine translation, to the contemporary role of AI in text processing. By doing so, we uncover how recent technological advancements reaffirm his claim that linguistic units must be analyzed beyond the apparent boundaries of words. The Concept of "Word" Across Languages One of Martinet’s ...