Vernacular Futurism: How Persian Language Users Imagine AI

Public discourse about artificial intelligence increasingly unfolds through compressed forecasts, moral warnings, and everyday speculation circulating at platform speed. This study examines how Persian language users on X construct and contest AI futures, analyzing a corpus of 4741 posts collected between January 2023 and December 2025, with volume concentrated overwhelmingly in 2025—a period of intensified public attention to generative AI. Drawing on the theoretical frameworks of sociotechnical imaginaries and affective publics, we apply multi‐label topic tagging across 12 thematic domains, latent dirichlet allocation topic modeling, and a five‐class sentiment scheme (delighted, happy, neutral, angry, and furious) to map the thematic and affective landscape of Persian language AI future talk. Findings reveal a cautiously energized public during this period: neutral (35.56%) and positive affect (47.10%) dominate, whereas concentrated pockets of anger and fury cluster around existential risk and truth crisis themes. Topic co‐occurrence analysis shows that model and platform competition functions as the primary narrative backbone through which users stitch together concerns about labor displacement, human identity, national positioning, and infrastructural dependency. We conceptualize this discursive practice as vernacular futurism—a distributed platform‐mediated mode through which technical events are translated into moral claims and social expectations. The study contributes an empirically grounded non‐Western perspective to communication research on AI imaginaries, highlighting geopolitical exclusion and sanctions as a plausible—though not yet comparatively demonstrated—source of the distinctive anxieties of Persian language publics.