Political Economy + AI = ?

Large language models (LLMs) promise personalized persuasion at near-zero cost, but their output has a slant of its own. In an experiment, U.S. respondents viewed an LLM-generated post advocating a policy stance, randomly personalized to up to six demographics. Messages persuade on average. Personalization raises persuasion only when matching on political party, almost exclusively among Democrats. Benchmarked against Congress members' language, messages slant Democratic even when prompted to target Republicans. Paradoxically, personalization with Democratic-slanted LLMs can benefit Republican positions by finding language to persuade Democrats. Our findings speak to debates about persuasion and slant in the age of generative AI.

States repress insurgency through public force and hidden coercion. We study Pakistan’s 2007–2008 campaign against Taliban-linked militancy, combining administrative records on enforced disappearances with sub-district panels on political violence and protest. The campaign generated visible military operations in Pashtun areas and covert spillovers against Baloch individuals outside the main theater of Baloch insurgency. This variation allows us to compare political responses to open and concealed repression. Visible repression is followed by armed backlash, with little protest response. Covert repression is associated instead with muted mobilization and a fall in militant violence. Overt and covert repressions produce sharply different political responses.