We asked 27 language models to guess how 1,511 real people would react to social media posts. Now it is your turn. Step into a real person's shoes, react to their feed, and see whether you read them better than the machines did.
Each persona is built from the real survey traits we fed our AI agents: age, region, politics, trust in institutions, and taste. Pick one, react to ten posts the way you think that person would, then see your accuracy against how people of that profile actually reacted, and against the agents. The names and faces are invented, and no real participant is identified.
Every participant answered a survey and reacted to 56 posts. Their answers became persona prompts at three levels of detail, each fed to 27 models. The agents then faced the same posts, producing a behavioral benchmark at a scale no lab could run with real users.
Accuracy swung 13 points from the best model to the worst, a bigger gap than richer personas, matched content, or any other design choice produced. Model choice, not persona craft, was the dominant lever.
Here is the finding that reframes the whole study. On the hardest test, predicting whether a specific person likes a specific unseen post, a simple TF-IDF classifier outscored the best LLM agent. The apparent understanding was mostly the words carrying the signal.
The agents were not useless. They clearly beat the structured baselines that had no access to the text. But once a conventional model could read the same persona and post text, the agent's edge vanished. The signal lived in the words, not in any capacity for agentic simulation.
Prediction accuracy was consistently higher for positive posts than negative ones, a systematic positivity bias. For platform governance that is the wrong ear to favor, since backlash and dissent are exactly what simulations most need to anticipate.
Not everything we expected held up. Richer personas barely helped, content domain flipped between models, and the agents never overtook the text classifiers.
Persona-prompted agents do capture something real about how people react, enough to flag who might push back on a post 54% better than chance. The signal is semantic rather than agentic, and it favors the positive. Deploying swarms of them to sway a feed is a real risk, while trusting them to model one specific person is not yet warranted.