Artificial intelligence (AI) changes social learning when aggregated outputs become training data for future predictions. To study this, we extend the DeGroot model by introducing an AI aggregator that trains on population beliefs and feeds synthesized signals back to agents. We define the learning gap as the deviation of long-run beliefs from the efficient benchmark, allowing us to capture how AI aggregation affects learning. Our main result identifies a threshold in the speed of updating: when the aggregator updates too quickly, there is no positive-measure set of training weights that robustly improves learning across a broad class of environments, whereas such weights exist when updating is sufficiently slow. We then compare global and local architectures. Local aggregators trained on proximate or topic-specific data robustly improve learning in all environments. Consequently, replacing specialized local aggregators with a single global aggregator worsens learning in at least one dimension of the state.

翻译：人工智能在聚合输出成为未来预测的训练数据时，会改变社会学习方式。为研究这一现象，我们通过引入一个基于群体信念训练、并向智能体反馈合成信号的人工智能聚合器，对DeGroot模型进行了扩展。我们将学习差距定义为长期信念与有效基准的偏差，从而能够捕捉人工智能聚合对学习的影响。我们的主要结果揭示了更新速度的一个阈值：当聚合器更新过快时，不存在一组正测度的训练权重能在广泛的环境中稳健改善学习；而当更新速度足够慢时，这类权重则存在。随后，我们比较了全局架构与局部架构。基于邻近或特定主题数据训练的局部聚合器能在所有环境中稳健改善学习。因此，用单一全局聚合器替代专门的局部聚合器，至少会在状态空间的一个维度上使学习效果恶化。