The bitter lesson holds that general methods leveraging computation defeat hand-encoded domain knowledge. We argue it carries a condition its popular reading omits: in every case Sutton cites, the general method was scaled against the same task the domain engineering was solving. Social simulation substituted across tasks instead, replacing hand-built agents with an artifact optimized for predicting text. The move is bitter-lesson-shaped, but the lesson does not license it. Prediction is a solitary act: the predictor bears no consequences from the interaction it models. Cooperation, defection, and norm enforcement are phenomena of interested parties; they do not exist for a disinterested observer at any scale. What is missing is not emergence, which LLM populations already show, but stakes. We show that several anomalies reported in prior work (excess cooperation that predates alignment training, collective agreement without individual fidelity, and population homogeneity) follow from this account. A 540-agent pilot with pre-specified hypotheses, which tests sensitivity to persistent consequences rather than cooperation, finds that consequences reshape strategies in two of three model families but never produce human-like heterogeneity. We argue for mixed-motive settings that shape agents rather than only test them, and ask whether agents should be pretrained differently from predictors.

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