World Action Models (WAMs) can condition robot actions on generated visual futures, but this additional context is neither cost-free nor uniformly beneficial. We examine what a predicted future actually changes: the action delta Δa between the future-conditioned and future-free execution paths of the same model. Under a shared WAM checkpoint, this delta barely shifts average benchmark success (+0.8 points on LIBERO, -0.8 on RoboTwin 2.0), while generating visual futures increases inference latency by 1.7–1.9×. These aggregate metrics conceal a concentrated failure mode that we term future-induced negative transfer: while predictive deltas benefit long-horizon tasks (+2.4 points on LIBERO-Long) and remain negligible on most routine manipulations, they induce severe degradation on precision tasks—with Press Stapler and Place Can Basket losing 54.0 and 34.0 points, recurring as top failure modes across independently trained WAM variants. We introduce Utility-WAM for in-place correction of future-conditioned control. Its primary learning component is a residual adapter that repairs future-induced velocity deltas under a frozen shared WAM by aligning them with ground-truth dynamic guidance and penalizing excess error beyond the future-free baseline. The adapter selectively eliminates the majority of severe task drops (reducing losses to 6.0 and 14.0 points) while preserving long-horizon foresight. To eliminate superfluous video computation, a lightweight pre-generation gate reuses counterfactual action outcomes to selectively bypass video generation on 75.5% of RoboTwin queries, cutting latency by 38% while improving average success to 93.3% on RoboTwin 2.0 and 98.9% on LIBERO. On physical bimanual manipulation across three real-world tasks, Utility-WAM succeeds in 53 of 60 trials, compared to 43 for FAST-WAM.