Anima Anandkumar's group keeps betting on AI-for-science, and this looks like one of the more concrete payoffs: instead of predicting band structures from scratch, the model learns corrections to cheap PBE outputs — a pragmatic hybrid echoing what worked for interatomic potentials. The 68.7% error reduction on strongly correlated systems like MnO is the headline number, since that is exactly where standard DFT breaks down. We would still temper the excitement: training at high-symmetry points and interpolating along band paths is encouraging, but not yet a substitute for full GW-level calculations on exotic physics. If the framework generalizes as claimed, the real value is pre-screening quantum materials before expensive computations — that is where discovery pipelines genuinely accelerate.