Resolves YES if deep learning models at the compute frontier* could be** trained using full homomorphic encryption (FHE) with a <10x slowdown before 2030/1/1.

[*] Say, within 1 OOM of the highest-compute model deployed.

[**] No need for frontier models to be trained with FHE. Empirical evidence of smaller models trained with FHE at <10x slowdown plus a heuristic argument (e.g. dumb extrapolation) that larger models would also satisfy this will suffice.