Machine learning has transformed vision, language, games-even the prediction of a protein's folded shape-and every one of those victories quietly relied on gifts financial markets withhold: abundant data, stable rules, a legible signal, and a target that does not move because it was predicted. Medium-horizon asset return forecasting is starved of all four, which is why the sophistication of a model says so little about whether it will survive a live market. Asset managers, ever keen on the cutting edge, keep adopting these methods in the pursuit of alpha-sometimes successfully, often not. We argue that what divides the models that endure from those that vanish is less the power of the box than the rigor built around and into it-governance by construction, not as an afterthought: controls and economic constraints designed into the model from the outset, uncertainty quantified so the system knows when to abstain, frictions and capacity priced before the first trade, and every claimed edge made auditable. Borrowing medicine's discipline, where trust rests on a protocol of evidence and vigilance rather than on a fully understood mechanism, we suggest that investors should shift the burden of proof from fully explaining the model to governing it-and close with the questions an allocator should ask before believing that any machine has learned to beat the market.