While widely used by the industry for over a half-century, the Sharpe ratio falls short in out-of-sample robustness for portfolio judgments that generalize to the future. Despite many attempts to improve it—like the Probabilistic Sharpe Ratio—the problem persists. With their implicit domain knowledge and code-generation capabilities, LLMs are proving powerful tools for evolving algorithms and formulas. From enhancing matrix algorithms to making scientific discoveries, their potential is immense. It turns out that LLMs can discover new risk-adjusted metrics with over 3x the rank correlation to future Sharpe ratios compared to the Sharpe itself. When these metrics are used to select the top 25% of assets, they double the risk-return performance of the Sharpe portfolio. The paper dives deeper into this breakthrough, and the discovered metrics are available in the repository for full reproducibility. This research can disrupt how we evaluate backtests, select assets, or optimize portfolios!