High-entropy alloys offer promising applications in aerospace and potential room-temperature superconductors, but their complex electronic structures and massive combinatorial configuration spaces make them exceptionally difficult to model. Because standard machine learning tools serve only as rough approximations, researchers must rely on immense computing power and automated high-throughput experimentation to navigate the vast material possibilities. Although industry research and development is accelerating, widespread commercial deployment remains limited by low technical readiness and the high cost of constituent metals.