Source: Understandingai
As AI systems move beyond narrow tasks into general-purpose applications, traditional metrics that once cleanly separated capable from incapable models are collapsing—making it genuinely difficult to know whether a new system is actually better or just different. This creates a real problem for enterprises and regulators trying to compare systems before deployment: you can’t optimize what you can’t measure, and vendors have strong incentives to game whatever metrics remain legible. The shift mirrors what happened in other maturing technologies, but the speed here is compressing years of measurement uncertainty into months, leaving the industry without stable ground truth as the stakes rise.