Many public AI conversations still revolve around leaderboards: which model ranks highest, which provider is 'winning', and which benchmark score matters most. But for organizations building AI in production, those questions are becoming less useful than understanding how AI is actually being used.
One reason is the growing debate around "tokenmaxxing" - the practice of maximizing AI usage, often driven by internal adoption targets, incentives or leaderboard-style competitions.
