The Sequence Opinion #750: The Paradox of AI Benchmarks: Challenges in Evaluation
Goodhart’s Law and some of the challenges around AI benchmarks...
Artificial intelligence has raced ahead, but knowing what counts as real progress is still surprisingly hard. We largely rely on standardized benchmarks and tidy metrics to declare winners and track improvement. Yet a central paradox undercuts this practice: as soon as a metric becomes the target, it often stops measuring what we intended. This is Goodhart’s Law in action—“when a measure becomes a target, it ceases to be a good measure.” In AI, that means a leaderboard gain may reflect success at gaming the test, not a durable leap in capability.
This essay examines the core challenges of AI benchmarks and evaluations across language, vision, and reinforcement learning (RL). We’ll look at how once-useful measures turn into misleading goals; why good evals should be accurate, inexpensive, and hard to memorize; where academic and real-world tests clash; and what RL environments promise—and fail—to deliver as evolving evaluations. Throughout, we weave in perspectives that many researchers and practitioners share: leaderboards shape behavior, static tests invite overfitting, and narrow metrics drift from what truly matters.