Giving your AI a Job Interview
One Useful Thing Ethan Mollick
Standard AI benchmarks like MMLU and AIME have significant flaws including public answer keys that models memorize, unclear measurement validity, and calibration issues, yet all major benchmarks trend upward suggesting they capture some real underlying improvement in AI capabilities. OpenAI's GDPval study tested models on realistic four-to-seven-hour expert tasks with blind evaluation by subject matter experts, revealing that performance varies substantially across domains—with AI excelling at software development and financial advising but underperforming at pharmacy and real estate. Organizations deploying AI at scale should conduct rigorous task-specific benchmarking rather than relying on general benchmarks alone, testing models on actual business scenarios multiple times to understand their strengths, weaknesses, and decision-making patterns across thousands of real decisions.
Why it matters
As AI advice becomes more important, we are going to need to get better at assessing it
