Who Is the Right Expert for AI?

High-quality AI training and evaluation data should reflect the full range of real workplace expertise rather than relying on mass-recruited experts, who may overrepresent junior, external-facing, or unusually available professionals. Patronus argues that representative datasets require contributors across seniority levels, roles, industries, and working styles, along with reviewers who can validate work and account for legitimate differences in practice. Complex professional tasks often involve long-running collaboration, incomplete information, contextual judgment, feedback from senior colleagues, and messy source materials, none of which is captured by polished final outputs alone. Effective AI experts are therefore not defined solely by credentials or titles, but by practical experience and the ability to identify multiple sound approaches and create nuanced grading criteria. The company also contends that common benchmarks can obscure uneven model performance when they use hand-picked tasks, and proposes broader expert-designed, peer-reviewed tasks as a more realistic measure of AI capability.

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