Look at the contribution before the ranking.
When assessing emerging AI expertise, the most useful question is what someone has demonstrably contributed—and how that work matters to the decision in front of you.
Cato & Veil · Research desk · 15 September 2026
Make the unit of analysis clear.
A prominent executive, an influential researcher and a technically indispensable engineer are not interchangeable categories. A single league table can obscure those differences. An assessment should specify whether it concerns published research, leadership responsibility, a technical specialism or another defined criterion.
Build a profile from attributable work.
Use named papers, documented contributions, dates and relevant professional roles. Read the work in context and, where necessary, involve a subject-matter specialist. Affiliation with a successful team is not sufficient evidence to predict an individual’s future performance.
Be careful with precision.
A score needs a reproducible method, comparable inputs and an explanation of uncertainty. If those are missing, a clear qualitative profile is more useful than a precise-looking number. Missing public information should remain a gap, rather than become a low score.
Apply it to a real mandate.
For a family office, a company or counsel, the output should address the brief: technical concentration, the people behind a proposed partnership, or the history relevant to a particular concern. Rankings can direct attention. They cannot substitute for due diligence.