There is a confident story circulating about AI and white-collar employment, and a surprising amount of it does not survive contact with the underlying sources. I went through the material most often cited — McKinsey's automation estimates, the Goldman Sachs projections, Anthropic's economic index work, and Bureau of Labor Statistics data — and read what they actually claim rather than how they get quoted. The picture is messier and more useful than the headline version: augmentation dominating replacement in most measured tasks, effects concentrating in specific job components rather than whole occupations, and timelines considerably longer than the discourse implies. This is not a reassurance talk. There are real displacement effects and they land unevenly. But leaders making workforce decisions off the headline version are planning for the wrong thing, and I'll cover what the evidence supports planning for instead. I built an interactive tool for board directors who need to interrogate these claims rather than accept them, and I'll show how to use it.

Target audience: executives, boards, policy audiences, general conference audiences. Preferred duration: 20-30 minutes; also works well as a panel contribution or podcast conversation. Level: non-technical. Sources and the interactive evidence tool are published at gittielabs.com/research.