AI displacement now shows up in the US government data at both ends of the career ladder: A Bloomberg analysis of new BLS figures finds every one of the eighteen occupations the BLS classifies as AI-exposed has lost jobs over the past year, even as US payrolls grew 0.8% overall. Customer service representatives shed 130,180 jobs, 4.8% in a single year; interpreters down 24% over three years; credit authorizers down 26%. The exception that confirms the rule: medical secretaries up 15.8%, the cluster that needs a body in the room. The same picture shows up at the other end of the funnel. The Economist this month plotted US graduate full-time employment against AI exposure: computer science and information sciences graduates are down 10 to 15 percentage points since 2022; philosophy and psychology graduates held steady or gained. The displacement isn't just to the people already doing those jobs. It's to the people trying to start in them, and what they should be studying may not be obvious to anyone yet — a thread the essay returns to via Elliott's homework.The UK's data regulator has put AI hiring tools on formal notice. Sixteen organisations have already had a letter: The Information Commissioner's Office issued formal guidance this week saying that AI-driven CV screening, candidate ranking, and video interview analysis without "meaningful human involvement at every consequential stage" may already breach UK data protection law. Sixteen organisations have been written to directly. The consultation closes on 29th May, six days after the edition lands. A concrete Monday-morning task for any leader running a hiring pipeline: get the full list of AI tools in use across the funnel, decide which involvements count as "meaningful" against the ICO's test, and put a response into the consultation. The window is genuinely short.Salesforce will spend close to $300 million with Anthropic this year. Marc Benioff says the engineering productivity gains made it the easiest line in the budget: Marc Benioff disclosed that Salesforce is on track to spend close to $300 million with Anthropic over 2026, with most of the spend on coding, justified by engineering productivity gains of more than 30%. Separately, Anthropic announced a $200 million partnership with the Gates Foundation focused on global health. A Fortune 100 chief executive treating the model layer as a procurement line item, not a research expense. The bigger question is who in your firm is allowed to commit that kind of capital, against what kind of evidence, and how quickly.When the output goes wrong, shrink the task: Justin Skycak put it as a principle for skill acquisition this week: shrink the unit of practice until the mistake has nowhere to hide. The same rule applies to working with language models. Sprawling prompts produce sprawling failures you can't diagnose. Break the task into its smallest meaningful unit, run it, inspect the output, then rebuild. If you can't immediately see where it went wrong, your chunk is still too large.Ask AI questions it can't possibly know the answer to: A marketing lead at a global firm told David this week she's running a five-minute stress-test on every AI tool she's thinking of trusting. She uploads her own data, asks the model to use only that data, then asks it questions she knows the data can't answer. Some models fabricate regardless ("53% of women in the northeast states feel..."). She's learned what its confident-but-wrong mode looks like before depending on it for an answer she can't independently check. Worth doing once on every tool you rely on.Run your day past AI before you start it: A senior leader described her commute habit to David this week. She opens Claude, asks it to review her calendar and her email, then asks it to surface what she needs to read before each meeting, what's carrying over from yesterday, and which emails in her inbox need replies before the day eats her. Five minutes on the train, and the day is scoped from outside her own head. "Just a nice little daily habit," she said. Try it tomorrow.Read the full edition with all links and sources
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