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Calibration, panels and month one, and whether the people in the room agree on what they saw.
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- Calibration Is a Standing Session, Not an Onboarding Slide Calibration has one measurable output: how far apart raters land on the same submission. Track the spread across sessions, and hunt the discount nobody wrote down.
- Three Failures, Three Deadlines, and Only One Is the Hire's Fault A skill gap, a judgment gap and a badly drawn role look identical at week six and run on different deadlines. How to tell them apart before the clock starts.
- Week One Buys One Thing: An Unrehearsed Look at How Someone Works A first week buys one thing later weeks cannot: an unrehearsed view of how someone works. What to schedule, what to compress, and what week one cannot tell you.
- Build the Reps Your First Job No Longer Hands You AI now does the cheap-to-fail tasks that taught junior judgment. Replace them: attempt the task yourself, then note where an assistant's answer diverged from yours.
- Forecasts Missed Both Ways. Track Your Own Task List. No forecast on job timelines has a good record in either direction. Here is the quarterly task review that updates your plan on evidence instead of the next headline.
- An Intake Meeting Produces a Decision Rule, Not a Wish List The deliverable is one page the manager has corrected: outcomes, requirements mapped to stages, the evidence each stage produces, and who breaks a tie.
- Prompt Engineering Went Into Every Job, Not Away The prompt engineer title mostly stopped appearing in postings, but the work behind it moved into analyst, support and marketing roles. Train for the role, not the title.
- AI Is the Reason Given. It Is Rarely the Whole Reason. An AI-attributed layoff can mean real automation, a bet that has not landed, or framing for investors. The tell: whether your old role returns to postings.
- The Internal Move Needs a Shipped Thing, Not a Course That evidence is a small working thing someone else already uses, not a certificate. Ask the receiving team its bar, and raise backfill before it stalls the move.
- Output Ramps in Weeks Now, Judgment Still Takes Months The eight-month ramp figure is HR self-report from a 2012 online survey. Carry two numbers instead: time to usable output, and the week you stopped checking the work.
- A Veto Is Fine If It Has to Name What It Saw When the panel disagrees: the manager decides, anyone can block, and every position names what was said or done and in which round. Uncited vetoes do not count.
- Lock the Scores Before the Debrief, and Keep the Split Visible A rating filed after seeing a colleague's is a vote, not an observation. File first, check whether the tool hides submitted cards, and treat a split as the result.
- Seniority Prices Judgment, and Judgment Just Got Scarcer Watching a junior match your output in minutes is not proof your experience stopped mattering. The throughput half is what compressed; the judgment half got scarcer.
- Switch for the Task Mix, Not for AI in the Job Title Career-switch marketing sells a destination title, not your risk. Compare task exposure on both sides of the move before you pay for a bootcamp or a certificate.
- Lock the Scorecards Before the Transcript and the Debrief Submit before you read another card, the transcript, or its automatic summary. Sequence is what protects the account, and a 24-hour deadline cannot see it.
- The Manager Writes the 30-60-90, Starting From the Loop's Open Question A 30-60-90 plan written by a candidate to win the job is a guess. What belongs in each column, who writes it and when, and the field every template leaves out.
- Interviewer Training Ends in a Rated Practice Round, Not a Deck Interviewer training that works ends in a rated practice round: two completed write-ups read, one recorded round scored cold, and a write-up a second reader can act on.
- Put the Hiring Manager in the First Pass, Not Only the Shortlist Recruiters should own the pass, the pace and the record. A manager's hour buys more spent writing criteria and reading rejected applications than grading a shortlist.
- AI Is Taking Tasks From Your Job Before It Takes the Job The research on AI and job loss operates at the task level, not the job level. How to size your own exposure instead of guessing from a headline.
- Advertising AI Tool Access and Rules in the Posting Name the tools, the plan tier, who pays for the seat, and one real restriction. What experienced AI users read a posting for, and what an AI-forward line tells them.
- Buy the Certificate Only When Someone Outside Asks for It Buy a credential only where an outside party demands one. Otherwise the same money buys protected hours on real work and a senior reader at the end of them.
- Should AI Be Its Own Competency in the Framework? Fold AI into an existing competency where it changed the method, and give it its own where it changed the deliverable. The split runs function by function.
- Why Do Candidates Who Demo Brilliantly With AI Fall Apart in Month One? A demo rewards a finished artifact made under supervision. Month one bills for the checks nobody watched, in four patterns, each with the question that catches it.
- The AI Skills Gap Is a Specification Problem First Write the skill as observable behavior for two or three roles, then look at real work. Most teams find a distribution they had not noticed rather than a shortage.
- Why Does an AI-Skills Hire Work Like the Rest of the Team After Six Months? The hire wasn't wrong. Six months taught them your real standard, and no review step ever asked for the framing, the source or the check they were hired to do.
- How AI Use Belongs in a Performance Review, and How It Doesn't AI use does not belong in a review as its own rating. Grade the work, and write the part of the job AI changed as observable behavior on competencies you already have.
- Apprenticeship, Internship, or Rotation: Which Survives AI? Match the shape to how long your field takes to make someone useful: rotations for long ramps, apprenticeships for regulated ones, internships as short paid trials.
- Do Assessment Scores Still Predict Performance With AI in the Workflow? Validity is measured against one job's performance data, so re-validate per role: the tests that measured production speed lost the most when AI arrived.
- How Do You Get an Interview Panel to Judge AI Use the Same Way? Three acts, anchored in the hiring role's own vocabulary, scored alone and in writing before the debrief opens. The one-hour calibration that ends five private standards.
- How to Build an AI Upskilling Program That Changes the Work Three layers: tool access, supervised practice on the team's own work, and a reader who compares one finished deliverable per person before and after.
- A Central AI Team Buys Demos; Embedded Capability Buys Work Centralize tooling and the verification standard, keep delivery in the functions that own the work product, and write the sunset condition before the first hire.
- The Case for Still Hiring Juniors When AI Does Junior Work Entry-level hiring is a task-by-task call now. Keep the roles whose work still gets read and graded, cut the ones producing drafts nobody opens, and price the lag.
- How Do You Structure an AI-Heavy New Hire's First 90 Days? Stop reading the finished work. For three weeks make the framing note, one intermediate artifact and one named check the deliverable, then remove them and watch.
- How Many People Do You Need If AI Takes Part of the Workload? Task exposure varies several-fold across occupations, so one productivity assumption misses high and low at once. Plan a range per occupational family instead.
- Should You Hire for AI Skills or Train the Team You Have? Train for the tool gap, hire for the verification gap. Last quarter's escaped errors tell you which gap your team has, and your field tells you how big it is.
- Hire a Person or Hand the Work to AI: How to Decide A salary against a subscription is the wrong comparison. Sort the tasks into producing and deciding, count the review hours automating creates, then open or hold the req.
- How Do You Stop Hiring Managers Rejecting Candidates for 'Sounding Like AI'? A memo won't stop it. Require every rejection to name the claim that's wrong, the check run and what it returned, then read flag rates by req and by reviewer.
- Should You Hire the Junior Who's Fast With AI Over the Senior? Seniority doesn't decide this. Hire for who catches a confident wrong answer in your field, and let the cost of one undetected error pick the candidate.
- Where Juniors Get Their Reps When AI Takes the Grunt Work Pick two or three tasks that historically built judgment, make them narrated rather than banned, put them on a calendar, and give one senior the teaching.
- The People Best With AI Leave When Their Judgment Stops Mattering People good with AI leave when their objections stop changing what ships. Count the overrides, then give the check a named point, an owner, and a deadline rule.
- Four Levels of AI Skill, Written So Two Managers Agree Accepts, edits, checks, refuses. Each rung names something you could watch a person fail to do, which is the only property that makes a level usable for hiring.
- A Manager Who Has Never Used AI Cannot Review AI Work Reviewing AI-assisted work takes recognition of three failure shapes, and that comes from hours in the tool. A course does not supply it. Here is the dose.
- The Minimum AI Literacy Is Three Judgments, Not a Curriculum What may be handed over, what has to be verified, and what data never goes into a prompt. Everything else, including how the technology works, is optional context.
- New Grads Who Never Worked Without AI: Training Gap or Hiring Mistake? A missing fundamental is trainable in weeks. Not noticing one was needed is a hiring signal. How to separate the two, and which fundamentals each field has to keep.
- Should You Care That New Hires Ask AI Before the Team? Asking AI first is not disloyalty. It matters only where your team's own context is the only check, which is exactly the context a new hire lacks.
- Promotion Criteria That Survive AI-Assisted Output Output volume rose for everyone at once, so shipped surface area no longer separates levels. Promote on the decisions instead, and make the packet prove them.
- Prompt Engineering Is Not the Skill Your Team Is Missing Three habits outlast any model release: specify before generating, source the claim that carries the decision, and test the output against something outside the chat.
- What Do You Hire For After AI Didn't Absorb the Work? The tasks that came back after the cut are the job description: exceptions, escalations, and checking confident output. Hire for those, not for the volume AI absorbed.
- What a Remote AI-Heavy Posting Has to Say About Review Remote removes watching someone work. AI removes the rough draft that used to get corrected. Say who reviews the work, against what, and what stays with the person.
- The Tasks Worth Keeping Out of the Model Security lists answer a containment question. Three structural tests decide which recurring tasks a model may assist with and which it must never produce.
- Before You Open the Req, What Can Your Team Already Do With AI? Pull three recent deliverables per person and mark where AI touched each one, what got checked, and what got kept. The gap is usually verification, not output.
- What Separates Heavy AI Use From Good AI Use Usage dashboards count seats and prompts, not judgment. Four traces separate good AI work from a lot of it, and a ten-minute work review finds all four.
- Which Roles Actually Need AI Skills Right Now? Rank roles by how much of the occupation's task list a model already drafts, how often that work runs, and what a wrong answer costs. The output is three to six roles.
- Name the Human Who Checks It, or Nobody Did Accountability never moved: it belongs to whoever shipped the work. What has to be built is the named check, on the named claim, done by a named person.
This beat is written by Drake Kelly & Olive.