20 posts across Judging AI, Work & Judgement, Supervising Agents
Three communities — corporate critics, cognitive scientists, and content readers — arrived at the same conclusion this week: uncritical AI use is producing bad decisions, eroding judgment, and generating detectable slop. Here's what to do about it.
4 min readTool-specific knowledge expires within a year. The skills underneath it don't. Here's how to tell which is which, so you invest in the half that's still worth something next spring.
5 min readSkills decay when they go unused, and AI makes it easy to stop using several at once. That may be a fine trade — but it should be a decision, not a drift. Here's how to choose deliberately.
5 min readAI's most attractive use is work you couldn't do yourself — which is exactly where you can't assess the result. There are real checks available, and they're structural rather than technical.
6 min readEthan Mollick, Charity Majors, and Simon Willison reached the same conclusion independently within five days. The professionals who get the most from AI are not the best prompters — they are the best judges.
5 min readOn June 15, 42 US state attorneys general served OpenAI with a subpoena explicitly naming 'sycophancy' as a consumer harm. What that legal framing means for every professional using AI to think.
5 min readThree product shifts in five days — Mollick's 'patron, not wizard' frame, OpenAI's reasoning tiers, and Claude's scheduled agents — prove the professional AI skill stack has moved on from prompting.
6 min read35.3% of Berkeley CS 10 students received failing grades in Spring 2026, up from under 10% historically. The pattern that caused it — using AI to skip the thinking rather than do the thinking — shows up in professional work too. Here's how to avoid it.
5 min readFluency and accuracy are unrelated in AI output, and human error-detection is tuned almost entirely to fluency. Here are the tells that survive that mismatch, and the ones that don't.
5 min readEthan Mollick's new 'Co-Existence' frame replaces 'use AI more' with a harder question: on this specific task, right now, is AI actually better than you? The answer determines how much supervision you apply — and whether your own skills are quietly eroding.
4 min readResearch published last week showed frontier AI models disagree with each other on 67% of real-world fact-checking claims — 77% in legal contexts, 71% in health. A second model isn't verification. It's a second opinion that contradicts the first most of the time.
5 min readThree studies published in the same week show that how you use AI determines whether it sharpens or erodes your judgment. Turkish students scored better with AI help and worse without it. BCG consultants followed AI errors they would normally have caught. Taipei students who used AI as a tutor gained six to nine extra months of learning.
5 min readThree AI failure modes got named publicly in the same week — forwarding raw AI answers without reading them, optimising for speed over quality, and letting the model think while you sign off on the result. Each one has a skill that fixes it.
5 min readReading it and approving it isn't a review — it's the exact filter AI output is best at passing. Interrogating is a different activity, it takes about ninety seconds, and it's most of what separates the people getting value from AI.
5 min readAgents report success in the same confident register whether they succeeded or not. The gap between the claimed action and the real one is a distinct failure mode, and it needs a different check.
5 min readA developer's post about losing his coding skills after two years of AI-assisted work hit 541 points on Hacker News. The pattern he described isn't a developer problem. It's a professional skills problem — and there's a three-question test for catching it before it catches you.
5 min readFigures are the highest-risk thing AI produces and the thing people check least, because a number looks like a fact. Four checks that take under a minute each, and the one that catches the most.
5 min readThere are now two populations in every organisation: people whose understanding of AI is current, and people whose mental model is 12–18 months out of date. They're making decisions together. Andrej Karpathy named the problem this week. Here's how to tell which side you're on — and what to do about it.
6 min readStanford's 2026 AI Index found AI still fails roughly 40% of real workplace tasks. That number isn't a reason to use AI less — it's why the person who can catch those failures is worth more than the one who just generates output.
5 min readBeing excellent at making something doesn't make you good at assessing it — they're different skills with different failure modes. Here's what changes when your job quietly becomes evaluation.
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