An AI ran a ransomware attack this week, and the headline said it did so on its own. The details said a human still chose the victim and built the setup. In the same seven days, Google confirmed that the tidy little file everyone hoped would fix their AI visibility does exactly nothing. Two stories, one lesson. The claim outran the reality, and the reality was quietly available if you checked. That is the thread through this edition. Falling organic traffic that means your metrics broke, not your content. Agents that execute fast but still cannot decide what matters. A workplace where the real blocker to AI is fear, not capability. A model layer you should keep separate from your agents so no single vendor owns your position. Underneath it all is a number that resets assumptions. ChatGPT reached 900 million weekly users, growing fastest in the markets the old playbook drew smallest. AI adoption is real, global, and uneven, which makes checking the claim against the data the most valuable habit you can build right now. Seven reads on the gap between what AI is said to do and what it actually does when you look. Let's get into it.
Run a Blind Model Eval Before You Trust the Hype
Every model launch arrives with benchmark claims, and most of them do not match your actual work. Steal the method from Claire Vo, who ran 64 blind generations across five frontier models to review Sonnet 5, scoring real tasks rather than vendor numbers. You do not need her scale. Pick one job you actually do, a brief, a first-draft page, a support reply, run it blind through two or three models with the labels hidden, and score the outputs yourself before you look at which is which. Twenty minutes tells you more than any launch post, and you end up choosing the model that fits your work instead of the one with the loudest announcement.
Happy Growth,
Dan
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