The uneven edge of what AI can do
Where it shines
Drafting and rewording, summarising long text, explaining a concept at your level, brainstorming options, turning notes into a structure, translating, and writing everyday code or spreadsheet formulas. These share a pattern: you can judge the result quickly, and a rough first version saves you real time even if you fix parts of it. The blank page is the thing AI removes best.
Where it still slips
Exact facts you cannot easily check, recent events without search, precise arithmetic in long chains, niche topics with little written about them, and anything where one wrong detail matters a lot, such as dosages, legal deadlines or money. It also struggles to know what it does not know. A model rarely says "I'm not sure" as often as it should.
The edge is jagged
Researchers studying consultants who used AI found the dividing line is uneven: tasks that seem equally hard can fall on opposite sides of it. People did better on tasks inside the edge and worse on tasks just outside it, often without noticing. The practical lesson is not to trust or distrust AI wholesale. Try it on your own tasks, check the output, and learn where your edge is.
A three-question test
Before handing over a task, ask: can I tell quickly whether the answer is good? What happens if it is subtly wrong? Does doing it myself teach me something I need? If you can judge it fast and an error is cheap, use AI freely. If errors are costly, use it for a draft and check carefully. If the doing is the learning, keep it for yourself; lesson 12 comes back to that.
Name one task you would happily hand to AI and one you would never hand over. What makes them different?