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I caught myself last Thursday accepting a slide deck I had not really written. I had asked Claude to draft an investor update, skimmed it, changed two words, and sent it on.
A colleague came back with one question, why was the second metric framed that way and I could not answer her. I had to open the chat, read the reasoning, and pretend I had thought it through.
I had outsourced not just the writing but the thinking behind it. That is when I started paying attention to how I was using these tools, not just whether I was using them.
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Three patterns, one that protects judgment.
A useful split has been forming in the research over the last two years. Three patterns of how people work with generative AI, and the gap in output quality between them is bigger than the gap between models.
The first is the Centaur. You decide what the work is, decide which sub-task the AI is actually good at, hand it that one piece, and do the rest yourself. Clear line down the middle, like the mythical creature.
The second is the Cyborg. You and the AI move together, sentence by sentence, prompt by prompt. The work gets fused. The third is the Self-Automator. You hand over the whole task and ship the output.
The Self-Automator pattern is where most of the quality loss is happening, and most people doing it have not noticed yet.
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What Priya changed.
Take Simin, a senior strategy lead at a B2B SaaS company I spoke with last month. She had been writing memos by prompting Claude with the brief, accepting most of the draft, and editing for tone.
Three months in, her manager flagged that her recommendations had started sounding generic. Priya switched approaches. She wrote the recommendation herself in rough form first, then asked the AI two specific things, sharpen this paragraph, stress-test this assumption.
Her next memo took twenty minutes longer to write. It also got picked up by the CEO in the quarterly review.
The shift was not about using AI less. It was about deciding what only she could do, and protecting that part.
The 60-27-14 split.
A 2025 study led by MIT Sloan professor Kate Kellogg tracked 244 Boston Consulting Group consultants on a strategy task using GPT-4. 60 percent worked as Cyborgs, 27 percent as Self-Automators, and only 14 percent as Centaurs.
The Centaurs produced the highest accuracy on business recommendations of the three groups. The Self-Automators delivered work the researchers described as polished but shallow, quick and clean on top, weak underneath.
The interesting part is the percentages. Most knowledge workers using AI right now default to one of the two patterns that hurt judgment. The pattern that protects it is the rarest.
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One task this week.
Pick the next significant piece of work on your plate. Before you open the AI tab, write down two things what only you can answer about this task, and the one sub-task you would hand off cleanly.
Do the first yourself. Hand off the second. That is the Centaur move, and it takes about ten minutes to set up. The MIT Sloan write-up has the underlying research if you want it.
—Prompt N Productive