We Were Already Offloading Our Thinking
A response to Yennie Jun: the autonomy we’re afraid of losing to AI was mostly delegated already — what matters is whether anyone reviews what comes back.
Yennie Jun published an essay last week asking whether we are offloading too much of our thinking to AI. It is the strongest version of that worry I have read — grounded in her own habits, honest about the benefits, and free of the usual doom. Her most unsettling figure is a man her friend met at a San Francisco startup event, wearing a small microphone that records every conversation he has so an AI can analyze his day. “I let Fable do all of my thinking these days,” he explained, with apparent enthusiasm. Her core claim is that autonomy depends on continuing to participate in forming our own desires, and that handing decisions to AI — even trivial ones, like what to eat or what to listen to — slowly erodes who we are.
She is right about more than is comfortable. And the essay still aims at the wrong danger. The threat was never that we delegate our thinking; we have always delegated it, to whatever was nearest. The threat is delegating without a review loop. The distinction matters because the first framing is existential, and leaves you nothing to do but worry. The second is operational. Operational problems can be fixed.
The alarm has a history
Socrates warned that writing would destroy memory. In the Phaedrus, he argues that people who trust the written word will stop exercising their memories and carry the appearance of wisdom rather than the real thing. He was not wrong. The trained memory of oral culture — the capacity to hold an epic poem or a body of law in your head — genuinely atrophied. We traded it for literacy, and literacy bought philosophy, science, and the common law.
The pattern has repeated on schedule ever since. Calculators reached classrooms in the 1970s, and a generation of parents was warned that numeracy would die. It largely did, at least the mechanical kind; almost nobody now does long division for a living, and nobody mourns it. GPS arrived, and spatial reasoning measurably declined — there is research associating habitual turn-by-turn navigation with weaker spatial memory. The losses in each case were real. Jun is honest about the costs of offloading, so the response should be honest too: this is not a story where the worriers were simply wrong.
What the worriers missed is what happened next. Each time, the freed capacity moved up a level, and the surrendered skill quietly stopped mattering. Every skill you maintain has a carrying cost — the practice hours required to keep it sharp, paid from a fixed budget of attention. The useful question about any capability AI can now perform is not “are we losing it” but “is it still worth its carrying cost.” Sometimes the answer is yes: writing-to-think survived the word processor, because the value was never the typing. Sometimes the answer is no, and pretending every loss is an erosion of self collapses a judgment call into a panic.
The autonomous baseline never existed
Her framing carries a quieter assumption: that before AI, we were forming our own desires. Mostly, we were not. The music was chosen by Spotify’s recommendation engine, the restaurant by an aggregate of strangers’ reviews, the shoes by a marketing budget, and the opinions — more than anyone likes to admit — by an engagement-ranked feed. Ken Liu’s “The Perfect Match,” the 2012 short story Jun builds her essay around, was not a prophecy about large language models. It was a barely exaggerated description of the recommendation systems we had already accepted.
Jun asks exactly the right question: who is making the final decisions for the things that matter in your life? But the honest pre-AI answer was rarely “me, unaided.” It was a network of delegations we never audited, because none of it looked like delegation. It looked like convenience.
Against that baseline, an AI assistant is a strange place to plant the flag, because it is the first delegate in the chain you can actually interrogate. Ask Claude why it recommended something and you get reasons — reasons you can push on, disagree with, and overrule. Try asking the TikTok algorithm why it fed you what it fed you. An articulate delegate carries its own risk; a confident explanation can persuade when it should not. But a delegate that shows its reasoning at least makes review possible. The feed never offered that. Measured honestly against what it replaces, this is the first delegation mechanism in twenty years that hands any autonomy back.
Delegation is a discipline
Most of my job is delegation. I lead development teams and a project management office at a national law firm, and very little that matters ships from my own hands anymore. Nobody describes this as a loss of capability. A director who insists on doing everything personally is not more capable than one who delegates well — they are less, and every organization knows it. Deciding what to hand off, with how much context, and how tightly to inspect what comes back is high-order judgment. It may be the highest-order judgment the AI era pays for.
The legal industry has run on this discipline for a century. Associates draft; partners review; the partner’s name goes on the opinion. Nobody argues that the partner has surrendered their autonomy to the associate. The profession also knows precisely what the failure looks like, because it has a name for it: the partner who signs without reading. The failure was never delegation. It was delegation without a review loop.
Read Jun’s essay through that lens and her best moment confirms it. In Portugal, she and her sister wondered why the country celebrates explorers the United States would call colonizers. Her sister reached for ChatGPT; Jun suggested they think first. They speculated, disagreed, backtracked — and only then asked the AI, using its answer to test and extend hypotheses they had formed themselves. That is not resistance to offloading. That is a review loop, run well. Her mother’s physics students, meanwhile, pasting assignment questions into a chatbot and submitting the output unread, are the partner who signs without reading. Same technology, opposite disciplines. What separates them is not how much thinking was offloaded. It is whether anyone reviewed what came back — an operating problem, and operating problems are trainable. Organizations worried that AI will erode their people’s judgment should be training exactly this muscle: what to delegate, what to hold, how to review.
The line that deserves the worry
One part of Jun’s argument deserves to be granted in full. There is a real difference between delegating task execution and delegating desire formation. “Draft this memo” is delegation. “Tell me what to want from my career” is something else, because if you outsource the wanting, no one is left standing behind the review loop. A reviewer needs preferences of their own to review against.
But this is not a new problem arriving with a new technology. We already share our desire formation with other minds that hold opinions about what we should want — mentors, spouses, consultants, therapists. A good mentor absolutely tells you what to think, and sometimes what to want. Centuries of social practice taught us to take that input as input: to hold advisers at the distance where they inform the wanting without replacing it. We also recognize instantly when the line fails — the mentor who dictates, the consultant who decides for you. The skill of holding that line exists. It survives, like any skill, only if it is practiced. That is Jun’s real warning, and it stands stripped of its fatalism.
The Microphone Man is genuinely alarming, but the microphone is not why. He is alarming because he announced, cheerfully, that his review loop is closed — the AI is smarter, so the AI does the thinking now. Socrates’ alarm about writing was real too, and the answer was never to stop writing things down. It was to remain the kind of reader who checks. That is still the answer. Delegation without review is not offloading your thinking; it is abdicating it. And abdication was never the tool’s decision to make.
Read Yennie Jun’s original essay, Are we offloading too much of our thinking to AI? — it is the strongest version of the worry, and it deserves the argument. If the operational side of AI adoption is your beat, subscribe; it is the only thing I write about.