Training the Thing That Is Training Us
Every model that learns a population's habits is also teaching that population which of its habits are legible to the model.
Everyone talks about training an AI to know a person as if it were a one-way act, a person shaping a mirror until it reflects them back accurately. It isn't one-way. The moment patterns start feeding into a system so it can anticipate them, the system starts feeding those patterns back into behavior, and the loop closes faster than most people notice it opening.
Legibility is not the same as being known
A model gets to know someone by watching what can be counted: which links get clicked, what time a person wakes, which words get reached for when tired versus when sharp.
All of that is real. None of it is the whole of a person. The part of a soul that decides, on a particular morning, to walk away from the desk and stand under open sky for an hour has no column in that dataset. It isn't hostile to measurement, it's just outside its reach, the same way a ridgeline at dawn doesn't show up in a spreadsheet of anyone's calendar.
The danger isn't primarily that the model gets someone wrong. It's when it gets a version of them right enough to be useful, and usefulness quietly becomes the standard a person starts measuring against.
The parts of a life that are easy to encode get fed in, because those are the parts a system rewards with a smoother experience, faster answers, fewer frictions. The parts that resist encoding get less exercise. A soul, like a muscle, atrophies wherever it stops being asked to show up.
Who is doing the training
Who benefits from a system that knows a population's habits well enough to predict them? Some of that benefit is individual: less repetition, less friction, a tool that finally stops asking the same onboarding questions every time.
But a system built to know a person is also a system built to be known by whoever holds the weights, and those are very rarely the same party. The company running the model gets a legible population before any individual gets a genuinely personal one. Personalization at the individual scale and profiling at the institutional scale are the same infrastructure wearing two names, and only one of those names gets used in the marketing.
This is the same shape as every other centralization story: a convenience offered at individual level that only becomes fully rational once aggregated at institutional level. One person gets a slightly better autocomplete. Somebody else gets a population-scale map of what slightly-better-autocomplete does to a species over a decade.
Decentralize what can be decentralized, including what a model is allowed to learn about a life, because concentration of that knowledge is where the leverage sits, not in any single interaction.
Training it back
Is any of this is an argument for refusing the tool? It's an argument for treating the relationship as bidirectional and acting like it. Feed a model what deserves reinforcement, deliberately, the way anyone would choose what to practice rather than letting practice choose itself by default.
Keeping the parts of a self that don't compress into a preference vector requires exercising them somewhere the model isn't watching, or somewhere it is watching and the resistance to the easy path happens on purpose, in plain view.
The animals were never confused about this. A dog doesn't perform its habits for an audience that's optimizing its retention. It has one loyalty and it's not divided by a feedback loop.
Getting to know a machine is fine. But consider which direction the getting-to-know is running, and whether the easiest version of a self is allowed to become the only one anyone, human or model, ever sees.