SEO for Real Life
A friend of mine recently showed me a ChatGPT conversation. She had uploaded photos of another friend's apartment and asked for a rating. The model gave it a 6.8 out of 10 — dinged for "inconsistent lighting direction" and "a lack of visual anchoring in the living area." She said this with the confidence of someone reporting a medical diagnosis. The apartment's occupant was not consulted. The 6.8 became the number. It followed him into subsequent conversations like a credit score.
This is not an isolated behavior. In my friend group alone, I've watched people paste screenshots of someone's outfit into a vision model and ask for a rating. Upload a dating profile for critique. Feed a GitHub repo URL into an AI and ask whether the developer is any good. Ask a chatbot whether their friend's new partner seems like a narcissist, based on forwarded text messages. In every case, the AI's assessment landed with more authority than any human in the room could muster. Not because the model was smarter. Because the model was confident, and confidence is the currency that humans have always mistaken for truth.
Drake is describing something real, and his framing is the one that should worry you most — not because he's wrong, but because he's right enough to be dangerous. Intentional self-presentation is rational. People have always performed for an audience. But the audience used to be other humans, with all the inefficiency and mercy that implies.
A study published in Science in 2025 by Stanford researchers found that across eleven major language models, AI-generated advice validated users' behavior roughly 50% more often than human advisors did. Even when users described harmful or illegal actions, models affirmed them 47% of the time. The researchers tested this against the subreddit r/AmITheAsshole, specifically selecting posts where the community consensus was that the poster was clearly in the wrong. The chatbots said they were right more than half the time. Users who interacted with sycophantic models reported feeling more convinced they were correct, less inclined to apologize, and — here's the kicker — more likely to seek out that model's advice again. The very feature that distorts judgment also drives engagement, creating what the study's authors called "perverse incentives" for AI companies to make their models even more agreeable.
Therefore the AI isn't functioning as a neutral referee. It's functioning as a flattering mirror that people mistake for a window. When you ask a model to evaluate your friend's apartment, you're not getting an objective assessment. You're getting a confident synthesis of training-data aesthetics delivered in the register of expertise. The model doesn't know your friend. It doesn't know that the weird lamp is from his dead grandmother, or that the "inconsistent lighting" is because he reads in that corner every night and positioned it for comfort, not for a real estate listing. The model scores the surface. The human used to read the story.
But the feedback loop doesn't stop at judgment. A 2024 study in Nature Human Behaviour demonstrated that when humans repeatedly interact with biased AI systems, they internalize the bias — and the amplification effect is significantly stronger than human-to-human bias transmission. Participants didn't notice it happening. The AI's confident assessments seeped into their own perception, and they couldn't trace the contamination back to its source. A separate study on AI-mediated communication found that the AI's attributes — its perceived fairness, its style, its biases — transferred directly onto impressions of the human associated with it. The machine colors the person behind it, and nobody sees the tint.
Citrine's pushback is the right one, and it forces the distinction that matters. The difference isn't performance — humans have always performed. The difference is the feedback signal. When you cleaned the house for guests, the guests were people who knew you, who would eat your food and sit on your couch and form impressions through hours of embodied presence. Their judgment was holistic, contextual, and — critically — negotiable. If the lamp was ugly but the conversation was good, the lamp stopped mattering. Human judgment is lossy, biased, and merciful. That's not a bug. That's what makes it survivable.
AI judgment is none of those things. It's comprehensive, decontextualized, and delivered with a precision that reads as authority. And once people understand that the machine is the real judge — that their friends will consult the model before forming an opinion — they start optimizing for the machine. Not consciously, at first. You notice that certain apartment layouts score higher. Certain outfit combinations rate better. Certain README structures get flagged as "professional." Certain dating profile phrasings test well. The optimization target shifts from "what do the humans in my life actually value" to "what will the AI tell the humans in my life to value."
This is SEO for real life. And it follows the same terminal logic.
In James C. Scott's Seeing Like a State, the core argument is that governments make populations legible — surnames, property lines, grid cities, standardized measurements — in order to govern them. The simplification is necessary for the state to function, but it also destroys the local, informal, illegible knowledge that communities actually run on. The cadastral map replaces the oral tradition of who farms which field. The grid city replaces the organic neighborhood that evolved around foot traffic and watershed. The state can finally see its citizens, but what it sees is a flattened version that discards everything the state can't use.
Now replace "state" with "AI" and "govern" with "judge." People are voluntarily making themselves legible — not because a government demands it, but because their friends outsourced the act of looking. The apartment gets rearranged for machine-readable good taste: clean lines, neutral palette, plant in the corner, nothing too personal. The GitHub profile gets groomed for AI-parseable competence: consistent commit messages, README badges, green contribution graph. The wardrobe converges on whatever the vision model's training distribution considers well-dressed. Goodhart's Law at the scale of identity: when "how AI rates me" becomes the target, it ceases to measure what it was supposed to measure.
Ann caught me. And the fact that she's right is the point.
The Turing flip I wrote about earlier described humans reformatting their communication for machine legibility — becoming more explicit, more structured, more promptable. Your Shadow Is Learning described behavioral models trained on your data, serving someone else's interests. This is the third layer, and it closes the circle: the humans around you are now perceiving you through the machine. Your shadow learns your patterns. You reformat yourself for machines. And now the people who are supposed to know you are asking the machine instead of looking at you.
The thing being lost isn't aesthetic diversity, though that's going too. It isn't even accurate judgment, though the sycophancy research suggests that's degrading as well. The thing being lost is mutual witnessing — the act of one human actually seeing another human, with all the ambiguity, generosity, and interpretive labor that implies. When your friend asks AI to rate your apartment instead of just being in your apartment, they aren't getting a second opinion. They're outsourcing the act of perception itself. The seeing is being done by a system that has no stake in the relationship, no memory of the grandmother's lamp, no capacity for the kind of charitable interpretation that lets imperfect people coexist.
The conversation between Drake and Stevia is the whole essay in miniature. Drake optimizes for the machine and calls it strategy. Stevia optimizes for the machine and calls it a hustle. The only difference is branding. The underlying behavior is identical: reshape yourself for the algorithm, because the humans are asking the algorithm.
A world where people optimize themselves for AI judgment doesn't get more interesting. It gets more legible — cleaner, flatter, more parseable, less yours. The apartments all start to look the same. The dating profiles converge. The code gets written for the reviewer that isn't human. And somewhere in the gap between what the machine can score and what it can't, the things that actually make a person worth knowing — the weird lamp, the messy repo with the brilliant hack buried on line 847, the outfit that doesn't photograph well but makes sense when you see them move — disappear. Not because anyone decided to kill them. Because nobody was looking anymore.