Reddit user tests whether AI can predict personal preferences better than chance

The user begins by recounting two objections that emerged from a prior post about personal AIs. The first objection—'How can an AI know you when you predict yourself badly?'—rests on the idea that preferences are unstable and poorly structured, leaving no inner truth for a model to read. The second—'Models don't understand lived experience'—argues that AI captures only surface patterns and then reinforces them, leading people to conform to their own caricatures. Rather than arguing against these critiques, the user agrees with the strong version of both: there is no stable inner self, and claiming an AI can know your true self is a losing argument. Instead, the user proposes a much weaker and more defensible claim: that a personal AI, trained through long correction and with explicit consent, can predict a specific person's stated preferences and objections better than chance on a defined set of questions. This is framed not as identity discovery but as prediction, and crucially, as falsifiable. To test this claim, the user generated fifty A/B/C questions about their own preferences and ran the test themselves. However, the user explicitly names the test as flawed ('Badly') and invites readers to take apart the methodology—suggesting that the post is less a finished study and more an invitation to critique the design and approach before drawing conclusions from the results.

The post sits at the intersection of two common critiques of personal AI: the philosophical objection that human preferences are unstable and poorly structured, and the practical concern that AI mirrors surface patterns rather than understanding lived experience. Rather than defending personal AI as a tool for self-knowledge, the user strategically retreats to a much narrower claim—that repeated correction can train a model to predict stated preferences on a defined question set better than chance. This move is deliberate; by lowering the stakes from 'knowing the real you' to 'predicting your answers,' the user makes the claim falsifiable and testable. The user's self-aware framing ('Badly') suggests awareness that the methodology has significant flaws, and the explicit invitation to scrutiny indicates this is a preliminary or exploratory test rather than a definitive study.