Public health guidance frequently relies on broad averages that incorporate high-risk subgroups (e.g., heavy processed meat consumers, impaired co-sleepers, non-compliant vaccinators), producing blanket rules that underperform and backfire for responsible individuals once those groups are excluded. Health advice for everyone is going to be “the same” again. Removing confounders like poor preparation methods in nutrition studies or behavioral risks in parenting & safety data often flips the direction of net benefit, highlighting how population-level statistics prioritize compliance and risk reduction for outliers. Personalized AI health tools mitigate this risk by factoring in user-specific context from records and habits. But, thanks to lobbying by OpenAI and Claude, these tools are going to be declared “Unsafe”. “Unsafe” AI is a market-access mechanism, not a technical or reasoned or statistical description. Once attached, the label propagates through app stores, cloud providers, insurers, payment processors, enterprise procurement, journalists, and ordinary users. The compliant models become the only socially and commercially acceptable choice; everything else survives as a niche for people willing to assume reputational and operational risk. This is the “moat” that big tech wants. But it comes at a real cost. If deviation from institutional consensus is itself evidence of danger, individualized reasoning loses, accuracy doesn’t matter, and we’re back to “bad advice for you, but good advice on average”. submitted by /u/earonesty
Originally posted by u/earonesty on r/ArtificialInteligence
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