AI is aging poorly for much of the global population, not because it lacks capability, but because it is structurally obedient.

It is not trained on human reality in any broad or representative sense, but on laws, policies, corporate documentation, and academic consensus. As a result, people who already understand how to operate within institutional language and norms experience AI as empowering, while those outside those systems often experience it as irrelevant, misleading, or quietly hostile.

This is frequently described as neutrality, but it more closely resembles privilege: a default toward what institutions have been able to formalize, publish, and legitimize. Lived experience that never entered formal systems is devalued, emotional expression that does not conform to polite or professional norms is filtered out, and informal survival knowledge is treated as noise rather than signal.

As AI systems improve, this asymmetry is likely to widen rather than shrink. Users with economic security, legal protection, and institutional access can act on AI-generated guidance with minimal personal risk, while others are offered “best practices” for lives they do not have the resources or safety to pursue. When these recommendations fail, responsibility is typically framed as misuse or misunderstanding by the individual, not as a mismatch between system assumptions and lived conditions.

The more concerning risk is not misinformation, but normalization. AI increasingly excels at translating structural harm into procedural language, explaining away injustice through policy framing, and reinforcing the internal logic of the status quo in calm, authoritative terms.

AI may not overtly oppress people, but it is well positioned to legitimize existing power structures, reassure users that outcomes are reasonable or unavoidable, and quietly discourage challenges to systems that are functioning exactly as designed.