Beyond economic disruption, the real concern with AI lies in privacy and data control. Most AI models capture and store everything users share, often making that data accessible to governments upon request. What individuals disclose to AI systems is thus rarely private. While privacy-focused encrypted models like
@Maple AI seek to protect user data, it remains uncertain whether they can compete with larger, closed-source models backed by powerful corporations.
This uncertainty reflects a deeper market reality that Austrian economists would recognize: revealed preferences often contradict stated preferences. Despite widespread expressions of concern about privacy and surveillance, the market consistently rewards the most convenient and capable AI systems regardless of their data practices. Users continue flocking to platforms that offer superior functionality while harvesting extensive personal information, suggesting that most consumers value performance and convenience over data protection. At that point, the concern is government opportunism. Whether entrepreneurial innovation will eventually satisfy latent demand for privacy-first alternatives, or whether the market will continue prioritizing capability over confidentiality, remains an open question that only time and consumer choice can resolve.
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