The AI Trap We're Walking Into (unvoid.substack.com) AI

The article argues that while LLM capability is becoming cheaper and more “commoditized,” the cost of running effective agentic systems is rising sharply due to repeated context and token-heavy reasoning, pushing advantage toward capital-rich enterprises. It also claims that improvements increasingly depend on extracted human labor—such as data labeling, corrections, and scraped open-source contributions—which the piece says concentrates value with AI providers while leaving non-buyers to do lower-paid handwork. The author concludes that policy and technical shifts like open weights, local inference, and paying people for “data dignity” are needed to avoid repeating past cycles of power concentration.