The AI Trap We're Walking Into

5 hours ago

The cost of LLM inference is dropping rapidly, with open models closing in on frontier models, making AI capabilities a commodity.
Despite falling token prices, agentic work becomes more expensive due to high and unpredictable token consumption, making large-scale deployment costly.
AI agents concentrate power with capital-rich entities who can afford high inference costs, potentially widening income inequality.
A global underpaid workforce corrects and labels AI data, often under poor conditions, creating a feedback loop that improves models without fair compensation.
Open-source contributions and creative work are scraped without consent to train AI models, then repackaged and sold back, exploiting generosity.
The AI economy is structured so that those with capital and platforms capture value, while those providing human input see diminishing returns.
This dynamic mirrors historical patterns of technology concentrating power, but different outcomes are possible through local inference, fair compensation for data labor, efficiency gains, and targeted regulation.
The key challenge is ensuring AI benefits society broadly rather than exacerbating inequality, requiring deliberate choices to steer its development equitably.

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