Moravec's Paradox and AI Automation Limits

These two 2025 research papers collaboratively examine **Moravec's Paradox**, which posits that skills effortless for humans (like perception and mobility) are computationally difficult for machines, while complex reasoning tasks (like math or chess) are comparatively easy for AI. The Wikipedia entry introduces the paradox, explaining its evolutionary basis: skills acquired over millions of years are deeply encoded and hard to reverse-engineer, while abstract thought is evolutionarily recent and less efficient. A research paper further demonstrates this gap with an **"auditory Turing test,"** where state-of-the-art AI models catastrophically fail (achieving less than 7% accuracy) on simple human listening tasks involving overlapping speech and noise, confirming the paradox in the auditory domain. Finally, an economics preprint incorporates the paradox into a **model of economic growth**, arguing that the high or infinite computational cost of automating physical, sensorimotor tasks means human labor in these bottleneck areas will persist, preventing the labor share of income from collapsing to zero as some AGI models predict.Sources:https://arxiv.org/pdf/2507.23091https://arxiv.org/pdf/2509.24466https://en.wikipedia.org/wiki/Moravec%27s_paradox

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