Artificial Effort
Real-effort tasks, in which participants perform cognitively costly activities whose outcomes depend on actual performance, are widely used in experimental economics. Their validity, however, rests on the assumption that a human performs them. We study whether this assumption still holds in the era of Artificial Intelligence (AI) and Large Language Models (LLMs). Using 8 canonical real-effort tasks and 23 LLMs from three major providers, we show that most tasks can now be solved accurately and at a negligible cost, while only a few resist automation. Performance improves with each model generation, and midtier models are rapidly closing the gap with frontier ones, broadening the set of wi...
| Authors | Federico Belotti, Stefano Coniglio, Antonio Cosma et al. |
|---|---|
| Primary category | cs.CY |
| Published | 17 April 2026 |
| Last updated at source | 17 April 2026 |
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