Real-Effort Tasks in the Age of AI: An Obsolete Assumption?

Real-effort tasks, once the bedrock of experimental economics, are coming under scrutiny thanks to the relentless march of Artificial Intelligence. The premise that these tasks gauge genuine human effort is being questioned in the face of AI's growing capabilities. With 23 large language models (LLMs) tested across eight canonical tasks, the findings are clear: AI achieves accuracy with remarkable ease and at minimal cost. This isn't some distant possibility. it's happening now.

The AI Performance Surge

AI models, spanning three leading providers, show an impressive ability to complete tasks traditionally requiring human cognitive effort. With each model iteration, performance improves significantly. Mid-tier models, in particular, are closing in on the frontier. The implications are straightforward: the accessibility of models capable of effectively automating these tasks is broadening, leaving fewer tasks out of reach for AI.

Color me skeptical, but the assumption that human effort is measurable through these tasks is looking increasingly shaky. If AI continues on this trajectory, how long before the very notion of measuring genuine human effort becomes obsolete?

Monetary Incentives: A Red Herring?

Interestingly, the study finds that offering monetary incentives verbally does nothing to enhance LLM performance. Quite the revelation, given how often incentives are considered a cornerstone of motivation. Apparently, AI isn't swayed by promises of financial gain, an advantage, perhaps, over their human counterparts who might be.

What they're not telling you: the traditional methods of ensuring authenticity in task completion are crumbling. When participants can effortlessly offload tasks onto an LLM, the authenticity of the outcome is no longer guaranteed. We're entering an era where these tasks may be more a measure of AI prowess than human capability.

Rethinking Methodologies

Let's apply some rigor here. As AI and LLMs continue to infiltrate domains previously dominated by human effort, the methodologies of experimental economics need a rigorous reevaluation. It's not just about identifying which tasks resist automation. it's about understanding how the integration of AI reshapes human performance measurement.

I've seen this pattern before: technology advancing faster than our frameworks can adapt. If our current systems fail to account for AI's influence, we risk basing conclusions on fundamentally flawed assumptions. In the end, only by acknowledging and adapting to these shifts can the field maintain its relevance and integrity.