
arXiv:2607.02467v1 Announce Type: cross Abstract: Whether pairing people with AI helps or hurts is usually reported as a single average effect. Using a real-money prediction market (Polymarket) as an objective, externally resolved benchmark, this pilot shows that the value of human-AI collaboration depends on a specific, measurable form of human capital. Analyzed at the level of the individual forecaster, hybrid performance is trimodal: most people either deferred to the model (matching it) or used it to rubber-stamp a prior guess (performing worse than the model alone), while a minority engag
The proliferation of accessible AI models makes human-AI collaboration a pressing question for productivity and decision-making across industries.
This research provides crucial insights into optimizing human-AI teams, moving beyond simplistic average effects to focus on specific human capital traits that drive effective collaboration.
The understanding of hybrid intelligence shifts from general model benchmarks to the specific human aptitude and interaction styles required for successful AI integration.
- · Organizations focused on upskilling human-AI collaboration
- · AI model developers designing for nuanced human interaction
- · Consultancies specializing in organizational hybrid intelligence
- · Organizations implementing AI without considering human capital
- · AI integration strategies based solely on model performance
- · Forecasters who defer or rubber-stamp AI without critical engagement
Companies will re-evaluate their AI deployment strategies to focus on human training and individual interaction styles rather than just model capabilities.
New metrics and assessment tools will emerge to identify and cultivate 'super-collaborators' in human-AI teams.
The definition of 'AI literacy' will expand to include explicit skills in discerning when and how to integrate AI insights for optimal outcomes, potentially leading to new educational pathways.
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Read at arXiv cs.AI