Collaborating with AI Agents: Field Experiments on Teamwork, Productivity, and Performance

Aral and Ju run a genuinely ambitious experiment — 2,234 participants, 11,024 real advertising outputs, a live field test on X generating ~5M impressions — and use it to identify three distinct mechanisms by which AI agents reshape teamwork: more task-oriented communication, increased delegation, and AI-recognition effects that moderate both.

The 'jagged frontier' finding (AI improves text quality but not image quality) connects to the Brynjolfsson task-based economics tradition while adding behavioural texture about what actually changes inside teams.

The 'diversity collapse' mechanism — higher average quality but homogeneous outputs — is the most consequential finding for product direction: organisations adopting AI agents may be trading creative variance for efficiency, a structural shift in organisational capability that compounds over time.

For product leaders, this paper provides empirical scaffolding for thinking about what human-AI collaboration actually does to teams, not as a philosophical proposition but as a measured causal claim about communication patterns, delegation, and output character.

The experimental apparatus itself — a controlled platform producing real-world evaluated outputs — models what rigorous AI organisational research looks like and sets a methodological bar that most adjacent work fails to clear.