AI and the Research Team

2026-08-08

AI Summary

This study investigates how artificial intelligence reshapes the optimal size of scientific research teams, with a focus on highly codifiable domains where individual researchers increasingly leverage AI to produce high-quality outputs. By developing a span-of-control model that quantifies the interplay among task codifiability, automation coverage, and team structure, the work reveals—for the first time—that team size exhibits a quasi-concave relationship with AI capability. It further derives a closed-form threshold for effective automation coverage at which team size peaks in fully codifiable settings. Theoretical analysis predicts that such teams will reach their maximum size between 2026 and 2030, with fields containing fewer irreplaceable human tasks attaining this peak earlier.

Abstract

Artificial intelligence is associated with larger research teams, yet in mathematics, among the most codifiable fields, individual researchers working with AI now produce research-grade results. A span-of-control model reconciles these observations. AI lowers execution cost, which expands laboratory scale, and automates codifiable tasks, which lowers the member share of each unit. Team size is therefore quasi-concave in AI capability, with at most one peak. The model predicts that a fully codifiable team peaks when effective automation coverage reaches a closed-form threshold, typically near complete coverage, and, among fields with shared primitives that possess an interior peak, those with less irreducibly human task content peak first. Under explicit priors, the 90 percent forecast intervals for the fully codifiable peak span 2026 to 2030.

Problem

• artificial intelligence
• research team size
• codifiability
• span-of-control
• automation

Innovation

• span-of-control model
• AI automation
• codifiable tasks
• research team size
• quasi-concave relationship