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Working Papers

As IDE scholars' projects progress, they share early-stage research through working papers that offer insights into their findings and methodology.

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Working Papers Chaining Tasks, Redefining Work: A Theory of AI Automation

Jan 2026

John Horton | Associate Professor, MIT Sloan School of Management

Peyman Shahidi | Ph.D. Candidate

Brendan Lucier | Microsoft Research

Nicole Immorlica | Yale University, Microsoft Research

Mert Demirer | MIT

 

This paper develops a model that predicts that (1) AI-executed steps co-occur in chains, (2) dispersion of AI-exposed steps lowers AI execution at the job level, and (3) adjacency to AI-executed steps increases the likelihood that a step is AI-executed.

Working Papers The Latent Role of Open Models in the AI Economy

Nov 2025

Frank Nagle | Research Scientist

Daniel Yue

 

The result uncovered in this working paper suggest that closed model dominance reflects powerful drivers beyond model capabilities and price – whether switching costs, brand loyalty, or information frictions – with the economic magnitude of these hidden factors proving far larger than previously recognized, reframing open models as a largely latent, but high-potential, source of value in the AI economy.

Working Papers How fast are algorithms reducing the demands on memory? A survey of progress in space complexity

Nov 2025

Jayson Lynch | Research Scientist, CSAIL

Neil Thompson | Research Scientist, MIT Sloan School of Management and CSAIL

Hayden Rome

Jeffery Li

Chirag Falor

 

In this paper, scholars present the first broad survey of how algorithmic progress has improved memory usage (space complexity) by analyzing 118 of the most important algorithm problems in computer science, reviewing the 800+ algorithms used to solve them.