Working Papers

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

Featured

The Diminishing Returns to Human Recruiting in Online Labor Markets

May, 2026

John Horton | Associate Professor, MIT Sloan School of Management

Emma Wiles | IDE Digital Fellow and Assistant Professor, Questrom School of Business, Boston University

Working Papers General Social Agents

Jun 2026

John Horton | Associate Professor, MIT Sloan School of Management

Benjamin Manning | Ph.D. Candidate

Useful social science theories predict behavior across settings. However, applying a theory to make predictions in new settings is challenging: rarely can it be done without ad hoc modifications to account for setting-specific factors. We argue that AI agents put in simulations of those novel settings offer an alternative for applying theory, requiring minimal or no modifications.

Working Papers Judgment-Free AI Advisors: The Comparative Advantage of Artificial Advisors for Information Disclosure

Eric So | Professor, MIT Sloan School of Management

Abigail Sussman

Yujin Yang

This paper finds that people rely on a competence-judgment tradeoff to choose between human and AI advisors, evaluating whether the benefit of expertise outweighs the cost of social judgment.

Working Papers Beyond AI Exposure: Which Tasks are Cost-Effective to Automate with Computer Vision?

May 2026

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

Martin Fleming | Research Scientist, IDE

Wensu Li | Postdoctoral Associate

Maja S. Svanberg
Brian C. Goehring

This paper’s findings suggest that AI job displacement will be substantial, but also gradual – and therefore there is room for policy and retraining to mitigate unemployment impacts.

Working Papers Is there “Secret Sauce” in Large Language Model Development?

May 2026

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

Matthias Mertens | Research Scientist at Future Tech

Natalia Fischl-Lanzoni

Do leading LLM developers possess a proprietary “secret sauce,” or is LLM performance driven by scaling up compute? Using training and benchmark data for 809 models released between 2022 and 2025, the authors estimate scaling-law regressions with release-date and developer fixed effects.

Working Papers The Augmentation Trap: AI Productivity and the Cost of Cognitive Offloading

Sinan Aral | Director, MIT IDE; Professor, MIT Sloan School of Management

Michael Caosun | Ph.D. Candidate

Authors develop a dynamic model in which a decision-maker chooses AI usage intensity for a worker over time, trading immediate productivity against the erosion of worker skill, testing when decision-makers opt for AI use and the productivity of workers as a result.

Working Papers The Diminishing Returns to Human Recruiting in Online Labor Markets

May 2026

John Horton | Associate Professor, MIT Sloan School of Management

Emma Wiles | IDE Digital Fellow and Assistant Professor, Questrom School of Business, Boston University

John Fallon | Boston University

This paper explores whether human intermediaries add value when employers already have access to algorithmic screening tools.

Working Papers Crashing Waves vs. Rising Tides: Preliminary Findings on AI Automation from Thousands of Worker Evaluations of Labor Market Tasks

Apr 2026

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

Martin Fleming | Research Scientist, IDE

Matthias Mertens | Research Scientist at Future Tech

Brittany Harris | Data Analyst, CSAIL

Wensu Li | Postdoctoral Associate

The authors propose that AI automation is a continuum between: crashing waves where AI capabilities surge abruptly over small sets of tasks, and rising tides where the increase in AI capabilities is more continuous and broad-based.

Working Papers Economics of Human and AI Collaboration: When is Partial Automation More Attractive than Full Automation?

Mar 2026

Wensu Li | Postdoctoral Associate

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

Martin Fleming | Research Scientist, IDE

This paper develops a unified framework for evaluating the optimal degree of task automation. Moving beyond binary automate-or-not assessments, we model automation intensity as a continuous choice in which firms minimize costs by selecting an AI accuracy level, from no automation through partial human-AI collaboration to full automation.

Featured Podcast

Featured Insights

Featured Research Projects

Upcoming Events

Featured Publication

Featured Bio

Education

Partner with the IDE

Search the site

The IDE Newsletter

MIT Initiative on the Digital Economy

Working Papers