Transformative AI Research Economist, Economic Research 

Anthropic
San Francisco, CAPosted 11 March 2026

Tech Stack

Job Description

About Anthropic Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems. About the Role As a Transformative AI Research Economist at Anthropic, you will build macroeconomic models of AI that could be genuinely transformative and develop the scenario-based forecasting tools that let us reason quantitatively about economic trajectories with no historical precedent. You will work on questions of aggregate growth, income distribution, and economic governance under scenarios that most of the profession has not yet modeled seriously. You will ground projections in microeconomic signals from the Anthropic Economic Index — usage patterns across millions of real-world AI interactions, surfaced through privacy-preserving measurement — so that scenario forecasts are disciplined by what we actually observe about task transformation and productivity. You will use frontier methods in growth theory, computational macro, and structural estimation, and contribute to AI-powered tools that expand what economic research can do. Our team combines rigorous empirical methods with novel measurement approaches. We're building first-of-its-kind datasets tracking AI's impact on labor markets, productivity, and economic transformation. Using our privacy-preserving measurement system , we analyze millions of real-world AI interactions to understand how AI augments and automates work across different occupations and tasks. Responsibilities Build macroeconomic models of transformative AI spanning growth, labor markets, and income distribution Develop and maintain scenario-based forecasting tools; publish forecasts for GDP, productivity, and unemployment under a range of AI-capability trajectories Ground macroeconomic projections in microeconomic data from the Anthropic Economic Index, constraining theory with observed patterns of adoption and task transformation Analyze questions of income distribution and economic governance under transformative-AI scenarios Contribute to the development of AI-powered research tools for economics Contribute to Economic Index Reports and publish Research Briefs on first-order questions as they arise Build and maintain relationships with academic institutions, policy think tanks, and other research partners Amplify external engagement through research publications, policy briefs, and presentations to diverse stakeholders You May Be a Good Fit If You Have PhD in Economics, or an exceptional candidate close to completion Background in macroeconomics, growth theory, or public finance ideally with exposure to task-based frameworks and labor economics A research record that engages seriously with the possibility of transformative AI — you treat the scenarios in this posting as live questions worth modeling rigorously, not speculation to be hedged against Relevant experience in some of: Macroeconomic modeling and structural estimation Scenario-based and time-series forecasting Task-based approaches to technological change Computational methods, agent-based modeling, or large-scale simulation Income distribution and inequality Using large language models in the research workflow Technical skills including: Proficiency in Python, Julia, or similar for computational economics Facility with AI coding agents as part of a research workflow Comfort learning new technical tools and frameworks Demonstrated ability to: Lead research projects from conception to publication Ship on tight timelines and revise in public as new data arrives Communicate technical findings to diverse audiences Strong interest in ensuring AI development benefits humanity Some Examples of Our Recent Work Labor market impacts of AI: A new measure and early evidence ... (truncated, view full listing at source)
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