Clinical Data Managers

In pre-registered experiments with experienced open-source developers, access to AI tools increased the number of tasks completed by about 26% relative to control.

Complements the Copilot study with a more experienced population and a pre-registered design. Reinforces a high task-automation component for implementation work; no employment effect is measured.

Source: Cui, Demirer, Jaffe, Musolff, Peng & Zheng — The Effects of Generative AI on High Skilled Work (arXiv:2412.04638) · Updated 5 Dec 2024

Claude Opus 4.1 extended sustained autonomous performance on multi-step coding and research tasks, with providers reporting multi-hour task horizons.

Agentic reliability is the capability that moves automation from single-prompt assistance toward completing whole workflows. It affects multi-step task exposure, not physical work.

Source: Anthropic · Updated 5 Aug 2025

Using O*NET task descriptions scored by GPT-4, the authors estimate that about 80% of the US workforce could have at least 10% of their tasks affected by large language models, and about 19% could see at least 50% of their tasks affected.

This is a *capability and exposure* estimate produced by having a model rate task descriptions — not a measurement of employment change. It is the most-cited exposure figure in the field and is included here as an exposure input, weighted accordingly.

Source: Eloundou, Manning, Mishkin & Rock — GPTs are GPTs (arXiv:2303.10130) · Updated 24 Aug 2023

The OECD Employment Outlook reports that occupations most exposed to AI are predominantly high-skill, and that exposure does not equal job loss, while noting emerging evidence of effects on task composition and entry-level work.

Cross-country official analysis. Used to inform the interpretation that exposure is not a prediction of unemployment.

Source: OECD — Employment Outlook 2024 · Updated 2 Jul 2024

The authors construct an AI Occupational Exposure Index by linking 10 AI application categories to 52 O*NET abilities, finding that high-exposure occupations are more common in high-wage, high-education industries.

Important because it establishes the *direction* of exposure: much AI exposure sits in high-skill work rather than routine manual work. Included here as a methodology reference and an exposure input.

Source: Felten, Raj & Seamans — Occupational, industry, and geographic exposure to artificial intelligence (Strategic Management Journal) · Updated 1 Apr 2021

The AI Index reports continued increases in the share of organisations using generative AI in at least one business function, alongside rapid benchmark gains on reasoning and coding tasks.

Survey-based adoption evidence across firms. Useful for the adoption component and for interpreting why capability and adoption diverge by occupation.

Source: Stanford HAI — Artificial Intelligence Index Report 2025 · Updated 1 Apr 2025

Aggregated and anonymised Claude conversation data mapped to O*NET tasks shows that usage is concentrated in a small number of occupations and tasks — notably software development, writing and technical analysis — and that a substantial share of usage is augmentation rather than full automation of a task.

This is the primary *observed adoption* input, and it is deliberately kept separate from capability. A model being able to do a task does not mean firms are using it. Usage data is self-selected (Claude users) and therefore over-represents digital and technical occupations.

Source: Anthropic Economic Index · Updated 27 Feb 2025

Using an AI exposure index, the IMF estimates that around 40% of global employment and around 60% of employment in advanced economies is exposed to AI, with exposure concentrated in high-skill occupations.

A projection-based exposure estimate across countries. It is a scenario measure, not an observed outcome, and is weighted as such in this index.

Source: IMF — Gen-AI: Artificial Intelligence and the Future of Work · Updated 14 Jan 2024