Occupation score change
IMPACT 10lawyer exposure increased 1.0 points
AI Exposure Score moved from a previous calculation to 55/100 under methodology v1.0. 4 evidence item(s) contributed.
55/100
increased by +1.0 vs last
Moderate exposure
3 Oct 2026
19 hours ago · sources: BLS OES (approx) · O*NET
4 sources
15 core tasks shown · 7 secondary hidden
Lawyers High exposure: 3 of 15 core tasks show high task automation potential : exposure 55/100 (moderate exposure).
No model has published evidence mapped to this occupation yet - the score comes from generic task assessment.
Score breakdown
Why this score?
Every component is stored with the score row and can be recomputed from the evidence table.
AI Exposure (composite)55/100
Methodology 2026-09-a · score v1.0 · input hash 22d4438e6dc4e26b
AI capability exposure73/100
Strength of current models on the capabilities this job's tasks require.
Task automation potential63/100
Task-level exposure, weighted by task importance. A component of the headline, not the headline itself.
Observed AI adoption40/100
Evidence that AI is actually being used for this work.
Labour-market signal0/100
Observed changes in postings, employment or earnings.
Evidence strength62/100
Quantity and source quality of the evidence behind the score.
4 evidence item(s) contribute. Calculated 19 hours ago. 3 Oct 2026 UTC.
Overview
Represent clients in criminal and civil litigation and other legal proceedings, draw up legal documents, or manage or advise clients on legal transactions. May specialize in a single area or may practice broadly in many areas of law.
Employment
≈730,000
approx. BLS OES
Median wage
≈$145,000
approx. BLS OES
Tasks tracked
22
linked to this occupation
Employment and wage values are approximate reference figures from the BLS Occupational Employment and Wage Statistics programme, stored with an approximation flag. Verify against the O*NET record and BLS before quoting them.
Tasks
Task-level automation exposure
Core tasks only. Exposure describes how much of the task output current AI can produce or substantially assist - not whether the task disappears. Secondary shared-baseline edges are hidden until per-occupation curation is complete.
core task · importance 4.5/5
core task · importance 4.4/5
core task · importance 4.4/5
core task · importance 4.3/5
core task · importance 4.3/5
core task · importance 4.3/5
core task · importance 4.2/5
core task · importance 4.2/5
core task · importance 4/5
core task · importance 4/5
core task · importance 4/5
core task · importance 3.9/5
core task · importance 3.8/5
core task · importance 3.7/5
core task · importance 3.6/5
Evidence
Evidence trail
4 attributed item(s). Confidence reflects study design: randomised and production deployments score higher than platform observations, which score higher than projections.
- researchneutralconfidence 90/100 · magnitude 5518 Sept 2023
In a field experiment with 758 consultants, GPT-4 users completed about 12% more tasks, about 25% faster, with about 40% higher quality on tasks within the model's capability frontier — but were about 19 percentage points more likely to produce incorrect answers on tasks outside that frontier.
The clearest published statement of the 'jagged frontier' problem: measured gains depend entirely on whether the task is inside the model's competence. This is why this site scores tasks rather than whole occupations and keeps capability separate from adoption.
Source: Dell'Acqua et al. — Navigating the Jagged Technological Frontier (Harvard Business School Working Paper 24-013) · Updated 18 Sept 2023
- adoptionneutralconfidence 80/100 · magnitude 402 Jul 2024
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
- researchincreasesconfidence 78/100 · magnitude 521 Apr 2021
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
- researchincreasesconfidence 70/100 · magnitude 5614 Jan 2024
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
Historical trend
Exposure over time
Backcast series: recomputed at quarterly checkpoints using only the models and evidence published by each date.
Reconstruction, not measurement. 5 observed daily snapshot(s) exist for this occupation; the earlier part of the curve is derived by applying the current methodology to the model releases and published evidence available at each past date.
Recent events
Signals affecting this occupation
- Research publishedIMPACT 40
Legal Research Bench: Measuring End-to-End Reliability in Long-Horizon Legal Research Agents
arXiv:2610.00609v1 Announce Type: new Abstract: Legal research is a core and time-consuming legal workflow. Lawyers must identify controlling authority, verify that it remains valid, reconcile statutes and cases, and synthesize a grounded answer. Language model agents are a natural fit for this retrieval-intensive workflow, and automating even part of it would be valuable. But that value depends on reliability: a single missing authority, stale citation, or wrong legal conclusion can make an otherwise plausible answer unusable. We introduce \textbf{Legal Research Bench} (LRB), a benchmark of 4
- Research publishedIMPACT 20
Harvey turns legal context into stronger drafts with GPT-6 Astra
GPT-6 Astra produces more structured, context-aware legal documents, freeing lawyers to focus on strategy.
- Research publishedIMPACT 20
How Cooley is accelerating IPO work with ChatGPT
Cooley built GO Public with ChatGPT Work to bring intelligence to the IPO process, helping lawyers surface issues earlier and focus judgment where it matters most.
- AI adoption signal
AI exposure is highest in high-skill occupations, with polarised effects
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.
- Research publishedIMPACT 56
About 40% of global employment is exposed to AI, 60% in advanced economies
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.
- Research published
Consultants performed better inside the AI frontier and worse outside it
In a field experiment with 758 consultants, GPT-4 users completed about 12% more tasks, about 25% faster, with about 40% higher quality on tasks within the model's capability frontier — but were about 19 percentage points more likely to produce incorrect answers on tasks outside that frontier.
- Research publishedIMPACT 52
AI Occupational Exposure Index built from 10 linked electronic advances
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.
Related
Related Occupations
Occupations sharing major SOC group (23) with Lawyers.
faq
Frequently Asked Questions
Common questions regarding Lawyers and AI exposure.
Will AI replace Lawyers?
Current AI models show an exposure score of 55/100 for Lawyers. This measures technical capability to assist or automate specific tasks rather than predicted immediate job loss. Moderate exposure reflects assistance in routine information-handling while core responsibilities require human judgment.
Which tasks in Lawyers have the highest AI exposure?
The most exposed tasks for Lawyers include "Search for and examine public and other legal records to write opinions or establish ownership." (70% exposure) and "Analyze the probable outcomes of cases, using knowledge of legal precedents." (65% exposure).
What aspects of Lawyers can AI not do yet?
Tasks with the lowest AI exposure for Lawyers include "Represent clients in court or before government agencies." (5% exposure) and "Gather evidence to formulate defense or to initiate legal actions by such means as interviewing clients and witnesses t…" (10% exposure). These responsibilities typically require physical dexterity, licensed human accountability, or complex interpersonal navigation.
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Notice: All ratings shown are AI-estimated technical exposure scores measuring technical capabilities across O*NET task statements, not predicted job loss. Contains information from O*NET 31.0 by the U.S. Department of Labor, ETA, used under CC BY 4.0.