Urban and Regional Planners
United States · BLS Employment Projections 2024–34 (figures for SOC 19-3051 occupational group)
AI exposure in published research
Figures below are reproduced from external datasets without modification. Where a dataset does not cover this occupation, the value is shown as — rather than as zero.
Data provider: OpenAI · "GPTs are GPTs"
Time basis: 2023 baselineex-ante estimate
Scored against GPT-4-generation capability.
- Human rater basis
- 58.0%
- GPT-4 rater basis
- 47.7%
β = direct exposure (E1) + 0.5 × exposure when tools are available (E2), per the source repository's definition.
25 rated tasks · 24 tasks with β ≥ 0.5 (96.0%)
- Version:
- gh-main-0471612
- License:
- MIT License · Copyright (c) 2024 OpenAI
Data provider: Anthropic Economic Index
Time basis: Published 2026-03-05composite index
- Observed exposure
- 9.6%
Theoretical exposure index weighted by measured Claude usage, per the original report's definition.
17 tasks · usage observed in 0 · mean 0.0%
Data: Anthropic Economic Index — labor_market_impacts, CC-BY, https://huggingface.co/datasets/Anthropic/EconomicIndex
AI Changing Work uses Anthropic Economic Index data under CC-BY; Anthropic does not endorse or sponsor this site or its analyses.
- Version:
- hf-lmi-2026-03
- License:
- CC BY 4.0
- Observation period:
- — (not applicable to this release)
- Model:
- — (not stated by the source)
- Definition source:
- https://www.anthropic.com/research/labor-market-impacts
2023 prediction vs observation-based index published 2026-03-05
One card (GPTs are GPTs) is a 2023 estimate of what AI could theoretically do; the other (Anthropic Economic Index) is built from observed usage and was published on 2026-03-05 — that is its publication date, not the period it observed. They measure different things, so the two figures cannot be added, averaged, or ranked against each other. The older figure is kept here as a baseline for comparison rather than removed.
Caution: the Anthropic figures — observed exposure and task penetration — take the Eloundou β as one of their inputs. The two sides resembling each other is therefore not evidence that the earlier prediction came true; reading it that way is circular reasoning.
The Anthropic figures are measured on Claude users, who are not the whole economy and not the whole workforce.
The mapping of O*NET tasks and occupation codes was performed by AI Changing Work. The source figures themselves were not modified.
These indices are not forecasts. Which point in time each one belongs to is stated on the badge on its card.
Task Breakdown
- Analyze demographic and geographic data
- Generate zoning and land use simulations
- Facilitate community engagement meetings
- Draft planning reports and policy recommendations
About This Occupation
If you work as a Urban and Regional Planners, AI is reshaping your profession. With an automation risk of 29/100 and overall exposure at 37%, this role faces medium transformation. The highest-impact area is analyze demographic and geographic data at 70% automation. This is classified as an 'augment' role. BLS projects +4% growth through 2034. AI-powered GIS and simulation tools are becoming indispensable for modern urban planning.
ISCO-08 classification
Town and Traffic Planners
Definition
ILO original text (English)
Town and traffic planners develop and implement plans and policies for the controlled use of urban and rural land and for traffic systems. They conduct research and provide advice on economic, environmental and social factors affecting land use and traffic flows.
Definition & vocabulary source
Source: International Labour Organization (ILO) — ISCO-08 Structure
License: ILO CC BY 4.0
Official occupational information
- ONET19-3051.00
Urban and Regional Planners
Develop comprehensive plans and programs for use of land and physical facilities of jurisdictions, such as towns, cities, counties, and metropolitan areas.
View original
Frequently Asked Questions
The Anthropic Economic Index puts observed exposure at 9.6%. The OpenAI "GPTs are GPTs" rubric puts occupation-level β at 58.0% under human raters. Both figures are reproduced from published research without modification.
They come from two published datasets: the Anthropic Economic Index (labor_market_impacts, CC BY 4.0) and the OpenAI "GPTs are GPTs" exposure rubric (MIT License, Copyright (c) 2024 OpenAI). AI Changing Work maps them onto O*NET occupation and task codes and does not calculate exposure scores of its own. AI Changing Work uses Anthropic Economic Index data under CC-BY; Anthropic does not endorse or sponsor this site or its analyses.
No. They are a diagnosis of exposure as measured at the time each source dataset was published. AI Changing Work publishes no prediction of future automation or job displacement for this occupation.
Recent Changes Affecting This Occupation
Mar 2026: New blog post: AI impact analysis for urban designers
[Source: aichanging.work]Mar 2026: Published evergreen blog post analyzing AI impact on urban planning: 37% exposure, 29% risk, community engagement at 12% is among the most AI-resistant tasks.
[Source: AI Changing Work Blog]