Segmental Pavers
United States · BLS Employment Projections 2024–34: this SOC code is not among the published items.
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
- 8.3%
- GPT-4 rater basis
- 4.2%
β = direct exposure (E1) + 0.5 × exposure when tools are available (E2), per the source repository's definition.
12 rated tasks · 1 tasks with β ≥ 0.5 (8.3%)
- 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
- —
This occupation code is not included in this dataset.
Theoretical exposure index weighted by measured Claude usage, per the original report's definition.
12 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
- Prepare and compact base materials for paver installation
- Lay pavers in specified patterns and maintain alignment
- Estimate project materials, labor, and costs from plans
About This Occupation
If you work as a Segmental Paver, AI has very limited impact on your outdoor manual trade. With an automation risk of 7/100 and overall exposure at 13%, this role faces very low transformation. Project estimation sees the highest automation at 42%. BLS projects +3% growth through 2034.
ISCO-08 classification
Concrete Placers, Concrete Finishers and Related Workers
Indirect mapping — this occupation is linked to the ISCO unit group by a rule-based fallback.
Definition
ILO original text (English)
Concrete placers, concrete finishers and related workers erect reinforced concrete frameworks and structures, make forms for moulding concrete, reinforce concrete surfaces, cement openings in walls or casings for wells, finish and repair cement surfaces and carry out terrazzo work.
Definition & vocabulary source
Source: International Labour Organization (ILO) — ISCO-08 Structure
License: ILO CC BY 4.0
Official occupational information
- ONET47-2051.00
Cement Masons and Concrete Finishers
Smooth and finish surfaces of poured concrete, such as floors, walks, sidewalks, roads, or curbs using a variety of hand and power tools. Align forms for sidewalks, curbs, or gutters; patch voids; and use saws to cut expansion joints.
View original - ONET47-2053.00
Terrazzo Workers and Finishers
Apply a mixture of cement, sand, pigment, or marble chips to floors, stairways, and cabinet fixtures to fashion durable and decorative surfaces.
View original - ONET47-4091.00
Segmental Pavers
Lay out, cut, and place segmental paving units. Includes installers of bedding and restraining materials for the paving units.
View original
Frequently Asked Questions
The OpenAI "GPTs are GPTs" rubric puts occupation-level β at 8.3% 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.