Floor Layers
United States · BLS Employment Projections 2024–34 (figures for SOC 47-2042 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
- 0.0%
- GPT-4 rater basis
- 0.0%
β = direct exposure (E1) + 0.5 × exposure when tools are available (E2), per the source repository's definition.
14 rated tasks · 0 tasks with β ≥ 0.5 (0.0%)
- 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
- 0.0%
Theoretical exposure index weighted by measured Claude usage, per the original report's definition.
14 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
- Measure and cut flooring material to fit rooms
- Prepare subfloor surfaces by scraping and leveling
- Apply adhesive and install floor coverings
- Estimate material quantities and project costs
- Inspect finished floors for quality and evenness
About This Occupation
If you work as a Floor Layer, AI has very little direct impact on your hands-on installation work. With an automation risk of 5/100 and overall exposure at 8%, this role faces very low transformation. The highest-impact area is estimating material quantities and project costs at 35% automation. This is classified as an 'augment' role. BLS projects +10% growth through 2034. Physical floor installation on varied surfaces remains beyond current automation capabilities, though AI estimation tools are improving.
ISCO-08 classification
Floor Layers and Tile Setters
Definition
ILO original text (English)
Floor layers and tile setters install, maintain and repair flooring, and cover floors, walls and other surfaces with carpets, tiles or mosaic panels for decorative or other purposes.
Definition & vocabulary source
Source: International Labour Organization (ILO) — ISCO-08 Structure
License: ILO CC BY 4.0
Official occupational information
- ONET47-2041.00
Carpet Installers
Lay and install carpet from rolls or blocks on floors. Install padding and trim flooring materials.
View original - ONET47-2042.00
Floor Layers, Except Carpet, Wood, and Hard Tiles
Apply blocks, strips, or sheets of shock-absorbing, sound-deadening, or decorative coverings to floors.
View original - ONET47-2044.00
Tile and Stone Setters
Apply hard tile, stone, and comparable materials to walls, floors, ceilings, countertops, and roof decks.
View original
Frequently Asked Questions
The Anthropic Economic Index puts observed exposure at 0.0%. The OpenAI "GPTs are GPTs" rubric puts occupation-level β at 0.0% 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.