Strength of current models on the capabilities this job's tasks require.
Task automation potential35/100
Task-level exposure, weighted by task importance. A component of the headline, not the headline itself.
Observed AI adoption0/100
Evidence that AI is actually being used for this work.
Labour-market signal0/100
Observed changes in postings, employment or earnings.
Evidence strength34/100
Quantity and source quality of the evidence behind the score.
1 evidence item(s) contribute. Calculated 19 hours ago. 3 Oct 2026 UTC.
Overview
Perform tasks involving physical labor at construction sites. May operate hand and power tools of all types: air hammers, earth tampers, cement mixers, small mechanical hoists, surveying and measuring equipment, and a variety of other equipment and instruments. May clean and prepare sites, dig trenches, set braces to support the sides of excavations, erect scaffolding, and clean up rubble, debris, and other waste materials. May assist other craft workers.
Employment
≈1,400,000
approx. BLS OES
Median wage
≈$42,000
approx. BLS OES
Tasks tracked
27
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.
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.
1 attributed item(s). Confidence reflects study design: randomised and production deployments score higher than platform observations, which score higher than projections.
capabilityincreasesconfidence 66/100 · magnitude 344 Jan 2024
Mobile ALOHA demonstrated bimanual mobile manipulation on everyday tasks using imitation learning on relatively low-cost hardware.
A marker of physical-task automation progress. Included with deliberately modest weight: laboratory demonstrations of manipulation do not translate into deployed automation in unstructured workplaces.
Backcast series: recomputed at quarterly checkpoints using only the models and evidence published by each date.
Score · 23 points · 2021-01-01 to 2026-07-01
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
Labour market update
▲ IMPACT 35
Artificial Intelligence and Economic Growth: Opportunities, Challenges, and Implications for Employment
Artificial Intelligence (AI) has emerged as a transformative general-purpose technology with significant implications for economic growth, productivity, industrial development, labour markets, and the future of employment. Advances in machine learning, deep learning, natural language processing, computer vision, generative AI, robotics, and automated decision systems are enabling organizations to perform increasingly sophisticated cognitive and operational tasks. AI has the potential to increase productivity, reduce production and transaction costs, stimulate innovation, improve resource alloc
Workforce data/Confidence:80 · high/Verified by AIKillsWhat
Research published
▲ IMPACT 40
Impact of Artificial Intelligence and Automation on Employment Patterns, Workforce Skills, and Future Career Opportunities
Artificial Intelligence (AI) and automation are transforming the global labour market by reshaping employment patterns, redefining workforce skill requirements, and creating new career opportunities across diverse industries. The integration of intelligent technologies such as machine learning, robotics, natural language processing, computer vision, and intelligent automation has significantly improved productivity, operational efficiency, and innovation while simultaneously altering traditional job roles and organizational structures. This study examines the impact of Artificial Intelligence
Labor research/Confidence:74 · moderate/Verified by AIKillsWhat
New capability
▲ IMPACT 34
Low-cost imitation learning for generalisable mobile manipulation
Mobile ALOHA demonstrated bimanual mobile manipulation on everyday tasks using imitation learning on relatively low-cost hardware.
Verified signal/Confidence:66 · moderate/Verified by AIKillsWhat
Common questions regarding Construction Laborers and AI exposure.
Will AI replace Construction Laborers?
Current AI models show an exposure score of 30/100 for Construction Laborers. This measures technical capability to assist or automate specific tasks rather than predicted immediate job loss. Low exposure indicates significant physical, clinical, or relational tasks that current AI cannot autonomously execute.
Which tasks in Construction Laborers have the highest AI exposure?
The most exposed tasks for Construction Laborers include "Read plans, instructions, or specifications to determine work activities." (15% exposure) and "Measure, mark, or record openings or distances to layout areas where construction work will be performed." (10% exposure).
What aspects of Construction Laborers can AI not do yet?
Tasks with the lowest AI exposure for Construction Laborers include "Dig ditches or trenches, backfill excavations, or compact and level earth to grade specifications, using picks, shovels…" (5% exposure) and "Clean or prepare construction sites to eliminate possible hazards." (5% 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.