AI exposure by occupation, O*NET 45-4023.00

Will AI replace Log Graders and Scalers?

Log Graders and Scalers: AI can already handle 2 of 12 tasks; most of the work is hands-on, and the part AI can address mostly needs a standard-tier model.

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Every task, scored

The 12 tasks O*NET 30.3 lists for Log Graders and Scalers, from most to least exposed. 2 reach 60 or more, the level at which AI already handles a task.

Task AI exposure Evidence Model tier Importance
Measure felled logs or loads of pulpwood to calculate volume, weight, dimensions, and marketable value, using measuring devices and conversion tables. 80 Estimated Standard 4.5
Evaluate log characteristics and determine grades, using established criteria. 65 Estimated Standard 4.7
Jab logs with metal ends of scale sticks, and inspect logs to ascertain characteristics or defects such as water damage, splits, knots, broken ends, rotten areas, twists, and curves. 20 Estimated Efficient 4.3
Drive to sawmills, wharfs, or skids to inspect logs or pulpwood. 20 Estimated Efficient 4.0
Record data about individual trees or load volumes into tally books or hand-held collection terminals. 15 Estimated No tier 4.6
Paint identification marks of specified colors on logs to identify grades or species, using spray cans, or call out grades to log markers. 15 Estimated No tier 4.4
Identify logs of substandard or special grade so that they can be returned to shippers, regraded, recut, or transferred for other processing. 15 Estimated No tier 4.3
Arrange for hauling of logs to appropriate mill sites. 15 Estimated No tier 4.2
Weigh log trucks before and after unloading, and record load weights and supplier identities. 15 Estimated No tier 4.2
Measure log lengths and mark boles for bucking into logs, according to specifications. 15 Estimated No tier 4.1
Communicate with coworkers by signals to direct log movement. 15 Estimated No tier 4.1
Saw felled trees into lengths. 15 Estimated No tier 3.9

Observed: scored from AI use recorded in the Anthropic Economic Index (June 2026 release), 0 of 12 tasks. Estimated: scored from the task's O*NET 30.3 work activity, 12 of 12. Importance is O*NET's 1 to 5 rating of how central the task is. Model tier is the lowest tier that can do the task under the published rubric; "No tier" marks hands-on work.

The model tier this role needs

A standard-tier model or lower covers 34% of the role's importance-weighted task work. 34% of the work is something AI can address at some tier, and 66% is hands-on.

Efficient, standard and frontier tiers come from the JobRoute model tier reference. The recommended tier is the lowest one that covers 80% of the work AI can address. How the method works.

Wages, employment and outlook

US employment
3,070
Median annual wage
$46,330
Middle half of earners
$37,780 to $56,250
Projected change, 2024 to 2034
-0.7%

Employment and wages: BLS Occupational Employment and Wage Statistics, May 2025, national. Projection: BLS Employment Projections 2024 to 2034.

Adjacent roles

The occupations that reuse the most of this role's skills, with the usual time to move. These are the first places to look when you plan to redeploy or retrain people in this role.

Skill overlap and transition time from the JobRoute role adjacency graph on O*NET 30.3 skills.

Questions about Log Graders and Scalers and AI

Will AI replace Log Graders and Scalers?

AI already handles 2 of the 12 tasks O*NET lists for this occupation, at a task exposure of 60 or more, and the role's importance-weighted exposure is 26 out of 100. JobRoute scores tasks, not whole jobs, so the score shows how much of the work changes rather than whether the job disappears. BLS projects employment to change by -0.7% from 2024 to 2034.

What is the AI exposure of Log Graders and Scalers?

26 out of 100 on JobRoute's task exposure measure, the importance-weighted average of the role's O*NET task scores. Higher means more of the work is AI-doable today. 0 of the 12 task scores are observed in the Anthropic Economic Index (June 2026 release), and the other 12 are estimated from each task's O*NET work activity.

Which Log Graders and Scalers tasks can AI already do?

The tasks with the highest exposure are: Measure felled logs or loads of pulpwood to calculate volume, weight, dimensions, and marketable value, using measuring devices and conversion tables; Evaluate log characteristics and determine grades, using established criteria. They are the tasks that reach 60 or more.

What AI model tier does this role need?

A standard-tier model or lower covers 34% of the role's importance-weighted task work, and 66% is hands-on work that no model tier covers. JobRoute assigns each task the lowest tier that can do it under a published rubric and recommends the lowest tier that covers 80% of the work AI can address.

How many people work as Log Graders and Scalers, and what do they earn?

3,070 people in the United States, with a median annual wage of $46,330 (BLS Occupational Employment and Wage Statistics, May 2025). BLS Employment Projections expect a change of -0.7% from 2024 to 2034.

How to cite

Data version 2026-11-02, JobRoute method v1.0.0. Download all occupations or this occupation as JSON. Methodology.

JobRoute (2026). AI exposure: Log Graders and Scalers. Data version 2026-11-02, method v1.0.0. Built on O*NET 30.3, BLS OEWS May 2025 and the Anthropic Economic Index (June 2026). CC BY 4.0. https://www.jobroute.ai/jobs/log-graders-and-scalers

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