Will AI replace Patternmakers, Wood?
Most of the work in Patternmakers, Wood still leans on things AI struggles with — research rates its theoretical AI reach at only ~26%, and real-world use lower still.
O*NET-SOC 51-7032
How your 19 core tasks split
Top = what GPT-4 judged AI could speed up. Bottom = how much AI was actually used for these tasks (Anthropic's March 2026 report, usage from Aug & Nov 2025). The gap is the real story.
Back in 2023, GPT-4 judged AI could, in theory, assist with a relatively low share of this job's tasks (~26%). By late 2025, real-world AI use had reached about 3% of its task activity (still rare). The gap between that 2023 forecast and today is the real story.
Where this job sits among 738 jobs
Each dot is one of 738 U.S. jobs. Right = AI can do more of it. Up = AI is actually used more.
The signals here line up
Theoretical reach (~26%), real-world use (~3%) and the task-level picture mostly agree — so this read is more reliable than for jobs where the signals contradict each other. Even so, AI-risk estimates shift by model (a 2026 study saw the "high-risk" share swing 2.7%–51.5%), so treat these as directional, not destiny.
See all 19 tasks, ratedBased on real task-level AI scores — click to collapse
- Estimate costs for patternmaking jobs.
- Maintain pattern records for reference.
- Compute dimensions, areas, volumes, and weights.
- Inventory equipment and supplies, ordering parts and tools as necessary.
- Read blueprints, drawings, or written specifications to determine sizes and shapes of patterns and required machine setups.
- Lay out patterns on wood stock and draw outlines of units, sectional patterns, or full-scale mock-ups of products, based on blueprint specifications and sketches, and using marking and measuring devices.
- Fit, fasten, and assemble wood parts together to form patterns, models, or sections, using glue, nails, dowels, bolts, and screws.
- Trim, smooth, and shape surfaces, and plane, shave, file, scrape, and sand models to attain specified shapes, using hand tools.
- Divide patterns into sections according to shapes of castings to facilitate removal of patterns from molds.
- Verify dimensions of completed patterns, using templates, straightedges, calipers, or protractors.
- Correct patterns to compensate for defects in castings.
- Set up, operate, and adjust a variety of woodworking machines such as bandsaws and lathes to cut and shape sections, parts, and patterns, according to specifications.
- Finish completed products or models with shellac, lacquer, wax, or paint.
- Mark identifying information such as colors or codes on patterns, parts, and templates to indicate assembly methods.
- Repair broken or damaged patterns.
- Glue fillets along interior angles of patterns.
- Construct wooden models, templates, full scale mock-ups, jigs, or molds for shaping parts of products.
- Select lumber to be used for patterns.
- Collect and store patterns and lumber.
How we measured this — and how fresh it is
AI's theoretical reach data: 2023
From GPTs-are-GPTs (Eloundou et al.), where GPT-4 rated how much of each task an AI tool could meaningfully speed up. This is the most recent open, commercially-usable occupation-level potential dataset — it dates to 2023. Newer multi-model re-runs exist but swing wildly (one 2026 study saw "high-risk" jobs range 2.7%–51.5% by model) and aren't openly licensed, so we show the stable 2023 baseline and pair it with newer real-world data.
Real-world AI use 2026 report
From the Anthropic Economic Index, which observes how real Claude conversations map onto each occupation's tasks. Published in Anthropic's March 2026 labor-market report, based on usage measured in Aug & Nov 2025 (Sonnet 4 / 4.5).
Task list & ratings O*NET 30.3
Tasks come from O*NET 30.3. Each task's "AI can do / speeds up / still on you" tier uses the real task-level exposure scores from GPTs-are-GPTs (E1 / E2 / E0) — not a guess from keywords.
Sources: O*NET 30.3 (CC BY 4.0) · GPTs-are-GPTs (MIT, 2023) · Anthropic Economic Index (CC BY, Aug & Nov 2025). Page compiled June 2026. "O*NET" is a trademark of the U.S. Department of Labor.
This page is for general informational purposes only and is not career, financial, or employment advice. AI exposure reflects research estimates of task overlap, not predictions about any individual's job, employer, or future employment.