Patternmakers, Metal and Plastic
51-4062.00Median wage $58,000/yr1,470 employed (US)Rank #215 of 923 scored · top 23% by substitution
Lay out, machine, fit, and assemble castings and parts to metal or plastic foundry patterns, core boxes, or match plates.
Sub-scores
0–100 · band = confidence interval from rater disagreement
Substitution38
Exposure33
Augmentation50
Substitution — the headline: capability discounted by cost, barriers and adoption.
Exposure — technical capability alone, regardless of whether anyone deploys it.
Augmentation — how much AI assists without replacing. High here + moderate substitution = a changing job, not a disappearing one.
Tasks on the substitution scale
15 rated tasks, binned by substitution score.
Position among all scored occupations
Distribution of 923 occupation scores; the marker is this occupation.
Tasks with substitution ≥ 70
7%
Run 1.0.0-draft.1 · computed 2026-08-05 · rater panel: claude-sonnet-5, claude-haiku-4-5-20251001 · intervals span rater disagreement.
Why this score
The five weighted dimensions of the composite, averaged across this occupation's tasks (importance-weighted, panel mean). Exact weights and formulas: /api/v1/methodology.
Task automatabilityw 35%35
panel mean rating 2.4/5 → substitution pressure 35/100
Technical feasibility todayw 20%29
panel mean rating 2.2/5 → substitution pressure 29/100
Cost vs. human wagew 15%30
panel mean rating 2.2/5 → substitution pressure 30/100
Adoption barriersw 20%inverted — strong barriers lower the score63
panel mean rating 2.5/5 (barrier strength) → substitution pressure 63/100
Sector adoption velocityw 10%26
panel mean rating 2.0/5 → substitution pressure 26/100
Task breakdown (15 tasks)
Substitution pressure per task, weighted by O*NET importance in the composite. Expand a task for the full rater audit trail — every rating, every model, every rationale.
Mark identification numbers or symbols onto patterns or templates.
75CI 72–77 · exposure 75 · augmentation 50 · importance 3.5/5 · click for rater detail
Mark identification numbers or symbols onto patterns or templates.
75| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 3/5 | Many mid-to-large manufacturing shops use automated marking systems, but adoption is uneven: small job shops and artisanal work often remain manual. Production adoption exists but is not as universal as in fully digitized industries like high-volume electronics. |
| Sector adoption velocity | claude-sonnet-5 | 3/5 | Manufacturing is a moderate adopter of automation broadly, and marking/engraving automation is common in CNC shops, though many small patternmaking shops still do this manually. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI-assisted tools (template recognition, mark-placement suggestions) can guide human workers and catch marking errors, improving speed and accuracy. However, the task itself is straightforward enough that augmentation is useful but not transformative compared to full automation potential. |
| Augmentation potential | claude-sonnet-5 | 3/5 | CAD/CAM software assists patternmakers by auto-generating and placing identification marks, saving time even where full automation isn't implemented. |
| Task automatability | claude-haiku-4-5-20251001 | 4/5 | Vision-based systems can reliably detect pattern positions and apply identification marks via robotic arms or print heads with high consistency. This is a well-defined, repetitive task with clear inputs and outputs that modern computer vision and robotic automation can handle end-to-end, easily achieving >50% time savings at equal quality. |
| Task automatability | claude-sonnet-5 | 4/5 | Marking identification numbers or symbols is a simple, well-defined labeling task that can be automated via CNC engraving, laser marking, or CAD/CAM-driven systems that already generate and apply such labels automatically. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | There are minimal regulatory or licensing barriers to automating pattern marking; no law requires a human to perform this task. The only friction is typical setup costs and operator training, which are business decisions rather than legal constraints. |
| Adoption barriers | claude-sonnet-5 | 1/5 | There is no licensing, liability, or regulatory requirement mandating a human perform this simple labeling subtask. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 4/5 | Once a robotic marking system is installed, the per-unit cost of marking (electricity, amortized equipment) is typically far lower than paying a human worker to hand-mark each pattern, especially at scale. Cost ratio favors automation by a significant margin. |
| Cost vs. human wage | claude-sonnet-5 | 4/5 | Once integrated into a CAD/CAM or CNC workflow, marking identifiers costs very little in machine time compared to manual labeling labor, though initial setup/integration has some cost. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 4/5 | Deployed industrial vision and robotic marking systems (laser marking, CNC engraving, print systems) perform this reliably in manufacturing today. While some setup and calibration is required per pattern type, these are mature production technologies used across metalworking and plastics shops. |
| Technical feasibility today | claude-sonnet-5 | 4/5 | Automated marking systems (laser etchers, engraving machines integrated with CAD/CAM software) are widely deployed in manufacturing settings and reliably mark parts with identifiers today. |
Program computerized numerical control machine tools.
62CI 49–75 · exposure 62 · augmentation 88 · importance 4.6/5 · click for rater detail
Program computerized numerical control machine tools.
62| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 4/5 | Manufacturing, especially CNC operations in metalworking and plastics, has been adopting CAM automation for decades and continues rapid deployment; most modern shops use at least semi-automated code generation. This is a digitized, information-heavy task in an industry with strong adoption infrastructure. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Manufacturing/machining sectors are moderate adopters of digital tools but lag behind information/professional services in deploying advanced AI-driven automation on the shop floor. |
| Augmentation potential | claude-haiku-4-5-20251001 | 5/5 | CAM software dramatically augments patternmakers' productivity by automating routine code generation and toolpath optimization, while humans retain control over design intent, material selection, and quality validation. This is a textbook example of human-AI collaboration in manufacturing. |
| Augmentation potential | claude-sonnet-5 | 4/5 | CAM software, simulation, and increasingly AI-assisted toolpath optimization substantially speed up programming work while the patternmaker still verifies and refines outputs. |
| Task automatability | claude-haiku-4-5-20251001 | 4/5 | Programming CNC machines involves converting design specifications into machine code; modern CAM software can automate much of this translation process, reducing manual programming time by 50%+ while maintaining output quality. Human oversight of toolpaths and parameters is typically still needed, preventing a full 5 rating. |
| Task automatability | claude-sonnet-5 | 3/5 | CAM software with AI-assisted toolpath generation can automate much of CNC program creation, but patternmaking often requires custom geometry and iterative fitting that still needs skilled human setup and verification.' |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | CNC programming has no formal licensing requirement or legal mandate for human approval, and automation faces minimal regulatory or organizational friction. Adoption is limited mainly by shop culture and trust in AI-generated code rather than hard legal barriers. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement mandates a human programmer, but liability for costly tooling errors and machine damage creates practical caution around fully autonomous deployment. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 4/5 | CAM software licenses are relatively inexpensive (hundreds to thousands annually) compared to the hourly wage of skilled patternmakers ($25–45/hour loaded), and inference/integration costs are minimal once set up. Cost per task is substantially lower than human labor. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | CAM software licenses plus skilled operator oversight remain significant costs; savings exist over pure manual programming but are not order-of-magnitude versus a technician's wage given verification needs. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 4/5 | CAM software (e.g., Mastercam, Fusion 360, SolidCAM) is mature and widely deployed in manufacturing environments to generate CNC code from designs; these systems reliably handle standard geometries and cutting strategies in production settings. Edge cases and complex optimizations may still require human intervention. |
| Technical feasibility today | claude-sonnet-5 | 3/5 | CAM/CAD software with automated G-code generation is widely deployed and used in production, but full autonomous programming for complex custom patterns still requires human review and adjustment. |
Create computer models of patterns or parts, using modeling software.
62CI 49–75 · exposure 62 · augmentation 88 · importance 4.2/5 · click for rater detail
Create computer models of patterns or parts, using modeling software.
62| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 4/5 | Manufacturing, automotive, and aerospace sectors are actively adopting CAD automation and generative design tools; adoption is rapid in digitized, capital-intensive industries where these tasks concentrate. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Manufacturing and industrial design sectors have historically slower digitization and AI adoption compared to purely digital/information industries, though CAD tools themselves are already entrenched. |
| Augmentation potential | claude-haiku-4-5-20251001 | 5/5 | AI-assisted CAD features (auto-dimensioning, design suggestions, parametric generation, constraint solving) demonstrably amplify human patternmaker productivity by handling routine geometry work while preserving human control over design intent and manufacturing feasibility. |
| Augmentation potential | claude-sonnet-5 | 4/5 | Modern CAD/CAM software with AI-assisted features (auto-dimensioning, generative design suggestions, simulation) substantially speeds up a patternmaker's modeling workflow while the human retains design control. |
| Task automatability | claude-haiku-4-5-20251001 | 4/5 | Current CAD and 3D modeling software with AI-assisted features (parametric design, auto-generation) can substantially automate pattern creation from specifications, though some design judgment and iteration typically remain human responsibilities. The task maps well to learned representations of geometry and manufacturing constraints. |
| Task automatability | claude-sonnet-5 | 3/5 | CAD/CAM modeling of patterns is a well-defined digital task, but translating physical part requirements, tolerances, and shrinkage/draft allowances into accurate models still requires significant human expertise and judgment, limiting full automation. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | No licensing requirement mandates human patternmakers; adoption is primarily driven by technical capability and cost. Some organizational inertia and quality assurance requirements exist, but they are not hard legal barriers. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement for this specific task, but quality/safety consequences of pattern errors in metal/plastic manufacturing create meaningful oversight and liability incentives to keep skilled humans in the loop. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 4/5 | Software licensing and compute costs are modest per pattern, while skilled patternmakers command significant wages; AI-assisted modeling is typically orders of magnitude cheaper per output unit once software is in place. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | Modeling software still requires a trained CAD/CAM operator or patternmaker to run it, review outputs, and correct for manufacturing-specific constraints, so labor costs remain substantial relative to any AI-driven efficiency gains. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 4/5 | Mature CAD software (SolidWorks, Fusion 360, AutoCAD) is deployed at scale in manufacturing; AI-assisted modeling plugins and generative design tools exist in production use, though most require human direction and refinement of results. |
| Technical feasibility today | claude-sonnet-5 | 3/5 | CAD software with parametric modeling, generative design, and AI-assisted feature recognition exists and is used in industry, but fully autonomous creation of production-ready patternmaking models without skilled operator input is not yet standard practice. |
Repair and rework templates and patterns.
57CI 26–87 · exposure 53 · augmentation 63 · importance 3.9/5 · click for rater detail
Repair and rework templates and patterns.
57| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 4/5 | Manufacturing and industrial sectors have been rapidly adopting AI-assisted CAD, automated quality control, and machine learning for defect detection over the past 3–5 years; these are not experimental but increasingly standard in digitized facilities. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Manufacturing and metalworking trades are slower AI adopters overall, with automation focused on CNC/CAD tools rather than full task replacement of physical rework. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI assists patternmakers by automating routine inspection, suggesting modifications, and handling repetitive rework tasks, significantly raising their efficiency while human expertise remains valuable for complex or non-standard patterns. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI-assisted CAD/CAM tools can help plan modifications, generate updated specs, or simulate fit adjustments, improving precision and speeding the design portion of rework. |
| Task automatability | claude-haiku-4-5-20251001 | 5/5 | AI-driven CAD/CAM systems can automatically generate rework specifications, detect pattern flaws via computer vision, and modify digital templates with minimal human intervention. This meets the 50% time-saving threshold through end-to-end automation of inspection, modification, and output. |
| Task automatability | claude-sonnet-5 | 2/5 | Repairing physical metal/plastic templates requires hands-on manual dexterity, machining, and fitting that current AI systems cannot perform end-to-end; at best AI can assist with CAD-based redesign planning. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | No licensing requirement, liability, or regulatory barrier mandates human patternmakers for template repair. Integration with existing manufacturing systems is the main friction, not legal or professional requirement. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No strict licensing requirement, but physical tooling, equipment access, and craft skill create practical barriers that prevent simple AI substitution. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 5/5 | Automated inspection and CAD-based rework cost per unit is orders of magnitude cheaper than skilled patternmaker labor when amortized across volume production and integrated into existing manufacturing pipelines. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | AI cannot perform the physical repair work itself, so the human machinist's labor cost remains necessary; there is no AI substitute cost to compare favorably. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 4/5 | Mature CAD software, automated inspection systems, and machine-learning-based defect detection are deployed in manufacturing environments today. While some complex bespoke rework may require human judgment, routine repair and rework of templates is increasingly handled by production systems. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product autonomously repairs or reworks physical patterns and templates in production; this remains a manual/skilled-trade task supported at most by CAD software, not AI agents. |
Read and interpret blueprints or drawings of parts to be cast or patterns to be made, compute dimensions, and plan operational sequences.
47CI 30–65 · exposure 45 · augmentation 75 · importance 3.8/5 · click for rater detail
Read and interpret blueprints or drawings of parts to be cast or patterns to be made, compute dimensions, and plan operational sequences.
47| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Manufacturing and metalworking remain traditionally slow to adopt digital transformation. While some advanced shops pilot AI-assisted design, broad production adoption of blueprint automation in patternmaking is still nascent, with most firms relying on experienced workers' manual processes. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Manufacturing and skilled trades are relatively slow adopters of AI compared to information-sector occupations, with CAD assistance more common than full task automation. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI can powerfully assist patternmakers by instantly extracting dimensions, suggesting optimal sequences, and flagging ambiguities in blueprints, significantly accelerating the interpretation phase while the worker retains judgment over final operational decisions and quality control. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI-assisted CAD tools, dimension calculators, and blueprint OCR/interpretation aids can meaningfully speed up reading drawings and computing dimensions while the patternmaker retains control of sequencing decisions. |
| Task automatability | claude-haiku-4-5-20251001 | 4/5 | Reading technical blueprints, computing dimensions, and sequencing operations are largely rule-based and data-extractable tasks that current AI systems can handle effectively. Computer vision and document understanding models can reliably interpret technical drawings, extract dimensional data, and generate operational plans with minimal human oversight, potentially reducing task time by >50%. |
| Task automatability | claude-sonnet-5 | 2/5 | AI can assist in interpreting blueprints and computing dimensions, but full end-to-end planning of operational sequences for physical patternmaking requires spatial reasoning and integration with shop-floor tooling that current systems cannot reliably replace. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | There are minimal legal or regulatory barriers to automating blueprint reading and dimension planning—no licensing requirement mandates a human perform this analysis. Adoption friction exists mainly from organizational inertia and worker resistance rather than hard regulatory or liability constraints. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No strict licensing requirement, but high liability for dimensional errors in cast parts and reliance on experienced human judgment for tolerances create meaningful organizational friction. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 4/5 | AI inference on document images and dimension extraction is cheap (pennies per blueprint), and integration overhead is moderate once initial setup is complete. The all-in cost per analyzed blueprint is substantially lower than paying a skilled patternmaker for the same interpretation work. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | Specialized CAM software plus AI vision tools have licensing and integration costs comparable to or higher than skilled labor for this task, especially given required verification and rework risk. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 3/5 | Document understanding and technical drawing interpretation products exist and are improving, but production deployments remain limited to narrow use cases. Most real-world blueprint reading still involves custom legacy formats, annotations, and context-dependent judgment that deployed AI systems handle with material error rates. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | CAD/CAM software with AI-assisted drawing interpretation exists, but no deployed product reliably reads arbitrary blueprints and autonomously plans full operational sequences for metal/plastic patternmaking in production settings. |
Verify conformance of patterns or template dimensions to specifications, using measuring instruments such as calipers, scales, and micrometers.
46CI 30–61 · exposure 45 · augmentation 63 · importance 4.4/5 · click for rater detail
Verify conformance of patterns or template dimensions to specifications, using measuring instruments such as calipers, scales, and micrometers.
46| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Patternmaking, particularly metal and plastic, is concentrated in small to mid-sized job shops and contract manufacturers with lower digitization levels. Adoption of automated inspection remains slower than in high-volume automotive or electronics. Pilots exist but production-scale AOI deployment in patternmaking is less common than in other manufacturing sectors. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Metalworking and plastics manufacturing is a lower-digitization physical sector where AI-driven inspection automation is progressing slowly compared to information/professional services sectors. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | Machine vision and automated measuring systems can directly augment human inspectors by flagging measurements, auto-logging results, and highlighting out-of-spec items, reducing tedium and error. An inspector working with AI-assisted measurement tools can focus on judgment calls and exceptions rather than rote measurement, substantially raising inspection productivity. |
| Augmentation potential | claude-sonnet-5 | 3/5 | Digital calipers, automated measurement software, and vision-assisted inspection tools can help patternmakers verify dimensions faster and more accurately, though the human remains central to the physical measurement process. |
| Task automatability | claude-haiku-4-5-20251001 | 4/5 | This task involves straightforward dimensional measurement and comparison to specifications—a process well-suited to AI-equipped measurement systems and machine vision. Current computer vision + caliper/micrometer integration systems can reliably capture dimensions and auto-check conformance, achieving significant time savings. Full end-to-end automation requires capturing a pattern/template, measuring key dimensions, and comparing to specifications—all technically feasible with ≥50% time savings over manual inspection. |
| Task automatability | claude-sonnet-5 | 2/5 | Precise physical measurement of patterns/templates requires manual instrument use and manipulation of physical objects, which current AI cannot perform end-to-end without robotic hardware and vision integration not commonly deployed for this niche task.' |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | No legal licensing or mandatory human sign-off is required for conformance verification itself, yet custom pattern work often demands human judgment about whether edge cases meet intent. Quality assurance oversight and customer confidence in automated inspection create organizational friction; many shops prefer human inspectors as final gatekeepers. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement blocks automation, but precision manufacturing quality control often has established human sign-off processes and physical setup requirements creating moderate organizational friction. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 3/5 | Machine vision inspection systems require upfront capital (camera, lighting, software licensing, integration) plus calibration and maintenance, while patternmakers' hourly labor costs are moderate. For small shops or low-volume work, the amortized cost per inspection task remains comparable to manual inspection. For high-volume patterning, automated systems can be cheaper, but breakeven depends heavily on volume. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | Automated CMM/vision inspection systems can be cost-effective at high volume but require significant capital investment in fixtures and integration, making the all-in cost comparable to or higher than a skilled patternmaker for low-to-medium volume specialty work. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 3/5 | Automated optical inspection (AOI) and machine vision products exist in manufacturing and are deployed in some quality-control environments, but their adoption in patternmaking shops remains spotty. Vision systems can measure simple geometric features reliably, yet complex or irregular patterns still challenge deployed systems. Products exist but lack the ubiquity and track record of mature solutions in higher-volume manufacturing. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Coordinate measuring machines and vision-based inspection systems exist and are used in manufacturing quality control, but general AI systems performing this specific verification task on metal/plastic patterns with calipers/micrometers is not a mainstream deployed product for this occupation. |
Lay out and draw or scribe patterns onto material, using compasses, protractors, rulers, scribes, or other instruments.
33CI 30–35 · exposure 25 · augmentation 63 · importance 3.2/5 · click for rater detail
Lay out and draw or scribe patterns onto material, using compasses, protractors, rulers, scribes, or other instruments.
33| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Manufacturing sectors show moderate adoption of CAD and CNC systems for high-volume work, but many patternmakers still perform manual layout and scribing, particularly in small shops and custom fabrication where automation ROI is unclear. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Manufacturing and machining trades adopt automation more slowly than digital/information sectors, though CAD/CNC adoption has been ongoing for decades in this niche field. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | CAD software and digital design tools meaningfully assist patternmakers by automating the geometry calculation and design phase, but the human remains essential for material assessment, positioning, and manual execution on the shop floor. |
| Augmentation potential | claude-sonnet-5 | 4/5 | CAD software substantially augments patternmakers by allowing precise digital layout, scaling, and design iteration before or in place of manual scribing, significantly boosting productivity while the skilled worker remains central to fabrication and verification. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | Pattern layout and scribing requires precise spatial reasoning and manual dexterity in a physical environment. Current AI can generate digital pattern designs but cannot reliably execute the physical scribing, marking, and material-specific adjustments that this task demands end-to-end. |
| Task automatability | claude-sonnet-5 | 2/5 | CAD/CAM software can generate patterns digitally, but the physical act of laying out and scribing onto material using hand instruments requires manual dexterity and physical presence that current AI/robotics cannot fully replicate end-to-end at equal quality across varied materials and shapes. |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | While there are no strict legal licensing requirements, material-specific expertise and quality-control requirements create organizational friction; supervisors often prefer human judgment for complex layouts and material-specific nuances. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement, but this is a physical, tactile skilled-trade task requiring precision judgment on materials, meaning organizational and technical friction (equipment retooling, custom part handling) limits substitution rather than legal barriers. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | Current automation (CAD + CNC marking systems) exists but requires significant capital investment and integration costs that may not outweigh the labor cost of skilled patternmakers, especially for varied or custom work. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | CAD/CAM software and CNC equipment have high upfront capital and programming costs; for one-off or custom pattern work the human skilled labor is often still cost-competitive versus automated tooling investment. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | While CAD software can design patterns digitally, no deployed system reliably automates the physical act of laying out, drawing, and scribing patterns onto actual material with the precision required in production settings. The task remains predominantly manual. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | CNC and CAD systems are deployed for pattern generation in many shops, but fully automated physical scribing/layout replacing skilled patternmakers on varied jobs remains limited to specific high-volume, standardized contexts rather than general practice. |
Set up and operate machine tools, such as milling machines, lathes, drill presses, and grinders, to machine castings or patterns.
31CI 30–32 · exposure 25 · augmentation 63 · importance 4.4/5 · click for rater detail
Set up and operate machine tools, such as milling machines, lathes, drill presses, and grinders, to machine castings or patterns.
31| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 3/5 | CNC adoption is well-established in manufacturing, but patternmaking shops—often smaller, specialized, and focused on custom work—adopt at a slower pace than mass-production sectors. Pilots and hybrid human-machine workflows are common, but full displacement remains limited. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Metalworking and pattern-making are traditional manufacturing trades with slower digitization and automation adoption compared to information-sector occupations, though CNC and robotics adoption is gradually increasing in machining generally. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI-assisted CNC programming, real-time tool wear monitoring, and defect detection can significantly boost patternmaker productivity by automating setup calculations and quality checks while the human remains in control of decisions and inspection. These tools are increasingly available and demonstrably raise output per operator. |
| Augmentation potential | claude-sonnet-5 | 3/5 | CAM software, CNC programming assistance, and simulation tools meaningfully help patternmakers plan tool paths and machining sequences, improving productivity while humans still perform physical setup and operation. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While CNC machines can be programmed to execute repetitive cutting operations, the task requires judgment about setup, tool selection, material properties, and quality inspection that varies by part geometry and casting defects. Current AI-assisted CNC systems can handle standard runs but struggle with the adaptive problem-solving and real-time adjustments needed for diverse pattern work. |
| Task automatability | claude-sonnet-5 | 2/5 | This requires physical machine setup, tool changes, and hands-on operation of milling machines, lathes, and grinders on physical materials, which current AI cannot perform end-to-end; only CNC programming portions are automatable, not the physical setup/operation described.' |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | Machine tool operation requires operator certification and safety licensing in many jurisdictions, and liability for part defects falls on the operator and employer. However, these are not absolute legal blockers to automation—only supervisory requirements, creating moderate friction rather than hard barriers. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No licensing requirement, but physical safety, precision tolerances, and the need for skilled judgment in setup create meaningful organizational and technical friction against full automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | CNC machine time and integration costs are significant, and the overhead of programming, setup, and human oversight for pattern variety means total cost per task remains comparable to or exceeds skilled patternmaker labor for non-standardized work. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | Industrial robotics/CNC automation for flexible, low-volume patternmaking work requires significant capital investment in robotics and fixturing that often exceeds the cost of a skilled machinist for varied, low-volume pattern work. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | Automated CNC systems exist and are widely deployed, but they operate under tight tolerances and require skilled human oversight for setup, tool changes, safety, and quality verification. No fully autonomous end-to-end system reliably handles the diversity of patterns, materials, and inspection without human intervention in production settings. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | CAM software and CNC controls exist and are mature for programming, but the task as stated includes physical setup and operation of multiple conventional machine tools, which robotic automation handles only in narrow, highly engineered production lines, not general patternmaking shops. |
Design and create templates, patterns, or coreboxes according to work orders, sample parts, or mockups.
28CI 25–30 · exposure 25 · augmentation 50 · importance 3.5/5 · click for rater detail
Design and create templates, patterns, or coreboxes according to work orders, sample parts, or mockups.
28| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Patternmaking occurs in specialized manufacturing sectors with slower digital adoption and heavy reliance on skilled craft workers; while CAD tools have penetrated some shops, widespread automation of the full task remains limited and adoption of autonomous pattern generation in production is minimal. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Manufacturing and metalworking trades are historically slower to adopt AI compared to information-sector work, with digitization of design workflows progressing but physical fabrication largely unchanged. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | Generative design and CAD software can assist patternmakers in visualizing geometries, exploring design variants, and documenting patterns, improving the design phase; however, the core task of crafting and validating the physical pattern still relies primarily on human expertise and decision-making. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI-assisted CAD/CAM tools, generative design, and 3D modeling software meaningfully speed up the design phase of pattern creation, though the physical crafting portion sees little AI assistance. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | Creating physical templates, patterns, and coreboxes requires understanding spatial geometry, materials, and manufacturing tolerances from work orders or samples, but also hands-on fabrication and iterative refinement that current AI cannot perform end-to-end. AI can assist in computational design steps but cannot execute the physical crafting or validate fit against real parts. |
| Task automatability | claude-sonnet-5 | 2/5 | This is a physical fabrication task requiring hands-on shaping of tooling and metal/plastic pattern creation; AI can assist with CAD design but cannot perform the physical construction, so the full task does not meet the 50% time-saving bar end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Patternmaking in regulated industries (aerospace, automotive) often requires human sign-off on specifications and fit; customer expectations heavily favor human expertise and accountability; and the physical embodiment of patterns demands human judgment and responsibility for quality and safety. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No licensing requirement blocks AI use, but quality-critical tooling for manufacturing carries liability risk for defects, and physical fabrication requires human operators and machinery, creating moderate organizational friction. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | Even with CAD automation, the specialized equipment, materials, and skilled labor required to fabricate and test patterns remain significant; AI tools for design can reduce some overhead but do not replace the full cost of materials, equipment maintenance, and human oversight needed to validate patterns. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | While AI-assisted CAD can reduce design time, the physical fabrication remains labor- and equipment-intensive, keeping overall costs comparable to or only modestly better than skilled human patternmakers. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | While CAD software and generative design tools exist for pattern generation, production patternmaking demands tactile judgment, material manipulation, and iterative testing against actual mockups and parts—tasks where deployed AI systems lack agency and validation in real manufacturing environments. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | CAD/CAM software with AI-assisted design features exist and are used in production, but no deployed product autonomously designs and physically creates coreboxes or patterns from sample parts without significant human machining and finishing work. |
Paint or lacquer patterns.
25CI 23–28 · exposure 16 · augmentation 13 · importance 3.1/5 · click for rater detail
Paint or lacquer patterns.
25| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Patternmaking is a traditional, craft-oriented occupation in small to medium shops with low digitization and high customization. Adoption of AI or robotics for this task remains extremely limited; sectors employing patternmakers (tool-and-die, mold-making) lag in automation compared to high-volume manufacturing. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Patternmaking is a low-digitization, small-scale manufacturing trade with minimal AI/robotics adoption reported for finishing steps like painting or lacquering. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could assist with pattern design generation or visualization, but direct augmentation of the painting or lacquering act itself is minimal. Current tools do not meaningfully enhance a patternmaker's ability to apply finish more productively while remaining in the loop on a per-pattern basis. |
| Augmentation potential | claude-sonnet-5 | 1/5 | AI offers essentially no direct assistance to a human physically painting or lacquering a pattern, as this is a hands-on physical task outside current AI/robotic tool capabilities in this context. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | Painting or lacquering patterns involves fine manual dexterity, spatial judgment, and aesthetic decisions that current AI systems cannot reliably perform end-to-end. While AI can theoretically control robotic arms, the precision needed to paint intricate patterns and the adaptive decision-making required (adjusting for surface variations, material behavior) fall short of the 50% time-saving threshold for general cases. |
| Task automatability | claude-sonnet-5 | 2/5 | Painting or lacquering physical metal/plastic patterns requires manual dexterity and physical manipulation of a real object, which current AI systems cannot perform end-to-end without robotic hardware not typically deployed for this niche task.robotics. |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | Patternmaking is typically a hands-on craft requiring direct inspection and manual adjustment; there is no strict legal licensing barrier, but quality standards and customer expectations for human craftsmanship create organizational friction. Health and safety regulations around coating operations add modest compliance overhead to any automated solution. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement exists for this task, but organizational friction (custom small-batch work, variable part geometry, need for tactile quality judgment) creates practical barriers to automating it. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | Current robotic systems capable of any painting task are capital-intensive (equipment, programming, maintenance) and require significant integration overhead. For bespoke or variable pattern work, the total cost per piece exceeds the loaded wage of a skilled patternmaker, making economic substitution unfavorable. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | Industrial robotic painting exists but requires costly setup, fixtures, and programming that would rarely be justified for the small-batch, varied nature of patternmaking, making automation costlier than a skilled worker performing this quickly by hand. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed product demonstrably performs pattern painting or lacquering reliably in production for the range of pattern types and substrates patternmakers encounter. Specialized industrial robots exist for specific, highly repetitive coating tasks, but general pattern painting remains research-stage or confined to narrow, pre-programmed scenarios. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed AI product performs pattern painting/lacquering in metal/plastic patternmaking shops today; this remains a manual craft task with occasional robotic paint spraying used only in high-volume industrial settings, not small-batch patternmaking. |
Select pattern materials such as wood, resin, and fiberglass.
24CI 14–35 · exposure 20 · augmentation 38 · importance 3.0/5 · click for rater detail
Select pattern materials such as wood, resin, and fiberglass.
24| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Patternmaking is a craft-based, physically distributed occupation with slow digitization and limited history of AI tool adoption; most shops remain small and rely on accumulated expertise rather than digital systems. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Metal/plastic patternmaking is a low-digitization manufacturing trade with minimal AI agent deployment or measured displacement. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI could assist by suggesting material options based on specifications, comparing properties, and flagging cost/performance tradeoffs, reducing research time while the patternmaker retains final judgment. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI could provide reference information on material properties or suggest options, but it offers limited hands-on assistance for this physically grounded judgment task. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | Material selection requires understanding project specifications, material properties, and cost tradeoffs, but current AI systems lack the embodied judgment and on-site assessment capabilities needed for end-to-end automation. An AI could assist in narrowing options based on input criteria but cannot reliably perform the full task without substantial human oversight. |
| Task automatability | claude-sonnet-5 | 2/5 | Material selection requires physical judgment about mold behavior, shrinkage, durability, and cost tradeoffs tied to hands-on manufacturing knowledge that current AI cannot independently execute end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Patternmakers typically work under direct supervision and client relationships where material choice reflects professional liability and craft judgment; organizational practice and the need for human accountability for material decisions create moderate-to-strong adoption friction. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No formal licensing requirement, but deep tacit craft knowledge and physical inspection of materials create practical friction against automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | AI material-selection tools (if they existed at scale) would require significant domain knowledge encoding and would still need human verification, making their all-in cost comparable to or exceeding a skilled patternmaker's hourly rate for this specific task. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI substitute performing this physical selection task, so cost comparison favors the human entirely. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | No production systems today independently select pattern materials; this task requires tactile evaluation, client feedback integration, and real-time project context that current AI tools do not reliably handle in deployment. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product autonomously selects patternmaking materials in production; this remains a skilled trade decision made by craftspeople on shop floors. |
Assemble pattern sections, using hand tools, bolts, screws, rivets, glue, or welding equipment.
20CI 10–30 · exposure 13 · augmentation 25 · importance 3.8/5 · click for rater detail
Assemble pattern sections, using hand tools, bolts, screws, rivets, glue, or welding equipment.
20| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Patternmaking is a skilled, small-batch, often specialized sector with limited digitization pressures. Adoption of advanced assembly automation remains sparse; most shops continue traditional hand-assembly methods or have invested in narrow robotic processes only for high-volume production runs. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Metal/plastic patternmaking is a small-scale, low-digitization manufacturing niche with minimal AI/robotics adoption reported, unlike high-velocity sectors like finance or software. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI vision and planning could assist layout planning or bolt-hole verification, but the hands-on assembly work—positioning, aligning, joining—offers limited augmentation value without human presence; guidance tools exist but do not substantially lift productivity for the core manual task. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI could assist with design specifications, CAD-based instructions, or quality checks, but offers little direct help with the physical hand-tool assembly and welding steps themselves. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | This task requires precise spatial assembly of physical parts using varied tools and joining methods, which demands real-time dexterity, force feedback, and adaptive problem-solving that current AI-robotic systems cannot reliably perform end-to-end at production speed. While industrial robots handle repetitive welding or fastening on rigid components, the variability in pattern sections and the need to align and fit sections using multiple methods makes consistent autonomous completion unrealistic today. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a physical manual assembly task requiring hand-eye coordination, tool manipulation, and fine motor skill that current AI systems cannot perform end-to-end; robotics for this specific unstructured assembly work is not deployed at scale.' |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | While there are no strict licensing barriers to automating assembly, organizational friction is moderate: shops have existing skilled workforce, tooling is already human-scaled, and the variability of work requires frequent retooling or retraining of systems. Some insurance and liability considerations apply if automation quality is unproven. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No licensing requirement specifically bars automation, but physical dexterity, variable workpiece geometry, and quality/safety concerns in welding and precision fitting create substantial practical barriers to substitution. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | Advanced robotic assembly systems (hardware, vision, integration, programming, maintenance) are capital-intensive and require significant setup per task, making the total cost per assembly comparable to or exceeding a skilled patternmaker's loaded wage, especially for lower-volume or varied work. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI/robotic substitute performing this task, so any hypothetical automation solution (custom robotics) would be far costlier than a skilled human patternmaker. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | Robotic arms can perform isolated welding or bolt-fastening in controlled settings, but deployable products that assemble arbitrary pattern sections across metal and plastic using mixed joining methods at human quality remain limited to narrow, pre-engineered scenarios. General-purpose assembly robots struggle with the judgment required to choose joining methods and detect fit issues. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product performs general-purpose pattern assembly with hand tools, bolts, welding, etc.; this remains far outside current robotic manipulation capability in unstructured shop settings. |
Clean and finish patterns or templates, using emery cloths, files, scrapers, and power grinders.
15CI 15–15 · exposure 0 · augmentation 13 · importance 3.5/5 · click for rater detail
Clean and finish patterns or templates, using emery cloths, files, scrapers, and power grinders.
15| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Patternmaking and finishing remains concentrated in small to mid-size traditional manufacturing shops with limited automation investment; digital adoption in this sector is slow relative to information industries. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Manufacturing trades involving manual pattern finishing show minimal AI adoption; this is a low-digitization, physical-labor-intensive niche within an already slow-adopting sector. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could potentially assist with task planning or quality inspection imagery analysis, but the core finishing work itself offers limited augmentation since it fundamentally depends on manual tool manipulation. |
| Augmentation potential | claude-sonnet-5 | 1/5 | AI offers essentially no assistance for hands-on cleaning and finishing with emery cloths, files, and grinders, as these require direct physical skill rather than information processing. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task requires fine motor control, spatial judgment, and tactile feedback to achieve smooth finishes on irregular surfaces. Current AI systems cannot operate physical tools like grinders or scrapers with the precision and dexterity needed for finishing work. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a manual, physical finishing task requiring fine motor manipulation of hand tools and power grinders on physical patterns; current AI systems cannot perform physical manipulation tasks at all. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | While there are no formal licensing requirements, the need for human judgment, quality control, and physical dexterity creates practical barriers to automation in manufacturing settings. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing or legal requirement mandates a human perform this specific task, but physical dexterity, tacit tool skill, and quality judgment create substantial practical barriers to automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Robotic systems capable of handling variable patterns and finishing work would cost significantly more than the loaded wage of a skilled patternmaker, including hardware, maintenance, and integration. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI-driven substitute for this physical task, so any hypothetical automation (e.g., robotic finishing cells) would require far greater capital investment than the human labor it replaces. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed product can autonomously clean and finish physical patterns using hand tools or power grinders at production scale. The task remains manual and requires human perception and adaptation. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed AI product performs manual finishing of metal/plastic patterns with hand tools; this remains firmly in the domain of human craftsmanship and robotics research at best. |
Apply plastic-impregnated fabrics or coats of sealing wax or lacquer to patterns used to produce plastic.
15CI 15–15 · exposure 0 · augmentation 0 · importance 2.9/5 · click for rater detail
Apply plastic-impregnated fabrics or coats of sealing wax or lacquer to patterns used to produce plastic.
15| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Pattern-making is a small, traditional craft sector with low digitization and low robotics adoption rates. The sector remains labor-intensive and has not shown rapid AI/automation adoption compared to other manufacturing domains. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Metal and plastic patternmaking is a small, highly specialized manufacturing trade with low digitization and no evidence of AI or robotic adoption for this specific coating task. |
| Augmentation potential | claude-haiku-4-5-20251001 | 1/5 | AI offers no meaningful assistance for applying coatings to patterns; the task is purely manual execution requiring human hands, sensory judgment, and physical presence, leaving no augmentation opportunity. |
| Augmentation potential | claude-sonnet-5 | 1/5 | AI offers essentially no assistance for this hands-on material application task, as it involves physical craftsmanship rather than information processing or planning. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task requires precise manual application of materials to irregular pattern surfaces with tactile feedback and judgment about coverage and finish quality. Current AI systems cannot physically manipulate materials or assess quality by touch, and the task is fundamentally manual/physical rather than cognitive. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a manual physical task requiring hands-on application of coatings to physical molds/patterns, which current AI systems cannot perform without robotic embodiment specifically engineered for this niche process. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | While there are no strict licensing barriers, the physical nature of the task and organizational reliance on skilled craftspeople create moderate adoption friction. Customer expectations for hand-crafted precision and material feel provide some protection. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement exists, but the task requires fine physical dexterity, material judgment, and tactile feedback that create practical (not regulatory) barriers to automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Specialized robotic systems capable of coating application (if they existed at scale) would be prohibitively expensive relative to the wages of skilled patternmakers, given the low production volumes typical in pattern-making. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no AI-based alternative to compare costs against; any robotic solution would require costly custom engineering far exceeding the wage cost of a skilled patternmaker performing this task. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed AI product can perform this physical manufacturing task end-to-end. The task requires embodied robotics with fine motor control and sensory feedback, which is not yet reliably available in production environments for this type of coating work. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed AI or robotic product exists that applies sealing wax, lacquer, or plastic-impregnated fabrics to metal/plastic patterns; this remains a specialized manual craft skill. |
Construct platforms, fixtures, and jigs for holding and placing patterns.
10CI 5–15 · exposure 0 · augmentation 25 · importance 3.7/5 · click for rater detail
Construct platforms, fixtures, and jigs for holding and placing patterns.
10| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Pattern making and fixture construction remains a skilled trade in manufacturing sectors with slower digital transformation. Adoption of AI tools in this domain is minimal; the work is largely hands-on and depends on experienced craftspeople in smaller, specialized shops. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Metal/plastic patternmaking is a low-digitization, physical manufacturing trade with minimal AI agent adoption in production settings. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | CAD and design simulation tools offer limited assistance with the planning phase, but AI cannot assist with the core physical construction, material handling, and hands-on adjustment work that dominates the task. |
| Augmentation potential | claude-sonnet-5 | 2/5 | CAD/CAM software and design tools can assist in planning fixture layouts, but the actual construction is unaided by AI, offering only limited planning-stage support. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | Constructing physical platforms, fixtures, and jigs involves complex spatial reasoning, material selection, hand fabrication, and iterative physical testing. Current AI systems cannot autonomously design and physically build these bespoke physical artifacts without extensive human intervention. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a physical fabrication task requiring manual construction of fixtures and jigs, which current AI systems cannot perform end-to-end as they lack physical manipulation capabilities. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Patternmakers often require apprenticeship training and domain expertise. The task involves safety-critical fixture design and hands-on fabrication that organizations typically require human craftspeople to execute and validate, creating strong organizational and expertise barriers. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing barrier specifically, but the physical nature of fixture-building and need for precise craftsmanship creates strong practical barriers to any automation short of robotics. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | The specialized equipment, materials, and skilled labor required to construct jigs and fixtures far exceed the cost of current AI systems, which lack physical manipulation and fabrication capabilities. AI cannot meaningfully reduce the human labor and material costs involved. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | AI cannot substitute for the physical labor and machine tool operation involved, so there is no viable AI cost comparison; human labor remains the only option. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed AI products can reliably construct physical fixtures and jigs end-to-end. While CAD software and generative design tools exist, they still require skilled patternmakers to validate designs, select materials, and perform the actual fabrication and assembly work. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed AI product constructs physical platforms, fixtures, and jigs; this remains a hands-on machining/fabrication task performed by skilled workers. |
Related occupations — Production
How to read this
A high substitution score does not mean this job disappears — it means a large share of its current tasks face replacement pressure, so the mix of tasks is likely to change. High augmentation alongside substitution typically means the occupation reorganizes around the protected tasks. Wide confidence intervals mean the rater panel disagreed: treat those scores as open questions, not verdicts.
What would change this score
New model capabilities (automatability, feasibility), falling inference costs (cost ratio), regulation and licensing shifts (barriers), and measured sector adoption (velocity) all re-enter at every index release. Each release is recomputed, versioned and kept queryable — scores are claims with a date on them, not permanent labels.