Layout Workers, Metal and Plastic
51-4192.00Median wage $63,870/yr5,970 employed (US)Rank #509 of 923 scored · top 55% by substitution
Lay out reference points and dimensions on metal or plastic stock or workpieces, such as sheets, plates, tubes, structural shapes, castings, or machine parts, for further processing. Includes shipfitters.
Sub-scores
0–100 · band = confidence interval from rater disagreement
Substitution27
Exposure20
Augmentation40
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
14 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
0%
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%22
panel mean rating 1.9/5 → substitution pressure 22/100
Technical feasibility todayw 20%18
panel mean rating 1.7/5 → substitution pressure 18/100
Cost vs. human wagew 15%19
panel mean rating 1.7/5 → substitution pressure 19/100
Adoption barriersw 20%inverted — strong barriers lower the score54
panel mean rating 2.8/5 (barrier strength) → substitution pressure 54/100
Sector adoption velocityw 10%21
panel mean rating 1.8/5 → substitution pressure 21/100
Task breakdown (14 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.
Inspect machined parts to verify conformance to specifications.
52CI 30–75 · exposure 50 · augmentation 63 · importance 4.0/5 · click for rater detail
Inspect machined parts to verify conformance to specifications.
52| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 4/5 | Manufacturing, especially automotive and electronics, is actively deploying automated inspection systems in production lines. Industry 4.0 adoption and pressure to reduce labor costs drive rapid deployment of vision-based quality control. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Manufacturing sectors adopt automated inspection unevenly; large OEMs use automated QC but many machine shops still rely on manual inspection, reflecting slower digitization typical of physical production trades. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI inspection assists human inspectors by flagging suspect parts, highlighting defect regions, and reducing false negatives—allowing humans to focus on ambiguous cases or secondary verification rather than 100% manual scanning. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI-enabled measurement tools and vision-assisted gauges can help workers verify specs faster and catch defects, improving productivity while the worker still performs setup and judgment calls. |
| Task automatability | claude-haiku-4-5-20251001 | 4/5 | Computer vision systems can reliably detect dimensional deviations and surface defects on machined parts, often matching or exceeding human accuracy. While 100% end-to-end automation may require some setup (fixturing, lighting standardization), the core inspection task achieves well over 50% time savings compared to manual visual inspection. |
| Task automatability | claude-sonnet-5 | 2/5 | Some inspection can be automated via computer vision/CMM systems, but layout workers often check complex geometries and use judgment on tolerances that require flexible physical measurement not fully replaceable by off-the-shelf AI today.' |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | Minimal regulatory barriers exist; inspection automation is not legally required to be performed by licensed humans. Main friction comes from customer requirements for human sign-off on critical parts and organizational inertia, but these are not hard blockers. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No licensing requirement, but quality assurance sign-off, liability for defective parts reaching customers, and need for physical handling of parts create moderate friction against pure automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 4/5 | Once deployed, automated vision inspection costs per part are a fraction of hourly labor (seconds of GPU inference plus amortized hardware); for high-volume production, this easily achieves 5–10× cost advantage over manual inspection per part. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | Vision/CMM inspection hardware and integration costs are substantial relative to a human visually inspecting parts with calipers/gauges, especially for low-volume or varied part runs typical in layout work. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 4/5 | Deployed vision-based inspection systems are in active production use across manufacturing (automotive, aerospace, electronics). Products from vendors like Cognex, Basler, and custom ML pipelines perform dimensional and defect detection reliably at scale, though occasional integration challenges and model retraining remain. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Automated optical/CMM inspection systems exist and are deployed in some manufacturing lines, but they require significant setup, fixturing, and calibration per part type, so general-purpose reliable deployment is narrow. |
Compute layout dimensions, and determine and mark reference points on metal stock or workpieces for further processing, such as welding and assembly.
33CI 30–35 · exposure 25 · augmentation 50 · importance 4.2/5 · click for rater detail
Compute layout dimensions, and determine and mark reference points on metal stock or workpieces for further processing, such as welding and assembly.
33| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Manufacturing adoption of AI is progressing but unevenly; advanced facilities use automated marking systems, but most small to mid-sized metal/plastics shops rely on manual layout workers due to equipment cost, setup complexity, and low production volumes that don't justify capital investment. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Manufacturing sectors employing layout workers show slower, more capital-intensive automation adoption compared to information/professional service sectors, with automation typically requiring dedicated CNC equipment rather than general AI tools. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | CAD integration and automated dimension computation can assist layout workers by reducing calculation time and providing digital reference overlays, but the human must still inspect workpieces, adapt to physical irregularities, and perform marking—a useful productivity lift rather than transformation. |
| Augmentation potential | claude-sonnet-5 | 3/5 | CAD software and digital measurement tools already assist layout workers with dimension calculations and precision marking, improving speed and accuracy while the worker still executes physical marking. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | Current AI can compute layout dimensions from digital specifications, but physically marking reference points on irregular metal/plastic stock requires robotic manipulation with precise spatial reasoning, visual feedback, and real-time adjustment—capabilities that exist in research but lack reliable end-to-end automation in typical shop environments today. |
| Task automatability | claude-sonnet-5 | 2/5 | While CAD/CAM systems can compute dimensions digitally, physically marking reference points on actual metal stock requires manipulation of physical materials that current AI systems cannot perform end-to-end without robotic hardware integration. |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | While no formal licensing requirement exists, quality control, liability for mislaid parts affecting downstream welding/assembly, and the need for human judgment about material defects or non-standard workpiece geometry create moderate organizational friction against full substitution. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement exists for this task, but precision and safety-critical downstream processes (welding, assembly) create quality-control friction that slows blind automation adoption. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | AI vision and computation systems for this task require specialized hardware (cameras, robots, fixtures) and integration costs that currently exceed the wage burden of a skilled layout worker, especially when accuracy and setup variability are factored in. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | Automated layout systems (CNC markers, laser scribing) require significant capital investment in machinery and integration, often exceeding the cost of a skilled worker for small-batch or variable work. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | CAD-to-manufacturing software can compute dimensions automatically, but physically inspecting workpieces, determining reference points considering material variation, and marking them reliably in production remains a manual task; no mainstream product reliably automates the full physical workflow. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | CNC and CAD-driven marking/layout systems exist and are used in some shops, but many layout tasks still require manual measurement and marking on irregular stock, especially in smaller shops or custom jobs. |
Plan locations and sequences of cutting, drilling, bending, rolling, punching, and welding operations, using compasses, protractors, dividers, and rules.
30CI 25–35 · exposure 25 · augmentation 50 · importance 4.4/5 · click for rater detail
Plan locations and sequences of cutting, drilling, bending, rolling, punching, and welding operations, using compasses, protractors, dividers, and rules.
30| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | While many metal/plastic shops use CAD tools, actual autonomous planning and sequencing remains limited to larger, digitally mature firms. Most small and mid-sized job shops still rely on experienced layout workers to make critical decisions; adoption of fully automated planning is slow. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Manufacturing and metalworking are historically slower adopters of AI compared to information/finance sectors, with digitization occurring mainly through CNC/CAM adoption rather than AI-driven planning specifically. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | CAD and visualization tools meaningfully assist layout workers by handling geometric calculations and draft generation, improving productivity on the computational aspects. However, the worker must still validate, interpret, and adjust plans based on tacit knowledge of materials and equipment. |
| Augmentation potential | claude-sonnet-5 | 3/5 | CAD/CAM tools and simulation software assist workers in planning cutting and drilling sequences more efficiently, though the hands-on layout marking with manual tools remains largely human-driven. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | Planning cutting and drilling sequences requires spatial reasoning about physical constraints, material properties, and tool interactions. While CAD software can assist with layouts, the integration of practical metalworking knowledge (material stress, tool wear, equipment limitations) and real-time problem-solving is not consistently automated end-to-end by current systems. |
| Task automatability | claude-sonnet-5 | 2/5 | This requires interpreting engineering drawings and spatial reasoning about physical fabrication sequences, which current AI can partially assist with via CAD/CAM software but cannot fully replace end-to-end given the physical measurement and layout marking involved. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Manufacturing workflows often have strict quality and safety requirements; errors in planning directly impact material waste, tool damage, and worker safety. Company-specific equipment configurations, material specifications, and regulatory compliance create organizational friction that slows autonomous deployment. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No formal licensing requirement, but this task occurs in industrial settings where errors in layout cause costly material waste or safety issues, creating moderate organizational caution before removing human judgment. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | CAD/CAM systems and their integration are expensive to deploy and maintain, and still require skilled operators and engineers. The all-in cost remains comparable to or higher than the labor cost of experienced layout workers, especially when accounting for setup and error correction. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | CAM/CAD software has upfront and ongoing licensing costs and still requires skilled human oversight to translate digital plans to physical layout, so cost savings versus a skilled layout worker are modest, not order-of-magnitude. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | CAD and CAM software exist for layout planning in manufacturing, but they typically require significant human input, custom toolpathing, and validation. No mature product reliably performs this task autonomously in production without expert oversight and adjustment. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | CAM software and nesting algorithms exist for planning cutting sequences, but the manual layout work with compasses, protractors, and dividers on physical stock is not something deployed AI products perform directly today. |
Locate center lines and verify template positions, using measuring instruments such as gauge blocks, height gauges, and dial indicators.
30CI 25–35 · exposure 25 · augmentation 50 · importance 4.3/5 · click for rater detail
Locate center lines and verify template positions, using measuring instruments such as gauge blocks, height gauges, and dial indicators.
30| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Metal and plastic fabrication remains a physical, fragmented industry with many small and mid-sized shops; adoption of measurement automation is slow and limited to high-volume, capital-intensive manufacturers. General AI adoption in this sector lags information and professional services. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Manufacturing metalworking is a moderate-to-low digitization sector; automation exists mainly in high-volume production, with slower uptake in small/medium job shops doing this task. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI-assisted vision tools can help human layout workers identify candidate centerlines and flag deviations from templates, reducing visual inspection time and improving consistency, but the worker must still operate instruments and make final verification decisions. |
| Augmentation potential | claude-sonnet-5 | 3/5 | Digital gauges, computer-assisted measurement, and software can help verify and log measurements, improving accuracy and speed while the human still performs physical setup and verification. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI vision systems can detect visual features and measure dimensions from images, this task requires precise physical measurement using specialized instruments (gauge blocks, height gauges, dial indicators) in three-dimensional space. Current AI cannot operate these instruments or physically verify template positions end-to-end; humans remain essential for the hands-on measurement work. |
| Task automatability | claude-sonnet-5 | 2/5 | This requires physical manipulation of measuring instruments on real workpieces, which current AI systems cannot perform without robotic hardware integration far beyond typical deployment.; some CMM/vision-guided automation exists but is not general-purpose AI replacing this manual task. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Quality and safety standards in metal and plastic fabrication often legally or contractually require a certified human worker to sign off on critical dimensional measurements. Physical access to parts and instruments, combined with liability for precision errors, creates strong organizational and regulatory barriers to full automation. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement, but precision quality control often has liability implications and organizational trust favors human verification, plus physical workpiece handling adds friction. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | Implementing robotic or AI-vision measurement systems requires significant capital investment and integration costs that typically exceed the loaded wage of skilled layout workers, especially for small to medium production runs or custom work. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | Robotic/automated measurement systems require significant capital investment (CMMs, fixtures, integration) that often exceeds the cost of a skilled layout worker for job-shop or low-volume work. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | Computer vision and measurement automation exist in limited industrial contexts, but production systems for this specific task of locating centerlines and verifying template positions with traditional measuring instruments remain immature. Most deployments require human operators to conduct and interpret physical measurements. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Automated coordinate measuring machines and vision systems exist in some manufacturing settings, but they are specialized hardware solutions, not general AI products, and widespread reliable deployment for this specific manual layout task is limited. |
Plan and develop layouts from blueprints and templates, applying knowledge of trigonometry, design, effects of heat, and properties of metals.
30CI 25–35 · exposure 25 · augmentation 50 · importance 4.3/5 · click for rater detail
Plan and develop layouts from blueprints and templates, applying knowledge of trigonometry, design, effects of heat, and properties of metals.
30| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Manufacturing and metal/plastic shops show moderate digitization; while CAD adoption is common, autonomous layout planning is rare in production. Adoption remains slow outside large industrial firms, reflecting low digitization in small shops and the specialized nature of the work. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Manufacturing and metalworking are historically slower to adopt AI compared to information/professional services, with CAD/CAM adoption mature but full AI-driven layout automation still uncommon in production shops. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI can assist by generating initial layout candidates, performing trigonometric calculations, and flagging design conflicts, meaningfully raising productivity for skilled workers who retain judgment over material properties and thermal considerations. |
| Augmentation potential | claude-sonnet-5 | 3/5 | CAD software, parametric design tools, and calculation aids meaningfully speed up blueprint interpretation and layout planning, though the worker still must apply judgment about material behavior and finalize physical markup. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI can assist with geometric calculations and pattern recognition from blueprints, the task requires integrating knowledge of material properties, heat effects, and design trade-offs that demand specialized expertise and physical validation. Current systems cannot reliably plan layouts end-to-end with 50% time savings at equal quality without substantial human supervision. |
| Task automatability | claude-sonnet-5 | 2/5 | This task blends spatial-geometric reasoning with physical material knowledge and hands-on marking of stock; AI can assist with calculations and CAD-based layout generation but cannot fully replace the physical fitting and judgment involved end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Liability and safety concerns are substantial: errors in layout planning can cause material waste, equipment damage, or unsafe structures, creating strong incentives for human sign-off. Organizational norms and workforce skill requirements also create friction against full automation. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing mandate requires a human to do this specifically, but quality/safety consequences of layout errors in manufacturing (scrapped material, structural issues) creates practical oversight friction favoring experienced workers. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | AI inference costs for layout assistance are low, but integration, validation, and the specialized human expertise required for oversight remain expensive. The total cost of AI-assisted layout planning is likely comparable to or exceeds the cost of a skilled layout worker. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | Software licenses and CAD/CAM tools have upfront and ongoing costs comparable to or sometimes higher than a layout worker's marginal cost, especially when factoring in setup, training and validation for each unique job. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | No mature production systems reliably perform full layout planning for metal and plastic work; CAD software assists but does not autonomously plan layouts incorporating material properties and thermal considerations. Research-stage tools exist, but deployed products are narrow and require expert human oversight. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | CAD/CAM and nesting software automate parts of layout planning, but integrating trigonometric calculations with real-world material behavior (heat distortion, metal properties) in production layout work still requires skilled human interpretation and shop-floor verification. |
Design and prepare templates of wood, paper, or metal.
30CI 25–35 · exposure 20 · augmentation 50 · importance 3.8/5 · click for rater detail
Design and prepare templates of wood, paper, or metal.
30| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Manufacturing and metal/plastic fabrication sectors show uneven digitization; CAD adoption is common in design, but actual AI-driven template automation is still emerging, with many smaller shops continuing manual practices. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Manufacturing and metalworking trades show slow, uneven AI adoption compared to information-sector jobs, with automation more focused on CNC and robotics than generative AI. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI can usefully assist by generating candidate template designs, optimizing for material efficiency, and automating parametric variations, allowing human workers to focus on validation, refinement, and physical preparation. |
| Augmentation potential | claude-sonnet-5 | 3/5 | CAD/CAM and generative design tools can assist in creating template geometries and specifications, improving speed and accuracy of the design portion of the task. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI can generate design concepts and parametric templates given clear specifications, the task requires spatial reasoning, material-specific constraints, and iterative physical validation that current systems struggle with end-to-end. The manual preparation of physical templates from designs remains largely dependent on human hands-on work. |
| Task automatability | claude-sonnet-5 | 2/5 | Template design requires physical measurement, layout on stock material, and manual marking that AI cannot perform end-to-end; CAD generation may assist but the fabrication/layout step is physical.2 |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | Moderate barriers exist: templates are typically part of licensed manufacturing workflows with quality and liability requirements, and human sign-off on tolerances and material suitability is generally expected in regulated environments. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement, but precision, safety, and material handling create practical friction against full automation without significant capital investment in robotics. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | AI-assisted design tools reduce some overhead compared to manual sketching, but the human operator remains essential for setup, material selection, and physical template preparation, making the all-in cost per template still comparable to or higher than the loaded human wage. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | Even where CAD/CAM software aids design, a skilled worker or technician is still needed for physical template preparation, so AI does not yet substantially undercut labor cost for the full task. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | CAD software can assist with digital template design, but deployed systems do not reliably handle the full workflow of translating designs into physical templates for metal and plastic, particularly accounting for real-world material properties, shrinkage, and precision tolerances required in manufacturing. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed AI product autonomously designs and physically prepares templates from wood, paper, or metal in production shop environments today. |
Fit and align fabricated parts to be welded or assembled.
28CI 21–35 · exposure 20 · augmentation 38 · importance 4.4/5 · click for rater detail
Fit and align fabricated parts to be welded or assembled.
28| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Manufacturing sectors show uneven adoption; large aerospace and automotive plants use some automated alignment, but small to mid-size metal/plastic fabrication shops—where most layout workers operate—have slow and limited AI adoption. Overall adoption remains concentrated in high-volume, standardized production. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Metal fabrication is a physical, moderately digitized sector where robotic automation is adopted mainly in high-volume, repetitive assembly lines, not in variable layout and fitting tasks typical of this occupation. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | Vision-based measurement and alignment feedback systems can assist layout workers by flagging misalignments and providing real-time correction guidance, improving speed and accuracy. However, the human still performs the core fitting and adjustment work. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI-based vision systems and CAD/CAM tools can assist with measurement verification or fit-up checks, but the physical alignment and adjustment work still relies almost entirely on human dexterity and judgment. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | Fitting and aligning parts requires precise spatial reasoning and physical dexterity in 3D environments. While vision AI can detect misalignments, the actual manipulation and adjustment of fabricated parts to tolerances remains primarily a manual, hands-on task that current AI cannot perform end-to-end without human intervention. |
| Task automatability | claude-sonnet-5 | 2/5 | This is a physical manipulation task requiring precise manual fitting and alignment of metal/plastic parts, which current AI systems cannot perform end-to-end without robotic hardware and extensive setup specific to each part geometry. |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | While there is no hard legal requirement for human sign-off, quality control and liability concerns around precision welding/assembly create significant organizational friction. Customers and safety standards often expect human verification of critical fit and alignment work. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement, but organizational friction is high because it requires custom fixturing, machine vision calibration, and physical robotic infrastructure not easily justified for variable job-shop parts. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | The cost of vision systems, robotic alignment hardware, and required infrastructure integration far exceeds the hourly wage of a layout worker for this task. The ROI remains poor except in high-volume, highly standardized scenarios. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | Robotic/vision-guided alignment systems require significant capital investment, tooling, and calibration per part type, making them costlier than a skilled layout worker for varied, low-to-medium volume fabrication work. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | Some computer vision systems can detect and measure alignment errors in manufacturing, but no deployed autonomous system reliably performs the full task of physically fitting and aligning metal/plastic parts for welding or assembly without human workers. Current products assist but do not replace the core manual work. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed general-purpose AI product performs freeform fitting and alignment of fabricated metal/plastic parts for welding across varied job shop conditions; robotic fixturing exists only in narrow, pre-programmed high-volume contexts. |
Add dimensional details to blueprints or drawings made by other workers.
28CI 25–30 · exposure 25 · augmentation 50 · importance 4.0/5 · click for rater detail
Add dimensional details to blueprints or drawings made by other workers.
28| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Manufacturing sectors show moderate digitization but remain cautious about automating specification tasks due to safety and liability concerns. Current adoption is limited to narrow CAD-assist pilots rather than production-scale displacement. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Manufacturing and metal/plastic fabrication sectors are traditionally slower to adopt AI compared to information/professional services, with CAD automation adoption still emerging and uneven. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI can assist by suggesting dimension placements, automating routine annotation, and flagging potential inconsistencies, helping a layout worker work faster on the dimensioning task while the human maintains judgment and final approval. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI-assisted CAD tools can speed up dimension placement and suggest values based on drawing geometry, aiding layout workers though final verification against physical specifications remains manual. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | Adding dimensional details requires understanding context from existing blueprints and applying precise specifications, which demands spatial reasoning and domain knowledge. While AI can extract some dimensions from images and suggest placements, it struggles with the contextual judgment and spatial interpretation needed to reliably complete this task end-to-end without human verification. |
| Task automatability | claude-sonnet-5 | 2/5 | This requires interpreting physical parts/specs and adding precise dimensional annotations to existing drawings, which involves spatial reasoning and physical measurement AI cannot fully replicate today without significant human verification.ed setup and integration with CAD systems. , |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Manufacturing and construction involve liability for errors in specifications; incorrect dimensions can result in costly or unsafe parts. Regulatory standards (ISO, ASME) and quality control procedures typically require human sign-off on dimensional accuracy, creating legal and organizational friction against full automation. |
| Adoption barriers | claude-sonnet-5 | 3/5 | Dimensional accuracy in manufacturing drawings has real liability and quality-control implications, requiring skilled human verification, though no formal licensing requirement exists specifically for this sub-task. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | AI-based drawing analysis tools exist but require significant human oversight and correction, making the integrated cost (inference + manual review + rework) comparable to or higher than paying a layout worker directly. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | CAD auto-dimensioning tools exist but require licensing, integration, and human oversight to verify accuracy against physical parts, keeping costs closer to comparable rather than dramatically cheaper. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | No deployed product reliably performs this task at production quality. CAD software can assist with dimension placement, but no current AI system demonstrably adds dimensional details to blueprints autonomously with acceptable accuracy for manufacturing use. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Some CAD tools offer auto-dimensioning features for CAD-native drawings, but adding dimensions to blueprints made by other workers in varied formats/physical layouts is not reliably automated in production. |
Mark curves, lines, holes, dimensions, and welding symbols onto workpieces, using scribes, soapstones, punches, and hand drills.
25CI 24–26 · exposure 16 · augmentation 38 · importance 4.4/5 · click for rater detail
Mark curves, lines, holes, dimensions, and welding symbols onto workpieces, using scribes, soapstones, punches, and hand drills.
25| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Layout work is concentrated in small-to-mid-size job shops and fabrication facilities with low digitization and high part variability. These sectors have historically shown slow AI/automation adoption compared to large-scale mass manufacturing or information sectors. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Manufacturing and metalworking sectors show slower, more capital-intensive automation adoption (via CNC/robotics) compared to information-based industries, and this specific hand-tool task is not a current AI adoption target. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could assist by auto-generating marking layouts from CAD files and providing visual guides or augmented reality overlays to workers, but the core hand-tool execution remains human-driven. The augmentation value is moderate—it simplifies planning but does not substantially reduce the manual marking labor itself. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI-assisted CAD/CAM and digital layout tools can help workers plan dimensions, curves, and welding symbol placement more efficiently before manual marking, offering moderate productivity gains in the planning phase. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | Marking physical workpieces with hand tools requires precise spatial positioning, depth sensing, and material interaction (scribing, punching, drilling) that current robots struggle with in varied real-world conditions. While vision systems can identify where marks should go, executing the actual marking with the required pressure, angle, and precision on diverse metal/plastic surfaces remains largely manual. |
| Task automatability | claude-sonnet-5 | 2/5 | This is a manual, physical marking task requiring hand-eye coordination with tools directly on physical workpieces; current AI cannot physically manipulate scribes or drills, though CAD/CAM software can generate the layout data that guides such marking.digital layout generation is automatable but the physical marking act is not.rating reflects only partial automatability of the overall task.rating capped low. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | Minimal regulatory barriers exist; marking is typically an internal manufacturing step without licensing or sign-off requirements. However, quality verification and rework expectations create organizational friction and preference for human judgment on mark placement. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement for this specific task, but physical presence and tool operation on a shop floor create inherent friction since AI software cannot directly manipulate physical marking tools without robotic embodiment. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Specialized industrial robots capable of precision marking with vision guidance are capital-intensive and require extensive setup; the total cost per task (equipment, integration, maintenance) exceeds the loaded wage of a skilled layout worker performing the same work. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | AI has no direct mechanism to perform this physical marking task, so there is no viable AI cost basis to compare against human labor; robotic/CNC alternatives exist but are not 'AI' per se and require significant capital investment. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed commercial products reliably perform end-to-end marking of complex curves, holes, dimensions, and welding symbols on varied workpiece geometries. Research systems exist for specific constrained cases, but production-scale automation of this manual marking task is not demonstrably in use. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed AI product physically marks metal or plastic workpieces with scribes and punches; this remains a manual trade skill performed by hand or CNC machines, not general AI systems. |
Apply pigment to layout surfaces, using paint brushes.
23CI 10–35 · exposure 13 · augmentation 13 · importance 2.9/5 · click for rater detail
Apply pigment to layout surfaces, using paint brushes.
23| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Layout work occurs primarily in manufacturing and construction—sectors with moderate digital adoption. While some large fabrication shops have invested in robotic painting, adoption remains limited and concentrated in high-volume standardized settings, not typical layout operations. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Metal/plastic manufacturing and layout work is a low-digitization, physical trade sector with minimal AI/robotic adoption for such fine manual tasks. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI offers minimal assistance to a human painter on layout tasks. Robotic guidance systems or vision-based defect detection could provide marginal value, but the core task of hand-brushing pigment with artistic or precision control remains largely human-dependent. |
| Augmentation potential | claude-sonnet-5 | 1/5 | AI offers essentially no assistance for the physical act of applying pigment with a paintbrush to a layout surface. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | Applying pigment with paint brushes involves fine motor control, spatial judgment, and visual feedback in real-time. While robotic arms exist, they require significant setup, calibration, and environmental control. Current AI systems cannot reliably handle the variability of layout surfaces, brush wear, pigment consistency, and quality standards without continuous human oversight. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a manual, physical shop-floor task requiring hand-eye coordination on physical metal/plastic surfaces; current AI systems cannot manipulate physical brushes and pigment on workpieces., so no meaningful automation is possible with off-the-shelf systems today. |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | This is manual work with no formal licensing requirement, but organizational friction exists around equipment investment, integration complexity, and the need to verify output quality. Workers may resist automation, and quality standards may require human sign-off on precision work. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing or legal barrier per se, but the physical nature of the task and need for a human physically present with tools creates practical friction against remote automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | The capital cost of robotic painting systems, integration, maintenance, and the need for human oversight and reprogramming for layout variations makes the all-in cost comparable to or higher than a skilled layout worker performing the task. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI system replacing this manual task, so AI cost is effectively infinite or inapplicable relative to a human performing it directly. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | Robotic painting systems exist in industrial settings but are designed for standardized, repetitive tasks with fixed surfaces. For layout work—which often involves custom surfaces, varied geometries, and precision requirements—deployed solutions are limited and typically require extensive programming and setup rather than off-the-shelf deployment. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product performs manual pigment application to layout surfaces; this would require specialized robotics, which are not standard or production-proven for this niche task. |
Lay out and fabricate metal structural parts such as plates, bulkheads, and frames.
20CI 10–30 · exposure 13 · augmentation 38 · importance 4.2/5 · click for rater detail
Lay out and fabricate metal structural parts such as plates, bulkheads, and frames.
20| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Metal fabrication and structural assembly are physical, skill-dependent trades in laggard sectors; while large aerospace and automotive firms use some CAM/robotics, the small-to-medium fabrication shops where most layout workers operate have slow, limited AI adoption. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Metal fabrication and structural trades are low-digitization, physical-labor sectors with minimal AI/robotics adoption at production scale today. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | CAD visualization, automated measurement, and digital layout guides assist skilled workers in positioning and planning, but AI assistance is partial and focused on specific steps rather than transforming overall productivity for the full range of layout and fabrication judgment required. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI/CAD-CAM software can assist with layout planning and design specifications, but core fabrication remains manual with limited AI-driven productivity gains for the hands-on task itself. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While CAD/CAM systems can design and partially guide fabrication, laying out and fabricating structural parts requires interpreting blueprints in context, managing material variation, and performing precise positioning—tasks that involve significant hands-on work and real-time judgment that current AI cannot end-to-end automate with 50% time savings at equal quality. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a physical fabrication and layout task requiring manual measurement, marking, and cutting of metal parts, which current AI systems cannot perform end-to-end; robotics for this remain narrow and non-generalized.' |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | Safety and quality standards for structural metal parts create some regulatory scrutiny and quality-assurance requirements, but there is no strict licensing requirement and automation is not explicitly prohibited—friction exists but substitution is not legally blocked. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No licensing mandates a human specifically, but physical dexterity, spatial reasoning, and quality/safety requirements in structural fabrication create strong practical barriers to automation without specialized robotics investment. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | Current AI for parts of this task (design automation, cutting guidance) are expensive to integrate and still require significant human oversight and manual finishing, making the all-in cost comparable to or exceeding the labor wage for routine layout and fabrication work. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI substitute performing this physical task, so AI cost comparison is not applicable; the human remains the only cost-effective option. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | No deployed AI system can reliably perform end-to-end layout and fabrication of metal structural parts. Computer vision and robotics exist for narrow sub-tasks (e.g., automated cutting), but integrated layout, material selection, and structural assembly remain dependent on skilled human technicians in production. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed general-purpose AI product performs metal layout and fabrication of structural parts like bulkheads and frames; this remains a skilled manual trade task. |
Install doors, hatches, brackets, and clips.
20CI 5–35 · exposure 13 · augmentation 25 · importance 4.2/5 · click for rater detail
Install doors, hatches, brackets, and clips.
20| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | While manufacturing has moderate automation adoption, general layout and assembly tasks remain heavily manual outside high-volume production. Most sectors using these workers (fabrication shops, field assembly, maintenance) have low digitization and remain laggards in AI-driven robotic automation. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Manufacturing and fabrication trades involving physical installation show minimal AI/robotics adoption for this specific task type, remaining a low-digitization physical sector. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI can provide some marginal assistance via computer vision for alignment verification or AR-guided instructions, but the task is fundamentally dependent on physical dexterity and spatial problem-solving where current AI offers limited productivity gains while the human remains the primary executor. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI could assist with instructions, quality checks, or planning via digital work orders, but offers minimal direct assistance to the physical act of installing components. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | Physical installation of doors, hatches, brackets, and clips requires dexterous robotic manipulation in varied spatial contexts. While some specialized automation exists in controlled manufacturing environments, current general-purpose AI systems lack the reliable end-to-end manipulation, spatial reasoning, and adaptive problem-solving needed for diverse real-world installation scenarios to achieve 50% time savings at equal quality. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a manual, physical installation task requiring dexterity, fitting, and hands-on manipulation of metal/plastic parts that current AI systems cannot perform end-to-end without robotics far beyond off-the-shelf availability. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Physical installation work has substantial adoption barriers: liability concerns for safety-critical fastening (structural integrity, airworthiness in aerospace/automotive), need for on-site adaptability to unforeseen spatial constraints, and organizational friction in retrofitting automation into existing production lines where human installers have domain expertise. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing or legal requirement mandates a human specifically, but the physical nature and precision needed create practical friction against automation without specialized robotics. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | Robotic installation systems require significant capital investment, specialized infrastructure, and ongoing maintenance that currently exceeds the loaded labor cost for most layout workers, especially when accounting for integration and task-specific customization across diverse installation scenarios. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI substitute for the physical labor involved, so any hypothetical robotic solution would be far more expensive than a human worker performing this task. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | Robotic systems can perform narrow repetitive installation tasks in structured factories, but deployed products lack the generality and reliability to handle the variable contexts (different metal/plastic types, mounting configurations, tolerances) present in typical layout work. Production systems are limited to highly controlled settings, not field deployment. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed AI product installs physical hardware components like doors, hatches, or brackets; this remains a manual trade skill performed by human workers. |
Lift and position workpieces in relation to surface plates, manually or with hoists, and using parallel blocks and angle plates.
20CI 10–30 · exposure 13 · augmentation 25 · importance 4.1/5 · click for rater detail
Lift and position workpieces in relation to surface plates, manually or with hoists, and using parallel blocks and angle plates.
20| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Manufacturing automation is progressing, but layout work in small to mid-sized shops remains largely manual because part variety is high and batch sizes don't justify custom automation. Adoption remains slow outside high-volume, standardized production runs. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Metalworking and machining trades are physically intensive, low-digitization sectors with minimal AI/robotic adoption for unstructured physical handling tasks. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI-powered vision systems could assist by identifying optimal block placement or verifying surface contact, but current tools offer limited real-time guidance for the dynamic, tactile aspects of positioning work. Augmentation remains marginal for typical layout operations. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI can assist indirectly through CAD/CAM planning or hoist automation software, but provides little direct assistance to the physical act of lifting and positioning workpieces. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While robotic arms and automated positioning systems can handle some aspects of lifting and positioning, this task requires adaptive judgment about workpiece geometry, surface contact verification, and real-time adjustment—especially the use of parallel blocks and angle plates. Current AI-enabled robotics lack reliable general-purpose object handling for diverse metal/plastic workpieces without extensive per-task setup, making end-to-end automation with 50% time savings unlikely. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a physical manipulation task requiring lifting, positioning, and fine physical adjustment of heavy metal/plastic workpieces using hoists and fixtures; no off-the-shelf AI system performs this physical labor today. |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | Manufacturing facilities operate with established workflows and worker roles; automation requires capital investment and reorganization. However, no hard regulatory or licensing barrier prevents substitution in principle—adoption is slowed mainly by cost justification and organizational friction rather than legal requirement. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No licensing requirement, but physical workspace safety standards, liability for damaging expensive workpieces or injury from hoist operations, and the need for tactile judgment create real friction against automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | Industrial robotic systems with vision, custom fixturing, integration, and oversight remain expensive relative to a layout worker's loaded wage, especially for shops with variable workpieces. The integration and reprogramming costs for different part types keep total cost of ownership high. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | Robotic systems capable of this dexterous, variable physical task would require expensive custom automation (grippers, sensors, hoist integration) far exceeding the cost of a human worker for typical job-shop volumes. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | Some industrial robots and vision systems can position objects on plates in controlled factory environments, but they typically require custom fixtures and programming for each part geometry. No deployed product reliably handles the full range of workpiece shapes, surface verification, and adaptive block/plate placement that human layout workers perform across diverse job runs. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product autonomously lifts and positions industrial workpieces against surface plates using hoists and angle plates; this remains firmly in the domain of skilled machinists and manual/robotic-assist equipment operated by humans. |
Brace parts in position within hulls or ships for riveting or welding.
7CI 5–10 · exposure 0 · augmentation 13 · importance 4.1/5 · click for rater detail
Brace parts in position within hulls or ships for riveting or welding.
7| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Shipbuilding and metal fabrication are capital-intensive, physically complex sectors with slow digital adoption and strong reliance on skilled manual labor; automation of in-hull bracing tasks has seen minimal real-world deployment. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Shipbuilding and heavy metal fabrication are low-digitization, physically intensive sectors with minimal AI/robotic adoption for such fine physical tasks. |
| Augmentation potential | claude-haiku-4-5-20251001 | 1/5 | AI offers no meaningful assistance to layout workers performing manual bracing; the task is fundamentally physical positioning work that does not benefit from data analysis, pattern recognition, or information processing assistance. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI could assist with planning layouts or providing digital work instructions, but it offers little direct assistance to the physical bracing and positioning work itself. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | Bracing parts in position within confined ship/hull spaces requires precise spatial reasoning, manual dexterity, and real-time tactile feedback in three-dimensional environments. Current AI systems lack embodied manipulation capabilities and cannot reliably perform this physical bracing task at acceptable quality in unstructured industrial settings. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a physical manipulation task requiring positioning and securing heavy metal/plastic parts within confined ship or hull structures, which current AI systems (software or robotics) cannot perform end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Maritime and aerospace fabrication involve strict safety and quality standards; human inspectors and sometimes licensed shipyard workers must verify positioning before welding/riveting due to structural integrity requirements and liability exposure from automation failures. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No licensing barrier specifically prevents automation, but safety requirements, quality/liability concerns in shipbuilding, and physical workspace constraints create moderate friction against automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | The capital cost of robotic systems capable of precise spatial manipulation in confined hulls, plus integration and maintenance, far exceeds the loaded wage of a skilled layout worker for this specialized task. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI/robotic substitute performing this task, so the human worker remains the only cost-effective option; any attempted automation would require expensive custom robotics exceeding human labor costs. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed AI product today can autonomously position and secure parts within ship hulls for riveting or welding. This requires specialized robotic manipulation in constrained spaces, which remains in research/prototype phases rather than production deployment. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed robotic or AI product reliably braces parts within ship hulls for riveting or welding; this remains far outside current production robotics capability due to unstructured environments and part variability. |
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.