Model Makers, Metal and Plastic
51-4061.00Median wage $63,340/yr2,610 employed (US)Rank #258 of 923 scored · top 28% by substitution
Set up and operate machines, such as lathes, milling and engraving machines, and jig borers to make working models of metal or plastic objects. Includes template makers.
Sub-scores
0–100 · band = confidence interval from rater disagreement
Substitution35
Exposure29
Augmentation51
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
16 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%30
panel mean rating 2.2/5 → substitution pressure 30/100
Technical feasibility todayw 20%27
panel mean rating 2.1/5 → substitution pressure 27/100
Cost vs. human wagew 15%30
panel mean rating 2.2/5 → substitution pressure 30/100
Adoption barriersw 20%inverted — strong barriers lower the score60
panel mean rating 2.6/5 (barrier strength) → substitution pressure 60/100
Sector adoption velocityw 10%30
panel mean rating 2.2/5 → substitution pressure 30/100
Task breakdown (16 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.
Program computer numerical control (CNC) machines to fabricate model parts.
69CI 51–87 · exposure 70 · augmentation 75 · importance 4.3/5 · click for rater detail
Program computer numerical control (CNC) machines to fabricate model parts.
69| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 4/5 | Manufacturing and precision-engineering sectors are rapidly adopting AI-assisted CAM and CNC programming as part of Industry 4.0 initiatives; many mid-to-large shops already use automated tool-path generation, though small shops lag. |
| Sector adoption velocity | claude-sonnet-5 | 3/5 | Manufacturing and machining sectors show moderate, steady CAM/CNC automation adoption but remain behind purely digital/professional-services sectors in AI-driven workflow integration. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI significantly accelerates CNC program generation, allowing programmers to iterate quickly on designs and optimize tool paths; the human remains in the loop for design intent and validation, making this a high-productivity augmentation. |
| Augmentation potential | claude-sonnet-5 | 4/5 | CAM software and AI-assisted toolpath generation substantially speeds up programming work, letting the machinist focus on verification and fine-tuning rather than manual coding. |
| Task automatability | claude-haiku-4-5-20251001 | 5/5 | Programming CNC machines is a structured, rule-based task where AI systems can generate G-code and tool paths from CAD models with high fidelity; current CAM software and AI-assisted code generation can produce fully functional programs with minimal human intervention, meeting the 50% time-saving threshold. |
| Task automatability | claude-sonnet-5 | 3/5 | AI/CAM software can generate toolpaths and G-code from CAD models, but complex model-making often needs custom geometry handling and physical verification that limit full end-to-end automation.4 |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | While some oversight and safety checks are prudent, there are no licensing requirements or hard legal mandates that a human programmer must sign off on CNC code; automation adoption faces only modest organizational and quality-control friction. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement bars CNC programming by AI-assisted tools, though quality control and machine safety oversight create some organizational friction before untested programs run unsupervised. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 5/5 | AI-driven CNC programming (integrated into CAM packages or standalone agents) costs a fraction of an experienced programmer's hourly rate while delivering comparable or better results at scale, easily achieving an order-of-magnitude cost advantage. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | CAM software licenses and setup costs are substantial, and human oversight for verification and error-correction on each new part keeps costs closer to comparable rather than order-of-magnitude cheaper. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 4/5 | Deployed CAM software and AI-enhanced CNC programming tools are mature and widely used in production; however, complex geometries or novel material requirements may still require expert human review, preventing a perfect 5. |
| Technical feasibility today | claude-sonnet-5 | 3/5 | CAM software with automated toolpath generation is widely deployed and used in production, but programming for novel or complex model parts still requires significant manual adjustment and verification. |
Record specifications, production operations, and final dimensions of models for use in establishing operating standards and procedures.
62CI 47–76 · exposure 58 · augmentation 63 · importance 3.9/5 · click for rater detail
Record specifications, production operations, and final dimensions of models for use in establishing operating standards and procedures.
62| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 3/5 | Manufacturing is undergoing digital transformation with increasing sensor and vision integration, yet many model-making shops remain smaller, traditional operations. Adoption of automated documentation is present in modern facilities but remains inconsistent across the sector—neither leading-edge nor laggard. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Metal/plastic model-making is a physical, lower-digitization manufacturing niche where AI adoption for documentation tasks remains slow compared to information-sector norms. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI can assist model makers by auto-capturing dimensions from photos, auto-populating templates, and flagging out-of-spec measurements, meaningfully reducing documentation burden while keeping the engineer or model maker in control of quality and final verification. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI-assisted dictation, template generation, and data aggregation from measurement instruments can meaningfully speed up the documentation process while the worker still performs and verifies measurements. |
| Task automatability | claude-haiku-4-5-20251001 | 4/5 | Recording specifications, operations, and dimensions is largely documentary work involving measurements and data entry. Current AI vision systems can extract dimensions from images, and automation can capture production parameters from digital logs or sensors, then compile them into standardized formats—readily achieving 50% time savings with minimal human intervention. |
| Task automatability | claude-sonnet-5 | 3/5 | Recording specifications and dimensions into structured documentation is largely a data-entry/transcription task that AI could handle via dictation, OCR of measurement tools, or integration with CAD/CMM outputs, but capturing accurate final dimensions still requires physical measurement by a human. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | Documentation tasks carry minimal regulatory or liability barriers; no license is required to record specifications. Some organizational friction may exist around changing established procedures, but automation is largely a convenience rather than a legal or compliance blocker. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement blocks automation, though quality/process documentation often ties into ISO or internal quality standards requiring sign-off, creating mild organizational friction. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 5/5 | Recording and transcription are labor-intensive but low-margin tasks. AI inference for reading documents, extracting measurements, and populating databases costs pennies per instance, orders of magnitude cheaper than skilled labor for routine documentation. |
| Cost vs. human wage | claude-sonnet-5 | 3/5 | Once integrated with digital measurement tools, automated logging is cheap, but the setup, calibration, and human measurement steps keep overall costs comparable to manual documentation in many smaller shops. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 4/5 | Deployed products reliably perform related tasks: OCR extracts specifications from documents, computer vision measures physical dimensions, and data-aggregation pipelines compile operational logs into structured records. Real manufacturing facilities use these systems in production, though some manual verification of complex or ambiguous dimensions may still be required. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Some manufacturing software can auto-log dimensions from connected measurement devices, but general deployed AI products for compiling this specific documentation workflow across model-making shops are narrow and not widespread in production. |
Study blueprints, drawings, and sketches to determine material dimensions, required equipment, and operations sequences.
56CI 35–76 · exposure 50 · augmentation 75 · importance 4.5/5 · click for rater detail
Study blueprints, drawings, and sketches to determine material dimensions, required equipment, and operations sequences.
56| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 3/5 | Manufacturing and engineering sectors show moderate AI adoption; CAD-integrated AI tools are gaining traction in larger firms, but small job shops and custom model makers still rely on manual review—adoption is in the pilot-to-early-production phase rather than deep mainstream penetration. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Manufacturing and skilled trades sectors adopt AI more slowly than white-collar office sectors, with CAD/CAM AI features still in early-to-moderate adoption stages. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI-assisted blueprint analysis (flagging dimensions, suggesting material and tooling, auto-populating operation lists) significantly accelerates a model maker's interpretation phase while the human retains final judgment, validating feasibility and catching edge cases. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI tools can help interpret drawings, cross-check dimensions, and suggest equipment/operations sequences, meaningfully speeding up the planning phase for a human model maker. |
| Task automatability | claude-haiku-4-5-20251001 | 4/5 | AI vision systems can reliably extract dimensions, identify materials, and infer operation sequences from digital blueprints and technical drawings at scale. While some complex spatial reasoning or non-standard notations may require human review, the majority of the task (80%+) can be automated with current OCR and computer vision, achieving the ≥50% time-saving threshold. |
| Task automatability | claude-sonnet-5 | 2/5 | AI vision-language models can interpret basic blueprints and suggest dimensions/operations, but complex CAD drawings and physical fabrication planning for metal/plastic models require nuanced spatial reasoning and shop-floor knowledge that current systems handle unreliably. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | No licensing or legal requirement mandates human review of blueprint interpretation; some shops may prefer human judgment for non-standard designs, but organizational inertia is the main friction—not regulatory or liability barriers. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement exists, but errors in interpreting specs can cause costly material waste or safety issues, creating moderate organizational caution before removing human review. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 5/5 | Cloud-based AI vision and document processing cost pennies per drawing, while a skilled model maker's loaded hourly wage (burden ~$50–80/hr) makes AI orders of magnitude cheaper for large-batch drawing analysis. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | While AI-assisted CAD tools reduce some analysis time, the need for skilled human verification of dimensions and process planning keeps effective costs closer to human-comparable rather than an order of magnitude cheaper. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 4/5 | Production systems for technical drawing interpretation exist in CAD software and specialized engineering AI platforms; they reliably parse dimensions, material specs, and operation sequences from standard blueprints. Minor edge cases with ambiguous or handwritten annotations persist, but deployed solutions handle the mainstream case reliably. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Some CAD/CAM software includes AI-assisted feature recognition and toolpath generation, but no mainstream deployed product reliably reads arbitrary blueprints and autonomously determines full material and operations sequences for model-making. |
Use computer-aided design (CAD) and computer-aided manufacturing (CAM) software or hardware to fabricate model parts.
55CI 35–75 · exposure 50 · augmentation 88 · importance 3.8/5 · click for rater detail
Use computer-aided design (CAD) and computer-aided manufacturing (CAM) software or hardware to fabricate model parts.
55| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 4/5 | Manufacturing, engineering, and product development sectors are actively adopting AI-enhanced CAD/CAM tools; pilots and early production adoption are widespread, particularly in automotive, aerospace, and consumer product companies. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Manufacturing and fabrication trades adopt automation more slowly than office/information work, though CAD/CAM tools are well-established as design aids. |
| Augmentation potential | claude-haiku-4-5-20251001 | 5/5 | AI transforms model maker productivity through real-time design suggestions, automatic optimization of complex geometries, rapid iteration, and intelligent toolpath generation, keeping the human designer in full control while dramatically accelerating output. |
| Augmentation potential | claude-sonnet-5 | 4/5 | CAD/CAM software significantly boosts precision and speed of model part design and toolpath generation, meaningfully augmenting the human fabricator's productivity. |
| Task automatability | claude-haiku-4-5-20251001 | 4/5 | CAD/CAM workflows are inherently digital and already heavily automated; AI can generate design variations, optimize toolpaths, and execute manufacturing instructions with minimal human intervention, though complex design intent and material-specific adjustments typically still require skilled oversight. |
| Task automatability | claude-sonnet-5 | 2/5 | CAD/CAM software can generate toolpaths and assist design, but physical fabrication of model parts requires hands-on machine operation, material handling, and fine-tuning that current AI cannot fully replace end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | Few regulatory or licensing barriers prevent AI-driven CAD/CAM use; organizations mainly face internal resistance to retraining and some hesitation about design quality, but technical adoption blockers are minimal. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement, but quality control, precision tolerances, and physical craftsmanship create practical barriers to full automation without human oversight. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 4/5 | AI-augmented CAD/CAM tools cost far less than skilled model makers' loaded wages when accounting for design iteration speed, toolpath optimization, and reduced manual setup time; the per-part economic advantage grows with batch size. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | CNC machines and CAD/CAM software reduce some labor costs, but capital equipment, skilled setup, and material costs keep overall cost comparable to or only modestly cheaper than human labor for custom model making. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 4/5 | Mature CAD/CAM software with AI-assisted design generation and automated toolpath optimization exists in production (Fusion 360, SolidWorks with AI plugins, dedicated manufacturing software); real manufacturers use these systems daily, though integration complexity varies. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Generative design and CAM automation tools exist and are used in industry, but actual model-making still requires skilled machinists to set up, monitor, and finish parts; no product fully automates this workflow. |
Inspect and test products to verify conformance to specifications, using precision measuring instruments or circuit testers.
54CI 30–77 · exposure 50 · augmentation 50 · importance 4.2/5 · click for rater detail
Inspect and test products to verify conformance to specifications, using precision measuring instruments or circuit testers.
54| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 5/5 | Automated inspection is deeply embedded in modern manufacturing across automotive, electronics, medical devices, and consumer goods production. Production deployment of AOI and automated testing systems is widespread, standard practice rather than pilot stage. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Manufacturing and model-making are physical, lower-digitization sectors where automated inspection adoption is slower and concentrated in high-volume production lines rather than custom model shops. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI-assisted inspection tools can highlight anomalies, flag marginal measurements, and organize data for human review, improving throughput and catch rates when humans remain in the loop. However, the task is largely mechanical data collection and conformance checking, so augmentation is useful but not transformative. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI-assisted measurement tools and defect-detection software can help flag anomalies and speed up documentation, meaningfully aiding but not replacing the inspector's judgment. |
| Task automatability | claude-haiku-4-5-20251001 | 4/5 | Inspection and testing of products using precision instruments can be largely automated with machine vision systems, automated measurement devices, and circuit testers integrated into production lines. Most measurement and conformance checks against specifications can achieve >50% time savings, though some complex visual judgment calls may still require human verification. |
| Task automatability | claude-sonnet-5 | 2/5 | Automated inspection with vision systems and CMMs can handle some conformance checks, but model-making often involves one-off or low-volume custom parts requiring flexible human judgment and instrument setup that off-the-shelf AI cannot fully replicate.' |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | Regulatory requirements in some industries (aerospace, medical device) may require human sign-off on critical specs, and liability concerns about missed defects create modest friction. However, most consumer and industrial manufacturing has adopted automated inspection with minimal legal or licensing barriers to deployment. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No licensing requirement, but quality assurance sign-off and liability for defective models/parts create organizational caution before removing human verification entirely. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 4/5 | Automated inspection systems and circuit testers have high upfront capital costs but very low per-unit marginal cost once deployed; across high-volume production runs, this is typically 1-2 orders of magnitude cheaper than manual inspection labor plus overhead. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | Precision measuring equipment and vision systems have high upfront integration and calibration costs for low-volume custom model work, often exceeding the cost of a skilled technician performing spot checks. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 4/5 | Deployed AI systems including computer vision for dimensional inspection and automated circuit testing hardware are in production use across manufacturing. Products like automated optical inspection (AOI) systems and coordinate measuring machines (CMM) with AI integration demonstrate reliable performance, though edge cases and complex anomalies sometimes need human review. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Automated optical/coordinate measuring systems exist in production manufacturing for standardized parts, but for custom model-making with varied geometries and circuit testing, deployed AI-driven inspection is narrow and not broadly reliable. |
Lay out and mark reference points and dimensions on materials, using measuring instruments and drawing or scribing tools.
33CI 30–35 · exposure 25 · augmentation 50 · importance 3.5/5 · click for rater detail
Lay out and mark reference points and dimensions on materials, using measuring instruments and drawing or scribing tools.
33| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Manufacturing is digitizing, but manual marking and layout remain performed by humans in most model-making shops. Adoption of automated marking is limited to high-volume, standardized production runs, not general model-making work. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Metal/plastic model-making is a physical, low-digitization manufacturing niche where robotic/AI adoption for layout tasks remains slow and largely confined to CNC-heavy shops rather than widespread deployment. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | CAD software and AI-assisted layout tools can help model makers plan dimensions and reference points before manual scribing, improving accuracy and reducing setup time. However, the actual marking remains primarily human-performed with AI in an advisory role. |
| Augmentation potential | claude-sonnet-5 | 3/5 | CAD software, digital calipers, and laser-guided marking tools assist workers in achieving precise measurements and layouts faster than fully manual methods, meaningfully boosting productivity while the human still performs the physical task. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | Marking reference points requires precise spatial reasoning and physical manipulation that current AI vision systems struggle with in real materials. While AI can theoretically identify where marks should go from CAD data, the actual scribing or marking task demands dexterous robotics and haptic feedback that remain immature for general application. |
| Task automatability | claude-sonnet-5 | 2/5 | This is a manual, physical layout task requiring hand-eye coordination with real materials and tools; current AI cannot physically mark materials, though CAD/CAM software can generate layout data for CNC or laser-guided marking in some workflows.dollar Full end-to-end automation at equal quality is not generally available off-the-shelf for varied model-making contexts. |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | This task sits in manufacturing where automation is common, but precision marking often requires human oversight and quality verification. Skilled trades maintain some organizational inertia toward human craftspeople, and quality liability for mismarked materials creates moderate friction. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement for this specific task, but physical dexterity, material handling, and quality-control judgment create practical barriers to full substitution by generic automation without task-specific robotic engineering. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | The capital cost of robotic marking systems, combined with programming, integration, and maintenance overhead, currently exceeds the loaded wage of skilled model makers performing this manual task. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | Setting up CNC or robotic marking systems requires significant capital investment (fixtures, programming, calibration) that often exceeds the cost of a skilled worker doing one-off or low-volume layout work. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | No deployed product reliably performs end-to-end marking and layout on arbitrary materials in production settings. Robotic marking systems exist in specialized manufacturing but are narrow in scope, require extensive setup, and handle only standardized geometries. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | CNC machining and CAD-driven marking systems exist in production for standardized parts, but the manual layout/scribing task itself for custom model-making is still largely done by hand with no mature deployed robotic system replacing it broadly. |
Wire and solder electrical and electronic connections and components.
33CI 30–35 · exposure 25 · augmentation 38 · importance 2.3/5 · click for rater detail
Wire and solder electrical and electronic connections and components.
33| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Soldering automation is largely confined to high-volume electronics manufacturing; small model-making shops and prototype labs remain primarily manual, with automation adoption measured and incremental rather than rapid or widespread. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Model making is a small-scale, physical, craft-oriented manufacturing niche with low digitization and slow AI/robotics adoption compared to information-sector work. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI-assisted vision inspection and fault detection during soldering can help humans identify defects and improve workflow, and robotic positioning or heating guidance offers some productivity gains, though the core skill remains human-performed. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI can assist with design specs, wiring diagrams, or defect detection via vision systems, but offers limited direct assistance to the hands-on soldering and wiring process itself. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While robotic soldering systems exist and can automate some high-volume assembly tasks, the work requires precise spatial reasoning, fine motor control, and judgment about joint quality that varies by component type and context. Current AI-driven robots cannot reliably handle the full diversity of soldering scenarios a model maker encounters, and setup overhead is substantial. |
| Task automatability | claude-sonnet-5 | 2/5 | Fine manual soldering and wiring of custom model components requires dexterity, visual judgment, and adaptation to irregular geometries that current AI-driven robotics cannot reliably replicate outside high-volume, standardized production lines. |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | Quality and safety standards for electrical connections create oversight requirements, and liability for faulty solder joints (especially in products shipped to customers) introduces organizational friction. However, there is no strict legal requirement that a human must perform soldering, only that it meet standards. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement exists, but quality/safety concerns for electrical connections and the custom nature of the work create moderate organizational friction against automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | Robotic soldering systems have high capital and integration costs that favor high-volume production; for model makers performing low-to-medium volume custom work, the amortized cost per task typically exceeds the loaded wage of a skilled technician. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | Setting up robotic soldering/wiring cells for low-volume, custom model work is expensive relative to a skilled technician's hourly wage, making automation cost-inefficient at this task's typical scale. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | Specialized soldering robots are deployed in industrial manufacturing for standardized repetitive work, but they require extensive programming and fixturing for each new design. No general-purpose AI agent can reliably solder arbitrary electrical connections in prototype or custom model-making contexts without human intervention. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Automated soldering exists in mass PCB manufacturing (pick-and-place, wave/reflow soldering), but for custom model-making with varied, low-volume, non-standard components, no deployed product performs this reliably. |
Devise and construct tools, dies, molds, jigs, and fixtures, or modify existing tools and equipment.
28CI 25–30 · exposure 25 · augmentation 63 · importance 3.9/5 · click for rater detail
Devise and construct tools, dies, molds, jigs, and fixtures, or modify existing tools and equipment.
28| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Tool and die shops are typically small to mid-sized, physically located operations with slow digital transformation; while CAD adoption is common, autonomous fabrication and AI-driven design remain in pilot phases, not production deployment. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Manufacturing and toolmaking sectors are traditionally slower adopters of AI compared to information/professional services, with automation focused on CNC and robotics rather than generative AI. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI can meaningfully assist in design iteration, stress analysis, and optimization recommendations, helping model makers refine specifications faster; however, the final judgment and hands-on construction remain human-driven, limiting the productivity multiplier. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI-driven CAD/CAM tools, generative design software, and simulation significantly speed up the design and planning phase for tools, dies, and fixtures, aiding the human toolmaker substantially. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI can assist in design optimization and CAD modeling, the core task requires physical construction, material selection judgment, and iterative testing that demand hands-on experimentation and embodied problem-solving; automated end-to-end tool fabrication at equal quality remains beyond current AI capabilities. |
| Task automatability | claude-sonnet-5 | 2/5 | This involves physical fabrication, precision machining, and hands-on assembly of tools/dies/molds that current AI cannot perform end-to-end; only design/CAM planning portions are automatable.' |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Significant barriers exist: functional requirements are often tacit and customer-specific, safety-critical applications require human sign-off and liability responsibility, and regulatory standards (e.g., for dies used in regulated manufacturing) typically require certified human craftsmanship and inspection. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No licensing requirement, but precision fabrication demands physical dexterity, judgment on tolerances, and hands-on quality verification that create practical (not regulatory) barriers to automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | AI-assisted design reduces some planning costs, but the skilled labor, materials, machining, and quality control required to physically construct and test tools remain expensive; total automation cost would likely exceed or match skilled tradesperson wages. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | AI-assisted design software reduces some engineering time cheaply, but the physical fabrication requires machinery, materials, and skilled labor that AI does not reduce significantly in cost. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | AI tools exist for CAD design and simulation, but no deployed system reliably automates the full cycle of devising, constructing, and validating physical tools and fixtures without human intervention and hands-on modification. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | CAD/CAM software with AI-assisted generative design exists, but actual construction and modification of physical tools/fixtures still requires skilled machinists operating equipment; no deployed product does the full task. |
Drill, countersink, and ream holes in parts and assemblies for bolts, screws, and other fasteners, using power tools.
26CI 14–38 · exposure 13 · augmentation 25 · importance 4.0/5 · click for rater detail
Drill, countersink, and ream holes in parts and assemblies for bolts, screws, and other fasteners, using power tools.
26| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 3/5 | CNC and robotic drilling is common in large-scale automotive and aerospace manufacturing, but model-making shops and small metal/plastic shops show slower adoption due to low batch sizes, custom geometries, and cost constraints. Adoption is uneven across the sector. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Model making is a low-digitization, small-scale manufacturing trade with minimal AI/robotics adoption for bespoke physical fabrication tasks. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI/vision-guided systems could assist by suggesting optimal hole positions or automating boring sequences, but current augmentation is limited. Power tools remain manual; AI assistance on this task is largely constrained to pre-drilling planning rather than active workflow support. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI-assisted CAD/CAM software can help plan hole placement and generate toolpaths, offering some upstream assistance, but does not meaningfully augment the physical drilling/reaming action itself. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While power drill operations are partially automatable (CNC machines handle many drilling tasks), this task requires positioning and aligning holes in assemblies with precision, spatial reasoning about fastener placement, and manual adjustment for material variation. Current AI/robotics can't fully handle the end-to-end problem at scale without significant human intervention and setup. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a physical manual/machine-tool task requiring hands-on manipulation, tactile feedback, and dexterity that current AI systems cannot perform end-to-end; robotics for this specific unstructured task are not deployed at scale. |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | Drilling is not licensed work, but there are modest organizational barriers: shops must invest in automation equipment, retool workflows, and maintain quality control. Customer preference for precision and the integration complexity into existing assembly lines create friction against full substitution. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement, but physical safety, precision tolerances, and liability for faulty parts create moderate organizational friction against blind automation of manual fabrication steps. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | Industrial robots and CNC systems for drilling carry high capital and integration costs. For model makers and small shops performing custom one-off or low-volume work, the equipment ROI is poor compared to skilled human operators using standard power tools. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | Robotic/CNC solutions for one-off precision drilling require expensive custom tooling, programming, and setup that likely exceeds the cost of a skilled model maker performing this task manually. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | CNC drilling systems exist in manufacturing, but they require extensive programming, setup, and fixture design for each part geometry. General-purpose robotics for ad-hoc drilling of varied assemblies remains research-stage; deployed solutions are highly specialized and not generalizable across typical model-making shops. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed general-purpose product autonomously drills, countersinks, and reams custom model-making parts; CNC machining exists for repetitive production but not for the flexible, one-off model-making context described. |
Set up and operate machines, such as lathes, drill presses, punch presses, or bandsaws, to fabricate prototypes or models.
24CI 16–32 · exposure 16 · augmentation 50 · importance 4.3/5 · click for rater detail
Set up and operate machines, such as lathes, drill presses, punch presses, or bandsaws, to fabricate prototypes or models.
24| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 3/5 | Manufacturing sectors show growing CNC and robotic adoption, but it remains concentrated in large-volume production rather than prototype and custom model shops. Smaller model-making shops and prototyping labs are slower to adopt due to cost and the diversity of bespoke work. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Manufacturing and machining trades are slow adopters of AI-driven automation for bespoke prototype work, though CAD/CAM integration is increasing gradually. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI-assisted design and CNC path generation can help model makers plan setups and reduce programming time. However, the hands-on nature of adjusting, troubleshooting, and adapting to material behavior limits how much AI can augment the core task of operating the machines themselves. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI-assisted CAM software, toolpath optimization, and generative design can help plan and streamline machine setup, improving efficiency while the human still operates equipment. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While CNC programming and machine operation can be partially automated, setting up machines for varied prototype work requires spatial reasoning, fixture design, and real-time adjustments that current AI cannot reliably perform end-to-end. Physical manipulation and adaptive responses to material behavior remain outside practical automation scope today. |
| Task automatability | claude-sonnet-5 | 2/5 | This requires physical manipulation of machinery, material handling, and real-time sensory feedback that current AI systems cannot perform without robotic embodiment, which is not generally available for this specialized fabrication work. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Workspace safety regulations, machinery operator licensing in some jurisdictions, and liability for defective prototypes create meaningful friction. Customer preference for human oversight of custom prototype quality and the physical presence required to troubleshoot machine setup add organizational and legal barriers to full substitution. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement dictates a human must perform this, but physical presence, machine safety protocols, and hands-on craftsmanship create practical barriers to remote AI substitution. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | Industrial robotics and CNC systems require significant capital investment and integration costs. While they may reduce per-unit labor once amortized, the upfront expense and need for skilled technicians to program and oversee them keeps all-in costs comparable to or higher than skilled model makers for small-batch prototype work. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI-only substitute for this physical task, so any comparison would require expensive robotic systems that remain more costly than skilled human labor for low-volume prototype work. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed AI system can independently set up and operate multi-step machine tooling for custom prototypes in production. Robotic arms exist for repetitive manufacturing, but prototype fabrication demands the contextual judgment and flexibility that current deployed systems lack. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product autonomously sets up and operates lathes, drill presses, punch presses, or bandsaws for prototype fabrication; CNC automation exists but requires human setup, tooling, and supervision. |
Cut, shape, and form metal parts, using lathes, power saws, snips, power brakes and shears, files, and mallets.
23CI 10–35 · exposure 13 · augmentation 38 · importance 4.2/5 · click for rater detail
Cut, shape, and form metal parts, using lathes, power saws, snips, power brakes and shears, files, and mallets.
23| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Small job shops and model-making firms (where this task predominantly occurs) have slow digitization and low capital investment in automation. While large manufacturers do adopt CNC and robotics, the broader model-making and skilled trades sector lags significantly in AI/robotic adoption relative to information-sector benchmarks. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Model making and small-batch metal/plastic fabrication is a low-digitization, physical trade sector with minimal AI/robotic deployment for this specific flexible task. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | CAM software and CNC assistance can help designers and machinists plan and preview cuts, reducing trial-and-error in setup and material waste. Augmentation exists on the planning and simulation side, but limited on the real-time sensorimotor guidance during complex forming operations. |
| Augmentation potential | claude-sonnet-5 | 2/5 | CAD/CAM software and some AI-assisted design tools can help plan cuts and shapes, but the physical execution with hand tools remains largely unassisted by AI. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While some subprocesses (e.g., CNC programming from designs) are automatable, the full task requires judgment about material properties, tool wear, setup, and real-time adjustment of cutting parameters. Current robotic systems can execute pre-programmed cuts but struggle with the adaptive, sensorimotor feedback loop needed for consistent quality across varying stock without human intervention. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a physical manual/machining task requiring hands-on manipulation of tools and materials; no off-the-shelf AI system can perform the physical cutting and shaping itself.' |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | There are no strict licensing barriers preventing automation of cutting and shaping itself, but workplace safety regulations, quality inspection requirements, and organizational investment in equipment create moderate friction. Customer demand for bespoke or prototype work, where human judgment is valued, also slows substitution. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement, but physical dexterity, workshop safety, and craftsmanship needs create practical friction against automation, though not a hard legal barrier. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | CNC equipment and industrial robots have high capital and integration costs; for small-batch or custom metal work, the amortized cost per task often exceeds a skilled machinist's wage. Only high-volume, standardized production achieves favorable cost ratios, which does not match the typical scope of model-making. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | Any robotic solution capable of this flexible, low-volume prototyping work would require expensive custom automation, far exceeding the cost of a skilled machinist for one-off model work. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | Industrial robots and CNC machines exist and perform cutting in controlled settings, but deploying them end-to-end for the full range of hand-tool and power-tool operations (snips, files, mallets, shaping with feedback) remains limited to narrow, high-volume scenarios. Most job shops still rely on skilled operators because setup, material variation, and quality control exceed current automation maturity. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | CNC and robotic machining exist for repetitive production, but flexible model-making with hand tools, mallets, and improvisational shaping is not performed by deployed AI/robotic products. |
Grind, file, and sand parts to finished dimensions.
23CI 10–35 · exposure 13 · augmentation 38 · importance 4.0/5 · click for rater detail
Grind, file, and sand parts to finished dimensions.
23| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | CNC finishing is adopted in larger job shops and production runs, but model-making remains a craft-oriented, small-batch sector with slower digitization; many shops still rely on hand finishing for precision and aesthetic control. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Model making and small-batch precision manufacturing are low-digitization, physical craft sectors with minimal AI/robotic adoption for finishing operations compared to information-sector benchmarks. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | Automated measurement feedback, defect detection via computer vision, and pre-programmed tool paths assist model makers by reducing manual trial-and-error and speeding rough passes, though the human ultimately controls fine finishing decisions and quality sign-off. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI can assist with CAD/CAM planning, toolpath generation, or quality inspection, but offers little direct augmentation of the physical hand-finishing motion itself. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While automated grinding and sanding machines exist, this task requires adaptive visual inspection, fine tolerance verification, and real-time adjustment to part geometry—capabilities that current AI systems cannot reliably perform end-to-end without human oversight, limiting time savings to below the 50% threshold. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a manual, physical hand-finishing task requiring fine motor dexterity, tactile feedback, and real-time adjustment that current AI systems cannot perform end-to-end without robotic hardware far beyond generally available AI tools. |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | No strict licensing barrier exists for automated finishing, but organizational friction remains significant: machinists must validate tolerances, inspect surfaces, and sign off on quality, and customer preferences often favor human craftsmanship in model-making—creating moderate adoption friction. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement exists, but organizational friction (custom fixturing, part variability, quality tolerances) and capital investment in robotics create moderate practical barriers to automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | CNC equipment and associated AI vision systems are capital-intensive and require skilled technician oversight; the total cost per part remains comparable to or exceeds skilled manual labor for small batch and custom model work typical in this occupation. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | Specialized robotic finishing systems capable of this precision work require expensive custom tooling, fixtures, and programming, making them costlier per unit than skilled model makers for varied, low-volume work. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | CNC grinding and sanding systems are deployed in manufacturing, but they operate within pre-programmed parameters and require human setup, inspection, and rework; AI vision systems can detect defects but cannot autonomously adjust cutting depth, tool wear, or handle the variability inherent in model-making work. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed AI product autonomously grinds, files, and sands custom metal/plastic parts to precise finished dimensions in production settings; such work remains research-stage robotics at best. |
Assemble mechanical, electrical, and electronic components into models or prototypes, using hand tools, power tools, and fabricating machines.
23CI 10–35 · exposure 13 · augmentation 38 · importance 3.0/5 · click for rater detail
Assemble mechanical, electrical, and electronic components into models or prototypes, using hand tools, power tools, and fabricating machines.
23| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Model making and prototyping remain primarily in small shops, educational settings, and specialized manufacturing; digitization and automation adoption lags information/finance sectors. Most firms still rely on human craftsmanship rather than invested robotics, indicating slow adoption velocity. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Model making and prototype fabrication occur in manufacturing, a sector with historically slow AI/robotics adoption for flexible, low-volume physical tasks. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI-assisted design tools, component positioning guides, and quality-check automation can help model makers optimize layouts and catch errors, moderately raising productivity. However, the hands-on nature of assembly limits how much assistance AI can provide without direct robotic intervention. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI can assist with CAD design, planning, or simulation feeding into the prototype, but offers little direct assistance during hands-on physical assembly itself. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | Assembly of models/prototypes demands physical dexterity, spatial reasoning, and adaptation to component variations that current robots handle only in highly structured, pre-programmed environments. Significant portions remain difficult without custom automation; general-purpose assembly AI lacks the flexibility for the diverse mechanical, electrical, and electronic integration required. |
| Task automatability | claude-sonnet-5 | 1/5 | Physical assembly of mechanical, electrical, and electronic components using hand and power tools requires fine motor manipulation and adaptive dexterity that current AI systems cannot perform end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | No strict licensing requirement exists for the task itself, but safety regulations around electrical and electronic assembly, quality standards, and liability for faulty prototypes create moderate friction. Physical workspace and worker safety rules add some organizational and regulatory friction to full substitution. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement exists, but organizational and safety practices around handling tools/machinery and quality assurance for prototypes create moderate friction. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | Customized robotic assembly systems are capital-intensive and require engineering setup that often exceeds the loaded cost of a skilled model maker, especially for low-to-medium production volumes or diverse prototypes. General-purpose robotic labor remains costlier than human assembly for this varied, small-batch work. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | Robotic/automated assembly systems capable of this flexible, low-volume prototyping work require expensive custom engineering, making them costlier than a skilled model maker for varied one-off builds. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | Industrial robotic arms can handle repetitive assembly in controlled settings, but deployed systems typically require task-specific programming and fixtures. Current AI-driven robotics rarely perform end-to-end heterogeneous assembly (mechanical + electrical + electronic) reliably in production without heavy human oversight and customization. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed AI product autonomously assembles multi-domain prototype components with tools; robotic assembly remains narrow, task-specific, and largely research or highly structured factory-line stage. |
Align, fit, and join parts, using bolts and screws or by welding or gluing.
20CI 5–35 · exposure 13 · augmentation 25 · importance 3.9/5 · click for rater detail
Align, fit, and join parts, using bolts and screws or by welding or gluing.
20| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Model making and small-scale fabrication remain largely manual or semi-automated in small-to-medium shops; adoption of full automation is slow outside high-volume mass production, where task standardization is highest. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Model making is a small-scale, craft-oriented manufacturing niche with low digitization and minimal AI/robotics adoption compared to high-volume manufacturing lines. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI offers limited assistance for this hands-on physical task; while AR guidance for alignment might help slightly, the core work—precise fitting, welding, and joining—remains primarily manual skill-based, with minimal productivity lift from current AI tools. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI-driven CAD/CAM tools and robotic arms can assist with planning or repetitive industrial welding, but for bespoke model-making assembly, current assistance is limited to design-stage support rather than the physical joining task itself. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While some robotic systems can perform bolting and welding in controlled manufacturing settings, the task's requirement to 'align and fit' parts demands visual inspection, measurement, and micro-adjustments that current general-purpose AI cannot reliably handle end-to-end without human intervention. Achieving the 50% time-saving threshold across diverse part geometries and tolerances remains undemonstrated. |
| Task automatability | claude-sonnet-5 | 1/5 | This requires physical dexterity, precise manual manipulation, and real-world sensory feedback to align and join metal/plastic parts—no current AI system performs this physical task end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Safety regulations, quality assurance requirements (especially for metal joining), and the need for human sign-off on structural integrity create significant adoption friction. Many applications require a qualified technician to inspect and verify joins, limiting full automation. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement specifically blocks automation, but physical safety concerns (welding, precision fitting) and the custom/low-volume nature of model making create practical organizational friction against automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | Specialized welding robots and assembly automation are capital-intensive and require significant setup; the all-in cost per task often exceeds that of a skilled model maker, particularly for low-to-medium volume or custom work where setup amortization is poor. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI substitute for this physical assembly task, so any comparison would require expensive custom robotics far exceeding the cost of a skilled model maker for low-volume, high-precision work. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | Specialized industrial robots exist for repetitive welding and bolting in high-volume production, but these are narrowly scoped to identical parts and fixed sequences. General-purpose AI systems and agents cannot reliably perform this task in production today across the variety of materials, geometries, and joining methods a model maker encounters. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed AI product autonomously aligns, fits, and joins model-making parts via bolts, welding, or gluing; industrial welding robots exist but require fixed, pre-programmed setups unlike the flexible custom fitting described here. |
Rework or alter component model or parts as required to ensure that products meet standards.
16CI 5–26 · exposure 13 · augmentation 38 · importance 4.1/5 · click for rater detail
Rework or alter component model or parts as required to ensure that products meet standards.
16| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Model making and similar precision manufacturing remains predominantly in smaller, less-digitized shops with limited automation infrastructure. Adoption of autonomous rework systems is negligible in this occupational context, with only larger aerospace/automotive suppliers experimenting with robotic inspection and simple rework. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Manufacturing and machining trades show slow, uneven AI adoption, with robotics/automation focused on repetitive production rather than adaptive rework tasks. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI-powered vision systems can assist model makers by flagging deviations from standards and suggesting rework approaches, improving inspection speed and consistency. However, the actual alteration remains human-driven, so augmentation is meaningful but limited to guidance and quality analysis rather than transforming the core manual work. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI-driven CAD/CAM tools and inspection software can help identify deviations from standards and suggest adjustments, but the physical reworking itself remains manual with limited AI assistance. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | Reworking or altering components requires physical manipulation, spatial judgment, and quality assessment against often context-specific standards. While AI can analyze whether parts meet specifications via vision systems, the iterative hands-on alteration and assembly work remains deeply manual and requires tactile feedback that current AI agents cannot execute end-to-end. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a hands-on physical task requiring manual rework of metal/plastic components using machining and fabrication skills; no current AI system can physically manipulate materials to correct model parts.atypically. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | This task faces substantial adoption barriers: it requires physical presence on a production floor, involves liability for quality failures, demands skilled judgment about whether rework meets standards, and typically occurs in small-to-medium shops with high organizational friction for automation. The human craftsperson's accountability for output quality creates a strong human-touch requirement. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No licensing requirement specifically bars automation, but physical dexterity, tacit craft knowledge, and quality-control judgment create substantial practical barriers to substitution by current technology. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Current AI systems capable of physical component rework (robotic arms with vision guidance) remain expensive in capital, integration, and oversight costs, far exceeding the loaded wage of skilled model makers, particularly for low-volume or custom alterations. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI substitute performing physical rework, so AI cost is effectively infinite relative to human labor for this hands-on task. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | No deployed product reliably performs unsupervised rework and alteration of physical model components to meet standards. Vision inspection systems exist to assess compliance, but autonomous physical manipulation with the precision and judgment required for model making remains at prototype stage in specialized labs, not production. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed AI product performs physical rework of model parts; this remains squarely in the domain of skilled machinists and technicians using tools. |
Consult and confer with engineering personnel to discuss developmental problems and to recommend product modifications.
14CI 7–21 · exposure 5 · augmentation 50 · importance 3.5/5 · click for rater detail
Consult and confer with engineering personnel to discuss developmental problems and to recommend product modifications.
14| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Model making and product development remain hands-on, relational domains with limited existing AI adoption. Most manufacturing advice still flows through human expertise networks; digital adoption of consultation tasks lags compared to information-heavy sectors. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Manufacturing and skilled trades sectors have historically slower AI adoption, especially for tasks requiring physical inspection and cross-functional in-person dialogue. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI could usefully assist by summarizing prior modifications, drafting technical notes, or flagging design considerations, but the core consulting dialogue still needs the model maker to interpret context and negotiate with engineers on feasibility and trade-offs. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI can assist by summarizing technical specs, drafting reports, or suggesting design modifications based on data, aiding preparation for these consultations even though it can't replace the interaction itself. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task requires real-time dialogue with domain experts to discuss complex developmental problems and make nuanced recommendations. Current AI lacks the embodied context, tacit knowledge access, and interactive problem-solving depth that engineers expect in such consultations. |
| Task automatability | claude-sonnet-5 | 1/5 | This requires real-time interpersonal negotiation, technical judgment about physical materials/processes, and collaborative problem-solving that current AI cannot conduct autonomously end-to-end.' |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Engineers typically expect to consult with peers or experienced model makers who can take accountability for recommendations that affect product safety and manufacturability. Organizational culture and liability concerns create high friction against substituting AI for human expert judgment in this advisory role. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No formal licensing requirement, but organizational trust, tacit craft knowledge, and the need for in-person judgment on physical prototypes create meaningful friction against automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | AI inference is cheap, but the task requires significant integration with engineering workflows, validation of recommendations by humans, and oversight to avoid suggesting flawed modifications. Setup and quality-control costs make the all-in cost closer to human labor. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | AI cannot independently perform this collaborative, physically-grounded consultation, so there is no viable AI-only cost comparison; human expertise remains necessary. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | While AI can draft technical suggestions based on written specs, no deployed product reliably conducts expert consultation with engineers on developmental problems at production scale. Some internal copilots assist documentation but don't replace the core consulting dialogue. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product substitutes for the human consultative role in engineering-shop floor discussions about physical model modifications; this remains research-stage at best. |
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.