Machinists
51-4041.00Median wage $58,750/yr287,050 employed (US)Rank #379 of 923 scored · top 41% by substitution
Set up and operate a variety of machine tools to produce precision parts and instruments out of metal. Includes precision instrument makers who fabricate, modify, or repair mechanical instruments. May also fabricate and modify parts to make or repair machine tools or maintain industrial machines, applying knowledge of mechanics, mathematics, metal properties, layout, and machining procedures.
Sub-scores
0–100 · band = confidence interval from rater disagreement
Substitution30
Exposure26
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
29 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%27
panel mean rating 2.1/5 → substitution pressure 27/100
Technical feasibility todayw 20%23
panel mean rating 1.9/5 → substitution pressure 23/100
Cost vs. human wagew 15%24
panel mean rating 2.0/5 → substitution pressure 24/100
Adoption barriersw 20%inverted — strong barriers lower the score49
panel mean rating 3.0/5 (barrier strength) → substitution pressure 49/100
Sector adoption velocityw 10%28
panel mean rating 2.1/5 → substitution pressure 28/100
Task breakdown (29 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.
Monitor the feed and speed of machines during the machining process.
65CI 55–75 · exposure 62 · augmentation 75 · importance 4.5/5 · click for rater detail
Monitor the feed and speed of machines during the machining process.
65| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 4/5 | Modern manufacturing, especially in electronics, automotive, and aerospace, is rapidly deploying machine monitoring systems and predictive maintenance platforms. Smaller job shops lag, but mid-market and large-scale production environments show clear adoption momentum. |
| Sector adoption velocity | claude-sonnet-5 | 3/5 | Manufacturing is a middling-adoption sector for AI; smart sensors and adaptive machining control are increasingly common in advanced shops but far from universal, especially among small and mid-size machine shops. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI monitoring dashboards and real-time adjustment suggestions substantially assist machinists by aggregating data from multiple machines and flagging deviations before quality or tool-life issues arise. The human operator gains situational awareness and frees cognitive load for higher-value problem-solving. |
| Augmentation potential | claude-sonnet-5 | 4/5 | Real-time monitoring dashboards, alerts, and adaptive control systems significantly help machinists catch feed/speed deviations faster and reduce scrap while the operator remains responsible for oversight and intervention. |
| Task automatability | claude-haiku-4-5-20251001 | 4/5 | Current AI systems with computer vision can monitor machine feed rates and spindle speeds in real-time via live camera feeds and sensor data streams, detecting anomalies and adjusting parameters with minimal human intervention. This meets or approaches the 50% time-saving threshold for routine monitoring, though complex troubleshooting may require human oversight. |
| Task automatability | claude-sonnet-5 | 3/5 | Sensor-based CNC monitoring systems can track feed/speed parameters automatically, but many machinists still work on manual or semi-automated equipment requiring human observation and adjustment, and full automation requires significant machine-specific setup. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | Few regulatory barriers exist specifically for AI-based machine monitoring; operator licensure does not legally mandate human observation of feed rates. Customer preference and organizational inertia (workforce retention, trust in legacy systems) present light friction rather than hard legal blocks. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement specifically for monitoring feed/speed, but liability for machine crashes, tool breakage, or workpiece scrap creates some caution about full automation reliance. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 4/5 | The cost of continuous sensor monitoring and AI-driven alerts is typically lower than stationing a human operator full-time to watch machines, especially across multiple workstations. Setup costs are moderate but per-task amortization favors automation. |
| Cost vs. human wage | claude-sonnet-5 | 3/5 | Sensor and monitoring hardware plus software integration costs are substantial upfront, though once installed the ongoing monitoring cost is low compared to constant human attention; net cost advantage is moderate, not dramatic. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 4/5 | Mature industrial IoT platforms and machine vision systems deployed in modern manufacturing already perform real-time machine parameter monitoring in production environments. Automated alerts and some adaptive control loops are in production at scale in larger shops, though coverage varies. |
| Technical feasibility today | claude-sonnet-5 | 3/5 | Modern CNC machines with adaptive control and IoT sensors are deployed in production shops, but many machinists work with older equipment or in contexts requiring hands-on monitoring, limiting universal deployment. |
Program computers or electronic instruments, such as numerically controlled machine tools.
64CI 39–89 · exposure 70 · augmentation 75 · importance 4.5/5 · click for rater detail
Program computers or electronic instruments, such as numerically controlled machine tools.
64| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 4/5 | Aerospace, automotive, and job-shop manufacturing sectors have actively adopted CAM and code-generation tools for NC programming over the past two decades; adoption continues to accelerate with AI enhancements and is well-established in digitized manufacturing. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Manufacturing is a comparatively slow-adopting, capital-intensive sector with high physical and safety stakes, so AI-driven programming tools are in pilot/early adoption stages rather than widespread deployment. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI assists machinists by auto-generating initial code, suggesting toolpaths, and optimizing feeds/speeds, allowing human experts to focus on validation, edge-case design, and quality oversight rather than manual code writing. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI and advanced CAM software meaningfully speed up drafting of toolpaths and code generation, letting machinists focus on verification, optimization, and fine-tuning rather than starting from scratch. |
| Task automatability | claude-haiku-4-5-20251001 | 5/5 | Current AI systems (code generation models, CAM software) can fully automate NC programming from part specifications, designs, or even sketches, meeting or exceeding the 50% time-saving threshold with comparable or superior code quality compared to human programmers. |
| Task automatability | claude-sonnet-5 | 3/5 | AI (LLMs and CAM-integrated tools) can generate G-code or CNC programs from specifications, but complex parts, tolerances, and machine-specific quirks still require significant human verification and setup, limiting full end-to-end automation today. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | There are minimal regulatory or licensing barriers to automating NC programming itself; the main friction is organizational (preference to retain skilled staff, validation workflows) rather than legal or liability-driven. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No licensing requirement, but liability for scrapped material, machine damage, or safety incidents from faulty programs creates strong organizational caution against fully unsupervised AI-generated programs. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 4/5 | AI-based CAM and code-generation tools cost a fraction of a machinist's hourly rate when amortized across projects; a single CAM license generates thousands of programs, while human programming demands per-program labor at loaded wages. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | AI-assisted programming tools reduce some time but still require skilled machinist oversight for verification, simulation, and adjustment, so total cost savings versus a human programmer are moderate rather than order-of-magnitude. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 5/5 | Mature CAM (Computer-Aided Manufacturing) software integrated with AI-assisted code generation already operates at scale in production machine shops; tools like Fusion 360, Mastercam, and increasingly AI code assistants are deployed and reliably generate G-code for CNC machines. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | CAM software with automated toolpath generation exists and some AI-assisted G-code generation tools are emerging, but reliable, error-free autonomous programming of NC machines in production is not yet standard practice across shops. |
Test experimental models under simulated operating conditions, for purposes such as development, standardization, or feasibility of design.
54CI 25–82 · exposure 53 · augmentation 63 · importance 4.0/5 · click for rater detail
Test experimental models under simulated operating conditions, for purposes such as development, standardization, or feasibility of design.
54| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 4/5 | Manufacturing and product development sectors have rapidly adopted automated test frameworks, simulation software, and continuous integration pipelines. Data from CAD vendors and manufacturing automation show deep penetration of automated testing in medium-to-large firms and fast-growing adoption in smaller operations. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Manufacturing and machining are lower-digitization, physically-oriented sectors where AI adoption for hands-on testing tasks remains in early pilot stages rather than production-scale deployment. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI-driven simulation and real-time test analytics substantially amplify a machinist's ability to design, refine, and validate models. Humans focus on interpretation and iteration while automated systems handle execution, data synthesis, and anomaly detection—a high-augmentation scenario with human oversight intact. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI can assist with simulation modeling, data logging, and predictive analytics to inform test design and interpret results, meaningfully aiding the machinist without replacing the physical testing process. |
| Task automatability | claude-haiku-4-5-20251001 | 5/5 | Simulation-based testing of experimental models can be largely or fully automated via software-defined test frameworks, data logging, and analysis pipelines. Current AI and automated testing systems can execute, monitor, and evaluate performance against specifications with minimal human intervention, easily meeting the 50% time-saving threshold. |
| Task automatability | claude-sonnet-5 | 2/5 | This task combines physical setup of experimental models, running physical simulations/tests, and interpreting results—largely a hands-on, judgment-driven activity that current AI cannot perform end-to-end.' |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | Standards compliance, liability for defects, and design validation requirements create moderate friction. Many organizations still require human sign-off on critical design tests; however, automation of routine testing is already normalized in quality-assurance workflows without legal mandates. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No licensing requirement specifically blocks automation, but organizational reliance on skilled machinist judgment, safety protocols, and physical equipment access create real friction to full automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 5/5 | Automated simulation and test infrastructure, once deployed, has very low per-test marginal cost compared to the loaded wage of a skilled machinist running physical or manual tests. Infrastructure investment is high but per-unit cost is orders of magnitude lower. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | The physical testing infrastructure, machinist labor, and equipment costs dominate; AI could reduce some data-analysis time but doesn't replace the core physical test costs, so overall cost savings are modest. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 4/5 | Deployed software solutions (CAD-integrated simulation, automated test benches, measurement systems) reliably execute standardized tests in production environments. Some edge cases and novel designs may require human judgment, but mature products perform core testing tasks at scale across manufacturing. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product autonomously conducts physical feasibility testing of experimental machined models; this remains a human-in-the-lab activity with AI only assisting in data analysis. |
Prepare working sketches for the illustration of product appearance.
52CI 34–70 · exposure 45 · augmentation 63 · importance 3.9/5 · click for rater detail
Prepare working sketches for the illustration of product appearance.
52| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Manufacturing and machine shops remain among the slower-adopting sectors for AI. Digitization is uneven across firm size, and while large aerospace/automotive shops explore CAD automation, small and mid-sized machine shops have limited AI adoption in design workflows. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Manufacturing and machining are traditionally slower to adopt AI tools compared to purely digital/professional service sectors, though CAD-adjacent AI tools are gaining some traction. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI can meaningfully assist by rapidly generating multiple sketch options, speeding up concept ideation, and helping visualize product appearance. However, a machinist must still validate technical correctness, add annotations, and integrate sketches into production workflows, limiting augmentation to idea generation rather than end-to-end task transformation. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI can significantly speed up sketch drafting and idea generation for appearance visualization while the machinist refines and finalizes the design. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | Current AI can generate visual sketches and renderings from text, but machinists typically need sketches that meet specific technical standards (dimensions, tolerances, annotations) and integrate with existing product specifications. AI struggles with the precision requirements and context-dependent judgment involved in translating vague product concepts into manufacturing-ready working sketches. |
| Task automatability | claude-sonnet-5 | 4/5 | Generating illustrative working sketches from descriptions or CAD data is a task current AI image/CAD-generation and sketching tools can do quickly, though final technical accuracy may need human review. |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | Machinists often work under quality standards, safety regulations, and organizational procedures that require human sign-off on working sketches. Customer expectations and liability concerns around manufacturing defects create friction to full automation, though there are no hard legal barriers preventing AI sketch generation as an assistive tool. |
| Adoption barriers | claude-sonnet-5 | 1/5 | No licensing or regulatory requirement mandates a human create appearance sketches; it's a low-stakes illustrative task with no liability barrier. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 3/5 | AI image generation has very low inference costs, but integration into a machinist's workflow (validation, revision, incorporation into CAD) involves overhead. For rough concept sketches the cost ratio might favor AI; for specification-grade working sketches requiring human oversight and rework, costs approach parity. |
| Cost vs. human wage | claude-sonnet-5 | 4/5 | AI-generated sketches or CAD-assisted illustrations cost a fraction of a machinist's or draftsperson's time to produce a rough visual representation. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | While AI image generation tools (DALL-E, Midjourney, Stable Diffusion) exist and can produce sketches, they lack reliable accuracy for technical specifications and cannot consistently integrate with CAD standards or manufacturing constraints. No mature product demonstrably handles this task reliably in production machine shop environments. |
| Technical feasibility today | claude-sonnet-5 | 3/5 | AI sketch and CAD-assist tools (e.g., generative CAD, image generation) exist and are used for concept sketches, but reliable production-grade technical illustration integrated into machining workflows is still narrow and not universally deployed. |
Calculate dimensions or tolerances, using instruments, such as micrometers or vernier calipers.
50CI 25–75 · exposure 45 · augmentation 63 · importance 4.8/5 · click for rater detail
Calculate dimensions or tolerances, using instruments, such as micrometers or vernier calipers.
50| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 4/5 | Manufacturing, especially precision machining and quality control, has actively adopted automated measurement systems over the past decade; adoption is well-established in larger production facilities and continues to expand into mid-sized shops. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Manufacturing/machining is a moderately digitized but physically-oriented sector where AI adoption for hands-on measurement tasks remains slow and pilot-stage at best. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI-assisted measurement and real-time tolerance feedback can significantly enhance a machinist's productivity by automating the routine measurement cycles and highlighting out-of-tolerance parts, allowing the human to focus on adjustment and problem-solving. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI-enabled digital measurement tools and software can assist in logging, computing tolerances, and flagging deviations, improving efficiency while the machinist still performs physical measurement. |
| Task automatability | claude-haiku-4-5-20251001 | 4/5 | Modern vision systems and AI can measure physical dimensions with high precision using automated optical inspection or laser scanning, achieving the 50% time-saving threshold. However, the task requires physical instrument manipulation and interpretation of complex tolerance specifications that may still benefit from human judgment in edge cases. |
| Task automatability | claude-sonnet-5 | 2/5 | The physical act of measuring with micrometers/calipers requires hands-on manipulation of instruments on physical parts, which current AI cannot perform; only the arithmetic/tolerance calculation portion is automatable.dxxx |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | While automation is technically viable and increasingly adopted, many shops require human sign-off on critical tolerance measurements for liability and quality assurance reasons, and some regulatory contexts demand certified personnel validation. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing barrier specifically for using calipers, but precision quality control often requires human verification and physical dexterity, creating practical friction against substitution. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 4/5 | Automated vision and measurement systems have dropping deployment costs and operate 24/7 without fatigue, making them substantially cheaper than a human machinist for high-volume dimensional verification. Integration and maintenance costs are modest relative to labor savings. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | Since the physical measurement must still be done by a human or specialized hardware, any AI component only handles a small calculation slice, so overall cost savings versus a human machinist are minimal. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 4/5 | Deployed automated optical inspection (AOI) and coordinate measurement machine (CMM) systems reliably perform dimensional measurement in production environments at scale, particularly in manufacturing. Some integration overhead remains for translating tolerance specifications into automated routines. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product performs the physical measurement step; digital calipers with data output exist but the integration of AI to fully replace this task in production is not demonstrated. |
Check work pieces to ensure that they are properly lubricated or cooled.
41CI 30–52 · exposure 38 · augmentation 50 · importance 3.9/5 · click for rater detail
Check work pieces to ensure that they are properly lubricated or cooled.
41| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Manufacturing adoption of AI for this specific task remains limited; most shops rely on periodic human checks and simple timers rather than autonomous monitoring agents. Larger, digitized facilities are beginning sensor integration, but deployment is still spotty and often manual-assisted. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Manufacturing, especially small-to-mid machine shops, is a slower-adopting sector for sensor-based automation compared to information/professional services, though larger manufacturers with smart-factory investments move faster. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI-driven sensor dashboards and alerts can assist machinists by flagging temperature/pressure anomalies and recommending checks, reducing their need to monitor continuously. Such tools improve productivity and safety awareness, though the human judgment about remedial action remains central. |
| Augmentation potential | claude-sonnet-5 | 3/5 | Sensors and alerts can flag lubrication/cooling issues in real time, helping machinists catch problems faster, but this is a narrow assistive function within the broader task of operating the machine. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While some aspects of lubrication/cooling (e.g., monitoring sensor data, timing intervals) could be partially automated, the task requires physical inspection and tactile assessment of work pieces in dynamic manufacturing environments. Current AI cannot reliably perform end-to-end lubrication and cooling checks without human intervention, especially when quality judgment or unusual conditions arise. |
| Task automatability | claude-sonnet-5 | 3/5 | Sensor-based monitoring of coolant flow, temperature, and lubrication can automate the checking function on modern CNC machines, but many manual/older machine setups still require physical human inspection.5:.0. |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | Manufacturing environments typically require operator certification and responsibility for machine health and product quality; there is liability exposure if automation fails and a defective part ships. However, no hard legal requirement mandates human sign-off on every lubrication check, creating moderate but not insurmountable barriers. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing or legal requirement mandates human inspection of lubrication/cooling; the main barrier is practical integration cost and retrofit requirements on existing machinery rather than regulation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | While sensor systems have become cheaper, integrating vision, temperature monitoring, and decision logic to replace a skilled machinist's periodic checks remains costly relative to the brief time the human task itself takes. The labor cost is low for the frequency of checking, making automation economics unfavorable. |
| Cost vs. human wage | claude-sonnet-5 | 3/5 | Sensor retrofit and monitoring systems have upfront capital costs but low marginal cost once installed; for high-volume shops this is cost-effective, though for small shops the manual check by an already-present machinist has near-zero marginal cost. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | Deployed products exist for limited monitoring (temperature sensors, flow meters) but lack comprehensive, reliable end-to-end automation of the full inspection task. Most implementations remain narrow (single parameter monitoring) or require significant human oversight, falling short of production-ready autonomous performance. |
| Technical feasibility today | claude-sonnet-5 | 3/5 | Industrial IoT sensors and automated coolant monitoring systems exist and are deployed in modern CNC shops, but many machine shops still rely on manual checks, especially on older equipment or manual lathes/mills. |
Measure, examine, or test completed units to check for defects and ensure conformance to specifications, using precision instruments, such as micrometers.
39CI 30–49 · exposure 38 · augmentation 50 · importance 4.6/5 · click for rater detail
Measure, examine, or test completed units to check for defects and ensure conformance to specifications, using precision instruments, such as micrometers.
39| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Manufacturing has adopted automated inspection in high-volume, standardized contexts, but most shops rely on skilled manual inspection. Adoption remains slow outside large-scale production due to customization needs and capital barriers. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Manufacturing is a comparatively slow adopter of AI-driven automation relative to information/professional services, though CNC and automated inspection are gradually penetrating machine shops. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI-assisted visual inspection tools can flag potential defects and reduce time on routine checks, augmenting a machinist's productivity, though the human must still make final dimensional judgments with precision instruments. |
| Augmentation potential | claude-sonnet-5 | 3/5 | Digital calipers/micrometers with data logging, SPC software, and automated measurement systems assist machinists in verifying conformance faster and with better record-keeping, though the physical measurement act often remains manual. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI vision systems can detect some surface defects, the task demands precise dimensional measurement with micrometers and judgment about specification conformance in diverse product geometries and conditions. End-to-end automation with 50% time savings at equal quality is not yet demonstrated at scale in general manufacturing contexts. |
| Task automatability | claude-sonnet-5 | 3/5 | Automated metrology (CMMs, laser scanners, vision systems) can perform much of this measurement work, but it requires substantial capital setup and integration with existing manual machinist workflows, so full end-to-end automation isn't universal today. |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | Quality assurance and metrology in machining often involve responsibility for product safety and customer liability; operators signing off creates friction but not a hard legal barrier. Automation requires validation and may face organizational resistance. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement for using measurement tools, but quality/traceability sign-off requirements in regulated industries (aerospace, medical) can require documented human-verified inspection records, adding some friction. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | High-precision measurement equipment and AI vision systems, plus integration and oversight, remain comparable to or exceed the labor cost of skilled machinists, particularly for small-batch or custom work. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | Precision automated inspection equipment (CMMs, vision systems) carries high upfront capital and programming costs that often exceed the marginal cost of a machinist doing manual checks, especially for low-volume production. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | Vision-based defect detection systems exist but struggle with the precision and variability required for dimensional measurement using micrometers, and they typically cannot assess conformance to multi-dimensional specs reliably. Deployed products cover narrow use cases (e.g., specific defect types) rather than the full task. |
| Technical feasibility today | claude-sonnet-5 | 3/5 | Automated inspection systems (CMMs, optical comparators, vision-based gauging) are deployed in production in many shops, but many machinists still rely on manual micrometer/caliper checks for in-process inspection, especially in small-batch or job shops. |
Operate equipment to verify operational efficiency.
38CI 30–46 · exposure 38 · augmentation 63 · importance 4.3/5 · click for rater detail
Operate equipment to verify operational efficiency.
38| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 3/5 | Manufacturing has adopted predictive maintenance pilots and condition monitoring at middling pace, particularly in larger operations; however, small to mid-sized shops and older equipment environments lag significantly in AI adoption, preventing deep, sector-wide displacement. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Manufacturing and machining are relatively slow adopters of AI compared to information/professional services, with automation focused on CNC control rather than general AI agents. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI-powered monitoring dashboards and anomaly detection systems substantially assist machinists by surfacing efficiency trends, fault predictions, and diagnostic data that would take humans much longer to gather manually, enabling faster decision-making while the operator remains responsible for action. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI-based sensors and analytics can help machinists monitor performance metrics and flag anomalies, improving efficiency verification without replacing the operator. |
| Task automatability | claude-haiku-4-5-20251001 | 3/5 | Monitoring equipment efficiency through sensor data collection and analysis can be partially automated with current AI systems, but the full task typically requires human judgment for unexpected anomalies and preventive maintenance decisions that demand domain expertise. |
| Task automatability | claude-sonnet-5 | 2/5 | Operating physical machinery and verifying it via sensory/manual checks requires physical presence and dexterity that current AI cannot perform end-to-end; only data analysis portions are automatable.' |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | Safety regulations and equipment liability create moderate friction—autonomous adjustments or shutdowns require human sign-off or embedded safety certification, and many manufacturing environments have organizational preference for human operators to remain in direct control. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No licensing requirement specifically, but safety protocols, physical equipment access, and liability for machine damage create meaningful organizational friction against full automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | While sensor systems and monitoring software have dropped in cost, the requirement for specialist setup, network infrastructure, and ongoing human oversight keeps total-cost-of-ownership comparable to or occasionally higher than employing experienced machinists for routine verification tasks. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | Sensor/analytics systems add cost on top of still-needed human operators and technicians, so total cost is not clearly cheaper than a machinist performing verification directly. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 3/5 | Condition monitoring and predictive maintenance products exist and operate in industrial settings, but they often require significant configuration, human validation of alerts, and integration with legacy equipment systems that limits their autonomous reliability. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Condition-monitoring and predictive maintenance products exist for machinery diagnostics, but they support human verification rather than autonomously operating equipment and confirming efficiency. |
Support metalworking projects from planning and fabrication through assembly, inspection, and testing, using knowledge of machine functions, metal properties, and mathematics.
30CI 30–30 · exposure 25 · augmentation 63 · importance 4.4/5 · click for rater detail
Support metalworking projects from planning and fabrication through assembly, inspection, and testing, using knowledge of machine functions, metal properties, and mathematics.
30| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Adoption of AI-assisted planning and inspection tools is growing in larger manufacturing facilities, but full end-to-end automation remains rare; most shops continue to rely heavily on machinist judgment and skill. Digitization is advancing but not yet displacing machinists at scale. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Manufacturing and machining are historically slower-digitizing sectors; AI-driven CAM optimization and inspection vision systems are being piloted but production-scale substitution of full task remains limited. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI significantly augments machinists through CAD/CAM software, design optimization, predictive tool life modeling, and automated visual inspection feedback—all of which can accelerate planning and reduce rework while keeping the skilled operator central to fabrication and adaptive problem-solving. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI-assisted CAD/CAM programming, toolpath optimization, and machine vision inspection meaningfully speed up planning and quality-check portions of the workflow while the machinist remains in control of fabrication and assembly. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI can assist with planning, calculations, and inspection via computer vision, the core tasks of fabrication and assembly require hands-on manipulation of machinery and materials that demand real-time physical judgment, tool setup, and troubleshooting. Current AI systems cannot reliably operate CNC machines end-to-end or perform adaptive assembly work, limiting automation to perhaps 20–30% of the overall workflow. |
| Task automatability | claude-sonnet-5 | 2/5 | This is a broad, multi-stage physical task spanning planning, hands-on fabrication, assembly, inspection, and testing; the manual machining and assembly steps require physical dexterity and real-world manipulation that current AI cannot perform end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | No hard legal requirement mandates human sign-off, but strong organizational and craft norms favor skilled machinists, quality liability for defective parts encourages human inspection, and the variability of projects creates friction in full automation. Safety and precision requirements add oversight burden. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No licensing mandate universally requires a human machinist, but quality/safety inspection sign-offs, liability for defective parts, and reliance on skilled trade judgment create moderate organizational and safety-driven friction. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | Integration of AI tooling (vision systems, CAD automation, planning aids) adds capital and maintenance cost, while the skilled machinist wage remains competitive. AI adoption reduces per-unit time on specific subtasks, but the overall all-in cost (hardware, software, operator oversight) is still comparable to or exceeds the loaded labor cost for complex, variable work. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | Software tools reduce some programming time cheaply, but physical fabrication, assembly, and testing still require skilled machinist labor and capital equipment, keeping overall cost comparable to or above human-only workflows. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | Some narrow tasks (e.g., design optimization, metal property lookup, inspection image analysis) have working AI tools, but no deployed system reliably performs the full machining workflow—from job planning through adaptive fabrication and physical assembly—without significant human oversight and intervention. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | CAM/CAD software and CNC programming assistants exist and are deployed, but they cover only planning/programming subtasks, not the full fabrication-assembly-inspection-testing chain described. |
Diagnose machine tool malfunctions to determine need for adjustments or repairs.
30CI 25–35 · exposure 25 · augmentation 63 · importance 4.1/5 · click for rater detail
Diagnose machine tool malfunctions to determine need for adjustments or repairs.
30| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Manufacturing remains a relatively laggard sector for AI deployment; even digitized shops often use legacy systems and rely on skilled technician expertise. Pilots of condition-monitoring AI exist but deep production adoption of autonomous diagnosis is limited, particularly in smaller and mid-sized machine shops. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Manufacturing and machining are historically slow-digitizing, capital-intensive sectors with uneven adoption of predictive maintenance and AI diagnostics, especially among smaller machine shops. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI-powered sensor dashboards, anomaly alerts, and predictive maintenance recommendations can significantly assist machinists by surfacing early warning signs and narrowing diagnostic hypotheses. A machinist remains in the loop to interpret context, validate findings, and make final repair decisions, raising their efficiency. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI-driven condition monitoring, vibration analysis, and diagnostic knowledge bases can meaningfully assist machinists in narrowing down potential causes of malfunction, speeding up their diagnostic process. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | Diagnosis of machine tool malfunctions requires real-time sensor data interpretation, physical inspection, and contextual understanding of complex mechanical systems. While AI can assist with pattern recognition on logged data, current systems lack the multimodal integration and real-world inference needed for reliable end-to-end diagnosis without human oversight. |
| Task automatability | claude-sonnet-5 | 2/5 | Diagnosing mechanical/CNC malfunctions requires physical inspection, sensory feedback (sound, vibration, tolerances), and hands-on testing that current AI cannot perform end-to-end; AI can assist with diagnostic checklists or log analysis but not replace the full diagnostic process. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Safety-critical systems and equipment require documented, auditable diagnostics; liability falls on the operator/maintainer if an automated diagnosis leads to incorrect repair. Regulatory and insurance frameworks often require or strongly incentivize human expert sign-off on maintenance decisions, creating organizational and legal friction. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement specifically for diagnosis, but physical access, machine-specific tacit knowledge, and safety concerns around misdiagnosis causing equipment damage create moderate practical friction. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | Implementing AI monitoring systems requires upfront integration costs, ongoing model maintenance, and human expert oversight to validate diagnoses. For a single diagnostic instance, the all-in cost (inference, integration, verification by a technician) likely exceeds the direct labor cost of experienced machinist troubleshooting. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | Deploying sensor-based diagnostic systems requires significant hardware integration and monitoring infrastructure, making costs comparable to or higher than relying on an experienced machinist for many shops, especially smaller ones. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | Some computer vision and sensor analysis products exist for predictive maintenance, but they typically flag anomalies rather than perform full diagnostic reasoning. Production deployments remain narrow and require substantial domain-specific tuning; reliable autonomous diagnosis of novel failure modes is not yet standard. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Some predictive-maintenance and condition-monitoring products exist for sensor-equipped CNC machines, but they typically flag anomalies rather than perform the full diagnostic reasoning a machinist does, and adoption on shop-floor legacy equipment is limited. |
Advise clients about the materials being used for finished products.
30CI 30–30 · exposure 25 · augmentation 63 · importance 3.6/5 · click for rater detail
Advise clients about the materials being used for finished products.
30| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Machining remains a traditional, hands-on sector with relatively slower digital adoption. While some shops use ERP and CAD systems, AI-driven client advisory adoption is nascent and not yet visible in production displacement data. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Manufacturing and machining are relatively slow-adopting sectors for AI compared to information/professional services, with most AI use concentrated in CAD/CAM and programming rather than client advisory work. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI can usefully assist a machinist by instantly retrieving material specifications, comparing properties, and suggesting alternatives, thereby speeding research and allowing the machinist to focus on client discussion and custom recommendations without drudgery. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI tools can quickly surface materials data, comparative properties, and cost/performance tradeoffs that a machinist can then translate into client-specific advice, meaningfully speeding up research and preparation. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | AI can provide factual material property information and basic recommendations from databases, but advising clients requires understanding context, tradeoffs, constraints, and client-specific needs that typically involve human judgment and negotiation. This task is mostly informational retrieval with limited automation potential at the 50% threshold. |
| Task automatability | claude-sonnet-5 | 2/5 | This requires synthesizing client-specific requirements, materials science knowledge, and practical machining experience in a live conversational advisory context, which current AI can support but not fully replace end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | Clients often prefer direct consultation with an experienced machinist for trust and accountability; liability concerns arise if incorrect material advice causes product failure. While not strictly licensed, the advisory relationship and reputational risk create meaningful friction against full automation. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No formal licensing requirement typically exists, but liability for wrong material recommendations, client trust dynamics, and organizational reliance on experienced staff create moderate friction. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | Material databases and AI systems are inexpensive to run, but integration with machinist workflows and the need for human oversight to ensure sound advice means total cost remains substantial relative to the value of a brief advisory conversation with an experienced machinist. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | While AI chat tools are cheap per query, the trust, liability, and technical nuance required in client-facing materials advice keep skilled human machinists cost-competitive for this specific task. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | While AI can query material databases and generate fact-based summaries, reliable client advisory requires domain expertise, situational judgment, and relationship management. No deployed product reliably handles the full advisory loop in production machining environments; existing tools are limited to lookup and suggestion. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | No deployed product autonomously advises clients on materials for machined products in production settings; this remains a human expert function, sometimes with AI-assisted reference lookups. |
Lay out, measure, and mark metal stock to display placement of cuts.
29CI 23–35 · exposure 25 · augmentation 38 · importance 4.0/5 · click for rater detail
Lay out, measure, and mark metal stock to display placement of cuts.
29| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Adoption of automated marking systems remains minimal in the machining sector. Most shops are small to mid-sized firms with legacy workflows, manual processes, and resistance to capital investment in robotics for a task that skilled workers perform reliably and quickly. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Manufacturing is a physically-oriented sector with slower digitization and AI adoption compared to information-based industries, though CAM/CNC automation has been adopted for decades in a narrow sense. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI can offer modest assistance through CAD-to-layout visualization tools and measurement verification, but the core spatial layout and marking work remains tactile and requires human judgment about material conditions, tool fit, and production sequencing that AI assistive tools currently provide only limited value for. |
| Augmentation potential | claude-sonnet-5 | 3/5 | CAD/CAM software and digital calipers/laser measurement tools significantly speed up and improve accuracy of layout and marking tasks, assisting machinists though not replacing them. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | Current AI systems cannot reliably perform end-to-end layout, measurement, and marking of physical metal stock. While vision systems can detect and measure stock dimensions in images, translating this to precise physical marking requires robotic integration, handling of variable materials, and real-time spatial reasoning that deployed systems do not consistently achieve at production quality. |
| Task automatability | claude-sonnet-5 | 2/5 | This requires physical manipulation, precise measurement, and marking of physical metal stock, which current AI systems cannot perform without robotic hardware integration that is not off-the-shelf. Vision-based CAM/CNC layout software can assist digitally but does not perform the physical layout task. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | High barriers exist due to precision and liability requirements—mistakes in layout lead to wasted material and safety risks. Many machine shops operate under quality certifications (ISO, AS9100) that require documented human inspection and sign-off, and customer contracts often specify human verification of markings before cutting begins. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement dictates this specific step, but quality/safety tolerances in machining create moderate liability and inspection requirements that favor human oversight. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | The cost of AI vision + robotic marking hardware, software integration, and ongoing maintenance would significantly exceed the wage of a skilled machinist performing this task. Barrier to cost parity remains high due to equipment specificity and relatively low labor cost for skilled manual work. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | Digital layout software has low marginal cost, but physical marking still requires human labor or expensive CNC/robotic systems, so overall cost savings versus a skilled machinist are modest at best. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | No mature production systems currently perform this task reliably at scale. While computer vision and CAM systems assist in design and planning, the physical act of laying out and marking metal stock in a machine shop remains primarily manual, with only narrow experimental deployments of robotic marking systems. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Some CAD/CAM and CNC programming tools automate the digital planning of cuts, but the physical layout, measuring, and marking on actual stock is still predominantly done manually or via CNC machines requiring machinist setup and verification. |
Study sample parts, blueprints, drawings, or engineering information to determine methods or sequences of operations needed to fabricate products.
28CI 25–30 · exposure 25 · augmentation 50 · importance 4.5/5 · click for rater detail
Study sample parts, blueprints, drawings, or engineering information to determine methods or sequences of operations needed to fabricate products.
28| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Manufacturing remains relatively slow in AI adoption compared to information-intensive sectors; small and mid-sized machine shops dominate the sector and lack digital infrastructure for seamless AI integration. While large aerospace and automotive manufacturers pilot AI tools, production deployment for sequence planning is still nascent. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Manufacturing is a moderate-to-low digitization sector; AI adoption in process planning is mostly pilot-stage with CAM automation being the more mature but narrower tool. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI can assist by automatically extracting and highlighting key features from blueprints, suggesting standard process sequences, or flagging potential tool conflicts, meaningfully speeding up human analysis without replacing the machinist's judgment on final method selection. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI-assisted CAD/CAM analysis and blueprint interpretation tools can help machinists speed up planning and catch errors, providing meaningful but partial productivity gains. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI can extract and analyze some information from blueprints and drawings through OCR and image recognition, determining the correct sequence of operations requires understanding complex manufacturing constraints, material properties, tooling availability, and trade-offs that demand domain expertise. Current AI systems cannot reliably produce a complete, executable fabrication plan without substantial human oversight and refinement. |
| Task automatability | claude-sonnet-5 | 2/5 | Interpreting blueprints and planning fabrication sequences requires spatial reasoning, tolerance judgment, and knowledge of shop-floor constraints that current AI can partially assist with but not fully replace end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Significant barriers exist: machinists are often bound by union agreements, safety and liability concerns are high (errors in fabrication sequences can result in dangerous products or scrap), and quality assurance requirements typically mandate human sign-off on manufacturing plans. Regulatory compliance and customer sign-off further protect this task. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No licensing requirement specifically for this task, but liability for part failure, tight tolerances, and reliance on experienced judgment create organizational friction against fully automating this step. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | The cost of developing, training, and validating AI systems for blueprint analysis plus the required human oversight and correction still exceeds the wage of an experienced machinist performing the analysis directly. Integration costs and error handling remain substantial. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | AI/CAM tools can speed up planning but still require skilled machinist oversight and correction, so total cost including integration and review is not dramatically cheaper than a human doing the task directly. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | Some CAD interpretation and feature recognition tools exist, but no deployed production system reliably converts blueprints into fabrication sequences at the quality and accuracy required for machining without human review. Research prototypes exist, but real-world adoption remains limited due to the complexity of manufacturing decision-making. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | CAM software and some AI-assisted CAD interpretation tools exist, but reliable autonomous generation of full machining process plans from arbitrary blueprints is still narrow and requires human verification. |
Evaluate machining procedures and recommend changes or modifications for improved efficiency or adaptability.
28CI 20–35 · exposure 20 · augmentation 50 · importance 4.3/5 · click for rater detail
Evaluate machining procedures and recommend changes or modifications for improved efficiency or adaptability.
28| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Manufacturing adoption of AI for process optimization remains slow relative to information services, with most adoption concentrated in large OEMs and tier-1 suppliers. Small and medium machine shops—where this task is most prevalent—lag significantly in digital tooling adoption. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Manufacturing is a moderate-to-slow adopter of AI compared to information sectors, with automation focused on execution (CNC, robotics) rather than judgment-based process evaluation. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI can assist by flagging anomalies in production data, suggesting parameter ranges based on material/tool databases, or surfacing historical precedents, helping machinists make better recommendations. However, the assistance is partial and requires expert judgment to translate into safe modifications. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI-driven analytics, simulation tools, and CAM software can help identify inefficiencies and suggest parameter changes, meaningfully aiding a machinist's evaluation process. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | Evaluating machining procedures involves domain expertise in manufacturing physics, tool geometry, and material science. While AI can analyze historical data and detect patterns in production metrics, recommending safe, effective procedural changes requires deep understanding of failure modes and contextual constraints that current systems struggle with reliably. |
| Task automatability | claude-sonnet-5 | 2/5 | This requires physical shop knowledge, tacit understanding of machine tolerances, and hands-on evaluation of processes that current AI cannot directly observe or assess without extensive human-provided data. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Strong barriers exist: liability for equipment damage or unsafe procedures is substantial, customer/supplier relationships depend on proven procedures, and regulatory compliance in aerospace/defense machining often mandates documented human accountability for process changes. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement specifically bars AI from this task, but organizational trust, safety implications of process changes, and reliance on experienced machinists create moderate friction. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | The cost of AI systems plus integration, validation, and human oversight for machining procedure evaluation remains comparable to or exceeds the cost of an experienced machinist's time, especially given the high cost of errors in manufacturing. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | AI tools could assist with data analysis (cycle times, toolpath simulation) cheaply, but the full evaluative judgment still requires costly expert oversight, keeping overall cost comparable to or above human-only work. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | No deployed product reliably performs end-to-end evaluation and modification recommendations for machining procedures in production environments. Some advisory tools exist for specific parameters (speed, feed rates), but they require substantial human validation and domain expertise to integrate safely. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product autonomously evaluates real-world machining procedures and issues actionable efficiency recommendations; this remains largely a human expert function supported by CAM software analytics at best. |
Design fixtures, tooling, or experimental parts to meet special engineering needs.
28CI 25–30 · exposure 25 · augmentation 50 · importance 4.1/5 · click for rater detail
Design fixtures, tooling, or experimental parts to meet special engineering needs.
28| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Machining and tool-and-die sectors lag in broad AI adoption; generative design tools see pilot use but rare production deployment at scale. Small shops and specialized manufacturers have low digitization and slow technology uptake. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Manufacturing and machining remain a sector with lower AI tool adoption depth compared to information/finance industries; generative design tools are used by early adopters but not yet widespread standard practice. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | Generative design and parametric CAD assist machinists in exploring design variants and optimizing dimensions faster, but the human remains central to validating feasibility, adjusting for real-world constraints, and taking responsibility for the final design. |
| Augmentation potential | claude-sonnet-5 | 3/5 | CAD-integrated generative design and simulation tools meaningfully help machinists brainstorm and validate fixture concepts faster, though the human remains central to final design decisions and shop validation. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | AI can assist with parametric design generation and optimization of fixtures, but designing for special engineering needs requires domain expertise, iterative testing feedback, and understanding of material constraints and manufacturing feasibility that exceed current autonomous capability. Significant human oversight and validation remain essential. |
| Task automatability | claude-sonnet-5 | 2/5 | Fixture and tooling design for novel engineering needs requires physical intuition, tolerance stack-up reasoning, and iterative shop-floor validation that current AI cannot fully replicate end-to-end, though CAD generative tools can accelerate parts of the process.' |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Designing fixtures for special engineering needs often requires professional engineering judgment, material science knowledge, and legal responsibility for safety and performance. Organizational practices, liability concerns, and the need for human sign-off on designs create substantial friction against full automation. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No licensing requirement blocks AI use, but liability for faulty tooling causing scrap, damage, or safety issues creates real caution, and engineering sign-off is often organizationally required. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | AI-assisted CAD tools reduce design time modestly, but integration, constraint specification, validation, and the skilled machinist's oversight remain labor-intensive and costly. Overall cost savings relative to human expertise remain marginal. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | Software licensing plus required expert oversight and iteration cycles make AI-assisted design cost savings modest, not dramatically cheaper than an experienced machinist/engineer doing this work. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | CAD automation and generative design tools exist but are narrow, requiring extensive human specification of constraints and frequent manual correction. No production systems reliably generate manufacturing-ready designs for novel special engineering needs without substantial skilled human iteration. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Generative design and CAD-integrated AI tools exist but are mostly used for suggestions or optimization within constrained geometries, not full autonomous design of custom fixtures for special engineering needs in production settings. |
Establish work procedures for fabricating new structural products, using a variety of metalworking machines.
25CI 20–30 · exposure 20 · augmentation 50 · importance 3.9/5 · click for rater detail
Establish work procedures for fabricating new structural products, using a variety of metalworking machines.
25| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Adoption of AI for procedure generation is slow in most machine shops, particularly outside large aerospace and automotive tiers. Most shops still rely on machinists' experience, handwritten notes, and incremental CAM use rather than end-to-end AI-generated procedures. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Manufacturing and machining are historically slower to adopt AI compared to information-sector work, with pilots for process planning emerging but production-scale autonomous procedure generation still rare. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | CAM software and generative AI can usefully draft procedure steps, suggest tool sequences, and highlight constraints, raising a machinist's efficiency in documentation and planning. However, the human remains essential for validating, adapting, and approving procedures for novel work. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI tools (e.g., CAM software enhancements, generative design aids) can help machinists draft initial procedures or suggest tool paths, improving efficiency while the machinist retains final judgment and validation. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI can assist in procedure planning and documentation, the task requires integrating diverse physical constraints, machine capabilities, and real-time problem-solving that current AI systems cannot reliably perform end-to-end. The creative process of selecting among multiple metalworking machines and sequencing operations remains deeply dependent on tacit expertise and trial-and-error. |
| Task automatability | claude-sonnet-5 | 2/5 | Establishing novel work procedures requires physical reasoning about tooling, tolerances, and machine capabilities specific to a shop floor, which current AI cannot fully replicate without heavy human oversight and hands-on validation. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Significant barriers exist: safety certification and liability (incorrect procedures risk worker injury and product failure), regulatory requirements for structural products, and organizational practice requiring sign-off by licensed/experienced personnel. Customers and regulators expect human judgment and accountability in procedure approval. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No formal licensing requirement exists, but safety, liability, and quality-control concerns in metal fabrication create meaningful organizational friction against fully automating new procedure design. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | Current CAM and AI-assisted design tools require substantial licensing, integration, and validation overhead. The cost of inference plus human review, testing, and correction likely exceeds or matches the hourly wage of an experienced machinist performing this planning work. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | While AI could assist in drafting or referencing procedures cheaply, the need for skilled human verification, iteration, and shop-specific calibration keeps overall cost comparable to or only modestly below human-only approaches. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | No deployed production system reliably generates complete, validated work procedures for fabricating novel structural products without significant human oversight and revision. CAM software can assist with tool paths, but establishing procedures for 'new' products requires domain knowledge and approval that remains human-driven. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | There are no deployed products that autonomously generate validated fabrication work procedures for new structural products across varied metalworking machines in production shops today. |
Confer with numerical control programmers to check and ensure that new programs or machinery will function properly and that output will meet specifications.
23CI 16–30 · exposure 20 · augmentation 50 · importance 4.3/5 · click for rater detail
Confer with numerical control programmers to check and ensure that new programs or machinery will function properly and that output will meet specifications.
23| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Machining remains a moderately digitized, physical-process sector where adoption of full automation for conferencing and verification tasks is slow. While digital tools and simulation exist, the collaborative human conferencing step is still entrenched in practice. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Manufacturing and machining are historically slower to adopt AI compared to information/professional services, though CAM/CNC software integration is growing gradually. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI could usefully assist by pre-analyzing NC code for anomalies, checking specifications against documentation, or generating summary reports before a human conference. These augmentation tools would raise productivity but leave the human machinist and programmer in the decision loop. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI-assisted CAM simulation and programming tools can help predict and flag potential program errors before physical trials, aiding the conferring process but not replacing it. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | This task requires domain expertise, interpersonal judgment, and real-time problem-solving in collaborative meetings. While AI could help draft or review program documentation, the core conferencing, cross-checking, and specification verification demands human judgment and communication that current systems cannot fully automate. |
| Task automatability | claude-sonnet-5 | 2/5 | This requires physical verification of machine setup, tooling, and real-world output against specs, which AI cannot perform end-to-end; only the communication/documentation portion could be partially assisted.name |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | This task involves critical safety and specification sign-off on machinery and NC programs. Regulatory and organizational liability requirements typically demand that a qualified machinist or engineer personally verify and confer on functional correctness—legal and professional standards create substantial barriers to full automation. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No licensing barrier specifically, but liability for defective parts, need for hands-on verification, and organizational reliance on experienced machinists create moderate friction against automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | AI tools for code review or specification checking would still require human conference facilitation, oversight, and final decision-making. The cost of integration and human oversight remains comparable to or higher than the value of partial automation, especially given the liability of errors. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | AI cannot replace the physical inspection and cross-functional judgment involved, so no meaningful cost substitution exists; a human machinist's involvement is still required. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | No deployed system reliably performs end-to-end conferencing and collaborative technical verification. AI can assist with document analysis or anomaly detection in code, but cannot independently participate in substantive technical conferences or sign off on machinery function—this remains research-stage. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product autonomously confers with programmers and physically validates machine output against specifications; this remains a human collaborative and physical verification task. |
Separate scrap waste and related materials for reuse, recycling, or disposal.
23CI 10–35 · exposure 13 · augmentation 25 · importance 4.0/5 · click for rater detail
Separate scrap waste and related materials for reuse, recycling, or disposal.
23| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Adoption of automated scrap sorting in machining shops remains slow and limited to larger facilities; most small and mid-sized machine shops rely on manual sorting. Pilot projects exist but production deployment is not yet widespread in the trades. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Manufacturing shop floors are a low-digitization, physical-labor sector with minimal AI/robotic adoption for such ancillary housekeeping tasks. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI-powered sorting assistants—such as automated material classifiers or robotic bin-feeding systems—can help organize and streamline the scrap-separation workflow, but the core task still requires human judgment on contamination, reuse eligibility, and safety decisions. |
| Augmentation potential | claude-sonnet-5 | 1/5 | AI offers essentially no productivity assistance for this manual sorting and disposal task performed alongside machining work. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | Sorting scrap materials involves visual inspection and physical handling in cluttered, variable environments where manual dexterity and spatial reasoning remain difficult for current robots. While AI can classify materials in controlled settings, end-to-end automation with 50% time savings would require reliable vision systems, robotic arms, and integration with waste streams—capabilities exist but not yet deployable at the quality and speed of experienced machinists. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a physical sorting and handling task requiring manipulation of metal scrap and shop waste, which off-the-shelf AI cannot perform end-to-end; it requires robotic manipulation, not just cognition.' |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | Workplace safety regulations and environmental disposal rules add some friction, but no strict licensing requirement mandates human-only sign-off on scrap sorting. Organizational inertia and capital cost are the main adoption hurdles rather than legal barriers. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing or legal barrier, but physical workspace integration, safety around machine tools, and variable waste streams create practical organizational friction. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | Robotic sorting and AI vision systems require significant capital investment and ongoing maintenance, making them more expensive than hiring labor for small to medium machine shops. Only large-volume operations might achieve cost parity with human sorting workers. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | Robotic sorting systems for small-scale, variable shop waste would be far more expensive to deploy and maintain than simply having a machinist toss scraps into labeled bins. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | Some sorting systems and computer-vision material classifiers exist in research and pilot deployments, but production-scale automation of mixed-material separation remains immature. Real-world scrap streams vary too much in composition, contamination, and layout for current deployed products to reliably handle without human oversight. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed products perform physical scrap sorting on machinist shop floors reliably; industrial waste-sorting robots exist mainly in large-scale recycling facilities, not machine shops. |
Machine parts to specifications, using machine tools, such as lathes, milling machines, shapers, or grinders.
20CI 7–32 · exposure 13 · augmentation 50 · importance 4.8/5 · click for rater detail
Machine parts to specifications, using machine tools, such as lathes, milling machines, shapers, or grinders.
20| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 3/5 | Large manufacturers have adopted CNC and some AI-assisted tool-path optimization in recent years, but small job shops and custom fabrication remain labor-intensive and slower to digitize; overall adoption is uneven across the sector. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Manufacturing/machining is a physical, moderately digitized sector where AI-driven robotic adoption is still nascent compared to information-sector AI adoption. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI-assisted CAM software and real-time sensor feedback for tool-wear prediction offer moderate assistance to machinists in planning and monitoring, but current systems require skilled human judgment on complex jobs and do not yet transform baseline productivity across the occupation. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI-assisted CAM software, generative toolpath optimization, and predictive maintenance can improve machinist efficiency and precision, though the core cutting/shaping remains human/machine operator-driven. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While CNC machines can automate repetitive part production, this task requires skilled setup, tool selection, tolerance verification, and real-time adjustment for variations—capabilities that current AI-integrated systems cannot fully autonomize end-to-end without significant human oversight and manual intervention. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a physical manufacturing task requiring manual/robotic operation of machine tools with real-time tactile and visual feedback; current AI systems cannot perform the physical machining itself. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Machining involves safety-critical equipment (spindle hazards, tooling risks), quality certification for aerospace/medical parts, and apprenticeship/licensure traditions in unionized shops; regulatory and human-supervision expectations create substantial organizational friction against full automation. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No licensing requirement specifically bars automation, but quality/safety liability, capital cost of retooling, and need for skilled setup and inspection create real organizational friction. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | CNC equipment and the AI-assisted systems that support them require substantial capital investment and skilled technician oversight; for small production runs or custom work, the per-part cost often exceeds a skilled machinist's loaded labor cost. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no AI system replacing the physical act of machining, so comparing AI inference cost to human wage is not applicable; any substitute (robotics/CNC) requires large capital investment exceeding typical labor costs for many shops. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | Deployed CNC systems execute programmed cuts reliably, but autonomous adaptation to material variance, tool wear, and quality inspection remains limited; no mainstream product today performs the full specification-to-finished-part workflow without human machinists validating setup and intermediate results. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed AI product autonomously operates lathes, mills, shapers, or grinders to machine parts to spec; CNC automation exists but is programmed automation, not AI-driven end-to-end task performance. |
Maintain machine tools in proper operational condition.
20CI 7–32 · exposure 13 · augmentation 63 · importance 4.5/5 · click for rater detail
Maintain machine tools in proper operational condition.
20| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 3/5 | Predictive maintenance analytics are gaining adoption in larger manufacturing operations, but widespread production deployment of fully autonomous maintenance remains limited. Adoption is concentrated in well-capitalized sectors with strong digitization, not across the machinist profession broadly. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Manufacturing and machining shops are relatively slow adopters of AI-driven automation for hands-on physical maintenance tasks compared to information-sector work. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI-driven condition monitoring, sensor data analytics, and predictive alerts significantly enhance machinist productivity by identifying maintenance needs before failures occur and guiding diagnostic work, while the machinist retains control over all corrective actions. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI-powered predictive maintenance and sensor analytics can help identify when maintenance is needed or diagnose issues, assisting machinists in planning and prioritizing upkeep even though it can't perform the physical work. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | Maintaining machine tools requires hands-on mechanical work (lubrication, adjustment, replacement of worn parts) and real-time sensing of machine condition that current AI cannot perform end-to-end. While AI can assist with predictive maintenance analytics, the physical maintenance work itself remains predominantly manual. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a hands-on physical maintenance task involving inspection, cleaning, lubrication, and adjustment of machine tools that requires physical presence and manipulation; current AI cannot perform the physical labor.' |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Significant barriers exist: machinists require trade certification and specialized training in many jurisdictions; liability and safety regulations require qualified humans to sign off on critical machine maintenance; and manufacturing environments prioritize human expertise for problem-solving on complex equipment failures. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No licensing mandate specifically requires a human for machine maintenance, but physical presence, safety protocols, and equipment-specific tacit knowledge create real organizational and physical barriers to automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | Current AI solutions (sensors + analytics software) add cost without eliminating the need for skilled machinists to perform the actual maintenance work. The all-in cost of AI monitoring systems plus human labor typically exceeds or matches the cost of experienced machinists performing preventive maintenance. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no AI substitute performing the physical maintenance work, so AI cost is not comparable—robotics/automation for this remains far more expensive or nonexistent versus a machinist's labor. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | Predictive maintenance products exist (e.g., condition monitoring software), but they address only the diagnostic portion of the task. No deployed autonomous system reliably performs the full spectrum of maintenance actions (adjustments, parts replacement, calibration) in operational manufacturing environments. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product performs physical machine tool maintenance autonomously; predictive maintenance software exists but the actual hands-on upkeep remains fully manual. |
Set up or operate metalworking, brazing, heat-treating, welding, or cutting equipment.
20CI 7–32 · exposure 13 · augmentation 50 · importance 3.9/5 · click for rater detail
Set up or operate metalworking, brazing, heat-treating, welding, or cutting equipment.
20| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 3/5 | Large manufacturers and job shops have adopted CNC and robotic welding in production, but adoption is uneven—small shops and custom fabricators still rely on skilled manual operators. Automation exists for high-volume standardized work but lags for low-volume, varied jobs. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Manufacturing is a physical, moderately digitized sector where robotic automation has existed for decades but full AI-driven autonomous operation remains limited and slow to expand. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI-assisted design and setup tools (CAM software, temperature monitoring, defect detection via computer vision) can improve efficiency, but the core task—operator control, adjustment, and judgment—remains human-driven. Assistance exists on specific subtasks but does not transform overall productivity. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI-enabled CAM software, predictive maintenance, and process optimization tools assist machinists in setup and quality control, improving productivity without replacing the operator. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | Modern CNC machines can automate cutting and some heat-treating, but setup requires spatial reasoning, part inspection, tool selection, and real-time troubleshooting that current AI cannot reliably perform end-to-end. Brazing and welding demand precise manual control and adaptive response to material behavior that remains beyond autonomous systems. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a physical, manual task requiring hands-on setup and operation of machinery; no off-the-shelf AI system can perform the physical manipulation involved.' |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Equipment operation requires hands-on control in hazardous environments; liability for defects, worker safety, and machinery damage falls on the operator. Many jurisdictions require licensed or certified operators for certain equipment (welding certification, pressure vessel work), creating legal barriers to full automation. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No licensing requirement per se, but safety regulations, equipment liability, and the need for skilled physical judgment create meaningful friction against pure automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | Robotic systems for welding and cutting carry high capital costs, require specialized integration, and need ongoing skilled operator supervision. For small-batch or custom work, the total cost per task remains comparable to or higher than skilled machinist labor. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | Robotic automation for this requires expensive specialized hardware and integration, generally exceeding the cost of a skilled machinist for flexible, varied tasks. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | CNC and robotic welding systems exist in production but are task-specific, pre-programmed, and require expert human setup and oversight. No general-purpose AI system can reliably set up or operate this range of equipment across variable job specifications without human intervention. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | While CNC automation exists, general setup/operation of diverse metalworking equipment by an AI agent without human physical presence is not deployed in production. |
Install experimental parts or assemblies, such as hydraulic systems, electrical wiring, lubricants, or batteries into machines or mechanisms.
20CI 10–30 · exposure 13 · augmentation 38 · importance 3.6/5 · click for rater detail
Install experimental parts or assemblies, such as hydraulic systems, electrical wiring, lubricants, or batteries into machines or mechanisms.
20| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Machining shops remain relatively fragmented and low-digitization relative to software and finance sectors. Adoption of robotic assembly is slower in job shops and facilities handling experimental or custom work, where human flexibility is still valued and ROI on automation is weaker. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Manufacturing and machining trades involving physical assembly of experimental parts show minimal AI/robotic adoption for unstructured, novel installation tasks. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI vision systems and digital assembly instructions can assist machinists by highlighting alignment, torque specs, and part compatibility in real time. However, the task remains heavily dependent on human judgment, dexterity, and improvisation when dealing with novel experimental components. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI can assist with design specifications, wiring diagrams, or troubleshooting guidance, but offers little direct support for the physical installation process itself. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | Installation of experimental parts requires physical manipulation, spatial reasoning, and adaptation to novel designs. While AI vision systems can inspect and guide placement, end-to-end autonomous installation—including precise alignment, torque application, and troubleshooting fitment issues—remains beyond current robotics without extensive bespoke setup. Most of the task still requires human hands. |
| Task automatability | claude-sonnet-5 | 1/5 | Physical installation of experimental hydraulic, electrical, or battery components requires manual dexterity, fitting, and adaptive judgment that current AI cannot perform without robotic embodiment, which is not generally available for this task. |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | While there are no hard legal licensing barriers preventing automation of assembly tasks, organizational friction is moderate: shops require skilled machinists for troubleshooting and quality assurance, and client trust in experimental work often depends on human expertise and accountability. |
| Adoption barriers | claude-sonnet-5 | 3/5 | While no formal licensing is required, physical safety, liability for experimental systems, and the need for skilled hands-on judgment create meaningful practical barriers to automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | Industrial robotic systems capable of precision assembly cost hundreds of thousands of dollars plus significant integration and programming labor. For experimental, low-volume installations, the capital and setup costs far exceed the loaded wage of a skilled machinist performing the work once. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI substitute for the physical labor involved, so any AI-based approach would require expensive robotics far exceeding human labor costs for this task. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | Robotic arms can perform guided assembly in controlled environments, but experimental parts by definition lack standardized procedures. Deployed solutions exist for routine assembly but not reliably for novel, non-standardized installations. Current systems lack the adaptability and real-time problem-solving required for experimental work. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product installs experimental machine assemblies; this remains a manual, skilled-labor task performed by machinists in shops or labs. |
Set up, adjust, or operate basic or specialized machine tools used to perform precision machining operations.
19CI 7–30 · exposure 13 · augmentation 50 · importance 4.6/5 · click for rater detail
Set up, adjust, or operate basic or specialized machine tools used to perform precision machining operations.
19| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | While CNC automation has existed for decades, true end-to-end AI-driven machining setup and operation is not widely deployed in production. Most shops still rely on skilled human operators for complex jobs; adoption of autonomous AI machining agents remains minimal. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Manufacturing is a physically-oriented, moderately digitized sector where robotic and AI-driven automation is growing but adoption of full autonomous machining setup remains slow and capital-intensive. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI-based vision systems and predictive maintenance tools can assist machinists by monitoring tool wear, detecting defects, and suggesting parameter adjustments, improving productivity without full automation. However, augmentation is currently limited to specific sub-tasks rather than the whole operation. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI-driven CAM software, tool-path optimization, and predictive maintenance can meaningfully assist machinists in planning and adjusting operations, though the hands-on setup and operation remain human-led. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While some machine setup and operation can be partially automated (CNC already automates much of this), the task requires real-time adjustment, troubleshooting, and judgment that current AI systems cannot reliably perform end-to-end. Humans remain essential for complex problem-solving and adaptive responses to variations. |
| Task automatability | claude-sonnet-5 | 1/5 | This requires physical manipulation of machine tools, workpiece handling, and fine motor precision that current AI systems cannot perform without robotic embodiment, which is not generally available for this task.' |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Regulatory requirements around precision, tolerances, and worker safety in manufacturing create strong barriers. Quality certification and liability for defects typically require human sign-off or supervision, and customer contracts often mandate trained operator involvement. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No licensing barrier per se, but safety requirements, quality control, and liability for scrapped parts or equipment damage create meaningful organizational friction against unsupervised automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | The capital and integration costs of deploying AI-capable robotic systems for precision machining are still substantially higher than skilled machinists' wages, especially when considering the need for custom setup and maintenance per job variant. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI-only substitute for the physical setup and operation, so any comparison favors the human machinist; robotic automation capital and integration costs remain high relative to labor. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | CNC machines exist and operate widely, but they are not AI-driven; they execute pre-programmed instructions. Current AI vision and robotics systems cannot reliably perform the full range of setup, adjustment, and real-time operation that this task encompasses without substantial human oversight. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed AI product autonomously sets up and operates general-purpose or specialized machine tools for precision machining; CNC automation exists but requires human setup, adjustment, and oversight. |
Align and secure holding fixtures, cutting tools, attachments, accessories, or materials onto machines.
19CI 7–30 · exposure 13 · augmentation 38 · importance 4.3/5 · click for rater detail
Align and secure holding fixtures, cutting tools, attachments, accessories, or materials onto machines.
19| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Most machining shops, especially small and medium-sized operations, remain semi-manual. While large automated facilities exist, the broad machinist workforce has seen slow adoption of autonomous workholding systems due to high capital costs, job variability, and the craft skill involved in setup. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Manufacturing/machining is a physical, moderately digitized sector where robotic automation of fixturing exists mainly in high-volume automated cells, not general adoption of AI-driven setup across shops. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AR/vision-guided overlays and force-feedback arms can assist a machinist in verifying alignment and applying correct clamping force, reducing setup time and rework. However, augmentation is limited by the physical nature of the task—the human still performs most of the critical manipulation. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI can assist with setup planning, CAM programming, or providing digital work instructions, but offers little direct assistance to the physical act of aligning and securing fixtures and tooling. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | Physical alignment and securing of fixtures and tools requires dexterous manipulation in three dimensions and precise tactile feedback. While some guided positioning systems exist, current AI/robotics cannot reliably handle the variability of real-world fixtures, tools, and attachment methods end-to-end to achieve the 50% time-saving threshold without extensive custom setup per job. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a physical manipulation task requiring dexterity, force sensing, and precision fixturing on the shop floor; no off-the-shelf AI system can perform this end-to-end today. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Machinists typically work in unionized or regulated manufacturing environments with safety certifications and operator oversight requirements. Machine tool operation and workholding carry liability exposure for part quality and worker safety, creating institutional and legal inertia against full automation without human sign-off. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No licensing barrier specifically, but safety, precision tolerances, and liability for misaligned fixtures causing scrapped parts or machine damage create meaningful organizational and quality-control friction against automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | Industrial robotic arms capable of precise alignment and force-controlled securing are expensive ($50k–$300k+), require integration, programming, and ongoing maintenance. For small job shops and variable work, the total cost per task-instance often exceeds the loaded labor cost of a skilled machinist. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI-driven robotic system that performs this at comparable or lower cost than a machinist; specialized robotic tool-changers exist but are capital-intensive and task-specific, not a cheaper general substitute. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | Industrial robots can perform repetitive placement tasks in controlled environments, but reliable end-to-end alignment and securing of diverse fixtures and cutting tools remains challenging due to tolerance requirements and the need for force-sensitive verification. No mature general-purpose product performs this task reliably across varied machinist scenarios. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product autonomously aligns and secures fixtures, cutting tools, or workpieces on general machining setups; this remains a manual or semi-automated (CNC pallet systems) operation performed by humans. |
Confer with engineering, supervisory, or manufacturing personnel to exchange technical information.
19CI 7–30 · exposure 13 · augmentation 50 · importance 4.0/5 · click for rater detail
Confer with engineering, supervisory, or manufacturing personnel to exchange technical information.
19| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Manufacturing remains moderately digitized; technical conferencing is still predominantly synchronous and human-centered. While some sectors are piloting AI meeting assistants, deep adoption in machining and production shops remains limited, with preference for direct human communication on technical matters. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Manufacturing is a comparatively low-digitization sector for interpersonal coordination tasks, with AI adoption mostly in design/analysis tools rather than replacing verbal technical conferences. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI can assist by preparing agendas, summarizing prior discussions, drafting meeting notes, or translating specifications into accessible language. These supports could improve meeting efficiency, but the core task of live technical exchange and decision-making still requires human participation. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI can help machinists prepare documentation, translate technical specs, summarize meeting notes, or draft communications, meaningfully aiding but not replacing the interpersonal exchange itself. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI can summarize documents and draft communications, conferring requires real-time exchange, clarification, and negotiation of technical nuances that demand genuine understanding of context and authority. Current AI cannot reliably replace the judgment and domain expertise needed to resolve technical disagreements or make binding decisions in these discussions. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a real-time, in-person collaborative communication task requiring contextual judgment, trust-building, and often physical demonstration on shop floor equipment that current AI cannot replicate end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Conferring with engineering and supervisory personnel typically requires organizational authority, liability for technical decisions, and established trust relationships. Replacing human presence in these meetings faces strong adoption friction due to regulatory and accountability concerns in manufacturing environments where safety and precision matter. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No formal licensing requirement blocks AI involvement, but organizational trust, tacit shop-floor knowledge, and the need for real-time physical-context communication create moderate friction against substitution. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | The cost of an AI system to participate meaningfully in technical conferences—including integration, training on domain knowledge, and human oversight to prevent miscommunication—likely exceeds the cost of a machinist's time spent in brief coordination meetings with internal staff. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no AI product performing this exchange autonomously, so no meaningful cost comparison applies; the human cost is the only viable option today. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | No deployed products reliably conduct multi-party technical conferences autonomously. AI chatbots can participate in structured Q&A but cannot navigate the dynamic, contextual nature of real engineering meetings where decisions are made and accountability is assigned. Deployment remains largely research-stage. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product substitutes for a machinist conferring with engineers or supervisors about specific technical/production issues; this remains a human-to-human interaction embedded in workplace dynamics. |
Fit and assemble parts to make or repair machine tools.
18CI 10–26 · exposure 8 · augmentation 38 · importance 4.4/5 · click for rater detail
Fit and assemble parts to make or repair machine tools.
18| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Adoption of robotic assembly in machining remains slow outside high-volume standardized production; most machine shops and repair operations remain labor-intensive due to the variety and customization involved in fitting and repair work. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Manufacturing/machining is a physical, lower-digitization sector where general AI adoption for hands-on assembly tasks remains minimal compared to information-based industries. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | CAD visualization, AI-assisted design of assembly sequences, and robotic guidance systems can improve a machinist's planning and efficiency, though the core manual and decision-making work remains human-led. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI can assist with CAD/CAM programming, tolerance calculations, or diagnostic guidance, but offers little direct help with the physical fitting and assembly process itself. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI vision systems can identify parts and guide assembly, the physical manipulation, precision fitting, and real-time problem-solving required for machine tool assembly remains beyond current robotic capabilities at production scale. Material variation, tolerance verification, and repair decisions still require human judgment and dexterity. |
| Task automatability | claude-sonnet-5 | 1/5 | Fitting and assembling machine tool parts requires physical dexterity, tactile feedback, and hands-on manipulation that current AI systems cannot perform; this is a physical manual task, not a cognitive/digital one AI can execute. |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | While no explicit licensing blocks automation, custom tooling, equipment integration challenges, and customer preference for human expertise on precision work create moderate friction. Liability for assembly quality on critical machines also incentivizes human sign-off. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No strict licensing barrier requires a human machinist by law, but precision fitting requiring judgment and tactile skill creates strong practical barriers against non-human-attended automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Robotic systems capable of precision assembly and fitting are capital-intensive (hundreds of thousands to millions), with significant integration costs, making them far more expensive than the loaded wage of skilled machinists for task-equivalent output in most settings. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI-driven substitute for this physical task, so any comparison is moot; robotic automation for this exists but is capital-intensive hardware, not general AI, and far more expensive than a skilled machinist for varied fitting work. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed commercial systems reliably perform end-to-end fitting and assembly of machine tools autonomously. Research prototypes exist, but production assembly for precision equipment requires integrated robotic systems with vision and force feedback that are not standardly deployed in general machine shops. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed AI product performs physical fitting and assembly of machine parts; this remains firmly in the domain of robotics research and human craftsmanship, not commercial AI products. |
Dispose of scrap or waste material in accordance with company policies and environmental regulations.
18CI 5–30 · exposure 13 · augmentation 38 · importance 4.0/5 · click for rater detail
Dispose of scrap or waste material in accordance with company policies and environmental regulations.
18| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Adoption of AI in waste management is limited; most firms rely on traditional disposal methods with manual compliance documentation. Industrial settings show slower digitization of waste processes compared to information-heavy sectors. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Manufacturing shop floor operations, especially physical material handling tasks like scrap disposal, show minimal AI adoption as this sector lags in automating physical, low-digitization tasks. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI can assist through waste classification suggestions, compliance checklist automation, and tracking system integration, meaningfully supporting workers in identifying proper disposal methods and ensuring regulatory adherence. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI could potentially help track waste logs, generate compliance documentation, or flag regulatory requirements, but offers little assistance with the physical act of sorting and disposing of materials. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI could assist in identifying scrap types and tracking disposal compliance, the physical handling and disposition of material requires human intervention. Current systems cannot fully automate the end-to-end process of material handling, segregation, and compliant disposal without human oversight. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a physical task requiring sorting, handling, and disposal of scrap metal/waste materials, which requires robotic manipulation and physical presence that current AI systems cannot provide. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Environmental regulations, company safety policies, and potential liability for improper disposal create significant legal and compliance barriers. A responsible human must typically sign off on or directly oversee waste disposal to maintain regulatory compliance and liability protection. |
| Adoption barriers | claude-sonnet-5 | 3/5 | Environmental regulations and company policies impose compliance and liability requirements around waste handling, though this doesn't strictly require a licensed professional, just proper procedure adherence. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | AI tools for waste tracking and compliance monitoring carry integration and maintenance costs, but the physical labor of disposal remains human-dependent, making the overall cost comparable to or higher than manual processes. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI-only solution for physical waste disposal, so AI cost is effectively irrelevant or would require expensive robotics/automation infrastructure exceeding human labor cost for this simple task. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | Some compliance tracking and waste classification tools exist, but no deployed system reliably performs the full disposal task autonomously. Production systems focus on documentation and sorting assistance rather than autonomous material disposition. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed AI product physically disposes of scrap or waste material; this remains a manual, physical operation performed by shop personnel. |
Dismantle machines or equipment, using hand tools or power tools to examine parts for defects and replace defective parts where needed.
16CI 10–21 · exposure 8 · augmentation 38 · importance 4.0/5 · click for rater detail
Dismantle machines or equipment, using hand tools or power tools to examine parts for defects and replace defective parts where needed.
16| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Manufacturing has moderate digitization, but dismantling/repair is typically small-batch, equipment-specific work done in traditional shops or field maintenance contexts. Adoption of autonomous agents for this task remains nascent outside high-volume industrial settings. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Manufacturing and industrial maintenance sectors show slow uptake of robotic automation for unstructured mechanical repair tasks, remaining a laggard domain for AI-driven physical labor. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | Computer vision systems and AR assistance can help machinists locate defects and access procedures, and automated part identification aids troubleshooting. These augment the human mechanic's productivity on diagnosis and planning, though hands-on work remains with the worker. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI can assist with diagnostics via sensor data analysis or documentation lookup, but offers minimal help with the core physical dismantling and part replacement work. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While some steps (inspection imaging, defect classification) can be partially automated, the core task requires dexterous physical manipulation of varied equipment in unstructured environments, which current robots and hand-tool agents cannot reliably perform. Dismantling and reassembly remain predominantly manual. |
| Task automatability | claude-sonnet-5 | 1/5 | This requires physical manipulation of hand and power tools to dismantle machinery, visually and tactilely inspect parts, and physically replace components—current AI systems cannot perform physical manual labor tasks. |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | There are no hard legal barriers to automation, but organizational friction exists: equipment-specific knowledge, liability concerns over improper reassembly, and safety certification of automated dismantling processes create moderate adoption friction. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No licensing mandates a human specifically, but safety, liability for damaged equipment, and the physical/tactile nature of diagnosis create practical barriers against automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Current AI inspection systems plus robot arms capable of precision dismantling cost tens of thousands to hundreds of thousands of dollars and require extensive setup per machine type, far exceeding the loaded wage of a skilled machinist for most tasks. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI/robotic solution for general-purpose disassembly and defect-based part replacement, so any hypothetical automation would be far more costly than a skilled machinist's wage. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed product reliably performs full machine dismantling, defect examination, and part replacement autonomously. Vision systems can detect some defects; mechanical automation exists only in high-volume, standardized scenarios. This task lacks production-scale general solutions. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed products perform physical disassembly and part replacement of industrial machinery; robotics for such unstructured mechanical maintenance remains research-stage or highly specialized/limited. |
Install repaired parts into equipment or install new equipment.
13CI 5–21 · exposure 8 · augmentation 25 · importance 4.1/5 · click for rater detail
Install repaired parts into equipment or install new equipment.
13| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Adoption of robotic installation systems remains limited outside large-scale manufacturing plants and aerospace/automotive suppliers. Most small to mid-sized machine shops lack the digitization, capital, or volume to justify such automation, keeping velocity low across the broader machinist workforce. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Machining and industrial maintenance are physical, low-digitization trades where AI/robotic adoption for hands-on installation tasks remains minimal and slow. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | Current AI offers minimal productivity assistance for physical installation work itself—no computer vision, robotic guidance, or planning tools are widely deployed in machine shops to augment human installers. Digital manuals or AR guidance remain niche, not transformative. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI can assist with diagnostics, documentation, or guided instructions prior to installation, but offers little direct assistance during the physical installation act itself. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | Physical installation of parts into equipment requires spatial reasoning, manipulation, and alignment in 3D space—capabilities modern AI systems lack reliably. While some assembly steps in controlled environments might be partially automated (e.g., with robotic arms in factories), the general task of installing varied repaired or new parts into diverse equipment remains beyond end-to-end AI capability today. |
| Task automatability | claude-sonnet-5 | 1/5 | Physical installation of machined or repaired parts into equipment requires manual dexterity, fitting, alignment, and adjustment that current AI systems cannot perform without embodied robotics far beyond generally available tools. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Installation work often requires verification of fit, quality control, and sign-off by a licensed or experienced machinist, creating both practical oversight requirements and organizational friction. Many shops lack infrastructure for full automation, and customer or regulatory expectations often favor human workmanship and accountability. |
| Adoption barriers | claude-sonnet-5 | 3/5 | While not formally licensed in most cases, safety, liability, and precision-fit requirements create strong practical barriers to non-human execution, though not legal licensure barriers specifically. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | The capital and integration costs of industrial robotic systems capable of flexible part installation far exceed the loaded wage of a skilled machinist for most applications. Deployment requires significant upfront investment, custom programming, and site-specific configuration. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI system to compare costs against for physical installation work, so AI is not cheaper—it's simply not a substitute today. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No commercially deployed AI or robotic system currently performs general-purpose part installation tasks in machine shops or field settings at production scale. Specialized industrial robots handle narrow, repetitive assembly in highly controlled conditions, but cannot generalize to the open-ended installation work machinists perform. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed AI product installs mechanical parts into industrial equipment; this remains a physical, hands-on task performed by skilled technicians. |
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.