Lathe and Turning Machine Tool Setters, Operators, and Tenders, Metal and Plastic
51-4034.00Median wage $50,620/yr16,710 employed (US)Rank #592 of 923 scored · top 64% by substitution
Set up, operate, or tend lathe and turning machines to turn, bore, thread, form, or face metal or plastic materials, such as wire, rod, or bar stock.
Sub-scores
0–100 · band = confidence interval from rater disagreement
Substitution23
Exposure18
Augmentation35
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
18 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%20
panel mean rating 1.8/5 → substitution pressure 20/100
Technical feasibility todayw 20%15
panel mean rating 1.6/5 → substitution pressure 15/100
Cost vs. human wagew 15%16
panel mean rating 1.6/5 → substitution pressure 16/100
Adoption barriersw 20%inverted — strong barriers lower the score46
panel mean rating 3.2/5 (barrier strength) → substitution pressure 46/100
Sector adoption velocityw 10%15
panel mean rating 1.6/5 → substitution pressure 15/100
Task breakdown (18 tasks)
Substitution pressure per task, weighted by O*NET importance in the composite. Expand a task for the full rater audit trail — every rating, every model, every rationale.
Program computer numerical control machines.
34CI 25–44 · exposure 38 · augmentation 63 · importance 3.9/5 · click for rater detail
Program computer numerical control machines.
34| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | CNC shops remain heavily dependent on human expertise and manual troubleshooting. While CAM software adoption is mature, AI-driven autonomous programming adoption in production is still negligible. The sector is relatively traditional and risk-averse due to capital-intensive equipment and quality consequences. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Manufacturing is a comparatively low-digitization sector where CAM/AI tools are used as aids but full automation of programming remains uneven across small and mid-size shops. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | Modern CAM software with AI-assisted suggestions (tool-path recommendations, parameter optimization hints) does help programmers iterate faster. However, the augmentation is partial—AI typically handles routine code scaffolding, while humans retain control over design logic and validation—making this useful but not transformative assistance. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI-assisted CAM tools significantly speed up toolpath generation and parameter selection, letting programmers focus on optimization and verification rather than manual coding. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | Programming CNC machines requires interpreting engineering drawings, selecting tool paths, and optimizing parameters for material properties—tasks with significant domain complexity. While AI can assist with code generation from simple specifications, the full task involves troubleshooting, material-specific adjustments, and quality validation that current systems cannot reliably perform end-to-end without substantial human oversight, falling well short of the 50% time-saving threshold. |
| Task automatability | claude-sonnet-5 | 3/5 | AI-assisted CAM software and code generators can produce CNC programs from CAD models, but complex geometries, tolerances, and machine-specific tuning still require skilled human verification and adjustment.5 |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Manufacturing environments have strict liability and quality control requirements; a failed program can damage expensive tooling or produce scrap parts. Additionally, CNC programming often requires licensed or certified technicians in regulated settings, and organizational inertia around validation procedures creates significant friction against autonomous code generation. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No licensing requirement, but liability for scrapped parts, tool crashes, and machine damage creates strong incentive for human verification before running new programs. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | Current AI-assisted CAM tools add licensing and integration costs on top of existing CAM software. When accounting for required skilled operator oversight and frequent code revision, the total cost per successful program likely exceeds the loaded wage of an experienced CNC programmer who can write working code with fewer iterations. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | CAM software licenses, setup, and required human oversight for safety-critical programming keep costs comparable to or only modestly below skilled machinist/programmer wages. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | No deployed product reliably programs CNC machines from scratch in production environments. Research prototypes exist for CAM code generation, and some CAM software incorporates AI suggestions, but these operate in narrow domains and require expert validation. The variability in materials, machine configurations, and job requirements means error rates remain material. |
| Technical feasibility today | claude-sonnet-5 | 3/5 | CAM software with automated toolpath generation and AI-assisted G-code suggestions exist in production, but shops still rely heavily on human programmers to validate and refine programs before running parts. |
Study blueprints, layouts or charts, and job orders for information on specifications and tooling instructions, and to determine material requirements and operational sequences.
33CI 30–35 · exposure 30 · augmentation 50 · importance 4.3/5 · click for rater detail
Study blueprints, layouts or charts, and job orders for information on specifications and tooling instructions, and to determine material requirements and operational sequences.
33| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Metalworking and machine shops remain among the slower-adopting sectors for AI, with most operations still paper-based or using legacy CAM systems. Pilots exist but production deployment of autonomous document interpretation remains limited and concentrated in large facilities. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Manufacturing, especially metal/plastic machining, is a physical, lower-digitization sector where AI adoption for blueprint interpretation lags behind information-sector applications, with CAM automation adoption proceeding slowly and unevenly. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI-powered document extraction and specification lookups can meaningfully reduce manual chart review time and flag missing parameters, but operators still need to interpret context, cross-check against equipment capabilities, and make final material and sequencing decisions themselves. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI-assisted CAD/CAM tools and digital blueprint annotation can help operators quickly extract specifications and suggest tooling sequences, meaningfully speeding up parts of this task while the operator retains final judgment. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While current AI can extract and parse structured information from technical documents with reasonable accuracy, the task requires synthesizing multiple document types (blueprints, layouts, charts, job orders) and making judgment calls about material requirements and operational sequences that depend on tacit domain knowledge and equipment-specific constraints that remain difficult to automate end-to-end reliably. |
| Task automatability | claude-sonnet-5 | 2/5 | AI vision-language models can extract some information from blueprints and charts, but reliably interpreting complex mechanical drawings, GD&T symbols, and translating them into precise tooling/operational sequences for physical machining still requires human expertise and shop-floor context. |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | Unionized shops and safety regulations often require a licensed/certified operator to sign off on material and tooling specifications; ISO and shop-floor quality systems typically mandate human review of critical parameters. Integration friction is moderate but not insurmountable. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No formal licensing requirement exists for reading blueprints, but liability for machining errors, need for tacit shop knowledge, and reliance on human judgment for ambiguous or nonstandard specifications create moderate friction against full automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | Current AI document processing systems require significant infrastructure, API costs, and operator oversight to verify outputs. The loaded cost of a skilled lathe operator reviewing AI extractions is often comparable to or higher than the AI system cost itself when integrated into a production workflow. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | Specialized CAM software and AI-assisted drawing interpretation tools require significant upfront investment, integration with CNC systems, and human verification, making the all-in cost not clearly cheaper than an experienced machinist performing this task. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 3/5 | Document processing and information extraction products exist and perform well on standardized formats, but blueprints and handwritten or legacy layouts introduce variability that causes material error rates in real shop-floor settings. Deployed OCR and document AI can assist but typically requires human verification before critical decisions. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Some CAD/CAM software includes automated feature recognition and toolpath generation, but fully autonomous interpretation of blueprints into complete material and operational sequencing for lathe work is not yet a mature, widely deployed product for this specific occupational task. |
Inspect sample workpieces to verify conformance with specifications, using instruments such as gauges, micrometers, and dial indicators.
30CI 25–35 · exposure 25 · augmentation 50 · importance 4.2/5 · click for rater detail
Inspect sample workpieces to verify conformance with specifications, using instruments such as gauges, micrometers, and dial indicators.
30| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Lathe operations remain concentrated in small and mid-sized job shops with low digitization; adoption of automated inspection is slow and limited to high-volume, repeatable production runs. Most facilities still rely on manual inspection with hand instruments. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Metalworking and machining are physical, moderately digitized sectors with slower and uneven adoption of AI/automated inspection compared to information-sector tasks. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | Digital gauges, calipers with data logging, and vision-assist tools can help operators record and compare measurements more efficiently, and AI could flag anomalies in size trends. These assist the operator but do not replace the judgment needed to interpret specifications and accept/reject parts. |
| Augmentation potential | claude-sonnet-5 | 3/5 | Digital gauges, statistical process control software, and vision-assisted measurement tools can meaningfully speed up and improve consistency of manual inspection while the operator remains in control. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While computer vision can detect some dimensional deviations, lathe inspection requires real-time physical measurement with precision instruments (micrometers, dial indicators) and judgment about acceptable tolerances in context. Current vision systems cannot reliably replicate the multi-dimensional, tactile inspection workflow with the accuracy demanded in manufacturing; setup for each part geometry would be substantial. |
| Task automatability | claude-sonnet-5 | 2/5 | Automated metrology systems (CMMs, in-process gauging) can measure workpieces, but this manual task involves handheld gauges/micrometers requiring physical dexterity and situational judgment on a shop floor, limiting full automation with off-the-shelf AI alone. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Manufacturing regulations, quality assurance standards (e.g., ISO, aerospace/automotive specs), and liability for dimensional non-conformance create strong requirements that inspection data be traceable and defensible. Many sectors require a qualified human operator or inspector to certify acceptance; automation is often treated as a pre-check only. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement for this quality-control task, but liability for defective parts and physical integration into machining workflow create moderate organizational friction against pure AI substitution. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | Automated vision or optical systems capable of accurate dimensional measurement require significant capital investment (camera, lighting, calibration, software), hardware mounting, and ongoing maintenance. For typical job-shop or small-batch lathe operations, these costs exceed the loaded wage of a single operator. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | Dedicated inspection automation (CMMs, laser scanners) can be cost-effective at high volume but requires significant capital investment, making it costlier than a human with a micrometer for low-to-medium volume or job-shop settings typical of this trade. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | Vision-based metrology exists but is limited to surface inspection and simple dimensional checks in controlled environments. Deployed systems do not reliably handle the full range of workpiece geometries, material finishes, or tolerance judgments that human operators perform; production use remains narrow and supervised. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Automated inspection systems exist in high-volume manufacturing but are typically integrated hardware/software solutions (CMMs, vision systems) rather than general AI products retrofitted to arbitrary lathe operations; adoption remains narrow relative to the broad occupation. |
Turn valve handles to direct the flow of coolant onto work areas or to coat disks with spinning compounds.
29CI 23–35 · exposure 20 · augmentation 0 · importance 3.5/5 · click for rater detail
Turn valve handles to direct the flow of coolant onto work areas or to coat disks with spinning compounds.
29| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Metal and plastic machining remains in laggard sectors for general AI adoption; small and mid-size shops dominate, digitization is low, and physical automation is spotty. Full production robotics for machine tending are costly and job-specific, so adoption of AI for this discrete valve-turning task has been slow and remains minimal. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Manufacturing floor tasks involving physical machine tending have historically slow AI/robotic adoption compared to information-based sectors, with automation typically achieved via dedicated CNC/automatic coolant systems rather than AI. |
| Augmentation potential | claude-haiku-4-5-20251001 | 1/5 | Turning a valve handle is straightforward manual work that offers little opportunity for AI assistance; there is no complex decision-making or information retrieval dimension that an AI system could augment. The operator learns by feedback and does not benefit from predictive or generative AI support. |
| Augmentation potential | claude-sonnet-5 | 1/5 | This is a low-level manual action with little cognitive component for AI to assist with; sensors and automatic coolant controls already exist as non-AI solutions. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | Turning valve handles is a simple physical manipulation, but it requires proprioceptive feedback, real-time adaptation to visual cues of coolant flow direction, and integration with the broader machine operation. Current robotics can handle repetitive reaching tasks, but adaptive in-situ sensing and real-time adjustment of valve positions based on work-area coverage or disk coating quality remain difficult for general-purpose AI systems deployed today. |
| Task automatability | claude-sonnet-5 | 2/5 | This is a simple physical manipulation task, but it requires physical presence and manual dexterity that current AI systems (software/LLM-based) cannot perform without robotic embodiment, which is not off-the-shelf deployable for this specific task.rehen.It could be automated via fixed automation/robotics rather than AI per se, so the 'AI does this end-to-end' bar is largely unmet today.rethinking: rated low. |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | Operators must be present to oversee machine safety, coolant levels, and part quality, and they typically perform valve-turning as part of a broader tending workflow. Regulatory and safety requirements around machinery operation create some friction to full substitution, though the valve task alone is not legally restricted. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing or legal barrier exists, but physical integration into legacy machinery creates practical friction since the human operator is already present for other tasks. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | A dedicated robotic system (arm, vision, sensors, integration, maintenance) for this single task costs tens of thousands of dollars upfront and ongoing; the loaded wage for a machine operator performing this component is far lower. Cost-effectiveness requires amortization across multiple similar tasks or very high labor rates, neither common in metal/plastic machining. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | Retrofitting robotic actuators or automated coolant systems to replace this simple manual action requires capital investment that often exceeds the marginal labor cost of a human already present tending the machine. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | Robotic arms capable of handle-turning exist in industrial settings, but reliable end-to-end deployment of a general system that monitors coolant flow, makes directional adjustments, and responds to changing work conditions is not mature in production metalworking shops. Narrow task-specific automation exists, but not general-purpose feasibility at scale. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No general AI product operates physical valve handles on manual or semi-manual lathes in production; this requires robotic actuation, not deployed today for this specific micro-task. |
Adjust machine controls and change tool settings to keep dimensions within specified tolerances.
28CI 25–30 · exposure 25 · augmentation 50 · importance 4.3/5 · click for rater detail
Adjust machine controls and change tool settings to keep dimensions within specified tolerances.
28| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Manufacturing adoption of AI-driven adaptive control is still in early stages, concentrated in high-volume automotive and aerospace suppliers. Most small to mid-sized job shops continue manual or conventional CNC operation, reflecting slow sector-wide digitization. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Manufacturing is a comparatively slow-adopting sector for full autonomy in physical control tasks, though CNC and adaptive machining technology has been gradually adopted for decades. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI can assist by monitoring dimensions, flagging out-of-tolerance trends, and recommending tool offsets, raising operator situational awareness. However, the operator must still execute the adjustments and validate results, limiting transformative impact. |
| Augmentation potential | claude-sonnet-5 | 3/5 | Modern CNC systems and monitoring software assist operators by flagging drift and suggesting adjustments, improving precision and reducing scrap, while the operator remains responsible for final settings. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI can monitor tolerances and suggest adjustments, current systems cannot reliably perform the iterative mechanical adjustments and real-time dimensional feedback loops required to keep a lathe within specified tolerances across varying materials and setups. The task demands physical actuation and continuous sensory feedback that exceeds current automation capabilities. |
| Task automatability | claude-sonnet-5 | 2/5 | Adjusting machine controls to hold tolerances requires physical manipulation, real-time sensory feedback, and fine motor calibration that current AI systems cannot perform end-to-end without robotic hardware and extensive integration.' |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Significant organizational and liability barriers exist: operators are legally responsible for part quality and safety; many shops use older equipment not designed for autonomous control; and precision manufacturing carries high error costs that discourage full automation without human sign-off. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No licensing requirement, but quality/safety liability for out-of-tolerance parts creates strong incentive for human oversight and sign-off, especially in regulated industries like aerospace or medical parts. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | Retrofitting legacy lathes with sensors and AI control systems, plus ongoing integration and oversight, remains expensive relative to a skilled operator's wage. Modern CNC machines reduce this gap, but pure AI adjustment systems are not yet cost-competitive for existing shop floors. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | Retrofitting sensors, adaptive control systems, and robotics to replace this task is capital-intensive compared to an operator's wage, though some high-volume shops may already have embedded automation. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | Automated tool changers and CNC pre-programming exist, but real-time adaptive control of lathe dimensions during production to maintain tolerances relies on human operator judgment and tactile feedback. No deployed AI system reliably performs this end-to-end adjustment task in unstructured production environments. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Some CNC systems have adaptive control and closed-loop feedback for tool wear compensation, but fully autonomous adjustment across varied machines and materials is not a mature, widely deployed product for this specific task. |
Move controls to set cutting speeds and depths and feed rates, and to position tools in relation to workpieces.
28CI 25–30 · exposure 25 · augmentation 38 · importance 4.3/5 · click for rater detail
Move controls to set cutting speeds and depths and feed rates, and to position tools in relation to workpieces.
28| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Adoption of AI-driven adaptive control in lathe operation is slow; most shops still rely on traditional CNC programming and skilled operators rather than autonomous AI systems. The sector has low digital maturity compared to information or finance, and capital-intensive nature slows experimentation. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Manufacturing, especially machine shops, has slower and shallower AI/automation adoption compared to information-sector work, though CNC automation has existed for decades as a distinct technology from AI. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI can assist operators by recommending optimal cutting speeds and depths based on material properties or predicting tool wear, helping them make better real-time adjustments. However, the core task of manual control input and real-time judgment remains necessary, limiting transformative augmentation. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI-driven monitoring and predictive analytics can assist operators in adjusting parameters or catching tool wear, but this is a niche and partial augmentation rather than transformative for the physical control-setting task itself. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While CNC machines can automate much of the tool positioning and speed setting once programmed, the task as stated requires real-time manual control adjustments based on material feedback and workpiece characteristics. Current AI systems cannot reliably replace the sensory judgment and adaptive control needed to handle variations in real production, though they can assist with initial setup calculations. |
| Task automatability | claude-sonnet-5 | 2/5 | This requires physical manipulation of machine controls based on real-time sensory feedback and tactile judgment; current AI cannot physically operate lathe controls without a robotic embodiment, though CNC programming portions could be pre-set. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Significant barriers exist: safety regulations mandate operator presence and control over high-speed machinery; liability for tool breakage or workpiece damage rests with the operator; and union rules in many shops restrict automation. Machine tool operation legally requires qualified human oversight. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No licensing requirement, but safety regulations, equipment liability, and the need for physical presence to monitor tooling wear and material behavior create moderate organizational and safety-driven friction. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | AI-based adaptive control systems, where they exist, remain expensive and require significant integration and oversight. The loaded cost of a skilled lathe operator remains competitive with or lower than the cost of advanced automation plus ongoing maintenance and supervision for this relatively specialized operation. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | Retrofitting older manual lathes with sensors, actuators, and control systems to replace a human operator is capital-intensive relative to a machine operator's wage, making all-in AI cost often higher for this specific micro-task. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | Automated CNC systems exist but are not AI-driven replacements for this task; they require human programming and monitoring. No deployed AI product demonstrably performs autonomous cutting speed/depth optimization and tool positioning in response to live workpiece conditions with reliability comparable to a trained operator. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | CNC machines already automate speed/feed/depth via programmed G-code, but the manual setter/operator task of physically adjusting controls and positioning tools for conventional lathes is not performed by deployed AI products; it remains largely manual or pre-programmed rather than AI-driven. |
Compute unspecified dimensions and machine settings, using knowledge of metal properties and shop mathematics.
28CI 25–30 · exposure 25 · augmentation 50 · importance 4.1/5 · click for rater detail
Compute unspecified dimensions and machine settings, using knowledge of metal properties and shop mathematics.
28| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Manufacturing, especially small- to mid-size job shops, has adopted CAM and simulation tools slowly. Most setups remain operator-driven with manual calculation; AI-native automation in this domain is minimal outside large aerospace/automotive primes with bespoke systems. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Manufacturing/machining is a physically-oriented, lower-digitization sector where AI adoption for shop-floor calculations remains limited to CAM tools with slow diffusion beyond larger firms. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI-assisted calculators and property lookups can speed routine computations and reduce arithmetic errors, but augmentation is limited by the need for operator judgment on unspecified parameters and the mature skill set already in use. |
| Augmentation potential | claude-sonnet-5 | 3/5 | Software-based calculators and simulation tools meaningfully speed up computing dimensions and settings, giving operators a productivity boost while they retain responsibility for verification and machine setup. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI can perform shop mathematics and look up metal properties from reference data, the task requires domain-specific judgment about 'unspecified' dimensions and context-dependent machine settings that depend on equipment condition, material lot variation, and operator experience. Current AI cannot reliably infer missing specifications or validate non-standard setups without human oversight. |
| Task automatability | claude-sonnet-5 | 2/5 | Some computation (dimensioning, feed/speed calculations) can be done by CAM/CAD software, but the task also requires real-time judgment tied to physical setup and material behavior, limiting full end-to-end automation with current off-the-shelf tools without integration into a broader CNC workflow. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Safety-critical task: incorrect machine settings cause equipment damage, scrap, and injury. Liability and regulatory expectations (OSHA, machine safety standards) require that a licensed or trained operator verify and sign off on settings, creating a hard authorization barrier to full automation. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No licensing requirement, but quality/safety consequences of miscalculated tolerances create liability concerns and organizational reliance on experienced setters to verify outputs before machining. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | AI tools for calculation assistance exist but require integration with shop systems, data entry, and human review of outputs. The total cost (licensing, setup, oversight) approaches or exceeds the labor cost for a skilled operator performing these computations directly. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | While software licenses are cheap per use, the need for skilled setup, calibration, and verification against physical outcomes keeps the effective cost of substituting the full task relatively high compared to a trained machinist doing it as part of their job. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | Some CAM software and machine-tool calculators can assist with standard calculations, but no deployed system reliably computes truly unspecified dimensions autonomously or adapts settings to variable shop conditions without human verification. Production use remains heavily manual. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | CAM software and calculators for cutting parameters exist and are used in production, but they are tools requiring skilled operators to interpret and apply them to actual machine setups, not autonomous replacements for the operator's judgment. |
Move toolholders manually or by turning handwheels, or engage automatic feeding mechanisms to feed tools to and along workpieces.
26CI 14–39 · exposure 20 · augmentation 25 · importance 4.2/5 · click for rater detail
Move toolholders manually or by turning handwheels, or engage automatic feeding mechanisms to feed tools to and along workpieces.
26| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Manufacturing environments adopting AI remain concentrated in large-scale automotive and aerospace; small to mid-sized machine shops where this task is common show slow digitization and continue to rely on skilled manual operators rather than advanced automation. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Manufacturing sectors adopt automation steadily but unevenly; small and mid-size shops lag, and CNC/automation adoption for this specific manual step is incremental rather than fast, AI-agent-driven change. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | Computer vision or sensor-based alerts could assist operators by flagging tool wear or positioning errors, but current AI systems offer minimal productivity enhancement for the core sensorimotor and judgment aspects of manual toolholder adjustment and feeding. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI-driven monitoring or programming assistance (e.g., optimizing feed rates, predictive maintenance) can support operators, but it doesn't directly enhance the physical act of moving toolholders or engaging feed mechanisms. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While CNC lathes can execute some feeding sequences automatically, the task as stated emphasizes manual movement of toolholders via handwheels or engaging feeding mechanisms in response to workpiece geometry and quality feedback. Current AI systems lack the real-time sensorimotor control and adaptive decision-making to reliably substitute for skilled manual adjustment and operator judgment. |
| Task automatability | claude-sonnet-5 | 2/5 | This is a physical manipulation task requiring manual dexterity and real-time tactile/visual feedback on a physical machine; current AI (software/LLM systems) cannot perform this without embodied robotics, which are not general-purpose or off-the-shelf for this task.imacy |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Significant barriers exist: the task requires close physical interaction with moving machinery and the workpiece, safety regulations mandate operator presence and control, and liability concerns around autonomous tool positioning near high-speed equipment strongly protect human oversight and sign-off. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement for this specific task, but organizational capital investment, machine compatibility, and safety/oversight practices create moderate friction to full replacement. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Implementing robotic or AI-driven toolholder positioning would require specialized hardware, integration, and maintenance costs far exceeding the hourly wage of a lathe operator, with no indication that such systems are economically competitive today. |
| Cost vs. human wage | claude-sonnet-5 | 3/5 | CNC automation is often cost-comparable or somewhat cheaper for high-volume repetitive runs, but retrofitting or deploying flexible robotic/AI systems for varied manual setups carries substantial capital and integration costs relative to a machine operator's wage. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed AI product autonomously performs manual toolholder positioning and feeding adjustment for lathe operations in production environments. Existing CNC automation is pre-programmed, not adaptive AI-driven, and does not address the interpretive and corrective elements of this task. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | CNC and automated feed systems exist and are mature, but these are pre-programmed mechanical automations rather than 'AI' performing adaptive judgment; general robotic AI systems reliably doing manual toolholder positioning across varied setups are not deployed at scale. |
Start lathe or turning machines and observe operations to ensure that specifications are met.
26CI 23–30 · exposure 25 · augmentation 50 · importance 4.0/5 · click for rater detail
Start lathe or turning machines and observe operations to ensure that specifications are met.
26| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Metalworking shops, especially small-to-mid-sized job shops and contract manufacturers, remain low-digitization sectors with older equipment; adoption of AI monitoring in production is rare outside large, capital-intensive manufacturers. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Manufacturing, especially machine shops, is a moderate-to-slow adopter of AI/automation technology outside large-scale automotive/aerospace plants; most metalworking is done in smaller enterprises with limited automation budgets. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI-powered vision dashboards or anomaly alerts could assist an operator in spotting drift or defects faster, but integration into shop-floor systems is still emerging and often bespoke rather than off-the-shelf. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI-enabled monitoring, predictive maintenance, and quality-control analytics can flag deviations and assist operators in catching defects faster, improving productivity without removing the human from the loop. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While machine startup can be partially automated via CNC control systems, the observational component—detecting defects, vibrations, or drift in real-time from physical inspection and auditory/tactile cues—remains difficult for current AI without expensive sensor integration and custom deployment on existing legacy equipment. |
| Task automatability | claude-sonnet-5 | 2/5 | Starting machines and monitoring operations requires physical presence, sensory judgment, and real-time intervention that current AI cannot perform end-to-end without robotic embodiment.；only monitoring software components could partially assist. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Safety regulations (OSHA) mandate operator presence and intervention capability; liability for scrap parts and equipment damage rests with the operator; many shops have union agreements protecting setup/tender roles; and the physical, non-remote nature of the task limits substitution. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No licensing requirement, but liability for defective parts, safety regulations around machine operation, and the need for physical presence to halt malfunctions create real friction against full automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | Retrofitting sensors, vision systems, and monitoring software onto existing lathe infrastructure, plus ongoing calibration and false-positive handling, typically exceeds the annual loaded wage of a single machine tender. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | Sensor/vision-based monitoring systems require significant capital investment (sensors, cameras, integration) that often exceeds the marginal cost of a human operator for many shops, especially low-volume operations. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | No mature product reliably automates the full observe-and-ensure-specs task end-to-end in production metalworking environments; computer vision systems exist but struggle with occlusion, lighting variation, and the judgment calls required when parts are mid-operation. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | CNC monitoring software and sensor-based quality systems exist in some advanced factories, but the physical act of starting machines and hands-on observation is still predominantly manual, especially in small/mid-size shops. |
Refill, change, and monitor the level of fluids, such as oil and coolant, in machines.
26CI 18–35 · exposure 20 · augmentation 38 · importance 3.9/5 · click for rater detail
Refill, change, and monitor the level of fluids, such as oil and coolant, in machines.
26| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Manufacturing remains heavily reliant on manual fluid maintenance; the task is routine but physically heterogeneous across machine types, and small-to-mid-size job shops—where most lathe work occurs—show minimal adoption of fluid-handling automation. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Manufacturing/machining is a sector with historically slower AI and automation adoption for physical maintenance tasks compared to information-based industries. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI can assist with monitoring (sensor logging and anomaly alerts, predictive maintenance scheduling), but the core refill and change work remains manual; augmentation is limited to scheduling and trend analysis rather than a transformative productivity gain on the physical act itself. |
| Augmentation potential | claude-sonnet-5 | 3/5 | IoT sensors and predictive maintenance software can alert operators to low fluid levels or degradation, improving efficiency of monitoring even though physical refilling remains manual. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | This task requires physical manipulation of machinery (refilling, changing fluids) and visual inspection of fluid levels—capabilities that current general-purpose AI systems lack. While fluid level sensors could enable monitoring via software, the refill and change components demand robotic hardware not yet reliably deployed in shop-floor settings at scale. |
| Task automatability | claude-sonnet-5 | 2/5 | This is a manual, physical task requiring presence on the shop floor to check fluid levels and physically refill or change fluids; current AI (software/LLM-based) cannot perform the physical actions, though sensors could monitor levels., e.g. IoT sensors handle monitoring but not the physical refill/change. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Significant safety and liability barriers exist: operators must physically access pressurized or hot fluid systems, and failures (overfill, contamination, spills) create worker injury and equipment damage risks that discourage automation without heavy engineering oversight and custom certification per machine. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing or legal requirement mandates a human for this task, but physical plant integration, safety protocols around machine fluids, and lack of universal automation infrastructure create moderate friction. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | The capital cost of robotic systems capable of safely manipulating fluids and machine access ports typically far exceeds the loaded wage of a machine tender for the time spent on this routine task, especially when amortized across a single machine or small shop. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | Retrofitting machines with automated fluid monitoring/dispensing systems requires capital investment that often exceeds the marginal labor cost saved for this narrow task. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed product today reliably performs end-to-end fluid refill and level management on general lathe machines. Existing industrial sensors monitor levels and can trigger alerts, but human operators still physically perform the refill and change work; full automation would require custom robotic integration per machine type. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Sensor-based fluid level monitoring exists in some modern CNC equipment, but automated refilling/changing of coolant/oil is rare and not a mainstream deployed product for this occupation. |
Clean work area.
26CI 24–28 · exposure 16 · augmentation 0 · importance 3.8/5 · click for rater detail
Clean work area.
26| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Manufacturing sectors have invested heavily in automation, but physical cleaning of lathe work areas remains primarily manual labor; adoption of autonomous cleaning systems in this niche is minimal. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Manufacturing shop floor tasks like this are in a low-digitization, physically-oriented sector with slow AI/robotics adoption for menial cleanup tasks. |
| Augmentation potential | claude-haiku-4-5-20251001 | 1/5 | Current AI and robotics offer minimal assistance to human workers performing manual cleaning; there is no meaningful productivity-enhancing tool available today for this specific task. |
| Augmentation potential | claude-sonnet-5 | 1/5 | AI offers essentially no meaningful assistance for the physical act of clearing swarf and debris from a machine work area. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | Cleaning a work area requires physical manipulation in unstructured environments (removing chips, coolant, debris from machinery) that current robots struggle with. While some narrow cleaning tasks are automatable, the full end-to-end cleaning of a lathe work area with quality assurance falls well short of the 50% time-saving threshold. |
| Task automatability | claude-sonnet-5 | 2/5 | Cleaning a physical work area requires manipulation of tools, chips, and coolant in a shop environment, which current robotics cannot reliably do end-to-end without significant custom engineering.ateing.but might partially be automated with fixed sweeping/vacuum systems in some shops.rate low. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | Physical safety requirements around active machinery, regulatory compliance for workplace cleanliness standards, and the need for human judgment about what constitutes proper cleaning create modest friction, though no absolute legal barrier requires human sign-off. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement, but safety concerns around metal shavings, coolant, and machine proximity create some organizational and safety-protocol friction for full automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Deployment costs for a robotic cleaning system (hardware, integration, maintenance, safety compliance) far exceed the loaded wage cost of a human worker performing routine cleaning. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | Specialized robotic cleaning or automated chip/coolant removal systems exist but require capital investment that often exceeds the marginal cost of a human operator doing this quick manual task. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed products reliably perform autonomous cleaning of industrial lathe work areas at production scale. This remains a research and robotics frontier, not a deployed commercial capability. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No mainstream deployed product autonomously cleans a metalworking machine shop workspace around a lathe; industrial cleaning robots are narrow and not integrated into this specific task context. |
Select cutting tools and tooling instructions, according to written specifications or knowledge of metal properties and shop mathematics.
24CI 18–30 · exposure 20 · augmentation 50 · importance 3.5/5 · click for rater detail
Select cutting tools and tooling instructions, according to written specifications or knowledge of metal properties and shop mathematics.
24| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Manufacturing, especially metal fabrication shops, tends toward laggard adoption of AI automation. Most lathe shops remain small, asset-intensive, and reluctant to displace skilled machinists; digital infrastructure is often legacy or fragmented, slowing integration of AI systems. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Manufacturing, especially metal/plastic machining shops, is a low-digitization sector with slow AI adoption for floor-level operational decisions. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI can provide real-time lookups of material properties, tool inventory, and geometric calculations, reducing manual reference checking and arithmetic. Digital assistants for tool suggestion and parameter optimization offer meaningful productivity gains while the operator retains final authority over selection. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI-driven CAM software and knowledge bases can help suggest tooling options and speeds/feeds calculations, assisting operators in decision-making while they retain final judgment. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | AI can assist in retrieving and suggesting cutting tools based on written specifications and material properties, but the task requires integration of shop mathematics, real-time machine feedback, and contextual knowledge of tool wear and part geometry that current systems cannot reliably automate end-to-end. Human judgment on tool selection remains critical for safety and quality. |
| Task automatability | claude-sonnet-5 | 2/5 | Tool selection requires interpreting specifications, physical material knowledge, and shop-floor context that current AI cannot reliably handle end-to-end without significant human oversight and physical interaction with machinery.'}, |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Safety-critical manufacturing operations have high liability exposure for incorrect tool selection (tool breakage, part damage, operator injury), and regulatory frameworks (OSHA, ISO standards) place responsibility on qualified personnel. Organizational culture and risk-aversion in manufacturing further resist full automation of this decision. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No formal licensing requirement, but liability for tool selection errors (scrapped parts, machine damage, safety) creates strong organizational reliance on experienced operators. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | The cost of developing and maintaining custom tooling selection systems, including integration with existing manufacturing software and oversight of recommendations, approaches or exceeds the cost of a skilled operator performing tool selection directly. Labor savings are marginal. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | Any AI assistance would require integration with CAM software and human verification, and given low task frequency relative to setup, cost savings versus a skilled machinist are marginal at best. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | While AI tools exist to provide material property lookups and basic tool recommendation systems, no deployed product reliably performs the full task of selecting cutting tools and generating tooling instructions in production environments without significant human oversight. Most systems remain research-stage or lab-tested rather than operationalized at scale. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product autonomously selects cutting tools and tooling instructions on shop floors today; this remains a human expert task tied to physical machine setup. |
Position, secure, and align cutting tools in toolholders on machines, using hand tools, and verify their positions with measuring instruments.
20CI 14–26 · exposure 16 · augmentation 38 · importance 4.1/5 · click for rater detail
Position, secure, and align cutting tools in toolholders on machines, using hand tools, and verify their positions with measuring instruments.
20| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Manufacturing sectors have adopted CNC programming and some robotic handling, but manual tool setup by skilled operators remains a bottleneck in small-to-medium shops and job shops where setup variability is high; adoption of AI-driven automated setup is minimal in production. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Manufacturing floor tasks involving physical tool setup show slow AI/robotics adoption; this sector lags far behind information-based industries in deploying autonomous physical automation for such granular tasks. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI-assisted measurement feedback, positioning hints, or automated toolholder alignment suggestions could meaningfully aid human operators in verification and adjustment, though current systems offer limited practical augmentation in this workflow. |
| Augmentation potential | claude-sonnet-5 | 2/5 | Digital measuring instruments and CNC systems can assist with verification data, but AI itself offers limited direct augmentation to the physical act of positioning and securing tools by hand. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While some tool positioning tasks could be partially automated via CNC integration or robotic arm assistance, the requirement to physically handle precision hand tools, securely seat cutting tools, and manually verify alignment with measuring instruments demands dexterous human judgment that current AI systems cannot reliably replicate end-to-end at production quality. |
| Task automatability | claude-sonnet-5 | 2/5 | This requires physical manipulation, tactile feedback, and precise manual alignment of tooling that current AI systems cannot perform without robotic embodiment, which is not standard or widely deployed for this specific task.the task is fundamentally physical, not cognitive/digital. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Manufacturing safety regulations, equipment liability, precision quality standards, and the critical nature of tool setup (mistakes cause product defects or machine damage) create substantial institutional and regulatory barriers to full automation without human sign-off. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement exists, but precision and safety-critical alignment create quality/liability concerns that favor experienced human judgment and tactile verification. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | The capital cost of specialist industrial robots or AI-integrated tooling systems capable of this task would far exceed the loaded wage of a skilled lathe operator, especially given the low error tolerance and need for human verification. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | Without a mature robotic solution for this specific fine-motor task, any automation attempt would require expensive custom robotic tooling systems far exceeding the cost of a human operator performing this routine setup step. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed AI or robotic systems today can independently perform this task reliably in production settings. Current industrial robots lack the fine tactile feedback and adaptive precision needed to position, secure, and verify tool alignment without human oversight, and no commercial products claim autonomous execution of this specific workflow. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed AI product autonomously positions and secures cutting tools in toolholders on lathes; this remains a manual skilled-trade task performed by human operators. |
Crank machines through cycles, stopping to adjust tool positions and machine controls to ensure specified timing, clearances, and tolerances.
16CI 5–26 · exposure 13 · augmentation 25 · importance 4.1/5 · click for rater detail
Crank machines through cycles, stopping to adjust tool positions and machine controls to ensure specified timing, clearances, and tolerances.
16| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Manufacturing has adopted CNC and some automation, but small and mid-sized job shops—where manual lathe operation remains common—lag in high-cost robotics adoption. Digitization is uneven, and true full-cycle automation of this task remains rare in production. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Manual machining/metalworking is a low-digitization, physically embedded sector where AI agent adoption for this specific hands-on task is minimal to none. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could assist by monitoring tolerances, predicting tool wear, or suggesting parameter adjustments, but current shop-floor vision and sensor integration is limited. The inherent tactile and real-time adjustment nature of the task limits meaningful AI co-working gains with today's technology. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI-enabled sensors or predictive analytics could inform tolerance adjustments, but current tools offer limited direct assistance to the operator performing manual cranking and adjustment. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI could theoretically monitor machine parameters and suggest adjustments, the task requires real-time physical intervention (manual cranking, hands-on tool positioning, tactile adjustment of controls) that current robotic systems struggle to perform reliably in unstructured shop environments. End-to-end automation with 50% time savings is not yet achievable with generally available systems. |
| Task automatability | claude-sonnet-5 | 1/5 | This requires direct physical manipulation of machine controls, tactile feedback, and real-time adjustment during a manual cranking cycle—no current AI system can perform this physical interaction end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Safety regulations, OSHA compliance, and liability for equipment damage or worker injury create strong organizational and legal friction. Additionally, real-time responsiveness to tool wear, part variations, and machine drift typically requires human judgment and certification. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No licensing barrier exists, but physical presence, machine-specific tacit skill, and safety/quality liability create meaningful friction against remote or software-only substitution. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Current robotic arms and automated tool-changers capable of performing machine adjustments and cycle management are capital-intensive and require significant integration cost, far exceeding the loaded hourly wage of a machine operator. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no AI system that substitutes for this physical task, so the cost comparison favors the human operator entirely; robotics for this remains costly and immature. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | Some CNC machines offer semi-automated cycle management and tool offset adjustment, but the cyclical manual cranking and real-time adjustment based on visual/tactile feedback from operators remain largely human-dependent. No deployed AI system reliably performs this full task in production metalworking shops today. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed AI product performs manual crank-cycle adjustment of lathes; CNC automation exists but replaces the task paradigm rather than performing this specific manual operation. |
Replace worn tools, and sharpen dull cutting tools and dies, using bench grinders or cutter-grinding machines.
14CI 5–24 · exposure 8 · augmentation 25 · importance 4.2/5 · click for rater detail
Replace worn tools, and sharpen dull cutting tools and dies, using bench grinders or cutter-grinding machines.
14| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Manufacturing sectors employing lathe operators remain relatively low-digitization, capital-intensive environments with long equipment lifecycles and strong craft tradition. Automation adoption in this segment is slow, with most facilities still relying on skilled human workers for maintenance and tool management. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Manufacturing shop floor tasks involving physical tool maintenance show slow AI/robotics adoption compared to information-sector tasks; this is a laggard, low-digitization physical task. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI tools could potentially assist with scheduling tool changes or recommending sharpening intervals via predictive analytics on machine wear data, but current systems offer minimal support for the physical sharpening task itself. Augmentation is limited and peripheral to the core manual work. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI could help with predictive maintenance scheduling or wear detection via sensors, but it offers minimal direct assistance to the hands-on grinding and replacement process itself. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task requires physical manipulation in a workshop environment—removing worn tools, positioning them for grinding, and sharpening to precise specifications—which current AI systems cannot perform end-to-end. No deployed autonomous system can reliably handle the tactile feedback, tool changeover, and real-time grinding adjustment required. |
| Task automatability | claude-sonnet-5 | 2/5 | This involves manual dexterity, physical tool replacement, and skilled sharpening operations that require physical manipulation AI cannot perform end-to-end; only inspection/scheduling aspects could be assisted.PROVIDER. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Tool replacement and sharpening typically requires operator judgment, certification, and direct responsibility for part quality and machine safety. Shop-floor work has strong union presence in many facilities, and error costs (damaged machines, bad parts) create liability concerns that favor human oversight and decision-making. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement exists specifically for this task, but physical workspace constraints, machine-specific setup knowledge, and lack of robotic infrastructure create practical friction against automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | The capital cost of specialized robotic systems capable of tool sharpening vastly exceeds the hourly wage of a skilled tool setter or operator, and integration and maintenance overhead is substantial. This task remains economically better performed by humans. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | Robotic automation of tool replacement and sharpening would require expensive specialized robotics far exceeding the cost of a skilled machine operator performing this routine task. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed products perform this task autonomously in production. Robotic systems for tool sharpening exist only as niche research prototypes; they do not form a reliable, scalable production capability in real manufacturing settings. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed AI product replaces worn tools or operates bench grinders/cutter-grinding machines autonomously in production settings; this remains a physical, hands-on machining task. |
Lift metal stock or workpieces manually or using hoists, and position and secure them in machines, using fasteners and hand tools.
11CI 5–18 · exposure 0 · augmentation 13 · importance 3.7/5 · click for rater detail
Lift metal stock or workpieces manually or using hoists, and position and secure them in machines, using fasteners and hand tools.
11| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Manufacturing sectors are gradually adopting robotics, but heavy reliance on manual setup and positioning persists due to workpiece variability, tight tolerances, and the need for skilled judgment. Adoption remains slow compared to information-sector tasks. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Metal/plastic machining is a physically-oriented manufacturing sector with historically slow, capital-intensive automation adoption cycles compared to information-sector AI adoption.General AI agent adoption patterns seen in white-collar work do not translate to this manual task. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI offers minimal assistance for the core actions of lifting, positioning, and fastening; computer vision might help with workpiece tracking or setup guides, but the primary task remains fundamentally manual and physical. |
| Augmentation potential | claude-sonnet-5 | 1/5 | Current AI (LLMs, vision models) offers no meaningful assistance to the physical act of lifting and securing workpieces in a lathe.Any productivity gains here come from traditional automation/robotics, not from AI augmentation of a human performing this specific task. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task requires physical manipulation of heavy metal stock, precise positioning in machine fixtures, and securing with fasteners—all in a dynamic manufacturing environment. Current AI systems lack the dexterity, spatial reasoning, and real-time physical interaction capabilities to perform these actions end-to-end without human intervention. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a physical manipulation task requiring manual lifting, positioning, and securing of workpieces with hand tools; no off-the-shelf AI system can perform this end-to-end physical work today.The task is inherently robotic/physical, not cognitive, so language/vision AI models have no direct application. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Safety regulations, operator licensing requirements for heavy equipment operation, and employer liability for equipment damage or worker injury create significant legal and organizational friction against automation. Human operators must bear direct accountability for proper setup and safety. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement exists for this task, but physical safety regulations (OSHA), workpiece variability, and capital costs create moderate friction against automation.The barrier is more physical/economic than regulatory or professional in nature. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Robotic systems capable of handling unstructured material handling and positioning are extremely expensive to acquire, integrate, and maintain, making them far costlier than direct human labor for this task. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | Robotic automation for this exact task requires expensive custom fixturing, integration, and engineering that typically costs far more than human labor for small-to-medium batch work.Only high-volume dedicated automated lines achieve favorable economics, not general-purpose AI systems. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed AI systems can reliably perform manual lifting, positioning, and fastening of metal workpieces in production environments today. While robotic arms exist for structured tasks, they require extensive integration and do not handle the variability and judgment in this task. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed AI product performs manual lifting and fastening of metal stock in machine shops; industrial robotic arms exist but are task-specific, expensive, and not 'AI' in the general sense being rated here.This remains research/specialized robotics territory, not a general AI capability deployed at scale. |
Install holding fixtures, cams, gears, and stops to control stock and tool movement, using hand tools, power tools, and measuring instruments.
9CI 5–13 · exposure 0 · augmentation 25 · importance 3.6/5 · click for rater detail
Install holding fixtures, cams, gears, and stops to control stock and tool movement, using hand tools, power tools, and measuring instruments.
9| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Manufacturing adoption of general-purpose task automation remains slow outside high-volume production lines. Small and mid-sized job shops—where lathe setup is common—have low digitization and prefer retaining skilled labor control over setup operations. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Manufacturing shop-floor setup tasks are in a low-digitization, physical sector with minimal AI/robotic adoption for this specific fixture-installation work.' |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could assist with documentation and measurement recording, but offers minimal productivity gain for the core physical task of installing and aligning fixtures, cams, and stops, which remains dependent on human judgment and tactile skill. |
| Augmentation potential | claude-sonnet-5 | 2/5 | Digital work instructions, measurement software, or CAM systems can assist planning and verification, but the physical installation itself receives little direct AI augmentation.' |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task requires precise physical manipulation of small parts, real-time tactile feedback, and assembly in three-dimensional space. Current AI systems lack the dexterous robotic hardware and sensorimotor control to reliably install fixtures, cams, and gears on lathe machines without human oversight. |
| Task automatability | claude-sonnet-5 | 1/5 | This requires physical manipulation of heavy fixtures, cams, and gears on a physical machine tool using hand and power tools plus measurement—no current AI system can perform this physical setup task end-to-end.' |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Union rules in many manufacturing plants require skilled tradespeople to perform setup and calibration tasks. Additionally, liability for equipment damage and tight tolerances create organizational and legal barriers to full automation without human sign-off. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No licensing barrier exists per se, but the physical dexterity, precision measurement, and safety-critical nature of machine setup create substantial practical barriers to automation.' |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Specialized robotic arms capable of precision assembly cost tens of thousands of dollars, require custom programming and tooling, and demand ongoing maintenance—far exceeding the loaded wage of a skilled lathe operator performing this setup work. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI substitute performing this physical task, so cost comparison favors the human by default; any robotic solution would require expensive specialized hardware exceeding human labor costs.' |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed AI product performs end-to-end fixture installation and calibration on lathe machines. While industrial robotics exists, it is task-specific, heavily customized, and does not generalize to the varied setups and tolerances this task demands. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product installs physical machine tooling fixtures autonomously; this remains firmly in the domain of skilled machinists and robotics research at best.' |
Mount attachments, such as relieving or tracing attachments, to perform operations, such as duplicating contours of templates or trimming workpieces.
7CI 5–10 · exposure 0 · augmentation 25 · importance 3.5/5 · click for rater detail
Mount attachments, such as relieving or tracing attachments, to perform operations, such as duplicating contours of templates or trimming workpieces.
7| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Manufacturing, particularly small-to-mid-sized lathe operations, has low automation adoption for fine-motor attachment tasks. Most facilities rely on trained human operators due to cost, flexibility, and the non-standardized nature of setups. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Machining and metalworking is a physical, lower-digitization sector with slow adoption of AI/robotics for hands-on tooling tasks like this. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI tools could potentially assist with template design or contour recognition, but the core physical task of mounting attachments offers minimal augmentation opportunity; the operator must perform the manipulation themselves. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI could potentially assist with generating CNC programs or suggesting attachment configurations, but it offers little direct assistance for the physical act of mounting attachments. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | Mounting physical attachments to lathe machines requires precise manual dexterity, spatial reasoning in physical space, and real-time adaptation to equipment—capabilities far beyond current AI. End-to-end automation would require robotics integration that is not standard in manufacturing settings today. |
| Task automatability | claude-sonnet-5 | 1/5 | This requires manual physical manipulation of heavy attachments, precise mechanical alignment, and tactile fitting on a physical machine tool—no current AI system can perform this physical mounting task end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Safety regulations, equipment-specific certification, and the requirement for human oversight of precision manufacturing create material adoption friction. Human operators must verify correct attachment mounting for quality and safety compliance. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No licensing barrier per se, but physical dexterity, safety requirements around machine tool setup, and lack of robotic infrastructure in most shops create substantial practical barriers to automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Robotic systems capable of mounting lathe attachments are expensive to purchase, integrate, and maintain, making them significantly more costly than a trained human operator performing this task. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no AI-based alternative to compare cost against; a human machinist remains the only viable means of performing this physical setup task, making AI substitution cost effectively infinite/inapplicable. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed AI product reliably performs the physical manipulation of mounting lathe attachments in production environments. This task requires embodied robotics with specialized end-effectors, which is research-stage for general manufacturing use. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed AI product performs physical attachment mounting on lathes; this remains purely a manual machinist task with no robotic or AI product addressing it in production. |
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.