Gem and Diamond Workers
51-9071.06Median wage $52,540/yr22,440 employed (US)Rank #531 of 923 scored · top 58% by substitution
Fabricate, finish, or evaluate the quality of gems and diamonds used in jewelry or industrial tools.
Sub-scores
0–100 · band = confidence interval from rater disagreement
Substitution26
Exposure21
Augmentation45
Substitution — the headline: capability discounted by cost, barriers and adoption.
Exposure — technical capability alone, regardless of whether anyone deploys it.
Augmentation — how much AI assists without replacing. High here + moderate substitution = a changing job, not a disappearing one.
Tasks on the substitution scale
14 rated tasks, binned by substitution score.
Position among all scored occupations
Distribution of 923 occupation scores; the marker is this occupation.
Tasks with substitution ≥ 70
0%
Run 1.0.0-draft.1 · computed 2026-08-05 · rater panel: claude-sonnet-5, claude-haiku-4-5-20251001 · intervals span rater disagreement.
Why this score
The five weighted dimensions of the composite, averaged across this occupation's tasks (importance-weighted, panel mean). Exact weights and formulas: /api/v1/methodology.
Task automatabilityw 35%22
panel mean rating 1.9/5 → substitution pressure 22/100
Technical feasibility todayw 20%18
panel mean rating 1.7/5 → substitution pressure 18/100
Cost vs. human wagew 15%23
panel mean rating 1.9/5 → substitution pressure 23/100
Adoption barriersw 20%inverted — strong barriers lower the score47
panel mean rating 3.1/5 (barrier strength) → substitution pressure 47/100
Sector adoption velocityw 10%15
panel mean rating 1.6/5 → substitution pressure 15/100
Task breakdown (14 tasks)
Substitution pressure per task, weighted by O*NET importance in the composite. Expand a task for the full rater audit trail — every rating, every model, every rationale.
Sort rough diamonds into categories based on shape, size, color, and quality.
45CI 30–60 · exposure 45 · augmentation 63 · importance 4.3/5 · click for rater detail
Sort rough diamonds into categories based on shape, size, color, and quality.
45| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Diamond and gem industries are relatively small, specialized, and conservative; while they are information-intensive, they lag behind broader manufacturing and retail in automation adoption, with pilot systems more common than production deployment. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | The gem and diamond industry is a small, traditional, low-digitization sector where automated sorting is used only at the largest mining/processing companies, with widespread adoption still limited. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI vision systems can assist human sorters by flagging borderline cases, highlighting key features, or pre-sorting to high confidence, reducing manual inspection load and increasing throughput while preserving expert human judgment for complex or ambiguous stones. |
| Augmentation potential | claude-sonnet-5 | 3/5 | Automated pre-sorting and spectral/imaging tools can assist human graders by narrowing categories or flagging attributes, improving throughput while final quality judgments remain human-driven. |
| Task automatability | claude-haiku-4-5-20251001 | 4/5 | Sorting diamonds by measurable properties (shape, size, color, quality) is well-suited to computer vision and classification algorithms. Current AI systems can reliably classify objects by visual features, though integration with specialized diamond-grading standards and edge cases may require some setup, approaching the 50% time-saving threshold. |
| Task automatability | claude-sonnet-5 | 2/5 | Sorting rough diamonds requires nuanced visual and sometimes physical assessment of irregular, translucent stones with subtle quality variations that current general-purpose AI vision systems cannot reliably replicate end-to-end without specialized hardware. |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | No hard licensing barrier prevents automation of sorting itself, but industry norms, quality assurance liability (high-value items), and potential preference for human expert verification add organizational friction and risk aversion that moderately slow substitution. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No strict licensing requirement, but high-value, irregular rough stones create strong liability concerns and quality-control incentives to retain experienced human graders, plus the physical handling requirement of gemstones adds friction. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 4/5 | Optical scanning and AI inference are inexpensive per unit, and the labor cost for manual sorting is substantial; a deployed automated system would likely achieve several-fold cost advantage over human sorters once amortized across volume. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | Specialized sorting equipment carries high capital costs and is only cost-effective at large industrial scale; for smaller operations, skilled human sorters remain cheaper than deploying custom machine vision systems. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 3/5 | Computer vision systems for object sorting exist in production (e.g., manufacturing quality control, agricultural sorting), and diamond grading incorporates quantifiable metrics, but deployed end-to-end systems specifically for rough diamond sorting with certified reliability are less common in industry, limiting this to material but not fully mature deployment. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Specialized industrial sorting machines (e.g., using X-ray fluorescence, spectral imaging) exist in large diamond processing operations, but these are narrow, expensive, custom systems rather than generally available AI products, and rough stone grading still often requires human expert judgment. |
Advise customers and others on the best use of gems to create attractive jewelry items.
39CI 35–43 · exposure 25 · augmentation 50 · importance 3.9/5 · click for rater detail
Advise customers and others on the best use of gems to create attractive jewelry items.
39| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Jewelry retail remains largely human-centered and relationship-driven; most shops rely on in-person expert consultation. AI adoption in this context is limited to informational chatbots on e-commerce sites, not genuine advisory replacement. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Jewelry retail and craft trades are a low-digitization, physically-oriented sector with slow AI adoption for customer-facing advisory tasks. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI could usefully assist human jewelers by offering design suggestions, gemstone property lookups, or historical references, raising productivity in certain phases of consultation without removing the human advisor from the loop. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI can assist with generating design ideas, visualizing combinations, and providing gem information, aiding but not replacing the human advisor. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI can provide basic information about gems and jewelry design principles, advising customers on 'best use' requires contextual judgment about aesthetics, personal preferences, budget, and occasion-specific suitability. Current AI systems struggle with the subjective, client-specific reasoning needed for genuinely useful jewelry consultation. |
| Task automatability | claude-sonnet-5 | 2/5 | This requires visual assessment of physical gems, aesthetic judgment tied to a specific customer's taste, and interactive trust-building that current AI cannot fully replicate end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | While there are no legal licensing requirements for this advisory task itself, customers strongly prefer human expertise and personal interaction when making jewelry purchases, and trust is a significant adoption barrier. Liability for poor recommendations is relatively low, reducing regulatory friction. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement generally, but customer preference for personal, trust-based interaction with a knowledgeable expert creates moderate friction. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 4/5 | An AI system providing baseline gem and jewelry advice has minimal marginal cost per query compared to the loaded wage of a skilled jewelry advisor, even accounting for integration and human oversight overhead. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | Skilled gemologist advice is moderately costly, but AI tools still require significant human oversight and physical inspection, limiting cost savings. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | No deployed products reliably perform end-to-end customer advisory on gem selection and jewelry design in production settings. Chatbots can offer generic information, but real jewelry consultation involves nuanced aesthetic judgment and client rapport that existing systems handle poorly at scale. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Some AI-powered jewelry design and virtual try-on tools exist, but no deployed product reliably substitutes for expert in-person gem advisory at scale. |
Examine gems during processing to ensure accuracy of angles and positions of cuts or bores, using magnifying glasses, loupes, or shadowgraphs.
29CI 23–35 · exposure 20 · augmentation 50 · importance 4.7/5 · click for rater detail
Examine gems during processing to ensure accuracy of angles and positions of cuts or bores, using magnifying glasses, loupes, or shadowgraphs.
29| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Gem and diamond processing remains a craft-oriented, labor-intensive sector with relatively slow digital transformation; most workshops still rely on manual inspection with magnification tools rather than automated systems, and adoption of AI-based inspection is concentrated in only the largest industrial operations. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Gem and diamond processing is a small, traditional, craft-based sector with low digitization and slow AI adoption compared to information or finance sectors. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI-powered imaging systems can assist inspectors by highlighting potential defects, providing real-time measurement overlays, and flagging gems that fall outside tolerance ranges, improving speed and consistency while the human makes final accept/reject decisions. |
| Augmentation potential | claude-sonnet-5 | 3/5 | Digital magnification, imaging software, and automated measurement tools can assist human inspectors in verifying angles and cut precision, improving accuracy and speed while the human remains the final judge. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI vision systems can detect visible defects and measure geometric features, the task requires real-time judgment of subtle angular deviations and three-dimensional spatial positioning under magnification, which current systems struggle with reliably. Automation would require sophisticated 3D imaging and specialized optical calibration that is not yet standard in gem processing workflows. |
| Task automatability | claude-sonnet-5 | 2/5 | This requires precise physical manipulation and optical inspection of gemstones during active processing, which involves fine motor coordination and real-time visual judgment that current AI cannot perform end-to-end without robotic hardware integration.' |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | While there is no legal licensing requirement to perform gem inspection itself, high error costs (cutting errors are permanent and financially significant) create operational and liability friction against full automation, and many firms prefer human expertise as a trust signal to customers. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement exists for gem inspection itself, but high value of gemstones creates strong error-cost asymmetry and craftsmanship trust preferences that favor experienced human inspectors. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | Building and maintaining specialized optical AI systems for gem inspection requires expensive hardware (high-resolution cameras, calibrated lighting, 3D reconstruction) and domain-specific training data, making the all-in cost comparable to or higher than skilled human gemologists' wages for routine inspection work. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | Specialized machine vision systems for gem inspection exist but require expensive custom optics, calibration, and integration, making them costly relative to a skilled human using simple magnification tools for this narrow task. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | AI vision inspection systems exist in manufacturing, but few are deployed specifically for gem quality assessment at the precision levels required (sub-degree angular accuracy). Existing solutions are research-stage or limited to coarse defect detection rather than the precise cut-angle and bore-position verification this task demands. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed products autonomously perform in-process gem cutting inspection using loupes or shadowgraphs; automated gem inspection remains largely research-stage or limited to pre/post-processing quality grading, not real-time cutting verification. |
Examine diamonds or gems to ascertain the shape, cut, and width of cut stones, or to select the cuts that will result in the biggest, best quality stones.
28CI 25–30 · exposure 25 · augmentation 63 · importance 4.5/5 · click for rater detail
Examine diamonds or gems to ascertain the shape, cut, and width of cut stones, or to select the cuts that will result in the biggest, best quality stones.
28| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Adoption remains limited to niche experimental applications and lower-stakes initial screening; the high-value gemstone industry has been slow to adopt autonomous AI for critical grading decisions due to liability, certification requirements, and client expectations for human expertise. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | The gem/diamond cutting industry is a niche, traditional, low-digitization sector where AI adoption is mostly limited to large firms using specialized scanning technology, not widespread agentic AI. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI-assisted imaging and measurement tools can meaningfully support a human grader by highlighting candidates and providing consistent measurements, but the task's core—expert judgment about optimal cut and quality trade-offs—still relies heavily on human experience and decision-making. |
| Augmentation potential | claude-sonnet-5 | 4/5 | 3D scanning and planning software (e.g., Sarine Galaxy) substantially assist human cutters by modeling optimal cut plans, significantly boosting yield and productivity while humans retain final judgment. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | Current AI can classify and measure gem properties from images with reasonable accuracy, but the task requires expert judgment about cut optimization and quality grading that involves subjective aesthetic and technical decisions beyond what automated vision systems reliably do today. Significant human oversight would remain necessary for high-value stones. |
| Task automatability | claude-sonnet-5 | 2/5 | AI vision systems can analyze gemstone dimensions and suggest cuts, but the actual judgment on optimal cutting for value maximization still requires expert human evaluation of unique inclusions and market factors.' |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Gem and diamond grading is governed by strict industry standards (GIA, AGS) and often requires certified graders whose credentials and liability cannot be delegated to automated systems. Customers in luxury markets explicitly value certified human expertise, creating strong adoption friction. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No formal licensing requirement, but high value per error, insurance/liability concerns, and industry reliance on trusted expert graders create real friction against full automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | Current AI vision systems plus infrastructure and ongoing calibration cost is comparable to or exceeds the labor cost for experienced gem examiners, especially given the high cost of errors on valuable stones and the need for expert human validation. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | Specialized imaging/planning systems are costly to acquire and integrate, and skilled human graders/cutters remain necessary, so cost savings are modest rather than dramatic. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | While computer vision and machine learning models for gemstone analysis exist in research and some commercial tools, no deployed product consistently and reliably performs the full scope of shape assessment, cut evaluation, and optimization decisions at the precision required by professional gem workers. Error rates remain material for high-value applications. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Some gemological scanning tools (e.g., Sarine, GemEx) exist and are used in production for rough diamond planning, but they are narrow-scope and still require human oversight and final decisions. |
Estimate wholesale and retail value of gems, following pricing guides, market fluctuations, and other relevant economic factors.
28CI 25–30 · exposure 25 · augmentation 63 · importance 4.4/5 · click for rater detail
Estimate wholesale and retail value of gems, following pricing guides, market fluctuations, and other relevant economic factors.
28| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Gem and diamond workers are concentrated in specialized, traditionally hands-on sectors with slower digital transformation. Adoption remains limited to supplementary data lookup rather than autonomous valuation in most high-value settings. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | The gem and jewelry trade is a low-digitization, relationship- and trust-based sector with slow AI adoption compared to finance or professional services. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI can assist by quickly retrieving comparable pricing data, market trends, and reference materials that inform human appraisers' estimates, but the core judgment and responsibility remain with the expert, making this a useful but not transformative augmentation. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI-based pricing databases, market trend analytics, and valuation software meaningfully speed up and inform human appraisers' judgments even though humans remain central to grading. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI can reference pricing guides and access market data, estimating gem value requires assessing physical characteristics (cut, color, clarity, carat weight) that typically depend on visual inspection and expert judgment of subtle factors not easily automated end-to-end. Current AI lacks reliable real-time integration of multiple economic factors with high-stakes accuracy. |
| Task automatability | claude-sonnet-5 | 2/5 | Value estimation depends heavily on physical inspection of clarity, cut, and inclusions plus tacit market knowledge, which current AI cannot perform end-to-end without human sensory assessment feeding in the data.' |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Gem valuation often carries significant liability exposure (errors can cause financial loss in transactions), and many jurisdictions require certified appraisers for official valuations. Insurance, legal, and customer-confidence requirements create strong friction against full automation. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No licensing mandate universally requires a human appraiser, but gemological certification (GIA, etc.) and buyer trust in credentialed graders create meaningful friction against pure AI substitution. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | AI systems for market data aggregation and pricing reference are relatively inexpensive, but the need for expert human oversight, authentication, and final judgment means the all-in cost remains comparable to or exceeds the cost of a skilled gem appraiser for high-value stones. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | AI software for price lookup is cheap, but the overall task still requires costly human grading and inspection, so total automation cost savings are limited. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | Some tools exist for basic price lookup against published guides and market indices, but deployed systems cannot reliably perform independent valuation of individual gems without human expert review. The complexity of gemstone-specific variables and authentication requirements means no mature product performs this task autonomously in production at scale. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Some pricing databases and AI-assisted valuation tools exist (e.g., for diamonds via 4Cs input), but they require accurate human-supplied grading data and are not fully autonomous production systems for appraisal. |
Measure sizes of stones' bore holes and cuts to ensure adherence to specifications, using precision measuring instruments.
26CI 23–30 · exposure 20 · augmentation 38 · importance 3.7/5 · click for rater detail
Measure sizes of stones' bore holes and cuts to ensure adherence to specifications, using precision measuring instruments.
26| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | The gem and jewelry industry remains relatively low-digitization and artisanal; adoption of automation in measurement remains limited, with most operations still relying on human specialists rather than deployed AI systems. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Gem and diamond crafting is a low-digitization, artisanal, small-firm-dominated sector with minimal AI/automation penetration for physical inspection tasks. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI-assisted measurement tools (computer vision for initial analysis, measurement suggestions) can meaningfully speed up human inspectors' verification work, though the human must retain final judgment on specification compliance. |
| Augmentation potential | claude-sonnet-5 | 2/5 | Digital microscopy and image analysis tools can assist in recording and comparing measurements against specs, but the core physical measurement process still relies on human-operated instruments with limited AI augmentation. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI-powered computer vision can detect and measure bore holes and cuts in images, the task requires handling physical stones with precision measuring instruments in real-world conditions, involving tactile feedback and manual instrument operation that current autonomous systems struggle to perform end-to-end reliably at 50% time savings. |
| Task automatability | claude-sonnet-5 | 2/5 | This requires physical manipulation of precision measuring instruments (calipers, gauges, microscopes) on physical gemstones, which current AI cannot perform without robotic hardware integration; only the data-analysis portion could be automated.5 |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | Quality assurance and liability concerns create moderate friction—errors in measurement directly affect gemstone value—and customer expectations for human expertise in luxury goods inspection add organizational resistance to full automation. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No licensing requirement mandates a human specifically, but quality control on high-value stones often requires trusted human verification and physical dexterity, creating moderate organizational and trust-based friction. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | The setup and integration costs of robotic systems capable of handling precision instruments on gemstones, combined with quality-control oversight, typically exceed the loaded wage of skilled gem workers who perform this specialized task. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | Automating this would require specialized machine vision and robotic handling systems whose setup and calibration costs likely exceed the wage cost of a skilled gem worker performing manual measurement, especially at small-batch scale typical in this industry. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | Deployed computer vision systems can assist with measurement from images or video, but reliable autonomous operation of precision measuring instruments on delicate stones remains largely research-stage; no production systems consistently perform this full task without human oversight. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product autonomously measures physical gem bore holes and cuts end-to-end; this remains a manual metrology task performed by skilled workers with physical tools. |
Examine gem surfaces and internal structures, using polariscopes, refractometers, microscopes, and other optical instruments, to differentiate between stones, to identify rare specimens, or to detect flaws, defects, or peculiarities affecting gem values.
25CI 25–25 · exposure 25 · augmentation 50 · importance 4.3/5 · click for rater detail
Examine gem surfaces and internal structures, using polariscopes, refractometers, microscopes, and other optical instruments, to differentiate between stones, to identify rare specimens, or to detect flaws, defects, or peculiarities affecting gem values.
25| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Gemstone and jewelry industries, while digitizing, remain heavily reliant on expert human examination and traditional processes; adoption of AI for core gemological assessment is minimal in production, with most activity limited to preliminary sorting or supplementary tasks. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | The gem and jewelry trade is a small, traditional, low-digitization sector where AI tools are only beginning to be piloted in specialized labs, not broadly deployed. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI-assisted image analysis and defect detection can meaningfully support human gemologists by highlighting regions of interest or flagging probable defects, improving examination speed and consistency, though the expert human must retain primary assessment responsibility. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI-driven imaging and spectral analysis tools can help gemologists spot anomalies or flag flaws faster, but human judgment remains central to final valuation and authentication decisions. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While image analysis AI can detect some surface flaws and defects in gemstone photographs, the task requires expertise in differentiating between similar stones, identifying rare specimens, and assessing nuanced structural peculiarities that significantly affect value—functions demanding contextual knowledge and judgment that current AI cannot reliably perform end-to-end at 50% time savings with equal quality. |
| Task automatability | claude-sonnet-5 | 2/5 | Optical gem examination requires fine physical manipulation of instruments and nuanced interpretation of subtle internal structures; while some image-analysis AI exists, it cannot yet replace the full hands-on inspection workflow.true automation would require robotic instrument handling plus vision, not yet integrated end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Significant regulatory and liability barriers exist: gemstone certification and valuation are often legally required to be performed by licensed or certified professionals, and high-value error costs create strong organizational and customer preference for human expertise and accountability. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Gem certification (e.g., GIA, AGS) requires credentialed human experts, and high liability for misidentifying valuable or fraudulent stones creates strong professional and trust barriers to automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | Current AI vision systems require significant training data, validation infrastructure, and expert human oversight for gemstone work; the specialized optical equipment and integration costs combined with necessary human verification make the all-in cost comparable to or exceed that of skilled gemologists for this precision-critical task. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | Specialized optical AI systems and imaging hardware remain costly relative to a skilled gemologist's assessment, especially given the need for human verification and certification for high-value stones. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | Some computer vision systems can identify obvious flaws and perform basic optical property analysis from images, but no mature production system reliably performs the full task of gemstone examination including differentiation, rare specimen identification, and defect assessment at the precision required for high-value transactions. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Some gemological labs use AI-assisted imaging tools (e.g., for diamond inclusion mapping) but these are narrow, research-adjacent aids, not full replacements for certified gemologists performing complete identification and grading. |
Assign polish, symmetry, and clarity grades to stones, according to established grading systems.
24CI 23–25 · exposure 25 · augmentation 50 · importance 4.6/5 · click for rater detail
Assign polish, symmetry, and clarity grades to stones, according to established grading systems.
24| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Adoption of AI-driven grading remains minimal in production. The jewelry and gemstone industry moves cautiously due to high financial stakes, regulatory entrenchment, and customer preference for human expertise; most firms still rely on certified human graders. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | The gem/diamond industry is a niche manufacturing/craft sector with relatively low digitization and slow adoption of AI compared to fast-moving information sectors, though some large labs are piloting automated tools. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI-assisted imaging and preliminary defect detection can support human graders by flagging potential issues and speeding up initial sorting, reducing time on obvious cases. However, the human expert remains central to final grading decisions on marginal stones. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI-assisted imaging and measurement tools can help graders more consistently assess symmetry and flag inclusions, improving speed and consistency while the human remains responsible for final grading decisions. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | Grading diamonds and gemstones requires assessment of subtle visual and structural qualities (polish, symmetry, clarity) against standardized systems. While AI can detect some features from images, achieving the precision and consistency demanded by certification standards (e.g., GIA) remains difficult; human expert judgment is still required for borderline cases and final certification. |
| Task automatability | claude-sonnet-5 | 2/5 | AI-based imaging systems can assist in measuring symmetry and detecting inclusions, but final grading of polish, symmetry, and clarity still relies heavily on trained human graders and certified lab judgment, especially for subtle distinctions and edge cases. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Gem grading is heavily regulated by standards bodies (GIA, AGS) and trade practices require certification by authorized human experts; many buyers and insurers will not accept AI-only grades. Liability and reputational risk for errors in expensive stones create strong organizational and legal barriers to full automation. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Grading certificates from major certification bodies (GIA, IGI, etc.) require accredited human graders for legal/market trust reasons, and industry-wide reliance on brand credibility acts as a strong barrier to pure AI substitution. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | Current AI solutions require expensive imaging equipment, specialized training, and significant human oversight to validate grades. The infrastructure and error-correction costs remain high relative to the labor cost of an experienced human grader working with standard tools. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | Specialized imaging and AI grading equipment plus calibration and oversight costs are significant relative to a trained human grader, and human graders remain necessary for certification credibility, keeping cost savings modest. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | Some computer vision systems can assist in preliminary grading or flag obvious defects, but no mature, production-deployed AI system reliably performs independent end-to-end grading at the precision required by major certification bodies. Attempts exist in research and pilot stages, but deployed products with sufficient accuracy and regulatory acceptance are absent. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Some automated grading tools (e.g., for cut/symmetry measurement) exist in labs like GIA/AGS pilot programs, but full clarity and polish grading via AI is not yet a mature, widely deployed production standard across the industry. |
Identify and document stones' clarity characteristics, using plot diagrams.
24CI 23–25 · exposure 25 · augmentation 50 · importance 4.3/5 · click for rater detail
Identify and document stones' clarity characteristics, using plot diagrams.
24| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Gem cutting and grading remain highly specialized, craft-oriented sectors with slow digital transformation. Small shops and specialized gem houses dominate the market, with limited institutional investment in automation, making this a classic laggard sector. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Gemology remains a niche, low-digitization craft sector where AI pilots exist mainly in large labs, with slow diffusion into broader trade practice. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI-assisted image annotation and preliminary defect detection can help gemologists document and speed the plotting process, though the final clarity assessment and plot diagram remain human-verified. This provides useful assistance on the documentation component without replacing the expert judgment step. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI-assisted imaging and pattern recognition tools can help highlight inclusions and suggest plot points, aiding but not replacing the trained grader's judgment. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI can analyze gemstone images and detect some clarity features, the nuanced interpretation of inclusions, feathering, and internal characteristics required for accurate clarity grading still requires expert human judgment. Automated systems lack the reliable 50% time-saving threshold when accounting for the need to verify and correct AI assessments. |
| Task automatability | claude-sonnet-5 | 2/5 | Identifying and plotting internal clarity characteristics requires physical inspection with a loupe/microscope and precise spatial mapping onto a diagram, which current AI systems cannot reliably do end-to-end without specialized imaging hardware not commonly deployed.' |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Gemstone grading carries significant liability and financial stakes; industry standards (GIA, AGS) require certification and human expertise. Regulatory and certification bodies recognize only accredited human graders, creating a hard barrier to full automation without professional sign-off. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Grading often needs certification from bodies like GIA and is tied to appraisal integrity and legal/insurance documentation, creating strong institutional and trust-based barriers to full automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | High-quality gemstone imaging and AI analysis systems are capital-intensive, and oversight by trained gemologists remains mandatory. The combined cost of imaging infrastructure, AI software, and expert verification often exceeds the loaded wage of a skilled worker for the same output quality. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | Specialized imaging and grading hardware plus software integration is costly relative to a trained grader's marginal task cost, so all-in AI cost is not clearly cheaper. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | Computer vision systems can be trained to detect some gemstone defects and generate basic clarity maps, but no deployed product reliably performs professional gem clarity documentation at the precision required by industry standards (e.g., GIA criteria). Existing tools are research-stage or demo-level with significant error rates. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Some automated gemological imaging systems (e.g., in grading labs) exist experimentally, but widespread production-grade tools that autonomously plot clarity characteristics to industry-accepted standards are not yet common. |
Secure gems or diamonds in holders, chucks, dops, lapidary sticks, or blocks for cutting, polishing, grinding, drilling, or shaping.
21CI 10–33 · exposure 13 · augmentation 13 · importance 4.3/5 · click for rater detail
Secure gems or diamonds in holders, chucks, dops, lapidary sticks, or blocks for cutting, polishing, grinding, drilling, or shaping.
21| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Gem and diamond work remains highly artisanal, concentrated in small workshops and specialist firms with low digitization. Adoption of automation in this sector has been slow, with most operations relying on skilled craftspeople and manual techniques. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Gem and diamond cutting is a traditional craft sector with low digitization and minimal AI/robotics adoption; this is a laggard sector for automation of physical handling tasks. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI vision systems could assist in initial gem positioning or holder selection, but the core task of secure fastening requires tactile feedback and judgment that current AI cannot meaningfully augment for human workers engaged in precision stone setting. |
| Augmentation potential | claude-sonnet-5 | 1/5 | AI offers essentially no assistance for the physical act of securing a gem in a holder or dop; this is a purely manual, tactile task outside current AI's scope of support. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While vision systems could identify gems and positioning might be partially automated, the precise tactile securing of delicate stones in various holder types to exact specifications for subsequent processing requires dexterous manipulation that current robotic systems struggle with at production speeds. The diversity of gem sizes, shapes, and holder types adds complexity that exceeds reliable end-to-end automation today. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a fine-motor physical manipulation task requiring precise handling and mounting of small, valuable, irregularly shaped objects; no current AI system (software) can perform this physical fixturing task, and robotic solutions are not off-the-shelf or widely deployed. |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | Gem and diamond work often involves valuable materials and requires quality assurance, creating moderate adoption friction. However, there is no strict licensing requirement for the securing task itself, though insurance and liability concerns around damage to expensive stones provide some organizational barriers. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement exists, but the physical dexterity needed, high value and fragility of the gems, and lack of any mature automation technology create strong practical barriers to substitution. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | Specialized robotic systems capable of handling delicate gems would require significant capital investment and integration costs that likely exceed the wage cost of skilled gem workers, especially given the relatively small scale and artisanal nature of much gem-setting work. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI/robotic system substituting for this task, so the human worker remains the only cost-effective option; any custom robotic fixturing system would be far more expensive than a skilled worker's wage for this task. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | No deployed commercial systems reliably perform the full task of securing diverse gems in various holders at production quality. Some robotic sorting and positioning systems exist in research or limited industrial settings, but consistent, reliable performance across the variety of gems and securing methods is not demonstrated in mainstream production. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed products perform automated gem-mounting in dops/chucks at production scale; this remains a manual skilled task in lapidary work, with any robotic gripping solutions confined to research or highly specialized custom setups. |
Immerse stones in prescribed chemical solutions to determine specific gravities and key properties of gemstones or substitutes.
20CI 10–30 · exposure 13 · augmentation 38 · importance 4.4/5 · click for rater detail
Immerse stones in prescribed chemical solutions to determine specific gravities and key properties of gemstones or substitutes.
20| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Gem and diamond working is a traditional, craft-oriented sector with relatively low digital-native adoption rates and small, specialized firms that move slowly on capital equipment investments and process changes. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Gem and diamond grading/testing is a small, specialized, low-digitization craft sector with minimal AI agent deployment for physical lab procedures. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI could assist by automating data logging, alerting gemologists to expected property ranges based on stone type, and flagging anomalies in immersion results, but the core interpretive task remains human-led and would benefit from such assistive tools. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI could assist with recording results, cross-referencing specific gravity data against reference databases, or interpreting readings, but cannot perform the physical immersion and observation itself. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While the chemical immersion procedure itself could be partially automated (solution preparation, timing), the critical task of observing and determining 'key properties' from the immersion results requires expert human interpretation of visual and physical cues that current AI systems cannot reliably assess end-to-end. The observation phase remains difficult to fully automate. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a manual, physical lab procedure requiring hands-on handling of physical gemstones and chemical solutions; no current AI system can physically immerse and manipulate stones.' |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | The gem industry values human expertise and trust in property assessment, and there may be quality-assurance or insurance expectations that a qualified human certify results, creating moderate organizational friction against full automation. |
| Adoption barriers | claude-sonnet-5 | 3/5 | While not formally licensed in most jurisdictions, gemstone identification for valuation/certification often relies on trusted human expertise and physical dexterity, creating practical barriers to automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | Building a fully automated immersion and analysis system would require specialized robotics, computer vision, and chemical handling infrastructure whose capital and integration costs would likely exceed the loaded wage of a skilled gem worker for several years of operation. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | AI cannot perform the physical task at all, so there is no viable AI cost basis to compare against human labor for this specific procedure. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | No deployed AI system reliably performs gemstone property determination via immersion observation at production scale. Robotic immersion systems exist but lack the visual and tactile interpretation capability to autonomously assess and report the full spectrum of gemstone properties without human oversight. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed AI product performs physical gemstone testing via chemical immersion; this remains a manual gemological technique performed by trained appraisers. |
Hold stones, gems, dies, or styluses against rotating plates, wheels, saws, or slitters to cut, shape, slit, grind, or polish them.
20CI 10–30 · exposure 13 · augmentation 38 · importance 4.3/5 · click for rater detail
Hold stones, gems, dies, or styluses against rotating plates, wheels, saws, or slitters to cut, shape, slit, grind, or polish them.
20| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Gem and diamond work remains concentrated in small, regional workshops with low digital maturity and high manual-craft tradition. Adoption of automation is slow; only large industrial cutters and labs have moved to robotic systems, while the bulk of the sector remains artisanal. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Gem and jewelry manufacturing is a low-digitization, artisanal, physical craft sector with minimal AI/robotics adoption reported in production settings. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI-assisted vision systems and robotic jigs can help by positioning and stabilizing stones, reducing human handling fatigue and improving consistency, though the human craftsperson still controls the cut and makes critical quality judgments. This represents meaningful but not transformative productivity gain. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI can assist with design, grading, or flaw-detection imaging, but offers little direct assistance to the physical act of holding and grinding stones against wheels. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While robotic systems can perform cutting and grinding motions, the task requires real-time sensory feedback to hold and position precious, irregularly-shaped stones with precision sufficient to avoid damage. Current AI-directed robots lack the dexterous, adaptive feedback control and cost-effective perception needed to match human skill across the variety of stone types and cuts at >50% time savings. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a physical, manual gem-cutting task requiring fine dexterity, tactile feedback, and real-time judgment on hard-to-see material flaws; no off-the-shelf AI system performs this physical manipulation today. |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | While no strict licensing bars robotic adoption, quality and reputation concerns create organizational friction—customers often seek hand-crafted work or certified human expertise. Liability for stone damage and the aesthetic/trust premium for human craftsmanship moderately slow substitution. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No licensing requirement exists, but high value of stones creates strong liability/error-cost asymmetry, and craftsmanship/tactile skill is highly valued, creating moderate organizational and trust-based friction against automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | Robotic systems capable of precision gemstone cutting are capital-intensive ($100k+), require skilled programming and maintenance, and demand custom tooling for each stone type. For typical gem workers in small to mid-size workshops, the all-in cost per task significantly exceeds loaded human wages. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | Specialized robotic/CNC lapidary equipment requires large capital investment, calibration, and human oversight, making it costlier than skilled manual labor for most gem workers, especially small operations. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | Specialized robotic systems exist for gem cutting in limited, controlled settings (labs, high-volume industrial production), but they remain niche, require extensive setup, and handle only standardized cuts. Deployed production systems that generalize across stone types and customer-specific cuts are rare; most high-value gemwork still relies on human craftspeople. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | While CNC gem-cutting machines exist as specialized industrial equipment, they are not 'AI' systems performing this task autonomously with judgment; no deployed AI product holds and manipulates stones against wheels reliably at scale. |
Select shaping wheels for tasks, and mix and apply abrasives, bort, or polishing compounds.
17CI 10–24 · exposure 8 · augmentation 25 · importance 3.6/5 · click for rater detail
Select shaping wheels for tasks, and mix and apply abrasives, bort, or polishing compounds.
17| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Gem and diamond work remains a small, highly specialized sector with traditional craftsperson practices; digitization and AI adoption in this domain is minimal compared to finance or information services. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Gem and diamond cutting is a small, traditional, low-digitization craft sector with minimal AI adoption or investment in automating physical polishing tasks. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could assist by recommending wheel and abrasive combinations based on stone type and desired outcome, but the hands-on selection and application work itself offers limited scope for meaningful productivity augmentation. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI could assist with some planning aspects (e.g., analyzing stone structure via imaging to suggest cut angles) but offers little direct help with physical wheel selection or abrasive application. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | Only initial material selection could be partially automated via image analysis and lookup tables, but the physical act of mixing compounds and applying them to wheels requires dexterous manipulation and real-time sensory feedback that current robots cannot reliably perform at speed. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a fine-motor, tactile physical craft task requiring hand manipulation of stones, wheels, and abrasive compounds; no current AI/robotic system performs this end-to-end with time savings at equal quality. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | While not strictly licensed, the task requires precise craftsperson judgment and quality control that trades value on human expertise; customer and quality standards create some organizational friction against full automation. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No licensing barrier exists, but the high value of gemstones creates strong liability/error-cost asymmetry and reliance on skilled artisan judgment, discouraging automation experimentation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Robotic systems capable of handling delicate abrasive compounds and wheel selection/application would be extremely expensive to develop and maintain, far exceeding the loaded wage of a skilled gem worker. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI substitute, so any AI-based attempt (custom robotics/vision R&D) would vastly exceed the cost of a skilled gem worker performing this routine task. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed production systems exist that reliably perform the full mixing and application of abrasives to shaping wheels; this remains a skilled manual task requiring human judgment on material consistency, pressure, and wheel condition. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed products perform gem-shaping wheel selection or abrasive mixing/application; this remains a specialized manual craft skill, not addressed by any commercial AI or robotics product. |
Dismantle lapping, boring, cutting, polishing, and shaping equipment and machinery to clean and lubricate it.
13CI 10–15 · exposure 0 · augmentation 25 · importance 3.2/5 · click for rater detail
Dismantle lapping, boring, cutting, polishing, and shaping equipment and machinery to clean and lubricate it.
13| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Gem and diamond working is a specialized, small-scale, low-digitization sector with limited capital investment in industrial automation. Adoption of AI or robotics for equipment maintenance remains minimal. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Gem and diamond manufacturing is a low-digitization, artisanal/physical trade with minimal AI/robotics adoption for equipment maintenance tasks. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could provide instructional guidance or step-by-step disassembly documentation via computer vision, but the core physical work cannot be meaningfully assisted by current systems without robotics. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI could assist with maintenance scheduling, diagnostics via sensor data, or providing repair guidance, but it offers little direct help with the physical cleaning and lubrication process itself. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task involves hands-on mechanical work requiring dexterity, spatial reasoning, and precise handling of delicate equipment. Current AI systems cannot perform the physical manipulation, disassembly, and reassembly needed for equipment maintenance in a gem-cutting workshop. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a hands-on mechanical disassembly, cleaning, and lubrication task requiring physical dexterity and manipulation of specialized machinery, which current AI systems cannot perform end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | While there are no strict licensing requirements for equipment maintenance in most jurisdictions, organizational and safety practices typically require trained personnel to handle delicate precision equipment, and liability concerns around equipment damage create moderate friction. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement exists, but physical access to specialized, often expensive equipment and the need for hands-on mechanical skill create practical friction against remote or AI-driven substitution. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Robotics capable of safe equipment disassembly and maintenance would be extremely expensive to acquire, program, and integrate compared to the loaded cost of a skilled maintenance technician performing this task. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI/robotic substitute for this physical maintenance task, so any hypothetical automation solution would be far more costly than a technician performing the work. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed AI products can reliably dismantle, clean, and lubricate specialized precision machinery. This requires integrated robotics with human-level dexterity and understanding of complex equipment layouts that do not exist at production scale. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product performs physical dismantling and lubrication of gem-cutting/polishing equipment; this remains firmly in the domain of skilled manual labor and robotics research at best. |
Related occupations — Production
How to read this
A high substitution score does not mean this job disappears — it means a large share of its current tasks face replacement pressure, so the mix of tasks is likely to change. High augmentation alongside substitution typically means the occupation reorganizes around the protected tasks. Wide confidence intervals mean the rater panel disagreed: treat those scores as open questions, not verdicts.
What would change this score
New model capabilities (automatability, feasibility), falling inference costs (cost ratio), regulation and licensing shifts (barriers), and measured sector adoption (velocity) all re-enter at every index release. Each release is recomputed, versioned and kept queryable — scores are claims with a date on them, not permanent labels.