Models
41-9012.00Median wage $48,470/yr3,780 employed (US)Rank #225 of 923 scored · top 24% by substitution
Model garments or other apparel and accessories for prospective buyers at fashion shows, private showings, or retail establishments. May pose for photos to be used in magazines or advertisements. May pose as subject for paintings, sculptures, and other types of artistic expression.
Sub-scores
0–100 · band = confidence interval from rater disagreement
Substitution37
Exposure33
Augmentation46
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
10 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
10%
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%36
panel mean rating 2.4/5 → substitution pressure 36/100
Technical feasibility todayw 20%29
panel mean rating 2.2/5 → substitution pressure 29/100
Cost vs. human wagew 15%39
panel mean rating 2.6/5 → substitution pressure 39/100
Adoption barriersw 20%inverted — strong barriers lower the score51
panel mean rating 3.0/5 (barrier strength) → substitution pressure 51/100
Sector adoption velocityw 10%27
panel mean rating 2.1/5 → substitution pressure 27/100
Task breakdown (10 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.
Record rates of pay and durations of jobs on vouchers.
83CI 76–90 · exposure 83 · augmentation 63 · importance 4.6/5 · click for rater detail
Record rates of pay and durations of jobs on vouchers.
83| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 3/5 | Adoption is moderate: larger modeling agencies and talent management firms have implemented some automation, but many smaller operations still rely on manual voucher processing; this is common in digitizing-friendly sectors but not yet universal. |
| Sector adoption velocity | claude-sonnet-5 | 3/5 | Modeling and talent agencies are a small, less digitized sector, so while automation tools exist, actual adoption of AI-driven administrative systems in this niche is moderate rather than fast. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI can assist by auto-populating fields, flagging anomalies in pay or duration, and validating entries, meaningfully reducing human workload and error rates in voucher preparation while a human retains oversight. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI-assisted tools can pre-fill, validate, and organize voucher data, meaningfully speeding up whoever manages these records even if a human still initiates or reviews entries. |
| Task automatability | claude-haiku-4-5-20251001 | 4/5 | This task involves straightforward data entry—recording numerical pay rates and job durations onto vouchers. Current OCR and form-filling AI systems can extract and populate such structured information with high accuracy, delivering significant time savings over manual entry. |
| Task automatability | claude-sonnet-5 | 5/5 | This is a simple data entry/record-keeping task involving structured information (pay rate, job duration) that current AI and basic automation tools handle trivially with full time savings. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | Minimal regulatory or legal barriers prevent automation; the main friction points are organizational (legacy systems, verification workflows, audit trails) and customer preference for human sign-off on employment records. |
| Adoption barriers | claude-sonnet-5 | 1/5 | There is no licensing, liability, or human-contact requirement for recording pay rates and durations; it is purely administrative. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 5/5 | Automating voucher data entry via software is substantially cheaper than paying human labor for routine keystroke and form-filling tasks, with per-transaction costs orders of magnitude lower. |
| Cost vs. human wage | claude-sonnet-5 | 5/5 | Automated data entry via software or AI-driven form processing costs a tiny fraction of a human's time compared to manually recording this information per job. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 4/5 | Deployed products including RPA platforms, intelligent document processing tools, and form-automation software reliably perform data entry and voucher population in production environments, though edge cases (ambiguous handwriting, non-standard formats) may require human review. |
| Technical feasibility today | claude-sonnet-5 | 4/5 | Standard software (spreadsheets, timesheet/payroll apps, OCR plus data extraction tools) already automates this reliably in production, though it may need integration with a specific voucher system. |
Gather information from agents concerning the pay, dates, times, provisions, and lengths of jobs.
66CI 47–85 · exposure 66 · augmentation 75 · importance 4.1/5 · click for rater detail
Gather information from agents concerning the pay, dates, times, provisions, and lengths of jobs.
66| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 3/5 | Talent and recruitment sectors show moderate adoption of AI-driven data collection systems, with many firms in pilots or early deployment; however, full production adoption across the modeling/talent space remains uneven, particularly in smaller agencies. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | The modeling/entertainment industry has low digitization and slow AI adoption for interpersonal negotiation tasks compared to finance or tech sectors. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI can greatly assist humans by pre-filling forms, suggesting completions, flagging inconsistencies in agent responses, and organizing data into dashboards—substantially raising productivity of anyone reviewing or coordinating job information. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI tools can effectively assist in organizing communications, summarizing terms, and tracking job details, meaningfully boosting efficiency while humans retain final negotiation control. |
| Task automatability | claude-haiku-4-5-20251001 | 5/5 | This task is entirely data collection and entry from agents—obtaining structured information about job parameters (pay, dates, times, provisions, length). Current AI systems can reliably extract, validate, and organize this information from emails, forms, or calls with minimal human intervention, achieving well over 50% time savings at equal quality. |
| Task automatability | claude-sonnet-5 | 3/5 | This is a communication/negotiation and data-gathering task that AI could partially automate via email/calendar assistants and CRM tools, but nuanced negotiation and relationship management with agents still requires human judgment.roughly half could be streamlined with scheduling and info-extraction tools. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | There are minimal legal or licensing barriers to automating information gathering from agents. Some organizations may prefer human contact for relationship reasons, and internal processes may require sign-off, but no regulatory or liability requirement mandates human performance of this task. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement, but industry relies on personal relationships and trust between models, agents, and clients, creating moderate organizational friction against full substitution. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 5/5 | The cost of automated extraction (API calls, LLM inference, minimal oversight) is typically a small fraction of paying a human agent to gather and compile this information across many job postings or inquiries. |
| Cost vs. human wage | claude-sonnet-5 | 3/5 | AI-assisted scheduling and information extraction tools are cheap, but human oversight and relationship management still add cost, making it roughly comparable to a human assistant doing this coordination task. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 4/5 | Deployed products (RPA, document processing APIs, contact management systems with AI extraction) routinely perform this type of structured data gathering in production. Some edge cases (ambiguous provisions, special arrangements) may require oversight, but core capability is mature and reliable. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Generic scheduling and email-summarization assistants exist but no deployed product specifically manages talent-booking negotiations with agents reliably at scale in the modeling industry. |
Report job completions to agencies and obtain information about future appointments.
60CI 47–72 · exposure 58 · augmentation 63 · importance 4.0/5 · click for rater detail
Report job completions to agencies and obtain information about future appointments.
60| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 3/5 | Modeling agencies and talent management firms are moderately digital but traditionally resistant to full automation of client-facing communication. Some agencies use scheduling bots and auto-responses, but widespread production adoption of end-to-end reporting automation is still emerging. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | The modeling/entertainment industry has low digitization of back-office workflows and adoption of AI agents for these administrative tasks remains nascent. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI scheduling assistants and intelligent communication systems can significantly reduce the time a model or agency assistant spends on administrative coordination, freeing capacity for relationship-building and higher-value client management. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI can help draft status updates, manage calendars, and track appointment requests, offering useful assistance while the model or their agent still manages actual relationships. |
| Task automatability | claude-haiku-4-5-20251001 | 4/5 | Reporting job completions and obtaining appointment information are largely structured, administrative tasks that involve data entry, email/communication, and database queries. Current AI systems can handle ~70-80% of these workflows autonomously via APIs and form-filling, though some tasks may require human verification of completion status. |
| Task automatability | claude-sonnet-5 | 3/5 | Reporting completions and checking future appointments is largely administrative communication that current AI tools (email/calendar agents, scheduling assistants) could handle with moderate setup, though it requires integration with agency systems and human judgment on relationship nuances.rating.rationale |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | Few regulatory or legal barriers exist; agencies typically accept automated reporting through standard systems. The main friction is organizational (legacy systems, agency-specific processes, preference for human confirmation), which are surmountable. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing or legal requirement mandates human performance, but agencies and clients often prefer personal relationship-based communication, creating some soft friction. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 4/5 | Automated reporting via APIs and chatbots costs negligibly compared to human labor (minutes of manual work). The integration and maintenance overhead is minimal relative to the wage cost of even one worker performing these repetitive administrative tasks. |
| Cost vs. human wage | claude-sonnet-5 | 3/5 | Basic AI scheduling/communication tools are cheap, but integration with idiosyncratic agency processes and oversight needs make the all-in cost only moderately cheaper than a human assistant or the model doing it themselves. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 4/5 | Production systems today reliably handle agency communications through automated email, scheduling systems, and CRM integrations. However, occasional ambiguities about job completion status or complex agency-specific procedures may still require human oversight in practice. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | While scheduling assistants and CRM tools exist, no widely deployed product specifically automates model-agency communication workflows in production at scale today. |
Pose as directed, or strike suitable interpretive poses for promoting and selling merchandise or fashions during appearances, filming, or photo sessions.
54CI 16–92 · exposure 45 · augmentation 38 · importance 3.6/5 · click for rater detail
Pose as directed, or strike suitable interpretive poses for promoting and selling merchandise or fashions during appearances, filming, or photo sessions.
54| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 4/5 | Fashion, e-commerce, and retail sectors are rapidly adopting AI model generation (Shopify, ASOS, and other major platforms now use AI-generated models in production), with measurable displacement of human models in photography and promotional content. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Fashion/retail is experimenting with AI-generated models for catalogs, but adoption for live posing, appearances, and film sessions remains minimal and slow. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI tools can assist human models by previewing poses, generating alternate compositions, and streamlining retouching workflows, but the core task—physical presence and real-time direction during shoots—remains human-centric in high-end and live contexts. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI can assist with post-production, image selection, or virtual try-on concepts, but offers little direct enhancement to the act of physically posing itself. |
| Task automatability | claude-haiku-4-5-20251001 | 5/5 | AI-driven computer vision and generative systems can now synthesize photorealistic model poses, body positions, and fashion presentations entirely end-to-end, replacing the human modeling task with significant time and cost savings in product photography, fashion e-commerce, and promotional content creation. |
| Task automatability | claude-sonnet-5 | 1/5 | Physical posing for live appearances, filming, or photo sessions requires a real human body present; AI cannot substitute for the physical act of modeling in person or on camera at all. |
| Adoption barriers | claude-haiku-4-5-20251001 | 1/5 | No legal licensing, regulatory, or liability barriers prevent substitution; AI-generated imagery for marketing has no formal restrictions, and companies face minimal organizational friction in adopting generative systems for this task. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No licensing barrier, but brand identity, live appearances, human contact with audiences/clients, and authenticity preferences create real friction against AI substitution for this specific physical task. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 5/5 | AI-generated model images cost pennies per output after one-time setup, versus a professional model's hourly/daily rates (typically $50–$500+ per hour), yielding at least a 100x cost advantage for bulk fashion and product photography. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | AI-generated fashion imagery is emerging as a cheaper alternative in some catalog contexts, but for live appearances and authentic photo/film sessions there is no comparable AI substitute, keeping the ratio low for the actual task as stated. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 4/5 | Deployed AI products (generative models like Midjourney, Stable Diffusion, and specialized fashion AI tools) reliably produce posed model images at commercial quality for e-commerce and marketing; while some niche interpretive requirements remain challenging, the majority of routine modeling tasks are already in production use. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product performs physical posing for photo/film shoots; AI-generated imagery can create synthetic 'models' but that is a different workflow, not automation of a human's physical posing task. |
Dress in sample or completed garments, and select accessories.
49CI 5–92 · exposure 45 · augmentation 50 · importance 2.5/5 · click for rater detail
Dress in sample or completed garments, and select accessories.
49| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 4/5 | Fashion, retail, and e-commerce sectors are rapidly adopting virtual try-on, AI styling assistants, and digital avatars in production catalogs and social platforms; major brands have deployed these at scale over the past 2–3 years. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Modeling and fashion styling remain a physical, low-digitization niche with essentially no AI displacement of the physical act of dressing and accessorizing. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI styling tools and virtual try-on platforms substantially amplify human designers' and stylists' productivity by automating outfit assembly, generating variant options, and reducing manual photography cycles, while humans retain final creative and brand judgment. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI can offer some styling suggestions or outfit recommendations digitally, but it provides minimal direct assistance to the physical act of dressing and selecting accessories in the moment. |
| Task automatability | claude-haiku-4-5-20251001 | 5/5 | This task can be substantially automated: virtual try-on AI and 3D garment simulation systems can automatically dress digital avatars in sample garments and select complementary accessories based on style rules, body type parameters, and design criteria, delivering photorealistic results in seconds—well exceeding the 50% time-savings threshold for photography and catalog work. |
| Task automatability | claude-sonnet-5 | 1/5 | Physically dressing a human body and selecting accessories in real time requires physical presence and embodiment that current AI cannot perform; no software or robotic system can substitute for this act.' |
| Adoption barriers | claude-haiku-4-5-20251001 | 1/5 | No legal or regulatory requirement mandates human models; fashion brands and e-commerce sites face no licensing or authorization barriers to deploying virtual models and automated styling—only market preference (which is shifting toward hybrid approaches). |
| Adoption barriers | claude-sonnet-5 | 4/5 | The task inherently requires a physical human body and hands-on styling judgment tied to modeling work, creating a strong structural barrier against automation, though not a formal licensing requirement. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 5/5 | AI-driven virtual dressing and accessory selection cost pennies per iteration (software licensing + compute), while hiring and paying models for photo shoots, fittings, and accessory selection runs hundreds to thousands of dollars per session, making AI two to three orders of magnitude cheaper. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no AI system capable of performing this physical task at all, so no cost comparison favors AI; the human is the only viable option. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 4/5 | Multiple deployed products perform this reliably: virtual try-on platforms (e.g., by major fashion retailers), 3D design software with AI styling suggestions, and AI-powered fashion recommendation engines are in production use, though accessory selection and styling consistency remain somewhat narrow in scope compared to human judgment. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product dresses live models or physically selects and applies accessories on a person; this remains outside current AI product scope entirely. |
Apply makeup to face and style hair to enhance appearance, considering such factors as color, camera techniques, and facial features.
12CI 5–19 · exposure 13 · augmentation 25 · importance 3.1/5 · click for rater detail
Apply makeup to face and style hair to enhance appearance, considering such factors as color, camera techniques, and facial features.
12| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | This sector (personal services, modeling, beauty) has shown minimal automation adoption; work remains highly localized, relationship-based, and physically hands-on. No measurable production deployment of robotic makeup application or hair styling exists in real workflows. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Modeling and makeup artistry sectors show essentially no AI displacement of physical application work, as this is inherently manual and tactile. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI can assist by suggesting color palettes, hairstyles, and camera angles based on facial features and lighting, but the core task of physical application and styling requires the human to remain fully operative. Assistance is limited to planning and design stages, not real-time augmentation during execution. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI can assist with color matching suggestions, mood boards, or virtual makeup previews to inform decisions, but does not enhance the physical application process itself. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI can analyze facial features and generate makeup/hairstyle recommendations, applying makeup and styling hair require fine physical manipulation and real-time adjustment based on tactile feedback, skin texture, and three-dimensional facial geometry. Current AI cannot reliably perform the physical execution end-to-end. |
| Task automatability | claude-sonnet-5 | 1/5 | This requires physical manipulation of cosmetics and hair on a live person, which current AI systems cannot perform; no robotic system does makeup/hair application reliably.' |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Client safety, liability (skin reactions, eye contact with tools), and strong preference for human touch and personalized interaction create substantial barriers. Many clients require assurance of professional expertise and personal attention that substitution with robots would face significant acceptance friction. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Physical touch, artistic judgment, and hands-on skill with tools (brushes, curling irons) create strong practical barriers, though not formal licensing in most cases. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | The specialized equipment, robotic systems, and computer vision infrastructure needed to physically apply makeup and style hair remain prohibitively expensive compared to hiring a skilled makeup artist or hairstylist, whose labor cost is modest relative to the capital investment required. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no AI alternative to compare costs against since the physical task cannot be automated; human labor is the only option. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | No deployed product reliably performs physical makeup application or hair styling. AI systems can generate style suggestions or virtual try-ons (image-based), but actual application requires robotic manipulation that is not mature in production. Virtual augmented-reality styling exists but is not the same as physical execution. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product performs physical makeup application or hair styling; this remains purely manual, skilled craft work performed by humans. |
Pose for artists and photographers.
11CI 0–21 · exposure 0 · augmentation 25 · importance 4.4/5 · click for rater detail
Pose for artists and photographers.
11| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | The modeling industry remains heavily dependent on human performers; while digital avatars and AI image generation exist, actual displacement of live modeling work in photography studios, fashion, and fine arts remains negligible in practice. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Some sectors (advertising, stock imagery) are adopting AI-generated models/synthetic media, but traditional modeling for photographers and artists remains dominated by human physical presence with slow uptake of full substitution. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI tools like pose-correction software or lighting simulation can assist photographers in planning, but they do not meaningfully augment a model's ability to perform the core task of posing; the model's role remains fundamentally manual and human-dependent. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI can assist in planning shoots, generating reference poses, or post-processing images, but offers little direct augmentation to the physical act of posing itself. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | Posing for artists and photographers is inherently a creative, embodied performance requiring physical presence and real-time human judgment about lighting, composition, and artistic vision. AI cannot physically occupy a pose or provide the subjective human presence that defines modeling work. |
| Task automatability | claude-sonnet-5 | 1/5 | Posing requires a physical human body present in the real world to be photographed or painted; current AI cannot physically substitute for this act, though AI-generated imagery can bypass the need for a model in some contexts.' |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | Posing for artists and photographers is fundamentally a human contact task where clients typically require a real person on set for collaboration, trust, and the authentic human element central to portraiture and figure work. Industry norms and artistic practice strongly favor human models. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement, but strong organizational and creative preference for real human models persists in fashion, art, and photography industries, with some contractual/model-release norms. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Deploying synthetic imagery or AI-generated models still requires human artists, photographers, and supervisors; the all-in cost remains higher than hiring human models for most commercial and fine-art applications. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | While AI image generation can be cheap, it produces a substitute image rather than performing the task of posing itself; if comparing to true substitutes (synthetic imagery replacing photoshoots), cost can be lower, but as the literal task defined, there's no AI equivalent cost to compare. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | While AI can generate synthetic images or digital avatars, no deployed product can substitute for a live model's physical presence, spontaneous expressions, and the collaborative interaction between model and photographer/artist that is central to the task. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product has an AI 'pose' physically for a photographer or artist since this is an inherently physical, embodied task, not a digital content-generation task. |
Work closely with photographers, fashion coordinators, directors, producers, stylists, make-up artists, other models, and clients to produce the desired looks, and to finish photo shoots on schedule.
10CI 7–13 · exposure 0 · augmentation 25 · importance 3.0/5 · click for rater detail
Work closely with photographers, fashion coordinators, directors, producers, stylists, make-up artists, other models, and clients to produce the desired looks, and to finish photo shoots on schedule.
10| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Fashion and photography sectors have experimented with CGI and virtual models in niche contexts (haute couture, digital-only campaigns), but mainstream production work still relies on human models. Adoption remains pilot-stage rather than deep production displacement. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Fashion/media production is a mixed-digitization sector; while AI image generation is growing in adjacent creative work, live shoot coordination with human models has seen minimal actual AI-driven displacement. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI can assist marginally with post-production retouching, lighting simulation, or outfit previsualization, but does not materially augment the model's core task of embodied performance, live collaboration, and real-time responsiveness on set. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI can assist with pre-visualization, mood boards, or scheduling logistics around the shoot, but offers little direct enhancement to the live interpersonal coordination task itself. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task requires physical presence, real-time collaborative interaction, embodied performance (posing, movement, expressions), and on-set responsiveness to direction. AI cannot substitute for the model's body and live interpersonal coordination with a crew. |
| Task automatability | claude-sonnet-5 | 1/5 | This task requires a physical human presence and real-time interpersonal collaboration during a live photo shoot; AI systems cannot physically pose, react to direction, or embody the creative vision on set. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Strong barriers exist: client preference for real humans, contractual and licensing requirements (model releases, rights), union representation (SAG-AFTRA in some contexts), and the embodied nature of the work creates organizational and legal friction against substitution. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No licensing requirement exists, but strong organizational and creative/interpersonal friction plus client preference for human presence and spontaneity limit substitution. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | A professional model's loaded cost is typically $100–500+ per hour; AI-generated imagery or virtual models currently require substantial custom development, human post-processing, and oversight, making end-to-end cost higher than hiring a model for routine shoots. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no AI substitute performing this exact physical collaborative task, so no meaningful cost comparison favors AI; a human model is the only current option. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed product can replace a model on a photo shoot. Digital avatars and synthetic models exist in limited research/niche contexts but do not reliably perform professional photo shoots at commercial quality or replace physical presence and real-time collaboration. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed AI product replaces a human model's physical presence and collaborative on-set interaction with a creative team; AI-generated imagery is a different workflow, not this coordination task. |
Follow strict routines of diet, sleep, and exercise to maintain appearance.
4CI 0–7 · exposure 5 · augmentation 50 · importance 3.3/5 · click for rater detail
Follow strict routines of diet, sleep, and exercise to maintain appearance.
4| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | While fitness and health tracking apps are common, they remain advisory rather than automating the core task. Models still manually perform all diet, sleep, and exercise actions with only monitoring assistance. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | This is not a task category where AI adoption applies since it is a personal lifestyle routine rather than an information or business process. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI-powered fitness tracking, meal planning apps, and personalized routine recommendations can meaningfully assist models in adhering to their regimens by providing feedback, reminders, and optimization suggestions. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI apps can track diet, sleep, and exercise data and provide personalized coaching or reminders, offering moderate assistance in maintaining the routine. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task fundamentally requires a human body to execute: diet consumption, sleep, and physical exercise cannot be automated by AI systems. AI can provide guidance or monitoring, but cannot perform the embodied actions required. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a personal, physical, behavioral task performed by the human's own body and willpower; AI cannot execute diet, sleep, or exercise on someone's behalf.' |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | The task is inherently personal and can only be performed by the human model themselves; no third party (human or AI) can eat, sleep, or exercise on their behalf. The human body is the irreplaceable execution unit. |
| Adoption barriers | claude-sonnet-5 | 5/5 | The task is inherently tied to the model's own body and personal choices, making it impossible for another agent, human or AI, to perform on their behalf. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | AI systems cannot substitute for the human's direct performance of this task. Monitoring and advisory costs add to the model's labor rather than replacing it, making total cost higher than having the model self-manage. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no AI substitute performing this task, so no meaningful cost comparison to a human doing it exists; the human must do it regardless. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | Current AI can track dietary intake via apps and recommend routines, but autonomous enforcement of diet, sleep, and exercise completion would require robotics and physical agency beyond today's deployed systems. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed AI product performs this task for a person; at best apps provide tracking or reminders, not execution of the regimen itself. |
Assemble and maintain portfolios, print composite cards, and travel to go-sees to obtain jobs.
3CI 0–5 · exposure 0 · augmentation 38 · importance 3.7/5 · click for rater detail
Assemble and maintain portfolios, print composite cards, and travel to go-sees to obtain jobs.
3| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Modeling remains a human-centered, relationship-driven sector with minimal digital automation of the core go-see and hiring process; adoption of AI for job-seeking is negligible. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Modeling and talent industries have minimal AI adoption for the physical, relational aspects of securing bookings via in-person meetings. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could assist in organizing portfolio images or printing materials, but the critical work—networking, travel, and in-person presentation—offers minimal scope for AI augmentation while the human remains in control. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI can help design and print composite cards, organize portfolio digital assets, and manage scheduling logistics for go-sees, offering moderate productivity assistance. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task requires significant in-person travel and subjective judgment about portfolio presentation tailored to specific clients; no current AI system can autonomously travel to go-sees or maintain physical composite cards at the quality and scale required for employment in modeling. |
| Task automatability | claude-sonnet-5 | 1/5 | This task requires physically embodying the model in front of clients and traveling to in-person meetings, which AI cannot perform since it involves the model's own physical presence and appearance being evaluated. |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | Modeling jobs inherently require human presence and subjective in-person evaluation by casting directors; there is a hard requirement for a licensed/professional model to physically attend go-sees and represent themselves, making legal and practical substitution impossible. |
| Adoption barriers | claude-sonnet-5 | 4/5 | The task inherently requires a human model's physical presence for casting evaluation and travel, creating a strong structural barrier to automation, though not a licensing requirement. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Any AI intervention would require human oversight, travel, and physical representation—the core constraint is irreducibly human labor, making AI cost-additive rather than cost-replacing for this task. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no AI substitute for physically attending go-sees or being the subject of a composite card, so cost comparison is not applicable in AI's favor. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed AI product performs the full end-to-end task of traveling to auditions, presenting portfolios, and securing modeling jobs; these activities fundamentally require human presence and real-time interaction with casting professionals. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed AI product performs go-sees or travels to meet clients in person; this is fundamentally a physical, in-person human activity tied to the model's body and presence. |
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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.