Cooks, Private Household
35-2013.00Median wage $47,940/yr1,100 employed (US)Rank #528 of 923 scored · top 57% by substitution
Prepare meals in private homes. Includes personal chefs.
Sub-scores
0–100 · band = confidence interval from rater disagreement
Substitution26
Exposure20
Augmentation44
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
13 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
8%
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%24
panel mean rating 2.0/5 → substitution pressure 24/100
Technical feasibility todayw 20%14
panel mean rating 1.5/5 → substitution pressure 14/100
Cost vs. human wagew 15%18
panel mean rating 1.7/5 → substitution pressure 18/100
Adoption barriersw 20%inverted — strong barriers lower the score57
panel mean rating 2.7/5 (barrier strength) → substitution pressure 57/100
Sector adoption velocityw 10%6
panel mean rating 1.3/5 → substitution pressure 6/100
Task breakdown (13 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.
Keep records pertaining to menus, finances, and other business-related issues.
74CI 65–84 · exposure 70 · augmentation 75 · importance 4.5/5 · click for rater detail
Keep records pertaining to menus, finances, and other business-related issues.
74| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 4/5 | High-income households and small private chef operations already use digital accounting tools, expense-tracking apps, and menu-planning software at scale; adoption is widespread in digitized household management and personal finance sectors. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Private household service is a low-digitization, small-scale employment context where AI tool adoption for administrative tasks lags behind larger institutional sectors. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI-assisted tools (accounting software with AI categorization, menu-planning aids, expense analytics) significantly enhance a household manager's productivity by automating data entry and generating summaries while keeping humans in control of decisions and approvals. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI tools (spreadsheet automation, receipt scanning, note-taking assistants) can meaningfully speed up and organize a cook's recordkeeping while the human still oversees accuracy and context. |
| Task automatability | claude-haiku-4-5-20251001 | 4/5 | Record-keeping for menus, finances, and business matters is highly structured and repetitive work involving data entry, categorization, and basic calculations—all strongly automatable by current AI systems (forms processing, accounting software integration, spreadsheet automation) with clear time savings. |
| Task automatability | claude-sonnet-5 | 4/5 | Recordkeeping for menus and finances is a structured data-entry and organization task that current AI/software (spreadsheets, expense trackers, LLM-assisted note organization) can largely automate, though setup and integration with a household's specific workflow reduce it slightly below full automation. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | No licensing requirement exists for household financial or menu record-keeping; minimal legal or regulatory barriers apply, though some households may prefer human oversight of sensitive financial data, creating modest organizational friction. |
| Adoption barriers | claude-sonnet-5 | 1/5 | There is no licensing, regulatory, or liability barrier to using software or AI for personal household financial and menu recordkeeping. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 5/5 | Automated record-keeping (accounting software, AI-assisted data entry, cloud-based management) costs a fraction of hiring a person for this work, achieving at least an order of magnitude cost reduction when amortized across multiple households or scaled operations. |
| Cost vs. human wage | claude-sonnet-5 | 4/5 | General-purpose finance/note apps and AI assistants cost very little compared to paying a human specifically for administrative recordkeeping time, though some setup and oversight cost remains. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 4/5 | Deployed products exist for accounting, expense tracking, menu planning, and document management across consumer and small-business applications; these perform reliably in production with minimal error rates for straightforward financial and menu record tasks. |
| Technical feasibility today | claude-sonnet-5 | 3/5 | Bookkeeping and expense-tracking apps with AI features exist and are used broadly, but for a private household cook's informal, ad-hoc recordkeeping, there's no dedicated deployed product performing this specific task reliably out of the box. |
Shop for or order food and kitchen supplies and equipment.
51CI 49–52 · exposure 50 · augmentation 75 · importance 4.7/5 · click for rater detail
Shop for or order food and kitchen supplies and equipment.
51| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Adoption is slowest in private household settings, which are fragmented, non-digitized, and reluctant to automate personal decisions. Broader grocery e-commerce adoption does not translate to private chef/cook automation. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Private household service is a small, low-digitization sector with slow, inconsistent adoption of AI/automation tools compared to larger commercial food service operations. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI significantly assists by automating inventory checks, price searches, delivery scheduling, and reorder reminders, allowing the cook to focus on menu planning and quality decisions. Current tools (shopping list apps, voice ordering) demonstrably raise productivity. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI tools can meaningfully help by tracking inventory, suggesting orders, comparing prices, and automating recurring purchases, improving efficiency while the cook still finalizes decisions. |
| Task automatability | claude-haiku-4-5-20251001 | 3/5 | AI can handle inventory tracking, price comparison, and order placement with current e-commerce APIs and LLMs, but requires human oversight for quality judgment, dietary preferences, and preference changes. End-to-end automation achieves roughly 40–50% time savings with significant integration setup. |
| Task automatability | claude-sonnet-5 | 3/5 | AI can generate shopping lists, compare prices, and even place online grocery orders, but physical shopping, quality inspection of fresh food, and equipment procurement still often require human judgment or physical presence. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | No licensing requirement or legal barrier exists; however, household preferences, dietary restrictions, and quality expectations create friction and favor retaining human judgment. Customer preference for personal shopping and trust in food selection adds organizational inertia. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing or legal requirement for a human to do shopping, though employer preference for personal judgment and trust in selecting food/equipment creates some friction. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | AI-assisted ordering (APIs, chatbots, algorithms) is relatively cheap, but integration, personal preference tuning, and oversight labor remain non-trivial. Full cost parity is achievable only with very simple, standardized households. |
| Cost vs. human wage | claude-sonnet-5 | 3/5 | Online ordering tools are cheap, but for a private household cook role, the overall task still requires human oversight/errands, keeping cost savings moderate rather than order-of-magnitude. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 3/5 | Deployed products (Amazon Fresh, grocery delivery apps, inventory management systems) perform basic ordering reliably, but struggle with household-specific preferences, quality assessment, and substitution decisions. Most solutions still require human review and input. |
| Technical feasibility today | claude-sonnet-5 | 3/5 | Grocery delivery apps and AI-assisted list/reorder tools exist and are used in households, but they don't reliably handle full sourcing of specialty kitchen supplies or nuanced food quality decisions. |
Plan menus according to employers' needs and diet restrictions.
34CI 23–45 · exposure 33 · augmentation 63 · importance 4.7/5 · click for rater detail
Plan menus according to employers' needs and diet restrictions.
34| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Private household cooking is a low-digitization, small-scale, relationship-driven sector with minimal organizational infrastructure for AI tool adoption. Public adoption data shows little to no uptake of algorithmic meal planning in private household contexts. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Private household service is a small, non-digitized, highly personalized sector with minimal AI tool adoption reported to date. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI can assist a cook by suggesting meal ideas that match dietary constraints, generating shopping lists, or proposing variations—useful support that raises productivity on constraint-matching and ideation portions of menu planning while the cook maintains control over final decisions. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI can meaningfully assist by suggesting recipes, checking nutritional/dietary compliance, and drafting weekly menus that the cook then customizes and finalizes for the household. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | Menu planning requires understanding employer preferences, dietary restrictions, budget constraints, and nutritional balance. While AI can generate menu suggestions and handle constraint-matching, the task requires significant human judgment about preferences and cultural/dietary nuance that AI systems struggle with in practice, and would require substantial human oversight and revision. |
| Task automatability | claude-sonnet-5 | 3/5 | AI can generate menu plans respecting stated dietary restrictions and preferences given text input, but it lacks direct knowledge of the employer's evolving tastes, pantry stock, and real-time feedback loop, requiring human curation.' |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Employers rely on personal relationships with household cooks and expect customized service responsive to their spoken preferences and evolving needs. Menu planning is deeply integrated into household management and trust; employers strongly prefer human judgment and direct input on what will be served in their home. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing or legal requirement mandates a human plan menus, but personal trust, taste preferences, and household-specific knowledge create moderate practical friction against pure AI substitution. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | An AI-assisted menu-planning tool has low marginal cost, but full integration into a household cook's workflow, including oversight and correction of misunderstood dietary needs, still requires significant human time. The cost savings are marginal compared to a cook's loaded wage. |
| Cost vs. human wage | claude-sonnet-5 | 3/5 | Generating a menu plan via an AI tool is cheap, but the private cook's overall wage covers many other duties, so the marginal cost savings on this narrow task is moderate rather than transformative. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | Some recipe and meal-planning tools exist with AI components, but none reliably handle the personalization required for private household employers—distinct preferences, evolving restrictions, and informal specification. No mature product demonstrates reliable autonomous menu planning at scale in private households. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Consumer-facing meal-planning apps and chatbots exist but are not deployed specifically in private household cook workflows; adoption in this niche domestic occupation is essentially absent in practice. |
Prepare meals in private homes according to employers' recipes or tastes, handling all meals for the family and possibly for other household staff.
30CI 10–50 · exposure 28 · augmentation 38 · importance 4.5/5 · click for rater detail
Prepare meals in private homes according to employers' recipes or tastes, handling all meals for the family and possibly for other household staff.
30| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Private household employment is a low-digitization, fragmented, predominantly human-staffed sector with minimal AI adoption; high-net-worth households exploring robotic cooking remain niche and anecdotal. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Private household service is a low-digitization, highly personal, physical-labor sector with essentially no measurable AI or robotic adoption for meal preparation. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI can assist via meal planning, recipe suggestion, nutritional analysis, and prep-work automation, but the social and sensory aspects of household cooking remain tied to human discretion and family dynamics. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI recipe generators and meal-planning apps can help a cook plan menus or adjust recipes to preferences, but they offer limited assistance for the hands-on cooking and serving itself. |
| Task automatability | claude-haiku-4-5-20251001 | 4/5 | Most of meal preparation—mise en place, cooking, plating—can now be substantially automated with robotic arms, computer vision for doneness detection, and recipe-following systems. A 50% time saving at equal quality is realistic for routine meals, though the long tail of dietary constraints and taste preferences adds friction. |
| Task automatability | claude-sonnet-5 | 1/5 | Physical meal preparation in a home requires manipulating ingredients, cooking equipment, and adapting to real-time sensory feedback (taste, texture), none of which current AI systems can perform end-to-end without robotics far beyond deployed capability. |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | No strict licensing requirement for household cooking, but significant friction exists: taste and preference verification, safety liability for meal quality in a residential setting, family comfort with robotic versus human preparation, and integration complexity. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing is required for private cooks, but strong household trust, food safety concerns, and personal taste preferences create meaningful friction against replacing a known human with an automated system. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | Full-stack robotic kitchen systems are capital-intensive (six figures+), and the loaded wage for a private household cook is modest (typically $35k–$60k annually); the amortized cost per meal often exceeds employing a human for all-in overhead. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI system performing this physical task, so any hypothetical robotic solution would carry far higher capital and integration costs than employing a private cook. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | Robotic cooking systems exist in research and limited commercial deployment (e.g., Flippy, Miso Robotics), but no product reliably handles the full scope of private household meal prep across diverse cuisines and family preferences at acceptable error rates in production. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product autonomously cooks and serves customized meals in private homes; kitchen robotics remain research/prototype stage and are not in production use for this role. |
Create and explore new cuisines.
21CI 9–33 · exposure 13 · augmentation 75 · importance 3.6/5 · click for rater detail
Create and explore new cuisines.
21| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Private household cooking remains a highly personal, artisanal domain with minimal digitization and slow AI adoption. This is not an information-age sector pursuing algorithmic replacement; households value bespoke human creativity and culinary personality. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Private household cooking is a low-digitization, highly personal, physically-performed occupation with minimal AI adoption in practice. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI can substantially augment a private chef's work by suggesting ingredient combinations, generating recipe variations, research on cuisines, and providing inspiration—while the human chef retains full creative control and sensory judgment. This is a strong assistive use case where AI boosts ideation without removing human decision-making. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI tools can meaningfully help brainstorm fusion ideas, generate recipes, and suggest ingredient pairings, augmenting a cook's creative exploration even though execution remains manual. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | Creating and exploring new cuisines requires significant creative ideation, sensory evaluation, and iterative refinement of flavor profiles—tasks where current AI can assist in recipe generation but cannot autonomously match a human's multisensory judgment and culinary innovation at equal quality. AI food systems offer suggestions but need human tasting, adjustment, and subjective creative direction. |
| Task automatability | claude-sonnet-5 | 1/5 | Creative culinary innovation requires physical execution, taste-testing, and iterative sensory judgment that AI cannot perform end-to-end; AI can suggest recipe ideas but cannot cook or taste them. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Private household cooking involves subjective artistic and creative judgment, strong customer preferences for human-curated culinary experience, and an implicit expectation of human creativity and taste. Employers typically value the human chef's personal style and exploration, creating organizational and preference-based friction against AI substitution. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing barrier, but household employers typically want a human cook physically present to prepare food, and sensory/physical execution can't be delegated to software. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | A private household cook's salary is relatively modest, and AI recipe/food-generation services often require ongoing subscriptions, human oversight, and testing ingredients. The all-in cost of AI-assisted exploration does not undercut human labor significantly when accounting for the need for human validation and iteration. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | AI recipe-idea generation is cheap, but since the actual cooking, tasting, and refinement must still be done by a human cook, there's no meaningful cost substitution for the full task. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | While recipe-generation tools and AI cooking assistants exist, none reliably perform end-to-end cuisine creation and exploration in production environments. Deployed systems (e.g., AI recipe apps) generate suggestions but lack the sensory feedback loop and iterative creative refinement required for genuine new cuisine development. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product actually creates and physically explores new cuisines in a household kitchen setting; this remains a human physical/creative task. |
Peel, wash, trim, and cook vegetables and meats, and bake breads and pastries.
17CI 10–24 · exposure 8 · augmentation 25 · importance 4.8/5 · click for rater detail
Peel, wash, trim, and cook vegetables and meats, and bake breads and pastries.
17| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Private household cooking is a low-digitization, physically embedded sector with minimal documented AI/robot adoption in production. Penetration remains negligible. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Private household service work is a low-digitization, physical-labor sector with essentially no AI/robotics adoption for meal preparation tasks. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI can assist with recipe suggestions, ingredient substitutions, and cooking timers, but does not materially transform the productivity of the core physical tasks of food preparation and cooking. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI recipe suggestion and meal-planning tools can assist with menu ideas or timing, but offer little help with the hands-on peeling, trimming, and cooking work itself. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | Current AI can assist with recipe retrieval and meal planning, but the physical manipulation of vegetables, meats, and dough—peeling, trimming, kneading, and precise cooking control—requires dexterity and real-time sensory feedback that general-purpose robots cannot reliably perform today. No single system achieves 50% time saving end-to-end. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a physical manipulation task requiring dexterity, sensory judgment, and coordination in an unstructured home kitchen; no off-the-shelf AI system performs peeling, trimming, or cooking end-to-end today. |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | No licensing is required, but liability for foodborne illness or injury, customer preference for human skill and judgment, and organizational/household friction around replacing trusted household staff create moderate adoption friction. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement exists, but food safety liability, physical kitchen variability, and strong preference for human food preparation in private homes create practical friction. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Acquiring, maintaining, and programming a multi-task robotic system with perception and manipulation capability costs orders of magnitude more than hiring a private cook. Integration and oversight costs further exceed human wages in this context. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | Any hypothetical robotic system capable of this would require expensive specialized hardware far exceeding a human cook's wage, with no mature product to compare costs against. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | While some specialized robotic arms exist for narrow tasks (e.g., vegetable cutting in industrial settings), no deployed product reliably handles the full pipeline of washing, peeling, trimming, cooking, and baking with the adaptability required in a private household. Research prototypes far exceed production reality. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed consumer or commercial product autonomously prepares and cooks meals in a private household; robotic cooking remains research/demo stage even in controlled settings. |
Cool, package, label, and freeze foods for later consumption and provide instructions for reheating.
17CI 10–24 · exposure 8 · augmentation 25 · importance 4.8/5 · click for rater detail
Cool, package, label, and freeze foods for later consumption and provide instructions for reheating.
17| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Private household employment is low-digitization, small-scale, and physically distributed; adoption of any automation technology in this sector is minimal. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Private household service is a low-digitization, highly manual, small-scale sector with essentially no reported AI/robotic adoption for physical food preparation and packaging tasks. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could potentially assist with generating reheating instructions (via language models) or suggesting optimal freezing containers, but the core physical and sensory tasks—cooling, packaging, labeling—offer minimal augmentation opportunity within current capabilities. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI can help draft reheating instructions or suggest labeling/storage best practices via a phone or assistant, but it offers no assistance with the physical cooling, packaging, or freezing work itself. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task requires physical manipulation of food items (cooling, packaging, labeling, freezing) and judgment about proper food safety and packaging methods. Current AI systems cannot perform these embodied, multi-step physical operations end-to-end. |
| Task automatability | claude-sonnet-5 | 2/5 | The physical actions of cooling, packaging, labeling, and freezing food require manual dexterity and kitchen presence that current AI systems cannot perform; only the instruction-writing sub-step is automatable., leaving the bulk of the task unaddressed. |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | Private household contexts involve customer preference for human food handlers and implicit trust in the cook's food-safety judgment; regulatory oversight is light but food safety liability remains a friction point. |
| Adoption barriers | claude-sonnet-5 | 2/5 | There's no licensing requirement for a private household cook, but physical presence and manual food safety handling create practical barriers to any non-human substitution, tempered by no formal regulation of home cooking tasks. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | The physical infrastructure (robotics, refrigeration, packaging equipment) required to automate this task far exceeds the wage cost of a private household cook performing these operations manually. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | AI cannot perform the physical labor at all, so the human cook remains the only viable option, making any AI cost comparison moot or AI effectively infinitely costlier for the physical portion. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed AI product or robotic system reliably performs the full sequence of cooling, packaging, labeling, and freezing food in a household kitchen context today. Specialized food robotics are research-stage or limited to industrial settings. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product performs the physical handling, packaging, or freezing of food; this remains squarely a manual kitchen task with no robotic or AI product in production for private household use. |
Specialize in preparing fancy dishes or food for special diets.
17CI 10–24 · exposure 8 · augmentation 50 · importance 4.1/5 · click for rater detail
Specialize in preparing fancy dishes or food for special diets.
17| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Private household cooking is a low-digitization, small-scale sector with strong human preference for personalized service; adoption of robotic or AI cooking systems is negligible today. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Private household service and hands-on culinary work are among the least digitized sectors, with essentially no measurable AI/robotic adoption for meal preparation in homes. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI can assist by suggesting recipes tailored to dietary restrictions, scaling ingredients, and recommending techniques, meaningfully raising a cook's productivity in menu planning and dietary compliance without replacing their core execution role. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI can meaningfully assist with recipe generation, dietary restriction planning, and menu customization for special diets, even though it cannot perform the physical cooking itself. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI can generate recipes and suggest ingredient combinations, actually preparing fancy dishes requires physical manipulation, real-time sensory feedback (taste, texture, temperature control), and creative adaptation to dietary constraints that current systems cannot execute end-to-end. AI might assist with recipe selection but cannot achieve 50% time savings on the full cooking task. |
| Task automatability | claude-sonnet-5 | 1/5 | Physical preparation of fancy dishes and specialized diets requires manual dexterity, sensory judgment, and real-time adaptation that current AI systems cannot perform end-to-end; no robotic system can substitute for a private household cook's hands-on cooking. |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | While food preparation itself is not heavily licensed in private households, there are customer preferences for human touch, trust in food safety and quality, and practical liability concerns around meal allergies and dietary accommodations that create moderate friction to full automation. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement exists for private household cooks, but strong customer preference for human-prepared meals, trust, and physical/manual nature of the task create moderate practical barriers to any automation attempt. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | The capital cost of robotic arms, food-handling infrastructure, and the narrow task scope make AI-based cooking more expensive than hiring a private household cook, even accounting for labor costs. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI substitute performing the physical cooking task, so any comparison would require expensive, non-existent robotic infrastructure far costlier than a human cook's wage. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed commercial product reliably prepares fancy or special-diet dishes autonomously today. Robotic cooking systems remain in research or narrow prototypes and lack the dexterity, adaptability, and sensory judgment this task demands. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product autonomously prepares fancy or diet-specific meals in a private household setting; this remains far beyond current robotics and AI kitchen automation, which is still research/demo stage for complex plating and diet customization. |
Plan and prepare food for parties, holiday meals, luncheons, special functions, and other social events.
17CI 10–24 · exposure 8 · augmentation 50 · importance 3.6/5 · click for rater detail
Plan and prepare food for parties, holiday meals, luncheons, special functions, and other social events.
17| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Private household cooking is a low-digitization, small-scale sector with minimal documented AI adoption. There are no known production deployments of AI cooking systems in homes for special events. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Private household services are a low-digitization, highly personal, physically-oriented sector with essentially no AI/robotic adoption for meal preparation. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI can usefully assist by generating customized menus, dietary-accommodation guidance, shopping lists, and timing schedules, raising a human cook's planning efficiency. However, the core preparation and cooking work remains manual and reliant on human judgment. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI can assist with menu planning, recipe suggestions, ingredient scaling for guest counts, and dietary accommodation research, meaningfully aiding the planning portion of this task even though execution remains manual. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI can generate meal plans, create recipes, and manage grocery lists, the core execution—food preparation and cooking—requires human manual skill, sensory judgment, and real-time adaptation to ingredients and equipment. Current systems cannot handle the full end-to-end task of physically preparing and cooking food to consistent quality. |
| Task automatability | claude-sonnet-5 | 1/5 | Physical food preparation and cooking for events requires manual dexterity, sensory judgment, and hands-on execution that current AI systems cannot perform; AI cannot chop, cook, or plate food. |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | Households typically prefer human expertise and personal service for special events, and food preparation carries hygiene and liability concerns. No legal licensing requirement exists for private household cooking, but customer preference and practical control barriers provide moderate friction. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement exists for private household cooks, but strong customer preference for human touch in personal meal preparation and hosting creates natural resistance to automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | A private household cook's loaded wage is modest, and any fully autonomous robotic cooking system (if it existed) would cost orders of magnitude more in capital and maintenance than hiring a human for event-based work. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI substitute performing the physical cooking, so the human cost is the only real option; any hypothetical robotic solution would be far more expensive than a private cook's wage. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed AI product can autonomously plan, source, and prepare meals for actual social events. Robotic cooking systems remain in R&D or highly constrained laboratory settings; there is no production-grade system doing this at scale in real households. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product physically prepares meals for private household events; this remains entirely a human physical labor task with no robotic kitchen products in real-world household deployment. |
Stock, organize, and clean kitchens and cooking utensils.
14CI 5–24 · exposure 8 · augmentation 25 · importance 4.4/5 · click for rater detail
Stock, organize, and clean kitchens and cooking utensils.
14| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Private household employment is low-digitization, low-automation sector with minimal production adoption of robotics; most households relying on human cooks show no measurable shift toward automation for this task. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Private household services are a low-digitization, physical-labor sector with minimal AI/robotics adoption for chores like kitchen organizing and cleaning. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could provide minor assistance via inventory-tracking apps or recipe-linked shopping lists, but most of the task—physical stocking, spatial organization, and cleaning—offers little room for meaningful human-AI collaboration given the embodied nature of the work. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI can offer minor planning assistance (e.g., generating shopping/stocking lists or cleaning schedules via an app) but provides little direct help with the physical execution of stocking, organizing, and cleaning. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | This task requires physical manipulation (handling varied utensils, organizing in specific spaces) and contextual judgment about storage placement that current AI cannot perform robotically at scale today. While inventory tracking of supplies could be partially automated, the core stocking, organizing, and cleaning work demands embodied dexterity and spatial reasoning beyond current robotic or vision-based systems in household settings. |
| Task automatability | claude-sonnet-5 | 1/5 | This requires physical manipulation of objects (stocking shelves, scrubbing utensils, organizing physical spaces) which current AI systems cannot perform without embodied robotics that are not generally available or reliable for household kitchen environments. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Household kitchens are highly variable private spaces with unique layouts, utensil sets, and organizational preferences; liability for damage to valuables, safety concerns with autonomous systems in food preparation areas, and strong household preference for trusted human staff create significant adoption friction. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing or legal barrier prevents automation, but physical/organizational friction (unstructured home environments, fragile items, unique home layouts) makes substitution impractical today. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Deploying a capable kitchen-stocking robot (hardware, integration, maintenance) would cost far more annually than employing a private household cook's time spent on this specific subtask, making AI substantially more expensive in practical household contexts. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI/robotic substitute at any reasonable cost for this physical task in a private household setting, so AI is effectively far more expensive or unavailable relative to human labor. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed product reliably performs the full end-to-end task of stocking, organizing, and cleaning a household kitchen and utensils autonomously. While robotic arms and computer vision exist in research, household kitchen automation at production scale remains absent from real homes. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed consumer or commercial product autonomously stocks, organizes, and cleans household kitchens; robotic dishwashing/cleaning solutions remain research or narrow pilot stage. |
Direct the operation and organization of kitchens and all food-related activities, including the presentation and serving of food.
14CI 5–23 · exposure 8 · augmentation 38 · importance 4.1/5 · click for rater detail
Direct the operation and organization of kitchens and all food-related activities, including the presentation and serving of food.
14| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Private household service is a small, fragmented, low-digitization sector where adoption of AI coordination tools remains minimal; household kitchens lack the digital infrastructure and organizational scale driving adoption in commercial food service. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Private household service is a small, highly manual, low-digitization sector with essentially no measurable AI adoption for kitchen management tasks. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI can assist with meal planning, dietary tracking, ingredient procurement reminders, and recipe suggestions, improving a private chef's productivity on administrative and planning tasks while the human retains full responsibility for execution and service. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI tools (recipe planning, menu suggestions, scheduling apps) can support some planning aspects, but core directing of staff and real-time food service execution sees minimal AI assistance today. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI can assist with meal planning, recipe retrieval, and inventory management, the core tasks of kitchen direction—coordinating staff, making real-time adjustments, plating, and serving—require human judgment, sensory assessment, and interpersonal coordination that current AI cannot perform end-to-end at scale. |
| Task automatability | claude-sonnet-5 | 1/5 | This task requires physical presence, real-time coordination of kitchen staff, hands-on food preparation oversight, and sensory judgment (taste, presentation) that current AI cannot perform end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Private household kitchens rely on personal trust, reputation, and direct accountability for food safety and quality; wealthy clients demand human expertise and presence, and liability concerns around food preparation and service create strong friction against full automation. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No licensing requirement exists, but strong household preference for a trusted human presence, food safety accountability, and the interpersonal nature of private service create real friction against automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | AI systems for meal planning and scheduling are inexpensive, but they cannot replace the human chef's wage for the supervisory, quality-control, and service coordination aspects that define the role, making the total cost still higher than substitution value. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI substitute performing this managerial/physical task, so AI cost is not comparable to a human cook's wage for this function. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed product reliably performs kitchen direction and food service coordination as a complete task; existing AI tools address only narrow components (recipe generation, inventory) without production-grade orchestration of kitchen operations. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product manages a private household kitchen's operations, staff direction, and food presentation; this remains firmly outside current AI product capability. |
Travel with employers to vacation homes to provide meal preparation at those locations.
7CI 5–10 · exposure 0 · augmentation 13 · importance 2.2/5 · click for rater detail
Travel with employers to vacation homes to provide meal preparation at those locations.
7| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Private household employment is highly fragmented, non-digitized, and resistant to technological disruption; adoption patterns show minimal AI/automation penetration in this sector. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Private household service work is a low-digitization, highly physical sector with essentially no measured AI adoption or displacement. |
| Augmentation potential | claude-haiku-4-5-20251001 | 1/5 | AI cannot meaningfully assist with meal preparation, travel logistics, or employer interaction in ways that enhance the cook's productivity in this bespoke, relationship-driven context. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI could help with recipe planning, meal ideas, or dietary accommodation research before travel, but offers little assistance during the actual on-site cooking task. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task requires physical presence in varying unfamiliar kitchens, real-time meal planning adaptation, and responsiveness to employer preferences—none of which current AI can perform end-to-end. The travel and on-site execution components are fundamentally non-automatable today. |
| Task automatability | claude-sonnet-5 | 1/5 | This task requires physical travel and hands-on meal preparation in varied, unstructured home environments—no AI system can perform physical cooking or accompany employers on trips. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | High barriers exist: employers typically prefer personal relationships and trust with household staff, strong privacy/access concerns in private homes, and implicit human-contact expectations for intimate household services create substantial friction against automation. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No licensing requirement, but strong employer preference for a trusted, known individual physically present and personal relationship with the household creates practical friction against substitution. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | The all-in cost of deploying robotic meal-prep systems (hardware, maintenance, infrastructure) vastly exceeds the loaded wage of a private household cook, making substitution economically infeasible. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no AI substitute for this physical, mobile service, so any comparison favors the human cook by default. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed product can physically travel, provision kitchens, or prepare meals at remote locations. This requires embodied robotic capabilities far beyond current commercial systems. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed AI product travels with people and cooks meals in real kitchens; this remains entirely a human physical service. |
Serve meals and snacks to employing families and their guests.
5CI 0–10 · exposure 0 · augmentation 0 · importance 3.2/5 · click for rater detail
Serve meals and snacks to employing families and their guests.
5| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Private household employment is a low-digitization, human-intensive sector with strong preference for in-person service; no meaningful adoption of AI for meal serving is occurring or foreseeable. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Private household service is a small, low-digitization, highly personalized sector with essentially no measurable AI or robotics adoption for this task. |
| Augmentation potential | claude-haiku-4-5-20251001 | 1/5 | The task of serving meals in a household context offers no meaningful opportunity for AI assistance; it is fundamentally a human-presence and human-interaction task that does not decompose into augmentable subtasks. |
| Augmentation potential | claude-sonnet-5 | 1/5 | AI provides no meaningful assistance to the physical act of serving meals and snacks in a private household context. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | Serving meals requires physical presence, social interaction, and real-time responsiveness to guest needs and preferences—capabilities entirely outside current AI capabilities. No meaningful automation of the end-to-end task is feasible today. |
| Task automatability | claude-sonnet-5 | 1/5 | Serving physical meals and snacks to people in a home requires physical manipulation, presence, and interpersonal service that current AI systems cannot perform end-to-end; no robotic system reliably automates this in home settings today. |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | Private household employment involves direct personal service, strong employer-guest preference for human staff, and implicit expectation of human judgment and discretion that creates powerful cultural and social barriers to automation. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No licensing requirement exists, but strong physical, safety, and personal-preference barriers (families wanting a trusted person serving food in their home) create real friction against automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | The task requires physical presence and human labor; AI systems have no cost advantage for a task that is inherently embodied and human-facing in a private household setting. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI substitute for this physical task, so any hypothetical automation (e.g., robotics) would be far more expensive than a human cook performing the same service. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed product can physically serve meals or engage in the interpersonal dynamics of household meal service. This task requires embodied presence and human judgment that current AI systems cannot deliver. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | There are no deployed AI or robotic products that reliably serve meals in private households; this remains far outside current product capability. |
Related occupations — Food Preparation & Serving
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.