Dishwashers
35-9021.00Median wage $34,810/yr477,450 employed (US)Rank #607 of 923 scored · top 66% by substitution
Clean dishes, kitchen, food preparation equipment, or utensils.
Sub-scores
0–100 · band = confidence interval from rater disagreement
Substitution23
Exposure11
Augmentation5
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
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%13
panel mean rating 1.5/5 → substitution pressure 13/100
Technical feasibility todayw 20%8
panel mean rating 1.3/5 → substitution pressure 8/100
Cost vs. human wagew 15%9
panel mean rating 1.3/5 → substitution pressure 9/100
Adoption barriersw 20%inverted — strong barriers lower the score76
panel mean rating 2.0/5 (barrier strength) → substitution pressure 76/100
Sector adoption velocityw 10%4
panel mean rating 1.2/5 → substitution pressure 4/100
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.
Wash dishes, glassware, flatware, pots, or pans, using dishwashers or by hand.
51CI 15–86 · exposure 42 · augmentation 0 · importance 4.5/5 · click for rater detail
Wash dishes, glassware, flatware, pots, or pans, using dishwashers or by hand.
51| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 5/5 | Automated dishwashing has been deeply adopted for decades across food service, hospitality, healthcare, and commercial sectors; it is standard infrastructure rather than an emerging technology. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Food service is a low-digitization, physical-labor sector with minimal AI/robotics adoption for this specific task; automation here remains rare and experimental. |
| Augmentation potential | claude-haiku-4-5-20251001 | 1/5 | Dishwashing automation offers no meaningful assistive capability—machines either handle the task or do not, with little scope for human-in-the-loop augmentation of the core washing function. |
| Augmentation potential | claude-sonnet-5 | 1/5 | Current AI tools offer essentially no assistance to a human physically washing dishes; there's no meaningful software augmentation pathway for this manual task. |
| Task automatability | claude-haiku-4-5-20251001 | 4/5 | Automated dishwashers already perform this task end-to-end with substantial time savings compared to hand-washing; commercial dishwashing systems are mature, reliable, and widely deployed in restaurants, institutions, and homes. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a physical manipulation task requiring loading/unloading, scrubbing, and handling varied items in unstructured kitchen environments; no current AI (software) system performs this end-to-end, and robotics for this remain experimental.software AI time-saving does not apply directly to a physical task. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | Minimal regulatory or legal barriers exist to automated dishwashing; primary friction is capital investment for equipment and minor operational setup, but no licensing requirement or mandatory human oversight mandates this task. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing or legal requirement mandates a human dishwasher, but physical environment constraints, capital cost, and kitchen workflow integration create practical friction against automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 5/5 | Automated dishwashing is orders of magnitude cheaper per cycle than hand labor when accounting for operator wages, water, energy, and detergent costs are absorbed across many cycles; initial capital cost is recouped quickly in high-volume operations. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | Robotic dishwashing systems, where they exist, require expensive specialized hardware, integration, and maintenance far exceeding the low wage cost of a human dishwasher. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 5/5 | Commercial dishwashing machines are production-ready, proven, and operating at scale in food service, hospitality, and institutional settings; they reliably clean dishes, glassware, flatware, and cookware to health and sanitation standards. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed commercial product autonomously washes dishes in commercial kitchens at scale; robotic dishwashing exists only in narrow pilots or research demos. |
Prepare and package individual place settings.
26CI 24–29 · exposure 16 · augmentation 0 · importance 3.2/5 · click for rater detail
Prepare and package individual place settings.
26| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Dishwashing is performed primarily in small to mid-size restaurants, hotels, and catering where digitization and automation adoption are lagging. Capital constraints and the prevalence of labor in high-employment jurisdictions mean very slow actual adoption of any robotic solution. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Food service and dishwashing are low-digitization, physical-labor sectors with minimal AI/robotics adoption for this kind of task. |
| Augmentation potential | claude-haiku-4-5-20251001 | 1/5 | AI offers no meaningful assistance for place-setting preparation and packaging; the task is primarily manual dexterity and does not benefit from software augmentation, data analysis, or AI-guided decision-making. |
| Augmentation potential | claude-sonnet-5 | 1/5 | There is little role for AI to assist a human in physically arranging and packaging place settings; it's a manual, dexterity-based task with no digital component to augment. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | This task involves handling fragile items, arranging them in specific patterns, and packaging them—all of which require dexterous manipulation and spatial reasoning. While sorting and basic arrangement could theoretically be semi-automated, current robotic systems struggle with the variability, breakage risk, and fine manipulation required to achieve 50% time savings at equal quality. |
| Task automatability | claude-sonnet-5 | 2/5 | This requires physical manipulation of dishware, utensils, and linens in varied environments, which current AI/robotics cannot reliably do end-to-end with major time savings; some robotic sorting exists but not general place-setting assembly. |
| Adoption barriers | claude-haiku-4-5-20251001 | 1/5 | No licensing, liability, or regulatory barriers protect this task. It is low-skill, physically hazardous, and highly substitutable in principle, with minimal organizational friction to automation adoption if the technology were cost-effective. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing or legal barriers exist, but organizational friction and low ROI for automating a low-cost, physically variable task discourage adoption. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Custom robotic solutions for dish manipulation and packaging would cost tens of thousands to hundreds of thousands in capital and integration, vastly exceeding the loaded hourly wage of a dishwasher (typically $12–18/hour including overhead), making AI/robotics uneconomical for this task. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | Robotic manipulation systems capable of this physical task would require expensive hardware and integration, far exceeding the cost of low-wage dishwasher labor for this task. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed commercial system reliably performs end-to-end place-setting preparation and packaging at scale. Robotic arms exist for limited grasping tasks, but none integrate the full workflow of handling diverse dishware types, arranging them correctly, and secure packaging in production dishwashing environments. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed commercial product performs place-setting preparation and packaging in restaurant/food service settings; this remains a manual kitchen task. |
Receive and store supplies.
26CI 24–28 · exposure 16 · augmentation 25 · importance 3.6/5 · click for rater detail
Receive and store supplies.
26| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Dishwashing and kitchen operations remain heavily manual, low-digitization environments with small, fragmented operations. Adoption of automation in these contexts is laggard, with few pilots of supply-management robotics in real restaurants or institutional kitchens. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Foodservice and dishwashing roles are in a low-digitization, physical-labor sector with minimal AI/robotics adoption for basic material handling tasks like this. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | Digital inventory systems and tracking tools offer limited augmentation for the physical receipt and storage task itself. While a dishwasher might use a handheld device to log items, AI provides minimal productivity enhancement for the core manual labor of handling and organizing supplies. |
| Augmentation potential | claude-sonnet-5 | 2/5 | Simple digital tools (inventory apps, barcode scanners) can assist with tracking supply levels, but they offer only marginal support for the core physical receiving and storing work. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | Receiving and storing supplies involves variable physical manipulation, spatial reasoning, and inventory tracking. While AI could potentially manage inventory records digitally, the core physical task of handling diverse items with different shapes and fragility requires robotic manipulation systems that are still immature and context-dependent. Current general AI systems cannot reliably perform the full end-to-end task. |
| Task automatability | claude-sonnet-5 | 2/5 | Receiving and storing physical supplies requires perception, mobility, and manipulation of real objects in varied kitchen environments, which current AI systems cannot handle end-to-end; only inventory-logging portions could be assisted digitally. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | While there are no strict licensing requirements for receiving and storing supplies, there are modest organizational barriers: the need for human judgment about storage locations, handling of different materials safely, and real-time adaptability to kitchen layout and inventory needs create friction for full automation. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing or legal barrier exists, but practical organizational friction (kitchen layout variability, need for human judgment on spoilage/quality checks) creates some resistance to automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | Robotic systems capable of handling and storing supplies would require significant capital investment, maintenance, and integration costs that far exceed the loaded wage of a dishwasher. The cost per task-equivalent would remain substantially higher than human labor. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | Physical robotic systems capable of receiving deliveries and stocking shelves in a kitchen would cost far more than a low-wage worker performing this task, given current robotics costs and lack of off-the-shelf solutions. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed product reliably performs end-to-end receipt and storage of supplies in a general dishwashing environment. While some industrial robots and inventory systems exist for specific controlled settings, they are not production-ready for the variable, unstructured conditions of kitchen supply management. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product performs physical receiving and storage of kitchen supplies; robotic manipulation for unstructured warehouse-like tasks in restaurant back rooms remains research-stage or extremely narrow pilot deployments. |
Maintain kitchen work areas, equipment, or utensils in clean and orderly condition.
24CI 15–33 · exposure 13 · augmentation 13 · importance 4.4/5 · click for rater detail
Maintain kitchen work areas, equipment, or utensils in clean and orderly condition.
24| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Dishwashing remains predominantly manual across nearly all restaurant, institutional, and food-service sectors; adoption of autonomous cleaning robots in kitchens is negligible and concentrated only in R&D or pilot settings, not production deployment. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Food service is a low-digitization, physically intensive sector with minimal AI/robotic adoption for cleaning tasks in commercial kitchens. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | Current AI and robotic tools offer minimal assistance to human dishwashers in core cleaning and organizing tasks; commercial dish machines automate a narrow subset (machine-washable items), but do not augment human productivity on hand-washing or broader kitchen maintenance. |
| Augmentation potential | claude-sonnet-5 | 1/5 | Current AI tools offer essentially no meaningful assistance to a human performing manual cleaning and organizing of kitchen equipment and utensils. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | Physical manipulation of diverse kitchen items and equipment in unstructured environments remains a significant challenge for current robotics. While some specialized dishwashing machines exist, maintaining broad kitchen cleanliness and orderliness—including scrubbing varied surfaces, organizing items, and handling delicate equipment—does not meet the 50% time-saving bar with off-the-shelf systems today. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a physical manual labor task requiring dexterity to handle utensils, scrub surfaces, and organize physical spaces; no off-the-shelf AI or robotic system can perform this end-to-end today. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | Food safety regulations, health code compliance, and liability for contamination or damage create moderate friction, though no single licensing requirement absolutely prohibits automation. Organizational preference for human oversight and sanitation verification adds additional practical barriers. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing or legal requirement mandates a human for this task, but food safety/hygiene standards and physical space constraints create practical friction against automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | Specialized robotic systems for general kitchen cleaning and organization are significantly more expensive to acquire, maintain, and integrate than the hourly wage of dishwashing staff, particularly when accounting for setup, supervision, and failure recovery. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | Robotic dishwashing/cleaning solutions would require expensive hardware, integration, and maintenance far exceeding the low wage cost of a human dishwasher. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | Deployed robotic systems for kitchen cleaning are extremely limited; most require highly structured environments or narrow task scope. General-purpose mobile manipulators capable of autonomous kitchen maintenance at production scale do not exist in reliable commercial deployment. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | There are no deployed commercial products that autonomously clean and organize kitchen work areas and utensils at scale; kitchen robotics remain research-stage or narrow pilots. |
Place clean dishes, utensils, or cooking equipment in storage areas.
24CI 15–33 · exposure 13 · augmentation 0 · importance 4.3/5 · click for rater detail
Place clean dishes, utensils, or cooking equipment in storage areas.
24| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Dishwashing remains concentrated in small and mid-sized food service operations with limited capital for automation and high physical-environment variability; adoption of specialized robotics in this sector is minimal. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Food service is a low-digitization, physical-labor-heavy sector with minimal AI/robotics adoption for this specific task. |
| Augmentation potential | claude-haiku-4-5-20251001 | 1/5 | Current AI offers no meaningful assistance to human dishwashers performing storage placement; the task is primarily manual and spatial, with no decision-support or predictive AI tools in typical deployment. |
| Augmentation potential | claude-sonnet-5 | 1/5 | There is no meaningful AI tool that assists a human in the physical act of placing clean dishes into storage. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While robotic arms can grasp and place objects, end-to-end automation of dish storage—including variable dish types, locations, and organizational schemes—remains challenging in real kitchens. Current AI/robotic systems lack the spatial reasoning and adaptability to reliably place diverse items in appropriate storage areas at speed comparable to humans. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a physical manipulation task requiring perception, grasping, and placement of varied items in a kitchen environment, which current general-purpose AI systems cannot perform end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | No licensing requirement mandates human performance, but kitchen safety codes, liability for damage, and the need for flexible physical adaptation in diverse storage environments create modest organizational and operational friction. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing or legal barrier exists, but practical organizational friction (kitchen layout variability, item diversity, cost of robotic retrofit) limits substitution. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | Industrial robotic arms with vision systems and integration infrastructure cost tens of thousands to six figures, plus ongoing maintenance, making them substantially more expensive than minimum-wage dishwashing labor for the foreseeable future. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | Robotic manipulation hardware capable of handling diverse dishware plus integration and maintenance costs far exceed the low wage cost of a human dishwasher performing this task. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | Some prototype robotic systems for dish handling exist in research settings, but no widely deployed commercial product reliably automates this task in production kitchens. The variability of storage layouts and item types limits current product readiness. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed commercial robotic product reliably performs putting away dishes and cookware in commercial kitchen storage today; this remains research-stage robotics work. |
Sweep or scrub floors.
24CI 15–33 · exposure 13 · augmentation 0 · importance 4.2/5 · click for rater detail
Sweep or scrub floors.
24| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Food service remains a laggard sector for automation due to tight margins, small establishments, and labor availability. Dishwashing floors are not a priority for capital-intensive robot deployment; adoption is negligible outside specialized, high-volume facilities. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Food service is a low-digitization, physical-labor-heavy sector with minimal robotic floor-cleaning adoption in kitchens specifically. |
| Augmentation potential | claude-haiku-4-5-20251001 | 1/5 | AI and current robotics offer minimal assistance to a human actively sweeping or scrubbing; the task is fundamentally physical and does not benefit from algorithmic guidance or partial automation while a human remains in the loop. |
| Augmentation potential | claude-sonnet-5 | 1/5 | No meaningful AI tool currently assists a human in the physical act of sweeping or scrubbing floors. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | Floor sweeping and scrubbing involve physical manipulation in unstructured environments with variable debris, wet surfaces, and obstacle navigation. Current robotics can perform narrow instances (e.g., autonomous floor buffers in controlled spaces), but end-to-end task completion with 50% time savings and equal quality is not reliably achievable by off-the-shelf systems in typical restaurant kitchens. |
| Task automatability | claude-sonnet-5 | 1/5 | Physical floor sweeping/scrubbing requires manipulation in unstructured kitchen environments; no off-the-shelf AI system performs this end-to-end today with time savings at equal quality. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | Physical safety standards and worker presence requirements in active kitchens create friction. Health code compliance around food preparation areas and operator liability for robot-caused accidents add organizational and regulatory friction, though no strict licensing requirement mandates human performers. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing or legal barrier, but health-code sanitation requirements and irregular kitchen layouts with staff/equipment create practical friction against automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | Robotic floor-cleaning systems (hardware + maintenance + integration) typically cost tens of thousands of dollars upfront, with ongoing operational expenses that exceed the loaded hourly wage of a dishwasher in most markets. Payback periods are long for this low-skill task. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | Robotic floor scrubbers are capital-intensive and require kitchen-specific navigation/obstacle handling; far more expensive than the marginal labor cost of a dishwasher's floor duties. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | Specialized floor-cleaning robots exist but are limited to controlled, mapped environments and require significant setup and oversight. Commercial deployment in active dishwashing areas remains rare; most systems lack the dexterity and adaptability needed for real kitchen conditions with obstacles, spills, and varying floor types. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | Commercial floor-cleaning robots exist for flat, open commercial spaces but are not deployed for the cluttered, greasy, obstacle-dense back-of-house kitchen floors dishwashers clean. |
Sort and remove trash, placing it in designated pickup areas.
22CI 15–29 · exposure 8 · augmentation 0 · importance 3.8/5 · click for rater detail
Sort and remove trash, placing it in designated pickup areas.
22| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Food service remains a laggard sector in AI/automation adoption due to low margins, high variability, and prevalence of small establishments with limited capital for robotics investment. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Food service is a low-digitization, physical-labor-heavy sector with minimal AI/robotics adoption for tasks like trash sorting and disposal. |
| Augmentation potential | claude-haiku-4-5-20251001 | 1/5 | AI provides no meaningful assistance to a human removing trash; the task is manual, well-defined, and does not benefit from predictive or analytical support. |
| Augmentation potential | claude-sonnet-5 | 1/5 | There is no meaningful AI tool that assists a human dishwasher in sorting or removing trash more efficiently. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While trash sorting and removal involve repetitive physical motions, current robotics and computer vision systems struggle with the unpredictability of dish residue, manual object placement, and navigating dynamic kitchen layouts. Meaningful automation would require significant hardware investment and controlled environments. |
| Task automatability | claude-sonnet-5 | 1/5 | This requires physical manipulation of varied trash items in a kitchen environment, which is beyond current off-the-shelf AI/robotics capability for reliable, general deployment. |
| Adoption barriers | claude-haiku-4-5-20251001 | 1/5 | There are no regulatory, licensing, or organizational barriers preventing automation of trash removal; it is a lower-skill, safety-tolerant task with no human-contact requirement or legal mandate. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing or legal barrier exists, but practical barriers like kitchen environment variability, hygiene requirements, and lack of suitable robotic infrastructure limit substitution. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Automated trash-sorting hardware (vision systems, robotic arms, integration) remains substantially more expensive than a dishwasher's loaded wage, especially given the low wage floor and simple task scope. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | Robotic systems capable of this physical task would require expensive specialized hardware plus maintenance, far exceeding the low wage cost of a human dishwasher performing this task. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed, production-grade systems reliably perform autonomous trash removal in real restaurant kitchens at scale today. Experimental robotic systems exist but remain research-stage or heavily supervised in narrowly controlled settings. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed commercial product performs sorting and removal of kitchen trash reliably in restaurant settings today; this remains research-stage robotics work at best. |
Stock supplies, such as food or utensils, in serving stations, cupboards, refrigerators, or salad bars.
19CI 15–24 · exposure 8 · augmentation 13 · importance 3.9/5 · click for rater detail
Stock supplies, such as food or utensils, in serving stations, cupboards, refrigerators, or salad bars.
19| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Food service remains a laggard sector for automation; most restaurants and commercial kitchens rely on manual stocking. Physical, low-digitization environments with high labor turnover show minimal AI agent adoption. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Food service is a low-digitization, physical-labor sector with minimal AI/robotic adoption for basic stocking tasks; restaurants overwhelmingly rely on manual labor for this work. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could assist by tracking inventory levels and optimizing stocking routes, but the core physical task of moving and placing items offers limited augmentation while the human remains in the loop. |
| Augmentation potential | claude-sonnet-5 | 1/5 | Current AI offers essentially no assistance for the physical act of stocking supplies in a kitchen; there's no meaningful software or AI tool that aids this manual task. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While robots could theoretically move and place items, the task requires navigating varied kitchen layouts, handling fragile items safely, and managing diverse supply types and storage locations. Current AI systems lack reliable end-to-end physical manipulation and spatial reasoning to achieve 50% time savings at equal quality in real kitchen environments. |
| Task automatability | claude-sonnet-5 | 1/5 | This requires physical manipulation of objects (moving food, utensils, dishes) into varied storage locations, which current AI systems cannot perform without robotic embodiment, and no such robots are deployed for this task today. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | Health and safety regulations around food storage and handling impose some friction, but there is no legal requirement that a human must personally stock supplies. Organizational resistance and the cost of adapting kitchen layouts to automation are the main barriers. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing or regulatory requirement mandates a human for this task, but practical/physical barriers (unstructured environments, varied objects, human coexistence in kitchens) create friction against automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | A dishwasher's loaded wage ($15–18/hour) is substantially lower than the capital, maintenance, and operational costs of a robotic stocking system, which typically runs tens of thousands of dollars with integration and oversight overhead. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI/robotic system performing this task at any cost comparable to a low-wage human worker; specialized robotics for this narrow task would be far more expensive than human labor. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed commercial products reliably perform this task autonomously today. While pick-and-place robots exist in controlled warehouses, deploying them to restock kitchen supplies with the required flexibility, safety, and speed is not demonstrated in production at scale. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed products autonomously stock kitchen supplies in restaurants; this remains firmly in the physical labor domain with no commercial robotic solution in production. |
Clean or prepare various foods for cooking or serving.
17CI 10–24 · exposure 8 · augmentation 13 · importance 3.8/5 · click for rater detail
Clean or prepare various foods for cooking or serving.
17| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Adoption of AI-driven food prep automation remains minimal across the dishwashing and prep workforce; most kitchens still rely on manual labor, and capital investment in specialized robotics is limited to a small number of high-volume operations. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Food service is a low-digitization, physical-labor-heavy sector with minimal AI/robotic adoption for prep tasks; automation here remains rare and experimental. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI tools could theoretically assist with recipe lookup or food safety reminders, but current systems offer minimal productivity enhancement for the core manual dexterity and sensory judgment required to clean and prepare foods safely and correctly. |
| Augmentation potential | claude-sonnet-5 | 1/5 | Current AI tools offer essentially no meaningful assistance to a human physically cleaning or prepping food items. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While some food prep (e.g., cutting uniform vegetables) could theoretically be automated with specialized equipment, the task encompasses diverse food types, variable conditions, and quality judgment that current general-purpose AI systems cannot reliably handle end-to-end. Most automation would require significant task-specific engineering rather than off-the-shelf AI. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a physical manipulation task involving handling, washing, cutting, and prepping diverse food items with variable shapes and textures, which current AI systems (software or robotics) cannot perform end-to-end reliably. |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | Food safety regulations (HACCP, local health codes) and liability for contamination create moderate friction, though automation itself is not legally barred. Customer expectations and the physical dexterity requirements also create practical barriers to wholesale substitution. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement, but food safety regulations, kitchen space constraints, and the need for flexible human dexterity create moderate practical friction against automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Specialized food-prep robotics (where they exist) remain capital-intensive and slow; integrated cost per task remains well above the hourly wage of a dishwasher or prep worker in most markets. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | Robotic food-prep systems capable of general food handling are expensive, require significant integration and maintenance, and are far costlier than a low-wage human worker for this task. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed commercial AI product reliably performs general food cleaning and preparation for cooking at restaurant or institutional scale. Robotic systems exist for narrow subtasks (e.g., peeling potatoes) but are not general-purpose solutions available in production today. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed general-purpose product performs food cleaning/prep reliably in commercial kitchens; food robotics remains narrow, research-stage, or limited to single fixed tasks like flipping burgers. |
Load or unload trucks that deliver or pick up food or supplies.
15CI 15–15 · exposure 0 · augmentation 0 · importance 4.0/5 · click for rater detail
Load or unload trucks that deliver or pick up food or supplies.
15| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Dishwashing occurs in laggard sectors (food service, hospitality, small to medium facilities) with low automation budgets, high fragmentation, and low digitization; robotics adoption in this domain remains negligible in production. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Food service and hospitality sectors have very low physical automation adoption; this task lags far behind digital/information tasks in AI deployment. |
| Augmentation potential | claude-haiku-4-5-20251001 | 1/5 | Current AI and robotics offer no meaningful assistance to a human performing manual truck loading and unloading; the task is purely physical manipulation where human judgment and strength are primary. |
| Augmentation potential | claude-sonnet-5 | 1/5 | Current AI tools offer essentially no assistance for the physical labor of loading or unloading trucks. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | Loading/unloading trucks requires physical manipulation of varied objects in unstructured environments, precise spatial reasoning, and real-time adaptation to weight distribution and damage prevention—capabilities far beyond current AI robotic systems in real-world deployment. |
| Task automatability | claude-sonnet-5 | 1/5 | Physical loading/unloading of trucks with varied food and supply items requires manipulation, sensing, and mobility that current general-purpose AI and robotics cannot perform reliably or affordably in dynamic kitchen/loading environments. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | This is a low-skill, non-licensed task with minimal regulatory barriers and no requirement for specialized credentials, making it vulnerable to substitution once technical feasibility improves, though physical site constraints provide some friction. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement, but physical workspace constraints, safety concerns around heavy lifting near vehicles, and lack of standardized loading environments create moderate practical friction. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Specialized robotics capable of truck loading would cost hundreds of thousands of dollars in capital and maintenance, vastly exceeding the minimum wage cost of a human dishwasher performing this task over the same period. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | Any robotic solution capable of this task would require expensive custom hardware and integration, far exceeding the low wage cost of a human dishwasher performing occasional loading tasks. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No commercially deployed general-purpose robot systems reliably perform this task at scale in production food service or supply chains; narrow robotics research exists but does not meet the reliability threshold for dishwasher workflows. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product performs general truck loading/unloading of mixed food and supplies in restaurant or institutional settings; existing robotic warehouse solutions are narrow, structured, and not deployed for this specific task. |
Transfer supplies or equipment between storage and work areas, by hand or using hand trucks.
15CI 15–15 · exposure 0 · augmentation 0 · importance 3.7/5 · click for rater detail
Transfer supplies or equipment between storage and work areas, by hand or using hand trucks.
15| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Dishwashing remains a labor-intensive, low-digitization sector with minimal robotics adoption relative to task volume. The small-firm and high-turnover nature of food service slows capital-intensive automation investment. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Food service is a low-digitization, physical-labor-heavy sector with minimal automation adoption for material handling tasks like this. |
| Augmentation potential | claude-haiku-4-5-20251001 | 1/5 | Current AI offers no meaningful assistance to a human transferring supplies or equipment by hand; the task is purely physical manipulation with no decision-support or analytical component that AI can enhance. |
| Augmentation potential | claude-sonnet-5 | 1/5 | Current AI tools offer no meaningful assistance for the physical act of carrying items between storage and work areas. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task requires physical manipulation and transport of items in dynamic, varied environments with spatial reasoning. Current AI systems lack embodied robotics deployment at scale and cannot reliably grasp diverse dishware and equipment or navigate complex kitchen spaces autonomously. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a physical manipulation and transport task requiring mobility, dexterity, and navigation in dynamic kitchen environments, which current AI systems (software-based) cannot perform; robotics for this remain research/pilot stage.4 |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | Limited regulatory barriers exist, but organizational friction is moderate: spaces are tight, unpredictable, and mixing automation with human kitchen staff creates safety and coordination challenges. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing or legal barriers exist, but practical organizational and physical-environment barriers (uneven layouts, tight spaces, mixed tasks) make substitution difficult without significant infrastructure investment. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Industrial robots capable of handling fragile dishware and navigating storage areas cost tens of thousands of dollars plus integration, while dishwashing labor remains inexpensive, making automation far more costly than human performance. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | Robotic solutions capable of this kind of flexible physical transport would require expensive hardware, sensors, and maintenance, far exceeding the low wage cost of a human dishwasher performing this task. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed, reliable product routinely performs this task in dishwashing environments today. Mobile manipulation in unstructured kitchens remains a research challenge without production systems in active use. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed commercial product reliably performs general-purpose carrying of kitchen supplies/equipment between storage and work areas in restaurant settings today. |
Clean garbage cans with water or steam.
15CI 15–15 · exposure 0 · augmentation 0 · importance 3.6/5 · click for rater detail
Clean garbage cans with water or steam.
15| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Food service and hospitality sectors where dishwashers work remain labor-intensive with low automation rates for ancillary manual tasks; capital constraints and operational simplicity favor human workers for this specific task. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Food service and sanitation work is a low-digitization, physical-labor sector with minimal AI/robotic adoption for cleaning tasks of this kind. |
| Augmentation potential | claude-haiku-4-5-20251001 | 1/5 | Current AI systems offer no meaningful assistance in physically cleaning garbage cans; the task requires embodied action rather than cognitive augmentation. |
| Augmentation potential | claude-sonnet-5 | 1/5 | AI tools offer no meaningful assistance to a human physically cleaning garbage cans with water or steam. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | Cleaning garbage cans requires navigating physical environments, handling unpredictable waste materials, and adapting to variable can sizes and conditions—capabilities current AI systems lack. No off-the-shelf robotic or software system achieves 50% time savings on this manual, physically-situated task. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a physical manual cleaning task requiring mobility, water/steam handling, and physical scrubbing that current AI systems cannot perform end-to-end; no software or general-purpose robot can do this today at scale. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | The task is manual and typically non-licensed, so regulatory barriers are minimal, but health/safety considerations and organizational reliance on simple human labor create moderate friction against automation. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing or legal requirement mandates a human perform this, but physical environment variability and hygiene/safety considerations create moderate practical friction against automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Specialized robotic systems capable of this task would require significant capital investment and maintenance, far exceeding the hourly wage of a dishwasher performing manual cleaning with standard equipment like water or steam. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI/robotic solution deployed for this task, so any hypothetical automation would require expensive custom robotics far exceeding the low wage cost of manual labor. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed commercial product reliably performs autonomous garbage can cleaning at scale. While industrial robots exist in controlled settings, none operate reliably in the outdoor, unstructured environments where garbage cans are typically located. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed commercial product performs autonomous garbage can cleaning with water or steam; this remains outside current robotics deployment even at research stage for this specific task. |
Set up banquet tables.
15CI 15–15 · exposure 0 · augmentation 0 · importance 3.2/5 · click for rater detail
Set up banquet tables.
15| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Hospitality and food service sectors show minimal AI/robotic adoption for table setup; labor remains the default approach due to task complexity and low margins. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Food service and hospitality are among the least digitized, lowest AI-adoption sectors, with virtually no robotic deployment for physical setup tasks. |
| Augmentation potential | claude-haiku-4-5-20251001 | 1/5 | Current AI systems offer no meaningful assistance with physically setting up banquet tables; the task is entirely manual and hands-on with no digital augmentation opportunity. |
| Augmentation potential | claude-sonnet-5 | 1/5 | Current AI tools offer no meaningful assistance for the physical act of arranging banquet tables and furniture. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | Setting up banquet tables requires physical manipulation of heavy furniture, precise spatial arrangement, and aesthetic judgment that current robotic systems cannot reliably perform without extensive custom engineering; no off-the-shelf AI or automation achieves 50% time savings. |
| Task automatability | claude-sonnet-5 | 1/5 | Physically setting up banquet tables requires mobile manipulation, carrying, and precise placement in varied physical environments, which is beyond current AI/robotic systems deployed in food service. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | There are minimal regulatory barriers, but the physical complexity and lack of deployable automation create practical adoption friction; banquet service remains labor-intensive by default. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing or legal barrier exists, but physical environment variability, lack of robotic infrastructure, and the need for adaptable manipulation create strong practical barriers to automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Robotic systems capable of this task would cost tens of thousands of dollars plus integration, while a dishwasher performing it earns modest wages, making automation prohibitively expensive. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI/robotic system for this task, so any theoretical solution would require expensive custom robotics far exceeding low-wage human labor costs. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed products exist that can autonomously set up banquet tables at commercial scale; the task involves dexterous manipulation, layout decisions, and environmental adaptation beyond current robotic capabilities. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product performs banquet table setup; this remains a manual physical task with no commercial robotics solution in restaurants or event venues. |
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.