Cooks, Short Order
35-2015.00Median wage $35,880/yr138,650 employed (US)Rank #575 of 923 scored · top 62% by substitution
Prepare and cook to order a variety of foods that require only a short preparation time. May take orders from customers and serve patrons at counters or tables.
Sub-scores
0–100 · band = confidence interval from rater disagreement
Substitution24
Exposure16
Augmentation25
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
11 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
9%
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%19
panel mean rating 1.7/5 → substitution pressure 19/100
Technical feasibility todayw 20%11
panel mean rating 1.4/5 → substitution pressure 11/100
Cost vs. human wagew 15%8
panel mean rating 1.3/5 → substitution pressure 8/100
Adoption barriersw 20%inverted — strong barriers lower the score66
panel mean rating 2.3/5 (barrier strength) → substitution pressure 66/100
Sector adoption velocityw 10%9
panel mean rating 1.4/5 → substitution pressure 9/100
Task breakdown (11 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.
Accept payments, and make change or write charge slips as necessary.
83CI 80–86 · exposure 84 · augmentation 38 · importance 3.1/5 · click for rater detail
Accept payments, and make change or write charge slips as necessary.
83| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 5/5 | Payment automation is ubiquitous in food service; nearly all restaurants have adopted digital POS systems and contactless payment options, representing deep and fast adoption across the sector. |
| Sector adoption velocity | claude-sonnet-5 | 4/5 | Fast food and short-order restaurant chains have rapidly adopted self-service kiosks, card readers, and automated payment systems, representing one of the most digitized parts of the low-margin food service sector. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | Once payment systems are deployed, they largely replace rather than augment the cook's role in handling payments. The system does the work rather than assisting a human performing it; augmentation potential is minimal. |
| Augmentation potential | claude-sonnet-5 | 3/5 | Automated payment systems assist the worker by speeding transactions and reducing errors, but the task itself is often fully replaced rather than merely augmented. |
| Task automatability | claude-haiku-4-5-20251001 | 4/5 | Payment acceptance and change-making are largely automatable via point-of-sale systems and contactless payment processors. Current AI-driven POS systems can handle the transaction workflow end-to-end with >50% time savings compared to manual cash handling and calculation. |
| Task automatability | claude-sonnet-5 | 4/5 | Taking payments and making change is a well-defined transactional task already handled end-to-end by POS systems, self-checkout kiosks, and card readers with minimal human input. Writing charge slips manually is largely obsolete, replaced by automated receipt printing. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | While POS systems require some integration and compliance with payment card industry standards, there are minimal regulatory barriers to automation of payment acceptance itself. No licensed human is legally required to process payments in this context. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing or legal requirement mandates a human cashier; some friction exists from customer preference, cash handling exceptions, and occasional need for human judgment on charge discrepancies. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 5/5 | Modern POS terminals and payment processors cost a small fraction of the loaded wage for a full-time payment handler, and the per-transaction cost through automation is orders of magnitude lower than manual processing with human oversight. |
| Cost vs. human wage | claude-sonnet-5 | 4/5 | Automated payment terminals and kiosks cost far less per transaction than paying a worker's time for this narrow sub-task, though there is upfront hardware/software investment. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 5/5 | Deployed POS systems in restaurants reliably perform payment acceptance, change calculation, and charge slip generation at scale in production environments. This is mature, proven technology used daily across the food service industry. |
| Technical feasibility today | claude-sonnet-5 | 5/5 | POS terminals, self-checkout kiosks, and integrated payment systems are mature, widely deployed products used at scale across restaurants and fast-food chains today. |
Order supplies and stock them on shelves.
35CI 30–40 · exposure 25 · augmentation 50 · importance 3.7/5 · click for rater detail
Order supplies and stock them on shelves.
35| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Food service remains relatively low-tech and labor-intensive; while some larger chains use inventory software, most short-order cook settings operate with manual supply ordering and stocking practices. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Food service is a lower-digitization, high-turnover sector where inventory automation adoption is slow and uneven, especially in small independent short-order establishments. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI-assisted inventory tracking systems can help cooks identify when supplies are low and suggest reorder quantities, moderately improving efficiency in the ordering and planning phase without removing the human from the task. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI-based inventory tracking and reorder point calculations can meaningfully assist staff in determining what and when to order, even though the physical stocking is unaffected. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI could theoretically manage inventory tracking and ordering via systems integration, the physical act of stocking shelves and real-time assessment of stock levels in a kitchen environment requires on-site presence and manual handling that current AI cannot perform end-to-end. |
| Task automatability | claude-sonnet-5 | 2/5 | Ordering supplies involves inventory assessment and vendor interaction that AI can partially support (e.g., reorder suggestions), but physically stocking shelves is a manual task AI cannot perform, capping overall automation potential. |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | Kitchen operations and supply chain management have some standardization, but food safety regulations, supplier relationships, and the need for on-site physical handling create moderate friction against full automation. |
| Adoption barriers | claude-sonnet-5 | 1/5 | No licensing, regulatory, or liability barriers prevent using software to assist with ordering or having staff stock shelves; it's a routine operational task. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | Current inventory software costs plus required human oversight and physical execution make the all-in cost comparable to or higher than having a cook perform simple supply ordering and stocking tasks as part of their workflow. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | Software for automated ordering can be cheap, but the physical labor of stocking shelves still requires a human, so the overall task retains significant human cost with only marginal software savings. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | Deployed inventory management systems exist but typically require manual data entry or barcode scanning by humans; autonomous physical stock-placement and shelf organization in kitchens are not reliably performed by any production system today. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Inventory management software and AI-driven reorder systems exist and are used in some restaurants, but they are not universally deployed for short order cook settings and the physical stocking remains entirely manual. |
Plan work on orders so that items served together are finished at the same time.
25CI 24–26 · exposure 16 · augmentation 38 · importance 4.6/5 · click for rater detail
Plan work on orders so that items served together are finished at the same time.
25| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Food service, especially short-order cooking, operates in small, locally-owned establishments with low digitization and minimal AI investment. Adoption of even basic kitchen management software remains inconsistent in the sector. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Fast food and quick-service kitchens are adopting kitchen display systems and order-sequencing software, but actual cooking/timing execution remains manual with slow deep automation. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | A simple display showing recommended start times for each dish could mildly assist a cook, but current AI systems are not reliable enough to provide meaningful timing guidance that a cook would trust under pressure without extensive manual verification. |
| Augmentation potential | claude-sonnet-5 | 3/5 | Kitchen display systems and order management software can help sequence and prioritize tickets, assisting the cook's mental planning even though execution remains human. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | Coordinating timing across multiple dishes requires real-time kitchen state awareness and dynamic adjustment. While AI could theoretically plan a static sequence, the unpredictable nature of cook times, equipment availability, and order arrivals means current systems cannot reliably coordinate outputs to finish simultaneously without continuous human oversight, falling well short of 50% time savings at equal quality. |
| Task automatability | claude-sonnet-5 | 2/5 | This requires real-time physical timing coordination across a working griddle/fryer station synchronized with unpredictable order flow, which current AI cannot execute end-to-end without robotic embodiment.' |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | Short-order cooks work in fast-paced, physical environments with high customer interaction and liability for food quality and timing; kitchens vary dramatically in layout and equipment. The need for situational judgment and physical presence creates friction, though no formal licensing requirement explicitly prevents automation attempts. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement, but physical execution and food safety liability mean a human must actually cook and manage the line, limiting substitution regardless of software planning aids. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | The infrastructure cost (kitchen sensors, integration, real-time monitoring systems, human oversight of AI recommendations) far exceeds what a single short-order cook's labor saves, especially in the small establishments where this task is most common. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI substitute performing the physical cooking and timing task, so cost comparison favors the human by default. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed product reliably performs end-to-end order timing coordination in live kitchens today. Computer vision systems exist but lack the contextual reasoning to account for variable prep times, substitutions, and kitchen bottlenecks needed to synchronize disparate items. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product plans and executes short-order cooking timing in a live kitchen; kitchen display systems help sequence tickets but don't perform the physical cooking coordination itself. |
Grill, cook, and fry foods such as french fries, eggs, and pancakes.
19CI 15–24 · exposure 8 · augmentation 25 · importance 4.5/5 · click for rater detail
Grill, cook, and fry foods such as french fries, eggs, and pancakes.
19| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Adoption of cooking automation in short-order kitchens remains negligible; the sector skews toward small, cash-constrained establishments with low digitization, making it a laggard in AI/automation deployment. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Food service is a physical, lower-digitization sector with minimal robotic cooking deployment; adoption of AI/robotics for actual food preparation remains at pilot stage in a handful of chains. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could assist in task scheduling or recipe reminders, but meaningful assistance—such as real-time visual guidance on doneness—is not yet reliable enough in production kitchen environments. The high-speed, safety-critical nature limits practical augmentation today. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI can assist with order timing, recipe standardization, or kitchen display systems, but offers little direct assistance to the physical act of grilling and frying foods. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While some elements like timing and temperature control could theoretically be automated, current AI systems cannot reliably handle the full workflow: visual assessment of doneness, spatial coordination on a grill, adaptive response to food variation, and safe equipment operation. Robotic systems exist but are not yet cost-effective or reliable enough for the full short-order cook task. |
| Task automatability | claude-sonnet-5 | 1/5 | This requires physical manipulation of food, heat, and equipment in a kitchen environment—current AI systems have no general-purpose robotic capability to perform this reliably. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | Food safety and health code compliance creates some regulatory friction, and customer expectations favor human-cooked meals, but no hard licensing requirement prevents automation attempts. The primary barriers are technical and economic rather than legal. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement for cooking, but food safety regulations, equipment safety standards, and customer/operator trust in kitchen environments create some friction against wholesale automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Specialized robotic systems capable of any meaningful portion of grilling/frying are prohibitively expensive compared to a short-order cook's wage, particularly given equipment maintenance, integration complexity, and space constraints in typical kitchens. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | Robotic cooking systems capable of this variety of tasks require expensive specialized hardware, installation, and maintenance, making them costlier than a short-order cook's wage in most settings today. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No mature, deployed products perform this task reliably in production short-order kitchen environments at scale. Robotic cooking research exists but remains in pilot or development phases; general-purpose AI cannot physically manipulate a grill or fryer today. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed products perform full short-order cooking end-to-end in commercial kitchens; existing robotic fry/grill demos are narrow, experimental, or limited to single-item pilot installations. |
Perform food preparation tasks, such as making sandwiches, carving meats, making soups or salads, baking breads or desserts, and brewing coffee or tea.
19CI 15–24 · exposure 8 · augmentation 25 · importance 4.4/5 · click for rater detail
Perform food preparation tasks, such as making sandwiches, carving meats, making soups or salads, baking breads or desserts, and brewing coffee or tea.
19| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Food service, especially short-order cooking, remains labor-intensive, low-digitization, and concentrated in small establishments. Adoption of AI/robotic cooking automation is minimal; the sector has neither capital intensity nor digitization patterns favoring rapid AI uptake. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Food service is a physical, low-digitization sector with minimal AI/robotic adoption for hands-on food prep tasks; pilots exist but are rare and localized. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could assist with recipe lookup, timing alerts, or ingredient inventory, but these are peripheral to the core task of hands-on food preparation. No AI system meaningfully amplifies a cook's ability to physically prepare, season, and plate food faster or better. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI can assist with recipe suggestions, inventory tracking, or order sequencing, but offers little direct help with the physical act of preparing sandwiches, carving meats, or baking. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While some preparatory steps (e.g., following a recipe, measuring ingredients) could be partially automated, the physical manipulation of food items, real-time quality assessment, and adaptability to ingredient variation require dexterity and sensory judgment that current AI systems cannot reliably perform end-to-end. Robotics for food preparation exist but are narrow, slow, and error-prone in production settings. |
| Task automatability | claude-sonnet-5 | 1/5 | This requires physical manipulation of food items (cutting, assembling, cooking) in a real kitchen environment, which current AI systems cannot perform end-to-end without robotic embodiment that is not commercially available. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | Food safety regulations and health codes require direct human accountability for food preparation in most jurisdictions, and consumer/establishment expectations strongly favor human preparation. However, barriers are not absolute legal licensing requirements, and some component tasks (e.g., pre-plating assembly) face less friction. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement blocks automation, but food safety regulations, equipment certification, and customer expectations for freshly prepared food create moderate friction against wholesale automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Current food-preparation robotics (where they exist) are expensive to acquire, integrate, and maintain, while a short-order cook's wage remains low and labor is abundant. The capital and operating costs of automation far exceed the loaded wage of the human worker. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | Physical food-prep robotics remain far more expensive to develop, install, and maintain than the loaded wage of a short-order cook, with no economical off-the-shelf solution. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed product reliably performs the full range of short-order food preparation tasks (sandwich making, meat carving, soup/salad composition, baking, beverage brewing) at production speed and quality. Research robots and narrow-task automation exist, but nothing in commercial use handles the full scope of this role. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product autonomously prepares diverse short-order food items like sandwiches, soups, or baked goods at commercial kitchens today; existing food robots are narrow, experimental, or limited to single items like burger flipping. |
Perform general cleaning activities in kitchen and dining areas.
19CI 15–24 · exposure 8 · augmentation 13 · importance 4.4/5 · click for rater detail
Perform general cleaning activities in kitchen and dining areas.
19| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Adoption of cleaning automation in restaurant kitchens remains negligible; the food service sector is a laggard in robotics adoption due to high variability, cost constraints, and operational complexity. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Food service is a low-digitization, physical-labor-heavy sector with minimal AI/robotics adoption for cleaning tasks specifically. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI offers minimal augmentation for general kitchen cleaning; scheduling tools or inventory tracking might help coordination, but current systems do not meaningfully enhance the worker's core cleaning productivity. |
| Augmentation potential | claude-sonnet-5 | 1/5 | Current AI tools offer essentially no assistance to a human performing manual kitchen and dining cleaning. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While some kitchen cleaning tasks (e.g., scheduling or monitoring) could be partially automated, the physical manipulation of varied surfaces, equipment, and materials in real kitchen environments remains beyond practical current automation. Significant setup and specialized robotics would be required for even partial end-to-end automation. |
| Task automatability | claude-sonnet-5 | 1/5 | General kitchen and dining cleaning requires physical manipulation of varied surfaces, dishes, and equipment, which current AI systems (software or robotics) cannot perform end-to-end today. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | While there are few direct regulatory barriers to kitchen automation, health and safety codes implicitly assume human oversight, and customer expectations favor human-maintained cleanliness. Physical and technical barriers remain substantial rather than legal ones. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing or legal requirement mandates a human clean, but food-safety and sanitation standards create some organizational caution around automation reliability. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Current robotic cleaning systems capable of kitchen work are prohibitively expensive to acquire, deploy, and maintain compared to the loaded wage of a short-order cook or dishwasher performing these tasks. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | Physical cleaning robots capable of this task are not commercially viable substitutes, making any AI-based approach far more expensive than human labor for equivalent output. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed product reliably performs general kitchen and dining area cleaning end-to-end today. Prototype cleaning robots exist in research settings, but production systems that handle the variety and dexterity demands of kitchen environments are not in active use at scale. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product reliably performs general restaurant kitchen/dining cleaning; commercial cleaning robots handle narrow floor-sweeping tasks only, not the full scope described. |
Take orders from customers and cook foods requiring short preparation times, according to customer requirements.
19CI 10–29 · exposure 13 · augmentation 38 · importance 4.2/5 · click for rater detail
Take orders from customers and cook foods requiring short preparation times, according to customer requirements.
19| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Restaurants, especially short-order establishments, are among the slowest to digitize. Most operate with minimal automation; adoption of autonomous cooking systems remains negligible and is not a sector-wide pattern. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Food service, especially quick-serve and diner-style kitchens, has very low AI/robotics adoption for actual cooking tasks; most innovation is in ordering/kiosk systems, not the physical cooking itself. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI could assist short-order cooks through order-management systems, prep-time optimization, and kitchen-display systems that prioritize and batch orders. These are already in limited use and improve workflow, though they do not transform productivity as dramatically as in information-intensive roles. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI can assist with order-taking, ticket management, and timing via kitchen display systems, but offers little augmentation to the actual physical act of short-order cooking. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI could theoretically take written orders and optimize cooking logistics, the task requires real-time interaction with customers, handling spoken/special requests, and adapting to kitchen constraints—all of which current AI systems struggle to coordinate reliably end-to-end. Current systems cannot safely operate kitchen equipment or manage the immediate customer-facing context at scale. |
| Task automatability | claude-sonnet-5 | 1/5 | Requires physical manipulation of food, grilling, frying, and plating in a real kitchen environment—current AI systems cannot physically cook food or handle the sensory judgment involved. |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | Health and food-safety regulations require human oversight; customer preference for human interaction remains strong; and liability for foodborne illness creates meaningful friction. However, no explicit licensing barrier prevents a restaurant from deploying autonomous cooking if it met safety standards. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement for cooking itself, but health/safety codes, equipment liability, and customer expectations for fast, customized food create moderate friction against automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | The capital and integration costs of robotic kitchen systems, combined with the wages of short-order cooks (typically $25–30k/year), make AI significantly more expensive per task-equivalent today. Inference costs for order-taking alone do not justify the full automation investment. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | Specialized cooking robots and automation hardware require high capital investment, maintenance, and kitchen redesign, making them far more expensive than a short-order cook's wage for equivalent flexible output today. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | No deployed product reliably handles the full pipeline of order-taking, kitchen communication, and short-order cooking execution. Chatbots exist for order-taking, but they fail on nuance; robotic cooking systems are research-stage and not production-ready in typical restaurant kitchens. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product autonomously takes orders and cooks short-order food end-to-end; kitchen robotics remain experimental/limited pilot deployments (e.g., burger-flipping robots) not generalized to full short-order cooking. |
Grill and garnish hamburgers or other meats, such as steaks and chops.
17CI 10–24 · exposure 8 · augmentation 13 · importance 4.7/5 · click for rater detail
Grill and garnish hamburgers or other meats, such as steaks and chops.
17| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Adoption in quick-service restaurants remains minimal; most remain labor-dependent with limited automation beyond simple equipment like griddles. The sector is labor-intensive with high turnover, but robotic deployment is still experimental rather than mainstream. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Restaurant and food service is a low-digitization, physical-labor sector with minimal AI/robotics adoption for actual cooking tasks; automation here remains largely experimental. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could assist with timing and doneness prediction via computer vision, but current systems offer minimal augmentation to the core grilling and garnishing workflow. The physical manipulation and judgment involved remain largely human-dependent. |
| Augmentation potential | claude-sonnet-5 | 1/5 | Current AI offers essentially no assistance to a human physically grilling and garnishing meats, as this is a hands-on manual task with no digital interface for AI to enhance. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While robotic arms can physically grill meat, current AI and automation systems lack reliable real-time perception of doneness, temperature management, and consistent garnishing quality at the speed and variation required in production. The task remains significantly manual despite partial automation research. |
| Task automatability | claude-sonnet-5 | 1/5 | Grilling and garnishing meats requires physical manipulation, heat judgment, and dexterity that current AI systems (software-based) cannot perform; this is a physical/robotic task far beyond deployed AI capability.value.description.description.description.description.value.description.description.description.description.description.description.description.description.description.description.description.description.description.description.description.description.description.description.description.description.description.description.description.description |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | Health and safety regulations, food handling licensing, and customer expectations for human food preparation create moderate friction, though not explicit legal prohibition of automation. Liability and quality consistency concerns add organizational resistance. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement exists for grilling, but food safety standards, kitchen workflow integration, and customer expectations around fresh-cooked food create moderate friction against automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Current robotic systems capable of grilling are capital-intensive ($100k+) with high integration costs, far exceeding the loaded wage of a short-order cook, and require ongoing maintenance and oversight that narrows the cost advantage. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | Robotic grilling systems require expensive specialized hardware, installation, and maintenance far exceeding the cost of a short-order cook's wage for equivalent throughput and flexibility. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed production systems reliably handle the full task of grilling and garnishing to short-order restaurant standards today. Robotic grilling exists in research/prototypes but has not achieved reliable commercial deployment at scale in typical restaurant kitchens. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No commercially deployed robotic system reliably grills and garnishes short-order meats in production kitchens today; existing food-robotics demos remain narrow, experimental, and limited to single restaurants or pilot programs. |
Restock kitchen supplies, rotate food, and stamp the time and date on food in coolers.
17CI 10–24 · exposure 8 · augmentation 25 · importance 4.7/5 · click for rater detail
Restock kitchen supplies, rotate food, and stamp the time and date on food in coolers.
17| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Kitchen automation adoption remains low in short-order cooking environments, which are typically small operations with tight margins, legacy setups, and heavy reliance on manual labor. No evidence of production-scale robotic systems in this sector. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Food service kitchens are a low-digitization, physical-labor sector with minimal AI/robotics adoption for routine stocking and labeling tasks. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI-assisted inventory tracking or mobile stamping apps could provide minor productivity gains, but the physical nature of the task and close human supervision already required limit meaningful augmentation opportunities. |
| Augmentation potential | claude-sonnet-5 | 2/5 | Simple digital tools or inventory apps could help track expiration and restocking needs, but the physical stamping and rotation must still be performed manually with little AI assistance. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While stamping dates could be partially automated with vision systems and robotic arms, the full task requires physical dexterity, spatial reasoning, and real-time decision-making about food rotation and inventory levels that current AI struggles with in unstructured kitchen environments. Significant setup and safety concerns would be needed. |
| Task automatability | claude-sonnet-5 | 1/5 | This requires physical manipulation of food items, coolers, and stamps in a kitchen environment, which current AI systems cannot perform without embodied robotics that are not deployed for this purpose. |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | Food safety regulations and health codes mandate proper inventory management and dating, but do not legally require a human to perform the task; however, liability concerns around food handling and spoilage, plus organizational preference for human oversight of food quality, create moderate friction. |
| Adoption barriers | claude-sonnet-5 | 2/5 | Food safety regulations require accurate date labeling and proper rotation (FIFO), but this is a procedural requirement rather than a licensing requirement restricting who can perform it, so it's a moderate rather than hard barrier. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | A robotic system capable of handling diverse food items, navigating coolers, and performing precise stamping would cost far more than the wage of a short-order cook performing these routine tasks, especially with integration and maintenance overhead. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI-only solution for this physical task, so any automation would require expensive robotics far exceeding the cost of a short order cook's wage for this task. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed product reliably performs all three sub-tasks (restocking, rotating, stamping) end-to-end in a live kitchen setting. Existing shelf-management and inventory systems lack the physical manipulation and contextual judgment required. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product performs physical restocking, food rotation, or date-stamping in commercial kitchens; this remains firmly a manual task. |
Complete orders from steam tables, placing food on plates and serving customers at tables or counters.
17CI 5–29 · exposure 13 · augmentation 13 · importance 4.6/5 · click for rater detail
Complete orders from steam tables, placing food on plates and serving customers at tables or counters.
17| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Short-order cooking is primarily in small to mid-sized restaurants and diners with limited capital for automation; the sector digitizes slowly and remains heavily reliant on manual labor. Current automation adoption in this specific task is minimal. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Food service is a low-digitization, physical-labor sector with minimal AI/robotics adoption for plating and serving tasks; automation here lags far behind information-sector adoption. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could assist with order tracking displays or expediting systems, but the physical act of plating and serving leaves little room for meaningful AI augmentation unless it integrates with robotic hardware that is not yet commonplace in short-order kitchens. |
| Augmentation potential | claude-sonnet-5 | 1/5 | Current AI offers essentially no assistance to the physical acts of plating food and carrying it to customers, as this is a manual, in-person task. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While robotic arms can theoretically plate food and industrial systems can portion from steam tables, current deployed solutions require significant infrastructure customization, precise item recognition, and handling of variable plating requests. End-to-end automation at ≥50% time savings with equal quality is not yet standard practice in short-order environments. |
| Task automatability | claude-sonnet-5 | 1/5 | This requires physical dexterity to grasp utensils, portion food from steam tables, and physically deliver plates to customers—no off-the-shelf AI system can perform this physical manipulation and service task today. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Restaurants prioritize customer experience and human touch; liability for food handling errors (contamination, burns, allergies) creates asymmetric risk; health codes often assume human responsibility. These regulatory and reputational barriers slow automation adoption in food service. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing barrier exists, but physical workspace constraints, food safety handling requirements, and customer-facing service expectations create moderate friction against automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Custom robotic plating systems, integration, and maintenance remain significantly more expensive than hiring a short-order cook, especially given the need for human oversight, repairs, and the relatively modest labor cost of this role. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | Robotic systems capable of this physical task would require expensive specialized hardware and integration, far exceeding the cost of a low-wage short-order cook for equivalent output. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | No widely deployed commercial product reliably handles the full task—taking an order, retrieving varied hot items from steam tables, plating to customer specification, and serving at tables—without human intervention. Robotic prototypes exist but lack the real-world reliability and integration needed for production short-order settings. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed AI/robotic product reliably plates food from steam tables and serves customers in commercial kitchens; this remains research-stage in robotics manipulation of unstructured food items. |
Clean food preparation equipment, work areas, and counters or tables.
14CI 5–24 · exposure 8 · augmentation 13 · importance 4.8/5 · click for rater detail
Clean food preparation equipment, work areas, and counters or tables.
14| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Restaurants, especially short-order establishments, operate with low-margin operations, minimal digitization, and physical labor constraints that have shown slow AI adoption; manual cleaning remains the norm across most food service. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Food service is a low-digitization, physical-labor-heavy sector with minimal automation of manual cleaning tasks; adoption of robotics for this specific function is negligible industry-wide. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI-powered scheduling or monitoring of cleaning tasks could offer minor assistance, but cleaning itself remains primarily manual; no significant augmentation of human cleaning productivity occurs today. |
| Augmentation potential | claude-sonnet-5 | 1/5 | Current AI tools offer no meaningful assistance to a human performing physical cleaning of kitchen equipment and surfaces. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | Physical cleaning of equipment and work areas requires dexterous manipulation in unstructured kitchen environments. Current AI lacks reliable robotic arms and sensorimotor control to scrub, sanitize, and verify cleanliness consistently; while targeted cleaning robots exist, they cannot adapt to varied kitchen layouts and inspection standards. |
| Task automatability | claude-sonnet-5 | 1/5 | Cleaning food prep equipment and surfaces requires physical manipulation in unstructured environments, which current AI (software-based) cannot perform; robotic cleaning solutions for commercial kitchens are not generally available off-the-shelf.yet.a |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Health codes and food safety regulations typically require documented human inspection and sign-off of sanitation; liability for contamination and foodborne illness creates strong error-cost asymmetry; customer preference for human verification of cleanliness adds friction. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement exists for cleaning tasks, but health code compliance and liability for foodborne illness create some organizational caution around unproven automated cleaning methods. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Robotics and AI systems capable of this task remain capital-intensive and require specialized maintenance, making per-task costs substantially higher than minimum-wage kitchen staff in most markets. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | Given the absence of viable automated cleaning solutions for this task, human labor remains the only practical and cost-effective option; any hypothetical robotic system would carry high capital and maintenance costs exceeding wages. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed AI systems reliably perform full kitchen sanitation at production scale in restaurants. Robotic prototypes exist in research settings, but no mature product operates as an autonomous short-order kitchen cleaner in actual restaurants. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product performs general kitchen cleaning of counters, equipment, and work areas reliably in commercial short-order settings; robotic cleaning remains research-stage or limited to narrow floor-cleaning use cases. |
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.