- O*NET-SOC code
- 35-3023.01
Prepare or serve specialty coffee or other beverages. Serve food such as baked goods or sandwiches to patrons.
- Data source: BLSPublished: 2026-08
Moderate· relative
LowFour relative bandsVery highGroup-level value
Scale, basis and source
Four relative bands (Low / Moderate / High / Very high)
831 detailed occupations in the BLS Employment Projections table. Assigned per National Employment Matrix (NEM) code, so occupations sharing a NEM code carry the same band
- Data source: AnthropicPublished: 2026-03
0.000
0.000Range of values carried here0.745Group-level value
Scale, basis and source
Observed exposure index, 0–1 as published
Mapped onto O*NET tasks
Published per SOC 2018 occupation; every O*NET occupation with the same SOC 2018 code carries this value
- Data source: ILOPublished: 2025
0.24
0.09Range of values carried here0.70Group-level value
Scale, basis and source
Generative AI exposure index, 0–1 as published
Published per ISCO-08 unit group. Linked to this occupation, wholly or in part, by applying U.S. Bureau of Labor Statistics crosswalks (ISCO-08 to 2010 SOC, 2010 SOC to 2018 SOC) as published
What kind of figure this source publishes
The BLS category is a relative rank, not an absolute level, and it is not a first-hand measurement: it groups an occupation's percentile ranks across several published studies into four bands. It is not an employment or wage forecast, not a probability of adoption, and it does not separate automation from augmentation.
Task-level exposure
Show 16 hidden tasks19 rated tasks · 3 tasks with β ≥ 0.5 (15.8%)
β = direct exposure (E1) + 0.5 × exposure when tools are available (E2), per the source repository's definition.
- Source unit: O*NET 27.2 tasks → O*NET 31.0 occupation code
- All rated tasks are in the O*NET 31.0 task list.
- Source
- OpenAI "GPTs are GPTs" exposure rubric
- Release
- gh-main-0471612
- License
- MIT License, Copyright (c) 2024 OpenAI
Tasks of O*NET-SOC 2010 occupation 35-3022.01 Baristas, linked to this occupation by the O*NET-SOC 2010 to 2019 crosswalk
| Task (O*NET 20.1) | Claude.aiRaw / share % | APIRaw / share % |
|---|---|---|
Prepare or serve menu items, such as sandwiches or salads.35-3022.01 | 0.006552.8 | —0 |
Prepare or serve hot or cold beverages, such as coffee, espresso drinks, blended coffees, or teas.35-3022.01 | 0.003427.6 | —0 |
Describe menu items to customers or suggest products that might appeal to them.35-3022.01 | 0.002419.5 | 0.0031100.0 |
Values in this tab are predicted labels, not observations. Eloundou et al. (2023) published two rating regimes — human raters and GPT-4 — and the β shown here is derived from the GPT-4 rater basis alone; the same task can take a different value under the other regime. The unit and the meaning differ from the observed shares (%) in the other tabs, so do not place them on the same axis.
| Task (O*NET 27.2) | βE1 + 0.5 × E2 |
|---|---|
Describe menu items to customers, or suggest products that might appeal to them.O*NET Task ID 17585 | 1.0 |
Provide customers with product details, such as coffee blend or preparation descriptions.O*NET Task ID 17587 | 1.0 |
Create signs to advertise store products or events.O*NET Task ID 17595 | 0.5 |
β = E1 + 0.5 × E2 · E1 = tasks where direct LLM access alone cuts time by at least 50%, E2 = tasks where software built on top of an LLM cuts time by at least 50%. Values take only 0 / 0.5 / 1.0.
Data sources & licenses — O*NET®, Anthropic Economic Index, Eloundou et al. (2023): see full notices on the Credits page
All O*NET 31.0 task statements (19)
All O*NET 31.0 task statements for this occupation (English, as published; task type core or supplemental). Exposure values are not listed here; they are in the table above, next to each task statement as worded in the O*NET version each source used.
| Task (O*NET 31.0) | Type |
|---|---|
| Prepare or serve hot or cold beverages, such as coffee, espresso drinks, blended coffees, or teas. | Core |
| Clean or sanitize work areas, utensils, or equipment. | Core |
| Clean service or seating areas. | Core |
| Check temperatures of freezers, refrigerators, or heating equipment to ensure proper functioning. | Core |
| Describe menu items to customers, or suggest products that might appeal to them. | Core |
| Order, receive, or stock supplies or retail products. | Core |
| Provide customers with product details, such as coffee blend or preparation descriptions. | Core |
| Receive and process customer payments. | Core |
| Serve prepared foods, such as muffins, biscotti, or bagels. | Core |
| Stock customer service stations with paper products or beverage preparation items. | Core |
| Take customer orders and convey them to other employees for preparation. | Core |
| Take out garbage. | Core |
| Weigh, grind, or pack coffee beans for customers. | Core |
| Wrap, label, or date food items for sale. | Core |
| Demonstrate the use of retail equipment, such as espresso machines. | Core |
| Prepare or serve menu items, such as sandwiches or salads. | Core |
| Set up or restock product displays. | Core |
| Create signs to advertise store products or events. | Supplemental |
| Slice fruits, vegetables, desserts, or meats for use in food service. | Supplemental |
1.04/ 3· Robot exposure index
Scale, basis and source
Each physical task is graded 0–3 by the setting in which robots that have been deployed, sold or demonstrated could do it (0 none · 1 purpose-built robot settings such as a fenced machine cell or a bottling line · 2 structured facilities such as a hospital pharmacy or a container port · 3 unstructured settings such as a private home or a construction site), and the grades are averaged by share of working time
Physical tasks among this occupation's O*NET 29.3 tasks
Anthropic estimated the grades and time shares with Claude Opus 5 using web search, as of 2026.
The robot exposure index is a different measure from the AI exposure figures on this site and cannot be added to or compared with them. It does not measure actual adoption or cost, and 0 also covers occupations with no physical tasks.
Tier of each physical task
Robot environment tiers (E0–E3)
E0 noneDetails
Rated not doable by robots that have been deployed, sold or demonstrated, even in a purpose-built setting
E1 purpose-built robot settingsDetails
Rated doable in a setting built around the machine, such as a fenced machine cell or a bottling line
E2 structured facilitiesDetails
Rated doable in a human work facility that an institution keeps to a working standard, such as a hospital pharmacy or a container port
E3 unstructured settingsDetails
Rated doable in a setting that no institution keeps to a working standard, such as a private home or a construction site
Only tasks classified as physical are listed; other tasks have no tier in the source.
| Task (O*NET 29.3) | Robot environment tier |
|---|---|
| Prepare or serve hot or cold beverages, such as coffee, espresso drinks, blended coffees, or teas. | E1 purpose-built robot settings |
| Clean or sanitize work areas, utensils, or equipment. | E1 purpose-built robot settings |
| Clean service or seating areas. | E1 purpose-built robot settings |
| Check temperatures of freezers, refrigerators, or heating equipment to ensure proper functioning. | E2 structured facilities |
| Order, receive, or stock supplies or retail products. | E1 purpose-built robot settings |
| Receive and process customer payments. | E2 structured facilities |
| Serve prepared foods, such as muffins, biscotti, or bagels. | E1 purpose-built robot settings |
| Stock customer service stations with paper products or beverage preparation items. | E1 purpose-built robot settings |
| Take out garbage. | E1 purpose-built robot settings |
| Weigh, grind, or pack coffee beans for customers. | E1 purpose-built robot settings |
| Wrap, label, or date food items for sale. | E1 purpose-built robot settings |
| Create signs to advertise store products or events. | E1 purpose-built robot settings |
| Demonstrate the use of retail equipment, such as espresso machines. | E0 none |
| Prepare or serve menu items, such as sandwiches or salads. | E1 purpose-built robot settings |
| Set up or restock product displays. | E1 purpose-built robot settings |
| Slice fruits, vegetables, desserts, or meats for use in food service. | E1 purpose-built robot settings |
Occupation information
Employment outlook (BLS)
Values for the whole SOC 35-3023 group (Fast food and counter workers), not for this O*NET occupation alone.
- Employment, 2025
- 3.9M
- Projected employment, 2035
- 4.1M
- Change (2025–35)
- +5.8%
- Median annual wage, 2025
- $31,200
- Typical education needed for entry
- No formal educational credential
Korea classification links (KSCO)
Links from the current release of the Korea Occupation Direct Crosswalk v1.1, made by AI Changing Work. Relation values are read from the O*NET-SOC occupation side.
| KSCO code | KSCO title | Relation | Status |
|---|---|---|---|
| 45211 | 커피 조리원 | Same scope | confirmed |
| 45212 | 전통차 조리원 | This occupation is broader | confirmed |
| 45219 | 그 외 음료 조리원 | This occupation is broader | confirmed |
Relation and status definitions(kr-direct v1.1)
ILO occupational description
AI Changing Work links the ISCO-08 unit groups below to this occupation by applying the U.S. Bureau of Labor Statistics crosswalks (2018 SOC to 2010 SOC, 2010 SOC to ISCO-08) as published, without case-by-case selection; each link may be whole or partial.
- ISCO-08 5246Food Service Counter Attendants
The definition, tasks and examples below are the ILO's description of that unit group, not of this occupation.
Food service counter attendants serve customers at food counters and finish preparation of simple food items in restaurants, cafes, hotels, fast food outlets, cafeterias, hospitals and other settings.
Tasks
- (a) serving food to customers at counters;
- (b) ascertaining the products desired by the customer, assisting them in making choices and taking orders;
- (c) cleaning, peeling, slicing and trimming foodstuffs using manual and electric appliances;
- (d) preparing simple food items and reheating prepared meals;
- (e) portioning and wrapping food or placing it directly on plates for service to patrons;
- (f) packaging takeaway food;
- (g) stocking refrigerators, salad, buffet bars and keeping records of the quantities of food used;
- (h) receiving payment for food items purchased.
Example occupations
Cafeteria counter attendant · Salad bar attendant
Sources: ILO ISCO-08 — details: Credits
ESCO occupational vocabulary
Each entry below is matched to this occupation in the published ESCO-O*NET Crosswalk (version 1, 2022). The match types are that crosswalk's; the wording is AI Changing Work's.
- baristaESCO 5132.1.1Same scope
Baristas prepare specialised types of coffee using professional equipment in a hospitality/coffee shop/bar unit.
Source: European Commission, DG EMPL — ESCO API 2026-08-08 (bridge v1.2.1)
License: CC BY 4.0 (EU Reuse Notice)
ESCO reuse terms and the modification notice are in Credits.
France classification links (ROME)
AI Changing Work's links between this occupation and ROME 4.0 job profiles, judged with AI models. Relations are read from this occupation's side. Links marked “provisional” were adopted on the judgments of two judges from the same model family (Claude) alone, or by an operator rule applied to their judgments, so they rest on weaker evidence than confirmed links.
| ROME code | ROME title (French) | Relation |
|---|---|---|
| G1809 | Barista | Same scopeprovisional |
Source: France Travail — Répertoire Opérationnel des Métiers et des Emplois (ROME 4.0), version 61, last updated 15 June 2026 (https://www.data.gouv.fr/datasets/repertoire-operationnel-des-metiers-et-des-emplois-rome). Licence Ouverte 2.0.
ROME codes and titles are reproduced unchanged; the links to O*NET-SOC occupations are AI Changing Work's own judgment and are not a France Travail correspondence.
(AI Changing Work link release: fr-direct v1)
Spain classification links (CNO-11)
AI Changing Work's links between this occupation and CNO-11 occupations, judged with AI models. Relations are read from this occupation's side.
| CNO-11 code | CNO-11 title (Spanish) | Relation |
|---|---|---|
| 5120 | Camareros asalariados | This occupation is narrowerprovisional |
| 5000 | Camareros y cocineros propietarios | Related, not a matchprovisional |
Source: Instituto Nacional de Estadística (INE), National Classification of Occupations 2011 (CNO-11, Royal Decree 1591/2010) — www.ine.es · Licensed CC BY 4.0. CNO-11 codes and Spanish titles are reproduced unchanged; the links to O*NET occupations are AI Changing Work's own judgment and are not an INE correspondence.
(AI Changing Work link release: es-direct v1)
Germany classification links (KldB)
AI Changing Work's links between this occupation and KldB occupations, judged with AI models. Relations are read from this occupation's side.
| KldB code | KldB title (German) | Relation |
|---|---|---|
| 63322 | Barkeeper/innen - fachlich ausgerichtete Tätigkeiten | This occupation is narrower |
Source: © Statistik der Bundesagentur für Arbeit — Klassifikation der Berufe 2010 – überarbeitete Fassung 2020, https://statistik.arbeitsagentur.de. KldB codes and titles are reproduced unchanged; the links to O*NET occupations are AI Changing Work's own mapping (eigene Zuordnung) and are not a Bundesagentur für Arbeit correspondence.
(AI Changing Work link release: de-direct v1)
Japan classification links (JSCO)
AI Changing Work's links between this occupation and JSCO occupations, judged with AI models. Relations are read from this occupation's side.
| JSCO code | JSCO title (Japanese) | Relation |
|---|---|---|
| 403 | 飲食物給仕従事者 | Related, not a match |
Source: Ministry of Internal Affairs and Communications (MIC), Japan Standard Occupational Classification (Dec. 2009, MIC Notification No. 555). JSCO codes and Japanese titles are reproduced unchanged; the links to O*NET occupations are AI Changing Work's own judgment and are not a MIC correspondence.
(AI Changing Work link release: jp-direct v1)
Sources, licenses and AI Changing Work's modification and translation notices for this page: Credits & sources