Baristas

AI & job outlook

What today's research says about this occupation's exposure to AI, how AI is actually being used in it, and where employment is headed. These are positions within published studies — measures of exposure and usage, not predictions that this job will disappear.

Exposure to current AI

Each study uses its own scale, so the raw scores are not comparable across rows — the percentile (this job's rank among all U.S. occupations with data) is the comparable figure, and sizes the bars.

OpenAI's exposure study scores tasks three ways: with a language model alone (α 0.1), with simple added tooling (β 0.1), and including AI-powered software (γ 0.2). Higher means more of the job's tasks could be done at least twice as fast — not that they will be automated away.

How AI is actually used in this job

Among measured AI assistant conversations mapped to this occupation (Anthropic Economic Index, 2026-01-15), these task types came up most. These are shares of observed AI conversations — not shares of the job, of worker time, or of what could be automated.

Job outlook

Independent U.S. Bureau of Labor Statistics employment projection for 2024–2034 — a labor-market forecast, not an AI-impact forecast.

“Annual openings” counts new jobs plus replacements for workers who leave the occupation, so it can be large even when growth is modest.

Tasks

All 19 tasks O*NET lists for this occupation, ordered by importance. Each links to its own page with AI-exposure and observed-use detail.

• Receive and process customer payments. Core · importance 4.7
• Prepare or serve hot or cold beverages, such as coffee, espresso drinks, blended coffees, or teas. Core · importance 4.6
• Clean or sanitize work areas, utensils, or equipment. Core · importance 4.5
• Take customer orders and convey them to other employees for preparation. Core · importance 4.5
• Describe menu items to customers, or suggest products that might appeal to them. Core · importance 4.4
• Clean service or seating areas. Core · importance 4.3
• Serve prepared foods, such as muffins, biscotti, or bagels. Core · importance 4.3
• Prepare or serve menu items, such as sandwiches or salads. Core · importance 4.2
• Set up or restock product displays. Core · importance 4.0
• Stock customer service stations with paper products or beverage preparation items. Core · importance 4.0
• Weigh, grind, or pack coffee beans for customers. Core · importance 4.0
• Wrap, label, or date food items for sale. Core · importance 4.0
• Provide customers with product details, such as coffee blend or preparation descriptions. Core · importance 4.0
• Take out garbage. Core · importance 3.9
• Order, receive, or stock supplies or retail products. Core · importance 3.9
• Slice fruits, vegetables, desserts, or meats for use in food service. Core · importance 3.8
• Check temperatures of freezers, refrigerators, or heating equipment to ensure proper functioning. Core · importance 3.8
• Demonstrate the use of retail equipment, such as espresso machines. Core · importance 3.7
• Create signs to advertise store products or events. Core · importance 3.1

Work activities

• Serve food or beverages.
• Prepare hot or cold beverages.
• Clean food service areas.
• Clean tableware.
• Assess equipment functioning.
• Present food or beverage information or menus to customers.
• Store supplies or goods in kitchens or storage areas.
• Order materials, supplies, or equipment.
• Process customer bills or payments.
• Stock serving stations or dining areas with food or supplies.
• Communicate dining or order details to kitchen personnel.
• Take customer orders.
• Remove trash.
• Measure ingredients.
• Package food or supplies.
• Write advertising or promotional material.
• Train food preparation or food service personnel.
• Cook foods.
• Set up merchandise displays.
• Cut cooked or raw foods.

Knowledge, skills & abilities

O*NET importance rating, from 1 (not important) to 5 (extremely important).

Knowledge

Abilities

Essential skills

Transferable skills

Skills in demand

Skills employers ask for in job postings for this occupation (Lightcast), with whether each is a common or specialized skill.

• Active Listening Common Skill
• Speech Recognition Specialized Skill
• English Language Common Skill
• Social Perceptiveness Common Skill
• Information Ordering Specialized Skill
• Finger Dexterity Common Skill
• Reading Comprehension Common Skill
• Microsoft Word Common Skill
• Microsoft Excel Common Skill
• Deductive Reasoning Common Skill
• Critical Thinking Common Skill
• Writing Common Skill
• Time Management Common Skill
• Inductive Reasoning Common Skill
• Active Learning Common Skill
• Mathematics Common Skill
• Negotiation Common Skill
• Visualization Specialized Skill
• Learning Strategies Specialized Skill
• Complex Problem Solving Common Skill
• Instructing Specialized Skill
• Equipment Maintenance Specialized Skill
• Depth Perception Common Skill
• Chemistry Specialized Skill
• Telecommunications Specialized Skill
• Foreign Language Common Skill
• Psychology Specialized Skill
• Systems Analysis Specialized Skill
• Equipment Selection Specialized Skill
• Geography Specialized Skill
• Physics Specialized Skill
• Biology Specialized Skill
• Installation Specialized Skill

Tools & technology

Work context

How characteristic each condition is of the job, on O*NET's 1–5 context scale (higher = more present in day-to-day work). Each condition links to how it varies across all occupations.

How to get in

Job zone

Zone 2 — Job Zone 1-2: Very Little to Some Preparation Needed

Education

Usually requires a high school diploma or GED, though some occupations may not.

Typical entry-level education

No formal educational credential · BLS, the typical path — not a requirement

Related experience

Some occupations may need little or no previous experience; others require several months to a year of experience. For example, landscaping and groundskeeping workers might require very little training or previous experience, while agricultural equipment operators can benefit from on-the job training.

Preparation level

SVP (Below 6.0) — total schooling plus on-the-job experience.

Education of current workers

Share of people in this occupation at each level of education.

Interests & work styles

The interests and personal qualities O*NET associates with people who do this work.

Career interests (Holland / RIASEC)

Interest areas

Work styles

Wages & employment

U.S. · annual wages (BLS OEWS)

Wages and employment are reported by BLS for the broader occupation group this specialty belongs to (SOC 35-3023), not for the specialty alone.

Industries that employ this occupation

Where these workers are employed, by number of jobs (national, BLS OEWS). Pay shown is the occupation's national median, not industry-specific.

Where this work is most concentrated

Industries where this occupation is far more common than in the economy as a whole. The location quotient is how many times more concentrated it is here (a value of 5 means five times its economy-wide share).

Related occupations

Part of the Hospitality, Events, & Tourism career cluster.

• Fast Food and Counter Workers · 35-3023.00 Compare →
• Dining Room and Cafeteria Attendants and Bartender Helpers · 35-9011.00 Compare →
• Food Preparation Workers · 35-2021.00 Compare →
• Food Servers, Nonrestaurant · 35-3041.00 Compare →
• Waiters and Waitresses · 35-3031.00 Compare →
• Cooks, Short Order · 35-2015.00 Compare →
• Bartenders · 35-3011.00 Compare →
• Cooks, Restaurant · 35-2014.00 Compare →
• Cooks, Private Household · 35-2013.00 Compare →
• Cooks, Fast Food · 35-2011.00 Compare →
• Chefs and Head Cooks · 35-1011.00 Compare →
• Bakers · 51-3011.00 Compare →

Side-by-side comparisons place two occupations’ pay, preparation, skills, and AI exposure on the same page — same data, same scale, no forecast.

What you can do with this

Options the data surfaces for Baristas — not advice or a forecast. Each is a real cross-link you can follow into the evidence.

Sources for this page

Every figure above traces to a named public dataset and the exact release below — not hand-written opinion. See the full methodology for what each measure does and does not mean.

• O*NET 30.3 U.S. Department of Labor / National Center for O*NET Development
• BLS Occupational Employment and Wage Statistics (OEWS) May 2024 U.S. Bureau of Labor Statistics
• BLS Employment Projections 2024–2034 U.S. Bureau of Labor Statistics
• Anthropic Economic Index v4 (2026-01-15) + v2 (2025-03-27) Anthropic
• Microsoft “Working with AI” working-with-ai Microsoft Research
• “GPTs are GPTs” (Eloundou et al.) arXiv 2303.10130 OpenAI / academic

Data compiled June 2, 2026. Figures are estimates, not advice.

@misc{singulariki-role-35-3023-01,
  title  = {Baristas},
  author = {{Singulariki}},
  year   = {2026},
  note   = {O*NET 30.3; BLS Occupational Employment and Wage Statistics (OEWS) May 2024; BLS Employment Projections 2024–2034; Anthropic Economic Index v4 (2026-01-15) + v2 (2025-03-27); Microsoft “Working with AI” working-with-ai; “GPTs are GPTs” (Eloundou et al.) arXiv 2303.10130. Accessed June 7, 2026},
  url    = {https://singulariki.com/roles/role-35-3023-01}
}