Serve ice water, coffee, rolls, or butter to patrons.[2]
Where your edge is
Time Machine
Dining Room and Cafeteria Attendants and Bartender Helpers
Scrub through 209 years of this role's history — from when it first emerged, through every wave of technology that reshaped it, to the cited projections for where it's heading next.
1850187519001925195019752000now
Drag the dot, click anywhere on the track, or use ← → arrow keys (Shift for 10-year jumps, PgUp/PgDn for 25).
2026
Known today as Dining Room and Cafeteria Attendants and Bartender Helpers (BLS SOC 35-9011)
US Employment
527K
BLS National Employment Matrix 2024-34 baseline employment for SOC 35-9011: 527,400 workers. Confirmed by O*NET 35-9011.00 citing BLS OEWS May 2024. This figure is the authoritative anchor for the BLS 2024-34 employment projection cycle and represents the occupation's highest-ever recorded employment, fully recovering the 2020 COVID crash and surpassing the 2019 pre-pandemic peak.
Median Annual Wage
$32,670
Source: BLS-OEWS
Tool of the era · Bear Robotics Servi — autonomous tray-delivery robot (founded May 2017, mass deployment 2022+)
In May 2017, former Google software engineer John Ha founded Bear Robotics in Redwood City, California, with the explicit goal of automating the food-runner function in casual-dining restaurants. The company's first robot, "Penny," was tested at Ha's own Willow Tree restaurant in Redwood City. The production version, "Servi," is a three-tray autonomous delivery robot that navigates restaurant floors using LiDAR and cameras, carrying food from the kitchen pass to the table. By 2022, Servi robots were logging significant mileage milestones (247,500 miles of total travel as of January 2022); by 2024 the company had deployed units across Denny's, Chili's, Olive Garden, and dozens of other casual-dining chains in the United States and South Korea. SoftBank led a $60M Series B in 2021. The deployment reality differs materially from the displacement narrative: Servi carries trays to tables but cannot load itself (a human at the kitchen pass loads each tray), cannot navigate stairs, cannot handle highly irregular floor layouts, cannot interact with guests when they ask "which is the salmon?", and cannot bus the dirty dishes back. The robot handles one sub-task of the food runner's job, not the job itself. Chains deploying Servi have reported it primarily frees the food runner's hands for higher-value interactions rather than reducing headcount.
Casual-dining chains deploying Servi (Denny's, Chili's, Olive Garden) have reported using robots primarily to assist rather than replace dining room attendants — the robot carries food while the human loads it and manages guest interactions. No major chain has reported headcount reductions attributable to Servi deployment as of 2024; the technology is more accurately described as a labor-assist tool in a labor-scarce market than as a displacement technology.
Projection cone · present → 2034
What credible sources project
Scrub the slider past now to anchor each scenario on the scrubber. The spread you see below is the range of futures credible sources project for this role.
BLS National Employment Matrix 2024-34
→ 2034
+6.3%
BLS National Employment Matrix 2024-34 cycle. Published figures for SOC 35-9011: base employment 527,400 (2024); projected employment 560,600 (2034); absolute change +33,100; percent change +6.3%. Full-service restaurant employment within the occupation (307,500 in 2024 to 331,400 in 2034) drives the majority of projected growth. O*NET cites projected annual job openings of 99,600 over the decade. BLS describes the outlook as "faster than average." This is the most authoritative near-term projection and does not model speculative robot-displacement scenarios — it projects under current technology trajectories and modeled productivity trends.
Eloundou et al. — "GPTs are GPTs" (2023)
→ 2028
-1%
GPT-4 task-by-task LLM exposure labeling on O*NET tasks for SOC 35-9011. Dining room attendants score very low on LLM exposure because the core tasks — carrying trays, clearing dishes, restocking bar supplies, refilling water and bread, resetting table covers — are physical tasks that a large language model cannot perform. The -1% estimate reflects the minimal near-term displacement risk from AI specifically (as opposed to robotics); the modest negative reflects the possibility that AI scheduling and staffing-optimization tools (Toast, Harri, Sling) modestly reduce the hours of dining room attendant coverage needed per shift by optimizing cover flow. LLM automation is essentially not a factor for this occupation.
Bear Robotics Servi displacement scenario
→ 2030
-10%
Bear Robotics-specific scenario: if Servi-class robots achieve saturation across the approximately 180,000 US full-service restaurant locations (per NRA) at a ratio of one robot per restaurant, and if each robot reduces food-runner headcount by 0.3 FTE, the net displacement would be approximately 54,000 positions — roughly 10% of the 2024 workforce. This is the pessimistic scenario and assumes full deployment saturation with real headcount reduction rather than the "labor assist" model observed in current deployments. Current evidence from Denny's, Chili's, and Olive Garden deployments does not support the headcount-reduction model; robots are deployed in labor-constrained environments to maintain service levels, not to reduce staffing. The -10% is the tail risk, not the central case.
Frey & Osborne (2013)
→ 2033
-25%
Gaussian-process classifier on O*NET task features. Frey & Osborne (2013) assigned Dining Room Attendants a high probability of computerization (~0.77), placing them in the upper quartile of automation risk. The task cluster driving this score: highly routine physical tasks (carrying trays, clearing tables, refilling water) that involve predictable motion in structured environments — precisely the physical tasks robotics researchers were targeting in 2013. The tray-delivery robot thesis was visible and reasonable in 2013. The -25% figure represents the implied employment ceiling if F&O's probability were substantially realized over 20 years. In practice, employment has grown substantially since 2013, suggesting the structural constraints on food-service robot deployment (floor layout variability, guest interaction requirements, tray loading at the kitchen pass) were underweighted by F&O's task-composition model.
Today, in this role
What's shifting in the work right now
The historical view above shows how this role has moved. This is the present-day detail: which AI tools are picking up which tasks, where the edge still is, and the natural directions this work can grow.
What's changing in your day
Three parts of your work where AI is already doing real lifting — and what stays yours.
Clean up spilled food or drink or broken dishes and remove empty bottles and trash.[2]
Where your edge is
Carry trays from food counters to tables for cafeteria patrons.[2]
Where your edge is
Present-day sources
Sources
Every claim on this page traces back to one of the following. Updated 2026-05-30.
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