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Hosts and Hostesses, Restaurant, Lounge, and Coffee Shop

Scrub through 206years 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.

2026drag to travel through time
1850187519001925195019752000now
2026
Known today as Hosts and Hostesses, Restaurant, Lounge, and Coffee Shop (BLS SOC 35-9031)
Latest actual · 2024
427K
OEWS is a point-in-time survey snapshot, not a continuous time series; BLS advises against using it for year-over-year trend comparison.
Latest actual · 2024
$30,380
Source: BLS-OEWS
Each dot is a cited figure over time; the dotted line only links them (values between aren't measured). Hollow dots are estimates.
Beat · 2025

Yelp launches Yelp Host in October 2025 at $149-$399/month per restaurant location -- the first major consumer-facing tech brand to enter the voice AI phone agent market for restaurant hosting. The product answers all inbound phone calls, takes and modifies reservations via Yelp Guest Manager integration, quotes wait times, and sends SMS waitlist links. Hostie AI, a competing platform, reports that early adopters are having approximately 84% of routine calls handled autonomously. This marks the beginning of the commercial market for AI phone agents in restaurant hosting -- a distinct phase from the AI-assisted reservation platforms (SevenRooms, OpenTable) that had been automating back-end reservation optimization for several years.

Tools of the era

The tools that defined the work

Select an era to see how it reshaped the work.

  • Reservation ledger and headwaiter protocol (pre-telephone era)

    The first technology of the hosting function was the reservation book: a physical ledger maintained by the headwaiter or maitre d'hotel in which guest names, party sizes, and table assignments were recorded by hand. In American-plan hotels of the 1830s-1900s, where meals were included in lodging costs, the headwaiter managed a dining room that was more like a theater than a restaurant -- knowing which guests were regulars, which had status, and which required special accommodation. The ledger gave the dining room its institutional memory. Before the telephone, reservations were made in person the day prior or via written note delivered by messenger; the headwaiter's job was as much social choreography as information management.

    Ledger workPaper recordkeeping
  • Telephone reservation system (post-WWI, mass telephone adoption)

    By the 1920s, telephone adoption had reached mass market in American cities. For restaurant hosts and hotel dining rooms, the telephone transformed the reservation from an in-person or written transaction into a real-time voice interaction. The host stand now required a person who could manage both arriving guests and an inbound call queue simultaneously. Tea rooms of the Prohibition era (1920-1933) brought the hostess role to a new female-dominant format: the society hostess greeted and seated, answered the phone for reservations, and set the social tone of the establishment. The telephone added volume (more reservations per day) and coordination complexity (cancellations, last-minute changes) without reducing the in-person seating workload.

    Work toolChanging equipment
  • Chain restaurant standardization (scripted host stand, laminated seating chart)

    The chain restaurant explosion of the 1960s and 1970s -- Howard Johnson's, Denny's, Perkins, Friendly's, Shoney's, then Applebee's, TGI Fridays, Chili's, Olive Garden in the 1980s -- transformed the host role from an individual performance into a standardized protocol. Every chain location had the same host stand configuration, the same greeting script ("Welcome to Chili's, how many in your party?"), the same laminated seating chart rotated to show server sections, the same pager system for waitlisted parties (when applicable). The host was no longer a personality hire; the role was defined by the operations manual. This standardization made the position replicable at scale and drove wages toward minimum wage, since the job was now a procedure rather than a craft.

    Effect on the work

    Chain restaurant standardization dramatically expanded the total number of host and hostess positions in the US, growing the occupation from an estimated 90,000 in 1970 to approximately 310,000 by 2000. It simultaneously deskilled the role: the headwaiter who memorized 200 regulars' preferences was replaced by a teenage hostess following a laminated chart.

    Work toolChanging equipment
  • OpenTable and digital reservation platforms (1998 launch, 55,000 restaurants by 2024)

    OpenTable was founded on July 2, 1998 by Chuck Templeton, initially incorporated as easyeats.com, Inc., in San Francisco. Its core proposition was simple: move the reservation book from paper to software and make it accessible to diners online in real time. Until OpenTable, all reservations were taken by phone and recorded manually. For the host stand, OpenTable replaced the physical ledger with a software-managed guest list, added automated SMS reminders to confirmed reservations, and surfaced guest history (dietary notes, prior complaints, VIP status) at the point of seating. The platform grew to cover more than 55,000 restaurants globally by 2024. It did not eliminate the need for a human host -- the seating and greeting functions remained in-person -- but it shifted the cognitive load of reservation management from memory and paper to screen.

    Effect on the work

    OpenTable and similar platforms (Resy, Tock, SevenRooms) modestly reduced the administrative burden on host stands but did not reduce headcount directly. They raised the service quality floor (fewer double-bookings, better guest tracking) and created a new skill expectation: proficiency with reservation software became a baseline requirement for host employment from the mid-2000s onward.

    Work toolChanging equipment
  • Digital waitlist and SMS paging (Yelp Guest Manager, NoWait, Waitlist Me)

    The paper waitlist -- a handwritten list of names on a clipboard, with a host shouting names across a lobby -- was systematically replaced by digital waitlist platforms between 2010 and 2020. NoWait (founded 2010, acquired by Yelp 2017) and Yelp Guest Manager, along with Waitlist Me and similar tools, sent automated SMS messages when tables became available, eliminating the need for guests to physically remain at the entrance while waiting. For the host, this technology reduced lobby congestion and the awkward public announcement of wait times, but added a new responsibility: managing a software dashboard while simultaneously greeting arriving guests. The transition also introduced quote-accuracy accountability -- SMS timestamps now documented whether wait times were accurate or not.

    Work toolChanging equipment
  • AI-assisted reservation platforms (SevenRooms AI Notes, OpenTable AI table optimization)

    The generation of AI features embedded in reservation platforms arrived commercially between 2020 and 2024. SevenRooms launched AI Notes (automatic standardization and enrichment of guest CRM records), AI Feedback Summaries (weekly guest sentiment distillation from reviews), and AI-optimized table assignment suggestions. OpenTable integrated AI-powered waitlist time estimation and demand forecasting. These tools did not replace the host but changed the nature of the informational environment at the host stand: the host now works alongside a platform that surfaces actionable intelligence -- which arriving VIP has a known allergy, which server section is running slow, which guest historically requests a quiet corner. SevenRooms reported an 85% reduction in time to respond to guest reviews at adopting restaurants.

    Work toolChanging equipment
  • Voice AI phone agents (Slang AI 2022, Yelp Host 2025, Hostie AI)

    Slang AI launched in 2022 and Yelp Host launched commercially in October 2025 at $149-$399/month per location. Both are voice AI systems that answer all inbound restaurant phone calls autonomously: taking and modifying reservations, quoting wait times, answering questions about hours and menus, and sending waitlist SMS links. Hostie AI, a competing platform, published case study data suggesting approximately 84% of routine incoming calls could be handled by the AI agent without human escalation. This is the first technology in the history of the role to directly automate a defined slice of what a host does -- not the in-person greeting and seating, but the phone-reservation intake that had been a core host responsibility since the 1920s. Restaurants using voice AI agents report freeing 20-30% of host stand hours from phone handling during peak service periods.

    Effect on the work

    Voice AI phone agents represent the clearest current threat to host employment levels. If 84% of inbound calls are handled autonomously and phone calls account for 25-30% of busy host time, the technology effectively reduces one host-equivalent of labor hours per shift at adopting locations. At scale, this could reduce the host-hours-per-cover ratio for full-service restaurants, contributing to the BLS-projected modest employment decline through 2034.

    Work toolChanging equipment
Projection cone · present → 2034

What credible sources project

Scrub the slider past now to anchor each scenario on the scrubber. The spread is the range of futures credible sources project for this role.

Employment outlook
Projected change in the number of people doing this work.
BLS OOH Food and Beverage Serving and Related Workers 2024-34
2034
+5%
BLS Occupational Outlook Handbook projects overall employment of food and beverage serving and related workers (the major group that includes 35-9031) to grow 5% from 2024 to 2034, faster than the all-occupations average. This is the broader group projection rather than the 35-9031-specific figure; the occupation-specific projection of -1.5% is more conservative than the group average, reflecting that hosts and hostesses are more exposed to delivery-format substitution (which reduces need for hosting) and voice AI automation of phone tasks than waiters or food prep workers within the same group. The two projections are reported together here to make the contrast visible: the overall food service sector is growing; the host and hostess slice within it is modestly contracting.
BLS National Employment Matrix 2024-34
2034
-1.5%
BLS Employment Projections, occupation-specific matrix for 35-9031. The 2024-34 cycle projects -1.5% employment change, from 429,900 (2024) to approximately 423,500 (2034) -- a decline of roughly 6,400 positions. BLS classifies this as "decline" against an all-occupations average of +4%. The methodology models continued growth in delivery and takeout (which reduces the need for in-person hosting at formats that shift toward takeout-heavy operations), modest increase in voice AI adoption for phone-reservation tasks, and partial offset from the continued expansion of full-service casual dining in suburban markets. The projection does not explicitly model the commercial rollout pace of Yelp Host, Slang AI, or SevenRooms AI features, which could modestly steepen the decline if adoption proves faster than the base case.
AI task exposure
Share of the role’s tasks that researchers estimate AI can do. This is a measure of task exposure, not a forecast of jobs lost.
Eloundou et al. -- "GPTs are GPTs" (2023)
2028
10%
of tasks
GPT-4 task-by-task LLM exposure labeling on O*NET tasks for food service and related occupations. Hosts and hostesses score in the low range for direct LLM exposure -- the dominant tasks (greeting guests in person, managing the physical seating of a dining room, reading body language, de-escalating complaints face-to-face, pacing service flow) require physical presence that language models cannot provide remotely. The 10% exposure estimate here reflects the tasks that do have LLM touchpoints: answering phone inquiries about hours and menus, managing reservation system entries, drafting confirmation messages. Voice AI phone agents represent the applied LLM product that most directly automates a host task; they operate on the phone-handling slice that Eloundou would classify as exposed. The in-person core of the role scores near zero for LLM exposure by the Eloundou methodology.
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.

AI is sitting alongside you hereHandle incoming phone calls for reservations, cancellations, modifications, and general inquiries (hours, parking, large-party policies, dietary accommodations)

Handle incoming phone calls for reservations, cancellations, modifications, and general inquiries (hours, parking, large-party policies, dietary accommodations); enter reservation details into the reservation platform accurately, capturing party size, special requests, and contact information; in restaurants using AI phone agents (Yelp Host, Slang AI), monitor the AI call log, handle escalated calls the AI flags, and configure the system with current specials and policy updates.[2],[3],[8]

Tools picking this up
Where your edge is

As AI phone agents handle routine calls, your comparative advantage shifts to handling escalated situations the AI cannot -- complex large-party negotiations, VIP calls, and guests with accessibility needs. Be the person in the restaurant who understands how to configure the AI system: set greetings, update specials, interpret analytics, and escalate issues to the vendor. That skill travels across every restaurant that adopts voice AI.

AI is sitting alongside you hereUpdate and act on guest profile data in the reservation platform: flag dietary restrictions and allergen notes when taking a reservation, check incoming VIP profiles before seating to brief the server, use SevenRooms AI Notes or equivalent to keep guest records clean and standardized, and add post-visit notes for guests who made special requests or lodged complaints so future visits are handled proactively.

Update and act on guest profile data in the reservation platform: flag dietary restrictions and allergen notes when taking a reservation, check incoming VIP profiles before seating to brief the server, use SevenRooms AI Notes or equivalent to keep guest records clean and standardized, and add post-visit notes for guests who made special requests or lodged complaints so future visits are handled proactively.[4],[5]

Tools picking this up
Where your edge is

Build a habit of adding one specific note per VIP reservation before the party arrives. A returning guest whose preferred seating is ready, whose name is used at the door, and whose anniversary the server already knows will leave a five-star review about "how they make you feel known." That outcome starts at the host stand, not the table.

AI is sitting alongside you hereManage the walk-in waitlist during peak service: accurately quote estimated wait times based on current turn rate (not optimistic guesses), add parties to the digital waitlist in the reservation platform, send SMS notifications when tables become available, and re-engage waiting guests to prevent silent walkouts.

Manage the walk-in waitlist during peak service: accurately quote estimated wait times based on current turn rate (not optimistic guesses), add parties to the digital waitlist in the reservation platform, send SMS notifications when tables become available, and re-engage waiting guests to prevent silent walkouts.[1],[5]

Where your edge is

Quote wait times conservatively and commit to a maximum, not a minimum. Guests who wait 30 minutes for a "20-minute wait" become hostile; guests who wait 25 minutes for a "30-minute wait" become grateful. A host who manages expectations accurately is the single biggest driver of positive lobby reviews.

Where this role is heading

Natural next steps for someone with your foundation: not exits, evolutions.

A direction you could grow

Food Service Managers

Experienced hosts who advance to floor supervisor and then develop operational breadth (scheduling, vendor management, P&L basics) are well-positioned for assistant general manager or food service manager roles. The host stand is the first place a restaurateur looks for GM potential: hosts who consistently run a calm lobby, de-escalate guests, and protect kitchen flow have already demonstrated the judgment that management requires. Median wages for food service managers are roughly double that of hosts, making this a meaningful income transition.

What you'd add
  • · Restaurant P&L literacy: food cost percentage, labor cost percentage, and how seating decisions affect table revenue per shift
  • · Inventory and ordering fundamentals: par levels, vendor relationships, waste tracking
  • · HR basics for hospitality: documentation, corrective action, scheduling compliance for tipped employees
  • · Reservation platform analytics: interpreting turn-time data, no-show rates, and cover-per-server metrics from OpenTable or SevenRooms to inform staffing decisions
What it takesSome new skills to pick up
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The data behind this timeline

On record since1830
Latest tracked employment427,150 (US, 2024)
Latest median pay$30,380 (2024)
Outlook-1.5% by 2034 (BLS National Employment Matrix 2024-34)
View all 26 cited data points
YearUS employmentMedian annual paySource
195045,000n/aESTIMATE
197090,000$3,800ESTIMATE
1990190,000n/aESTIMATE
2000310,000$14,100ESTIMATE
2003294,300$15,380BLS-OEWS
2004316,400$15,630BLS-OEWS
2005328,930$15,840BLS-OEWS
2006340,390$16,170BLS-OEWS
2007342,960$16,790BLS-OEWS
2008349,990$17,510BLS-OEWS
2009334,310$18,110BLS-OEWS
2010329,020$18,450BLS-OEWS
2011329,070$18,560BLS-OEWS
2012341,400$18,580BLS-OEWS
2013360,970$18,640BLS-OEWS
2014372,670$18,720BLS-OEWS
2015391,150$19,180BLS-OEWS
2016404,360$19,980BLS-OEWS
2017414,540$20,930BLS-OEWS
2018416,950$22,160BLS-OEWS
2019423,380$23,090BLS-OEWS
2020316,700$23,880BLS-OEWS
2021324,690$24,600BLS-OEWS
2022400,420$27,720BLS-OEWS
2023425,020$29,220BLS-OEWS
2024427,150$30,380BLS-OEWS
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