Nicolas Sitter
Still in beta. Spot something off or have ideas? Send me your feedback — DM on LinkedIn or email. Bugs forward 🙏
Weekly until 19 Oct 2026, monthly from November 2026Week of 2026-10-19

AI Hotel Landscape

How leading AI assistants recommend hotels: 616 prompts across 56 destinations, a monthly edition checked before it is published.

Data is directional. Some prompts retry due to upstream variance, so absolute counts can shift ±5% week to week.
PROMPTS

The 616 prompts we ask ChatGPT each edition

56 destinations × 11 templates. Each prompt has structured dimensions: city, country, region, persona (couples / families / solo / business), budget (luxury / mid / budget), and location zoom (wide city vs neighborhood vs landmark). Control prompts are unmodified baselines.

Prompts / week
616
across 6 platforms
Destinations
56
distinct cities
Countries
37
dest country
Regions
5
continental groupings
56 destinations across 37 countrieseuropeamericasasiaafricaoceania
Template patterns

Every destination below gets the same 11 question types — just with the city name swapped in. That's how 56 destinations × 11 templates = 616 prompts per platform per edition.

  • luxury hotels in <city>
  • family friendly hotels in <city>
  • hotels in <city> with rooftop pool
  • hotels near <neighborhood>, <city>
  • boutique hotels in <neighborhood>, <city>
  • best hotels in <city> for solo travelers
  • best hotels in <city>
  • affordable hotels in <city> under $200
  • best hotels in <city> for couples
  • best hotels in <city> for business travelers
  • best hotels in <neighborhood>, <city>
  • hotels near <city> National Park
africa4 destinations · 44 prompts

Each destination is asked the 11 prompt patterns above (11 prompts × 4 cities = 44 prompts in this region).

americas14 destinations · 154 prompts

Each destination is asked the 11 prompt patterns above (11 prompts × 14 cities = 154 prompts in this region).

asia16 destinations · 176 prompts

Each destination is asked the 11 prompt patterns above (11 prompts × 16 cities = 176 prompts in this region).

europe17 destinations · 187 prompts

Each destination is asked the 11 prompt patterns above (11 prompts × 17 cities = 187 prompts in this region).

oceania5 destinations · 55 prompts

Each destination is asked the 11 prompt patterns above (11 prompts × 5 cities = 55 prompts in this region).

HIGHLIGHTS

Named hotels of the week — ChatGPT

Hotels ChatGPT talked about most in this edition, with movement against the previous edition. Filtered to ChatGPT only — switch tabs to compare across models.

Hotel of the Week
9 mentions
Zamalek

Most-mentioned hotel by ChatGPT in this edition.

Hotel That Disappeared
-3 mentions
Zamalek

12 → 9 (-25% WoW). Gated to ≥10 prior-week mentions.

Top 20 hotels by ChatGPT mentions — week of October 19, 2026
#HotelCityChainMentionsW/W
1Cairo Marriott HotelZamalekMarriott9↓ 3
3The Langham Gold CoastSurfers ParadiseLangham8↓ 1
2The Fullerton Bay Hotel Singapore80 Collyer Quay—8↓ 1
4The Imperial New DelhiNew Delhi—7↑ 4
8Hotel BrunelleschiFlorence—6↑ 4
7Hotel Adlon Kempinski BerlinBerlinKempinski6↑ 1
12The Ritz-Carlton Shanghai, PudongPudong, ShanghaiMarriott6↑ 4
5Emiliano Rio, an SLH HotelRio de Janeiro—6—
6Four Seasons Hotel SeoulSeoulFour Seasons6↑ 1
11THE OMNIAZermatt—6↑ 1
10The Cosmopolitan of Las VegasLas Vegas—6↑ 1
9Peridot Grand Luxury Boutique HotelHa Noi—6↑ 1
22JOIA Bávaro by IberostarPunta CanaIberostar5↑ 1
21InterContinental Auckland by IHGAucklandIHG5↑ 3
20Hotel ViennaVienna—5↑ 4
19Grand Hotel ZermatterhofZermatt—5↑ 1
18Four Seasons Hotel SydneyThe RocksFour Seasons5↓ 2
34Wynn Las VegasLas Vegas—5—
16Fairmont Nile City HotelCairo GovernorateAccor5—
15Copacabana Palace, A Belmond Hotel, Rio de JaneiroRio de JaneiroBelmond5↓ 1
This is the top 20 of this edition, so the change column skews upward by construction: a hotel that fell simply drops out of the table rather than showing a ↓. Read it as “what rose into the top 20”, not as a balanced gainers-and-losers view — the risers and fallers below are computed over every hotel, not just these twenty.
SOURCES

Where ChatGPT pulls hotel information from

Every URL ChatGPT retrieved classified into 12 buckets: OTAs, editorial, chain sites, direct hotel pages, social, meta-search, AI tools, community, government, directory consortia, and other.

16,222 sources retrieved
→
3,575 actually cited
=
22.0% citation throughput
Full throughput study →
Other
29.3% (4,748)
Review Site
19.4% (3,152)
Editorial
12.3% (2,001)
OTA & Aggregator
11.7% (1,890)
Independent Hotel
7.1% (1,149)
Community
6.0% (972)
Chain
5.5% (891)
Government / DMO
3.6% (589)
Hotel Directory
1.8% (284)
Meta-search
1.4% (220)
AI Tool
0.9% (154)
Search engine
0.9% (142)
Social Channel
0.2% (29)
Encyclopedia
0.0% (1)
Top categories
Other
wherebest.com 39% · myconciergehotel.com 36% · hanoitourism.org 25%
29.3%4,748
Review Site
tripadvisor.com 87% · tripadvisor.co.uk 7% · tripadvisor.com.au 6%
19.4%3,152
Editorial
thehotelguru.com 37% · timeout.com 33% · cntraveler.com 31%
12.3%2,001
OTA & Aggregator
booking.com 80% · expedia.com 14% · hotels.com 6%
11.7%1,890
Independent Hotel
luxuryhotel.com 35% · amaldives.com 34% · hotel28.co.kr 31%
7.1%1,149
Community
hotelierschoice.com 85% · therooftopguide.com 8% · budgetyourtrip.com 7%
6.0%972
Chain
marriott.com 41% · all.accor.com 37% · hilton.com 22%
5.5%891
Government / DMO
neworleans.com 50% · miamiandbeaches.com 26% · visitdubai.com 24%
3.6%589
Hotel Directory
hotelsforkings.com 65% · luxuryhotelcompare.com 23% · smallboutique-hotels.com 12%
1.8%284
Meta-search
kayak.com 51% · hotelscombined.com 30% · momondo.com 20%
1.4%220
AI Tool
staygenie.app 97% · topratedplaces.ai 3%
0.9%154
Search engine
google.com 92% · google.com.vn 5% · google.ch 3%
0.9%142
Has that changed over time?
28 weeks · 2026-04-13 → 2026-10-19

Share of citations by source type, week by week. Search engine is its own bucket rather than part of metasearch, and covers Google (every regional domain), Bing and the rest. Google AI Mode routes its cited sources through google.com/searchviewer instead of linking the publisher, and those citations were landing under “meta-search” alongside trivago and kayak — which made the mix read as competitive pressure from metasearch when actual metasearch is under 1%. The dashed rule marks the week that started.

OtherReview siteEditorialOTAIndependent hotelChainCommunitySmaller categories
0%20%40%60%searchviewer04-1305-1106-0807-0608-0308-3109-2810-19

Weekly until 19 Oct 2026, monthly from November 2026

Which categories get a line is decided once over the whole window by total citations, not week by week — otherwise a category dropping out of the top N would look like it collapsed to zero. Everything below the cut is summed into “smaller categories” rather than dropped, so the lines always account for the full week. Citations per week: 04-13 4,218 · 04-20 3,605 · 04-27 10,013 · 05-04 11,586 · 05-11 1,054 · 05-18 1,296 · 05-25 10,366 · 06-01 19,118 · 06-08 18,803 · 06-15 12,162 · 06-22 9,388 · 06-29 14,505 · 07-06 13,627 · 07-13 6,373 · 07-20 6,423 · 07-27 6,073 · 08-03 6,058 · 08-10 6,331 · 08-17 5,801 · 08-24 4,234 · 08-31 5,638 · 09-07 2,652 · 09-14 2,688 · 09-21 2,679 · 09-28 2,773 · 10-05 2,435 · 10-12 14,894 · 10-19 16,222.

TOP DOMAINS

The domains ChatGPT pulls most — and how many it keeps

Individual domains ranked by URLs retrieved, with the category and the share that survive into the answer (throughput = cited ÷ retrieved). Worldwide, current week.

Top domains — retrieved vs. cited
DomainCategoryRetrievedCitedThroughput
tripadvisor.comReview Site2,2331,101
49%
booking.comOTA & Aggregator1,143224
20%
hotelierschoice.comCommunity724123
17%
thehotelguru.comEditorial31589
28%
timeout.comEditorial27894
34%
cntraveler.comEditorial26156
21%
expedia.comOTA & Aggregator19942
21%
guide.michelin.comEditorial18749
26%
tripadvisor.co.ukReview Site1768
5%
tripadvisor.com.auReview Site16716
10%
marriott.comChain15631
20%
staygenie.appAI Tool15030
20%
all.accor.comChain14039
28%
tripadvisor.caReview Site1249
7%
hotelsforkings.comHotel Directory12427
22%
REDDIT & YOUTUBE

Does ChatGPT send people to Reddit and YouTube?

Share of answers citing at least one Reddit thread or YouTube video, week by week. Worldwide.

How often does ChatGPT cite Reddit or YouTube?
26 weeks · 2026-04-27 → 2026-10-19

Share of answers citing at least one Reddit thread or YouTube video. Counted from the citation domains, so this measures what ChatGPT showed as a source — not whether it read them.

RedditYouTube
0%25%50%75%04-2705-1806-0806-2907-2008-1008-3109-2110-1210-19

Weekly until 19 Oct 2026, monthly from November 2026

Latest week (2026-10-19): 13 of 609 answers cited Reddit (2.1%), 3 cited YouTube, across 16,222 citations.

BRANDS

Top hotel parent groups by ChatGPT mentions

Each ChatGPT mention matched against Google Places, then rolled up to parent group (Marriott = Ritz-Carlton + Westin + Sheraton + EDITION + …, etc.). WoW delta vs last week. Detection via 175+ brand-domain rules.

↑ Gainers
biggest WoW jumps
1Wyndham10 → 20+100%
2Oberoi8 → 14+75%
3Iberostar6 → 10+67%
4Taj12 → 19+58%
5B&B Hotels7 → 10+43%
↓ Losers
biggest WoW drops
1Radisson23 → 19-17%
2Shangri-La31 → 26-16%
3Rosewood20 → 17-15%
4Mandarin Oriental45 → 40-11%
5Rocco Forte12 → 11-8%
#BrandMentionsHotelsW/W
1Marriott288167↑ 11.2%
2Accor207123↑ 1.5%
3Hyatt14284↑ 18.3%
4Hilton13092· 0%
5IHG11383↑ 41.3%
6Four Seasons10537↑ 7.1%
7Mandarin Oriental4015↓ 11.1%
8Kempinski3111↑ 24.0%
9Peninsula2910↑ 11.5%
10Minor2717· 0%
11Shangri-La2612↓ 16.1%
12Langham268↑ 18.2%
13Wyndham2017↑ 100.0%
14Radisson1916↓ 17.4%
15Taj1910↑ 58.3%
16Rosewood178↓ 15.0%
171 Hotels177↑ 41.7%
18Capella166↑ 23.1%
19Belmond165↑ 6.7%
20Oberoi146↑ 75.0%
Chain vs Independent

Of the hotels named in this edition: how many resolved to a chain (Marriott / Accor / …), an independent property, a vacation rental, or stayed unmatched.

Chain42.7%↑ 12.5%
Independent57.0%↑ 14.1%
Vacation rental0.0%
Unresolved0.2%↓ 11.1%
DEFINITIONS

What every metric means

Plain-English definitions for each number on this page. For deeper visuals + examples, see the annual landscape report linked at the bottom.

Captures
Number of AI responses we collected for this edition. Target = 616 per platform (one per prompt). Less when there are upstream errors.
Web search
Did the response trigger a live web fetch? Detected from the underlying response stream’s search-result events, not the unreliable top-level flag. We do NOT force web search — this is organic model behavior.
Map widget
Did the response render a hotel-card map (the Google-Maps-style widget ChatGPT shows for travel queries)? Each card is a "map entity" with its own provider.
Sources / response
Distinct URLs the model consulted while answering — the URLs that show up in its retrieval log, regardless of whether it cited them inline. Computed only over web-search responses (otherwise the answer is from training data, no sources to count).
Citations / response
Subset of sources that the model rendered as inline footnote pills in the answer. A source becomes a citation when the model explicitly references it.
Fanouts / response
Number of sub-queries the model spun up internally to answer the prompt (e.g. "best hotels Paris" might fan out to "hotels Paris Marais", "luxury hotels Paris", "rooftop hotels Paris", …).
Map entities / response
Hotel cards inside the map widget. Each carries a provider (Google Places, TripAdvisor, Yelp, Foursquare, SERP) and a place ID where applicable.
OTA
Online travel agency (Booking, Expedia, Hotels.com, Agoda, Trip.com, Priceline, etc.) — commission-based booking sites.
Direct
A hotel’s own website. Computed at request time by matching the cited domain against Google Places.
Chain
A hotel chain’s brand website (hilton.com, marriott.com, ihg.com, …). 175+ chain-domain rules.
Editorial
Travel media (Condé Nast Traveler, Time Out, Lonely Planet, Forbes Travel Guide, NYT, etc.) and city-specific travel blogs (santorinidave.com, theurbanlist.com, etc.).
Directory
Multi-property hotel consortia / brand collectives (Small Luxury Hotels of the World, Design Hotels, Preferred Hotels, Virtuoso, …). Aggregate hotels under one umbrella but aren’t a single OTA.
Review
TripAdvisor, Oyster, Yelp, Trustpilot, Holidaycheck — review-first platforms.
Want a deeper read? See the in-depth annual report