{"@context":"https://schema.org","@type":"Blog","name":"Nicolas Sitter","url":"https://nicolassitter.com","author":{"@type":"Person","name":"Nicolas Sitter","url":"https://nicolassitter.com/about","sameAs":["https://www.linkedin.com/in/nicolassitternolleau/","https://github.com/Nicositter88","https://hotelrank.ai"]},"blogPosts":[{"@type":"BlogPosting","headline":"The Cost of AI Crawling (2026): What AI Bots Cost, Measured From First-Party Logs","description":"A first-party measurement of AI-bot crawling: 119,609 AI-bot requests across a real hotel and a marketing landing page (Dec 2025–Jul 2026). An 81% OpenAI/Claude duopoly on the hotel, ByteDance #2 on the landing page, on-demand fetches (ChatGPT-User/Claude-User) as a server-side impression counter that rose 7 → 389/mo, a crawl-to-referral picture too thin to state, and a search-crawler worked example where a Vercel→Supabase log drain grew one table to 5.6 GB before triage cut it to 952 MB.","datePublished":"2026-07-24T00:00:00.000Z","dateModified":"2026-07-24T00:00:00.000Z","url":"https://nicolassitter.com/research/cost-of-ai-crawling","category":"research"},{"@type":"BlogPosting","headline":"AI Citation Throughput in Hotel Search (2026, Live): Retrieved vs. Cited","description":"A live weekly measurement of citation throughput (selection rate) — cited sources ÷ retrieved sources per AI engine, computed from the AI Hotel Landscape corpus (616 prompts × 56 destinations weekly, 1.1M+ retrieved sources). ChatGPT ~26%, Google AI Mode 9–10%, Grok 0.3% historically; Reddit tops every major domain at 59% throughput within hotel intent.","datePublished":"2026-07-22T00:00:00.000Z","dateModified":"2026-07-22T00:00:00.000Z","url":"https://nicolassitter.com/research/ai-citation-throughput-2026","category":"research"},{"@type":"BlogPosting","headline":"ChatGPT's result_source for Flights (2026): A Different Retrieval Stack","description":"A flights replication of the hidden result_source study: 2,007 ChatGPT captures from US and UK vantage points. The licensed labrador tier drops from 99.85% (hotels) to 74.6%, bright carries 23.1%, serp appears for the first time, only 8.6% of flight questions skip the web, half of cited documents bypass the tagged retrieval layer, and Reddit is cited on 83% of its fetches.","datePublished":"2026-07-21T00:00:00.000Z","dateModified":"2026-07-21T00:00:00.000Z","url":"https://nicolassitter.com/research/chatgpt-flights-retrieval-tiers-2026","category":"research"},{"@type":"BlogPosting","headline":"Where the AIs Sent L'Étape du Tour Riders to Sleep (2026)","description":"An event-anchored lodging study fired in the days before L'Étape du Tour 2026 (Le Bourg-d'Oisans → Alpe d'Huez). 5 AI engines (ChatGPT, Perplexity, Gemini, Copilot, Google AI Mode), EN + FR, 14 lodging prompts that name the event but never the towns — the engines had to know the route. 252 captures, 901 lodging mentions matched to a 74-place Oisans registry (55 real hotels/B&Bs). Consensus pick: Hôtel Oberland in Le Bourg-d'Oisans (29 mentions), then the Alpe d'Huez block (Royal Ours Blanc 24, Le Castillan 23, Grandes Rousses 22). Only 29% of named lodging verifies as real route-area properties; 8% are whole towns, 4% cycling tour operators. ChatGPT alone recommended Albertville/La Plagne hotels — the 2025 edition's geography, 100 km away — 26 times; the four live-search engines never did. Airbnb/camping/gîtes: ~3% of mentions despite being how much of the field actually stays.","datePublished":"2026-07-21T00:00:00.000Z","dateModified":"2026-07-21T00:00:00.000Z","url":"https://nicolassitter.com/research/letape-du-tour-hotels-ai-search-2026","category":"research"},{"@type":"BlogPosting","headline":"Argentina vs Spain in Paris: Who Wins the AI Restaurant War? (2026)","description":"A World-Cup-final head-to-head, fired with France knocked out. We asked 5 AI engines (ChatGPT, Perplexity, Gemini, Copilot, Google AI Mode), EN + FR, for the best Argentinian vs the best Spanish restaurant in Paris — 20 balanced prompts, 360 captures, 2,006 mentions matched to 81 real venues (42 Argentinian, 39 Spanish, seeded from Google Maps using only cuisine-specific categories). Argentina wins decisively: a front-three consensus (LOCO 124, Santa Carne 116, Les Grillades de Buenos Aires 103, each beating Spain's best pick Bodega Potxolo at 91) and tighter venue concentration on every engine. Two asides: ChatGPT's structured map widget crowns Bistro Caminito (#1 there, only #8 in prose), and \"Rosario\" — an Argentine city — gatecrashes the Spanish arm. AI Mode × FR proxy rejected by Bright Data, absent.","datePublished":"2026-07-17T00:00:00.000Z","dateModified":"2026-07-17T00:00:00.000Z","url":"https://nicolassitter.com/research/argentina-vs-spain-paris-ai-search-2026","category":"research"},{"@type":"BlogPosting","headline":"AI Search for Tattoo Studios in Berlin (2026): Same City, New Vertical — the Baseline Snaps Back","description":"The series' first same-city control: Berlin, already measured for yoga, re-run for tattoo studios with identical languages and proxies. 26 prompts × 5 AI engines, EN + DE, against a 551-studio registry. Perplexity's booking-platform share collapses from 31% (yoga) to 4%, ChatGPT returns to 35% studio-website citations, EN/DE top-5 overlap hits a series-high 67%, and famous private artists are recommended despite having no Google Maps listing.","datePublished":"2026-07-15T00:00:00.000Z","dateModified":"2026-07-15T00:00:00.000Z","url":"https://nicolassitter.com/research/tattoo-studios-berlin-ai-search-2026","category":"research"},{"@type":"BlogPosting","headline":"AI Search for Bistros in Paris (2026): The Guidebook City Where Restaurant Websites Disappear","description":"A cross-vertical field test: 26 prompts × 5 AI engines (ChatGPT, Perplexity, Gemini, Copilot, Google AI Mode), EN + FR, matched to 579 Paris bistro venues. ChatGPT cites bistro websites just 0.9% — the series floor — while the Paris guide layer (parisjetaime.com, Time Out, the Michelin Guide) absorbs the citations and the predicted TheFork takeover never happens (~2% booking share). 94% arrondissement accuracy overall; Perplexity the outlier at 47%.","datePublished":"2026-07-08T00:00:00.000Z","dateModified":"2026-07-08T00:00:00.000Z","url":"https://nicolassitter.com/research/bistros-paris-ai-search-2026","category":"research"},{"@type":"BlogPosting","headline":"How ChatGPT Pulls Hotel Prices (2026): It Scrapes the OTAs, Then Cites Reddit","description":"A network-source forensic study of how ChatGPT sources hotel prices. Across 240 captures and 3,092 fetched documents: price questions hit the live web 98.8% of the time, OTAs are 46% of everything fetched but cited only 11%, Reddit is cited 100% of the time it is fetched, and the cited price rides one licensed retrieval tier (labrador) not the Bright Data scraper tier.","datePublished":"2026-06-26T00:00:00.000Z","dateModified":"2026-06-26T00:00:00.000Z","url":"https://nicolassitter.com/research/chatgpt-hotel-price-sources-2026","category":"research"},{"@type":"BlogPosting","headline":"How to Measure AI Hotel Traffic and Bookings (2026)","description":"A practical framework for measuring AI-driven hotel traffic and bookings when attribution is broken: GA4 referrers as a floor, branded-query growth, pre-booking forms, and ChatGPT Apps. Includes the attribution-gap benchmark — analytics likely undercounts AI influence by ~2-5x for hotels.","datePublished":"2026-06-25T00:00:00.000Z","dateModified":"2026-06-25T00:00:00.000Z","url":"https://nicolassitter.com/research/how-to-measure-ai-hotel-traffic-2026","category":"research"},{"@type":"BlogPosting","headline":"ChatGPT's Hidden result_source (2026): How It Sources Hotel Answers","description":"An undocumented field, result_source, tags every page ChatGPT retrieves with the pipeline that fetched it. Across 30,002 hotel citations, 99.85% come from one licensed tier (labrador), serp never appears, and 37.3% of questions never search the web. Within the tier, brand sites are cited while aggregator listicles are retrieved then discarded.","datePublished":"2026-06-25T00:00:00.000Z","dateModified":"2026-06-25T00:00:00.000Z","url":"https://nicolassitter.com/research/chatgpt-result-source-retrieval-tiers-2026","category":"research"},{"@type":"BlogPosting","headline":"AI Hotel Memory 2026: What Chatbots Remember About Hotels Without Searching","description":"A parametric-recall study with web search OFF. Three cheap models (GPT-5.4-nano, GPT-5.4-mini, Gemini 3.1 Flash-Lite) named hotels in JSON, returning each hotel's website so it could be verified by DNS. ~1,400 generations across global chains and Paris/Dubai/London/New York. Findings: hotel chains are known cold (~99% correct websites everywhere); individual-hotel websites resolve only 47%–97% of the time depending on model and city; the failure mode is the model knowing a real hotel but inventing its web address (Le Bristol Paris → dead bristolparis.com vs real oetkercollection.com); Paris is the hardest city (independent palaces on collection domains); and the cheapest model, Gemini 3.1 Flash-Lite, had the most accurate hotel memory.","datePublished":"2026-06-16T00:00:00.000Z","dateModified":"2026-06-16T00:00:00.000Z","url":"https://nicolassitter.com/research/ai-hotel-memory-2026","category":"research"},{"@type":"BlogPosting","headline":"AI Search for Specialty Coffee in Marseille (2026)","description":"Marseille breaks the entity-engine pattern. Across Paris yoga, Berlin yoga and Amsterdam bikes, ChatGPT cited shop/studio websites ~32% of the time. For Marseille specialty coffee it's only 10% — instead 31% Reddit + 32% review-aggregators + 14% French local blogs = 77% third-party. 27 prompts × 5 AI engines × EN/FR (9/10 platform-proxy batches; AI Mode × FR rejected at the Bright Data trigger), 413 captures, 3,442 citations, 786 map entities against 280 specialty cafés. Three Marseille-only findings: Instagram at 237 cites (the highest social signal we've measured in any city/vertical), Gemini swinging to global specialty press (baristamagazine.com = 34% of its citations) when local trade press is absent, and a two-metric leaderboard split — Deep is the text-mention consensus winner (216 mentions across all five engines, 61.5% of ChatGPT) while Nua tops the cite-counted score (356) only because its brand_key is instagram.com and every Instagram cite attributes to it. EN vs FR control prompt = 11% overlap, the most language-divergent result so far.","datePublished":"2026-06-15T00:00:00.000Z","dateModified":"2026-06-15T00:00:00.000Z","url":"https://nicolassitter.com/research/specialty-coffee-marseille-ai-search-2026","category":"research"},{"@type":"BlogPosting","headline":"Are Hotels in Common Crawl? 39% Are Missing From AI Training Data (2026)","description":"108,109 hotel websites checked against the May 2026 Common Crawl snapshot: 60.6% are in it, 39.4% absent. Independents (61%) beat chains (45.9%); local-market TLDs are present but shallow; .es lags at 37%. Includes an interactive coverage map and a free checker.","datePublished":"2026-06-09T00:00:00.000Z","dateModified":"2026-06-09T00:00:00.000Z","url":"https://nicolassitter.com/research/hotels-in-common-crawl-2026","category":"research"},{"@type":"BlogPosting","headline":"AI Search for Yoga Studios in Berlin (2026)","description":"Direct replication of the Paris yoga study in Berlin: 27 prompts × 5 AI engines × EN/DE, 540 captures, 5,293 citations matched to 631 studios. The three engine personalities replicate almost to the percentage point (Copilot 95% entity, ChatGPT 32% studios + 19% Reddit, AI Mode 59% google.com) — strong evidence they're structural, not city-specific. The Berlin twist: booking platforms (Urban Sports Club, Eversports, ClassPass) climb to 31% of Perplexity citations, with blog.urbansportsclub.com and classpass.com the two most-cited domains overall.","datePublished":"2026-05-27T00:00:00.000Z","dateModified":"2026-05-27T00:00:00.000Z","url":"https://nicolassitter.com/research/yoga-studios-berlin-ai-search-2026","category":"research"},{"@type":"BlogPosting","headline":"AI Search for Bike Shops in Amsterdam (2026)","description":"27 prompts × 5 AI engines × EN/NL against 228 Amsterdam bike shops, 378 captures, 3,010 citations. The cleanest cross-engine entity consensus in the study: Copilot 97% shop websites, Reddit the single most-cited domain anywhere (198 cites, 4 platforms), AI Mode 82% google.com self-citation. Perplexity's exposed search query (fanout_count=1, prompt language preserved) is the mechanism behind 0% EN/NL overlap on repair and commuter queries.","datePublished":"2026-05-26T00:00:00.000Z","dateModified":"2026-05-26T00:00:00.000Z","url":"https://nicolassitter.com/research/bike-shops-amsterdam-ai-search-2026","category":"research"},{"@type":"BlogPosting","headline":"AI Search for Bookstores in Tokyo (2026)","description":"22 prompts × 4 AI engines (Perplexity returned no usable Tokyo data), EN + JA, US/JP proxies, matched to 584 Tokyo bookstores. Tokyo is where AI search stops looking Western: Gemini cites store sites just 5% of the time and runs on a 58% local-guide web (whenin.tokyo, Tokyo Weekender, GaijinPot), Japanese prompts cite .jp domains 5× more than English (the sharpest language→TLD coupling in the study), and DAIKANYAMA T-SITE / Kinokuniya tie at the top (122 each).","datePublished":"2026-05-26T00:00:00.000Z","dateModified":"2026-05-26T00:00:00.000Z","url":"https://nicolassitter.com/research/bookstores-tokyo-ai-search-2026","category":"research"},{"@type":"BlogPosting","headline":"AI Search for Yoga Studios in Paris (2026)","description":"First non-hotel field test of the AI-search methodology. 27 prompts across 5 AI engines (ChatGPT, Perplexity, Gemini, Copilot, Google AI Mode), EN + FR, matched to 369 Paris yoga studios. Copilot cites studio sites 96% of the time, ChatGPT leans on Reddit (its #1 source at 17%), Google AI Mode cites its own SERP back 52%. The per-engine citation split from the hotel studies generalises intact.","datePublished":"2026-05-24T00:00:00.000Z","dateModified":"2026-05-24T00:00:00.000Z","url":"https://nicolassitter.com/research/yoga-studios-paris-ai-search-2026","category":"research"},{"@type":"BlogPosting","headline":"The ChatGPT Direct-Traffic Explosion for Hotels (May 2026)","description":"On May 7, 2026, ChatGPT started embedding hotel-brand URLs inline in answers. Across The Hotels Network's panel of 17,000+ hotels, daily AI referrer sessions jumped +62% (31,688 → 51,282/day) and held through May 25. A skewed tail: 286 hotels now draw 5%+ of new sessions from AI, 43 get 10%+. Perplexity and Claude lost share — it's a ChatGPT-only story.","datePublished":"2026-05-21T00:00:00.000Z","dateModified":"2026-05-29T00:00:00.000Z","url":"https://nicolassitter.com/research/chatgpt-hotel-direct-traffic-explosion-2026","category":"research"},{"@type":"BlogPosting","headline":"The Schema.org Debate (2026): Why It Still Matters for Hotels","description":"AEO oversold schema as an LLM unlock. The pushback that transformers read tokens, not JSON-LD, is right for the general case. For hotels, every major AI still grounds against Places / KG / OTA aggregators — surfaces that sit downstream of schema. The four fields that actually move the needle: Hotel, sameAs, starRating, alternateName.","datePublished":"2026-05-13T00:00:00.000Z","dateModified":"2026-05-13T00:00:00.000Z","url":"https://nicolassitter.com/research/schema-org-grounding-loop-2026","category":"research"},{"@type":"BlogPosting","headline":"How Mistral Searches Hotels","description":"Captured Le Chat event streams. One Brave web_search call per entity (parallelised for brand-vs-brand prompts), prompt-language preserved with per-term rewrites, the current year injected as a freshness anchor, snippet paraphrase. Niche queries surface real specialists; generic queries surface SEO-spam aggregators. Authority-laundering patterns turn single reviews and self-marketing into asserted features — and one hallucinated a hotel that doesn’t exist.","datePublished":"2026-05-05T00:00:00.000Z","dateModified":"2026-05-05T00:00:00.000Z","url":"https://nicolassitter.com/research/how-mistral-searches-hotels-2026","category":"research"},{"@type":"BlogPosting","headline":"How Claude Searches Hotels","description":"Captured event streams across several Claude hotel conversations. With Connector Discovery off (the default), almost everything goes through one Google Places call. Turn it on and Claude branches into a small curated OTA-connector picker (Booking.com / Tripadvisor / Trivago and a few others) — no ads by design, so the curation logic itself becomes the product.","datePublished":"2026-05-01T00:00:00.000Z","dateModified":"2026-05-04T00:00:00.000Z","url":"https://nicolassitter.com/research/how-claude-searches-hotels-2026","category":"research"},{"@type":"BlogPosting","headline":"ChatGPT Hotel Ads Are Live — CPC Pivot, Ads Manager, $50K Entry","description":"Sponsored ads in 20-35% of hotel queries. Booking.com at 43.5%. April 29 update: OpenAI launched a self-serve ads manager, moved from CPM to CPC, dropped entry to $50K, ~$100M annualised revenue six weeks in.","datePublished":"2026-04-29T00:00:00.000Z","url":"https://nicolassitter.com/research/chatgpt-hotel-ads-live-2026","category":"research"},{"@type":"BlogPosting","headline":"ChatGPT 5.3 Halved Its Hotel Sources — March 5, 2026 Cutover","description":"Daily ChatGPT UI runs of 140 world hotel prompts from 4 locales. On Mar 5, URLs per answer fell 49% (24→12). Booking −82%, Expedia −76%, Reddit −93%.","datePublished":"2026-04-17T00:00:00.000Z","url":"https://nicolassitter.com/research/chatgpt-hotel-source-shift-2026","category":"research"},{"@type":"BlogPosting","headline":"Hotel YouTube Channels — Activity Study 2026","description":"We analyzed YouTube channels linked from 9,889 hotel websites. 43.7% are ghost channels. Only 11.3% post monthly.","datePublished":"2026-04-16T00:00:00.000Z","url":"https://nicolassitter.com/research/youtube-hotel-visibility-2026","category":"research"},{"@type":"BlogPosting","headline":"ChatGPT Hotel Index vs Live Web — What Changes When Search Goes Offline","description":"400 hotel queries, 2 models, 2 search modes. 83% of cited domains differ between live and cached.","datePublished":"2026-04-09T00:00:00.000Z","url":"https://nicolassitter.com/research/chatgpt-hotel-index-vs-live-web-2026","category":"research"},{"@type":"BlogPosting","headline":"How Dirty Is Google Maps Hotel Data? 179K Study","description":"17% of Google Maps hotel listings fail QA. 8,167 OYO vacation rentals. Belgium loses 54% after cleaning.","datePublished":"2026-04-01T00:00:00.000Z","url":"https://nicolassitter.com/research/google-maps-hotel-data-quality-2026","category":"research"},{"@type":"BlogPosting","headline":"ChatGPT Hotel Data Sources: 100K Entity Study","description":"Google dropped from 100% to 70.3% in 90 days. TripAdvisor descriptions are 8.8x longer.","datePublished":"2026-03-24T00:00:00.000Z","url":"https://nicolassitter.com/research/tripadvisor-chatgpt-hotels-study-2026","category":"research"},{"@type":"BlogPosting","headline":"What Hotel Footers Reveal — 98K Study","description":"Instagram is in 40.8% of hotel footers. 24% of copyright years are 3+ years stale. 10% link to OTAs.","datePublished":"2026-03-23T00:00:00.000Z","url":"https://nicolassitter.com/research/hotel-footer-analysis-study-2026","category":"research"},{"@type":"BlogPosting","headline":"Hotel llms.txt Adoption Study 2026","description":"105,002 hotel websites scanned for llms.txt. Only 6.3% have one. US leads at 12.4%.","datePublished":"2026-03-21T00:00:00.000Z","url":"https://nicolassitter.com/research/hotel-llms-txt-adoption-study-2026","category":"research"},{"@type":"BlogPosting","headline":"Hotel robots.txt & AI Blocking Study 2026","description":"105,002 hotel robots.txt files parsed. Only 3.3% block any AI crawler. France leads at 7.5%.","datePublished":"2026-03-20T00:00:00.000Z","url":"https://nicolassitter.com/research/hotel-robots-ai-blocking-study-2026","category":"research"},{"@type":"BlogPosting","headline":"What Hotels Are Actually Called: A Naming Study","description":"Analysis of naming conventions across 121,425 hotels in 7 countries","datePublished":"2026-03-10T00:00:00.000Z","url":"https://nicolassitter.com/research/hotel-naming-study-2026","category":"research"},{"@type":"BlogPosting","headline":"Hotel Schema.org Adoption Study 2026","description":"121,425 hotels scanned — 36.3% have no schema at all","datePublished":"2026-03-05T00:00:00.000Z","url":"https://nicolassitter.com/research/hotel-schema-adoption-study-2026","category":"research"},{"@type":"BlogPosting","headline":"Anatomy of a ChatGPT Hotel Search","description":"12 systems, 7 providers, 424 A/B tests — a technical teardown","datePublished":"2026-03-01T00:00:00.000Z","url":"https://nicolassitter.com/research/anatomy-chatgpt-hotel-search-2026","category":"research"},{"@type":"BlogPosting","headline":"Yelp in ChatGPT: Hotel Data Study","description":"33% Yelp integration rate in US hotel queries across 14 destinations","datePublished":"2026-02-20T00:00:00.000Z","url":"https://nicolassitter.com/research/yelp-chatgpt-hotels-study-2026","category":"research"},{"@type":"BlogPosting","headline":"How Consistent Are AI Hotel Rankings?","description":"Only 50.5% position stability across query reruns","datePublished":"2026-02-15T00:00:00.000Z","url":"https://nicolassitter.com/research/ai-hotel-rankings-consistency-study-2026","category":"research"},{"@type":"BlogPosting","headline":"Google AI Mode: Where Do Hotel Clicks Actually Go?","description":"79% of hotel clicks in Google AI Mode go to Google Business Profiles","datePublished":"2026-02-10T00:00:00.000Z","url":"https://nicolassitter.com/research/google-ai-mode-hotel-study-2026","category":"research"},{"@type":"BlogPosting","headline":"Do French Hotels Blog? A 15,000-Hotel Study","description":"49.3% have blogs but only 1 in 4 are active","datePublished":"2026-01-20T00:00:00.000Z","url":"https://nicolassitter.com/research/french-hotel-blog-study-2026","category":"research"},{"@type":"BlogPosting","headline":"The AI Hotel Landscape 2026","description":"How 6 AI Models Rank 12,500+ Hotels Across 1.2 Million Citations","datePublished":"2026-01-15T00:00:00.000Z","url":"https://nicolassitter.com/research/ai-hotel-landscape-2026","category":"research"}]}