Looking for a nearby eatery to silence your growling stomach? Where you go could vary widely, depending on your search choices.
New research from SEO and AI search platform Local Falcon found a significant gap between searches conducted with AI search and Google Maps for restaurants.
The study examined 10,000 restaurants across all 50 states and Washington, D.C., to test whether they appeared in Google Maps results and in AI-generated restaurant recommendations.
The company found that nearly three in four restaurants (74.9%) were invisible in Google’s AI recommendations, never surfacing at a single nearby search when a diner asked the AI where to eat.
“For a restaurant, that means getting shut out of AI Overviews completely, right as those overviews have become the way most people search because Google has promoted AI Overviews to the very top of the page,” observed Local Falcon CEO David Hunter.
“A restaurant is almost four times more likely to be invisible on Google’s AI surface than on Google Maps,” he told TechNewsWorld.
Meanwhile, for consumers, it means a much shorter menu of options, he added. “The top 10% of restaurants take 74.5% of all AI visibility, against 54% on Google Maps, so you’re picking from a short, often repetitive list while most of the places near you never come up,” he said.
Different Rewards Systems
Josh Stanaland, partner and CTO of Shark AI Solutions, a product development, AI, and client account management company in St. Petersburg, Fla., maintained that the core problem is that Google AI Overviews and other AI search tools do not work the way traditional search does. “Traditional search rewards review volume and backlinks,” he told TechNewsWorld. “AI search rewards structured, machine-readable content.”
“Most restaurants have invested years into getting Google reviews and building their Maps presence,” he explained. “None of that translates directly into AI visibility, because AI systems are looking for something different.”
“They’re looking for citable content, schema markup, and structured data that tells them what the business is, where it is, and who it serves,” he continued. “Most restaurant websites have none of that. So the AI ignores them, regardless of how many reviews they have.”
An important caveat to the new research is that there are other opportunities for discovery on the Google search results page, including the “map pack,” typically below AI Overviews, and organic listings, added Greg Sterling, co-founder of Near Media, a market research firm in San Francisco.
He acknowledged, though, that AI search can be a problem for consumers. “AI recommendations feel authoritative,” he told TechNewsWorld. “When someone asks ChatGPT or Google AI where to eat nearby and gets three suggestions, they assume those are the best options. In reality, they are the three options that happened to have the right technical infrastructure. The best restaurant in the area may not be showing up at all.”
Synthesizing Curated Sources
“Being easy to find on Google and being recommended by AI have become two different games,” noted Raúl Menoyo, founder of Citora, an AI visibility company in Madrid.
“A restaurant can own the Google Maps pack and still disappear the second a diner asks an AI ‘where should I eat near me,’ because the AI isn’t ranking the map — it’s writing an answer from the sources it trusts,” he told TechNewsWorld.
The 74.9% invisibility figure isn’t surprising, declared Chris McCarron, founder of GoGoChimp, an AI conversion-rate-optimization company in Glasgow, Scotland.
“It matches a broader pattern in AI citation research,” he told TechNewsWorld. “AI engines don’t crawl and rank like Google. They synthesize from a heavily curated source corpus that over-represents a small set of trusted domains.”
He noted that one citation analysis found that Wikipedia accounts for 47.9% of ChatGPT’s top-10 source share, Reddit for 46.7% of Perplexity’s top-10 share, and only 11% of domains are cited by both ChatGPT and Perplexity. For Google AI Overviews, Reddit and YouTube account for 21% and 18.8% of top-10 source share, respectively.
“Most restaurants live on Google Maps, Yelp, and TripAdvisor,” he said. “Those are excellent local-discovery surfaces, but AI engines don’t ingest them at the same scale they ingest Wikipedia, Reddit, and high-authority editorial.”
“So, a restaurant with 2,000 Google reviews can be invisible to ChatGPT because ChatGPT isn’t reading Google reviews,” he continued. “It’s reading what was written about the restaurant on Reddit, in editorial coverage, and in Wikipedia entries, which most restaurants don’t have.”
Diminished Value of Reviews
Local Falcon also found that restaurants with more than 1,000 Google reviews were left out of AI recommendations 70.9% of the time. Among the restaurants AI did recommend, 5.4% were rated below 3.5 stars, even though researchers explicitly asked for highly rated places in every search.
“With AI search, you’re probably skipping past some genuinely good restaurants with a long track record,” Local Falcon’s Hunter said. “A place with over 1,000 reviews has been tested by tens of thousands of real customers, and it’s still left out 70.9% of the time, roughly the same as a spot with a couple hundred reviews.”
“The restaurants people have clearly loved for years are often the ones the AI never brings up,” he added.
Alexandra Hayes, a GTM and AI product consultant in Austin, Texas, explained that, traditionally, a higher volume of reviews indicated a restaurant was more trustworthy, but AI may be factoring in more complicated metrics. “These may include contextual relevance, review quality, sentiment, recency, and potentially third-party sources,” she told TechNewsWorld. “Therefore, a restaurant that is well reviewed may not be visible at an AI recommender’s discretion.”
The findings show that the old local search playbook does not automatically carry over into AI search, added Jim Yu, CEO of BrightEdge, an enterprise SEO and content performance marketing company in San Mateo, Calif.
“Review volume still matters, but it is no longer a guarantee of visibility,” he told TechNewsWorld. “AI engines are weighing a broader set of signals, including the sources they cite, the way information is structured across the web, and how consistently a business appears across trusted third-party platforms.”
“This is important because restaurants have spent years optimizing for Google Maps and review volume,” he said. “Those signals still matter, but they are no longer enough on their own. AI search is forcing businesses to think about visibility across an ecosystem, not just rankings in one destination.”
Future Winners
Jeff Goyette, co-founder and CTO of Reel Estate, an AI-powered real estate video marketing platform, and former manager and server at a Logan’s Roadhouse franchise, pointed out that AI engines surface only 1% to 11% of eligible locations for a given query.
“AI visibility is up to 30 times harder to earn than a normal local ranking, and fewer than half the brands that rank well on Google are among the most-cited in AI results,” he told TechNewsWorld.
“The uncomfortable truth is that AI search does not inherit the signals restaurants spent 15 years building,” he said. “Reviews, star ratings, Maps position — none of it automatically carries over.”
“The restaurants that win the next phase will not be the ones who spend the most,” he predicted. “They’ll be the ones who treat AI visibility as its own young craft with clean structured data, consistent business information everywhere they appear online, and content an AI can actually quote.”
“The owners who figure that out early won’t be the biggest names,” he added. “They’ll be the ones who realized the rules changed before anyone bothered to tell them.”
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