What TikTok Did to Destinations and What AI Is Doing Now
More than half of Americans are already choosing where to travel by asking AI. How it worked before, what changed in each era, why the answer you receive can be bought, and seven predictions for the years ahead.
Last summer, 55% of Americans used an AI application to plan their vacations, up from 38% the previous year, according to McKinsey.
TripAdvisor attributed an estimated one-fifth decline in its own website traffic over a single year to AI.
That means the most important decision in any trip, where to go and where to eat, has just changed hands. For the fourth time in 180 years.
And every transition changed tourism in ways that only became visible afterward.
It is worth understanding how things worked before, because that is the only way to see what is coming now.
How It Worked Before: Four Owners of the Same Decision
Era 1: The travel agent. Starting in 1841, with Thomas Cook, someone chose for you, and you paid them to do it. The curator was a person with a name, an address, and a reputation at stake. If the hotel was bad, you knew who to complain to.
The effect on destinations was slow and predictable. A place became fashionable over the course of years.
Era 2: The printed guidebook. From the 1970s through the 2000s: Lonely Planet, Routard, and, in Brazil, Guia Quatro Rodas. The curator was an author applying editorial judgment and publishing once a year.
This is where the first significant concentration effect appeared: a mention in Lonely Planet could fill a hostel in Bangkok or a restaurant in Kathmandu.
But notice the speed. The list changed once a year. The hostel owner had twelve months to prepare, hire staff, and expand. So did the city.
Era 3: The review platform. From the 2000s onward: TripAdvisor and similar platforms. Curation became the average opinion of the crowd, transformed into a ranking.
And the effect became measurable. Dante Donati, from Columbia Business School, showed that when the European Union abolished roaming charges in 2017, the increase in tourists consulting TripAdvisor while traveling produced a 5% increase in revenue for tourist-oriented restaurants in Rome, concentrated among the highest-rated establishments.
This was the first time the influence of the discovery layer on the revenue of an entire neighborhood could actually be measured.
Concentration stopped being annual and became continuous. But it was still public: anyone could open the website, see who ranked first, and understand why.
Era 4: Social media. From 2015 onward, and especially with TikTok. The curator became an algorithm optimized for screen time, not for quality or a destination’s capacity.
What changed was speed and randomness. A video takes off, the system distributes it to millions, and the crowd shows up.
Hallstatt, a small Austrian lakeside town with only a few hundred residents, was overwhelmed. Previously unknown restaurants suddenly had hours-long lines with no editorial explanation whatsoever.
Concentration became sudden and unfair. But, and this is the most important point in this entire article, it was still visible.
You could see the hashtag. Count the views. Find the original video. Know whether the influencer had been paid, because advertising is regulated and there are authorities responsible for enforcing the rules. The local government could see the wave coming.
How It Works Now: The Fifth Era, and the First Invisible One
Now AI is the curator.
And here is the comparison that summarizes everything:
In the guidebook era, you and your neighbor read the same page. In the TripAdvisor era, you saw the same ranking. In the TikTok era, you might see different videos, but those videos existed somewhere in public.
Now the two of you can ask exactly the same question, receive different answers, and neither of you knows it.
The recommendation has no source, no ranking, no counter, no negative comment underneath it, and no advertising disclosure.
And the immediate measurable effect is concentration, not dispersion.
Research from the University of Westminster found a clear ChatGPT preference for established tourism hubs: ask for a hidden gem, get the Eiffel Tower.
More precise is the work of Daniel Paül i Agustí, from the University of Lleida. He collected 10,000 ChatGPT-generated recommendations for places to visit in Barcelona and compared them with photos that actual tourists posted on Instagram, coding the location of each one.
The photos came from around 1,300 different places.
AI recommended 215.
Six times less diversity.
And one detail that almost all the coverage ignored: among the suggestions, 38 attractions appeared to be fictional. Thirty-eight places that do not exist, recommended with the same confidence as places that do.
On the other side, there is an optimistic argument, and it has a real basis: different models produce different answers, the same model varies between responses, and personalization tends to send vegans, people with celiac disease, and architecture enthusiasts to different places. An analysis by Evertune demonstrated this with pizzerias in Rome, with each system drawing from different sources and Gemini citing 24 different establishments across its responses.
Both sides are right, which is precisely why the debate over whether AI concentrates or disperses tourism leads nowhere.
Concentration is today’s effect, with generic questions.
Dispersion is tomorrow’s possible effect, with specific questions.
The question that determines the future is a different one.
The Right Question: Who Controls the Answer, and How Much Does It Cost to Buy It?
TikTok concentrated attention by chance.
AI can concentrate it by contract.
An industry already exists for this, with a name and a price: generative engine optimization. The market was estimated at around $1 billion in 2025, with projections above $1.4 billion in 2026, growing by more than 45% annually.
The method is public. Agencies map the surfaces that models read, including forums, review websites, transcripts, comparison platforms, and publisher pages, and reinforce the client’s presence across those surfaces.
Apply that to a restaurant.
It no longer needs to go viral, which is expensive, uncertain, and short-lived. It needs to appear in the places the model consults. A dozen well-constructed discussion threads, consistent presence on aggregators, structured content on its own website.
Going viral on TikTok is a lottery.
Getting into the model’s answer is a service you can buy.
And if this can be hired to shape a state’s foreign-policy narrative, as has already happened with a front organization funded to influence AI responses, paying to influence recommendations for the neighborhood pizzeria is pocket change.
Meanwhile, the World Is Kicking Influencers Out the Front Door
Here is the irony that defines this moment.
At the exact moment recommendation is migrating to an invisible layer, destinations and establishments are rushing to ban the visible one.
Bali. Indonesia created a task force with hundreds of officers monitoring social media to catch foreigners producing paid content while on tourist visas. Penalties include fines, immediate deportation, and bans on re-entry lasting at least ten years.
United States. The government has formally warned foreign creators that producing paid content while on a tourist visa constitutes illegal work.
Kyoto. In Gion, the geisha district, a council of residents and business owners banned tourists from entering private residential alleys, with fines of ¥10,000. Photography on those streets had already been prohibited in 2019, and access restrictions followed in 2024 after years of tourists chasing and cornering geishas for photographs.
Portofino. The Italian town created zones where stopping to take photographs is prohibited, with fines exceeding $300.
And restaurants. In the United States, at least eleven states already have establishments imposing some form of cellphone restriction. The Delilah restaurant group adopted a “no phones, no posting” policy in Dallas, Las Vegas, Los Angeles, and Miami.
Notice what is being banned in all these cases.
It is not the person.
It is the record.
The photograph, the video, the post.
The logic is coherent: if the damage comes from an image going viral, prohibit the image.
Except that this defense was built against the threat of the previous decade.
A language model does not need a photograph. It does not need to enter a Gion alley, film the dish, or obtain a visa.
It reads text: reviews, forums, blogs, comments, transcripts.
The restaurant that banned phones at the table remains perfectly indexable. And there is no rule preventing someone from paying to get that restaurant into an AI answer, because there is no image to prohibit.
Cities are locking the front door while the new entrance is around the back.
And it does not even have a door.
What a City Loses When Recommendation Becomes Private
There is a second-order effect that almost nobody discusses, and to me it may be the most serious of all.
When everyone saw the same list, there was a shared public good: information about where people were going was public.
The local government could see the trend. Hotels could prepare. Restaurants knew they were about to fill up. Trail managers knew they would need additional staff.
Personalized recommendation destroys that predictability.
A thousand people receiving a thousand different, invisible suggestions still add up to the same crowd over the weekend, except now there is no advance warning.
The city discovers the demand when the bus pulls up.
Tourism planning depends on anticipation.
AI is removing the anticipation while keeping the crowd.
This is not a problem of too many tourists.
It is a problem of blindness.
Europe Is Closing the Door While Brazil Opens Its Own
It is worth looking at what mature destinations are doing, because this is our scenario ten years from now.
Venice increased its day-visitor fee to €10, limited groups to 25 people, banned loudspeakers, and introduced fines of up to €350. Barcelona doubled its tourist tax and plans to eliminate tourist-apartment licenses by 2028. Spain removed around 65,000 short-term rental listings that did not comply with regulations. Nice and Cannes began limiting cruise passengers.
Protests against overtourism took place in more than 40 Spanish cities, organized primarily by neighborhood associations, with a central complaint that is not actually about tourists.
It is about rent.
And in March 2026, the European Parliament approved a resolution whose central objective is redistributing visitors, not merely taxing them.
The irony: redistribution is exactly what the optimistic version of AI promises to do automatically.
Europe is turning into public policy what Silicon Valley offers as a product feature.
And public policy has debate, accountability, and elected officials who can be held responsible.
A recommendation algorithm has none of the three.
Brazil is moving in the opposite direction, and for good reasons.
The country received 3.74 million international tourists in the first quarter of 2026, an all-time record, with air arrivals increasing by 19.4%. Embratur projects that Brazil will surpass 10 million international visitors in 2026 and is talking about 12 million in 2027.
Excellent economic news.
And exactly the right moment to ask the question Europe failed to ask in time.
Because in Brazil, the cost of getting it wrong is higher.
Go back to Barcelona’s 38 invented places: in a European capital, a fictional attraction is frustrating. You walk for ten minutes and curse.
Apply the same hallucination rate to an itinerary through Jalapão, Lençóis Maranhenses, Chapada Diamantina, or the Amazon, with a waterfall that does not exist, an underestimated distance, or a dirt road described as easily accessible.
Here, a fictional attraction is not a frustration.
It is a rescue operation.
Seven Predictions, and the Signals We Can Already See
Now for the predictions, with commitment.
1. AI optimization will become a standard marketing package item for restaurants and hotels within two years.
Signal: the market already exists, is growing by more than 45% annually, and is already selling services to governments. When it reaches small businesses, it will be sold as “appearing in ChatGPT,” by the same type of agency that currently sells Instagram management.
2. Guides and platforms will stop being destinations and become suppliers.
Signal: TripAdvisor has already lost roughly one-fifth of its traffic. Value is shifting from being visited by humans to being read by machines, and the revenue model will shift from advertising toward content licensing.
3. Disclosure rules for AI recommendations will emerge.
Signal: influencer advertising is already regulated, synthetic election content already requires labeling in Brazil, and Europe already requires identification of generated content. It is only a matter of time before someone asks why an AI recommendation for a restaurant does not have to disclose sponsorship.
My bet is that the first rule will come from Europe.
4. Tourism authorities will become producers of machine-readable data.
Signal: formats that make websites easier for models to read already exist and are already being used by those who want to be cited.
The viable public response is not prohibition. It is better publishing: daily capacity, opening hours, rainy seasons, trail warnings, everything structured so the model can read it.
Those who do not publish their own information will be described by third parties.
5. A new category will emerge: “places that do not want to be found.”
Signal: Gion, Portofino, Bali.
The logical next step after prohibiting photography is asking not to be indexed, a kind of opt-out from the physical world.
It will begin with small communities containing fragile attractions.
6. Overtourism will become more unpredictable, not less.
Testable prediction: the next small town overwhelmed by tourism will not have an identifiable viral video as the cause.
People will simply start showing up, and nobody will be able to explain where they came from.
7. AI errors will stop being jokes and become safety incidents.
Signal: the 38 fictional attractions.
Prediction: the first rescue operation involving a tourist who followed an AI-invented itinerary into a remote area will become global news.
And it will be that case, not the overtourism debate, that triggers the first regulation specifically targeting AI travel recommendations.
And one prediction specifically for Brazil: with 10 million international visitors on the way, Brazil will discover that it has no idea what AI models are telling people about its own destinations.
Embratur will have to stop being only a tourism advertising agency and become a data curator as well.
It is not glamorous work.
But it is what determines whether tourists arrive informed or arrive lost.
End-of-the-World Spoiler™
For 180 years, whoever told tourists where to go had a name.
The travel agent had an office.
The guidebook had an author and a publisher.
The platform had a public ranking.
The influencer has a face, and the law requires them to disclose when they have been paid.
AI has none of that.
It gives you a name, in a neutral tone, with no source, no alternative, no history, and nobody to complain to.
That is the real change.
It is not about concentrating or dispersing tourists.
It is that the layer deciding the destinations of millions of people, and billions in revenue, has become the first one in history that nobody can audit.
If the answer is organic, great.
If it was bought, you will never know.
And that is exactly why it will be bought.
Three questions:
When AI recommends a restaurant to you, can you tell whether someone paid for that name to appear?
If tourism demand remains invisible until tourists actually arrive, how does a small city plan water supply, guides, transportation, and ambulances?
And the Brazilian question: with 10 million international visitors on the way, who is checking what AI models are saying about our destinations, and how many of those answers describe places that do not exist?
Tech Gossip follows whoever controls the layer where decisions are made, because that is always where the money is. Follow us at www.techgossip.com.br.
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