The National Day holiday has long been a key window for China's annual consumption. This year, more and more travelers are no longer manually scrolling through social platforms and comparing guides, but instead directly inputting their travel budget, destination, and taste preferences into large models and AI agents to generate a complete holiday plan with one click. AI is becoming the "travel buddy" for more and more people.

This shift is backed by data. From the summer of 2026 to the National Day holiday, the popularity of AI travel tools continued to rise. Data from Fliggy shows that AI interactions related to travel on the platform increased by 297% quarter-on-quarter during the summer; DeepTrip, the travel agent under Tongcheng Travel, saw hourly visits increase more than threefold during peak hours on the first day of the National Day holiday. Users can input their travel time, destination, number of people, budget, and preferences, and generate daily itineraries, attraction suggestions, and accommodation recommendations, and can continue to request modifications, adjust the order, and add or remove attractions.

A relevant person in charge of Tongcheng Travel's R&D and Cloud Empowerment Center previously stated publicly that the core value of AI travel planning should not stop at text-based plan output, but should also address the real problem of "what to do after reading the guide." The efficiency gains AI brings to travel are clearly visible. Holiday travel is inherently an overload of information: flights, hotels, tickets, routes, opening hours, queue times—every item requires repeated comparison. AI compresses this scattered information into a single conversation, saving the scarcest resource for ordinary people: time. From this perspective, technology reducing the cost of information search is itself a form of inclusiveness.

But when AI moves from "helping you search" to "deciding for you," a more hidden problem surfaces: recommendation itself is a business. Yin Jie, a professor at the School of Tourism of Huaqiao University, previously said in a media interview that some AI travel guides not only have advertising tendencies, but in certain dimensions, this tendency is more hidden and more manipulative than traditional search platforms, because "recommendation" itself is a business model, and platforms will use algorithms to prioritize "paid partners," "high-commission merchants," and "self-owned supply chain products."

This is precisely where AI recommendations diverge from traditional advertising. Some commercial promotions in search results are labeled "advertisement," letting consumers clearly know which positions were bought with money; but if AI says, "Based on your budget, travel companions, and accommodation needs, I recommend Hotel A," it sounds more like a consultant who fully understands you, rather than an ad slot. Commercial influence has not disappeared; it has simply retreated from a prominent position into a smooth tone of voice.

Li Muxin, an associate professor of economics at the Shanghai Institute for Mathematics and Interdisciplinary Sciences, previously stated publicly that an important value of large models is reducing consumers' information search costs, so maintaining a clear boundary between natural recommendations and commercial payments is very important; if commercial factors influence recommendations and consumers have difficulty identifying them, it may not only affect user trust but also further strengthen the platform's market power as an information gateway. Compared with traditional online travel platforms, AI recommendations also have a more obvious "black box" characteristic—why A is recommended instead of B is often harder for outsiders to judge.

According to media reports, a journalist asked an AI assistant to recommend hotels near a scenic area and received a quote lower than the same room type on a large online travel platform. But when verifying with hotel staff, the staff member said they were unaware of that booking channel and instead advised consumers to "better book through platforms like Ctrip"; another test showed that flight prices recommended by AI were about 10 yuan more expensive than on other platforms. In other words, AI has already brought users to the transaction page, but merchant awareness and fulfillment capabilities matching this capability have not yet been fully established. In addition, AI also struggles to grasp in a timely manner holiday crowds, road conditions, weather changes, and store operating status, so guides still need manual verification.

What is even more worth watching is how algorithms shape consumption patterns themselves. When itineraries, hotels, tickets, and dining are packaged into the same chat window and completed with one click, consumers' price comparison behavior is quietly dissolved. People used to shop around; now they often only need to accept one "comprehensively optimal" answer. What is saved is time; what is surrendered is judgment. And the National Day Golden Week is precisely the period when consumption is released most intensively in the year—Ctrip data shows that after entering the National Day Golden Week, the proportion of cross-province travel jumped to 82%, and among those choosing to combine the Mid-Autumn Festival and National Day holidays, nearly half traveled for more than 8 days. The more concentrated the consumption, the more significant a tiny deviation in algorithmic ranking becomes when magnified across massive orders.

None of this is meant to deny AI's entry into consumption scenarios. The issue has always been about boundaries: consumers have the right to know which results they see come from the algorithm's objective judgment and which come from commercial cooperation. This requires all three parties to respond together. On the platform side, "recommendation" and "charging" should be clearly separated, and commercial cooperation should be identifiable and explainable, so consumers can tell whether a suggestion was calculated or "bought"; on the regulatory side, algorithm filing and labeling requirements need to extend to consumer recommendation scenarios, making transparency an access condition for traffic gateways; and as consumers, perhaps we should also leave ourselves a little room to "think one step further"—treat AI's plan as a draft rather than a decision, compare prices once more before booking a room, and verify opening hours once more before departure.

The meaning of a long holiday has never been just about packing the itinerary more fully. What is truly precious in a journey is where you want to go, how much you are willing to spend, and what you are willing to pay for—these decisions are still made by people themselves. Algorithms can calculate the shortest route and the most cost-effective room for you, but they should not define what is "worthwhile." After all, a holiday is meant for living the life you want, not for running through an optimal solution.