AI helps you plan the National Day holiday, but don't let algorithms define "how to spend your money"

The National Day holiday has long been an important window for domestic annual consumption. This year's October 1st holiday, more and more tourists 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 more and more people's "travel buddy."

This change is already backed by data. From the 2026 summer vacation to the National Day holiday, the popularity of AI travel tools has continued to rise. Fliggy data shows that during the summer vacation, the platform's travel-related AI interactions increased by 297% month-on-month; Tongcheng Travel's travel agent DeepTrip saw hourly visits during peak hours on the first day of the National Day holiday increase by more than 3 times.

Users input their travel time, destination, number of people, budget, and play preferences to 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 in travel planning should not stop at outputting text plans, but should also solve the real-world problem of "what to do after reading the guide."

The efficiency improvement in travel brought by AI is clearly visible. Long holiday travel is inherently an overload of information: flights, hotels, tickets, routes, business hours, and queue times, any one of which requires repeated comparison. AI compresses this scattered information into a single conversation, saving what ordinary people lack most: time. From this perspective, technology reducing the cost of information search is itself a form of universal benefit.

Yin Jie, a professor at the School of Tourism of Huaqiao University, previously said in an interview with the media that some AI travel guides not only have an advertising tendency, but in certain dimensions this tendency is more hidden and more inducing than on 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 with the word "advertisement," letting consumers clearly know which positions were bought with money; but if AI says, "Based on your budget, companions, and accommodation needs, I recommend Hotel A more," it sounds more like a consultant who fully understands you rather than an ad slot. Commercial influence has not disappeared; it has merely retreated from conspicuous positions into a smooth tone.

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 it is very important to maintain a clear boundary between natural recommendations and commercial payments; if commercial factors influence recommendations and consumers find it difficult to identify them, it may not only affect user trust but may also further strengthen the platform's market power as an information gateway. Compared with traditional online travel platforms, AI recommendations also have more obvious "black box" characteristics—why A is recommended rather than B is often harder for outsiders to judge.

According to media reports, a reporter asked a certain 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 other party said they did not know that booking channel and instead suggested that consumers "had better book through platforms such as Ctrip"; another actual test showed that the airfare recommended by AI was about 10 yuan more expensive than on other platforms. In other words, AI has already brought users to the transaction page, but the merchant awareness and fulfillment capabilities matching this capability have not yet been fully established. In addition, during holidays, AI also finds it difficult to grasp in real time crowd flows, road conditions, weather changes, and store operating status, so guides still need manual verification.

What is more worthy of vigilance is the way algorithms shape consumption patterns themselves. When itineraries, hotels, tickets, and dining are packaged into the same conversation window and completed with one click, consumers' price-comparison behavior is quietly dissolved. In the past, people compared three sellers; now they often only need to accept a "comprehensively optimal" answer. What is saved is time; what is conceded is judgment. And the October 1st Golden Week is precisely the period when consumption is released most intensively in the year—Ctrip data shows that after entering the October 1st Golden Week, the proportion of cross-province travel jumped to 82%, and among people choosing to take the Mid-Autumn and National Day holidays together, nearly half traveled for more than 8 days. The more concentrated the consumption, the more considerable a tiny deviation in algorithmic ranking becomes when magnified across massive orders.

This requires a joint answer from three parties. On the platform side, the two things—"recommendation" and "charging"—should be clearly separated; commercial cooperation should be identifiable and explainable, so that consumers can tell whether a suggestion was calculated or "bought." On the regulatory side, algorithm filing and labeling requirements need to be extended to consumer recommendation scenarios, making transparency an access condition for traffic gateways. And as consumers, perhaps we should also leave ourselves some room to "think one step more"—treat AI's plan as a draft rather than a decision, compare prices one more time before booking a room, and verify business hours one more time before departure.

The meaning of a long holiday has never been merely to fill the itinerary more densely. 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 for you what is "worthwhile." After all, holidays are for living the life you want, not for running through an optimal solution.

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