Summary of Key Points
Ctrip was fined 5.179 billion yuan for abusing its dominant market position. On the surface, this seems like a case of a platform exploiting merchants and deceiving consumers, but in essence, it reflects the deprivation of pricing power under the hegemony of algorithms. Ctrip quietly controls merchants' pricing through technical means such as a listing system and a price adjustment tool, while also misleading consumers with tactics like "big data price discrimination." This highlights the trend of technological malpractice evolving from the internet era to the AI age, revealing new challenges where monopolies are more concealed and regulation more difficult. Global regulators are attempting to foster a symbiotic relationship between platforms and merchants rather than one of exploitation through measures such as fee regulation and algorithm transparency.
I. The Truth Behind Ctrip's Fine: How Do Algorithms “Steal Money?”
Ctrip doesn't coerce merchants with contracts; instead, it uses technology for a form of "soft bondage":
- Ranking Merchants to Control Traffic: Hotels are categorized into special, gold, and non-ranked levels. Special-level hotels require exclusive partnerships to access Ctrip's traffic, while gold-level hotels must promise the lowest prices online (20 yuan or 5% lower than competitors). If a merchant disobeys (for example, by listing on another platform), Ctrip downgrades their status and limits their traffic, essentially cutting off their customer base. For instance, a homestay in Lijiang that earns 100,000 yuan per month during the peak season may have 40,000 yuan taken away in fees by Ctrip, leaving the merchant in a vicious cycle where they lose business if they don't cooperate or suffer losses if they do.
- The Secretive Price Adjustment Tool: This tool automatically scans prices on other platforms and lowers Ctrip's prices without notifying the merchants. In one egregious case, a hotel's price was changed from 480 yuan to 130 yuan during holidays, causing significant financial losses for the merchant.
- “Big Data Price Discrimination” Against Regular Customers: Ms. Hu, a diamond-level member of Ctrip, paid 2,889 yuan for a hotel but found that the actual listing price was only 1,377 yuan upon check-out—indicating that regular customers are being exploited through precise algorithmic pricing.
All these practices are implemented through code and algorithms, making them more concealed and harder to resist than traditional contractual agreements.
II. The Evolution of Technological Malpractice: From “Storing Products” to “Acting as Personal Shop assistants”
The invention of the internet and AI was meant for good, but they have been misused:
- Internet Era: Platforms controlled traffic distribution, similar to how supermarkets place products on shelves—want your product to be seen? You had to comply with their terms (such as paying commissions or entering into exclusive partnerships).
- AI Era: AI acts like a personal shopping assistant or even a proxy, tracking your preferences and deciding what you see and what you don't. For example, when searching for hotels, AI might intentionally hide non-Ctrip partners and only recommend those that result in higher fees for Ctrip.
Ctrip's problem lies in its use of outdated internet-era tactics (such as forced price adjustments and explicit traffic restrictions). Its technology is not advanced enough to avoid detection by regulators. However, in the AI era, malpractice will become even more concealed, with algorithms claiming to be for “system optimization” without any clear indication of whether they are truly beneficial or just a form of deception.
III. New Challenges of Monopolies in the AI Age: Why Is Regulation Such a Problem?
Traditional monopolies (e.g., companies holding 80% of the market) are easier to identify, but AI platform monopolies are more hidden:
- Incomprehensible Behavior: Platforms appear beneficial to consumers (e.g., offering free services or bonuses), but they charge high commissions from merchants. They don't explicitly prohibit using other platforms; instead, they silently lower merchant rankings through algorithms, making it hard to determine whether these actions are intentional.
- Difficult to Prove: Evidence is in the hands of the platforms, which can change their algorithms at any time. For example, a platform might rank you high today and then lower your ranking tomorrow, claiming it was due to system optimization. Even if regulators examine the code, AI models are often too complex for programmers to explain the reasoning behind the recommendations.
- Laws Lag Behind Technology: Regulations often lag behind rapid technological advancements. For instance, just as laws banning forced choices were enacted, platforms have already shifted to using traffic control and AI shopping assistants. By the time regulations are implemented, the tactics used by platforms may have changed.
Moreover, platforms use “innovation” as a shield, claiming that strict regulation will hinder their competition with American AI companies. This puts regulators in a difficult position: if they relax the rules, merchants may be exploited; if they tighten them, innovation could be stifled.
IV. Global Regulatory Approaches: Putting Brakes on Platforms, Not Destroying Them
Countries do not aim to eliminate platforms but to ensure they coexist peacefully with merchants:
- Fee Regulation: South Korea limits platform commissions to reduce costs for small and medium-sized businesses. The U.S. restricts payment monopolies to prevent platforms from using them to control transactions.
- Algorithm Transparency: The EU requires platforms to disclose their ranking algorithms, explaining why certain hotels are ranked lower. This prevents hidden practices.
- Protecting Consumers’ Rights: Regulations protect merchants from being unfairly excluded by AI-driven recommendations, ensuring that platforms don’t favor their own products.
- Other Measures: These include encouraging merchants to negotiate collectively with platforms, allowing them to operate across multiple platforms with their credit records, and strengthening data protection (preventing platforms from misusing merchant data).
The core of these measures is the concept of symbiosis: platforms need to make money, but they must not drive merchants to extinction; merchants rely on platforms yet also have the right to choose and negotiate. After all, the market is an ecosystem where everyone's survival is essential for sustainable development.
In Conclusion
Ctrip's fine marks the beginning of anti-monopoly efforts in the AI era. We must be vigilant against more subtle forms of technological malpractice and find a balance between efficiency and symbiosis, ensuring that technology serves people rather than becoming a tool for exploitation.