Why "Cheaper Prices" Can Still Trigger Anti-Monopoly Fines: A Legal & Economic Breakdown of Platform Regulation in China

 Editor’s Note: Antitrust enforcement against digital platforms is no longer just a Western phenomenon. As global regulatory regimes—from the EU’s Digital Markets Act (DMA) to US FTC actions—tighten their grip on big tech, China has simultaneously developed one of the world's most aggressive and sophisticated platform antitrust enforcement frameworks. In this analysis, we deconstruct the regulatory logic behind China's massive antitrust penalties on digital platforms (such as the landmark online travel and e-commerce cases), and examine what cross-border businesses must know about platform compliance.

Introduction: The Misconception of "Consumer Welfare"

In traditional antitrust law, regulators primarily intervened when a dominant player artificially inflated prices, harming consumer welfare. However, in the modern digital platform economy, a paradox has emerged: How can a platform that offers users "guaranteed lowest prices" across the web be guilty of monopolistic behavior?

When Chinese antitrust regulators (the State Administration for Market Regulation, SAMR) handed down multi-billion RMB penalties against major internet conglomerates, many foreign observers and corporate executives struggled to grasp the core rationale.

To understand these enforcement actions, one must look beyond short-term consumer pricing and analyze the structural distortion of market competition.

The Core Violation: "Choose-One-of-Two" and Parity Clauses (MFNs)

The primary target of Chinese platform antitrust enforcement revolves around two distinct practices: "Choose-One-of-Two" (二选一) and Most-Favored-Nation (MFN) / Price Parity Clauses.

1. Price Parity Clauses (Most-Favored-Nation Rules)

Major Travel Platforms (OTAs) and e-commerce giants often impose strict clauses requiring merchants (e.g., hotels, airlines, or retailers) to offer their lowest prices exclusively on that specific platform.

• The Platform Logic: "We guarantee consumers the best price, which builds trust and efficiency."

• The Regulatory Reality: These clauses prevent merchants from discounting their products on competing channels or their own direct-to-consumer websites. By locking in price floors across the market, the dominant platform effectively eliminates inter-platform price competition, suppressing potential disruption from smaller competitors.

2. Algorithmic Retaliation and Traffic Suppression

Antitrust violations in the digital age rarely rely on explicit contractual coercion alone. Instead, dominant platforms leverage algorithmic enforcement:

• Merchants who refuse exclusivity or list lower prices elsewhere face subtle, systemic penalties—such as downgraded search algorithms, removal from recommendation feeds, or revocation of promotional badges.

• Because merchants depend heavily on the platform’s traffic for survival, this implicit economic pressure achieves the exact same exclusionary effect as a formal restrictive covenant.

Economic Impact: Why Regulators Intervened

Why do antitrust authorities view these platform tactics as severe threats to market economy principles?

Regulatory Perspective	Platform Action	Market Distortion
Merchant Autonomy	Dictating pricing & channel strategy	Merchants lose operational independence and pricing power.
Commission Rates	Extracting high take-rates (抽成)	Platforms can charge exorbitant commission fees because merchants cannot leave.
Market Entry	Blocking smaller competitors	New entrants cannot attract merchants even by offering lower commission rates.

In short, while consumers enjoy low prices in the short term, price parity clauses and "Choose-One-of-Two" practices allow platforms to extract high rent from merchants. In the long run, this diminishes product innovation, inflates merchant operating costs, and ultimately shifts the financial burden back onto consumers.

A Comparative Look: China SAMR vs. EU DMA & US FTC

China’s enforcement against platform monopolies is not an isolated initiative; it mirrors a global paradigm shift toward ex-ante (preemptive) regulation of digital gatekeepers.

1. European Union (EU DMA): The EU Digital Markets Act explicitly bans gatekeepers from enforcing parity clauses and anti-steering rules, imposing fines up to 10% of global turnover. China's SAMR enforcement mirrors this approach by outlawing algorithm-driven exclusive dealing.

2. United States (FTC/DOJ): While US antitrust litigation against Big Tech (e.g., Amazon, Google) relies heavily on lengthy federal judicial trials, China’s SAMR combines swift administrative investigations with public "rectification guidance" (行政指导), forcing rapid industry-wide compliance shifts.

Key Takeaways for Cross-Border Businesses & Counsel

For multinational corporations, cross-border e-commerce brands, and digital platform operators expanding into or out of China, navigating this regulatory landscape requires a recalibration of compliance strategies:

1. Audit Channel Agreements for Hidden MFNs: Ensure that supply and distribution contracts do not contain rigid price parity requirements, whether direct or indirect.

2. Beware of Algorithmic & Data Compliance Overlaps: In China, antitrust regulation operates in tandem with the Data Security Law (DSL) and Personal Information Protection Law (PIPL). Using consumer data or automated decision-making to execute dynamic pricing or price discrimination can trigger multi-agency investigations.

3. Re-evaluate Platform Distribution Risk: Brands operating on major platforms must diversify their sales channels and document any instances of platform coercion (e.g., traffic throttling) to protect their regulatory standing.

Conclusion

The multi-billion RMB antitrust penalties in China mark a permanent shift from unregulated platform expansion to structured regulatory oversight. A business model that achieves market dominance by restricting vendor freedom and squeezing out competitors can no longer hide behind the defense of "low consumer prices."

For international executives and legal practitioners, understanding the nuances of China's Anti-Monopoly Law is essential for building resilient, compliant cross-border commercial operations in the modern digital economy.

For more comparative legal analyses bridging Chinese law, US litigation, and global business regulation, subscribe to Talkchinalaw or contact our team for cross-border advisory services.

When We Talk About AI Across Disciplines—AI, Kant, and Legislation (1)

 

Welcome to this series. It spans technology, philosophy, law, linguistics, and other fields, offering a new perspective for discussing AI. No reproduction or secondary editing is allowed without the author’s permission. All rights reserved.

I have previously written about AI + healthcare, AI + cryptography, AI + personhood, AI + data, and more. Today, we return to the foundations and examine two questions: What is the ultimate legal dilemma brought forth by the rise of AI? And what contribution has a German philosopher named Kant made to the development of AI law several centuries later?

What kind of AI do we hope to coexist with in the long term?

What exactly is the bottleneck in current AI law roundtables?

What standards should guide future aesthetic judgments of AI?

I. The Real Problem of AI Law: Applying the Law Requires an Aesthetic Judgment About “What We Want AI to Become”

Over the past decade, advancements in artificial intelligence have pushed law from passively responding to technological risks toward actively shaping the future contours of technology.

Whether it is China’s principles for AI governance and its information-security and data-protection frameworks, or the European Union’s Artificial Intelligence Act, all point toward a shared core objective: ensuring that AI integrates into social order in a “form that is broadly acceptable according to human expectations.”

But how do we define “a form broadly acceptable to human expectations”? For legislators, this remains a difficult and ongoing exploration.

Looking at global regulatory trends, policymakers are attempting to outline “acceptable” versus “unacceptable” AI through distinctions framed as universalizable values. Acceptable AI generally exhibits controllability, transparency, auditability, non-discrimination, respect for privacy and personal autonomy, and the ability to enhance public governance. Unacceptable AI typically includes systems with high manipulative capacity (those that undermine human judgment through suggestion, inducement, or attention steering), opaque and high-risk predictive models, algorithms used to erode democratic processes, and fully non-auditable deep-generation architectures.

However, this categorization faces two challenges. First, these items cannot be exhaustively listed; they must be assessed case by case by courts, regulators, or ethics committees. Second, issues of degree are unavoidable: overemphasizing transparency sacrifices trade secrets and innovation; overemphasizing safety may stifle low- or medium-risk applications; overemphasizing anti-discrimination may suppress necessary modeling based on group differences. As a result, the governance of AI is under constant reconstruction, and legal roundtables never seem to run out of topics.

AI systems are complex and often opaque. As things stand, the law cannot fully cover all AI-generation mechanisms using predetermined rules. What then?

Several centuries ago, the German philosopher Immanuel Kant proposed the concept of “aesthetic judgment,” or more precisely, “reflective judgment” (German: Reflexionsurteil), aiming to answer how we can arrive at universally valid judgments in situations where no fixed concepts or rules exist. In simple terms, Kant offered a methodological insight: even when concepts are incomplete or the object cannot be fully grasped, we can still form universally applicable judgments, rooted not in utility or personal desire but in a rational structure capable of generating universally valid moral laws.

Kant’s theory of aesthetic judgment was never meant merely to teach people “how to appreciate beauty.” In the eighteenth-century context of collapsing rituals, weakening religious authority, and Enlightenment calls for new norms, Kant addressed a deeper philosophical problem: when facing an object that cannot be exhaustively defined by existing concepts, how can human beings still produce judgments that carry universal validity?

“The key lies in discovering purposiveness without purpose.” This is how Kant formulates it. It requires the subject, in the absence of exhaustive concepts, to engage in a process of synthetic balancing in order to reconcile causality with teleology. Such judgment is neither mechanical deduction nor dependent on personal desire or utility; rather, it is grounded in reason’s pursuit of a form of universal law.

We continue to discuss aesthetic judgment today precisely because it offers this model: a mode of judgment that is “without concept and without purpose,” yet enables the subject to locate purposiveness between imagination and understanding. Because it lacks utilitarian ends, it is free—and this freedom, in turn, compels the subject to sustain a harmonious order that transcends material interests. Kant believed that the cultivation of this faculty of judgment ultimately fosters moral subjectivity, allowing aesthetic experience to function as a bridge from the sensible world of nature to the free world of morality.

If we apply this to contemporary AI, are we not facing a kind of “collapse of ritual and order” in the technological age?

At its core, an artificial intelligence model is a network with innumerable parameters, designed to simulate, reconstruct, and output specific types of data. At initialization, what it produces is nothing but disordered noise. Only through exposure to massive training datasets and iterative parameter adjustment can the model begin to generate outputs that correspond to patterns present in the data. In this sense, the process bears some resemblance to human learning. However, when attempting to understand physical phenomena with human-intuitive properties like spatial or color distribution, the model relies strictly on statistical correlation rather than intuitive perception (Anschauung). Its grasp of the world is therefore fundamentally probabilistic. In any given context, it can only produce “possible configurations” in a distributional sense, rather than judgments or consciousness identical to that of a human subject at a specific moment. The limitation does not stem from insufficient technological maturity, but from a fundamental divergence between the operational logic of the model and the structure of human cognition.

In this context, traditional causal explanations no longer suffice. The internal structure of technological systems is opaque, indeterminate, and inexhaustible. The law can neither treat AI as a natural object subject to mechanical causation nor as a free, living subject. Instead, the relationship between law and AI more closely resembles the situation described by Kant: how a subject, when confronted with an object that cannot be exhaustively understood, constructs a universal norm through reflective judgment.

In other words, when we are faced with a technological system that is neither a living entity nor fully comprehensible through traditional causal chains, is it still possible, as Kant suggests, to arrive at universally valid norms through reflective judgment? This is the methodological premise that law must confront in the domain of AI.

A contemporary illustration can be found in the ongoing U.S. litigation often referred to as “New York Times vs. OpenAI.” One of the central factual disputes in that case concerns whether AI models may use copyrighted materials. The U.S. Supreme Court’s approach to such questions operates within the “fair use” framework. So-called “fair use” does not provide rigid ex ante rules; instead, it requires courts to conduct a contextual balancing of four core factors: (1) the purpose and character of the use, including whether it is commercially transformative; (2) the nature of the original work; (3) the amount and substantiality of the portion used in relation to the work as a whole; and (4) the effect of the use on the existing or potential market.

A court’s decision does not mechanically apply the four factors; instead, it forms a holistic judgment by weighing these factors against each other in the specific case, determining whether a particular use may be considered “fair” in a particular context.

However, the four factors of “fair use” fall far short of anything like Kant’s universal moral law. Under Kant’s rigorous rational test, when someone, “out of self-love,” wishes to commit suicide to end suffering, he must also acknowledge that out of self-love, a person may choose to continue living and preserve life. In other words, self-love could justify both life and death. Therefore, a maxim such as “ending one’s life out of self-love” cannot qualify as a universalizable law; it contains an internal contradiction.

By the same logic, the four-factor framework in U.S. copyright law—purpose and character, nature of the work, amount used, and market impact—is a set of experiential indicators that must be weighed against one another. There is no strict logical hierarchy among them; instead, they must compromise amid conflict. For example, a given use may serve a public-interest purpose (weighing in favor of permissibility) but involve a high proportion of the original work (weighing against it). The court must determine the extent to which one factor may be sacrificed to accommodate another.

This legislative orientation is not merely a matter of risk control. Traditional risk rules assume that the danger of technology arises from observable empirical properties; measurement, calibration, and review can reduce such risks to manageable levels. But the complexity and inexhaustibility of AI’s structure require legislators to answer a more fundamental question: What kind of “good form” do we want AI to exhibit within society? Once this question is on the table, law enters a domain resembling aesthetic judgment.

For Kant, aesthetic judgment does not describe what an object “is in itself.” Rather, it expresses a universal demand experienced by the subject: that the object “ought to be viewed in this way, and others ought to view it likewise.” It concerns how one ought to regard something, not a cognitive judgment about what that thing empirically is.

Although legislators may define AI, such definitions are inevitably shaped by their time. As model architectures, applications, and capabilities evolve, legal boundaries for what counts as AI will correspondingly shift. What remains constant is not the definition, but the “aesthetic-judgment-like tradeoff” operating behind the law. Kant’s theory of aesthetic judgment was later criticized by Hegel, Schiller, and others. For instance, for relying too heavily on the structure of the subject or neglecting historical and social conditions. Yet the core insight it offer, namely, that when fixed concepts are lacking, the subject must still make judgments capable of universalization, precisely illuminates the structural dilemma facing AI legislation today.