曼昆律師普法｜美國「先跑」、中國「先管」？AI企業如何理解兩種監管路徑

人工智慧正在快速從技術概念變成真實的商業基礎設施。從大模型、AI Agent 到智能硬體、AI 陪伴、企業軟體，當越來越多產品真正進入市場之後，企業面對的問題已經不只是模型能力夠不夠強、融資成本夠不夠低，而是一個越來越現實的選擇：同一個 AI 產品放在不同國家營運，可能需要面對完全不同的法律環境。

中美恰好提供了兩個很有代表性的樣本。

如果一定要用一句簡單的話概括，可以說，美國整體更接近「先跑」，中國則更強調「先管」。但這樣的概括只能作為理解兩套制度的入口，而不能直接當作法律結論。美國並不是 AI 企業可以不受監管地先做起來，中國也並不是所有 AI 產品都必須經過審批才能上線。兩國真正的差異，在於監管介入產品的時間、方式和風險承擔機制不同。

對於準備進入中美市場的 AI 企業來說，這一點遠比單純討論「哪個國家監管更嚴」重要。

真正的區別，不是一個管、一個不管，而是誰先承擔風險

截至 2026 年 8 月，美國仍然沒有形成一部統一覆蓋所有 AI 產品和應用場景的聯邦綜合性 AI 法律。現實中的美國 AI 治理，更像是一套由既有法律、行業監管、州級立法、行政執法以及司法訴訟共同組成的網路。

這意味著，對於大多數一般性的 AI 產品而言，企業通常不會因為「使用了人工智慧技術」本身，就需要先向某個統一的聯邦 AI 監管機關申請許可。模型可以研發，產品可以上線，商業模式也可以先進入市場接受驗證，但產品一旦真正觸碰到版權、消費者保護、隱私、就業歧視、兒童安全、虛假宣傳或者受監管行業的規則，既有法律就會迅速介入。

這也是為什麼「美國先跑」這個說法具有一定解釋力。美國監管更常見的邏輯並不是在產品出現之前判斷「你能不能做」，而是在企業已經進入市場之後，通過監管執法、州級規則和具體案件不斷劃定邊界。

但「先跑」絕不等於「出了事再說」。AI 企業在美國面對的真正壓力，是很多風險不會在產品上線前以一張明確的審批清單出現，而是會在產品擴大規模之後，以版權訴訟、消費者索賠、監管調查或者州檢察機關執法的形式出現。企業前期獲得的創新空間更大，但也意味著需要自己承擔更多判斷成本。

中國的監管思路則明顯不同。

近年來，中國並沒有等到一部完整的《人工智慧法》出台之後再統一管理 AI，而是圍繞算法推薦、深度合成、生成式人工智慧、AI 生成內容標識以及擬人化互動等不同場景，不斷建立專門規則。監管因此更容易在產品上線和營運過程中提前出現。對於部分 AI 服務來說，備案、安全評估、內容治理、生成內容標識、未成年人保護和數據合規，已經不再只是產品發生問題以後才處理的事項，而逐漸變成產品上線之前就必須考慮的問題。

因此，「中國先管」真正值得企業理解的，並不是所有 AI 項目都需要等待審批，而是監管要求更容易被前置到產品設計和營運流程中。如果把這種差異進一步拆開來看，中美 AI 治理在產品上線、訓練數據、AI 內容、未成年人保護、數據跨境以及監管邏輯上，已經呈現出較為清晰的不同路徑。

曼昆製圖｜中美 AI 治理的核心差異

美國劃的是「行為紅線」，中國越來越深入「產品機制」

這種差異，在具體規則層面會表現得更加明顯。

美國現階段對 AI 的監管，很大程度上仍然藉助既有法律完成。如果一家企業誇大 AI 產品的能力、安全性或者收益，就可能落入消費者保護和虛假宣傳監管；如果訓練數據涉及版權作品，就可能進入版權法爭議；算法用於招聘、信貸或者住房領域產生歧視結果，也可能直接受到已有反歧視法律約束。

換句話說，美國很多時候關注的並不是「你有沒有使用 AI」，而是你使用 AI 之後做了什麼，以及最終造成了什麼法律後果。

這也是為什麼美國的 AI 法律實踐會高度依賴具體案件。AI 技術發展速度通常快於專門立法，很多新的問題最終只能先回到舊法律中尋找答案，再由監管機關和法院逐漸劃出新的邊界。

中國的變化則正在向更深一層發展。

最初的 AI 治理更多集中在內容是否合法、算法是否備案、數據來源是否合法等問題，但隨著 AI 產品越來越具有交互性和人格化特徵，規則開始直接進入產品功能本身。2026 年 7 月 15 日正式實施的人工智慧擬人化互動服務相關規則就是一個很典型的例子。對於 AI 陪伴、擬人化聊天等產品而言，監管已經不僅關心模型會不會生成違法內容，而開始進一步關注平台是否清楚提示用戶正在與人工智慧互動、是否採取措施防止過度依賴、連續使用時間過長後是否進行提醒，以及未成年人能夠接觸什麼類型的擬人化服務。

這些要求已經開始影響 UI、用戶流程、賬號體系、產品交互和風控機制。

從這個角度來看，中國 AI 治理正在出現一個非常值得企業關注的變化：合規正在從「內容審核」進入「產品設計」。

對於企業而言，這種變化意味著，法律部門如果只在產品準備上線時進行最後一次審核，往往已經太晚。真正有效的 AI 合規，需要在產品經理定義功能、技術團隊設計模型交互、營運部門制定用戶機制的時候就開始介入。

兩國最大的風險，也不是簡單的「罰款誰更高」

討論中美 AI 治理時，另一種常見的簡化方式，是把美國理解為「靠法院打官司」，把中國理解為「靠政府行政監管」。

這個判斷有一定基礎，但仍然不夠準確。

美國當然存在大量私人訴訟，但 FTC、州檢察長以及金融、醫療等行業監管部門同樣會直接執法；中國以行政監管為重要手段，但 AI 企業同樣可能因為版權、人格權、個人資訊或者消費者權益問題遭遇民事訴訟。真正值得比較的，不是誰只用哪一種手段，而是哪一種風險更可能成為企業經營過程中最重要的約束。

Anthropic 的版權案件就是一個很典型的美國樣本。2026 年 7 月，美國法院批准 Anthropic 與作者群體達成約 15 億美元的版權訴訟和解。如果只把這個案件概括成「AI 公司使用版權作品訓練模型，因此賠了 15 億美元」，其實會忽略案件真正複雜的地方。

法院此前對於 AI 模型訓練是否構成合理使用作出了非常值得關注的判斷，而案件中另一個重要爭議，則涉及企業取得和保存盜版書籍副本的方式。最終的 15 億美元也是雙方達成的和解金額，而不是法院直接判決的侵權賠償。

這個案件真正給 AI 企業的提醒並不是「有版權的內容絕對不能訓練模型」，而是：AI 時代的數據合規不能只檢查最後模型怎麼使用數據，還必須往前追溯數據究竟從哪裡獲得、企業是否有權取得、保存和使用這些數據。

很多美國 AI 企業真正需要擔心的，也正是這種風險。產品可能已經運行幾年、獲得大量用戶甚至完成多輪融資，之後才因為一個訓練數據來源、一個產品設計或者一項宣傳方式陷入長期訴訟。創新空間更大，並不意味著法律成本更低，只是風險出現的時間不同。

中國企業面臨的壓力則常常更加直接地體現在「產品能否繼續經營」上。

備案沒有完成、內容治理機制不符合要求、AI 內容標識沒有落實、涉及未成年人保護的設計存在問題，或者某個產品功能與監管要求發生衝突，都可能帶來整改、功能調整甚至暫停服務。

對於一家正在高速增長的網路企業來說，這類風險未必能夠簡單用罰款金額衡量。一個核心功能被迫下線，或者產品需要重新設計用戶流程，帶來的商業成本可能遠遠高於行政罰款本身。

因此，從企業視角看，美國和中國的核心風險可以理解為兩種不同的問題：美國企業要防止「跑起來之後被追責」，中國企業則更需要防止「產品因為前置合規沒有做好而跑不起來」。

AI 出海真正應該比較的，不是哪裡監管更松

正因為監管邏輯不同，AI 企業在選擇市場時最容易犯的錯誤，就是直接比較「美國是不是更寬鬆」「中國是不是更嚴格」。

這種問題本身就把複雜的商業決策過度簡化了。

同樣是一家 AI 公司，做基礎模型、企業內部 Agent、AI SaaS、教育產品、AI 陪伴、醫療 AI 和智能硬體，面對的監管問題完全不同。一個不直接面向消費者的企業級效率工具，與一個每天和數百萬未成年人進行持續對話的 AI 產品，即使底層調用的是同一個模型，也不可能使用同一套法律判斷。

企業真正應該先看的是自己的風險結構。

如果產品高度依賴大規模版權數據訓練，那麼進入美國以後，數據授權、合理使用邊界以及潛在集體訴訟風險就必須成為核心問題；如果產品大量處理中國境內個人資訊，並需要調用境外模型，則數據跨境路徑和個人資訊保護會成為重要事項；如果是一款 AI 陪伴產品，那麼內容安全、未成年人保護、人格化設計和用戶沉迷機制就會直接進入產品合規範圍。

所以，一家 AI 公司決定去哪裡落地，實際上不是簡單地選擇「寬鬆市場」或者「嚴格市場」，而是在選擇自己更有能力管理哪一種法律風險。

這也是中美 AI 治理比較真正有價值的地方。

美國給予企業更大的前期創新空間，但要求企業有能力承擔複雜而分散的後期責任；中國對部分產品設置了更多前置要求，但企業在規則明確之後，也更容易知道哪些合規事項必須提前解決。

兩套制度都在管 AI，只是風險被放在了不同的位置。

曼昆製圖｜AI 出海風險對齊自查表

接下來，中美都不會越來越「松」

如果再往前看，中美 AI 監管雖然選擇了不同路徑，但有一個趨勢其實越來越接近：隨著 AI 從工具進入真實社會關係，監管都在變得更加具體。

美國未來一個很重要的變量，是聯邦政府與各州之間如何重新劃分 AI 監管邊界。聯邦層面希望保持美國在全球 AI 產業中的技術競爭力，而加州、紐約等州已經圍繞前沿模型、算法透明度、深度偽造、兒童保護等問題持續建立自己的制度。對 AI 公司而言，未來面對的很可能不是「美國到底管不管 AI」，而是如何同時處理聯邦政策、州法、行業規則和既有法律之間的關係。

中國則在另一條路徑上持續推進。過去幾年形成的算法、生成式 AI、數據、內容標識以及特定應用場景規則，已經構成了一套相當具體的監管體系；2026 年國務院年度立法工作計劃又進一步提出加快推進人工智慧健康發展綜合性立法。因此，未來中國 AI 治理大機率不會簡單推倒現有規則重來，而是在現有制度基礎上逐步形成更系統的上位法律框架。

對於企業來說，這意味著一個非常現實的結論：未來做 AI，很難再把法律合規理解成產品完成之後的輔助工作。

無論是在美國還是中國，真正成熟的 AI 公司最終都需要把合規能力放進研發、產品、數據、市場和營運全過程之中，只是兩邊最需要提前解決的問題不同。

結語

「美國先跑，中國先管」仍然是一個很容易理解的概括，但如果真正站在 AI 企業經營者的角度，這四個字只能解釋第一層。

美國並不是允許企業不受約束地先跑，而是更多通過既有法律、監管執法、州級立法和司法訴訟，在市場運行過程中持續劃定邊界；中國也不是所有 AI 產品都要先審批，而是在部分高影響、面向公眾或者涉及特定風險的服務中，把備案、評估、內容治理、用戶保護和產品機制等要求進一步前置。

因此，中美 AI 治理真正的差別，不是「一個鼓勵創新，一個限制創新」，而是兩套制度選擇了不同的風險分配方式。

對於準備出海或者同時進入中美市場的 AI 企業而言，與其先問「哪裡監管更寬鬆」，不如先問三個更重要的問題：自己的產品究竟屬於什麼場景，法律會在哪一個階段介入，以及一旦判斷錯誤，哪一種風險是企業最承擔不起的。

當這些問題想清楚以後，「去哪裡做 AI」才不只是一個市場問題，而是一項真正意義上的商業與法律決策。

*本文僅代表作者個人觀點，不構成針對任何具體項目的法律意見。具體 AI 產品及跨境業務，應結合產品功能、業務模式、用戶所在地、數據處理方式及具體應用場景進行專項判斷。

「Move Fast」 in the U.S., 「Govern First」 in China? How AI Companies Should Understand Two Regulatory Approaches

Artificial intelligence is rapidly evolving from a technological concept into real commercial infrastructure. From foundation models and AI Agents to smart hardware, AI companions, and enterprise software, more and more AI products are entering real-world markets. For companies, the question is no longer simply whether their models are powerful enough or whether capital is sufficiently available. A more practical choice is emerging: the same AI product may face fundamentally different legal environments depending on where it is launched and operated.

The United States and China offer two particularly representative examples.

If we had to summarize the difference in one simple phrase, the U.S. is generally closer to a 「move fast」 model, while China places greater emphasis on 「govern first.」 But this is only a useful entry point for understanding the two systems, not a legal conclusion in itself. The U.S. does not allow AI companies to innovate without regulatory constraints, nor does China require every AI product to obtain approval before launch. The real distinction lies in when regulation intervenes, how it intervenes, and how legal risks are allocated.

For AI companies considering the U.S. and Chinese markets, understanding this distinction is far more important than simply asking which country has 「stricter」 AI regulation.

I. The Real Difference Is Not Whether AI Is Regulated, but When Risk Is Allocated

As of August 2026, the United States still does not have a single comprehensive federal AI law governing all AI products and use cases. In practice, U.S. AI governance resembles a network made up of existing laws, sector-specific regulation, state legislation, administrative enforcement, and litigation.

For most general-purpose AI products, this means that a company does not typically need to apply to a centralized federal AI regulator for permission simply because it is using artificial intelligence. Models can be developed, products can be launched, and business models can enter the market for validation. But once a product implicates copyright, consumer protection, privacy, employment discrimination, child safety, misleading advertising, or rules governing regulated industries, existing law can quickly come into play.

This is why describing the U.S. as allowing companies to 「move fast」 has some explanatory value. The more common regulatory logic is not necessarily to decide before launch whether a company is permitted to build a particular AI product, but rather to define and refine legal boundaries after products enter the market through regulatory enforcement, state-level rules, and specific cases.

But 「move fast」 should never be confused with 「deal with the law only after something goes wrong.」 The real pressure for AI companies in the U.S. is that many risks do not appear in the form of a clear pre-launch approval checklist. Instead, they may surface only after a product has scaled, in the form of copyright litigation, consumer claims, regulatory investigations, or enforcement actions by state attorneys general. Companies may enjoy greater room to innovate at an earlier stage, but they also bear more responsibility for making their own legal judgments before the boundaries are fully settled.

China follows a noticeably different regulatory approach.

In recent years, China has not waited for a single comprehensive Artificial Intelligence Law before regulating AI. Instead, it has continuously introduced rules targeting specific technologies and scenarios, including algorithmic recommendation, deep synthesis, generative AI, labeling of AI-generated content, and anthropomorphic interactive services. As a result, regulatory requirements are more likely to appear earlier in the product launch and operation cycle.

For certain AI services, filing requirements, security assessments, content governance, AI-generated content labeling, protection of minors, and data compliance are no longer issues to be addressed only after a problem occurs. They are increasingly becoming matters that companies must consider before launch.

What companies should therefore understand by China』s 「govern first」 approach is not that every AI project must wait for regulatory approval. Rather, compliance obligations are more likely to be embedded earlier into product design and operational processes.

When these differences are broken down further, the U.S. and China already show relatively distinct approaches across product launch, training data, AI-generated content, protection of minors, cross-border data transfers, and the overall logic of regulation.

Mankun Graphic | Key Differences in AI Governance Between China and the U.S.

II. The U.S. Draws 「Conduct Red Lines,」 While China Is Moving Deeper into 「Product Design」

These differences become even more apparent when we look at specific rules.

At present, much of U.S. AI regulation still operates through existing legal frameworks. If a company exaggerates the capabilities, safety, or expected benefits of an AI product, it may trigger consumer protection or deceptive advertising rules. If copyrighted works are involved in training data, copyright disputes may arise. If algorithms used in hiring, lending, or housing produce discriminatory outcomes, existing anti-discrimination laws may apply directly.

In other words, the U.S. often focuses less on the fact that a company is 「using AI」 and more on what the company does with AI and what legal consequences result from that use.

This is also why U.S. AI law is highly dependent on individual cases. AI technology typically develops faster than AI-specific legislation. New disputes therefore often have to be analyzed first under existing legal doctrines, with regulators and courts gradually defining new boundaries through enforcement and litigation.

China, meanwhile, is moving into a deeper stage of regulation.

Earlier AI governance focused heavily on questions such as whether content was lawful, whether algorithms had been properly filed, and whether data had been lawfully obtained. But as AI products become increasingly interactive and anthropomorphic, regulation is beginning to reach directly into product functionality itself.

The rules governing anthropomorphic AI interactive services that took effect on July 15, 2026 provide a useful example. For AI companions, anthropomorphic chatbots, and similar products, regulators are no longer concerned only with whether a model may generate unlawful content. They are also paying attention to whether platforms clearly inform users that they are interacting with AI rather than a human being, whether measures are in place to prevent excessive dependence, whether users receive reminders after extended periods of continuous use, and what types of anthropomorphic services may be provided to minors.

These requirements are already beginning to affect UI design, user journeys, account systems, product interactions, and risk-control mechanisms.

From this perspective, an important shift is emerging in China』s AI governance: compliance is moving from 「content moderation」 into 「product design.」

For businesses, this means that if the legal team only conducts a final review when a product is nearly ready to launch, it may already be too late. Effective AI compliance increasingly requires legal considerations to enter the process when product managers define functionality, technical teams design model interactions, and operational teams establish user mechanisms.

III. The Biggest Difference Is Not Simply 「Who Imposes Larger Fines」

Another common simplification in discussions of U.S. and Chinese AI governance is to say that the U.S. relies on litigation, while China relies on administrative regulation.

There is some truth to this distinction, but it is still incomplete.

The U.S. certainly has extensive private litigation, but the Federal Trade Commission, state attorneys general, and regulators in sectors such as finance and healthcare may also take direct enforcement action. China relies heavily on administrative regulation, but AI companies can likewise face civil litigation involving copyright, personality rights, personal information, or consumer rights.

What matters is therefore not which country relies exclusively on which enforcement mechanism, but which type of risk is most likely to become a meaningful constraint on a company』s business operations.

Anthropic』s copyright litigation provides a useful U.S. example. In July 2026, a U.S. court approved an approximately US$1.5 billion copyright settlement between Anthropic and a group of authors. Describing the case simply as 「an AI company used copyrighted works to train its model and therefore paid US$1.5 billion」 would overlook the complexity of the dispute.

The court had previously made an important determination regarding whether AI model training could constitute fair use. Another major issue in the case concerned the manner in which the company acquired and retained copies of pirated books. The US$1.5 billion ultimately represented a settlement between the parties, rather than a damages award directly imposed by the court.

The real lesson for AI companies is therefore not that 「copyrighted materials can never be used for AI training.」 It is that data compliance in the AI era cannot stop at examining how data is ultimately used by a model. Companies must also trace where the data came from, how it was obtained, and whether they have the legal right to acquire, retain, and use it.

This is precisely the kind of risk that many U.S. AI companies need to take seriously. A product may have operated for years, attracted a large user base, and completed several rounds of financing before a dispute over training data, product design, or marketing practices develops into prolonged litigation. More room to innovate does not necessarily mean lower legal costs. It may simply mean that the risks emerge at a different stage.

For Chinese companies, the pressure often appears more directly in the question of whether a product can continue operating.

Failure to complete required filings, inadequate content-governance mechanisms, failure to implement AI-content labeling, product designs that do not satisfy requirements for protecting minors, or functions that conflict with regulatory rules can all lead to rectification requirements, product modifications, or even suspension of services.

For a fast-growing technology company, these risks cannot be measured solely by the size of an administrative fine. If a core function must be taken offline or an entire user flow needs to be redesigned, the commercial impact may far exceed the financial penalty itself.

From a business perspective, the core risks in the U.S. and China can therefore be framed as two different problems: in the U.S., companies need to avoid being held liable after they have already scaled; in China, companies need to avoid being unable to scale because they failed to address compliance requirements early enough.

IV. For AI Companies Going Global, the Real Question Is Not Which Market Is More Relaxed

Because the two systems follow different regulatory logics, one of the easiest mistakes for AI companies to make when choosing a market is to compare whether 「the U.S. is more relaxed」 or 「China is stricter.」

That framing oversimplifies a much more complex business decision.

A foundation model, an internal enterprise Agent, an AI SaaS product, an education application, an AI companion, a medical AI system, and an AI-enabled hardware product all face very different regulatory issues. An enterprise productivity tool that does not directly serve consumers cannot be evaluated under the same legal framework as an AI product that continuously interacts with millions of minors every day, even if both ultimately rely on the same underlying model.

What companies should examine first is their own risk profile.

If a product depends heavily on large-scale copyrighted training data, then data licensing, fair-use boundaries, and potential class-action litigation may become central issues in the U.S. If a product processes significant volumes of personal information in China while calling overseas models, cross-border data transfer mechanisms and personal information protection may become major concerns. If the product is an AI companion, content safety, protection of minors, anthropomorphic design, and mechanisms addressing user dependence may directly fall within the compliance framework.

Choosing where to launch an AI business is therefore not simply a choice between a 「relaxed market」 and a 「strict market.」 It is a choice about which type of legal risk the company is better equipped to manage.

That is where comparisons between U.S. and Chinese AI governance become genuinely useful.

The U.S. gives businesses more room for innovation at an earlier stage, but companies must be prepared to manage complex and fragmented downstream liability. China imposes more front-loaded requirements on certain types of products, but once the applicable rules are identified, companies may also have greater clarity about which compliance steps need to be completed before launch.

Both systems regulate AI. They simply place risk at different points in the product lifecycle.

Mankun Graphic | AI Global Expansion Risk Alignment Checklist

V. Neither the U.S. nor China Is Likely to Become 「More Relaxed」

Looking ahead, although the U.S. and China have chosen different regulatory paths, they are moving in one similar direction: as AI moves from being a tool into real social relationships and high-impact applications, regulation in both countries is becoming more specific.

One of the most important variables in the U.S. will be how the federal government and individual states ultimately divide authority over AI regulation. At the federal level, policymakers continue to emphasize maintaining U.S. technological leadership and competitiveness in AI, while states such as California and New York continue to develop their own rules around frontier models, algorithmic transparency, deepfakes, child safety, and related issues. For AI companies, the future challenge is therefore unlikely to be whether the U.S. 「regulates AI at all.」 It will be how to navigate the interaction between federal policy, state law, sector-specific regulation, and existing legal frameworks.

China is continuing down a different path. Rules developed over the past several years governing algorithms, generative AI, data, content labeling, and specific application scenarios have already formed a relatively detailed regulatory system. China』s 2026 State Council legislative agenda also calls for accelerating comprehensive legislation to promote the sound development and governance of artificial intelligence.

China』s future AI governance framework is therefore unlikely to discard the existing rules and start again. More likely, it will build a more systematic, higher-level legal framework on top of the regulatory architecture that already exists.

For businesses, the implication is increasingly clear: AI compliance can no longer be treated as an auxiliary legal review conducted only after a product has been completed.

Whether operating in the U.S. or China, mature AI companies will ultimately need to integrate compliance capabilities throughout research and development, product design, data governance, marketing, and operations. What differs is which risks need to be addressed first in each market.

Conclusion

「Move fast in the U.S., govern first in China」 remains an easy way to understand the basic contrast between the two systems. But from the perspective of an AI business operator, those few words explain only the first layer.

The U.S. does not allow companies to move fast without constraints. Rather, it relies more heavily on existing law, regulatory enforcement, state legislation, and litigation to define legal boundaries as products operate in the market. China, likewise, does not require every AI product to obtain prior approval. Instead, for certain high-impact, public-facing, or risk-sensitive services, it places filing, assessment, content governance, user protection, and product-design requirements earlier in the product lifecycle.

The real difference between U.S. and Chinese AI governance is therefore not that one system 「encourages innovation」 while the other 「restricts innovation.」 The two systems allocate legal risk differently.

For AI companies preparing to expand internationally or operate simultaneously in both China and the U.S., the better questions are not simply 「Which market is more relaxed?」 Instead, companies should ask: What type of product are we building? At what stage will the law intervene? And if we make the wrong judgment, which risk would be the most difficult for the business to absorb?

Once those questions are clear, deciding 「where to build AI」 becomes more than a market-entry decision. It becomes a genuine business and legal strategy.

This article reflects the author』s personal views only and does not constitute legal advice regarding any specific matter. Compliance assessments for particular AI products and cross-border businesses should be conducted based on product functionality, business model, user location, data-processing practices, and specific application scenarios.

本文作者：范川理 Joanna

來源：金色財經

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