Read More《中国AI应用出海:算力筑基，场景聚力——《2025年中国AI应用出海企业发展需求洞察报告》发布!》
Positive Reviews: Computing Power Foundation and Scenario-based Customization, Breaking through the Core Competitiveness of Chinese AI Applications Going Global
Against the backdrop of the global AI market expected to exceed $990 billion in scale by 2027, Chinese AI application enterprises are shifting from “testing the waters” to “in-depth development” in the global market. The release of the “Insight Report on the Development Needs of Chinese AI Application Enterprises Going Global in 2025” (hereinafter referred to as the “Report”) not only reveals the momentum of Chinese enterprises going global supported by technological accumulation, scenario innovation, and policy support, but also outlines a clear path of “computing power foundation + marketing breakthrough + payment closed-loop” through the actual combat data of 700 enterprises, injecting key confidence into the industry.
The “Cloud-based” Breakthrough of Computing Power Infrastructure Solves the Core Bottleneck of Global Services
Computing power is the “water, electricity, and coal” of AI applications, and its deployment efficiency directly determines the model training, inference response, and service coverage capabilities. The Report shows that over 52.7% of enterprises going global are troubled by the insufficient global computing power layout, resulting in high cross-regional latency and inefficient data collaboration – this pain point is particularly prominent in scenarios where AI applications rely on real-time interaction (such as emotional companionship, education, and games). The popularization of GPU cloud (87% of enterprises rely on GPU cloud) is the key innovation for Chinese enterprises to solve this bottleneck.
The value of GPU cloud lies not only in the cost optimization of “pay-as-you-go”, but also in its comprehensive capabilities of “globalization + elasticity + scenario customization”. For example, 60% of enterprises value the cluster management and resource scheduling capabilities of cloud platforms to handle cross-regional loads through automated computing power allocation; 51% of enterprises choose cloud services with global node coverage to achieve low-latency deployment in multiple regions, meeting the real-time interaction needs of users during the inference stage (such as millisecond-level response in games and real-time interaction in education). This “cloud-based computing power” not only reduces the heavy asset pressure of enterprises building their own computing power centers, but also directly solves the latency problem of cross-regional services through the global node layout of cloud providers.
More notably, GPU cloud has been upgraded from a single computing power resource to a comprehensive solution of “technological collaboration + cost optimization + global adaptation”. For example, the application of technologies such as mixed-precision inference and heterogeneous computing power collaboration enables enterprises to significantly reduce computing power costs while ensuring performance; emerging cloud service providers like GMI Cloud have entered the top three with a popularity of 36.3%, reflecting the market’s urgent need for cloud services “more suitable for the needs of Chinese enterprises” – this includes both cost competitiveness (59.6% of enterprises are concerned) and comprehensive capabilities in technical support (58.7%) and global compliance (31.0%).
Scenario-based Customization and In-depth Development in Niche Markets Build Differentiated Competitive Barriers
The “practical implementation” of AI applications determines their survival ability in the overseas market. The Report’s analysis of 7 popular fields such as AI productivity tools, emotional companionship, and audio-video generation reveals the key logic of “scenario-defined infrastructure”: there are significant differences in computing power requirements during the training and inference stages in different fields, requiring customized solutions.
For example, emotional companionship applications during the training stage need cross-cultural emotional recognition and persona fine-tuning capabilities, while audio-video generation requires multi-device compatibility and small-language data enhancement; game applications during the inference stage emphasize millisecond-level real-time interaction and graphics computing power optimization, and embodied intelligence focuses on microsecond-level motion control and real-time decision-making at the edge. This “scenario-based” demand forces enterprises to cooperate deeply with cloud service providers to develop computing power solutions suitable for specific scenarios.
The value of this differentiated strategy is that it helps Chinese enterprises avoid direct competition with international giants in “general computing power” and instead build technological barriers in vertical scenarios. For example, customized computing power solutions can improve model training efficiency for the needs of cross-age model adaptation and small-language annotation in the education field; edge-cloud collaborative inference solutions can reduce the computing pressure at the edge for the needs of multi-device collaboration and model lightweighting in AI terminals. This “scenario-driven” technological innovation is the core advantage of Chinese enterprises in the overseas market to “compete with the big with the small”.
The Practical Value of the Report: Providing “Precise Decision-making Guidelines” for Different Roles
Different from the macro analysis of traditional industry reports, the core value of this Report lies in the “systematic sorting of front-line actual combat data”. For enterprise decision-makers, the Report clarifies the strategic priority of “computing power foundation” and provides key data such as the proportion of computing power investment (over 70% of enterprises’ computing power investment accounts for over 10% of R & D) and the direction of cost optimization (mixed-precision inference, heterogeneous computing power collaboration); for technical teams, the Report analyzes the differentiated computing power requirements of 7 fields, directly guiding technology selection and architecture design (such as the 7×24-hour dynamic response optimization of emotional companionship applications); for investment institutions, the Report quantifies market opportunities (the market scale of AI applications exceeds $407 billion) and reveals high-potential tracks (such as the microsecond-level motion control requirements of embodied intelligence). This “role-adapted” content design makes the Report a “reference book” for enterprises to formulate global strategies.
Negative Reviews: Potential Risks and Challenges in the Global Ecosystem: From Technological Dependence to Localization Shortcomings
Although Chinese AI applications are showing strong momentum in going global, the challenges disclosed in the Report cannot be ignored. From the potential risks of computing power dependence, to the “last mile” problems in the marketing and payment links, to the pressure of resource dispersion in scenario-based customization, Chinese enterprises still need to overcome multiple obstacles.
Technological Dependence and Compliance Risks behind the “Cloud-based” Computing Power
87% of enterprises rely on GPU cloud services, which solves the urgent problem of computing power deployment but also implies risks of technological dependence. On the one hand, leading cloud service providers (such as AWS and Google Cloud) have an advantage in the global computing power node layout. If Chinese enterprises rely too much on their services, they may face “supply cut-off” or “data sovereignty” disputes (such as strict restrictions on cross-border data flow in some countries); on the other hand, although emerging cloud service providers (such as GMI Cloud) are more suitable for the needs of Chinese enterprises, their global node coverage capabilities and technological stability still need market verification – if the cloud service fails, it may lead to a complete stagnation of enterprises’ overseas business.
In addition, compliance risks run through the entire computing power chain. The Report shows that 31.0% of enterprises are concerned about the compliance certification of cloud services, but the storage and transmission rules for AI data (such as user privacy and industry-sensitive data) vary greatly in different countries (such as the EU GDPR and the US CCPA). For example, user data in the education field involves the privacy of minors. If the computing power deployment does not meet local compliance requirements, it may lead to legal disputes; sensor data in embodied intelligence (such as equipment operation data in industrial scenarios) may be regarded as “critical infrastructure information”, and cross-border transmission is restricted. This “compliance complexity” requires enterprises to not only consider cost and efficiency when choosing cloud services but also evaluate the regulatory risks of the target market in advance.
Localization Shortcomings in the Marketing and Payment Links Restrict the Implementation of the Business Closed-loop
Marketing and payment are the “last mile” of the global business closed-loop, but the data in the Report exposes obvious shortcomings. At the marketing end, 64.0% of enterprises face the problem of high social media operation costs, 57.7% lack accurate user portraits, and 57.3% have difficulty quantifying the ROI of advertisements – this reflects the lack of “user understanding” ability of Chinese enterprises in the overseas market. For example, emotional companionship applications need to deeply understand the cultural habits of the target market (such as the difference in the openness of “emotional expression” between European and American users and Asian users). If only standardized content marketing is used, it may lead to insufficient user resonance; the promotion of AI productivity tools needs to be combined with the work processes of local enterprises (such as European and American enterprises relying more on collaborative tools, while Southeast Asian enterprises may pay more attention to mobile device adaptation). If the marketing content is not localized, the conversion rate will be greatly reduced.
At the payment end, 61.3% of enterprises are troubled by complex compliance reviews, 54.0% face insufficient multi-currency settlement, and 51.7% need to deal with exchange rate fluctuation risks – these problems directly affect enterprises’ capital turnover and profitability. For example, e-wallets are popular in the Southeast Asian market (such as GrabPay and DANA). If enterprises do not connect to local payment methods, they may lose a large number of users; the exchange rate in the Latin American market fluctuates sharply (such as the Argentine peso depreciating by more than 100% annually). If there are no exchange rate hedging tools, enterprises’ profits may be greatly eroded. Although 65.0% of enterprises desire “one-stop compliance management”, the local adaptation of cross-border payment (such as supporting mainstream payment methods in the region) still requires enterprises to invest a lot of resources to cooperate with local financial institutions, which is particularly difficult for start-up enterprises with limited resources.
The Risk of “Resource Dispersion” in Scenario-based Customization Tests Enterprises’ Focusing Ability
The Report emphasizes that “scenario-based customization” is the key to differentiated competition, but the computing power requirements of 7 popular fields are highly dispersed (with different focuses in the training and inference stages), which may lead to resource dispersion for enterprises. For example, the AI terminal field requires multi-device collaboration and model lightweighting, while embodied intelligence requires microsecond-level motion control and real-time decision-making at the edge – the technical paths of the two are very different. If enterprises layout multiple fields at the same time, they may face problems such as insufficient R & D resources and shallow technological accumulation. In addition, scenario-based customization requires enterprises to cooperate deeply with cloud service providers and local partners (such as cooperating with local language service institutions for the small-language annotation needs in the education field), which puts forward higher requirements for enterprises’ cross-departmental collaboration and external resource integration capabilities.
Suggestions for Entrepreneurs: Building Sustainable Competitiveness from the “Computing Power – Marketing – Payment” Triangle System
Based on the opportunities and challenges revealed in the Report, Chinese AI application enterprises going global can optimize their strategies in the following directions:
Computing Power Level: Prioritize Cloud Services with “Globalization + Compliance + Scenario Adaptation”
- Evaluate the Globalization Ability of Cloud Service Providers: Choose cloud services with a high-density computing power node layout in the target market (such as Southeast Asia and Europe and America) to reduce cross-regional latency (such as in real-time interaction scenarios like games and education); at the same time, pay attention to the elastic scheduling ability of cloud services (such as to meet the sudden traffic expansion needs of audio-video generation).
- Strengthen Pre – compliance Design: Before computing power deployment, clarify the data compliance requirements of the target market (such as whether user data needs to be stored locally and the approval process for cross-border transmission), and give priority to cloud service providers that have passed local compliance certifications (such as the EU GDPR and the Singapore PDPA).
- Promote Scenario-based Computing Power Customization: Combine with your own business scenarios (such as the 7×24-hour dynamic response of emotional companionship and the microsecond-level motion control of embodied intelligence), and cooperate with cloud service providers to develop customized computing power solutions (such as low-power consumption optimization and edge-cloud collaborative inference) to improve resource utilization efficiency.
Marketing Level: Use AI Tools as a Lever to Deepen Localized User Understanding
- Use AI to Optimize Marketing Efficiency: Track user feedback in the target market in real-time through AI public opinion monitoring tools (a function expected by 67.7% of enterprises in the Report) and quickly adjust marketing strategies; use intelligent advertising delivery tools (a need of 57.0% of enterprises) to achieve accurate delivery based on user behavior data and improve ROI.
- Build Localized Content Production Capabilities: Use automated multi-language generation technology (such as AI to generate marketing copy and videos that conform to local culture) to reduce the cost of localized content production; at the same time, form or cooperate with local operation teams (such as hiring cultural consultants in the target market) to ensure that the content meets the language habits, aesthetic preferences, and values of local users.
Payment Level: Plan Compliance and Financial Tools in Advance to Ensure Capital Flow
- Establish a “One-stop Compliance Management” System: Integrate the payment regulations of the target market (such as anti-money laundering and tax compliance requirements), cooperate with third-party payment service providers (such as PayPal and local e-wallets) to simplify the compliance review process; for the need of multi-currency settlement, choose payment platforms that support multi-currency collection (such as Stripe and WorldFirst).
- Deal with Exchange Rate Fluctuation Risks: Use exchange rate hedging tools (such as forward contracts and options) to lock in the exchange rate and reduce the uncertainty of returns; for high-volatility markets (such as Latin America), consider local capital retention to reduce the frequency of cross-border settlement.
Strategic Level: Focus on Core Scenarios to Avoid Resource Dispersion
- Clarify the Priority of Core Tracks: According to the enterprise’s technological accumulation (such as whether it is good at multi-modal fusion and small-language data processing) and market demand (such as the demand for AI terminals in Southeast Asia and the industrial scenario demand for embodied intelligence in Europe and America), choose 1 – 2 core fields to develop in-depth and avoid layout in multiple scenarios with excessive differences at the same time.
- Strengthen Ecosystem Cooperation: Establish long-term cooperative relationships with cloud service providers, local marketing agencies, and payment service providers to share resources and data (such as the computing power optimization experience of cloud providers and the user portrait data of marketing agencies) and reduce the single-point investment cost.
Chinese AI applications going global have entered the “deep water area”. Computing power foundation solves the “technical feasibility”, marketing breakthrough solves the “user reachability”, and payment closed-loop solves the “business sustainability”. Enterprises need to be guided by the actual combat data in the Report and build the core competitiveness of “scenario-driven + ecosystem collaboration” under the multiple constraints of technology, market, and compliance to occupy an irreplaceable position in the global AI ecosystem.
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