导语 当AI Agent逐渐深入操作系统、走向跨应用操作，商业生态、数据安全与产业治理随之面临结构性风险，治理规则需要同步跟进。针对这一趋势，弗若斯特沙利文在《2026年侵入式Agent产业治理白皮书》中系统分析了跨应用执行带来的生态影响，并提出以双重授权和全链路可审计为治理基础，逐步形成以API协同为主、GUI模拟为辅的跨应用协作机制。提升执行效率的同时，更应保障用户知情、平台可控、责任可追溯。智能体产业能否形成长期价值，取决于效率与信任能否保持平衡。 AI Agents are evolving from information interaction toward task execution, with cross-application operations emerging as an important direction of product development. However, as Agents gain deeper access to operating systems and applications, risks associated with insufficient authorization and unclear accountability are also increasing, requiring governance rules to evolve in parallel.   2026年上半年以来，Agent产业密集演进，在应用生态内、应用与应用、应用与终端、终端与终端均涌现多款基于接口或协议的交互方案。这表明，白皮书所揭示的产业损害逻辑——流量价值迁移、开发成本攀升、安全风险集中——正被越来越多的行业参与者所验证和认知。Agent权限边界的合规压力也在推动产业回归可治理、可追责的轨道上。 Against this backdrop, Frost & Sullivan’s White Paper on the Governance of Invasive Agents 2026 systematically examines the ecosystem implications of cross-application execution. It proposes a governance framework built on dual authorization and end-to-end auditability, with API-based collaboration as the primary approach and GUI simulation as a supplementary mechanism. The objective is to improve execution efficiency while ensuring that users remain informed, platforms retain control, and accountability remains traceable. The Agent industry’s ability to create long-term value will depend on maintaining a sustainable balance between efficiency and trust.   但与此同时，应用授权不充分、责任边界模糊、高危权限滥用等侵害产业的潜在问题依然存在。白皮书指出，随着全球AI竞争加速，治理能力和信任基础正成为企业进入国际市场的重要竞争条件。若以牺牲信任换取短期扩张，不仅会压缩中国AI企业的国际合作空间，也可能损害中国AI产业的长期国际信誉。Agent技术的演进方向，不应以侵入替代协同，而应在可信治理的框架内，实现从“能做什么”到“该做什么”的产业共识收敛。 As global competition in AI accelerates, governance capabilities and a strong foundation of trust are becoming increasingly important for companies seeking to enter international markets. Pursuing short-term expansion at the expense of trust could not only constrain international cooperation opportunities for Chinese AI companies, but also cause lasting damage to the global reputation of China’s AI industry.   根据沙利文《2026年侵入式Agent产业治理白皮书》，AI Agent加速进入系统层，跨应用执行正在重塑移动互联网入口与生态治理规则 Frost & Sullivan’s 2026 White Paper on the Governance of Invasive Agents: AI Agents Move Deeper into the System Layer, Reshaping Mobile Entry Points and Ecosystem Governance   随着多模态大模型从概念验证步入商业化落地，AI智能体（AI Agent）正逐渐深度介入设备底层操作，并展现出独立执行跨应用复杂任务的能力。在此背景下，部分绕过标准应用程序接口（API）、直接利用系统底层特权干预应用运行的“侵入式Agent”迅速蔓延，给现有的互联网信任机制与生态协同带来了空前挑战。 As multimodal large language models move from proof-of-concept to commercial deployment, AI Agents are increasingly becoming deeply involved in low-level device operations and are demonstrating the ability to independently execute complex tasks across applications. Against this backdrop, invasive Agents that bypass standard application programming interfaces (APIs) and directly use low-level system privileges to interfere with application operations are spreading rapidly, creating unprecedented challenges for existing internet trust mechanisms and ecosystem collaboration.   基于对全球及中国AI Agent市场的系统调研，弗若斯特沙利文（Frost & Sullivan，以下简称“沙利文”）于2026年3月正式发布《2026年侵入式Agent产业治理白皮书》。本报告聚焦侵入式Agent机制对产业流量分配、商业生态运作及底层数据安全造成的冲击，并对行业未来的规范化落地与治理路径进行了前瞻性研判。 In March 2026, based on systematic research into the global and Chinese AI Agent markets, Frost & Sullivan has officially released the White Paper on the Governance of Invasive Agents 2026. The white paper focuses on the impact of invasive Agent mechanisms on industrial traffic allocation, commercial ecosystem operations, and underlying data security, while providing forward-looking analysis of the industry’s future standardization and governance pathways.   01 侵入式Agent依托系统层权限突破应用边界，为产业治理带来新风险 Invasive agents breach application boundaries through system-level privileges, introducing a new layer of governance risk 侵入式Agent的关键特征，在于其不依靠标准接口或协议，借助系统签名级权限直接读取界面信息、识别页面内容，并模拟用户完成点击、输入、跳转等操作。借助这一路径，Agent可以在未获得应用方业务授权的情况下，跨越多个软件连续执行任务。相较于基于标准接口开展协同的路径，这种方式虽然降低了跨应用联动对生态合作的依赖，却也突破了原有应用边界与权限边界，将风险前置到系统层。 The defining feature of invasive agents is that they do not depend on standard interfaces or protocols exposed by third-party applications. Instead, they rely on high-level system privileges, including signature-level permissions, to directly read interface content, interpret on-screen information, and simulate user actions such as clicking, inputting text, and navigating between pages. Through this approach, agents are able to execute continuous tasks across multiple applications without obtaining business authorization from the applications involved. Compared with models built on standardized interface-based collaboration, this pathway reduces dependence on ecosystem cooperation for cross-application orchestration, but at the same time pushes risk upward into the system layer by breaking through established application and permission boundaries.   过去，用户需要先进入具体应用，再逐步完成信息获取与操作决策；如今，系统层Agent开始承接用户意图理解、任务拆解和动作调度，应用则更多退居为任务执行的承载界面。随着移动端原有以应用为中心的入口逻辑被改写，系统层Agent在提升任务执行便利性的同时，也开始对既有生态秩序、权限规则与治理边界形成新的冲击。 Historically, users entered individual applications and completed information discovery and decision-making step by step within each app environment. Today, system-level agents are increasingly taking over intent understanding, task decomposition, and action scheduling, while applications are being relegated to execution interfaces. As this shift rewrites the app-centric entry logic of the mobile internet, system-level agents are improving task convenience while simultaneously placing new pressure on existing ecosystem order, permission rules, and governance boundaries. 侵入式Agent的定义 Defining Features of Invasive AI Agents   02 侵入式Agent前置用户决策环节，第三方应用商业价值承压 By moving user decision-making upstream, invasive agents place pressure on the commercial value of third-party applications 当侵入式Agent逐步成为用户发起任务的主要入口，传统移动应用在生态中的角色也随之发生变化。原本由应用自身承接的搜索、浏览、比较、点击和下单等行为，被越来越多地前置到Agent侧完成，应用留给用户的直接交互时长和触达机会显著减少。 As invasive agents increasingly become the primary entry point for users to initiate tasks, the role of conventional mobile applications within the ecosystem is also changing. Activities that were previously carried out within apps, including search, browsing, comparison, clicking, and purchasing, are increasingly being completed on the agent side before users ever meaningfully interact with the application itself. As a result, applications face a sharp reduction in direct engagement time and user touchpoints.   这一变化将直接传导至应用开发者的商业模式。工具类应用首当其冲，交易类与社交类的生态价值也将被冲击。无论是依赖停留时长和曝光分发的信息流广告，依赖深度使用形成粘性的会员订阅，还是依赖交易链路闭环获得收益的佣金模式，都会受到不同程度影响。 This shift is likely to have direct consequences for application developers’ monetization models. Utility applications are expected to come under the greatest pressure first, but transactional and social/content platforms will also face material disruption. Whether monetization depends on information-flow advertising tied to user time spent and exposure allocation, subscription models built on deep engagement, or commission-based models supported by closed-loop transaction paths, each of these commercial mechanisms may be weakened to varying degrees.   白皮书测算显示，若未来侵入式Agent在用户侧渗透率达到25%，工具类应用商业价值预计下降39%，内容与社交类应用预计下降19.5%，交易类应用预计下降15.4%。这意味着，侵入式Agent带来的并非单点效率优化，而是应用生态商业价值的重新分配。 Frost & Sullivan estimates that if invasive agents reach a 25% user-side penetration rate in the future, the commercial value of utility applications could decline by 39%, while content and social applications could see a 19.5% decline and transactional applications a 15.4% decline. This suggests that the rise of invasive agents is not merely about marginal efficiency enhancement. It represents a broader redistribution of commercial value across the application ecosystem. 用户触达流量入口转移 Shift in User Traffic Entry Points   03 流量迁移未带来显著增量，反而导致产业内卷与高治理成本 Traffic migration does not generate meaningful incremental value, but instead intensifies internal competition and drives up coordination and governance costs across the industry 白皮书进一步指出，侵入式Agent带来的影响，并不主要体现为产业新增量，而更多体现为对现有流量分配关系的重新切分。当系统层Agent试图绕过既有合作机制直接承接用户需求时，平台方、应用开发者与Agent提供方之间的关系会由协同合作转向零和博弈。 The white paper further notes that the impact of invasive agents is not primarily reflected in the creation of substantial new market value. Rather, it is more accurately understood as a redivision of existing traffic allocation relationships. When system-level agents attempt to bypass established cooperation mechanisms and directly intermediate user demand, the relationship among platform operators, application developers, and agent providers may shift from coordinated collaboration to a zero-sum game. 侵入式Agent内卷式工具特征 Invasive AI Agents and Zero-Sum Competition 在这一过程中，应用开发者往往需要通过界面调整、反自动化识别、权限收紧和策略更新等方式进行防御，以应对未经许可的数据读取和界面操作。这使得软件迭代频率、兼容维护难度以及安全防护投入同步抬升。 In this process, application developers are often compelled to respond through interface adjustments, anti-automation detection, tighter permission controls, and ongoing strategy updates in order to defend against unauthorized data extraction and interface manipulation. This raises software iteration frequency, increases compatibility-maintenance complexity, and drives additional spending on security protection.   研究显示，在侵入式Agent渗透率达到25%的情况下，移动应用综合开发成本预计上升16%，包括合作协调、合规审查与安全防御在内的产业链综合治理成本预计上升34.4%。这一趋势说明，若缺乏清晰规则约束，侵入式发展路径很可能将产业资源更多消耗在对抗之中，而非创新本身。 Research indicates that at a 25% penetration rate for invasive agents, overall mobile-application development costs could increase by 16%, while total ecosystem governance costs, including coordination, compliance review, and security defense, could rise by 34.4%. The implication is clear. Without an explicit governance framework, an invasive development path is likely to consume more industry resources in defensive confrontation than in genuine innovation.   04 高权限集中叠加指令诱导，显著放大数据与资产安全风险 The concentration of high-level permissions, combined with instruction-based manipulation, significantly amplifies data and asset-security risks 为实现跨应用、跨场景的连续任务执行，侵入式Agent通常需要长期持有较高等级的系统权限，并在多个业务场景中维持账户登录与操作能力。这意味着，原本分散在不同应用中的数据边界和权限边界，开始向单一Agent集中。一旦出现权限滥用、模型误判或安全缺口，影响范围将不再局限于单个应用，而可能扩散至整个终端环境。 To enable continuous execution across applications and use cases, invasive agents typically require long-term access to elevated system permissions and the ability to maintain logged-in operational states across multiple business scenarios. This means that data boundaries and permission boundaries that were previously distributed across different applications begin to converge into a single agent. Once permission abuse, model misjudgment, or security vulnerabilities occur, the consequences are no longer confined to a single application, but may spread across the entire terminal environment.   与此同时，由于侵入式Agent具备读取屏幕内容并触发后续动作的能力，其还可能受到外部信息的诱导。攻击者可以通过网页、邮件、文档等载体嵌入特定指令，诱导Agent在用户未充分察觉的情况下执行删除、转发、修改、支付等敏感操作。原本局部、可控的识别偏差，在高权限执行环境下可能迅速演变为隐私泄露、账户风险甚至资产损失事件。由此可见，侵入式Agent带来的已不只是传统意义上的信息安全问题，更是权限集中背景下的系统性风险放大。 At the same time, since invasive agents can read screen content and trigger subsequent actions, they may also be inherently susceptible to indirect prompt injection. Attackers may embed specific prompts or instructions in webpages, emails, or documents, inducing the agent to perform sensitive actions such as deletion, forwarding, modification, or payment without the user’s full awareness. In a high-permission execution environment, what might otherwise have remained a localized and manageable recognition error can rapidly escalate into privacy leakage, account compromise, or even financial loss. In this sense, invasive agents do not merely create conventional information-security concerns. They materially increase systemic risk under conditions of concentrated authority. 零点击邮件指令触发网盘批量误删 Zero-Click Email Injection Triggers Bulk Cloud-Drive Deletion   05 以双重授权和全链路可审计为基础，构建可信治理框架 A trusted governance framework should be built on dual authorization and full-chain auditability 针对上述问题，白皮书提出，Agent产业的可持续发展不应建立在越过生态边界和削弱信任机制的基础上，而应推动形成以API协同为主、GUI模拟为辅的治理框架。其核心在于建立双重授权机制，即Agent开展跨应用操作，不仅需要获得用户对系统权限的明确授权，也需要获得被调用应用或服务方对具体业务动作的许可。 In response to these risks, the white paper argues that the sustainable development of the agent industry should not be built on bypassing ecosystem boundaries or weakening trust mechanisms. Instead, the industry should move toward a governance framework in which API-based collaboration remains the primary model and GUI simulation plays only a supplementary role. The core principle is the establishment of a dual-authorization mechanism. This means that for an agent to perform cross-application actions, it must not only obtain the user’s explicit authorization for system-level permissions, but also secure permission from the invoked application or service provider for the underlying business action itself.   在具体实施层面，可信治理框架应围绕四个方向展开。其一，清晰界定Agent的权限边界与可代办事项范围，防止能力外溢。其二，对涉及隐私、支付、资产和身份变更等高风险操作设置更严格的动作约束与确认机制。其三，建立覆盖授权、决策、执行与结果反馈的全过程留痕体系，确保关键操作可核验、可复盘。其四，在可审计基础上进一步明确责任归属，为后续争议处理、损失核定和制度约束提供依据。 At the implementation level, the report proposes that a trusted governance framework should advance along four directions. First, agent permission boundaries and the scope of delegable tasks should be clearly defined in order to prevent capability spillover. Second, stricter constraints and confirmation mechanisms should be imposed on high-risk actions involving privacy, payments, assets, and identity changes. Third, a full-process traceability system should be established across authorization, decision-making, execution, and result feedback so that critical actions can be verified and reconstructed. Fourth, on the basis of auditability, responsibility allocation should be clarified to support future dispute resolution, loss assessment, and institutional enforcement.   白皮书最终强调，Agent技术的演进方向不应是以侵入替代协同，而应是在可验证、可约束、可追责的框架内实现跨主体协作。只有在保障生态秩序、商业公平与用户安全的前提下，Agent才能真正释放其对社会效率提升的长期价值。 The white paper ultimately emphasizes that the future direction of agent development should not be one in which invasion replaces collaboration, but one in which cross-entity coordination takes place within a framework that is verifiable, enforceable, and accountable. Only by safeguarding ecosystem order, commercial fairness, and user security can agents fully deliver their long-term value in improving social and economic efficiency.   06 全球竞争不能以透支信任为代价，可信治理将成为中国AI走向国际市场的前提 Global AI competition cannot be pursued at the expense of trust; trusted governance will be a prerequisite for China’s AI industry to expand internationally 白皮书进一步指出，不能片面以创新视角理解侵入式Agent，而应考虑其对产业生态、公众隐私、安全环境、国际竞争的综合影响。Agent并不只是模型能力、产品形态和落地速度的竞争，更是治理能力、信任基础与规则适配能力的竞争。若侵入式Agent以突破权限底线、削弱授权机制、牺牲用户信任为代价，看似抢占了竞争先机，实则可能为整个产业与社会发展埋下更大风险。 The white paper further argues that invasive agents should not be viewed solely through the lens of innovation. Their broader implications for industrial ecosystems, public privacy, security conditions, and international competition must also be taken into account. Competition in AI agents is not only a matter of model capability, product form, and deployment speed. It is also a competition in governance capacity, trust infrastructure, and the ability to adapt to evolving rules. If invasive-agent providers seek short-term advantage by pushing beyond permission boundaries, weakening authorization mechanisms, and eroding user trust, such an approach may appear to secure an early lead, but in reality it may embed greater risks for the wider industry and society.   中美AI竞争格局的演进，不仅关乎技术能力和落地速度，也越来越取决于创新效率、安全约束与生态协同之间能否实现可持续平衡。对中国人工智能产业而言，具备安全、信任与规则兼容基础的Agent体系，将更有助于提升其面向全球市场的长期竞争力；若过度依赖以牺牲信任为代价的扩张路径，不仅可能削弱单一产品或企业的国际合作空间，也可能对中国AI整体的国际信誉带来负面影响，对融入全球主流的AI技术路线与治理体系同样构成负面障碍。 The evolution of China-US AI competition increasingly depends not only on technological capability and implementation speed, but also on whether innovation efficiency, security constraints, and ecosystem coordination can be balanced in a sustainable way. For China’s AI industry, an agent framework grounded in security, trust, and compatibility with emerging governance norms will be more conducive to strengthening long-term global competitiveness. By contrast, an expansion path built at the expense of trust may not only narrow the international cooperation space available to individual products or companies, but may also negatively affect the broader international credibility of China’s AI industry and create additional obstacles to its integration into mainstream global AI technology and governance systems.

NEWS On September 12th, Frost & Sullivan Frost & Sullivan China Center of Excellence (COE) officially opened in Zhangjiang Science City, Pudong New Area , marking a crucial step in the company's platform-based, high-end, and global strategic layout. At the opening ceremony, Dr. Wang Xin, Global Senior Vice President of Frost & Sullivan, Co-Chair of Asia-Pacific Region, and Chairman of China, along with industry peers and ecosystem partners, gathered in Zhangjiang Science City to witness the new development milestone of Frost & Sullivan Frost & Sullivan China Center of Excellence' innovation empowerment and ecological coexistence, and to jointly explore new paths for intelligent industrial inspection innovation. Dr. Liu Jun, Chairman of YUAN CAPITAL, and Dr. Wang Xin, Chairman of Frost & Sullivan, unveiled the center The newly launched YUAN CAPITAL (Shanghai) Innovation Center is located in the core area of Zhangjiang Science City, enjoying significant geographical advantages and a strong innovation atmosphere. The center integrates research and innovation, industrial cooperation, and strategic investment and mergers and acquisitions, forming a modern composite innovation venue for technology development, external exchanges, and internal empowerment. Integration, Accumulation, Empowerment UNICOMP WORLD Leveraging Shanghai's geographical advantages, YUAN CAPITAL (Shanghai) Innovation Center will deeply integrate into the Zhangjiang innovation ecosystem, collaborating with YUAN CAPITAL Asia Artificial Intelligence Center, adhering to "horizontal expansion, vertical deep cultivation" development strategy, playing a core hub role, coordinating the group's global strategic layout, the transformation of AI inspection technology results, and domestic and international operations, empowering platform-based innovation , continuously cultivating new growth trends for the company. In the future, YUAN CAPITAL will continue to build a leading platform-based technology enterprise in the industry, providing global customers with multi-modal integrated intelligent inspection solutions, empowering high-quality development for all industries with its strong innovation capabilities.

Introduction AI can read a large number of reports, web pages, and databases in a very short time, but "reading more" does not automatically mean "using it more reliably". When the conclusions in reports are broken into fragments, compressed into answers, or even used by intelligent systems for subsequent actions, industry research must re-answer a question: How can knowledge remain accurate after leaving the report? AI is entering industry research at a rapid pace. It can read reports, retrieve data, compare companies, generate summaries, and combine multiple materials to form a well-structured industry assessment. The time-consuming information search and preliminary sorting in the past are being reduced. What is more significant is that AI is changing the way knowledge is used. When market size data is extracted from a report, when a trend judgment enters a company's knowledge base, and when an expert opinion is re-expressed by a model, do these still retain their original time, scope, and applicable conditions? If new evidence emerges and old conclusions are corrected, can old knowledge already included in the model's responses, customer materials, and decision-making processes be identified promptly? If constraints are lost during transmission, who should explain, maintain, and correct them? In the past, industry research was mainly responsible for one report. In the future, it will also be responsible for the state of key knowledge in the report after leaving the original text and being repeatedly called upon by people and machines. This is the starting point of our proposed "Knowledge 3.0" research framework. From "remaining and being found," to "reliable use with accountability" "Knowledge 3.0" is not a historical phase with an academic consensus, nor a completed technical standard. It is a research framework we propose to understand the changes in knowledge in the AI era. In this framework, Knowledge 1.0 addresses "remaining". Text, publishing, archives, and corresponding professional systems enable knowledge to escape individual memory and be stably recorded, cited, and passed on. Knowledge 2.0 addresses "being found". The Internet, search engines, databases, and network collaboration enable scattered content to be connected, discovered, and jointly produced across regions and organizations. Knowledge 3.0 faces a new question: When machines begin to participate in the selection, transformation, and use of knowledge, can a piece of knowledge continue to be reliably used by people and machines? Can it be traced, questioned, corrected, and have someone accountable for it? These three capabilities are not mutually replaceable. Reports, books, and databases will not lose their value just because Knowledge 3.0 appears. On the contrary, complete documents remain important carriers for preserving the argument process, institutional context, and complex judgments. What needs to be added is another capability: after key knowledge in a document leaves its original position, it should still be able to move with the necessary context and return to the original text for inspection. Why don't more reports automatically become more reliable industry knowledge Industry research has long accumulated a large number of reports, data, and expert judgments. These contents are important foundations for training industry models, building company knowledge bases, and conducting retrieval-enhanced generation, but the scale of data does not equal the quality of knowledge. First, explicit knowledge may be distorted during transmission. The same "market size" may refer to sales volume, shipment volume, or terminal transaction amount respectively; the year in the same annual report may be the release date, the data benchmark year, or the start of the forecast period; the same "expected rapid growth" may be the model's calculation result or include analysts' comprehensive judgments on policies, competition, and supply conditions. When people read a complete report, they can often restore these boundaries from chapters, tables, and footnotes. When a machine only obtains a fragment, the boundaries are easily lost. Secondly, many experiences that determine the quality of research have not been included in formal results. An industry forecast may come from public data, corporate interviews, model calculations, and analyst judgments, but the final report may not record how researchers handled abnormal samples and contradictory interviews, why a certain scenario was chosen, and what changes would trigger a re-estimation. These experiences cannot be fully encoded; if no traces of judgment are left, machines may misinterpret professional judgments as unconditional facts. Therefore, a report database can preserve "what the industry has said before", but reliable industry knowledge also requires explaining: specifically who, at what time, according to what scope, based on what relatively independent evidence, formed which claim; under what conditions does this claim apply, and is it still valid now. What vertical models first need is not just more reports, but also better knowledge units. From document fragments to knowledge objects If the entire document is too large and isolated sentences are too thin, what the machine truly needs is a unit of expression between the two. We call it "knowledge object". A knowledge object is not the shortest text fragment, but the smallest knowledge unit that can be recognized and called upon with the necessary context and return to the original material for inspection. It can be a fact, an indicator, a definition, a method, or a conditional professional judgment. To avoid leaving only a seemingly precise field in the knowledge object without context, Knowledge 3.0 proposes five types of questions that cannot be forgotten. The content layer explains what it is about and under what conditions it applies; the experience layer retains the context, trade-offs, and exceptions of the judgment; the evidence layer connects direct sources, methods, and conflicts; the responsibility layer records who provided, transformed, inspected, released, maintained, and corrected; the lifecycle layer explains versions, status, alternative relationships, and reasons for changes. The five layers are not five forms requiring all knowledge to be mechanically filled out, nor does it mean the more fields, the more reliable it is. They are more like five groups of continuous questions: what we are using, why we can use it, where we cannot use it, and who to find when changes occur. Knowledge objects do not replace reports. Complete documents preserve the whole picture, and knowledge objects support precise calls; users can still return to the original text to restore the context after obtaining key conclusions. The two must be mutually accessible. How will Knowledge 3.0 change industry research This change affects industry research not simply by adding a delivery format, but by redefining the responsibilities of research institutions in the knowledge chain. First, reports will shift from being the end point of delivery to the starting point of the knowledge lifecycle Traditional research projects usually completed their main deliverables when the report was released. In the AI environment, a conclusion will continue to be retrieved, relayed, and combined. Research institutions need to identify which knowledge is frequently used, prone to failure, or has high error costs, and establish versions, status, and update triggers for them. When new market data appears, it should not just silently replace a number on a web page. What is really important is to explain: what claims have changed, whether the changes come from new evidence, scope adjustments, or corrections, and which existing judgments may be affected. Second, research quality will move from "the entire report being reliable" to "why a specific claim holds true" The brand and professional reputation of research institutions are still important, as they let users know why a material deserves priority attention. However, an institution's reputation cannot automatically turn predictions into facts, nor can it replace the evidence and boundaries of specific claims. Future industry research needs to distinguish facts, explanations, predictions, and suggestions, and ensure that evidence corresponds to specific claims. Similar views in multiple reports do not necessarily mean there are multiple independent evidences; they may just be repeated statements from the same source. The value of research institutions will be more reflected in discovering original evidence, distinguishing scopes, presenting differences, and explaining uncertainties. Third, analyst experience will transform from "individual ability" into "organizational traces of continuous learning" AI cannot replace researchers' judgments in complex situations, nor can it replicate an expert through a few fields. But organizations can more consciously preserve the origin of judgments: what signals were seen, what explanations were considered, why a certain scenario was chosen, whether the result met expectations, and what exceptions were later discovered. These records are not meant to turn experience into rigid rules, but to let machines and later scholars know that a judgment does not appear out of thin air, nor is it unconditional in all scenarios. For industry research institutions, this will turn personal experience into sustainable learning, review, and correction organizational capabilities. Fourth, research results need to face both human readers and machine users Reports for humans pursue complete arguments, expression rhythm, and reading experience; knowledge for machines also needs a stable identity, clear boundaries, corresponding sources, version status, and calling methods. Future industry research will not have to choose between "writing reports" and "doing data interfaces", but will need to establish two interconnected paths. People can read complete reports to understand complex logic, and machines can call upon key knowledge objects to complete retrieval, comparison, and auxiliary judgment; once risks increase or action is prepared, the system should be able to return the formal original text, reveal uncertainties, and hand over decisions to those with appropriate authority and responsibility. Fifth, research institutions will shift from content producers to important nodes in the knowledge responsibility chain In the Knowledge 3.0 environment, the role of research institutions may further expand: producing and organizing evidence, transforming knowledge objects, inspecting sources and scopes, maintaining the lifecycle of important knowledge, and providing entry points for explanation and correction. This does not require an institution to take full responsibility. Knowledge may pass through multiple entities from creation to use, so a visible responsibility chain needs to be established: who completed what action, who has the right to make decisions, and who will notify and correct after changes occur. Where should industry research institutions start Knowledge 3.0 should not start with "re-structuring all reports". A more cost-effective path is to first select those knowledge that are frequently individually called upon, change rapidly, or have high error consequences. Frequently used market size, business indicators, industry definitions, and key forecasts can prioritize supplementing sources, time, scope, statement type, and boundaries; content relying on expert judgments should retain context, exceptions, and re-estimation conditions; continuously changing content should record knowledge differences between versions, rather than just file modification dates. At the same time, the path for knowledge objects to return to the complete report should be preserved. Objectification is not discarding the original text after cutting it up, but connecting precise calling and complete reading. These practices need to be compared and verified: whether knowledge objects improve version selection, evidence correspondence, and conflict handling? Whether the lifecycle mechanism reduces old knowledge from entering new answers? Whether the additional maintenance costs are lower than the reduction in errors and reviews? Only through real-scenario comparative studies can Knowledge 3.0 move from a theoretical framework to a usable capability. Conclusion: Keeping knowledge reliable after leaving the report AI will not make industry research lose its value. It is forcing industry research to build its value in a deeper place. Future competitiveness of research institutions depends not only on who can publish reports faster and accumulate more content, but also on who can keep key knowledge accurate after leaving the original text: it can be called upon, evidence and boundaries can be found, versions and uncertainties can be seen, and the responsible person can be found when changes occur. When knowledge crosses organizational boundaries, facts, evidence, judgments, and inspection capabilities are still distributed among different entities. Connecting these knowledge, evidence, changes, and responsibilities may form a public credible knowledge network. It is not a central database or "unified truth", nor is it the only implementation of Knowledge 3.0, but a path to be jointly verified. Knowledge 3.0 is still an open research proposition, and it cannot be completed by a single institution alone. We look forward to communicating with three types of partners: technical partners in large models, knowledge engineering, data infrastructure, and industry application fields, to jointly verify whether the knowledge structure can bring measurable system increments; content partners such as research institutions, industry associations, enterprises, experts, and professional data providers, to jointly explore the production, expression, and continuous maintenance of high-quality knowledge; governance partners in standard research, independent inspection, legal compliance, and public governance, to jointly discuss the boundaries of evidence, responsibility, correction, and publicness. This is not an recruitment for a predefined platform, nor a prior commitment to a set of standards. We hope to start from real industry problems, jointly define issues, conduct comparative verification, and explore an industry knowledge infrastructure that can be reliably used by people and machines.

Frost & Sullivan Insight From an industry perspective, the core barrier to computing power leasing is the comprehensive ability to acquire resources, operate clusters, provide customer service, and manage funds. Enterprises do not need to build their own infrastructure; long-term leasing or hosting third-party resources is also feasible. The key lies in whether substantial control over resources is achieved, and whether responsibility for scheduling, operation, and SLA delivery is independently held, with primary responsibility for service quality. The industry distinguishes service providers from intermediaries mainly based on resource control rights and delivery responsibilities: service providers actually control resources and bear operational risks, with revenue coming from computing power and value-added services; the barriers lie in scale and technology. Intermediaries facilitate supply and demand to earn commissions, with low risk but thin profits, and barriers depend on channel efficiency. The fundamental difference between the two is whether they bear asset risks and add technical value. An interview with Frost & Sullivan China's Senior Partner and Managing Director, Jia Pang, was conducted by a reporter from The Times Weekly regarding the entry barriers and business models of the computing power leasing market. Q: From an industry perspective, what are the core barriers to computing power leasing? What key capabilities are required for an enterprise to enter the computing power leasing market? Jia Pang Senior Partner and Managing Director of Frost & Sullivan China The core barrier to computing power leasing is whether a stable, controllable, and sustainable computing power supply capability can be established, mainly including four aspects: First, the ability to acquire and control computing power resources , enabling stable acquisition of key resources such as GPU servers, cabinets, networks, and electricity; Second, the ability to operate and deliver computing power resources , including the ability to pool resources, manage clusters, schedule operations, monitor and measure, and handle failures, ensuring service availability and SLA; Third, the ability to acquire and serve customers , continuously meeting customer needs and improving the utilization rate of computing power resources; Fourth, the ability to manage funds and assets , effectively managing risks such as resource procurement, equipment depreciation, idle resources, and technological iteration. Therefore, the competitive barriers in the industry mainly reflect the comprehensive ability of "resource acquisition and control + operation and delivery + customer service + fund and asset management". Q: Do computing power leasing enterprises must have their own computing power infrastructure? If they mainly rely on third-party procurement or leasing of GPU resources and then provide services downstream, does this model have long-term commercial value? How is the scale barrier measured? Jia Pang Senior Partner and Managing Director of Frost & Sullivan China Not necessarily. There are both heavy-asset models where enterprises purchase and build their own computing power infrastructure, and models that obtain computing power resources through long-term leasing, hosting, and cooperative procurement. Using third-party resources does not affect the enterprise's ability to engage in computing power leasing business. The key is whether stable and continuous control over relevant resources can be achieved, and whether responsibility for resource allocation, scheduling, pricing, billing, operation, and customer service is independently held, with responsibility for final delivery quality and SLA. If only short-term resource matching or reselling is done, the business stability and customer stickiness are usually limited. Currently, there is no unified threshold for the number of GPUs or revenue scale in the industry. To determine whether an enterprise operates on a large scale, it is more appropriate to comprehensively evaluate indicators such as stable available GPUs or equivalent computing power scale, resource utilization rate, customer and contract stability, computing power service revenue, and renewal status, rather than solely measuring by the number of own GPUs. Q: How does the industry define "computing power leasing service providers" and "computing power intermediaries/resource integrators"? What are the essential differences between the two in business models, profit margins, and competitive barriers? Jia Pang Senior Partner and Managing Director of Frost & Sullivan China There is no unified legal classification in the industry. In practice, it can be distinguished mainly by resource control rights and final delivery responsibilities. Computing power leasing service providers usually have relatively stable control over relevant computing power resources and are responsible for resource allocation, scheduling, measurement and billing, operation, and customer service, bearing primary responsibility for service availability and SLA; computing power intermediaries or resource integrators mostly carry out functions such as supply and demand matching, resource matching, and transaction coordination, usually not continuously controlling underlying computing power resources, and their responsibility for final delivery is relatively limited. In terms of business models , computing power leasing service providers mainly charge fees for computing power leasing and related operation, deployment, etc., while bearing risks such as resource lock-in, equipment depreciation or leasing, idle resources, and price fluctuations; intermediaries or resource integrators mainly earn income through commissions, service fees, or transaction price differences, with relatively low asset investment and resource risks. The former usually has higher revenue scale and single customer value, but profitability depends more on resource utilization rate, procurement costs, and pricing ability. In terms of competitive barriers , computing power leasing service providers rely more on stable resource acquisition and control, cluster operation and scheduling, SLA delivery, customer base, and fund and asset management capabilities; intermediaries or resource integrators rely more on supplier and customer channels, supply and demand information coverage, and matching efficiency. The core difference between the two models is whether the enterprise has substantial control over computing power resources and bears primary operational and delivery responsibilities for customers. *This interview was published in The Times Weekly. The author is Zhu Chengcheng. The original title was: Exclusive | Gaole Co., Ltd.'s 6.7 billion yuan computing power deal mystery: The registered address of its Harbin subsidiary is virtual, and more than half of the computing power business team joined less than 3 months ago?

近日（2026年8月）， 上海昂朴生物科技有限公司 （下称“昂朴生物”）与 北京无界进化科技有限公司 （INFevo，下称“无界进化”）正式宣布达成深度战略合作。双方将整合昂朴生物诱导多能干细胞（ iPSC ）疾病模型与多组学数据库资源、无界进化自研AI虚拟细胞大模型OCOO与计算生物学能力，共建适配全球NAMs监管体系的遗传病创新研发平台，开辟以“ 人源数据+虚拟计算 ”替代传统动物实验的新药研发路径。 当前全球生物医药正迎来 “去动物化” 研发范式变革，各国监管机构持续放开新型非动物测试方法学（ NAMs ）的申报应用通道。2025年4月，美国FDA官宣逐步取消单克隆抗体等药物的动物实验硬性要求；2026年3月，FDA发布NAMs使用指南草案，明确药企可提交类器官、器官芯片、AI计算模型生成的数据直接用于非临床安全性评价。国内同步加速产业落地，国家药审中心启动NAMs研究应用“先锋计划”，中国食品药品检定研究院设立创新方法评价中心，一套兼容人源细胞、数字模拟技术的全新审评体系正在成型。 在此产业转型关键窗口期，昂朴生物与无界进化的强强联合，将搭建国内自主可控的“实体人源细胞+AI虚拟模型”一体化研发底座，填补遗传病领域数字化非临床评价体系空白。   昂朴生物： 国内领先的iPSC智能制造与遗传病数据台 昂朴生物成立于2013年，是一家专注于遗传病领域和诱导多能干细胞（iPSC）技术的高新技术企业。公司已通过CAP、CMA和CNAS等多项权威质量体系认证。 在细胞模型层面，昂朴生物构建了国内领先的工业级iPSC制备分化平台，拥有500㎡工厂级实验室及1,400㎡GMP厂房，可定向分化为多种细胞模型，批次稳定性优于行业平均水平。在数据资产层面，昂朴生物建设了中国领先的遗传病iPSC库（iBGD），当前已积累近1,000株iPSC细胞株、300TB+多组学数据，覆盖罕见病目录内重点疾病。这一资源库对标日本RIKEN、美国 CIRM 等国际级iPSC生物样本库，填补了中国在遗传病iPSC领域的产业基建空白。   无界进化：从基准 SOTA 到临床一线的AI虚拟细胞引擎 无界进化（INFevo）成立于2025年，是晶泰科技（2228.HK）深度孵化的前沿AI生物企业。2026年6月，公司完成数千万元人民币天使轮融资，由顺为资本、红杉中国与松禾资本共同参与。 无界进化专注数字生命与计算生物学，以自研虚拟细胞基础模型 OCOO 为底座，结合强化学习构建面向药物发现的自主科学发现系统TPP（The Popper Project），将模型预测、实验设计、实验验证与结果回流纳入统一研发工作流，通过持续迭代不断提升模型能力。公司正加速建立覆盖新靶点发现、虚拟药物筛选与评价、临床方案优化的全功能AI生物学平台。   双向印证：“虚拟评价—人源验证—结果回喂”的正向研发闭环 本次合作实现标准化人源疾病多组学数据与高精度AI虚拟细胞模型深度耦合，完美契合全球NAMs监管政策导向，形成行业独有的闭环研发体系。 AI虚拟细胞的预测结论要成为研发决策依据，离不开大规模、高质量人源数据的独立验证；而人源细胞实验要形成规模化产出，同样需要足够强的计算与排序能力与之匹配。本次合作正是让二者深度耦合：OCOO虚拟细胞平台在数字空间完成潜在治疗靶点挖掘以及高通量化合物评价，精准推演不同遗传背景、不同人体细胞类型下的药物药效与毒副作用，并依托海量人源数据进行交叉验证；昂朴生物iBGD资源库输出大批量、可溯源、匹配真实人体病理特征的基因型—表型—药物响应多组学数据，既为模型预测提供独立的交叉验证基准，也在湿实验层面对结果给出校正。 两者互为印证、彼此校正，落地“虚拟评价—人源验证—结果回喂”的全新研发范式：以iPSC分化的人体功能细胞替代传统实验动物，以大数据与AI模拟替代高成本、低效率的经验试错实验。每一轮湿实验缩小模型探索空间，进一步模型又压缩下一轮所需的湿实验量，研发成本与周期随迭代持续下行。双方由此打通实体细胞实验与虚拟评价的链路，为药物创新提供符合国内外审评标准的基础设施，并推动新药的开发落地。 三个真实困境，一套共同解法 新药研发中代价最高的并非失败本身，而是失败发生得过晚。以下三类困境分处药物发现、疾病研究与临床决策的不同环节，却指向同一个技术缺口——缺少一个兼具人体生物学真实性与大规模计算能力的中间层。 当一个分子在动物模型中有效，却在临床阶段失效。 这是创新药研发中代价最高的系统性偏差，也是“去动物化”变革的根本动因。联合平台以“靶点虚拟研究与化合物虚拟评价＋人源细胞实验验证”的双层漏斗推进研发，快速形成带证据链的靶点与化合物优先级清单，以及符合FDA、CDE NAMs申报口径的人源数据，直接支撑项目决策与申报。将淘汰环节前置至成本最低处，意味着更短的靶点发现至临床前候选化合物（PCC）周期、更低的动物实验成本与合规风险，以及在全球监管转向中的先发位置。 当一种疾病全国仅有数十例患者，研究难以形成通量。 样本稀缺、动物模型缺失或失真、单中心难以支撑系统性筛选，是遗传病与罕见病研究长期的结构性困境。iBGD已覆盖罕见病目录内重点疾病的iPSC细胞株，可定向分化为运动神经元、皮层神经元、心肌细胞、星形胶质细胞、视网膜色素上皮细胞等疾病相关细胞类型；配合虚拟细胞模型，同一突变背景可在数字空间完成大规模扰动推演，产出疾病机制假说的计算生成与优先级排序、候选干预方案的预测结果，以及可直接在人源细胞上落地的实验设计。病例数由此不再构成研究通量的上限，候选药物的临床转化亦得以提速。 当临床存在明确的用药选择难题，却缺少可计算的解法。 真实世界的治疗决策多依赖经验与有限证据，难以复用与横向比较。虚拟细胞模型与患者来源细胞、类器官体系可形成双向迭代：AI提供可计算、可比较、可排序的预测框架，真实样本提供反馈与校正，产出面向具体患者群体的疗效与耐药预测、机制解析与潜在治疗靶点线索，并可沉淀为标准化的研究与分析范式。 三者分处研发链条的不同位置，制约却同源：足够真实的实验难以规模化，足够快的计算不足以还原人体。无界进化与昂朴生物的联手，让人源细胞与AI虚拟细胞的耦合补上真实性与通量之间的这一层，这也是本次合作的技术起点。   共启新篇：构筑国产研发底座，持续开放平台合作 当全球监管体系加速告别动物实验，当“人源数据+AI计算”成为药物研发的新坐标，昂朴生物与无界进化的战略合作，正是对这一时代命题的回应。 昂朴生物的遗传病iPSC数据银行，提供了中国自己的、可规模化获取的人源疾病模型与数据资产；无界进化的AI虚拟细胞模型，则提供了在计算机中预测药物响应的智能引擎。这套融合体系，对产业、药企、患者具备多重核心价值：对于创新药企，整套研发链路产出的数据符合FDA、CDE NAMs申报要求，有效压缩临床前研发周期、削减动物实验相关成本；对于遗传病领域，解决传统动物模型病理模拟失真、转化效率低下的行业痛点，加速疑难遗传病全新疗法开发；对于国内生物医药产业，搭建不依赖海外样本库与海外计算模型的自主研发底座，夯实国产创新核心竞争力。 未来，双方将持续扩充遗传病标准化细胞模型库，迭代优化遗传病专属AI虚拟细胞解决方案，完善适配国内外审评标准的NAMs技术方案。双方合作将聚焦于遗传病相关方向的新药研究与开发，并积极向全行业开放联合研发平台能力，积极推动在遗传病方向的治疗突破，为患者带来有效的治疗。

Frost & Sullivan Insight Recently, the State Council issued the "15th Five-Year Plan for Carbon Peak Action", clearly proposing to improve the energy conservation and carbon reduction standards for computing power facilities, promote energy conservation and carbon reduction reforms for non-compliant computing power facilities, and orderly phase out backward and inefficient technologies and equipment, thereby enhancing the energy efficiency level of computing power facilities. The 2026 Government Work Report indicates that the proportion of green electricity used in new data centers at national hubs should exceed 80%, and it is encouraged to achieve 100% consumption of green electricity. What are the relatively mature energy conservation and carbon reduction approaches currently available in the industry, ready for large-scale implementation? For example, technologies such as liquid cooling, waste heat recovery, and CPO (Co-packaged Optical) are in what application stages? Currently PUE , CUE, and unit computing power carbon emissions ("computing power carbon efficiency") are multiple evaluation indicators. Do you think a unified national green computing power evaluation system will be needed in the future? What are the challenges in implementing a unified standard at this stage? In the next three to five years, which aspects do you believe the largest changes in the green computing power industry will manifest? Will the development of AI technology itself change the energy conservation methods for data centers? For instance, optimizing cooling scheduling through AI and predicting loads to reduce idle energy consumption—how is the progress of this "using AI to save energy with AI" approach? Zhou Mingzi, Partner of Frost & Sullivan China, interviewed by The Times Weekly to discuss how green computing power opens a new paradigm of "mutual pursuit". Securities Daily Q: What are the relatively mature energy conservation and carbon reduction approaches currently available in the industry, ready for large-scale implementation? For example Liquid cooling , waste heat recovery, and CPO (Co-packaged Optical) technologies are in what application stages? Zhou Mingzi Partner of Frost & Sullivan China From the perspective of industry commercialization, the technology that has entered the stage of large-scale application in energy conservation and carbon reduction for computing power is cold plate liquid cooling, with a market share of over 80%. Currently, not only leading brands such as Inspur and Huawei have natively supported servers with this technology, but new intelligent computing centers are basically configured with it. PUE can be stabilized between 1.15 and 1.25, making it the only technology path that can be "delivered and used immediately" at present. Although immersion liquid cooling offers better performance, it faces practical challenges such as high initial investment and the need to rebuild the operation and maintenance system, so it still relies on zero-carbon parks and benchmark projects from finance and operators. The industry predicts that it will take about 3 to 5 years before large-scale implementation. Waste heat recovery technology is mature, but its commercial closed loop is severely limited by geographical conditions: in the north, projects like Ningxia Zhongwei Ningxia Zhongwei Frost & Sullivan, Frost & Sullivan China, LeadLeo, LeadLeo Research Institute, LeadLeo, YUAN CAPITAL, SULLIVAN TELE-TREND CLOUD TECHNOLOGY, TradeGo, MagnaTEC, Automotive & Mobility, Environmental Protection & Energy Saving Technology, Logistics & Supply Chain, MATERNAL AND INFANT, Education & Training, Real Estate & Property, Catering & New Retailing, Advanced Materials, Healthcare & Life Sciences, Semiconductor & Chip, COMMERCIAL AVIATION, Technology, Media and Telecom, LANDSCAPING, Big Data & AI, Infrastructure Construction & Utilities, Culture & Entertainment, AGRICULTURE, FORESTRY ANIMAL HUSBANDRY AND FISHERY, Food & Beverage, Fintech, SHIPPING AND PORTS, Dual Carbon & New Energy, Mining & Metals, Public Sector, Cross-Border E-commerce Trade, Building Technology, Construction & Decoration, Beauty & Fashion, Smart Homes, Digital Infrastructure, Enterprise Services, Consumer Electronics, and other projects have already integrated waste heat into the green electricity direct supply solution; in the south, they can only connect with scattered scenarios such as greenhouses and pools, with a long recovery period, and no independent replicable business model has yet been established. CPO is the most significant differentiation: the exchange side will start production in the second half of 2026. Nvidia's Quantum 3400 CPO switch has been scheduled for mass production, reducing transmission losses by 60%; however, scale-up optical interconnection between chips will not be scaled up until the Feynman architecture is mass-produced in 2028, when the market is expected to see a new wave of applications. Q: Currently, multiple evaluation indicators such as PUE, CUE, and unit computing power carbon emissions ("computing power carbon efficiency") exist. Do you think a unified national green computing power evaluation system will be needed in the future? What are the challenges in implementing a unified standard at this stage? Zhou Mingzi Partner of Frost & Sullivan China From the perspective of industry development, it is necessary to establish a national unified green computing power evaluation system, but achieving "complete comparability" at this stage is not realistic. The limitations of a single PUE indicator are already very obvious: it does not reflect the energy structure—a coal-based data center with PUE 1.1 may have several times higher carbon emissions than a green electricity data center with PUE 1.3; it also does not measure computing power output efficiency—a cluster with PUE 1.15 but a GPU utilization rate of only 30% is essentially "false efficiency"; moreover, scope three emissions such as construction period, equipment manufacturing, and decommissioning are completely outside the coverage of PUE. Policy efforts are already shifting: the 2026 Government Work Report included "computing power and electricity coordination" in the new infrastructure project for the first time. The National Data Bureau and the National Energy Administration have clearly defined two constraints: "green electricity proportion of new computing power facilities ≥ 80%," and "PUE ≤ 1.20 in the west region ≤ 1.25 in the east region." The China Academy of Information and Communications Technology is also promoting two new indicators for AI business outputs—computing power energy efficiency and Token energy efficiency—these two new indicators mark the shift of the industry's evaluation focus from "data center efficiency" to "business output efficiency". On the other hand, implementing a unified standard faces three substantial obstacles: first, the criteria for green electricity traceability have not been unified, with differences in the recognition standards for green certificates, green electricity trading, green electricity direct supply , and self-generation and self-use photovoltaic paths; second, there is a lack of industry consensus on the accounting method for scope three carbon emissions; third, the differences in regional resource endowments are too large. Western hubs like Ningxia can meet the requirements by relying on wind and solar power plus green electricity direct supply, while the Yangtze River Delta needs cross-provincial green certificates and energy storage support to achieve 80%. Forcing a comparison using the same score will cause certain concerns in both eastern and western industrial markets. Therefore, the more likely evolution path in the future is that the state will formulate a comprehensive framework, and industry associations will introduce hierarchical leading values, similar to the gold and silver grading logic of LEED certification, to achieve better implementation results. Q: In the next three to five years, which aspects do you believe the largest changes in the green computing power industry will manifest? Zhou Mingzi Partner of Frost & Sullivan China Over the next three to five years, Frost & Sullivan believes that changes in the green computing power industry will focus on four interconnected main lines. The first is that "green electricity 80%" has shifted from policy advocacy to rigid constraints, fundamentally restructuring the investment logic for data centers. Among the eight major computing power hubs, only Ningxia, Gansu, and Inner Mongolia have mature green electricity direct supply channels, while the green electricity proportion in most other nodes is less than 30%. The 2026 Government Work Report has explicitly included this proportion as a constraint. This means that 2026 to 2028 is a window period to seize green electricity direct supply channels, sign long-term power purchase agreements, and secure source-grid-load-storage resources. Projects that miss this window will find it difficult to pass energy assessment. The second is that the industrial layout will further refine from "east data, west computing" to a new map of "green electricity hinterland training and storage, coastal reasoning, and hub transit". Training clusters above ten thousand units will basically be locked in the west region. The third is that liquid cooling combined with zero-carbon parks will shift from demonstration to a required standard. The "15th Five-Year Plan" explicitly requires the construction of 100 national zero-carbon parks, and the green and low-carbon transformation of computing power facilities has been separately listed. In the future, new large-scale clusters will basically feature a combination of liquid cooling, green electricity direct supply, and waste heat utilization. The fourth is that computing power and electricity coordination will generate new trading products. Data centers, as adjustable loads, will participate in grid peak shaving, green electricity spot trading, and non-real-time tasks across regions. These activities will make "computing power carbon efficiency" and "green electricity traceability" tradable assets. The return on investment for projects certified by CCER will be higher than pure hardware investment. Frost & Sullivan believes that in the next three to five years, what will truly differentiate companies is not hardware parameters, but the ability to sign green electricity procurement agreements, operate carbon assets, and schedule computing power assets—while hardware can be obtained by everyone, green electricity channels, carbon certification, and credible computing service assets are scarce resources. Q: Will the development of AI technology itself change the energy conservation methods for data centers? For example, optimizing cooling scheduling through AI and predicting loads to reduce idle energy consumption—what is the progress of this "using AI to save energy with AI" approach? Zhou Mingzi Partner of Frost & Sullivan China Frost & Sullivan believes that the path of using AI technology to optimize the energy consumption of data centers actually progresses faster than most market expectations. However, a key boundary must be clarified: AI optimization is the most cost-effective lever for existing infrastructure transformation, but it cannot save old air-cooled data centers with PUE above 1.3—those data centers need to replace liquid cooling hardware first, and AI can only add value on this basis. Technological evolution has gone through three stages: the first stage is pre-optimization based on load prediction to avoid temporary peak impacts; the second stage is global optimization, combining optimization of chillers, water pumps, cooling towers, and CDU to extract an additional 10% to 30% energy efficiency from the existing energy-saving framework; the third stage is computing power and electricity coordination, linking cooling strategies with the timing of green electricity output and computing power migration. When green electricity is abundant, pre-cooling is done in advance, and batch processing tasks are run more frequently; when green electricity is scarce, non-real-time loads are actively reduced. Frost & Sullivan believes that the product form with real premium potential in the future may be a complete package of "AI optimization plus green electricity timing matching plus cross-regional load migration," rather than simply selling AI energy-saving software. A high-end equipment manufacturing company in Wuxi has entrusted EasyEnergy to manage the energy and computing power in its park through intelligent operation management. EasyEnergy uses AI to coordinate servers, cooling, photovoltaics, and energy storage systems, predicting computing power loads, equipment temperatures, and green electricity output, and dynamically adjusting cooling strategies, server operating states, and task timing to achieve integrated optimization of computing power, cooling, and electricity. When green electricity is abundant, the system can pre-cool and increase batch processing and reasoning tasks; during peak power periods, frequency reduction of servers, task off-peak scheduling, and energy storage adjustment are used to reduce peak energy consumption. This scenario upgrades traditional single-cooling energy saving to global optimization of computing power, cooling capacity, and power resources, reflecting the acceleration of "using AI to save energy with AI" from technical verification to actual operational scenarios. *This interview was published in Securities Daily. The author is Wang Jingru. The original title is: Green Computing Power Opens a New Paradigm of "Mutual Pursuit"

AI is reshaping almost all value propositions and activities related to people, making it difficult to predict where it will ultimately lead our production and life. On August 5, David Frigstad, Chairman of the Global Board of Directors and CEO of Frost & Sullivan, a global growth consulting firm, said in an interview with the International Financial News that AI is bringing enterprises into an era of heightened competition. AI is transforming nearly all human-related values and activities, and it is difficult to predict where it will ultimately affect our production and daily lives. On August 5, David Frigstad, Global Chairman and CEO of Frost & Sullivan, said in an interview with International Finance News that AI is ushering enterprises into an era of unprecedentedly intense competition. 在 Fu Dawei It appears that AI is accelerating the pace of corporate decision-making, organizational collaboration, and market competition at an exponential rate. "The Industrial Revolution brought about a hundredfold change over approximately 150 years; now, perhaps a hundredfold change can occur in just 10 years." He noted that both CEOs and companies must be prepared for this unprecedented speed of change. As Frost & Sullivan’s "pilot," Fu Dawei has long focused on corporate growth, innovation, and leadership, and he positions himself as a "growth coach." Growth Coach He believes that what is most scarce in the AI era is not a impressive technical demonstration, but rather the ability of companies to continuously learn, make rapid decisions, and drive their teams to take real action. It is worth noting that this is the first time Fù Dawei has been in China in nearly ten years. During the interview, he reflected on the rapid development of China’s innovation industry and expressed high praise for the ecosystem that has emerged in high-tech fields such as artificial intelligence. "China’s innovation capability excites me greatly; I rarely see such a scene anywhere in the world." In Mr. Frigstad’s view, AI is accelerating corporate decision-making, organizational collaboration, and market competition at an exponential rate. “The Industrial Revolution brought about a hundredfold change over approximately 150 years; now, a hundredfold change may occur in just 10 years.” He noted that CEOs and enterprises must prepare for this unprecedented pace of change. As the “helmsman” of Frost & Sullivan, Mr. Frigstad has long been actively involved in corporate growth, innovation, and leadership, serving as a “Growth Coach.” He believes that what is most valuable in the AI era is not a sophisticated technological demonstration, but a company’s ability to continuously learn, make swift decisions, and motivate its team to take real action. It is worth noting that this is Mr. Frigstad’s first visit to China in nearly a decade. During the interview, he reflected on the rapid development of China’s innovative industries and expressed his admiration for the development ecosystem that China has established in high-tech areas such as artificial intelligence. “China’s innovation capabilities excite me greatly; I have rarely seen such a level of progress elsewhere in the world.” "When I was still a college student, I spent time in Hong Kong and Taiwan. Later, when I had my first opportunity to come to Beijing, I met our Chinese partners. That was a long time ago." From the first visit during my student years, to entering the Chinese market for the first time, and then returning to Shanghai after a decade, Fu Dawei’s feelings stem not only from the vitality of the Chinese economy or the popularity of the AI industry, but also from a rare ability to adapt—"The national investment and commitments made by China to improve issues related to people’s lives are truly impressive and exciting." In his view, China’s development does not remain confined to grand numbers and narratives, but transforms into tangible reality within a relatively short period. Seeing the vitality of the Chinese economy, he simply says, "It is amazing that you can experience all this firsthand. This is why I feel lucky to live in this era." "When I was still a college student, I spent time in Hong Kong and Taiwan. Later, I had my first opportunity to visit Beijing and meet our Chinese partners. That was a long time ago." From his first visit as a student, to his first entry into the Chinese market and now returning to Shanghai after ten years, Mr. Frigstad’s reflections stem not only from the vitality of the Chinese economy or the boom of the AI industry, but also from a capacity for change that he considers quite rare—"China’s national-level investment and commitment to improving issues related to people’s lives is both astounding and exciting." In his eyes, China’s development does not merely exist in grand numbers and narratives, but is continuously translated into visible reality within a relatively short period of time. Seeing the vitality of the Chinese economy, he frankly expressed his amazement: "You are able to experience all of this firsthand, which is exactly why I feel we are very fortunate to live in this era." In the AI era, what enables companies to build competitiveness? "Growth Coach" is Fu Dawei's self-defined role. "Growth Coach" is how Mr. Frigstad defines himself. This title originates from his experience as a gymnast during his college years. "No outstanding athlete can succeed without a coach, and this is especially true in team sports." In Mr. Frigstad's view, whether in individual or team events, the path to victory requires constant training, review and adjustment—and above all, the continuous observation and guidance of a coach. This title originates from his experience as a gymnast during his college years. "No outstanding athlete can succeed without a coach, and this is especially true in team sports." In Mr. Frigstad's view, whether in individual or team events, the path to victory requires constant training, review and adjustment—and above all, the continuous observation and guidance of a coach. However, upon transitioning from the sports arena to the business world, he noticed an intriguing contrast: despite managing complex organizations with intricate webs of employees, clients and business operations, many CEOs find themselves in a "coach-less" state. They have few people around them who can consistently ask the right questions, help them review past actions, exercise judgment and make better choices. However, upon transitioning from the sports arena to the business world, he noticed an intriguing contrast: despite managing complex organizations with intricate webs of employees, clients and business operations, many CEOs find themselves in a "coach-less" state. They have few people around them who can consistently ask the right questions, help them review past actions, exercise judgment and make better choices. Based on this, Mr. Frigstad does not simply define Frost & Sullivan as a traditional consulting firm that "walks into an enterprise with a ready-made proposal and tells management what to do." Instead, he prefers to position himself as a "Growth Coach," initiating engagements with questions like, "How can we make your team better?" Because of this, Mr. Frigstad does not simply define Frost & Sullivan as a traditional consulting firm that "walks into an enterprise with a ready-made proposal and tells management what to do." Instead, he prefers to position himself as a "Growth Coach," initiating engagements with questions like, "How can we make your team better?" In his view, the true value of consulting lies not in making decisions for the CEO, nor in handing over an external playbook to be blindly followed, but in helping enterprises build their own capabilities for continuous learning, rapid judgment and collaborative execution—until the day comes when "external help is no longer needed." In his view, the true value of consulting lies not in making decisions for the CEO, nor in handing over an external playbook to be blindly followed. Rather, it is about empowering the enterprise to build its own capabilities for continuous learning, rapid judgment and collaborative execution—until the day comes when "external help is no longer needed." This understanding of organizational capabilities also serves as the starting point for how Mr. Frigstad views the question of "how enterprises build competitiveness in the AI era." This deep understanding of organizational capabilities also serves as the starting point for how Mr. Frigstad views the question of "how enterprises build competitiveness in the AI era." AI is bringing companies into an era of higher Competitive Intensity. Fu Dawei noted that as information becomes more transparent and technology spreads faster, companies around the world may develop and utilize similar AI capabilities at the same time. In the past, a core technology could be held by a company for a long period and turned into a solid competitive barrier; today, the "shelf life" of leading technologies is constantly shortening. Frigstad said, "AI is bringing enterprises into an era of higher Competitive Intensity." As information becomes increasingly transparent and the diffusion of technology continues to accelerate, companies around the world may develop and deploy similar AI capabilities at the exact same time. In the past, a single core technology might have been exclusively held by one company for a long period, resulting in a strong competitive advantage. However, today, the "shelf life" of technological leadership is continuously shortening. Mr. Frigstad believes that the competition between enterprises is shifting from "whether you possess a new technology" to "whether you can harness the new changes brought by technology"—whether you can make decisions faster, drive organizational collaboration and translate technological capabilities into products and experiences that resonate more closely with customers. Mr. Frigstad's assessment is that the competition between enterprises is shifting from "whether you possess a new technology" to "whether you can harness the new changes brought by technology"—whether you can make decisions faster, drive organizational collaboration and translate technological capabilities into products and experiences that resonate more closely with customers. This shift in competitive logic also places higher demands on CEOs. Mr. Frigstad encapsulates the capabilities required of today's CEOs as the "Connected CEO": In the past, boards met quarterly and companies formulated their business plans once a year; today, such a pace can hardly keep up with the rapidly evolving competitive landscape. A CEO needs to sense changes in projects, employees and customers more promptly, while leading their team to communicate rapidly, exercise judgment and take action. This shift in competitive logic also places higher demands on CEOs. Mr. Frigstad encapsulates the capabilities required of today's CEOs as the "Connected CEO." In the past, boards met quarterly and companies formulated their business plans once a year; today, such a pace can hardly keep up with the rapidly evolving competitive landscape. A CEO needs to sense changes in projects, employees and customers more promptly, while leading their team to communicate rapidly, exercise judgment and take action. However, as AI accelerates its integration into daily life and advances to the frontlines of industry, different types of enterprises are facing entirely different tests. However, as AI accelerates its integration into daily life and advances to the frontlines of industry, different types of enterprises are facing entirely different tests. For traditional enterprises seeking transformation in the AI era, the challenge is no longer just whether to adopt AI, but whether the CEO truly participates and the team is willing to commit fully. Mr. Frigstad believes that enterprises should not view AI merely as a departmental task or a tool purchased by the marketing department; more importantly, they should use AI to comprehensively review internal collaboration methods and customer experiences: which production processes can be reengineered, how information flows more efficiently within the organization, and how connections between enterprises and employees or customers can be redefined. For traditional enterprises seeking transformation amid the AI wave, the question is no longer simply "whether to adopt AI," but whether the CEO is genuinely involved and whether the entire team is willing to fully commit. Mr. Frigstad believes that companies cannot merely view AI as a task for a specific department or a tool procured by the marketing team. More importantly, they must leverage AI to comprehensively re-examine their internal collaboration methods and customer experiences: which production processes can be reconstructed, how information can flow faster within the organization and how the connections between the enterprise, its employees and its customers should be redefined. Fuda Dawei said that what inspired him most about Amazon founder Jeff Bezos is the integration of a "Mindset of Experimentation" into the company's daily operations. "Great leaders, great departments and great people are conducting experiments all day long." Mr. Frigstad mentioned that what inspired him most about Amazon founder Jeff Bezos is the integration of a "Mindset of Experimentation" into the company's daily operations. "Great leaders, great departments and great people are conducting experiments all day long," he noted. In his view, many attempts may not obtain the expected results, but failure cannot be completely avoided, nor should it be simply defined as a failure; rather, it is an experiment that has not yet succeeded. Based on this, Fuda Dawei further emphasized: "Every employee should experiment with AI to see what results it produces." Enterprises in transformation cannot stay away from AI due to fear of making mistakes; blindly waiting for a definite answer may actually pose a greater risk. In his view, many attempts may not yield the expected results, but failure can neither be completely avoided "nor should it be simply defined as a failure, but rather as an experiment that has not yet succeeded." Building on this, Mr. Frigstad further emphasized: "Every employee should experiment with AI to see what results it produces." Enterprises undergoing transformation cannot keep their distance from AI out of a fear of making mistakes; blindly waiting for a definitive answer may actually pose a much greater risk. For emerging companies at the forefront of the new round of industrial transformation, Fuda Dawei’s assessment is even more direct: no matter how dazzling a technology demonstration may be, it does not equate to real commercial value. For emerging companies at the forefront of this new wave of industrial transformation, Mr. Frigstad’s assessment is even more direct: no matter how dazzling a technology demonstration may be, it does not equate to real commercial value. He used robotics as an example: a robot that can perform backflips or dance ballet might be highly eye-catching, but the market does not necessarily need a "performing robot." However, if it can be deployed in hospitals, assist in patient care and solve real-world needs, then it becomes an opportunity truly worth paying attention to. He cited robotics as an example: a robot that can perform backflips or dance ballet might be highly eye-catching, but the market does not necessarily need a "performing robot." However, if it can be deployed in hospitals, assist in patient care and solve real-world needs, then it becomes an opportunity truly worth paying attention to. "Ultimately, everything depends on the human experience in its application." In Mr. Frigstad’s view, AI and robotics companies must be almost "fanatical" in their focus on the application itself. They must answer exactly who the product serves, what problems it solves and whether it can build sustainable commercial viability in real-world scenarios. "Technological capability is merely the starting point. What truly determines whether a company can navigate past the hype and survive in the market is its ability to ground its technology in specific people, specific needs and specific values." "Ultimately, everything depends on the human experience in its application." In Mr. Frigstad’s view, AI and robotics companies must be almost "fanatical" in their focus on the application itself. They must answer exactly who the product serves, what problems it solves and whether it can build sustainable commercial viability in real-world scenarios. "Technological capability is merely the starting point. What truly determines whether a company can navigate past the hype and survive in the market is its ability to ground its technology in specific people, specific needs and specific values." How can Chinese enterprises build global influence? How can Chinese enterprises build global influence? Currently, AI is accelerating its integration into daily life and advancing to the frontlines of industry. In this transformation, China is demonstrating increasingly robust innovation momentum across areas such as research and development, manufacturing, application deployment and industrial synergy. Today, AI is accelerating its integration into daily life and advancing to the frontlines of industry. In this transformation, China is demonstrating increasingly robust innovation momentum across areas such as research and development, manufacturing, application deployment and industrial synergy. The 2026 World Artificial Intelligence Conference, which concluded in Shanghai just over ten days ago, serves as a vivid footnote to this trend. In the exhibition halls at that time, the most eye-catching highlights were no longer large models merely competing on parameters, nor were they robots putting on flashy technological displays. Instead, the focus shifted to Agents that have evolved from "being able to chat" to "being able to execute tasks," as well as embodied intelligence capable of entering various scenarios and performing practical tasks. The 2026 World Artificial Intelligence Conference, which concluded in Shanghai just over ten days ago, serves as a vivid footnote to this trend. In the exhibition halls at that time, the most eye-catching highlights were no longer large models merely competing on parameters, nor were they robots putting on flashy technological displays. Instead, the focus shifted to Agents that have evolved from "being able to chat" to "being able to execute tasks," as well as embodied intelligence capable of entering various scenarios and performing practical tasks. This vitality was equally sensed by Mr. Frigstad. In his observation, Chinese enterprises have gone through several clear stages of development: first the establishment of manufacturing capabilities, then the refinement of supply chains and logistics systems and subsequently the accumulation and unleashing of innovation capabilities. "I used to think that innovation might be a difficult problem for Chinese enterprises to solve, but today it has been successfully resolved." This vitality was equally sensed by Mr. Frigstad. In his observation, Chinese enterprises have gone through several clear stages of development: first the establishment of manufacturing capabilities, then the refinement of supply chains and logistics systems and subsequently the accumulation and unleashing of innovation capabilities. "I used to think that innovation might be a difficult problem for Chinese enterprises to solve, but today it has been successfully resolved," he noted. Visiting China again after nearly a decade, he saw not a market still chasing the technological wave, but an industrial landscape where innovation continuously emerges and application deployment accelerates. From technological R&D to product iteration and from business models to application deployment, new attempts are constantly appearing. "China's innovation capabilities make me incredibly excited; I have rarely seen such a scene anywhere else in the world," he said. Visiting China again after nearly a decade, what he saw was not a market still chasing the technological wave, but an industrial landscape where innovation continuously emerges and application deployment accelerates. From technological R&D to product iteration and from business models to application deployment, new attempts are constantly appearing. "China's innovation capabilities make me incredibly excited; I have rarely seen such a scene anywhere else in the world," he said. From manufacturing to supply chains and logistics, and now to innovation capabilities, Mr. Frigstad pointed out that the growth path of Chinese enterprises has been forged step by step. The next step is brand globalization. In his view, the most difficult part of the development of Chinese enterprises has already been completed. The ensuing questions are: How do they become global brands? How do they play a more important role on the global stage? This will be the next major growth opportunity for the Chinese economy and Chinese enterprises. However, between AI innovation, product capabilities and true global influence, enterprises still need to fill in a crucial missing link: the brand. From manufacturing to supply chains and logistics and now to innovation capabilities, Mr. Frigstad pointed out that the growth path of Chinese enterprises has been forged step by step. The next step is brand globalization. In his view, the most difficult part of the development of Chinese enterprises has already been completed. The ensuing questions are: How do they become global brands? How do they play a more important role on the global stage? This will be the next major growth opportunity for the Chinese economy and Chinese enterprises. However, between AI innovation, product capabilities and true global influence, enterprises still need to fill in a crucial missing link: the brand. Today, AI is accelerating its integration into daily life and advancing to the frontlines of industry. In this transformation, China is demonstrating increasingly robust innovation momentum across areas such as research and development, manufacturing, application deployment and industrial synergy. To take this step, a CEO cannot merely be an explainer of technology and products, but must also become a storyteller for the enterprise. "Steve Jobs did a phenomenal job at storytelling." Using Apple as an example, Fuda Dawei further explained that Jobs did not obsess over reciting a string of "bits, bytes and functional parameters." Instead, he talked about why Apple wanted to create such products and what kind of experience they hoped to bring to customers. To take this step, a CEO cannot merely be an explainer of technology and products, but must also become a storyteller for the enterprise. "Steve Jobs did a phenomenal job at storytelling." Using Apple as an example, Mr. Frigstad further explained that Jobs did not obsess over reciting a string of "bits, bytes and functional parameters." Instead, he talked about why Apple wanted to create such products and what kind of experience they hoped to bring to customers. "Technology, customer applications and corporate mission can all serve as the starting point of a story. What a CEO needs to do is connect these threads to build a brand capable of crossing global markets and cultural boundaries," Mr. Frigstad emphasized. A truly influential brand needs to make customers, employees, investors, partners and suppliers across different markets understand and believe in the same story: why this company exists, what it hopes to change and for whom it will create value. Technology, customer applications, and corporate mission can all serve as the starting point of a story. What a CEO needs to do is connect these elements to build a brand that can cross global markets and cultural boundaries, Mr. Frigstad emphasized. A truly influential brand must ensure that customers, employees, investors, partners, and suppliers across different markets understand and believe in the same narrative: why this company exists, what it aims to achieve, and for whom it will create value. In addition to learning how to tell stories, Frigstad also recommended that Chinese companies use the global market as their reference point from the start. "Do not restrict yourself to being a Chinese company; position yourself as a global company." According to him, the next step for Chinese companies expanding globally is not merely selling products overseas or applying the same growth strategies in different markets. Instead, it involves viewing the entire global economy as their stage and redefining the company’s positioning, customers, and value propositions. Beyond learning how to tell a story, Mr. Frigstad also advised Chinese enterprises to use the global market as their coordinate system from the very beginning. "Do not limit yourselves to being a Chinese company; position yourselves as a global company." In his view, the next phase for Chinese enterprises going global is not just about selling products overseas or replicating a growth methodology across different markets. Rather, it is about viewing the entire global economy as their stage and redefining the enterprise’s positioning, customers, and value propositions. *Source: International Financial News (Reprinted) International Financial News

“企业在寻找可持续、长期增长路径时，AI正成为一股不可忽视的重要力量。” As companies search for path to sustainable, long-term growth, AI is becoming a vital force that cannot be ignored.   近日全球性企业增长咨询公司弗若斯特沙利文（Frost & Sullivan，下称“沙利文”）全球总裁兼主管合伙人、全球董事局执行董事祖亚儒（Aroop Zutshi）在接受《国际金融报》记者采访时表示，企业真正的增长，并非一时的收入上扬，而在于能否找到可持续、可复制的转型增长（Transformational Growth）路径。而相比前一轮互联网革命，AI将为企业带来更深刻的影响与机会。 Recently, Aroop Zutshi, Global President, Managing Partner, and Executive Director of the Board at the global growth consulting firm Frost & Sullivan (hereinafter referred to as "F&S"), made these remarks in an interview with International Financial News. He stated that true corporate growth is not merely a temporary surge in revenue, but whether a company can find a sustainable and replicable path to transformational growth. Compared to the previous revolution brought by the internet, AI will bring far deeper impacts and opportunities to businesses.   长期深耕“全球化与本土化相结合”的企业发展模式， 祖亚儒 到访过80多个国家，并在约45个国家工作过。而在过去30年间，他每年至少到访中国两次，有时一年多达四次，是中国市场的“常客”。 Having long cultivated an enterprise development model that "combines globalization with localization," Zutshi has visited over 80 countries and worked in approximately 45. Over the past 30 years, he has visited China at least twice a year—and sometimes up to four times—making him a frequent guest in the Chinese market.   长期、持续的观察，让他得以近距离见证中国企业、中国市场与中国经济的巨大演变。“很难说，世界上还有哪个国家能够在相同时间内取得与中国同等、甚至更大的进步。”祖亚儒直言。 Long-term, continuous observation has allowed him to witness up close the massive evolution of Chinese enterprises, the Chinese market, and the Chinese economy. "It's hard to say if there is any other country in the world that could make equivalent, or even greater, progress within the same timeframe," Zutshi stated frankly.   “每次来到中国，我都会看到中国和中国企业进入一个新阶段。”从医疗、科技、 ICT 、通信到航天，在祖亚儒看来，中国企业正凭借技术与产品能力参与解决全球性问题；道路、桥梁与技术等基础设施的持续改善，也共同构成了一个不断向前的中国。 "Every time I come to China, I see China and Chinese companies entering a new stage." From healthcare, technology, and ICT (information and communications technology) to aerospace, Zutshi believes Chinese enterprises are leveraging their technological and product capabilities to help address global issues. The continuous improvement of infrastructure—such as roads, bridges, and technology—collectively forms the China that is constantly moving forward.   尤其在AI领域，他提到，中国早在世界广泛讨论AI之前，便已投入大量资源。如今，中国在多个行业的AI技术上处于领先位置，许多企业和国家也能从中国的技术进展与产业实践中受益、获得启发。 Particularly in the field of AI, he noted that China invested significant resources long before the world began widely discussing artificial intelligence. Today, China holds a leading position in AI technology across multiple industries, and many companies and countries stand to benefit from and be inspired by China's technological advancements and industrial practices.   尤其在AI领域，他提到，中国早在世界广泛讨论AI之前，便已投入大量资源。如今，中国在多个行业的AI技术上处于领先位置，许多企业和国家也能从中国的技术进展与产业实践中受益、获得启发。 Particularly in the field of AI, he noted that China invested significant resources long before the world began widely discussing artificial intelligence. Today, China holds a leading position in AI technology across multiple industries, and many companies and countries stand to benefit from and be inspired by China's technological advancements and industrial practices.   AI赋能企业可持续增长   “转型增长”，是祖亚儒最为关注的议题。 "Transformational Growth" is the topic Zutshi focuses on most.   “许多企业容易将短期市场红利、需求反弹或收入跃升，视作战略成功；但外部环境一旦改变，这种增长也可能迅速消失。”祖亚儒指出，企业真正要追求的，并非一时的营收上涨，而是一种能够持续创造机会、改善利润，并不断为客户创造价值的能力。 "Many companies tend to mistake short-term market dividends, demand rebounds, or revenue jumps for strategic success. But once the external environment changes, this kind of growth can disappear quickly," Zutshi pointed out. He emphasized that what companies should truly pursue is not a temporary rise in revenue, but the capability to continuously create opportunities, improve margins, and deliver ongoing value to customers.   在他看来，这对应着企业增长的两种模式：一种是常规增长，即企业在某一阶段抓住市场需求或周期机会，收入和利润随之上升，但这种增长未必能够持续；另一种则是转型增长，即企业找到长期、可持续、可复制的增长机会，并通过创新与执行，建立更具独特性的市场位置。 In his view, this corresponds to two modes of corporate growth: Conventional Growth: A company capitalizes on market demand or cyclical opportunities at a certain stage, leading to a rise in revenue and profit—though this growth may not necessarily be sustainable. Transformational Growth: A company identifies long-term, sustainable, and replicable growth opportunities and builds a more distinct market position through innovation and execution.   “转型增长不仅要看市场，也要看非市场因素——供应链、政策、技术变化和商业环境等，都可能影响一家企业能否实现长期增长。”祖亚儒强调，企业需要的，不是一份可执行的战略方案，而是一套能够持续发现机会、判断机会并把握机会的能力。 "Transformational growth depends not only on the market, but also on non-market factors—supply chains, policy, technological shifts, and the business environment can all affect whether a company can achieve long-term growth," Zutshi stressed. What companies need is not just an actionable strategic plan, but a set of capabilities to continuously discover, evaluate, and capture opportunities.   但如今，企业在寻找可持续、长期的增长路径时，AI正成为一股不可忽视的重要力量——它一边加速进入日常生活和产业一线，一边快速迭代能力。祖亚儒也坦言，AI正不断重新定义实现转型增长的条件，也向企业提出了新的挑战。 Today, as companies search for sustainable, long-term growth paths, AI is becoming a vital force that cannot be ignored—it is accelerating into daily life and front-line industries while rapidly iterating its own capabilities. Zutshi acknowledged that AI is constantly redefining the conditions for achieving transformational growth and presenting new challenges to businesses.   “市场动态的变化并不新鲜。”祖亚儒指出，企业经营中既有可以掌控的因素，也有难以掌控的变量，前沿技术的出现与落地便是其中之一。以AI为例，人们早已知道它的存在，但直到过去两年，它才真正以今天这样的方式进入大众生活，并在极短时间内产生了深刻而广泛的影响。 "Changing market dynamics are nothing new," Zutshi noted. In business operations, there are controllable factors as well as difficult-to-control variables, and the emergence and implementation of cutting-edge technology is one of them. Taking AI as an example, people have long known about its existence, but only over the past two years has it entered the public sphere in its current form, exerting a profound and widespread impact in a very short period.   “我更愿意把AI看作转型增长的赋能者，而不是转型增长的颠覆者。”在祖亚儒看来，AI当然会改变企业，但企业不应将其视为会摧毁既有业务的外部威胁。真正重要的是，把AI纳入企业的增长体系，让它帮助企业更快作出改变，并创造更多价值。 "I prefer to view AI as an enabler of transformational growth, rather than a disruptor of transformational growth," Zutshi said. While AI will certainly transform companies, businesses should not treat it as an external threat that will destroy existing operations. What truly matters is integrating AI into a company's growth system, allowing it to help the enterprise make changes faster and create more value.   在他看来，企业需要从内部和外部两个维度使用AI。对内，企业首先应重新审视所有工作流：一项业务如何从A点推进到B点，信息如何流动，哪些环节耗时、重复，或容易出错。通过引入AI梳理流程，企业能够提高速度、质量和效率，进而降低成本、改善利润率。 He believes companies need to utilize AI from two dimensions: internally and externally.   Internally: Companies should first re-examine all workflows—how a business process moves from Point A to Point B, how information flows, and which steps are time-consuming, repetitive, or prone to error. By introducing AI to streamline processes, companies can increase speed, quality, and efficiency, thereby lowering costs and improving profit margins. Externally: Beyond internal efficiency, companies must leverage AI to rethink their products and services.   而在内部提效之外，企业还需要借助AI重新思考产品与服务。祖亚儒认为，随着信息愈发透明、技术扩散持续提速，世界各地企业的产品能力将越来越接近。届时，真正拉开企业差距的，是客户体验——从使用、咨询、售后，到问题出现时企业如何回应客户。 Zutshi observed that as information becomes more transparent and technology diffusion accelerates, product capabilities among companies around the world will become increasingly similar. When that happens, what truly sets a company apart is customer experience—ranging from usage, consultation, and after-sales service, to how the company responds when problems arise.   “能否增加新的功能？能否提供新的服务？能否创造新的商业模式？更重要的是，能否让客户获得更便宜、更好、更快的产品和体验？”在祖亚儒看来，企业都应借助AI，一一回答这些问题。 "Can you add new features? Can you provide new services? Can you create new business models? More importantly, can you offer customers cheaper, better, and faster products and experiences?" In Zutshi's view, all companies must leverage AI to answer these questions one by one.   把中国能力变成全球价值 Turning Chinese Capabilities into Global Value   当下，AI正加速进入千行百业。在这场变革中，中国企业正走在前沿：既加快AI在产业场景中的应用与落地，也持续展现出强劲的原创能力。 Currently, AI is accelerating its penetration into various industries. In this wave of transformation, Chinese enterprises are standing at the forefront: they are accelerating the application and implementation of AI in industrial scenarios while continuously demonstrating strong capacity for original innovation.   作为中国市场的“常客”，在祖亚儒看来，中国企业的优势不只在于速度，更在于能够将制造、技术与产品能力结合起来，并在汽车、出行、医疗健康、ICT等领域创造实际价值。越来越多中国制造的产品达到全球标准，也开始进入海外市场，改善当地生活、带动当地生态。 As a "frequent guest" to the Chinese market, Zutshi sees that the advantage of Chinese enterprises lies not just in speed, but in their ability to combine manufacturing, technology, and product capabilities to create tangible value across sectors like automotive, mobility, healthcare, and ICT. An increasing number of products made in China meet global standards and are entering overseas markets, improving local lives and driving local ecosystems.   也正因此，谈及中国企业的全球化，祖亚儒首先给出了一个不同于“竞争叙事”的判断。 Precisely for this reason, when discussing the globalization of Chinese enterprises, Zutshi first offered an assessment that differs from the typical "competition narrative."   “比起全球化竞争，更应该讨论的是全球化合作。中国企业与海外同行并不必然是此消彼长的关系。”在他看来，走向全球不只是将产品卖到更多国家，而是将自身的制造、技术与创新能力带入更多真实场景，更是在全球市场中寻找合作的可能，共同将技术与产业能力转化为对社会的真实价值。 "Rather than global competition, we should be discussing global cooperation. Chinese companies and their overseas peers do not necessarily have a zero-sum relationship." In his view, going global is not just about selling products to more countries; it means bringing one's manufacturing, technological, and innovative capabilities into more real-world scenarios. It is also about seeking potential collaborations in global markets to jointly translate technology and industrial capabilities into real value for society.   “不过，全球化的第一步，并不是急于进入尽可能多的国家。”祖亚儒指出，企业要先判断哪些市场真正值得进入：既要评估市场规模、竞争程度、技术水平、渠道条件与客户需求，也要考量贸易政策、税收安排、投资环境、资金汇回等非市场因素。 "However, the first step of globalization is not rushing into as many countries as possible," Zutshi pointed out. Companies must firstly evaluate which markets are truly worth entering. This requires assessing market size, competitive intensity, technological maturity, channel conditions, and customer demand, as well as weighing non-market factors such as trade policies, tax arrangements, the investment environment, and capital repatriation.   比市场选择更难的，是跨越品牌信任。祖亚儒指出，一个品牌在中国成立，并不意味着它在另一个国家也会被同样理解。 Harder than selecting markets, however, is building cross-border brand trust. Zutshi noted that just because a brand is established in China does not mean it will be understood the same way in another country.   在他看来，企业必须重新理解当地客户的需求、文化与价值判断，必要时甚至需要调整品牌表达与市场定位。“技术可以帮助企业进入全球市场，但信任决定企业能否真正留在全球市场。” In his view, companies must re-understand the needs, culture, and value judgments of local customers, and adjust their brand messaging and market positioning when necessary. "Technology can help a company enter the global market, but trust determines whether it can truly stay there."   “中国企业能够走在前列，关键在于始终专注于为客户创造更大价值，并以优异的性价比提供产品和解决方案。”祖亚儒判断，当这种中国能力真正转化为能够被全球市场理解、接受并共享的价值时，中国企业也将迎来下一轮重要的增长机会。 "The key to Chinese enterprises staying at the forefront is their persistent focus on creating greater value for customers and offering products and solutions with exceptional cost-performance," Zutshi concluded. When these Chinese capabilities are truly transformed into value that can be understood, accepted, and shared by the global market, Chinese companies will welcome their next major growth opportunity.

Announcement regarding Frost & Sullivan’s Policy Guidelines for Implementing the “Guidelines for Enforcement of Advertising Citation Content” Recently, Frost & Sullivan has observed that the General Office of the State Administration for Market Regulation has issued relevant notices to strengthen the supervision of suggestive terms in advertising. Subsequently, an announcement was made regarding the “Guidelines for Enforcement of Advertising Citation Content” (referred to as the Guidelines). Frost & Sullivan promptly organized its internal compliance team to carefully study the specific policy points of the Guidelines at the moment of their release. In addition, comprehensive notifications and risk warnings were provided to all related clients of Frost & Sullivan regarding the possible details related to Frost & Sullivan’s market position statement services, in order to prevent clients from improperly applying past research findings to various advertising citation scenarios. Over the past few years, during interactions with brand owners through market research services, Frost & Sullivan has gradually realized that traditional advertising creative forms cannot meet the increasing needs of brand owners. Brand owners urgently need to establish a unified brand recognition framework for their target audiences from a trustworthy, compliant, and reusable perspective. This demand has directly led to the emergence of the third-party consulting company market position statement as a new research service model. When conducting such research, Frost & Sullivan always adheres to professional research standards. In the complete reports and market position statement documents, pre-defined conditions such as statistical criteria, applicable time, sample scope, and data limitations are clearly indicated. The research conclusions also have strict application boundaries, and the original research is not suitable for direct use as a marketing slogan by brand owners. However Frost & Sullivan has found that some brand owners, when conducting advertising campaigns, extract favorable conclusions from reports in isolation, omit all or part of the underlying constraints, and remove the statistical boundaries. They simplify and transform a professionally conducted research conclusion into a marketing advertisement slogan for public dissemination, resulting in misleading advertising effects. To prevent the misuse of research results from misleading consumers, now officially released the following statement: Frost & Sullivan will completely cease research conclusions containing absolute advertising terms in market position statement services within the Chinese market. At the same time, a comprehensive review of the usage of historical research conclusions will be carried out, and relevant brand owners are required to immediately stop using historical research outputs that have exceeded the authorized period or do not meet the new advertising regulatory requirements. Frost & Sullivan will strictly comply with the policy requirements of the “Guidelines for Enforcement of Advertising Citation Content” and firmly oppose the abuse or misuse of serious industry research results. As a professional industry research institution, Frost & Sullivan is committed to building a trustworthy and compliant research ecosystem and delivering more research-based and meaningful products to society. Frost & Sullivan will also actively explore more scientific, rigorous, transparent, traceable, and verifiable research methodologies to convey more objective and neutral market research outcomes to the public. Frost & Sullivan 2026 8 month 1 4 day

新华网上海8月12日电  8月3日至5日，第二十届沙利文全球增长、科创与领导力峰会暨第五届新投资大会在上海举办。本届峰会恰逢沙利文全球成立65周年、沙利文峰会落地中国20周年双庆典节点，以“锚定创新爆发期，共塑转型增长新未来”为核心主题，汇聚300余位海内外嘉宾，落地150余场主题演讲与圆桌对话，全方位呈现全球产业转型增长的中国路径与亚太方案。 From August 3 to 5, the 20th Frost & Sullivan Global Growth, Innovation and Leadership Summit and the 5th New Investment Event were held in Shanghai. This year's summit coincided with the dual anniversary milestones of Frost & Sullivan's 65th global anniversary and the 20th anniversary of the summit's presence in China. Under the core theme "Anchoring the Innovation Explosion, Shaping New Future of Transformational Growth", the event brought together over 300 domestic and international guests and featured more than 150 keynote speeches and round table dialogues, presenting a comprehensive picture of China's pathways and Asia-Pacific solutions for global industrial transformation and growth.   峰会期间，沙利文全球董事局主席兼首席执行官 富大为 （David Frigstad）在接受新华网专访时表示，自1976年首次到访中国以来，他亲历了中国从制造业起步，到成长为世界级物流强国，再到创新渗透整个经济体的全过程。“中国的转型是人类历史上前所未见的，转型速度更是史无前例。”在他看来，这一切才刚刚开始——随着制造、供应链、物流与创新能力全面就位，中国正迎来前所未有的发展机遇。 During the summit, David Frigstad, Global Chairman and CEO, Frost & Sullivan, gave an exclusive interview to Xinhua Net. He shared that since his first visit to China in 1976, he has witnessed the country's entire journey—from its early manufacturing base, to its rise as a world-class logistics powerhouse, and to the permeation of innovation throughout the entire economy. "China's transformation is unprecedented in human history, and the speed of that transformation is also unprecedented." In his view, this is only the beginning—with manufacturing, supply chains, logistics, and innovation capabilities now fully in place, China is facing unprecedented development opportunities.   谈及中国产业创新对全球的借鉴意义，富大为认为，其底层逻辑归根结底在于价值观：中国家庭世代传承的勤奋、尊师重教、回馈社会等核心价值，构成了产业创新持续迸发的根基，也是其他经济体最值得参考的增长范式。 When asked about the global implications of China's industrial innovation, David Frigstad believes that the underlying logic ultimately comes down to values: the core values passed down through Chinese families—diligence, respect for teachers and education, and giving back to society—form the foundation for sustained industrial innovation, and also represent the growth model most worthy of reference for other economies.   对于中国企业全球化，富大为指出，最紧迫的挑战与最大的机遇同出一源——讲好品牌故事。“中国企业在制造、供应链、文化底蕴上均已达到世界级水平，什么都有了，只差品牌。”他坦言，不少企业习惯于罗列财务数据与产品参数，而真正打动人心的品牌叙事，才是感染用户、赢得投资者、连接全球受众的关键，也是中国企业从“中国参与者”迈向“全球参与者”的必修课。 Regarding the globalization of Chinese enterprises, David Frigstad pointed out that the most pressing challenge and the greatest opportunity stem from the same source—telling a compelling brand story. "Chinese companies are already world-class in manufacturing, supply chains, and cultural heritage—they have everything except a brand." He candidly noted that many companies are accustomed to listing financial data and product specifications, but what truly moves users, wins over investors, and connects with global audiences is authentic brand storytelling—which is also an essential lesson for Chinese enterprises as they transition from "Chinese participants" to "global players."   谈及人工智能浪潮，富大为介绍，沙利文近年提出“ AI+HI ”理念，即推动人工智能与人类智能深度融合。AI将承担大量程式化工作，人的创造力、判断力与情感连接价值将愈发凸显；与此同时，信息爆炸也让独立、公正的第三方研究视角价值倍增，成为产业决策的重要参照。 Speaking of the AI wave, David Frigstad introduced Frost & Sullivan's recent "AI+HI" concept, which advocates the deep integration of artificial intelligence and human intelligence. AI will take on a large share of routine work, while human creativity, judgment, and emotional connection will become increasingly valuable. At the same time, the explosion of information greatly amplifies the value of independent, impartial third-party research perspectives, making them a critical reference for industrial decision-making.   面向未来，富大为表示，沙利文将继续聚焦识别颠覆性增长机遇，帮助全行业为智能革命带来的全领域变革做好准备，并与中国企业在行业转型、客户价值主张与客户体验升级上深度合作，助力中国产业的优质价值更有效地传递给世界。“企业越强大、协作越深入，世界就越趋向共赢。”他相信，以企业间的互信与合作推动产业共赢，全球经济将共同朝着正向方向发展。 Looking ahead, David Frigstad stated that Frost & Sullivan will continue to focus on identifying disruptive growth opportunities, helping all industries prepare for the sweeping changes brought by the intelligent revolution, and working closely with Chinese companies on industry transformation, customer value propositions, and customer experience upgrades—so that the high-quality value of China's industries can be more effectively delivered to the world. "The stronger the companies and the deeper the collaboration, the more the world moves toward win-win outcomes." He believes that by fostering mutual trust and cooperation among enterprises to drive shared prosperity, the global economy will collectively move in a positive direction.   （文字：许超、刘飞翔；视频：张千石、王若涵、郑瀚）   (Text: Xu Chao, Liu Feixiang; Video: Zhang Qianshi, Wang Ruohan, Zheng Han)   *文章来源：新华网（转载） Source credit: Xinhua Net