Under the great changes in the past century, emerging transnational technologies have been brought into the radius of American game against China. Scientific analysis of the narrative content of American science and technology in China can provide a mirror for American science and technology issues and American public opinion research on China. Based on the strategic narrative theory, this paper clarifies the strategic narrative of American science and technology to China in the topic of "Ban TikTok". It is found that the operational path of American strategic narrative of science and technology to China is divided into framing strategic scope, emphasizing strategic connection, intensifying strategic contradiction and solidifying strategic narrative, which embodies the narrative thinking that the United States links stereotypes and stigmatizes China's science and technology, forms a whole-subject narrative layout of "government-media-think tank", establishes narrative skills based on wedge strategy, and finally achieves the goal of suppressing rising countries by science and technology. The narrative of American science and technology strategy to China protects American international discourse right, but it is limited by the intermittent characteristics of American science and technology policy, the large-scale development of China's science and technology going to sea and the common conclusion of emerging global science and technology norms. The U.S. strategy of suppressing Chinese science and technology has been transferred to other China science and technology products. Therefore, the narrative suppression analysis of American science and technology to China should be transformed into relevant strategic industries, so as to help China science and technology to respond to the situation and follow the trend.

AI Agent technology is different from traditional large language models in that it can make autonomous decisions, perform multi-step tasks, and sense the environment; thus, it is now changing the world of technology. The AI Agent will use the large language model in the scaffolding software to plan and carry out a series of tasks independently without continuous human intervention, and it can do more than just answer questions. The purpose of this architecture is dual-use, and it can be employed for civil and military applications; thus, the same framework of autonomous planning and tool invocation that has optimised commercial workflows can also be used in the military for command and control, intelligence analysis, autonomous weapons systems, etc. Therefore, the dual-use boundary is determined not by the category of goods but by how they are used and connected in the system; thus, the old way of classifying or declaring the end-use of goods is no longer effective. With the intensification of strategic competition between China and the United States and the changes in the economic security strategy of the European Union, many countries have begun to add artificial intelligence to the mandatory foreign investment security review. The Committee on Foreign Investment in the United States has expanded the scope of application for inbound investment under the Foreign Investment Risk Review Modernization Act, introduced the Outbound Investment Security Program to curb outbound investment in countries of concern, and by 2025, the National Security Presidential Memorandum will increase control over sensitive technologies such as artificial intelligence. The new Foreign Direct Investment Screening Regulation in the EU lists artificial intelligence, quantum technology and semiconductors as important areas to be monitored for all member states, and the EU has started to study the screening of outbound investments. China's Measures for the Security Review of Foreign Investment have covered significant amounts of information and key technologies in the field of information technology and core industries, but specific judgment criteria for the new form of technology, AI agents, have not been established. Building on the foundation of technological security paradox, this paper thinks that the commercial value of AI Agent technology is directly related to its autonomy, adaptability and generalisation ability; at the same time, these are also the reasons for concern. Based on the criteria and practice in the United States, the European Union and China, as well as through cases such as Shield AI, OpenAI and the merger of SpaceX and xAI, this paper introduces the three structural defects of the current review system. The first is the accuracy of technical identification; static classification cannot consider that the capability changes according to the application environment. The second is the coordination of norms, as the security of a country is increasingly used to achieve technological containment in the background of geopolitical competition. The third is dynamic adaptability; that is to say, with the exponential growth in agents' capabilities, the revision of review norms has been slow. A new hierarchical review model based on functions, rather than product labels, will be put forward in this paper. The five parts of the AI Agent model are Autonomy, Connectivity, Adaptability, Collaboration and Accumulation, and one of them has been assigned to each of the four risk levels. If any aspect reaches the third level, a deep audit will be conducted; if two or more aspects reach the third level or any single aspect reaches the fourth level, then strict measures will be taken. Some proposals for the construction and operation of the Chinese review system were also made in this paper, such as adding a particular review guide for AI agent technology, establishing a joint expert assessment team to conduct practical capability tests, and introducing a regular update mechanism for threshold parameters. By changing the focus of the review from what a technology is to what it can do, the model aims to provide a theoretical basis and an institutional reference for China to improve the judgment criteria for the civilian and military dual-use attributes of AI Agent technology in foreign investment security review.

Artificial intelligence is a key driver of cultivating new quality productive forces in China. Exploring how the synergy between digitalization and green transformation can propel future industrial development is crucial for overcoming bottlenecks. This study addresses three main questions. First, it examines whether digital transformation, green transformation, and their synergy can drive China’s future industrial development and thereby inform industrial layout. Second, it analyzes how this synergy influences future industrial development by evaluating three mediating channels: green financial investment, green innovation capacity, and industrial structure. Third, it investigates whether the combined digital-green transformation generates spatial spillover effects, providing theoretical support for intercity industrial linkages and spatial planning. Key findings are as follows. First, digital transformation significantly drives future industrial development, whereas green transformation shows mainly long-term effects. The synergy between digitalization and green transformation compensates for the short-term limitations of green transformation, enabling joint short-term and long-term promotion of future industrial development. Second, the synergy advances development through three pathways: attracting green financial investment, strengthening green innovation capabilities, and optimizing industrial structure. Third, heterogeneity analysis reveals that the synergistic effects are stronger in larger cities, in cities with more developed digital economies, and in cities with higher environmental awareness. Fourth, this synergy generates significant spatial spillover effects, facilitating coordinated industrial layouts across cities. Overall, this study deepens our understanding of the drivers of future industrial development, the mechanisms through which dual transformations empower that development, and the spatial heterogeneity of these processes. This study offers several policy implications. First, it highlights that digital transformation can empower future industrial development by accelerating industrial digitalization and digital industrialization, thus promoting the advancement of Digital China and consolidating the digital dividends in industrial growth. Second, guided by sustainable development principles, policymakers should formulate long-term plans to extend the benefits of green transformation to a broader range of industries. Additionally, it is crucial to fully leverage the synergistic effect of dual transformation—“1+1>2”—to ensure that green dividends are delivered more swiftly and reliably to industrial development. Third, understanding the mechanisms through which dual transformation impacts future industrial development can harness green finance, innovation capacity, and industrial layout as new drivers for industrial growth. Finally, the synergistic integration of digitalization and green transformation demonstrates significant spatial spillover effects. This integration facilitates the coordinated development of digital and green industries across cities, thereby unlocking their spatial potential for future industrial growth.

As an intrinsic requirement and a crucial focal point for achieving high-level technological self-reliance and driving high-quality economic and social development in China, the cultivation and formation of new quality productive forces represent a complex process confronted with multifaceted theoretical and practical challenges that require systematic investigation and urgent resolution. This process fundamentally depends on the emergence of revolutionary technological breakthroughs to provide sustained momentum and structural support for its advancement. Within this context, understanding the foundational mechanism of technological formation and fostering the emergence of breakthrough technologies have become critical priorities for China's innovation-driven strategy.

Building upon the theoretical foundation of knowledge recombination, this study systematically investigates how specific characteristics of knowledge recombination—primarily knowledge distance and knowledge diversity—influence the formation of breakthrough technologies. The study further examines the moderating role of inventor collaboration intensity, making a critical distinction between inter-organizational and intra-organizational cooperation. To empirically validate the proposed theoretical framework, we constructed a comprehensive dataset comprising patent records from the United States synthetic biology sector spanning the period from 2004 to 2021. This sector constitutes an ideal empirical setting due to its inherently interdisciplinary nature, rapid knowledge obsolescence, and high-stakes innovation environment. These characteristics collectively create an amenable context for examining the dynamics between knowledge recombination and breakthrough technological outcomes.

The empirical analysis yields several significant findings that contribute to both theoretical understanding and practical application. First, the research confirms that both knowledge distance and knowledge diversity maintain inverted U-shaped relationships with the formation of breakthrough technologies. This nonlinear pattern suggests that while moderate levels of knowledge heterogeneity stimulate novel connections and creative synthesis, excessive cognitive distance or overwhelming diversity may exceed organizational absorption capacities and impede meaningful knowledge integration. Second, regarding contextual moderators, the intensity of inter-organizational collaboration significantly strengthens these observed inverted U-shaped relationships between knowledge recombination characteristics and technological breakthroughs. Conversely, intra-organizational collaboration demonstrates no statistically significant moderating effect. This differential impact highlights the unique value of cross-organizational partnerships in introducing complementary knowledge stocks, diverse problem-solving approaches, and alternative interpretive frameworks that collectively enhance the innovative potential of knowledge recombination processes.

This research makes substantive contributions to the theoretical discourse surrounding the origins and drivers of breakthrough technologies by providing nuanced insights into the complex interplay between knowledge structures, collaborative contexts, and innovative outcomes. From a practical perspective, the findings offer valuable guidance for scholars, innovation policymakers, and organizational leaders—particularly within China's ongoing pursuit of technological sovereignty and quality-driven development. By strategically configuring knowledge portfolios, optimizing collaboration patterns, and implementing governance mechanisms that foster productive cross-boundary exchanges, organizations and nations can systematically enhance their capacity to generate transformative technologies and accelerate the cultivation of new quality productive forces in strategic emerging sectors.

This study has theoretical and empirical contributions, but it also has limitations that point to future research. First, the generalizability of the findings, derived from synthetic biology, requires further validation. Second, the underlying mechanisms for the distinct effects of technologically-measured versus semantically-measured knowledge recombination on breakthrough technology remain unclear. Finally, while we examine the macro-level moderating role of collaboration, future work should investigate its micro-level foundations.

Breaking through the composite barriers in key core technologies is a necessary path to achieving technological self-reliance and strength. Based on the knowledge gap, path dependence and niche lock-in analytical framework, our paper systematically reveals the formation mechanisms of the triple barriers: Knowledge gap manifests as a dual imbalance between the fragmentation of explicit knowledge and the interruption in the inheritance of tacit knowledge. To break through it, a full-chain reconstruction mechanism is required, which includes the systematic integration of explicit knowledge, the explicit transformation of tacit knowledge, and the iterative optimization of knowledge systems; Path dependence arises from the self-solidification of technological systems, the synergistic lock-in of industrial chains, and the constraints of institutional combinations. To achieve trajectory transition, it is necessary to carry out modular decomposition of technical architectures, reconstruct technical standards, and design flexibly adjustable policy tools, among other measures. Niche Lock-in, in essence, refers to the multi-dimensional power solidification of technical standards, institutional rules, and knowledge networks. Achieving niche climbing relies on synergistic strategies including the reorganization of technical architectures, the remolding and integration of institutional rules, and the cognitive reconstruction of knowledge power. Furthermore, there exists a dynamic correlation between the triple barrier-breaking paths: knowledge reconstruction provides theoretical support for the leap of technological trajectories, the leap of technological trajectories creates interface conditions for niche ascension, and niche ascension feeds back to drive the innovation of knowledge production, thus forming a positive cycle that starts from knowledge reconstruction, moves to the leap of technological trajectories, then to niche ascension, and finally returns to knowledge reconstruction.

Based on the above conclusions, the following implications are derived: ① Establish a knowledge factor transformation system to resolve the dilemma of tacit knowledge inheritance. A national industrial knowledge graph platform should be built to integrate multimodal perception technologies and systematically capture high-dimensional tacit knowledge. ② Innovate dynamic policy tools to address the dilemma of technological trajectory lock-in. It is necessary to design a special policy package for intergenerational technological leaps and embed a flexible buffer mechanism to cope with path dependence. ③ Implement an innovation ecosystem power restructuring project to break through standard nesting blockades. A technological standard upgrading plan should be launched, and a mechanism for transforming international rules on standard nesting should be established. ④ Improve the collaborative barrier-breaking mechanism to enhance systematic evolution capability. An industry-university-research-government collaborative platform covering barrier diagnosis, path adaptation, and innovation ecosystem restructuring should be constructed, and a real-time monitoring indicator system for the three types of barriers should be developed.

In recent years, "involutionary competition" has emerged as a salient and defining feature of China's new energy vehicle (NEV) industry. Between 2018 and 2024, approximately 400 NEV firms exited the market amid intensified price wars, excessive capacity expansion, and escalating cost pressures. While involutionary competition may initially accelerate market selection and resource reallocation, prolonged competition driven by aggressive price-cutting and extreme cost compression has increasingly undermined technological innovation, distorted industrial ecosystems, and constrained high-quality development. Against this backdrop, a critical question arises: how can firms break through involutionary traps and construct sustainable competitive advantages under conditions of persistent and system-wide competitive pressure?

Existing studies on involution primarily adopt a macro-institutional perspective, emphasizing policy regulation, industrial governance, and the restoration of market order. These studies have contributed valuable insights into the structural and institutional origins of involutionary competition, particularly the role of government intervention in shaping industry dynamics. However, they provide limited explanations of how firms, at the organizational level, respond to and survive under sustained involutionary pressure. This limitation is especially evident in the NEV industry, where rapid technological iteration, high capital intensity, and converging technological trajectories expose firms to continuous environmental shocks. As a result, the micro-level mechanisms through which firms adapt to involution and achieve strategic breakthroughs remain theoretically underexplored.

To address this gap, this study introduces an organizational flexibility perspective and develops a process-oriented analytical framework to explain how firms achieve the continuous emergence of competitive advantages in involutionary environments. Drawing on theories of organizational flexibility and emergence, this research conceptualizes competitive advantage not as a deliberately designed or static outcome, but as an evolutionary process shaped by ongoing interactions between firms and their environments. Organizational flexibility is understood as a firm's capacity to adjust resource configurations, strategic orientations, and organizational arrangements in response to environmental disturbances. Extending prior static interpretations, this study emphasizes the dynamic evolution of flexibility itself under sustained competitive pressure.

Methodologically, this research adopts a comparative case study design, examining XPeng Motors and WM Motor-two Chinese NEV manufacturers that were exposed to similar external conditions but experienced divergent outcomes. By systematically comparing a "breakthrough" firm with an "exited" firm, this study constructs an analytical framework of "involutionary environment-flexible construction-advantage emergence" to reveal how different flexibility trajectories shape organizational evolution. The comparative approach enables the identification of key mechanisms through which flexibility influences firm survival and strategic outcomes under involutionary competition.

The findings reveal three main insights. First, the involutionary environment of the NEV industry evolves through three distinct stages: uncertainty, structural convergence, and polarization. Each stage imposes different adaptive pressures on firms. Second, under escalating involutionary pressure, organizational flexibility evolves from foundational flexibility to partial flexibility and ultimately to global flexibility. Foundational flexibility enables low-cost experimentation and rapid adjustment in uncertain environments; partial flexibility supports coordinated responses at critical organizational nodes; and global flexibility integrates resources, structures, and strategies into a system-wide adaptive capability. Third, driven by the evolution of flexibility, firms' competitive advantages emerge along a continuous path, progressing from agile trial-and-error advantages to path-based collaboration advantages and finally to system-level synergy advantages.

This study makes three primary theoretical contributions. First, it develops a process model that explains firm breakout mechanisms within China's involutionary context, thereby localizing organizational theory to a distinctive competitive environment. Second, it extends organizational flexibility theory by demonstrating how flexibility dynamically evolves under high-frequency environmental disturbances. Third, it reconceptualizes competitive advantage as a multi-stage emergent process driven by flexible evolution rather than static resource endowments.

As a core strategic resource in the era of artificial intelligence, the establishment of a governance system for computility is an urgent requirement for the modernization of national governance. This paper proposes for the first time the theoretical framework of "computility governance," elucidating its connotations through dual dimensions of resource attributes and instrumental attributes. In the resource dimension, computility as a novel derived resource necessitates addressing challenges such as sovereignty assurance, resource allocation, and cross-domain interoperability, enhancing national resource control capabilities through institutional design. In the instrumental dimension, computility empowers the modernization of national governance through dynamic adaptation, symbiotic governance, and digital-intelligent applications to enhance governance efficacy. Computility governance must transcend traditional resource governance paradigms to construct a novel framework aligned with its unique characteristics. Future efforts should innovate resource allocation models, reshape national governance logic, and promote the transformation of global power structures, achieving a transition from "technology adaptation" to "institutional empowerment," thereby providing a new governance paradigm for the era of digital civilization.

Artificial intelligence model distillation, as a key technology driving lightweight deployment of large models, serves as the core pathway for achieving “AI+” industrial integration. However, during its technological diffusion, it faces governance challenges stemming from inadequate intellectual property system adaptability. AI model distillation refers to the process of transferring knowledge from a pre-trained large teacher model to a lightweight student model through strategies such as softening the output distribution. This achieves model compression, enabling cost-effective and efficient deployment on terminal devices like smartphones and intelligent robots. Model distillation can be categorized into three approaches: soft label distillation, feature map distillation, and relational distillation. These correspond respectively to the distribution probability data output by the teacher model, the feature data input to the teacher model, and the inference process data from input to output within the teacher model. These data may potentially hold intellectual property rights. The distribution probability data output by the teacher model, the feature data input to it, and the inference process data from input to output may constitute works. If student model developers improperly replicate these data through model distillation, they may infringe copyright. If the aforementioned data constitute trade secrets, model distillation may infringe trade secrets by constituting improper means or violating confidentiality obligations. Distillation practices violating user agreements carry risks of breach of contract or unfair competition. Regarding these issues, international practice presents two typical models: the open innovation model, exemplified by DeepSeek, aims to achieve effective information utilization and sharing through open-source intellectual property initiatives, thereby reducing subsequent innovation costs. For copyright protection, it advocates automated, permissive open-source copyright licenses with licensed and restricted content; for trade secret protection, it proposes that open-source initiatives waive trade secret protection for much of the teacher model's data. Regarding unfair competition and breach of contract, it advocates for relatively lenient user agreements to completely exempt model distillation from breach of contract and unfair competition risks. The anti-competition model, exemplified by OpenAI, prohibits distillation through closed-source technology and strict agreements. Regarding copyright protection, it restricts other developers from directly appropriating the trainer model's data through closed-source models. It also supplements copyright protection by limiting other developers' use of model outputs for distillation via user agreements when copyright law rules are ambiguous. For trade secret protection, it safeguards the model's data as trade secrets through closed-source models and expands the scope of trade secret rights by restricting reverse engineering by other developers via user agreements. Regarding unfair competition and breach of contract, user agreements restrict model distillation by other developers, strengthening protection of their intangible competitive advantage—first-mover advantage. Open innovation pathways accelerate technological accessibility, foster innovation ecosystems, control industry standards and regulatory discourse, and counter external technological blockades and competitive pressures. Thus, they better align with China's industrial needs and scientific development patterns. China should cultivate an intellectual property-friendly industrial environment to promote open innovation models. It is recommended that, while respecting open-source agreements, China systematically resolve intellectual property conflicts in technology diffusion by clarifying copyright law rules for model distillation and defining the scope of anti-distillation agreements. This will foster a supportive institutional environment for AI technological innovation.

Against the backdrop of advancing high-level technological self-reliance, data asset disclosure has become a critical lever for firms to build competitive advantages in digital technological innovation. Based on panel data from Shanghai and Shenzhen A-share listed companies during 2012-2023, this study employs a fixed-effects panel regression model to empirically examine the impact of data asset disclosure on corporate digital innovation.The findings reveal that data asset disclosure significantly promotes both the quantity and quality of digital innovation. Heterogeneity analyses show that the disclosure effect on innovation quality is more pronounced among manufacturing firms, while non-manufacturing firms primarily benefit in terms of innovation quantity. The impact of disclosing self-use data assets is stronger than that of transactional data assets. Furthermore, technology-intensive firms experience greater improvement in innovation quality.Mediation and moderation analyses indicate that enhanced positioning in technology trading networks serves as a key mediating mechanism. Moreover, intellectual property (IP) protection positively moderates the relationship between data asset disclosure and innovation quantity, but has no significant effect on innovation quality. This study offers practical guidance for enterprises seeking technological upgrading through data asset strategies and provides policy implications for improving data disclosure regulations.

Digital technology has brought an end to the era where enterprises pursued innovation alone. Digital collaborative innovation, an innovative activity centered on digital innovation and conducted through multi-agent collaboration, is the result of the integration of digital innovation and collaborative innovation. Based on multi-agent collaboration, enterprises can easily break through technological barriers, expand their innovation networks, and unleash the vitality of innovation factors. A favorable intellectual property environment is an important factor in promoting cross-organizational collaborative innovation. Existing research has treated the construction of intellectual property demonstration cities as a quasi-natural experiment, discussing its impact on innovation, collaborative innovation, or digital innovation separately. However, how does the impact of intellectual property demonstration cities affect enterprise digital collaborative innovation, which centers on digital innovation and adopts the form of collaborative innovation? This topic has rarely been discussed in existing literature. With the accelerated penetration of digital technology in product collaborative innovation and industrial chain collaborative innovation, digital technology innovation consortia have become increasingly important; revealing the conditional mechanisms through which intellectual property demonstration cities trigger enterprise digital collaborative innovation holds theoretical value and practical significance.

Based on data from A-share listed companies from 2011 to 2023, this paper uses the construction of intellectual property demonstration cities as a quasi-natural experiment, characterizes digital collaborative innovation using jointly authorized digital patents, and examines the effects and mechanisms of intellectual property demonstration cities on digital collaborative innovation. The research finds that the construction of intellectual property demonstration cities has promoted an average increase of 22% in the rate of digital collaborative innovation among enterprises, indicating that public policies on intellectual property rights help to solve the property rights challenges of cross-organizational digital innovation and open up the channel for enterprises to integrate digital and physical innovation. Heterogeneity tests reveal that the impact of intellectual property demonstration cities on digital collaborative innovation only exists in the local creation effect, with no significant spatial spillover; enterprises with weak digital innovation foundations, those experiencing initial growth stages, and private "weak" enterprises are more likely to leverage public policies on intellectual property rights to gain the "wings" of digital-creative collaboration. In terms of mechanisms, enhancing multi-agent relationship connections, promoting the diffusion of basic research, and strengthening intellectual property enforcement are all important mechanisms through which intellectual property demonstration cities promote digital collaborative innovation among enterprises.

The potential academic contributions of this paper lie in the following aspects: Firstly, from a research perspective, this paper is an early empirical literature that examines the impact of intellectual property policies on enterprise digital collaborative innovation from the micro perspective of the interactive integration of digital innovation and collaborative innovation. Secondly, in terms of empirical strategy, this paper employs a multi-period difference-in-differences model to identify the causal relationship between intellectual property demonstration policies and enterprise digital collaborative innovation, providing incremental evidence for how to build a digital innovation consortium. Thirdly, in terms of mechanism analysis, this paper clarifies the theoretical mechanism through which the construction of intellectual property demonstration cities promotes enterprise digital collaborative innovation from three aspects: enhancing multi-agent relationship connections, promoting the diffusion of basic research, and strengthening intellectual property law enforcement. This provides theoretical guidance and practical insights for improving the intellectual property system, strengthening product collaborative innovation and building a digital innovation consortium.