Mapping competition concerns along the generative AI value chain

As China rapidly advances in AI innovation and development, especially in frontier AI, its regulatory and ethical frameworks are under increasing pressure to ensure that technological progress aligns with human interests and societal values. This Article argues that AI value alignment—the process of ensuring AI systems act in accordance with human values, norms, and ethical principles—should be adopted as a strategic pillar in China’s evolving AI governance architecture. While China has already established a comprehensive legal, ethical, and self-regulatory landscape to address AI risks, these mechanisms often rely on reactive enforcement and external compliance. In contrast, AI value alignment offers a proactive, intrinsic approach that embeds safety and ethical constraints directly into AI systems, making them safer, more trustworthy, and responsive to human needs. This study begins by mapping China’s current AI governance landscape, including national legislation such as the Cybersecurity Law, Personal Information Protection Law, and a growing set of regulations targeted at algorithms and generative AI. It also evaluates China’s normative commitments, such as the “human-centric” and “tech for good” principles articulated in national policy documents, and the increasing role of corporate self-regulation among major technology firms. While commendable in scope and ambition, these governance mechanisms often fall short in ensuring that AI behavior aligns with safety constraints and ethical intent—particularly when AI systems (such as agentic AI) become more autonomous and capable. This gap highlights the urgent need for a systematic value alignment strategy. The Article then delves into the conceptual and technical foundations of AI value alignment, identifying both engineering challenges—such as reward misspecification, data bias, and model deception—and normative dilemmas, including moral pluralism, value aggregation, and dynamic ethics. Special attention is paid to frontier models like large language models and artificial general intelligence (AGI), which pose alignment challenges at a scale previously unseen. Drawing on contemporary alignment techniques such as RLHF (Reinforcement Learning from Human Feedback) and principle-based alignment, such as Anthropic’s Constitutional AI, the Article explores their limitations and calls for a more diversified, interdisciplinary, and forward-looking alignment research agenda. Finally, the Article offers a roadmap for operationalizing AI value alignment across three key governance domains: Law and regulation, ethical norms, and industry self-regulation. Recommendations include the incorporation of alignment assessments into regulatory filings, the development of technical standards for value alignment and ethics-by-design guidelines, and institutional investments in safety and alignment research. The Article concludes by asserting that value alignment is not merely a technical safeguard but a governance imperative for the age of autonomous AI and agentic AI. By integrating alignment into its AI governance strategy, China can not only enhance domestic safety and public trust but also better coordinate with global AI ethics and safety initiatives—ultimately contributing to the shared goal of human-aligned and beneficial artificial intelligence.

The many initiatives on trustworthy AI result in a confusing and multipolar landscape that organizations operating within the fluid and complex international value chains must navigate in pursuing trustworthy AI. The EU’s AI Act will now shift the focus of such organizations toward conformance with the technical requirements for regulatory compliance, for which the Act relies on Harmonized Standards. Though a high-level mapping to the Act’s requirements will be part of such harmonization, determining the degree to which standards conformity delivers regulatory compliance with the AI Act remains a complex challenge. Variance and gaps in the definitions of concepts and how they are used in requirements between the Act and harmonized standards may impact the consistency of compliance claims across organizations, sectors, and applications. This may present regulatory uncertainty, especially for SMEs and public sector bodies relying on standards conformance rather than proprietary equivalents for developing and deploying compliant high-risk AI systems. To address this challenge, this paper offers a simple and repeatable mechanism for mapping the terms and requirements relevant to normative statements in regulations and standards, e.g., AI Act and ISO management system standards, texts into open knowledge graphs. This representation is used to assess the adequacy of standards conformance to regulatory compliance and thereby provide a basis for identifying areas where further technical consensus development in trustworthy AI value chains is required to achieve regulatory compliance.

The paper addresses data sharing contracts for AI development and training, entered into by parties participating in AI value chains, in order to assess the incentives for responsible data sharing under European data law. As the governance of AI training data is mainly entrusted to party autonomy and the principles of the GDPR, the analysis first assesses whether contract theory, as it is traditionally conceptualized, is an appropriate tool to promote responsible data sharing. Then the paper delves into the impact of the mandatory data protection framework on data sharing contracts, to conclude that the principle of accountability in its organizational dimension requires the parties to 'contractualize' good data sharing practices. Lastly, the analysis comprises a brief overview of the relevant provisions of the AI Act that deal with data governance and AI value chains, to highlight their impact on the relationship between the entities participating in these value chains.

Although contract cheating—students outsourcing their educational assessments to other people—is fraudulent, it is not typically labeled as corruption. This chapter illustrates how contract cheating can facilitate corruption within higher education. Through two case studies based on recent events, we illustrate how contract cheating could allow people to benefit from courses of study that they do not complete themselves. These cases show how such corruption could also benefit higher education institutions financially. We argue that low detection rates of contract cheating and the widespread misperception that it is impossible to prove enable schemes like those illustrated in our cases to operate with plausible deniability. We suggest that the recent rise of accessible Generative AI tools has shifted this into the realm of implausible deniability. We conclude by recommending that substantive efforts to improve the detection of contract cheating are needed to prevent it from facilitating corruption in higher education.

This study breaks through the traditional industry boundary perspective and constructs a cross-industry collaborative integration model of the digital economy and the real economy. Based on an in-depth analysis of 12 industry cases in Asia, Europe and the United States, it is revealed for the first time that the blockchain data governance system can reduce the risk of enterprise data leakage by 37% (p<0.05), which is 12 percentage points higher than similar studies. The innovative discovery that the blocking effect of organizational inertia on transformation is significantly higher than that of technical factors confirms that a flat management structure can speed up the digitalization process of enterprises by 28%. Different from the existing technological determinism paradigm, the study proposes a three- dimensional collaborative framework of “policy-technology-organization” (PTO) and verifies its theoretical validity through a cross-cultural case comparison of Haier Intelligent Manufacturing and Siemens Digital Factory. The specially constructed digital transformation maturity assessment matrix (DMAM) solves the dilemma of policy lag, and regional pilots show that it improves policy adaptation efficiency by 31%. The study provides a new analytical tool for the reconstruction of the global value chain and has forward-looking guiding value for industrial transformation under the impact of generative AI.

Recent Generative AI advancements, resulting in sophisticated image-generation tools, are revolutionising the Cultural and Creative Industries, impacting the entire value chain. In fashion design, these tools have become integral to the industry pipeline, supporting designers at various stages of creative processes. In particular, by leveraging vast datasets to generate synthetic images, AI has the potential to transform fashion archives into dynamic resources, enabling brands to explore and reimagine their visual heritage. Within this transformative context, the paper explores how designer-AI co-created outputs can be applied across three distinct areas of fashion design: Product development, Communication, and Retail. Through case studies analysis, three trajectories are delineated to enhance fashion visual language and renew creative processes. Ethical issues related to reconfiguring the fashion designer’s role will be addressed, contributing to the ongoing debate on the relationship between human and AI creativity.

Safeguarding intangible heritage music increasingly depends on endtoend digital value chains that can faithfully model, generate, track, and remunerate culturally embedded musical knowledge. This paper proposes a generative AI framework that jointly (i) learns a multilayer, ethnomusicologyaware representation across raw audio, symbolic structure, and contextual metadata, (ii) constrains a controllable transformerdiffusion pipeline to preserve modality, microtonal tuning, ornamentation practice, and rhythmic grammar, and (iii) embeds blockchainanchored provenance and smartcontract royalty logic inside the creative pipeline. Using 1,842 recordings (126.3 hours) spanning Southeast Asian gamelan, Chinese Buddhist chant, and Andean panpipe repertoires, we compare our system against an archiveonly baseline and an unconditioned generative baseline. Conditioned generation reduces average modal deviation from canonical tunings from 12.34.1 cents to 4.91.8 cents, decreases rhythmic dynamictimewarping distance by 44.5%, and raises expert authenticity ratings from 3.120.61 to 4.470.28 (ICC(2,k)=0.82). On the valuechain layer, median royalty settlement time drops from 23.7 days to 1.92 days, the Theil inequality index falls from 0.218 to 0.071, and Jains fairness index rises from 0.63 to 0.89. Listenerside evaluation shows higher longtail coverage (+31.4%), improved NDCG@20 (0.3820.497), and a 26% reduction in the hazard of early session abandonment. The findings demonstrate that culturally bounded, rightssensitive generative pipelines can simultaneously enhance preservation fidelity, creative reuse, and equitable community remuneration.

This research article proposes an integrated AI platform designed to revolutionise the apparel industry. The platform, envisioned as a comprehensive ecosystem, aims to enhance every stage of the apparel value chain, from design ideation to marketing and supply chain management. The architecture is built around three core components: an AI-Powered Design Studio, an Intelligent Production & Supply Chain Backbone, and a Hyper-Personalised Marketing & Engagement Engine. The AI-Powered Design Studio leverages generative AI, deep learning, and computer vision to transform the design process. A Trend Forecasting Engine utilises diverse data sources (social media, e-commerce, runway shows) to predict trends with high accuracy, providing data-driven insights that directly inform the AI Co-Creation Suite. This suite employs GANs, diffusion models, and sketch-to-image translation to generate design variations, refine concepts, and simulate fabric drape and fit, resulting in faster design cycles and commercially viable products. A Hyper-Personalisation Module further enhances this by generating personalised designs tailored to individual customer styles and body measurements, bridging the "aspiration gap" between desired and attainable styles. The Intelligent Production & Supply Chain Backbone focuses on efficiency and sustainability. A Material Optimisation and Waste Reduction System uses computer vision to detect fabric defects and optimise cutting layouts, minimising waste. Predictive inventory management and AI-powered logistics orchestration, combined with blockchain technology for enhanced traceability, create a responsive and transparent supply chain. Automated quality control, utilising computer vision, reduces defects and enables predictive maintenance of machinery, optimising production efficiency. A Sustainability and Circularity Management Dashboard provides a holistic view of environmental and social impact, facilitating data-driven decision-making and transparency for consumers. Finally, the Hyper-Personalised Marketing & Engagement Engine uses AI to deliver tailored experiences. A Personalised Marketing and Dynamic Campaign Engine, powered by a Customer Data Platform (CDP) and a "Latent Style" algorithm, provides personalised product recommendations, marketing messages, and promotions. An Automated Content Generation Engine generates marketing assets (product descriptions, social media posts, email copy) at scale, while a Unified Consumer Insights Platform provides real-time market analysis. Conversational AI, through chatbots and virtual stylists, enhances customer support and creates personalised interactions. The use of 3D digital twins and virtual prototyping, including virtual try-on (VTO) capabilities, enhances consumer engagement and reduces return rates. The article concludes with a phased implementation roadmap, prioritising data infrastructure, key modules with high ROI (such as waste reduction), and subsequent integration of generative design and personalisation. The overall goal is a symbiotic relationship between human creativity and AI's efficiency, resulting in a future-ready apparel industry characterised by enhanced speed, sustainability, and personalisation. Case studies of industry leaders like Zara, Stitch Fix, H&M, and Nike illustrate the successful application of similar AI strategies.

As they attempt to nurture and govern AI, some nations are acting in ways that – with or without direct intent – discriminate among foreign market actors. For example, some governments are excluding foreign firms from access to incentives for high-speed computing, or requiring local content in the AI supply chain, or adopting export controls for the advanced chips that power many types of AI. If policy makers in country X can limit access to the building blocks of AI – whether funds, data or high-speed computing power – it might slow down or limit the AI prowess of its competitors in country Y and/or Z. At the same time, however, such policies could violate international trade norms of non-discrimination. Moreover, if policy makers can shape regulations in ways that benefit local AI competitors, they may also impede the competitiveness of other nations’ AI developers. Such regulatory policies could be discriminatory and breach international trade rules as well as long-standing rules about how nations and firms compete – which, over time, could reduce trust among nations. In this article, the author attempts to illuminate AI nationalism and its consequences by answering four questions: – What are nations doing to nurture AI capacity within their borders? – Are some of these actions trade distorting? – Are some nations adopting twenty-first century beggar thy neighbour policies? – What are the implications of such trade-distorting actions? The author finds that AI nationalist policies appear to help countries with the largest and most established technology firms across multiple levels of the AI value chain. Hence, policy makers’ efforts to dominate these sectors, as example through large investment sums or beggar thy neighbour policies are not a good way to build trust.