The AI Era of Knowledge Infrastructure: New Frontiers in Digital Media, Humanities, and Global City Competition

Introduction: When AI Enters the Deep Waters of Knowledge

Artificial intelligence has already crossed the watershed from experimentation to application. But in the critical knowledge domain of libraries, digital archives, and academic publishing, AI applications have long remained on the margins. A study recently published in Humanities and Social Sciences Communications systematically maps the global research trajectory of AI in digital media, humanities, and information science using a mixed-method approach of bibliometric analysis and network sentiment analysis. Its core finding is that AI has the most significant impact on automated metadata indexing, citation analysis, and recommendation systems. This may seem like a technical detail, but it actually signals a deep transformation in knowledge infrastructure.

From Tools to Infrastructure: AI Redefining Knowledge Organization

Traditional knowledge management relies on manual classification and retrieval. With AI intervention, knowledge organization shifts from manual labor to algorithm-driven automated processes. Automated metadata indexing enables vast amounts of content to be rapidly identified, citation analysis reshapes the logic of academic evaluation, and AI recommendation systems determine what knowledge researchers and the public encounter. Together, these three constitute a new knowledge pipeline. The study points out that these directions are precisely where AI has the deepest impact—they sit at the core of knowledge dissemination.

This shift is not merely about efficiency gains; it also means that the visibility and accessibility of knowledge are beginning to be determined by algorithms. When recommendation systems replace human curation, the logic of resource allocation shifts. The bias and privacy concerns the study warns about are a direct manifestation of this structural power.

Urban Perspectives: New Competition Among Knowledge Nodes

Global cities have long been the spatial anchors of knowledge production, with libraries, universities, and research institutions forming the backbone of urban knowledge ecosystems. AI is reshaping this backbone. As AI-driven knowledge management systems become mainstream, cities that can take the lead in integrating algorithms, data, and academic networks will gain an advantage in global innovation competition. Conversely, cities that rely on traditional models may become marginalized in the global circulation of knowledge. This is no longer a simple "smart city" narrative, but a competition over deep knowledge infrastructure. Cities in the Global South especially need to be vigilant: if AI knowledge systems are dominated by a few tech giants, inequality in knowledge dissemination will be further entrenched.

Governance Deficit: The Ethics and Politics of AI Knowledge Systems

The study calls for AI transparency and interdisciplinary collaboration, but behind this lies a larger governance problem. When algorithms determine which papers are recommended and which data are indexed, epistemic justice becomes a political issue. AI is not value-neutral; biases in training data can be amplified, and marginalized voices can be further drowned out. Meanwhile, academic data privacy faces new challenges in the AI environment. As local governance actors, cities can promote the publicness and accountability of AI knowledge infrastructure through public library systems, open data policies, and academic networks. This is the deeper meaning of the study's call for "interdisciplinary collaboration": librarians, computer scientists, ethicists, and urban planners must jointly formulate the rules.

Long-Term Trends: The Restructuring of the Global Knowledge Order## Long-term Trends: The Restructuring of the Global Knowledge Order

From a historical perspective, the penetration of AI into information science represents a paradigm shift in the mode of knowledge production. The Internet era migrated knowledge onto the network, while the AI era turns knowledge into a computable resource. Whoever controls the algorithms controls the valve of knowledge flow. This raises new questions for the relationship between states and cities: knowledge infrastructure is increasingly becoming a field of geopolitical competition. The method adopted by the study itself—combining quantitative bibliometrics with qualitative sentiment analysis—suggests that the impact of AI needs to be understood from the dual dimensions of systems and users. The future challenge is not only technological implementation, but also how to build an inclusive, transparent, and sustainable knowledge ecosystem. This requires cities, academia, and public institutions to form new alliances.

Conclusion: Toward Knowledge Long-termism in the AI Era

The value of this research lies not only in depicting the current situation, but also in providing direction for the future. The role of AI in digital media, the humanities, and information science will continue to deepen, and global cities need to proactively shape this transformation with a long-term strategic vision. Policymakers should invest in public AI infrastructure, cultivate interdisciplinary talent, and establish ethical regulatory frameworks. Otherwise, although AI can optimize knowledge management, it may widen the knowledge divide.