Artificial intelligence is reshaping the organization and practice of research in ways that extend far beyond gains in productivity. AI systems now accelerate discovery, reorganize scholarly labour, and mediate access to expanding scientific literatures. At the same time, generative models capable of producing text, images, and data at scale introduce new epistemic and institutional vulnerabilities. They exacerbate challenges of reproducibility, blur lines of authorship and accountability, and place unprecedented pressure on peer review and editorial systems. These risks coincide with a deeper political-economic shift: the centre of gravity in AI research has moved decisively from universities to private laboratories with privileged access to data, compute, and engineering talent. As frontier models become increasingly proprietary and opaque, universities face growing difficulty interrogating, reproducing, or contesting the systems on which scientific inquiry increasingly depends. This article argues that these developments challenge research integrity and erode traditional bases of academic authority, understood as the institutional capacity to render knowledge credible, contestable, and independent of concentrated power. Rather than competing with corporate laboratories at the technological frontier, universities can sustain their legitimacy by strengthening roles that cannot be readily automated or commercialized: exercising judgement over research quality in an environment saturated with synthetic outputs; curating the provenance, transparency, and reproducibility of knowledge; and acting as ethical and epistemic counterweights to private interests. In an era of informational abundance, the future authority of universities lies less in maximizing discovery alone than in sustaining the institutional conditions under which knowledge can be trusted and publicly valued.

翻译：人工智能正在以远超生产力提升的方式重塑研究的组织与实践。AI系统如今加速科学发现、重组学术劳动，并作为中介管理日益扩张的科学文献获取。与此同时，能够大规模生成文本、图像和数据的生成模型引入了新的认知与制度脆弱性。它们加剧了可重复性挑战，模糊了作者身份与责任归属的界限，并对同行评审与编辑系统施加了前所未有的压力。这些风险与更深层的政治经济转型同时发生：AI研究的重心已决定性地从大学转移至那些在数据、算力和工程人才方面享有特权准入的私营实验室。随着前沿模型日益专有化和不透明，大学在审视、复现或质疑科学探究日益依赖的系统方面面临越来越大的困难。本文认为，这些发展态势挑战了研究诚信，并侵蚀了传统学术权威的基础——后者被理解为使知识可信、可辩驳且独立于集中权力的制度能力。大学无需在技术前沿与公司实验室竞争，而可以通过强化那些不易被自动化或商业化的角色来维持其合法性：在合成产出饱和的环境中行使研究质量评判权；策展知识的来源、透明度与可重复性；并作为抗衡私人利益的伦理与认知制衡力量。在信息丰裕的时代，大学未来的权威不在于单纯最大化发现，而更在于维系知识能够被信任并获得公共价值的制度条件。