University Research and Leadership in the Age of AI: Mobilizing Intellectual Capacity and Building the Infrastructure for Discovery
Abstract
Artificial intelligence (AI) is expected to disrupt research across academia, changing how knowledge is produced and how research is organized. Recent developments in mathematics provide an early example of strong AI research capabilities and the professional disruption accompanying them. Universities should respond by building the scientific, computational, and organizational capacity to develop AI and shape the research it enables. The relationship is reciprocal: AI can advance disciplinary research, while scientific questions, experimental evidence, and disciplinary expertise can guide AI development. This article proposes connecting shared computational resources with sustained technical teams, interdisciplinary research, experimental facilities, and knowledge infrastructure that makes findings traceable and reusable. It distinguishes three commitments with different resource requirements: operating existing models, developing AI for disciplinary research, and investigating the foundations of AI. Commercial services can support this agenda alongside institutional capabilities for independent investigation, evaluation, and preservation of the research record. Consortia and national facilities can broaden participation and support capabilities beyond individual campuses. Initial programs should assess scientific value, total costs, and contributions to AI development, with expansion guided by evidence. These changes require sustained funding, professional support, and recognition of the work that makes research reliable and cumulative. Building this capacity will enable universities to pursue new research directions, train future researchers, and exercise scientific leadership as AI transforms academic inquiry.
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BibTeX
@misc{alali2026university,
title = {{University Research and Leadership in the Age of AI: Mobilizing Intellectual Capacity and Building the Infrastructure for Discovery}},
author = {Bacim Alali},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2610.35919},
eprint = {2610.35919},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2610.35919},
note = {CC BY-NC-ND 4.0},
}
RIS
TY - UNPB
TI - University Research and Leadership in the Age of AI: Mobilizing Intellectual Capacity and Building the Infrastructure for Discovery
AU - Bacim Alali
PY - 2026
DA - 2026/10/29
PB - arXiv
DO - 10.48550/arxiv.2610.35919
UR - https://arxiv.org/abs/2610.35919
AB - Artificial intelligence (AI) is expected to disrupt research across academia, changing how knowledge is produced and how research is organized. Recent developments in mathematics provide an early example of strong AI research capabilities and the professional disruption accompanying them. Universities should respond by building the scientific, computational, and organizational capacity to develop AI and shape the research it enables. The relationship is reciprocal: AI can advance disciplinary research, while scientific questions, experimental evidence, and disciplinary expertise can guide AI development. This article proposes connecting shared computational resources with sustained technical teams, interdisciplinary research, experimental facilities, and knowledge infrastructure that makes findings traceable and reusable. It distinguishes three commitments with different resource requirements: operating existing models, developing AI for disciplinary research, and investigating the foundations of AI. Commercial services can support this agenda alongside institutional capabilities for independent investigation, evaluation, and preservation of the research record. Consortia and national facilities can broaden participation and support capabilities beyond individual campuses. Initial programs should assess scientific value, total costs, and contributions to AI development, with expansion guided by evidence. These changes require sustained funding, professional support, and recognition of the work that makes research reliable and cumulative. Building this capacity will enable universities to pursue new research directions, train future researchers, and exercise scientific leadership as AI transforms academic inquiry.
ER -