The rapid advancement of Artificial Intelligence is reshaping nearly every aspect of modern life, from scientific research to governance. However, beneath this explosive growth lies a troubling trend: AI research may be converging toward a “scientific monoculture,” increasingly dominated by a few key technologies like transformers and large language models, while abandoning a greater diversity of conceptual and methodological approaches. This potential loss carries serious risks—reduced innovation, diminished resilience, and vulnerability to technological dead-ends—yet remains largely unknown. Without quantitative tools to track how AI’s intellectual landscape has evolved over its 75-year history, we lack the empirical evidence needed to determine whether this perceived narrowing is real or perceived. This gap in understanding is critical: as AI becomes deeply embedded in society, with profound implications for its societal impact, rigorous, data-driven insights into its evolution are needed to guide future research directions and ensure the field remains innovative and epistemically resilient.

Project Goals and Approach

This project aims to create an unprecedented, data-driven “map” of AI’s 75-year history through comprehensive analysis of publications, conference papers, citations, patents, funding records and other documents from the largely informal literature at its beginnings. Core objectives include constructing a longitudinal dataset from sources like OpenAlex and Dimensions; developing computational methods to visualize the field’s evolving structure; tracking how AI subfields emerge, branch, and converge; and defining quantitative metrics for epistemic diversity—the variety of research approaches within AI.

The methodology combines cutting-edge techniques: Natural Language Processing models will generate embeddings capturing conceptual similarities across decades of research and complex network analysis will map relationships between publications, authors, and institutions. On the conceptual side, the project is based on understanding the history of AI as a living epistemic ecosystem. Phylogenetic networks will reveal how different concepts pollinated across subfields, how shifts in meaning, unlikely combinations and ‘roads not taken’ have continually expanded AI to where we are today.

Potential Impact

The project will provide solid quantitative data of the history of AI to understand whether AI research is narrowing into a dangerous monoculture or maintaining a healthy epistemic diversity. By revealing hidden biases in AI’s trajectory and identifying seemingly abandoned approaches, i.e. neglected but promising subfields, it will allow to treat the history of AI not as a closed chapter but as a living resource. The interactive visualizations will enable strategic discussions by charting past trajectories which can inform future directions to be taken. Such discussions will involve not only researchers but can reach out to policymakers and the public, inviting them to see AI not as the story of inevitable progress, but as a living web of research threads embedded in a larger context. Such an understanding is needed to see what AI can become in the future. 

This research program and related results were made possible by the support of the NOMIS Foundation.