Impact of Generative AI on AI Research Monoculture and Evaluation Regimes

Determine how the rise of generative AI will reshape artificial intelligence research (AIR) and other scientific fields, with particular attention to whether benchmarking-led evaluation and industry-dominated organizational structures that produced a deep learning monoculture will persist, evolve, or be supplanted by new practices.

Background

The paper argues that over the past decade AIR has become an epistemic monoculture centered on deep learning and formal benchmarking, with organizational power shifting toward large technology companies possessing the data and compute needed for scaling. This configuration has delivered rapid progress but narrowed epistemic values and entrenched industrial interests.

In the concluding section, the authors note that the advent of generative AI may disrupt key elements of this monoculture. They highlight emerging alternatives to static benchmarks (e.g., dynamic, crowdsourced human-preference evaluations) and shifting practices around open versus proprietary model releases. However, they emphasize that the long-term effects of these changes on evaluation methods, organizational power, and the broader scientific enterprise remain uncertain.