  There are pronounced differences in the extent to which industrial and
academic AI labs use computing resources. We provide a data-driven survey of
the role of the compute divide in shaping machine learning research. We show
that a compute divide has coincided with a reduced representation of
academic-only research teams in compute intensive research topics, especially
foundation models. We argue that, academia will likely play a smaller role in
advancing the associated techniques, providing critical evaluation and
scrutiny, and in the diffusion of such models. Concurrent with this change in
research focus, there is a noticeable shift in academic research towards
embracing open source, pre-trained models developed within the industry. To
address the challenges arising from this trend, especially reduced scrutiny of
influential models, we recommend approaches aimed at thoughtfully expanding
academic insights. Nationally-sponsored computing infrastructure coupled with
open science initiatives could judiciously boost academic compute access,
prioritizing research on interpretability, safety and security. Structured
access programs and third-party auditing may also allow measured external
evaluation of industry systems.
