  Increasingly, modern Artificial Intelligence (AI) research has become more
computationally intensive. However, a growing concern is that due to unequal
access to computing power, only certain firms and elite universities have
advantages in modern AI research. Using a novel dataset of 171394 papers from
57 prestigious computer science conferences, we document that firms, in
particular, large technology firms and elite universities have increased
participation in major AI conferences since deep learning's unanticipated rise
in 2012. The effect is concentrated among elite universities, which are ranked
1-50 in the QS World University Rankings. Further, we find two strategies
through which firms increased their presence in AI research: first, they have
increased firm-only publications; and second, firms are collaborating primarily
with elite universities. Consequently, this increased presence of firms and
elite universities in AI research has crowded out mid-tier (QS ranked 201-300)
and lower-tier (QS ranked 301-500) universities. To provide causal evidence
that deep learning's unanticipated rise resulted in this divergence, we
leverage the generalized synthetic control method, a data-driven counterfactual
estimator. Using machine learning based text analysis methods, we provide
additional evidence that the divergence between these two groups - large firms
and non-elite universities - is driven by access to computing power or compute,
which we term as the "compute divide". This compute divide between large firms
and non-elite universities increases concerns around bias and fairness within
AI technology, and presents an obstacle towards "democratizing" AI. These
results suggest that a lack of access to specialized equipment such as compute
can de-democratize knowledge production.
