In PNAS, Chu and Evans (1) argue that the rapidly rising number of publications in any given field actually hinders progress. The rationale is that, if too many papers are published, the really novel ideas have trouble finding traction, and more and more people tend to “go along with the majority.” Review papers are cited more and more instead of original research. We agree with Chu and Evans: Scientists simply cannot keep up. This is why we argue that we must bring the powers of artificial intelligence/machine learning (AI/ML) and open access to the forefront. AI/ML is a powerful tool and can be used to ingest and analyze large quantities of data in a short period of time. For example, some of us (2) have used AI/ML tools to ingest 500,000+ abstracts from online archives (relatively easy to do today) and categorize them for strategic planning purposes. This letter offers a short follow-on to Chu and Evans (hereafter CE) to point out a way to mitigate the problems they delineate.