Technological advancements are significantly influencing the legal services landscape in a myriad of respects. Automation, machine learning, and other advanced analytics are experiencing unprecedented acceptance and adoption across the legal industry. Discovery is no exception: over the past decade, the expansion of technology-assisted review has fueled speculation that attorneys will be largely replaced by machines. Commentators have prophesied an “artificial intelligence invasion” that brings about the “extinction of the legal profession.” Specifically, in the e-discovery process, predictive coding has been tagged as this sort of disruptive, impactful technology that would make attorneys no longer necessary. Predictive coding is a type of technology-assisted review that employs algorithms to help classify documents (relevant or not, privileged or not, etc.). This technology has surged in popularity, supported by a conventional wisdom that posits that predictive coding is faster, cheaper, and more accurate than manual (i.e., human) review. While experience supports an emerging–and powerful–role for artificial intelligence in data review, research has not actually shown that predictive coding is simply ‘better than’ humans, or that predictive coding should ever be employed without human training, iteration, and final review.