Attracting Commercial Artificial Intelligence Firms to Support National Security through Collaborative Contracts

Abstract

The United States Department of Defense (‘DoD’) has determined it is not ready to compete in the Artificial Intelligence (‘AI’) era without significant changes to how it acquires AI. Unlike other military technologies driven by national security needs and developed with federal funding, this ubiquitous technology enabler is predominantly funded and advanced by commercial industry for civilian applications. However, there is a lack of understanding of the reasons commercial AI firms decide to work with the DoD or choose to abstain from the defence market. Although there are several challenges to attracting commercial AI firms to support national security, this thesis argues that the DoD’s contract law and procurement framework are among the most significant obstacles. This research indicates that the commercial AI industry actually views the DoD as an attractive customer. However, this attraction is despite the obstacles presented by traditional contract law and procurement practices used to solicit and award contracts. Drawing on social exchange theory, this thesis introduces a theoretical framework – ‘optimal buyer theory’ – to understand the factors that influence a commercial AI firm’s decision to engage with the DoD. It develops evidence-based best practices in contract law that reveal how the DoD can become a more attractive customer to commercial AI firms. This research builds upon research at the nexus of national security and defence contracts as it studies business decision-makers from AI firms through an explanatory sequential mixed methods design. In the study’s first phase, participants are surveyed to discover the perceptions, opinions, and preferences at AI firms of all sizes, maturity, location, and experience within the DoD marketplace. In the second phase of the study, interviews from a sample of the participants explain why the AI industry holds such perceptions, opinions, and preferences about contracts generally and the DoD, specifically, in its role as a customer. This thesis concludes that commercial AI firms are attracted to contracts that are consistent with their business and technology considerations. These considerations align with contractual relationships that are collaborative, flexible, negotiated, iterative, and awarded promptly as opposed to those with fixed requirements and driven by regulations foreign to the commercial market. Additionally, it develops best practices for leveraging existing contract law, primarily other transaction authority, to align the DoD’s contracting practices with commercial preferences and the machine learning development and deployment lifecycle. Armed with this understanding, the DoD can better attract commercial AI firms to support its national security objectives.