Two Essays On Innovation In Artificial Intelligence: Corporate R&D And Firm-level Publications Strategy

Corporate research and development (R&D) plays an important role in firm-level innovation strategies. To maintain competitive advantage, firms tend to disclose their internal research strategically. Essay 1 of this dissertation examines what motivates firms to publish more papers in Artificial Intelligence (AI). Combining two disparate literatures— R&D disclosure strategy and strategic human capital literature— I argue that scientists have a preference to publish research and when scientists have higher bargaining power, firms tend to disclose more internal research to recruit talent. To test my propositions, I use a comprehensive dataset of 200 million US job postings (from Burning Glass Technologies) and 1 million firm-level peer-reviewed publications from AI firms. Using rich qualitative data, I document that, in AI, there is a shortage of talent which increases scientists’ bargaining power. Next, I demonstrate that firms’ AI job posts lead to AI publications, which supports my proposition. This relationship is even more salient when job posts require PhD degrees, indicating that bargaining power is a key driver of increased R&D disclosure. Interestingly, scholars have documented a secular decline of firm-level publications across many different knowledge-intensive industries. Contrary to this, in Essay 2, using a rich dataset of 171,394 papers from 57 prestigious computer science conferences, I document that in AI, firms have increased publications relative to non-AI research fields. The unexpected resurgence of AI is due to deep learning, a sub-field of AI, which requires significant computing power and large datasets. Building on the resource-based view and economics of innovation literature, I hypothesize and find that access to key resources provides competitive advantages to large technology firms and elite universities. On the other hand, the rise of deep learning creates entry barriers for non-elite universities, which are struggling to publish in top-tier conferences. Taken together, the results suggest that the rise of deep learning has resulted in a “de-democratization” of the AI research field. Taken together, these two essays advance our understanding of corporate R&D, the role of resources in knowledge production, and firms’ publications strategy. Further, both essays have significant policy implications on increasing societal welfare