Daniel Yue

I am an assistant professor in the Information Technology Management area at Georgia Tech's Scheller College of Business.

My research explores why firms openly share innovative knowledge without directly profiting, a strategy called “open disclosure.” My projects use scientific publications and open source software in AI research as an empirical setting to develop and test new theories.

Before Georgia Tech, I completed my PhD at Harvard Business School, my undergrad in Physics at Harvard College, and worked as a product manager of analytics software at Mastercard. Please find my CV here.

Research

Igniting Innovation: Evidence from PyTorch on Technology Control in Open Collaboration

(with Frank Nagle) - slides, working paper, under revision.

Many companies offer free access to their technology to encourage outside add-on innovation, hoping to later profit by raising prices or harnessing the power of the crowd while continuing to steer the direction of innovation. They can achieve this balance by opening access to the technology (access rights) but still maintaining governing control over it (control rights). However, how this continued exertion of control influences other companies’ choice to invest in furthering that technology is not well understood. This study looks at the impact of technology control on external contributions in open collaboration contexts by examining the case of PyTorch, a popular machine learning framework, which shifted its governance from a for-profit corporation (Meta) to a non-profit foundation in 2022. The results show that this shift led to a significant decrease in contributions from Meta but a notable increase from external companies. In particular, participation increased from complementors (Chip Manufacturers); by contrast, users (App Developers and Cloud Providers who rely on PyTorch as input) did not change their rate of participation. These findings are consistent with the notion that the governance change resolved complementors’ hold-up concerns.

I, Google: Estimating the Impact of Corporate Involvement on AI Research
(solo-authored) - working paper, R&R (Major Revision) at Management Science.

Abstract. While corporate involvement in modern scientific research is an indisputable fact, the impact of corporate involvement on scientific quality is controversial. While corporate interests can constrain research agendas, directing focus toward applied, commercially relevant questions, firms also provide resources—funding, data, engineering talent, and technical challenges—that can catalyze breakthroughs. This paper empirically assesses the impact of corporate involvement on scientific research by focusing on dual-affiliated artificial intelligence researchers located at the intersection of academia and industry. After controlling for the researcher's quality and topic preferences, I find that corporate involvement leads to up to a +44% increase in field-weighted citations received by a paper. I document evidence that this effect arises because the average benefit of a firm's scientific resources exceeds the cost of that firm's scientific constraints. Specifically, I find that corporate involvement raises the likelihood of producing breakthrough work, with these effects amplified when firms have greater resources. However, corporate involvement also alters the direction of the dual-affiliate author's research to align more with the firm's commercial interests. This paper is one of the first large-scale studies in any scientific field to estimate a positive impact of corporate involvement on science or to detail the mechanism driving this impact.

Nailing Prediction: Experimental Evidence on the Impact of Tools in Predictive Model Development
(with Paul Hamilton and Iavor Bojinov) - working paper, R&R (Major Revision) at Management Science.

Predictive model development is understudied despite its centrality in modern artificial intelligence and machine learning business applications. Although prior discussions highlight advances in methods (along the dimensions of data, computing power, and algorithms) as the primary driver of model quality, the tools that implement those methods have been neglected. In a field experiment leveraging a predictive data science contest, we study the impact of tools by restricting access to software libraries for machine learning models. By only allowing access to these libraries in our control group, we find that teams with unrestricted access perform 30% better in log-loss error — a statistically and economically significant amount, equivalent to a 10-fold increase in the training data set size. We further find that teams with high general data-science skills are less affected by the intervention. In contrast, teams with high tool-specific skills significantly benefit from access to modeling libraries. Our findings are consistent with a mechanism we call `Tools-as-Skill,' where tools automate and abstract some general data science skills but, in doing so, create the need for new tool-specific skills.

How Open Source Machine Learning Software Shapes AI

(with Max Langenkamp) - published in Artificial Intelligence, Ethics, and Society (2022)

If we want a future where AI serves a plurality of interests, then we should pay attention to the factors that drive its success. While others have studied the importance of data, hardware, and models in directing the trajectory of AI, we argue that open source software is a neglected factor shaping AI as a discipline. We start with the observation that almost all AI research and applications are built on machine learning open source software (MLOSS). This paper presents three contributions. First, it quantifies the outsized impact of MLOSS by using Github contributions data. By contrasting the costs of MLOSS and its economic benefits, we find that the average dollar of MLOSS investment corresponds to at least $100 of global economic value created, corresponding to $30B of economic value created this year. Second, we leverage interviews with AI researchers and developers to develop a causal model of the effect of open sourcing on economic value. We argue that open sourcing creates value through three primary mechanisms: standardization of MLOSS tools, increased experimentation in AI research, and creation of communities. Finally, we consider the incentives for developing MLOSS and the broader implications of these effects. We intend this paper to be useful for technologists and academics who want to analyze and critique AI, and policymakers who want to better understand and regulate AI systems.

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