Rate My Professor Daniel Yue

His key projects examine the local economic effects of data center entry, finding increases in employment by 3.5%, wages by 5.0%, business establishments by 4.7%, and median household income by 1.9%, particularly in metropolitan areas; the latent role of open models in the AI economy, where closed models dominate despite higher costs, with open models potentially saving $24.8 billion; technology control in open collaboration via PyTorch governance changes; corporate involvement boosting AI research citations by up to 44%; tools improving predictive model performance by 30% in experiments; and open source machine learning software creating $30 billion in annual value through standardization and communities. Notable publications include 'The Local Economic Effects of Data Center Entry' (with Yiyang Zeng), 'The Latent Role of Open Models in the AI Economy' (with Frank Nagle), 'Igniting Innovation: Evidence from PyTorch on Technology Control in Open Collaboration' (with Frank Nagle), 'I, Google: Estimating the Impact of Corporate Involvement on AI Research,' 'Nailing Prediction: Experimental Evidence on Tools and Skills in Predictive Model Development' (2022, with Paul Hamilton and Iavor Bojinov), and 'How Open Source Machine Learning Software Shapes AI' (2022, with Max Langenkamp). In 2025, he received the Best Dissertation Award from the Academy of Management's Technology and Innovation Management Division for his work on AI firms' openness.