嘉   宾：Chen Wei  Associate Professor  University of Connecticut

主持人：魏煊  助理教授  上海交通大学安泰经济与管理学院

时   间：2024年07月08日（周一）14:00-15:30

地   点：上海交通大学 徐汇校区安泰经济与管理学院A403

内容简介:

AI is undergoing a paradigm shift with the rise of foundation models (e.g., GPT-4, Claude 3, Gemini, Llama 3, Stable Diffusion) trained on broad data using self-supervision at immense scale, which can then be adapted to myriad downstream tasks. This paper offers an economic theory of foundation model ecosystems that consist of upstream model developers and downstream model deployers. We model how fine-tuning games arise as downstream deployers competing in foundation model adoption and fine-tuning effort, and how model openness affects the fine-tuning games. Our theory provides insights into the economic implications and trade-offs for multiple stakeholders throughout the AI value chain (including developer, deployers, and consumers) and sheds light on how to harness the full potential of the foundation model value chain interactions and avoid pitfalls. An important implication of our findings is that the policymakers should explicitly consider potential unintended economic consequences of AI regulation on the ecosystem around open foundation models, in particular, the so-called ``openness trap" (i.e., a range of medium openness levels that should be avoided). Furthermore, we explore the welfare implications of prevalent market strategies employed by upstream developers, such as vertical integration and offering free trials. Our findings reveal that vertical integration proves effective when model openness is relatively limited. The developer's strategy of providing free trials can negatively affect the leading deployer within a moderate range of model openness, while benefiting all other stakeholders. Overall, our theory suggests that vertical integration and free trial are effective strategies for relatively closed foundation models, and more importantly, competition regulations on limiting such strategies at the developer level are most likely unnecessary.

演讲人简介：

Wei Chen is an Associate Professor of Operations and Information Management (OPIM) at the School of Business, University of Connecticut. He is also the academic director of the UConn Digital Frontiers Initiative, which fosters collaborations between industry and academia, with a current emphasis on the business applications of generative AI. Wei's research studies platforms & crowds, financial technologies (FinTech), and the design and governance of generative AI. Utilizing theoretical models and granular data, he seeks to provide rigorous evidence to informs system and market design in these areas. His work has been published in leading academic journals, such as Management Science, Information Systems Research, MIS Quarterly, and Production and Operations Management. He has received several best paper awards and nominations in information systems conferences and workshops, and the Gordon B. Davis Young Scholar Award from the INFORMS Information Systems Society.

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