Cross-disciplinary Research

Our faces are said to be windows into the soul. But can they also reflect who we are as consumers? Can facial images predict brand preferences? To answer these questions, we analyze a unique dataset of over 100,000 single-face Twitter profile pictures linked with brand followership data for 444 brands across categories and brand personality metrics. Using advanced machine learning for automated face analysis, we demonstrate that consumers’ social media profile faces can reveal their preferences between rival brands (study 1).

As generative AI systems rapidly improve, a key question emerges: how do users adapt to these changes, and when does such adaptation matter for realizing performance gains? This paper studies prompt adaptation—how users adjust their inputs in response to evolving model behavior—using a common experimental design applied to two preregistered tasks with 3,750 total participants who submitted nearly 37,000 prompts. We show that the importance of prompt adaptation depends critically on task structure.

Screening human capital based on signals such as job applications or entrepreneurial pitches is crucial for organizations. Signals are often informative insofar as they require differential knowledge and effort to produce. Generative AI (GAI) complicates screening by lowering the cost of producing impressive signals. We model the informational effects of GAI, showing that applicants' access to GAI can increase—but also decrease—an evaluator's screening mistakes. This result depends on how GAI affects experts' signals compared to non-experts'.

Concerns that interacting with generative AI homogenizes human cognition are largely based on evidence from text-based interactions, potentially conflating the effects of AI systems with those of written communication. This study examines whether these patterns depend on communication modality rather than on AI itself. Analyzing 957 open-ended debates between university students and a knowledgeable AI adversary, we show that modality corresponds to distinct structural patterns in discourse.

This paper analyzes the AI infrastructure boom as a physical capital buildout centered on data centers, power infrastructure, cooling systems, and specialized chips. It studies how this buildout is financed through hyperscalers, third-party developers, REITs, private credit, and structured finance, and discusses the implications for leverage, risk allocation, and financial stability.

The arts are widely recognized for their profound psychological and social benefits. Although historically viewed as a uniquely human pursuit, art is increasingly created with artificial intelligence (AI). In the current work, we explore whether AI-generated art evokes the same emotional reactions and inspires the same interpersonal benefits as human-created art. Integrating appraisal theories of emotion and philosophical accounts of the arts, we propose that art believed to be AI-generated elicits less awe than human-created art, which in turn diminishes empathy.

Salespeople frequently face the dynamic screening decision of whether to persist in a conversation or abandon it to pursue the next lead. Yet, little is known about how these decisions are made, whether they are efficient, or how to improve them. We study these decisions in the context of high-volume outbound sales where leads are ample, but time is scarce and failure is common.