Large language models (LLMs) have shown significant potential to change how we write, communicate, and create, leading to rapid adoption across society. This dissertation examines how individuals and institutions are adapting to and engaging with this emerging technology through three research directions. First, I demonstrate how the institutional adoption of AI detectors introduces systematic biases, particularly disadvantaging writers of non-dominant language varieties, highlighting critical equity concerns in AI governance. Second, I present novel population-level algorithmic approaches that measure the increasing adoption of LLMs across writing domains, revealing consistent patterns of AI-assisted content in academic peer reviews, scientific publications, consumer complaints, corporate communications, job postings, and international organization press releases. Finally, I investigate LLMs' capability to provide feedback on research manuscripts through a large-scale empirical analysis, offering insights into their potential to support researchers who face barriers in accessing timely manuscript feedback, particularly early-career researchers and those from under-resourced settings.

Benchmark, the venture capital firm known for its investments in Uber and Snapchat, is expected to join the rush of investors into generative AI with a deal for startup Langchain. The company has developed an AI-powered platform that can generate natural language from data sets. The investment comes at a time when many companies are looking to capitalize on the potential of generative AI technology. Generative AI is an artificial intelligence system that uses machine learning algorithms to create new content from existing data sources. This type of technology could be used to create text or images based on inputted information, allowing businesses to quickly produce large amounts of content without having to manually write it themselves.