How People Use ChatGPT

Paper · Source
Knowledge After the Web

Source: Chatterji, Cunningham, Deming, Hitzig et al. (OpenAI, Harvard); NBER w34255 · 2025-09

Despite the rapid adoption of LLM chatbots, little is known about how they are used. We document the growth of ChatGPT’s consumer product from its launch in November 2022 through July 2025, when it had been adopted by around 10% of the world’s adult population. Early adopters were disproportionately male but the gender gap has narrowed dramatically, and we find higher growth rates in lower-income countries. Using a privacy-preserving automated pipeline, we classify usage patterns within a representative sample of ChatGPT conversations. We find steady growth in work-related messages but even faster growth in non-work-related messages, which have grown from 53% to more than 70% of all usage. Work usage is more common for educated users in highly-paid professional occupations. We classify messages by conversation topic and find that “Practical Guidance,” “Seeking Information,” and “Writing” are the three most common topics and collectively account for nearly 80% of all conversations. Writing dominates work-related tasks, highlighting chatbots’ unique ability to generate digital outputs compared to traditional search engines. Computer programming and self-expression both represent relatively small shares of use. Overall, we find that ChatGPT provides economic value through decision support, which is especially important in knowledge-intensive jobs.

Lines of inquiry this paper opens 24

Research framings built by reading the notes related to this paper — the questions it feeds into.

Does personalization in chatbots strengthen engagement or undermine authentic interaction? What makes decomposed prompts more reliable than monolithic instructions? How does AI exposure shape employment demand and worker earnings? How do AI-generated summaries affect traffic and engagement with original sources? What organizational factors determine whether firms successfully adopt and integrate AI? How should organizations govern autonomy and access for agentic AI systems? How do inference costs, efficiency, and market competition shape AI adoption economics? How should control and autonomy be allocated in human-AI workplace collaboration? Does generative AI shift knowledge work away from collaborative tasks? Why do firms choose automation over worker augmentation when adopting AI?