We examine the uptake of GPT-assisted writing in economics working paper abstracts. Using data from the IZA DP series, we detect a clear stylistic shift after the release of ChatGPT-3.5 in March 2023. This shift is evident in core textual metrics—mean word length, type-token ratio, and readability—and reflects growing convergence with machine-generated writing. While the ChatGPT launch was an exogenous shock, adoption is endogenous: authors choose whether to use AI. To capture this behavioral response, we combine stylometric analysis, machine learning classification, and prompt-based similarity testing. Event-study regressions with fixed effects and placebo checks confirm that the change is abrupt, persistent, and not explained by pre-existing trends. A similarity experiment using OpenAI’s API shows that post-ChatGPT abstracts resemble their GPT-optimized versions more closely than pre-ChatGPT resemble theirs. A classifier, trained on these variants, flags a growing share of post-March 2023 texts as GPT-like. Rather than suggesting full automation, our findings indicate selective human–AI augmentation. Our framework generalizes to other contexts such as e.g. resumes, job ads, legal briefs, research proposals, or programming code.