AI Agents Transform Research Production, Raising Questions on Verification and Understanding
Artificial intelligence agents are increasingly capable of autonomously producing empirical research manuscripts, from formulating research questions and identifying data to writing full papers with code and exhibits. This capability, which can generate a 'submissible manuscript' in as little as 30 minutes, marks a significant advancement in research automation. However, experts are emphasizing that while AI excels at 'production,' human involvement remains critical for 'verification' and 'understanding.' A proposed framework suggests that research, especially in causal inference, must pass through these three distinct stages. While AI can automate tasks like generating maps, writing code, and organizing datasets, the 'bite stage'—a deep dive into understanding the initial impact of an intervention—is deemed least suitable for full automation, as it requires human insight to conceive the project and interpret the treatment assignment mechanism. 
