The pieces in this series draw from my ethnographic research on how AI systems are being taken up in scientific practice. My aim is more descriptive than evaluative: to document how scientists decide what to trust and what to consider “good enough” as new tools become part of the routine of doing science. These posts stay close to the scenes where such judgments are made: lab meetings and draft manuscripts, tool demos and troubleshooting sessions, arguments over validation, and small acts of verification that rarely make it into published accounts. If you come to this series with suspicion about the usefulness of AI, you are not alone. If you come with enthusiasm that these tools will inevitably transform science for the better, you are not alone either. I’m not asking you to suspend your critical faculties. I’m asking you to treat this series as an experiment in attention to practice: to watch how AI becomes ordinary in research, how standards of evidence and “good enough” judgment get renegotiated in the process, and what kinds of authority, responsibility, and constraint travel with these systems as they move from novelty to infrastructure.