The AI Could Keep Fixing Things. We Had to Decide When to Stop.
A PDF investigation shows how sensible suggestions from an AI agent can add up to unplanned work—and why deciding what is enough takes human attention.
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66 articles
A PDF investigation shows how sensible suggestions from an AI agent can add up to unplanned work—and why deciding what is enough takes human attention.
AI agents use interfaces designed for people who bring context and learn through experience. What happens when they arrive without either?
Chicago's Deep Tunnel offers lessons for investing in AI defense, finding bottlenecks, and adjusting when future demands exceed our plans.
Alex Cannella applies familiar problem-solving methods to active AI incidents, smoldering problems, and conditions that could fuel future failures.
A retrospective lab notebook tracing experiments, measurement mistakes, and imperfect results when an AI must pause an unsupported action while continuing useful research.
Use disagreement with a coding agent to uncover missing context, preserve decisions, and improve how you work together.
Separate feelings, evidence, constraints, and assumptions when working with AI, while keeping research findings and practical hypotheses distinct.
How unspoken questions and missing dissent can prevent ideas from meeting—and what organizations can do to protect discovery.
How unchecked assumptions, unspoken thoughts, and gaps in shared context can steer an AI conversation—and how to make room for correction.
A newsletter access-control test prompted questions about login authority, lasting changes, and what an approval warning does not explain.
A reasonable question from an AI agent can arrive before we know enough to answer it. Decide when to commit, when a provisional choice is enough, and when to keep learning.
Why the next big win is not just faster workflow, but better work selection
Navigating the hype while meeting the future head on.
How rapid replacement separates useful life from market value, and what that means for buying, building, and switching technology.
A military leadership experiment offers a way to think about uncertainty, information provenance, and learning to lead systems that contain AI.
After roughly a thousand issues, tickets appear to help humans and AI coding agents preserve constraints, failed approaches, and prior decisions. Shared memory also needs human care to stay useful.
Kirk, raccoons, and AI agents offer a humorous look at how persistent learners discover gaps between intended rules and the possibilities a system actually permits.
A review of duplicated deployment work cut our deployment time by about a third. Periodic technical-debt reviews can reveal costs that have become routine, with humans deciding what to change.
AI can move the work, but people and organizations remain accountable for the choices, safeguards, and outcomes of its use.
Responsible AI use is a learning loop: anticipate what could go wrong, observe what happens, and use what you learn to improve the next decision.
Backup copies are a starting point. Recovering AI work also means restoring the conversations, instructions, connections, and context that make a project usable.
Recommendations need a receiving owner, a reasoned decision, and a way for learning to shape what comes next—even when the answer is no.
Repeatable habits keep human judgment involved when working with GenAI: develop the substance, pause to review, then give it shape.
Shared false memories and real differences between versions can both shape disagreements. Ask whether people were shown the same thing.