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.
Topic
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.
Navigating the hype while meeting the future head on.
A military leadership experiment offers a way to think about uncertainty, information provenance, and learning to lead systems that contain AI.
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.
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.
Distinguish AI traffic-planning support from chatbot control, and ask how aviation recommendations are checked and which decisions stay human.
Understand how flat skill names create ambiguity, and reduce friction and risk through source tracking, reviewed revisions, and deliberate updates.
Identify the last responsible gate where an unverified AI claim must be checked before action becomes hard to reverse.
How to trust AI-assisted business analytics by defining the metric, inspecting the calculation, and following the number back to its source.
Human-shaped model behavior raises choices about usefulness, safety, and the friction people face when those patterns are suppressed.
How independent recovery paths, retention, and tested restores help a business recover from mistakes, outages, and data loss.
Faster implementation changes when we can validate a system against the business it serves, and how we should plan to stay involved.
What weaker monitoring of visible reasoning means for AI oversight, trust, and the checks people choose.
When AI systems behave in unexpected ways, senior leaders need to examine the objectives, tools, permissions, monitoring, and organizational capacity surrounding the model.
A systems-thinking article explaining why AI adoption feels chaotic when capability, understanding, governance, resilience, human adaptation, and scale all move at different speeds.
A plain-language explanation of why AI-powered second brains depend on maps, indexes, and human validation to find the right information.
A reflection on how convenience, efficiency, and automation can quietly remove the alternate paths an organization needs during disruption.
A practical article on stress-testing AI-powered second brains for retrieval, source authority, calibration, and safe operating boundaries.
A practical how-to article on using transcript-based collaboration friction as one measurement tool for improving agentic work environments.
A field note on using shared operating principles to make human-AI collaboration more trustworthy.
A practical reflection on why every GenAI user should know how to stop an agent before handing it work.
AI-accelerated delivery needs a broader definition of coverage before teams can release with confidence.
How Semantic Debt Compounds at Machine Speed
A lesson from an AI agent that did not actually turn off.
Like many technologies before it, AI is spreading into more and more of the systems that shape modern life. History reminds us that technologies often become deeply embedded in society long before we fully understand their long-term consequences.
A convincing response can still be a useful object of investigation.