What Kind of Fires Does AI Start?
Alex Cannella applies familiar problem-solving methods to active AI incidents, smoldering problems, and conditions that could fuel future failures.
Purpose
Alex Cannella applies familiar problem-solving methods to active AI incidents, smoldering problems, and conditions that could fuel future failures.
A military leadership experiment offers a way to think about uncertainty, information provenance, and learning to lead systems that contain AI.
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.
Repeatable habits keep human judgment involved when working with GenAI: develop the substance, pause to review, then give it shape.
Distinguish AI traffic-planning support from chatbot control, and ask how aviation recommendations are checked and which decisions stay human.
Visible behavior can carry different cultural meanings. What goes unrecorded creates blind spots for language models and for us.
How to trust AI-assisted business analytics by defining the metric, inspecting the calculation, and following the number back to its source.
Shared Conceptual Vocabulary for Working with AI
An invitation to revisit an old curiosity and see where a conversation with generative AI might lead.
Generative AI can produce fluent language before purpose, audience, and evidence are fully formed. That changes the thinking loops we need.
Relevant context helps define an AI task through useful constraints, which makes a fitting answer more likely.
A grounded reflection on brainstorming with AI, chasing creative rabbit holes, and keeping human judgment in charge.
A short PSA about choosing the right tool for the job, and teaching Generative AI through guided, low-stakes practice.
A reflection on generative AI as an invitation for broader participation, patient teaching, and community learning.
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 reflection on why every GenAI user should know how to stop an agent before handing it work.
Before asking for the implementation, collaborate on the design.
What it took to turn scattered notes, an AI agent, and a shifting workspace into a collaboration that actually got better over time.
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 useful answer is not proof that a chatbot knows, remembers, or has checked the answer.
A convincing response can still be a useful object of investigation.
A practical guide to clearer AI prompts through specific instructions, useful structure, roles, and iterative refinement.
You can catch contradictions without knowing the whole story.
How Society Might Emerge from Noise
The Dirty Secret of Free Text Overloading
A practical guide to choosing an AI assistant for the work, comparing model strengths, integrations, and the cost of switching mid-project.
Estimate the speed of light with a microwave, a ruler, and a chocolate bar.