The AI Understanding Blog
Clear thinking about AI.
Original essays on using AI well — how to judge tools, read benchmarks, give models better context, and build real literacy. Plain English, no hype, no jargon.
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Essays
More in AI Literacy
Evergreen writing meant to stay useful long after this week's model launch is forgotten.
AI Literacy
The end of the 'Allow' button: Navigating the shift to autonomous execution
As AI systems move from passive chat to autonomous action, the 'human-in-the-loop' is being replaced by internal AI supervisors. Here is how to evaluate the risks of this transition and maintain control over your digital environment.
AI Literacy
The accountability gap: Why autonomous agents require new oversight
As AI shifts from passive chat to autonomous action, our legal and security frameworks are struggling to keep pace. Here is how to evaluate the risks of agentic systems and build meaningful safeguards.
AI Literacy
The Governance Gap: Why Your AI Policies Are Failing
Recent security leaks prove that written AI policies are no match for autonomous agents. Here is how to move from passive guidelines to active, identity-bound governance.
AI Literacy
The new security perimeter: Protecting your system from autonomous agents
As AI moves from passive chat to active task execution, your computer's security model is changing. Here is how to evaluate the risks of agentic tools and protect your local environment.
AI Literacy
The shift to agentic AI: Moving from chat to action
As AI systems transition from passive chatbots to autonomous agents capable of executing real-world tasks, the risks shift from misinformation to loss of control. Here is how to evaluate the reliability and safety of these new systems.
AI Literacy
The shift from static models to autonomous agents: A framework for control
As AI moves from answering questions to executing tasks, the risks shift from misinformation to loss of control. Here is how to evaluate the safety and reliability of agentic systems.
AI Literacy
Beyond the Doomsday Narrative: A Practical Framework for AI Governance
Public discourse is currently dominated by existential risk narratives and corporate posturing. Here is how to look past the headlines to evaluate the actual, mundane, and immediate risks of AI systems in your organization.
AI Literacy
How to tell whether an AI safety promise is real
AI companies increasingly promise monitoring, safeguards, and responsible deployment. Here is a practical framework for judging whether those promises create evidence, accountability, and meaningful limits.
AI Literacy
When AI becomes public infrastructure, what should we ask?
AI is moving from chat windows into highways, government offices, military records, and workplaces. A practical framework for judging these systems by their evidence, limits, permissions, and effects on people.
AI Literacy
What should schools teach when AI access rules keep changing?
School bans can limit immediate risks, but they cannot replace judgment. A practical framework for teaching students when to use AI, when to question it, and when to leave it out.
AI Literacy
When should an AI system stop, ask, or hand off?
Reliable AI is not just about producing good answers. It is about recognizing uncertainty, checking evidence, and knowing when a response should not become an action.
AI Literacy
Does local AI make your data safer?
Running an AI model on your own device can reduce exposure to outside services, but it does not automatically make the system private, reliable, or safe. Here is a practical framework for judging the tradeoffs.
AI Literacy
When should you trust an AI agent with real work?
An AI agent is not dependable just because it succeeds once. Here is a practical framework for judging its reliability, permissions, security, and readiness for real-world tasks.
AI LiteracyWhat AI Literacy Means in 2026 (and a 30-Day Plan to Get There)
AI literacy is not coding and it is not prompt tricks — it is judgment. A four-part definition and a realistic 30-day plan built on free resources.
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