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
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Evergreen writing meant to stay useful long after this week's model launch is forgotten.
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.
Benchmarks
Before AI takes a task, test the whole chain
A model can answer questions well and still fail at real work. Here is a practical framework for evaluating AI systems across evidence, tools, state, timing, and failure recovery.
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.
Tool StrategyHow to Evaluate an AI Tool Before You Pay
Demos are built to impress, not to inform. A practical, vendor-neutral process for testing an AI tool against your real work before a subscription starts.
PromptingPrompting Is Overrated — Context Is What Matters
Magic phrases and secret prompt formulas miss the point. The quality of an AI answer is mostly decided by the information you give it, not the incantation you wrap it in.
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.
AI EconomicsThe Real Cost of "Free" AI Products
Free AI tools are genuinely useful — and genuinely not free. What you actually pay in data, dependence, and switching costs, and how to use free tiers wisely.
BenchmarksHow to Read an AI Benchmark Without Being Fooled
Every model launch comes with a chart where the new model wins. What benchmark scores actually measure, the classic tricks to watch for, and the only benchmark that matters.
New essays are published regularly — check back soon, or start with the guides below.
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