Artificial Intelligence / Explainer
Why a fluent AI answer can still be wrong
The most persuasive error is the one that sounds ordinary. Reliable AI use starts with a verification process matched to the consequence.
THE TOPIC FILE
AI evaluation
A capable demonstration is a starting point. Explore benchmarks, realistic testing, missing-answer behavior and the operational measures that help distinguish useful AI from convincing mistakes.
Stories in this collection
AI Security / Field guide
An AI red team should test the system you will deploy
A clever jailbreak is only one signal. Useful adversarial testing follows the data, permissions and consequences of the full application.
Companies / Field guide
How to compare OpenAI and Anthropic for enterprise use
A durable comparison begins with workload evidence, data flows, integration boundaries, and operating requirements. A model leaderboard cannot settle those questions.
Artificial Intelligence / Explainer
A bigger context window is not a better memory
Long-context models can accept more information. Whether they use the right information remains a separate question.
Startups / Analysis
An AI pilot needs a stop rule
A trial becomes an investment decision only when the team agrees what would justify expansion, further testing, a narrower use, or an orderly stop.
Artificial Intelligence / Explainer
Multimodal AI needs more than eyes
Reading a picture, a chart and a document requires different checks. A model that accepts all three has not necessarily understood them.
Artificial Intelligence / Analysis
What an AI benchmark leaves out
A leaderboard measures a test under particular conditions. It does not settle which system will work best inside your organization.
Startups / Field guide
How to read an AI startup’s claims before you buy
Turn a persuasive demonstration into a testable purchasing decision by specifying the job, the evidence, the data path, and the work that remains with your team.