Guide

How to read AI news without the hype

AI coverage swings between miracle and doom. A few habits help you see what is actually new.

Ask: what exactly was shown?

• A demo is not a product. Demos are chosen to look good; ask whether the public can use it and how often it fails.
• A research paper is a claim awaiting replication. Many results shrink or vanish when others try them.
• A product launch tells you what is available today, usually with limits and pricing.

Read benchmark claims carefully

Benchmarks are standard tests. They are useful but imperfect: models can be tuned for them, test questions can leak into training data, and a high score on a narrow test does not guarantee real-world reliability. Look for results from independent evaluators, and for tests that match the work you care about.

Follow the incentives

Companies announce to attract customers, talent and investment. Founders predicting that their own field will change the world are not neutral sources. The same goes for critics selling fear. Prefer reporting that quotes outside experts and shows evidence.

Watch the language

• "Could", "may" and "up to" are not "will".
• "AI-powered" often means ordinary software with a model added on.
• "Human-level" or "PhD-level" refers to specific tests, not general ability.
• Percentages without a baseline ("50% faster") need a "compared to what?"

Separate the layers

Keep four things apart: research (what is possible), products (what you can use), business (money and competition) and policy (rules and safety). A funding record is business news, not proof that the technology works.

Our approach

AI Pulse links to original publishers and the primary sources (papers, company posts) whenever possible, and shows where each headline comes from, so you can judge for yourself.