EDITORIAL STANDARDS

How we decide what is real.

AI changes fast. Our method is designed to keep benchmark results, real-world evidence, interpretation and future scenarios from quietly blending together.

1. Start with a narrow question

Each research brief should investigate one question that can be answered with evidence available today. We prefer a small, useful question over a broad article that says little.

2. Prefer primary sources

We prioritize research papers, official datasets, benchmark creators, government or intergovernmental reports, and direct company documentation. Secondary reporting can add context but should not replace the strongest available source.

3. Label the evidence

4. Do not turn benchmarks into jobs

A benchmark score is evidence about performance in that benchmark. It is not automatically evidence about employment, economic impact, general intelligence or real-world autonomy. We explicitly state important scope limits.

5. Treat predictions as conditional

When we discuss a future scenario, we try to state what evidence would make that scenario more or less plausible. We avoid invented probability percentages when the evidence cannot support them.

6. End with preparation

We prefer actions that remain useful across multiple futures: deeper domain knowledge, AI literacy, verification skill, financial flexibility, human relationships and ownership of useful work.

7. Correct the record

If a source is corrected, a statistic is superseded or our wording overstates the evidence, we should revise the page. Material changes should update the modification date where practical.