Revolutionary. Surpasses humans. Jobs that will vanish. AI coverage runs on unusually large words. Having spent a long time sorting these stories, we are publishing the criteria we actually apply before deciding a claim is worth reporting.
Five checks will filter most of the noise.
Check 1: announced, or shipped?
A large share of AI news is not we built this but we intend to build this. Demo videos, research papers and waitlists all describe things nobody outside the company has used.
Ask first: can anyone use this today? The gap between announcement and general availability is routinely months, and capabilities are frequently scaled back along the way.
Check 2: who produced the number?
Claims like twice the performance or 95 percent accuracy live or die on who measured them.
| Source of the number | Weight | Note |
|---|---|---|
| Vendor self-published | Low | Chosen conditions favor the vendor |
| Independent third party | High | The standard worth waiting for |
| Consistent across benchmarks | High | Not tuned to one specific test |
| Large volume of user reports | Medium to high | Reflects real conditions |
Then ask compared to what. A doubling measured against a two-year-old model is close to meaningless.
Check 3: were the demo conditions disclosed?
The more impressive the demo, the more the conditions matter. Was it a single unedited take, or the best of many attempts? Was a human intervening off-camera? Is it real time or sped up?
A demonstration that does not disclose its conditions should be read as advertising, not evidence.
Check 4: follow the incentive
Ask who benefits from this being believed right now.
- A company announcing results shortly before raising capital
- A counter-announcement timed against a competitor launch
- A market forecast from an analyst whose firm sells related products
- Research funded by a party with a stake in the outcome
None of this makes a claim false. It tells you how much weight to assign before independent confirmation arrives.
Check 5: examine the premise, not the prediction
Forecasts that a category of work disappears within five years attract clicks, but the underlying analysis is usually a projection under specific assumptions.
Read for the premise rather than the conclusion. If the premise fails, so does everything built on it. This applies symmetrically: extreme optimism deserves the same scrutiny as extreme pessimism.
How we apply this
We use these five checks to decide what to cover, and we preserve attribution in the wording — the company said, according to reporting — rather than stating unverified claims as fact. We also separate reporting from analysis.
We still get things wrong. If you find an overstatement or an error, tell us through our contact page. We will correct it and say what changed.
The takeaway
Information advantage in this field comes from filtering well, not from reading more. Shipped or announced, who measured it, what conditions, whose interest, which premise — five questions, applied habitually.
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