AI Visibility Forecasting
A signal is not an event
Most coverage in this field reports events. A forecast is a claim about what an event implies, and that is the harder half that usually gets skipped.
The distinction
An event is something that happened: a model release, a feature launch, a policy change, an announcement. Verifiable, dated, and requiring no interpretation.
A signal is an observation that conditions are moving: citation behaviour shifting, a class of source gaining weight, volatility rising in a category.
An event may or may not produce a signal. Most do not, and the ones that do frequently produce it months later.
Why coverage stops at events
Events are easy to report, impossible to be wrong about, and arrive with a press release. Implications are hard, take time to observe, and can be wrong in public. So the field is saturated with accurate reporting of things that happened and thin on what any of it meant.
The two errors this produces
Treating an announcement as a change. A feature announced is not a feature adopted, and adoption is what moves conditions. The gap is routinely months and sometimes never.
Missing changes with no event. The most consequential shifts frequently have no announcement at all: gradual reweighting, quiet expansion of a source set, slow changes in how answers are assembled. Nobody publishes them, and they matter more than most launches.
How AI Visibility Forecasting separates them
Every reading states the observation and the inference as two different things. What was measured or seen, then what we conclude from it, with the confidence attached to the second rather than to the pair.
This makes it possible to be right about an observation and wrong about what it meant, which is the most common way a forecast fails and the one that is hardest to detect when the two are blended into a single sentence.
What to do with an event
Note it and wait. The useful question is not what was announced but whether anything measurable changed afterwards, and answering that requires a baseline taken before it. A practice that only starts measuring after an event has nothing to compare against.