A Phenomenon That Puzzles Many People
If you have ever asked ChatGPT the same question multiple times, you have certainly noticed the answers vary—sometimes mentioning brand A, sometimes brand B, sometimes no brand at all and just describing category characteristics. This randomness is not accidental; it is a fundamental property of AI language models.
A 2026 paper, Quantifying Uncertainty in AI Visibility (Sielinski, arXiv:2603.08924), systematically quantified this uncertainty for the first time: AI visibility measurement exhibits significant temporal and cross-sample variation, and single observations are insufficient as a reliable basis for decisions.
Why AI Answers Fluctuate
AI model answer randomness operates at multiple levels: temperature parameter—most language models use a temperature parameter during text generation, introducing randomness at each word prediction so that even identical inputs produce different outputs. Retrieval index changes—for AI systems with web search enabled, retrieval results change over time to reflect the latest web content, affecting which sources enter the final answer. Model version updates—AI platforms periodically update underlying models, potentially changing answer style and content selection for the same questions. Region and language variables—the same question in different languages or accessed from different regions triggers different retrieval and answer logic.
How Large Is the Variation: Core Research Findings
The core finding of this research is striking: for the same brand-related queries, brand visibility can vary very significantly across repeated cross-day sampling. This means: if you run a GEO analysis today and find brand appears in 60% of relevant question answers, retesting tomorrow might yield 40% or 80%. That gap does not necessarily reflect any change in your content—it is measurement noise.
Conclusions like rankings improved by X% based on a single measurement may be statistically meaningless.
Sample Size Determines Conclusion Reliability
The key practical implication of this research: GEO analysis reliability depends heavily on sample size—how many repeated tests were run for the same prompt.
A single test only tells you in this one instance AI answered like this. Ten repetitions begin to reveal the approximate distribution. Fifty or more repeated samples provide statistically meaningful confidence intervals.
For most practical applications, key prompts should have at least 10 to 30 repeated samples, with testing time windows noted to distinguish short-term random fluctuation from genuine visibility changes.
Confidence Intervals Rather Than Single Numbers
This research drives the evolution of GEO analysis tools from giving a score toward giving a confidence interval. The difference: a single number—your GEO visibility score is 65—may be statistically meaningless if the 95% confidence interval spans 40 to 90. A confidence interval—based on 30 samples, brand mention rate is 60% with a 95% confidence interval of 45% to 75%—is information that can actually support decisions.
This is why Broccoli AI GEO consistently shows sample size, sampling timestamp, and confidence notes in every report: transparent uncertainty expression is more valuable than a seemingly precise but misleading score.
How to Handle Uncertainty in Practice
Understanding AI visibility uncertainty makes the following practical approaches more helpful: do not make major decisions based on a single test—use multiple tests to establish a baseline, then retest multiple times after optimization, comparing distributions rather than points to judge change. Distinguish short-term fluctuation from trend changes—only investigate root causes if brand visibility shows systematic decline across multiple consecutive tests, since occasional low values may be random noise. Focus on consistently missing patterns—if across multiple tests a certain question type never includes your brand, that is more significant than occasional absences. Track relative changes versus competitors—even when absolute numbers are noisy, your gap versus key competitors is typically more stable and provides a more reliable signal.