Does AI use the same words for competing brands?

The same normalized word or short phrase described more than one ranked brand in 250,754 of 523,049 observed multi-brand AI answers, or 47.9408%. Each eligible answer described at least two consolidated brand families.

BrightEdge has documented that brand recommendations diverge across AI platforms. This study isolates a different layer inside one answer: literal language overlap, not whether the brands are interchangeable. It identifies answers where a buyer may see the same attribute attached to multiple choices.

Excellent was shared across brands in 17,550 answers, followed by best in 10,684 and strong in 8,667. Specialized, robust, comprehensive, and reliable also appeared near the top.

A frequent word can still be accurate, but frequency weakens its value as evidence of a distinct position. The practical question is whether the answer also gives the brand a specific, defensible reason to own that language.

Google AI Mode reused at least one exact description across brands in 138,226 of 274,912 eligible answers, or 50.2801%. ChatGPT Search did so in 112,528 of 248,137, or 45.3491%.

The two engines had different answer and brand mixes, so the 4.9310-point gap is an observed comparison rather than a causal engine ranking. Keep the engine attached to every brand-language audit.

Among answers describing exactly two brand families, 12,501 of 70,405 reused a description, or 17.7558%. The rate rose to 35.9604% with three or four brands, 57.7842% with five to seven, and 81.3483% with eight or more.

This exposure effect is mechanical as well as editorial: more brands and more terms create more chances for a match. Compare answers with a similar number of described brands before treating one category as unusually generic.

At least one adjective described multiple brands in 187,183 of 431,882 answers containing adjectives, or 43.3412%. The corresponding rate was 19.9511% for descriptive phrases and 17.2191% for positioning claims.

Semrush frames AI visibility as a topic-ownership problem. This cut adds a language-ownership boundary: short evaluative labels travel across brands more readily than fuller claims. The type groups overlap because one answer can contain more than one kind of description.

In 118,928 of 442,539 eligible answers with one unambiguous first-place brand, or 26.8740%, at least one description attached to the winner also described another ranked brand.

Controlled experiments have tested whether authority-style marketing language can influence AI recommendations. This observational result makes no such causal claim: it shows that recommendation position and language differentiation are separate signals. Winning the list does not guarantee that the answer gives the winner an exclusive reason to be first.

Among selected industries with at least 1,000 eligible answers, Messaging and Telecommunications recorded description reuse in 631 of 1,149 answers, or 54.9173%. Consumer Electronics recorded 1,460 of 4,472, or 32.6476%.

The mean number of described brands moved with the rate, so this is a prioritization view rather than an industry-quality ranking. Category, prompt, engine, brand, and answer-size mix can all change the observed result.

Prometheus and Grafana shared at least one exact description in 1,168 of 2,140 answers that described both, or 54.5794%. OpenAI and Anthropic recorded 1,129 of 2,649, or 42.6199%; Ahrefs and Semrush recorded 968 of 3,840, or 25.2083%.

These named rows are comparison opportunities, not brand scores. They count co-described answers and do not say whether the shared description was favorable, accurate, important to buyers, or caused by either company's marketing.

The main rule lowercased descriptions, converted punctuation to spaces, collapsed repeated spaces, and required an exact term match. It returned 47.9408%. Preserving punctuation returned 47.7628%, requiring confidence of at least 0.8 returned 47.5781%, and starting June 1 returned 47.8453%.

Hua and colleagues show why rigid matching can miss synonyms and paraphrases. The result is therefore lexical, not semantic. The final denominator had zero duplicate answer rows and zero answers that failed the two-described-brand rule. The study consolidates brand families, excludes descriptions below 0.7 confidence, and makes no claim about accuracy, causation, buyer preference, or movement over time.

Audit the full competitive answer, not only your own mention. Record the exact terms assigned to your brand and the alternatives, keep engine and recommendation rank beside them, and normalize for the number of brands the answer describes.

Separate generic category-entry words from claims your brand can substantiate and own. Then reinforce the distinctive claim across authoritative first-party pages and credible external evidence, rerun the same prompt set next quarter, and report movement only against that fixed baseline.