How AI assistants perceive Linear
What ChatGPT, Claude, and Gemini actually say when a buyer asks about Linear, across 120 recommendation prompts, 420 head-to-heads, and 56 web-grounded searches.
Ask ChatGPT, Claude, and Gemini about Linear and you get a recommendation. Ask what Linear is, and the picture gets more specific. Across 120 recommendation prompts and 420 forced head-to-heads, here is what the models actually say.
How the models label Linear
Every time a model weighed in, we captured how it described Linear and sorted the descriptions into buckets.
The label the models use most often is sleek issue tracker for startup engineering teams, in 98% of answers. The label a model reaches for quietly narrows the set of buyers it recommends the product to.
Head to head
Forced to choose between Linear and a named rival, the models pick Linear 64% of the time across 10 competitors.
The toughest matchup is Jira, which holds Linear to 48%. Its weakest attribute is company-wide work, at 10%, well off its 97% peak on issues and sprints.
Who authors the record
Everything above is what the models remember from training. With web search on, Linear appears in 77% of grounded answers. The more revealing question is who writes the pages behind those answers.
The most-cited domain is siit.io, a competing vendor. When a rival writes the pages that ground the answers, every comparison starts on terms a rival chose.
What buyers actually search for
Buyer demand still concentrates in the classic-PM terms, which draw roughly 47x the monthly volume of the newer AI-and-alternatives terms.
And Linear's own homepage positioning has moved over time:
Methodology. 120 aided recommendation prompts and 420 forced head-to-heads across ChatGPT, Claude, and Gemini; a web-grounded battery recording which pages the models cite; and a crawl of Linear's own pages plus the third-party pages the models cite. Part of the AI Perception Index. See the full interactive lab →