Open your analytics and there it is: Referral: chatgpt.com — or Perplexity, or Copilot. A traffic source that didn't exist in your planning docs is now a permanent line in your report, and it's growing quarter over quarter.
What this traffic actually is
It arrives in two forms. The first is a human who asked an AI — the assistant recommended you, the person clicked through. High intent, but they landed wherever the model linked them, which is often not the page you'd have chosen. The second is an agent acting for a human — comparing your product, checking your price, sometimes attempting the task itself. That second kind frequently never registers as a session at all, or shows up as a bounce with zero events.
Why your funnel numbers look weird for this segment
Standard analytics was built to observe humans: pageviews, scroll depth, click events. An agent reads your DOM and either finds what it needs or doesn't — no scrolling, no hesitation, no rage-clicks to alert you. When it fails on an unlabeled form or a price that only exists as pixels, the failure is silent. The referral line shows the arrival; nothing shows the abandonment or the reason.
The most expensive failures in your AI-referral segment are the ones your analytics is structurally unable to record.
What to do this quarter
First, segment it: isolate AI-assistant referrers and compare their conversion against organic. A large gap is your leak estimate. Second, test the actual mechanics: run an agent through your top flows and see where it stalls — that's measurable today, deterministically, without waiting for better analytics. Third, fix what the test finds; the failure classes are usually markup-level and cheap relative to what the traffic is worth.
The teams treating this referrer line as a curiosity are the same teams that treated mobile traffic as a curiosity in 2010. The line only goes one direction from here.