#seo

Ahrefs analyzed 75,000 brands and found that mentions, not backlinks, correlate most strongly with showing up in ChatGPT, AI Mode, and AI Overviews. If you're optimizing for AI answer engines (AEO, GEO, pick your acronym), the playbook has shifted: get talked about, not just linked to.

The study used Spearman correlation across millions of AI responses. Branded web mentions land between 0.66 and 0.71. Raw backlink counts and URL rating barely move the needle. Number of site pages is essentially noise at ~0.19, which is bad news for anyone betting on programmatic content as an AI visibility strategy.

YouTube punches above its weight

The surprise: YouTube mentions correlate at ~0.737, beating every other factor across all three AI surfaces. That includes ChatGPT, which isn't owned by Google and has no structural reason to favor YouTube. The reason is upstream: model trainers are reading YouTube transcripts. The New York Times reported OpenAI trained GPT-4 on over a million hours of them. Google has done the same for its own models.

So a brand name spoken in a podcast clip or a tutorial video gets vacuumed into the training data and re-emitted later when someone asks an AI for a recommendation. Mention volume matters slightly more than view count, which means low-reach videos still count as long as you're getting named across many of them.

What this changes

Backlinks aren't worthless. They still matter for classic search. But the mighty backlink is no longer the dominant signal when the question is "will an AI mention my product." Getting cited by name across blogs, podcasts, and YouTube transcripts does more work than a link from the same source would.

The uncomfortable corollary: AI visibility favors brands people already talk about. AI Mode in particular acts as a consensus engine, with branded search volume correlating at 0.466. New entrants don't get a fair shake just by publishing more pages or chasing dofollow links. They get a shake by being mentioned, in plain text, in places models read.

A friend pointed me at a14y.dev, which scans your site for "agent readability" and hands back a scored fix-list. It's the obvious next thing after llms.txt, and the suggestions are sharper than I expected.

The scorecard is 38 checks pinned at v0.2.0, split across discoverability, parsing, and comprehension. Some are the ones you'd guess: llms.txt exists, robots allows AI bots, canonical links, lang attributes, JSON-LD breadcrumbs. The interesting ones are the suggestions I hadn't seen pushed as a standard yet.

The less obvious suggestions

A Markdown mirror of every page, served at the same URL with a .md suffix, plus a <link rel="alternate" type="text/markdown"> in the HTML head so agents can find it without guessing. Content negotiation on the canonical URL so a request with Accept: text/markdown gets the Markdown directly. A glossary page, because agents resolving acronyms and project-specific terms benefit from one canonical place to look. Language tags on every code block. A /sitemap.md alongside the XML one.

None of these are exotic. They're the kind of thing you'd do for a thoughtful human reader, just written down as pass/fail checks.

The loop they're pushing

The CLI ships with an --output agent-prompt mode that writes a Markdown brief aimed at a coding agent: every failure, its detection rule, the fix, and a link back to the scorecard page. The intended workflow is to pipe that into Claude Code or Codex, let it patch, then re-run with --fail-under 80 in CI. There's also a skills add package for agents that speak the open skills format.