AI Search vs Google Search: What Actually Changed

Google gives you a list of links. ChatGPT gives you a name. That is the simplest way to describe the shift.

We built an open-source platform called canonry, the agent-first operating system for AEO. It monitors both sides of this: agents track indexing via Google Search Console and Bing Webmaster Tools, and separately run queries against ChatGPT, Gemini, Claude, and Perplexity to record which businesses get cited. The two systems overlap in interesting ways, but they are not the same. Here is what the data shows.

Why AI search returns names instead of a list of links

Google returns a ranked list of 10 pages and the user clicks one. AI search returns a direct answer with 3 to 5 names and reasons. There is no page two. There is no position 7 that still gets some traffic.

In one canonry dataset (11 keywords, 66 runs for a local service business), the split is binary. For branded + location queries where the site is well-positioned, it gets cited 82-90% of the time. For informational queries where the site has no content, citation rate is 0%. There is no "almost cited" or "showing up on the second page of AI results." You are in the answer or you are not.

Where the data comes from

Each AI system has its own retrieval pipeline. The exact details are not fully public, and they change frequently. Here is what we know and what we have observed:

ChatGPT (OpenAI)

• Publishes crawler and publisher guidance, including OAI-SearchBot
• Can use web information in supported experiences, but the full retrieval mix is not public
• Also relies on model knowledge that changes by release
• What this means: Keep pages accessible and indexed in Google and Bing. Treat any claimed retrieval mix as provisional.

Gemini (Google)

• Google documents grounding with Google Search for some products and contexts
• The full retrieval path for every consumer answer is not public
• What this means: Google indexing is an important foundation, not a guarantee of inclusion in a Gemini answer.

Perplexity

• Publishes crawler guidance, including PerplexityBot
• Shows source citations in many answer experiences
• What this means: Keep pages accessible, current, and easy to verify. Do not infer a fixed crawl cadence from individual answers.

Claude (Anthropic)

• Publishes documentation for web search and its crawlers
• Also relies on model knowledge
• What this means: The retrieval path can vary by product and setting, so test the queries that matter to your business rather than assuming one universal behavior.

Important caveat: These retrieval systems are opaque. We are making educated guesses based on announced features, observed behavior in canonry monitoring, and published documentation. Any of this could change tomorrow. The practical takeaway is: do not bet on understanding one provider's pipeline. Be indexed and structured well enough that any retrieval system can find and parse your content.

The key insight: these are independent systems. Canonry data shows queries where a site gets cited by one provider but not another. Optimizing only for Google and assuming AI will follow is a mistake.

Signals that overlap vs signals that diverge

Both Google and AI care about:

• Content quality. Thin content fails in both systems. Google's helpful content guidelines are a reasonable baseline.
• Authority. Strong backlinks and external mentions help in both, though the mechanisms differ.
• Technical health. Clean HTML, HTTPS, fast load times. Table stakes.

Signals that can make a page easier for AI systems to interpret:

• Structured data. In Canonry's published scoring model, Structured Data has the highest base weight (12 before normalization). In this dataset, the cited site scored 100 and the uncited site scored 42. JSON-LD gives parsers a clear representation of core facts, but it does not guarantee citation.
• Content extractability. This is the gap most SEO-optimized sites miss. Our cited site scores only 65/100 on extractability despite strong content depth (87/100). The content exists but the markup makes it harder to parse. Sites built with heavy page builders score worse here.
• Entity consistency. AI models cross-reference your business across the web. NAP consistency matters for AI in a way it has not mattered for Google ranking in years. BrightLocal's citation research covers the fundamentals.
• Definition blocks. "X is Y" opening statements. Google does not care whether your page starts with a definition. AI models do, because they need something to extract as an answer. The uncited site in the dataset scores 0/100 on this factor.
• llms.txt. An emerging machine-readable convention. Provider support varies, so use it as a supplement to crawlable HTML, sitemaps, and clear internal navigation.

AI models care less about:

• Keyword density. AI understands semantics. Keyword stuffing does not help.
• Internal linking structure. Clear internal links help people and crawlers discover and contextualize important pages. Their effect on any specific AI answer is not public.
• Meta descriptions for ranking. AI models extract from page content, not meta tags.

The indexing disconnect

Here is something that shows up regularly in canonry data: a site is fully indexed by Google (Search Console shows all pages crawled and indexed) but gets zero citations from Gemini.

Google knowing your page exists does not mean an AI system will use it in an answer. Inclusion can vary with the query, available sources, product settings, and the system's changing retrieval behavior.

The reverse also happens. A site can be poorly indexed by Google but picked up by Perplexity's real-time search because Perplexity crawls independently.

This is why monitoring across providers matters. Canonry runs the same queries against multiple AI systems and tracks citation state independently. Without that, you are guessing about which providers see you and which do not.

Google is also becoming an answer engine

Google's AI Overviews are blurring the line. They make clear, verifiable source material useful across more search experiences, even though each product has its own selection systems.

The same structured data and extractable content that helps you get cited by ChatGPT also helps you appear in Google's AI Overviews. The investment is the same. The surface area is expanding.

How to optimize for AI search and Google in 5 steps

Based on the citation monitoring and audit scores described above, this is the order that has moved the needle for sites we track.

1. Step 1: Submit your sitemap to both Google and Bing. This improves discovery in the two major search ecosystems. It is a foundation, not proof that a provider will cite the page.
2. Step 2: Add structured data. The biggest gap between cited and uncited sites in the dataset is schema quality (100 vs 42). Start with LocalBusiness and Service. The schema guide has copy-pasteable JSON-LD.
3. Step 3: Fix your opening paragraphs. Add definition blocks to your key pages. The uncited site scores 0 here. Clear opening facts make the page easier to interpret for people and machines.
4. Step 4: Publish llms.txt. Use it as a supplementary access file. Its support differs by provider, so do not use it in place of crawlable HTML, a sitemap, or clear page structure.
5. Step 5: Monitor across providers. Do not check one AI system and assume the others agree. Run a free audit for your baseline, then set up monitoring through canonry.

The free onsite technical audit provides a point-in-time assessment across 16 factors. Canonry handles the ongoing monitoring.