Has content chunking AI SEO advice got you rewriting pages into short, standalone pieces so AI tools will quote them? Google’s answer is simple: don’t. On its Search Off the Record podcast, released on 8 January 2026, Google’s Danny Sullivan said Google does not want publishers chopping content into bite-sized chunks for large language models (LLMs, the AI models behind chatbots). That doesn’t make structure pointless. The useful part is clear structure written for readers. The harmful part is breaking pages apart for machines.
In this guide I cover what Google actually said, where the line sits between good structure and over-chunking, and how I write pages that work for people, classic search results and AI answers at the same time.
What Google Said About Bite-Sized Content
The comments came in a Search Off the Record episode called “SEO, AIO, GEO, your site, & third-party support to optimize for LLMs”. It was hosted by John Mueller and Danny Sullivan of Google. It was the second part of a talk that began in an episode released on 17 December 2025, “Thoughts on SEO & SEO for AI, part 1”.
About 18 minutes into the January episode, Sullivan spoke about the trend of breaking content into small pieces so LLMs can pick it up more easily. His position was direct: “We don’t want you to do that.”
He added: “We don’t want people to have to be crafting anything for Search specifically.” He also said publishers should not feel they need to make two versions of their content, one for LLMs and one for everyone else.
“But It Works for Me”
Sullivan expected the obvious pushback. Some site owners believe chunked content is getting them cited in AI answers right now. His reply was that even if a tactic gives an edge today, “tomorrow the systems may change.”
That is the heart of the argument. Effort spent shaping content around how one system reads text today is fragile. Systems are tuned over time to reward content made for people. So the shortcut tends to stop working, and you are left with pages that were never great for readers. In my experience, this is how most SEO shortcuts end.
Optimizing for AI Search Is Still SEO
The chunking advice fits a wider message Google keeps repeating. In the December part-one episode, Sullivan and Mueller treated SEO for AI as basically the same work as SEO for normal search. Sullivan described labels like GEO and AIO as part of SEO, not a separate field. The format of the results changes, but the basics don’t.
Google’s own documentation says the same. Its page on AI features and your website says SEO best practices still apply to AI Overviews and AI Mode. It says there are no extra requirements or special optimizations needed to appear in them. It also says you don’t need new machine-readable files, AI text files or special markup.
So if someone sells you an “AI-ready” rewrite of your whole site, ask what it does for human readers. If the honest answer is “nothing”, Google has told you it isn’t needed. I’d save that budget. I go through the rest of Google’s guidance in my piece on Google’s advice on succeeding in AI search.
What Is Content Chunking in AI SEO?
“Chunking” comes from AI engineering, not SEO. Systems that find information for an AI answer usually split documents into smaller passages. They turn those passages into numbers, and pull back the ones that best match a question. Those passages are the “chunks”.
The SEO version of the idea went one step further. The thinking was: if AI systems pull passages, write pages as a stack of standalone passages so each one is easy to lift out. In practice, that has led to a few patterns you may recognise:
- Breaking one article into many very short sections, each with its own question-style heading.
- Writing every paragraph so it repeats the topic name and makes sense with no context at all.
- Splitting one solid guide into several thin pages, each answering one narrow question.
- Cutting out explanation, examples and nuance because they “dilute” the answer.
- Creating separate “AI versions” of pages, or summary blocks written for bots, not people.
The key point is that you don’t control how any AI system splits your page. Different systems use different methods, and they change them. Writing to match one imagined chunk size is guessing.
Google has also been able to understand single sections of long pages for years. When it announced passage ranking in 2020, it said it could find a relevant passage deep inside a long page. It could then use that to help the page rank for a specific search. Your content does not need to be pre-cut for Google to find the right part of it.
Clear Structure Versus Fragmenting Content for Machines
This is where many people get confused. Google is not telling you to write walls of text. Good headings, short paragraphs, lists and direct answers are still good writing. They help readers skim. They help any system understand what each part of a page is about.
The difference is who the structure serves. Clear structure organises a complete piece of content for a reader. Fragmenting breaks a piece apart because you think a machine wants smaller pieces.
| Clear structure (good) | Fragmenting for machines (bad) |
|---|---|
| Headings describe what each section actually covers | Every heading is a question, whether or not readers ask it |
| Sections are as long as the topic needs | Sections are cut to a fixed length regardless of the topic |
| Paragraphs build on each other and connect ideas | Paragraphs are isolated and repeat context to stand alone |
| Answers come first, with explanation and examples after | Answers come with no reasoning, examples or caveats |
| One thorough page covers a topic and its close questions | One topic is split across many thin pages |
| One version of the content, written for people | A second version written for LLMs or crawlers |
Here is the test I use. If you removed every search engine and AI tool from the world, would you still structure the page this way? If yes, it’s good structure. If the only reason is “the AI might like it”, you are probably fragmenting.
Why Over-Chunking Can Hurt You
Google asking you not to is one reason. There are also practical reasons why broken-up content tends to do worse.
It Reads Badly
Pages built from loose snippets feel like a list of facts with no thread. Readers lose the reasoning that links one point to the next. They leave, they don’t trust the page, and they don’t come back.
It Strips Out What Makes Content Worth Citing
Chunking advice often cuts context, examples, trade-offs and first-hand detail. But those are exactly what make a page more useful than every other page on the same topic. A bare one-line answer is easy for an AI to write on its own. It has little reason to send anyone to you for it.
It Drifts Toward Search Engine-First Content
Google’s guidance on creating helpful, reliable, people-first content lists warning signs. They include content made mainly to attract visits from search engines, and writing to a set word count because you’ve heard Google prefers it. Rebuilding pages just around an assumed chunk length sits very close to both.
It Doubles Your Workload for a Moving Target
Keeping one version of your content up to date is hard enough. Keeping a human version and a machine version, and rewriting the machine one every time AI systems change, costs time with no promised return. If you are setting priorities for the year, I’d spend that time on the basics in our 2026 SEO plan for small businesses.
How to Write for Readers and AI Search at the Same Time
The good news is that the habits that help readers also make your content easy for any system to understand. None of the following needs a separate version of your page.
Lead Each Section With the Answer
Put the main point in the first sentence or two of a section, then explain. Readers who skim get what they came for. Readers who want depth keep going. This is the single habit I push hardest with writers.
Weak: “There are many factors to consider when thinking about how often you should post on your Google Business Profile, and every business is different.”
Better: “Post on your Business Profile when you have something worth saying, such as an offer, event or new service. For most local businesses, that means a few times a month. Here’s how to decide what’s right for you.”
The better version answers first, then sets up the detail. It wasn’t rewritten for a machine. It is simply clearer.
Write Headings That Describe, Not Tease
A heading should tell the reader what the section contains. “Pricing” is vague. “How much a boiler service costs in 2026” is specific. Question headings are fine when they match questions people really ask. But they shouldn’t be forced onto every section.
Let Sections Be as Long as They Need to Be
Some points take two sentences. Others take six paragraphs and an example. Don’t pad short sections or cut long ones to hit a target length. If a section is getting very long, the fix is usually a subheading or a list, not moving the topic to a new page.
Keep the Thread Between Sections
It’s fine for sections to connect. Phrases like “as covered above” or “this matters because” help readers follow the logic. You don’t need to repeat the full topic in every paragraph so it can be lifted out alone. Modern systems read the page around it. Readers certainly do.
Use Lists and Tables When the Content Is Actually List-Shaped
Steps in a process belong in a numbered list. A comparison of options often works as a table. A careful explanation usually works best as paragraphs. Match the format to the content, not to what you think an AI will pull out.
Add What Only You Can Add
Real examples from your work, original photos, prices you actually charge, mistakes you see customers make. This is what makes a page worth reading and worth referencing. Generic summaries are the easiest content for AI to replace. If I had to pick one thing to add to most pages, it would be this.
Keep Related Questions on One Strong Page
Someone asking “how long does a kitchen fitting take” will also want to know about disruption, costs and preparation. Cover those on the same page. Splitting each into a thin page scatters your expertise and gives each page less reason to rank.
A Before-And-After Example
Imagine a plumbing company page about fixing a dripping tap.
The over-chunked version has twelve question headings: “What is a dripping tap?”, “Why does a tap drip?”, “Is a dripping tap serious?”, “Can I fix a dripping tap?” and so on. Each has a one-or-two sentence answer that repeats “dripping tap” and never refers to anything else on the page. There are no photos, no detailed steps, and no sign of when to call a professional.
The well-structured version opens by answering the main question. Most drips come from a worn washer or cartridge, and can be fixed in under an hour with basic tools. It then has a handful of sections:
- Why taps drip, with the common causes in a short list.
- How to tell which type of tap you have, because the fix depends on it.
- Step-by-step instructions for each type, as numbered lists.
- When it’s not a DIY job, with the specific warning signs.
- What a call-out usually involves, based on the company’s real experience.
Both pages are “structured”. Only the second is built around what the reader needs to do. It’s also the one with clear passages any system can understand, because each section has a real purpose.
Auditing Existing Content for AI-driven Chunking
If your site has already been rewritten around chunking advice, don’t panic or undo everything overnight. Go through your most important pages and look for the signs in the table above. For a wider review, I explain the full process in my guide on how to audit your site for unhelpful content.
- List your priority pages. Start with pages that bring in leads or sales. Then add any that were restructured just for AI.
- Read each page top to bottom as a customer. Note where it feels repetitive, choppy or thin, and where you would want more detail.
- Check the headings. Keep question headings that match real customer questions. Rewrite forced ones so they describe the section.
- Restore the connections. Remove repeated topic names and standalone restatements. Add linking sentences where the logic jumps.
- Merge thin pages. Where one topic was split across several short pages, combine them into one full page. Redirect the old URLs to it.
- Add real substance. Put back examples, steps, caveats and first-hand detail that were cut to keep sections short.
- Remove duplicate “AI versions”. If you created separate summaries or pages for LLMs, retire them so there is one version written for people.
I’d put pages that matter for sales and pages that lost visibility recently at the top of the list. If you are also checking the effects of the last core update, our guide to what changed in the December 2025 core update covers what to review first. Some of the same pages may show up in both checks.
When you track results after changes, look at trends over weeks, not days. The weekly and monthly views in Search Console make it easier to see whether a page is moving the right way without being fooled by daily noise.
The Bottom Line on Content Chunking AI SEO
Google’s message is the same across its podcast and its documentation. There is no separate rulebook for AI features in Google Search. The pages that do well are useful, clearly organised and written by people who know the subject.
That doesn’t mean ignoring AI search. It means you don’t need a different strategy for it. Clear headings, answers up front, short easy paragraphs and real expertise serve every part of Google, from classic blue links to AI Overviews.
What you should avoid is anything that exists only because you think a machine prefers it. That means fixed chunk lengths, forced question headings, standalone paragraphs that repeat themselves, duplicate “LLM versions” and thin pages split from one good guide.
My practical next step for you: pick your five most important pages and read them as a customer would. If any section exists only for a machine, rewrite it for a person. That is all Google is asking for on content chunking AI SEO: structure that helps readers, and nothing built just for bots.
