In early January 2026, Google’s Search team delivered a clear message to publishers experimenting with “AI-optimization”: don’t reshape your site into “bite-sized chunks” just to appeal to large language models (LLMs). The warning surfaced in the episode description of Google’s Search Off the Record podcast (Jan 8, 2026), which explicitly cautioned creators not to “rush to break your content into bite-sized chunks” for LLMs.
Within a day, the guidance spread across the SEO press, with multiple outlets attributing a blunt line to Google Search Liaison Danny Sullivan, “we don’t want you to do that”, specifically about turning content into bite-sized chunks for LLM/AI results. The consistent theme across the coverage: Google wants content designed for people first, and it does not treat “LLM-friendly chunking” as a ranking advantage.
1) What Google actually said, and where it came from
The most direct source is Google’s own Search Off the Record podcast ecosystem. The Jan 8, 2026 episode description (“SEO, AIO, GEO, your site, & third-party support to optimize for LLMs”) included a pointed line advising site owners not to break content into “bite-sized chunks” merely to satisfy LLMs. That phrasing matters because it targets intent: restructuring for machines rather than clarity for readers.
On Jan 9, 2026, Search Engine Land reported guidance attributed to Danny Sullivan that reinforced the same idea, including the quote “we don’t want you to do that” in the context of chunking content for LLMs. This report framed the message as discouraging publishers from redesigning pages to chase AI answer formats.
Search Engine Roundtable echoed the warning the same day, emphasizing a second point that often gets overlooked: Google also cautioned against creating “two versions” of content, one optimized for LLMs and one for humans. The implication is that attempts to split audiences (bots vs. people) are strategically fragile and may invite misalignment with what Search is trying to reward.
2) Why “content chunking for LLMs” became trendy in the first place
As AI Overviews and other AI-driven interfaces spread, marketers noticed that short, modular passages often appear in featured summaries, answer boxes, and conversational responses. The leap many teams made was: if an LLM “likes” clean snippets, then writing in snippet form must improve visibility.
This gave rise to a cottage industry of “AIO/GEO tools” and checklists that recommend rewriting pages into micro-sections, sometimes with repetitive ings, stripped nuance, and overly templated Q&A blocks. The promise is that this structure will make the content easier for AI systems to lift into direct answers.
But Google’s January 2026 statements push back on the assumption that LLM consumption patterns should dictate how webpages are authored. In other words, what appears to work in a narrow set of AI display scenarios does not necessarily translate into durable search performance or better user satisfaction.
3) Google’s core objection: ranking should reward content written for humans
Ars Technica’s Jan 9, 2026 coverage summarized the discussion as a direct challenge to the “LLM-friendly chunking” narrative, calling it an SEO misconception and stating that it is not used as a ranking boost. That is a major correction for publishers who were treating chunking as a new, unofficial ranking signal.
The rationale reported by Ars Technica is consistent with Google’s longstanding guidance: systems should reward content created primarily for people. Sullivan’s point, as reported, is that human-focused content is the best long-term strategy precisely because search systems evolve; building pages around a transient machine preference is a bet against inevitable change.
In practice, Google’s stance implies that “format hacking” for LLM output is less important than the fundamentals: usefulness, completeness, clarity, and satisfying user intent. If chunking undermines those fundamentals, by removing context or dumbing down complex topics, Google is signaling it won’t be the kind of optimization that stands the test of time.
4) The “two versions” warning: don’t build one page for LLMs and another for the web
Search Engine Roundtable’s recap highlighted a particularly important line: Google warned against producing separate versions of content, one tailored for LLMs and another for human visitors. That approach often shows up as cloaking-adjacent behavior (even when not intended) or as an internal workflow where “AI pages” become thin, snippetized mirrors of deeper human pages.
Maintaining dual versions also increases the risk of inconsistencies, outdated information, and broken canonicalization. Even if a publisher believes they can keep both versions aligned, the operational burden tends to produce drift over time, which can harm trust and user experience.
More broadly, Google is signaling that “writing for bots like Gemini” is not a path to better rankings. Moneycontrol’s Jan 9, 2026 summary framed the message in exactly those terms: avoid rewriting pages for AI systems and don’t expect it to improve Search performance.
5) Edge cases: why chunking might seem to work today, and why Google says that can vanish
One reason the chunking myth persists is that there are edge cases where the tactic appears to help, at least temporarily. For example, simplifying a page into short blocks can incidentally improve scannability, reduce redundancy, or make answers easier to extract. A publisher may then attribute the improvement to “LLM chunking,” when the real driver was basic readability.
However, Google’s warning also addresses the seductive nature of short-term wins. Search Engine Roundtable quoted Sullivan acknowledging that some approaches may work now, but cautioned that “tomorrow the systems may change.” If your gains come from matching a fleeting extraction pattern rather than delivering better content, those gains may be inherently unstable.
This is a useful lens for decision-making: if a change makes the page more helpful to humans, it is likely to remain beneficial. If a change makes the page less helpful but more “machine-snippable,” Google is strongly hinting that any benefit could be temporary, and may reverse as ranking systems adapt.
6) What publishers should do instead: structure for readers, not for myths
Google’s January 2026 guidance is not an argument against structure. Clear ings, logical sections, summaries, and well-written definitions are valuable, because they help people navigate. The distinction is intent and execution: don’t “rush” into reducing everything to tiny fragments solely to satisfy perceived LLM preferences.
A practical alternative is to write comprehensive pages with strong information architecture: start with an accurate overview, then go deeper with examples, caveats, and supporting details. If you add a short TL;DR or FAQ, it should exist to help readers, not to manufacture extractable snippets at the expense of substance.
Industry coverage underscored how broadly the warning resonated. Search Engine Roundtable’s daily recap (Jan 9, 2026) tied the statement to the wider market of AI-optimization tools, while Slashdot amplified Ars Technica’s line and the “we don’t want you to do that” quote, evidence of how quickly a simple “chunk for LLMs” idea can spread, even when Google rejects it.
7) How to interpret the warning in an AI-first search era
It’s easy to read “don’t chunk” as “ignore AI surfaces,” but that’s not the message. Google is warning against a narrow, mechanical tactic, turning webpages into “bite-sized chunks” purely to please LLMs, rather than discouraging thoughtful content design.
A healthier interpretation is: optimize for understanding. If AI systems summarize your page, they will do so based on what you publish; your best defense is accuracy, clarity, and depth. That means sourcing claims, defining terms, addressing common misconceptions, and keeping information up to date.
Technology.org’s Jan 12, 2026 coverage captured the takeaway in simple terms: “AI Content Chunking Will Not Improve Search Rankings,” urging creators to “write for people, not for AI.” When multiple independent outlets converge on the same message, the safest move for publishers is to treat it as strategic guidance, not a passing comment.
Google’s warning against chunking content for LLMs is, at its core, a reminder that search visibility should be earned by serving users well, not by reshaping pages to match a perceived machine template. The consistent reporting around Search Off the Record and Danny Sullivan’s attributed remarks makes the intent hard to miss: don’t turn your site into “bite-sized chunks” just to chase AI-driven presentation formats.
The practical path forward is not anti-structure; it’s pro-reader. Use ings and summaries where they genuinely help, avoid maintaining “two versions” of the same content, and focus on durable quality signals: helpfulness, completeness, and clarity. If systems change tomorrow, as Google explicitly warns they can, human-first content is the asset most likely to keep performing.
