How Often to Update Content for AI Search (2026 Answer)

Update a page when a claim on it has expired, not when a calendar says so. The cadence tables circulating in this category — product pages monthly, blog posts quarterly, everything annually — are not traceable to a study, and the study they are usually justified by says something narrower than the tables do.

Here is the cadence, before the evidence — on two axes, because they answer different questions:

And the same decision from the other axis — not what the page contains, but what kind of page it is:

The two tables answer different questions and you need both. The first tells you whether a page has anything to update. The second tells you what the update consists of, and it is the axis the published data is organised around whether the publishers noticed or not — because the genre of a page also decides whether it carries a visible date at all. That turns out to be the confound underneath every study below.

This survives either outcome of the freshness question. If freshness is causal, you updated the pages that had reason to change. If the 25.7% turns out to be an artefact, you still corrected content that had become wrong.

Key takeaways

• Two independent datasets converge on one interval: 74-75% of AI-cited pages carry an update date inside 12 months, and 25-26% do not (Seer 75%, AirOps 26.2% outside — different engines, industries and years, landing about a point apart).
• No published evidence supports a monthly or quarterly timer. Every cadence table we could find traces to no study, and the most-repeated figure in this topic — "50% of citations come from content under 13 weeks old" — is credited to four different origins by different republishers.
• The most-quoted study says the opposite of how it is quoted. Ahrefs report cited pages averaging 1,064 days against 1,432 for organic — and in the same post state that cited pages are still 2.9 years old on average and that Google is the surface least influenced by freshness.
• Refreshing is most of the freshness. Of pages with both dates, 72% look fresh by update date and 42% by publish date — the same pages (Seer, grade △▲). Roughly a quarter to a third of everything "fresh" in the citation set is old content that was updated.
• A genre confound sits under all of it, and nobody controls for it. Listicles carry the year in the title and get re-dated annually; vendor product pages often carry no visible date. If citations over-select listicles, the citation set looks younger with no freshness preference existing anywhere.
• Changing the date without changing the page is the version multiple sources agree does not work — including the authors of the largest study, who cite Google's John Mueller on it.

How often should I update content for AI search?

There is no published cadence to follow, but two independent datasets converge on one interval: 74-75% of AI-cited pages carry an update date inside 12 months, and 25-26% do not. No published evidence supports a monthly or quarterly timer. Update when a claim on the page has expired, not when a calendar says so.

The most-quoted figure sits beside that. The largest study reports AI-cited pages averaging 1,064 days old against 1,432 days for organic results — 25.7% "fresher" — and in the same post says cited pages are still 2.9 years old on average, and that Google is the surface least influenced by freshness.

Marks used below: △▲ observational study, no control group, published by a company selling a related product. ○▲ preprint with a statistical result, vendor-affiliated authors. ◐ our own first-party measurement. ✗ quoted widely, no method we could locate.

The number everyone quotes, traced

Ahrefs, by Ryan Law and Xibeijia Guan, published 28 July 2025. Method as stated: 16,975,000 cited URLs collected via their Brand Radar product from ChatGPT, Perplexity, Gemini, Copilot and AI Overviews, plus organic Google SERPs for comparison, with both publication date and last-updated date recorded. Grade △▲ — observational, no control, and Ahrefs sells the tool the data came from. (This is not their 75,000-brand study on what correlates with AI Overview presence; that one is a different dataset and we unpack it separately in Brand mentions beat backlinks.)

The average age of URLs cited by AI assistants is 1064 days, compared to 1432 days for URLs in organic SERPs — 25.7% "fresher."

That is a real comparison with a stated denominator, and it is the strongest thing in this area. Note precisely what it is: a difference between two average ages. It is not a measurement of what happens to a page when you update it.

If you have seen it quoted as "AI search relies 26% more on recent information" — that is a paraphrase, and it changes the claim. "The pages it cites are on average 25.7% younger" and "it depends 26% more on recency" are different sentences. The second implies a weighting inside a system nobody outside Google or OpenAI can see.

The same post contains the correction

This is why it is worth reading rather than quoting. The authors state, in their own words:

• "The average age of cited pages is still 2.9 years." Like traditional search, AI assistants still prefer citing long-lived content.
• "Google is the least influenced by content freshness" — and Google is still where the majority of searches happen.
• "Content freshness is one among many factors. Low-quality, irrelevant content that's updated every day will not have a magic positive effect."
• They explicitly warn against updating publish dates without changing the content, citing Google's John Mueller.

So the honest one-sentence summary of the largest study here is: pages cited by AI assistants skew younger than organic results, by about four months on a base of nearly three years.

A second study: refreshing is most of the freshness

Seer Interactive, published 2026, grade △▲ (a marketing agency selling work in this area). Method as stated: 7,683 pages carrying 47,097 citations, March-June 2026, across ChatGPT, Gemini and Perplexity, in four industries. Two-thirds of pages had readable last-modified dates from schema, sitemaps and headers.

The last three rows are the most useful lines published on this topic. The gap between 72% and 42% is the entire practical question: if you look at publish dates most cited content looks old, and if you look at update dates most of it looks new — and these are the same pages.

Roughly a quarter to a third of everything that looks fresh in the citation set is old content that was updated, not new content that was written. Refreshing is not a lesser substitute for publishing here. It is most of the freshness.

Note the engine spread too — 78% to 65% — the same shape as everything else in AI search. The surfaces are not one thing.

A third dataset, and the only one that splits by intent

AirOps, grade △▲ — they sell AI-visibility software. Method as stated: more than 4,000 pages cited by ChatGPT across 900 high-intent queries in 15 industries. No collection window, confidence interval or limitation is stated anywhere in the report.

The 26.2% is the useful line, because Seer's data says the same thing from the opposite end. Seer put 75% of cited pages inside a one-year update window; AirOps puts 26.2% outside one. Different engines, different industries, different years — and they land about a point apart. That is the closest thing to replication this topic has, and it is why the answer near the top of this post is a range rather than a number.

Then the part that stops being a freshness finding. Commercial-intent pages are fresher than informational ones in their data, and AirOps read that as the ranking system being intent-sensitive. There is a duller reading available: commercial pages are product pages, pricing pages and category rosters — the genres that change on their own because the underlying facts change. Nothing has to prefer freshness for that gap to open. Which reading is correct is the confound below, now carrying a number.

⚠ Two sentences in that report we would not repeat as findings. Pages not updated in over 12 months are "more than twice as unlikely to be cited," and pages not refreshed annually "lose over 50% of their chance to be cited." Both describe a probability of citation conditional on something, and the report does not say conditional on what — retrieved pages? indexed pages? pages inside their customers' tracked sets? Without the denominator the ratio cannot be checked, which is the same objection this post makes to the 13-week number further down. The 26.2% we can use because its denominator is stated. Those two we cannot.

The fourth study measured decay rather than stock

Scrunch AI with Stacker, published March 2026. Method as reported: over 3 million citation events, eight industries, six AI platforms, tracked across 26 weeks, analysed with cohort-based survival analysis and 200 bootstrap simulations. Headline: the median citation half-life for non-distributed, single-domain content is 4.5 weeks. Grade △▲ — Scrunch sells AI visibility software, Stacker sells content distribution, and the finding is flattering to precisely what Stacker sells.

Survival analysis is the right instrument for this question and, as far as we can tell, nobody else in this area is using it. Average age describes the stock of cited pages at a moment. A half-life describes the rate at which an individual page falls out, which is what a refresh cadence is trying to counter.

We have not opened the original. We read it through secondary write-ups and are reporting the method as they describe it. Until we have read the primary, we would not put weight on 4.5 weeks as a value — only on the observation that somebody has finally measured decay.

The 13-week number, and why we grade sources

You will meet the claim that 50% of AI citations come from content under 13 weeks old. It is the single most repeated figure on this topic. Tracing it, we found it credited to four different origins by different republishers — Seer Interactive, Ahrefs, Scrunch/Stacker, and an analysis by Lily Ray presented at Tech SEO Connect 2026.

We read the Ahrefs post directly and it contains no 13-week figure. It reports average ages of 1064 and 1432 days. So at least one of those four attributions is wrong, and a reader has no way of telling which without repeating the work.

This is not us calling the number false. It may be perfectly good. The problem is structural: a figure whose provenance changes depending on who repeats it cannot be checked, which means it cannot support a decision you will be held to.

Still untraced after looking:

The confound none of them control for

AI citations and organic results are not drawn from the same population of page types. Comparing their average ages compares two mixtures, and a difference in the mixtures produces a difference in average age with no freshness preference existing anywhere in the system.

Our own citation data makes that concrete rather than theoretical ◐ — one brand, one category, July 2026:

Now consider how age behaves per genre, before anyone optimises anything:

• A "best tools 2026" listicle carries the year in its title and gets re-dated annually as routine. It is fresh by construction.
• A YouTube page has a publish date and effectively never gets updated — but the ones surfacing for a live query tend to be recent uploads.
• A vendor product page may be edited constantly and carry no visible date at all.
• A community thread is dated by its newest reply.

If AI citations over-select listicles and video relative to organic results, the citation set will look younger even if freshness is not a factor at all. Sorting by age would recover the genre mix, not a preference.

AirOps' intent split is this confound with a number on it. Their commercial pages are fresher than their informational pages — and "commercial page" is very nearly a list of the genres in the second table at the top of this post, the ones whose facts expire on someone else's schedule. A system with no freshness preference whatsoever would produce that result.

This confound is unproven in both directions. Settling it means holding genre constant — listicles against listicles, product pages against product pages, how-to guides against how-to guides. None of the three published studies reports doing that, and it is the one thing that would turn this topic from description into evidence. What the table establishes is the precondition: the genre mixes differ sharply by surface, which is all the confound needs in order to bite. The engine split behind that table is in Why your brand isn't in AI Overviews, and the YouTube row is unpacked in Does YouTube help you get cited in AI search?.

Why a naive before-and-after won't settle it either

Suppose you skip the studies and just test it: update thirty pages, re-measure, see if citations rise.

That design produces a number, and the number is close to meaningless, because the citation set churns on its own. Published measurement puts the overlap between consecutive days' cited sources at a Jaccard index of roughly 0.336-0.423 — around 65% of cited sources change overnight with nobody touching anything (Schulte et al. 2026, arXiv 2604.07585, grade ○▲). At a single sample, the 95% interval on a brand-detection rate is about ±72 percentage points.

Two numbers drawn once from each end of a distribution that wide will contain almost any story you want to tell. This is also the reason a daily-refresh dashboard is not the answer to it: of the eight vendor pages we read for 8 AI visibility tools compared, four publish a cadence and all four say daily, while none documents how many times it asks each question — and it is the sample count, not the refresh rate, that narrows the interval above.

The test that would settle it

1. Matched pairs, not a single group. Pair pages by genre, topic and current appearance rate. Update one of each pair; leave the other alone. The untouched half is the whole experiment — it absorbs the overnight churn.
2. Repeated sampling at both ends. A rate per question per engine, never a single observation, never pooled across engines.
3. Substantive updates only, and log what you changed. Seer's 72%-versus-42% gap means "date changed" and "content changed" are separable in the data. If your treatment is a date bump, you have tested date bumps.
4. Hold genre constant. Do not update your listicles and compare against untouched product pages. That reproduces the confound inside your own test.
5. Fix the observation window in advance. Nobody has published how long an intervention takes to reach these surfaces. We do not know either.
6. Publish the null result if you get one. This field currently has a publication filter on it: the studies that exist were run by companies selling adjacent products, and a null is worth nothing to them commercially.

Nobody has run this test, including us. Everything above is observation on citation sets, not intervention on pages.

Frequently asked questions

Should I update an old page or write a new one?

Updating is not the lesser option here. Of pages with both dates in Seer's citation set, 72% looked fresh by update date and only 42% by publish date — the same pages (grade △▲), and they put 27-28% of all "fresh" cited content as refreshed older pages rather than new ones. Decide by whether a claim on the existing page has expired; if it has, updating it is the cheaper path to the same state.

Does changing the publish date without changing the content work?

Nothing published supports it, and the largest study in the area explicitly warns against it, citing Google's John Mueller. Seer's data is what makes the warning checkable rather than moral: because their 72%-versus-42% gap separates "date changed" from "content changed," a date bump is a measurable non-event rather than an invisible one. If your treatment is a date bump, you have tested date bumps.

Is there a recommended update frequency for each content type?

Not one traceable to a study. The tables circulating in this category — product pages monthly, blogs quarterly — are not sourced. What is defensible is decay rate rather than calendar: pricing, plan limits, version numbers and competitor rosters go wrong on their own and need updating when they change; definitions and mechanisms barely decay at all. The two tables at the top of this post are that decision, not a schedule.

Is content freshness a ranking factor for AI search?

We could not trace that claim to any stated method, and we grade it untraceable ✗. It is a statement about weights inside a system nobody outside the vendors can see. What is measured points the other way: Ahrefs find cited pages average 2.9 years old and that Google is the least influenced by content freshness — while remaining the surface where most searches happen.

What we would not do

Bulk date-bumping. Scheduled republishing with no substantive edit. Rewriting an evergreen explainer on a quarterly timer because a table on a vendor blog said to. The one thing multiple sources here agree on — including the authors of the largest study — is that changing the date without changing the page is the version that does not work.

The part of this we did build

There is a smaller problem hiding inside "update the page": knowing whether the edit is actually live on the page an engine reads. Staging, caching, a component that renders the fix only after JavaScript runs, an edit that never got deployed — all of these look identical to a finished task in whatever list you keep.

That is the loop peekr closes. The page check returns concrete items across AI crawler access, AI legibility and on-page SEO. You can mark an item as fixed, and marking it does not remove it. It leaves the list when a later check re-reads the page and no longer finds it. If you marked something and the next check still sees it, the tool says so in those words: either the change is not live on the page we read, or it is somewhere this check does not look.

The tool is also explicit about the limit this whole post is about: everything on that list being gone does not mean AI mentions you more. Only asking the engines tells you that, and that is a separate measurement with a separate number — see How to track your brand in AI answers.

Run the page check on one URL One site, no account.

Method

External figures are attributed inline with publisher, date, method as stated by the publisher, and a conflict-of-interest mark. We read the Ahrefs, Seer Interactive and AirOps write-ups directly; we did not receive or inspect any underlying dataset, and none appears to be public. We did not open the Scrunch AI original and say so in that section. Figures marked ✗ are ones we searched for and could not trace to any stated method — we have listed rather than used them.

All four sources here sell something adjacent to the answer they publish, which is why the two figures that agree across two of them are the only ones this post leans on.

Figures marked ◐ come from our own stored measurements on one brand in the AI video-editing category, July 2026: Google AI Overviews retrieved through a SERP API across 13 queries, and engine responses through the OpenAI and Google APIs with retrieval enabled, producing 154 citations in the head-term query set. Raw responses retained. We do not crawl third-party sites.

If you have run a matched-pair freshness test — in either direction — I would like to read it, and will link it here.