AI Brand Visibility and Content Recency

Do you remember that year in SEO where everyone was convinced that simply updating the publish date of your content would improve your organic performance? The thought process being Google favored recent content?  Here we are again. 

We’re asking that same question, now for LLMs. As LLMs continue to flip our world upside down, we’re focusing on our ability to influence these models. What factors affect your brand’s visibility in LLMs? Enter: content freshness, or content recency. 

In this study, we dug into whether or not this is where marketers should be focused. Is there a material impact. Well, nearly 65% of AI bot hits target content published in just the past year. However, this was not a steadfast rule, behavior varies across industries. 

How do you Measure Content Recency? 

To figure this out, we needed to track two key things: when content was published or updated and how often AI systems actually interact with it.

1. Publish date: The core of “recency” revolves around when your content was published or last updated. We grouped our data by the year it was last updated to see which years attracted the most attention from AI crawlers.
2. AI Log Hits: These hits reflect how frequently AI bots visit your pages. Specifically, we analyzed activity from three ChatGPT bots. (Check out the Seer blog for details on using log files to track visibility)

🔮 Tie this back to measurable impact: Layer in AI referral traffic to understand the average age of content that is actually resulting in visits to your site from LLMs. 

 

We analyzed 5000+ URLs that we were able to extract publish dates from that were being cited across models like ChatGPT, Perplexity, and AI Overviews. What we found is that recency played a factor in ALL three.

The Big Picture: AI Bots Love Fresh Content

There is a strong recency bias in LLMs.

• Nearly 65% of log hits were for content published within the past year (2025).
• 79% of total hits targeted content from the last two years (2024–2025).
• 89% of hits were on content updated within the last three years (2023–2025).
• 94% of hits occurred on content published within the last five years (2021–2025).
• Only 6% of hits were on content older than six years.

Clearly, freshness significantly influences AI interactions with content. However, this isn’t universal. Industry matters more than you might think.

But Here’s Where it Gets Interesting

Different industries show distinct patterns in how content age flows back to LLM visibility.  The charts below illustrate the distribution of AI log file hits by the content’s last updated year for a few of the most impacted industries. 

Financial Services Industry

There’s some pretty extreme recency bias – thousands of hits on 2024-2025, almost none pre-2020

Topics such as payroll, taxes, and HR regulations require frequent updates because outdated information rapidly loses relevance. Regularly refreshed content is crucial in finance, as both users and AI systems prioritize current and timely information.

Travel Industry

Travel is also a top industry for content recency, although it has a slightly broader window than financial services. 92% of hits focus on last 3 years, peaking from 2023 content

Much of this content is somewhat evergreen, like guides on “best places to travel in July” or “when to book holiday flights.” These topics remain relevant beyond their initial publish dates.

Energy Industry 

This is where it gets interesting. Recency matters, yes. But it’s much less extreme. There’s a wider window and the information the AI crawlers gravitated toward was different. 

AI crawlers gravitated toward informational evergreen content that won’t become outdated next month like “what is environmental sustainability?” and “Green vs renewable energy.” This tells us that topics in the energy space, likely have a longer shelf life due to their evergreen, educational nature. 

A Sub-Industry Deep Dive: What Decking Taught Us

Yes, decking. Bear with us here.

Before you skip this section thinking ‘I’m not in decking’, the lessons here apply to industries that don’t have major changes year over year, where what was true 10 years ago is still true today, and where instructional or ‘how-to’ content tends to perform. 

While experiencing a large amount of log hits to recent content, showcases that sometimes quality, instructional content, can hold up even 10-15 years later, with AI crawlers hitting content from as far back as 2004.  

Does this mean that content wouldn’t be worth updating? Absolutely not, rather it showcases that AI crawlers do interact with your older content. 

While it would be nice to say “that means you’re great, your content will stay relevant” if you’re in those industries. Instead, (1) that may not remain true, and (2) could updating that older content catapult even further and increase the number AI bots hitting it? 

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The industry analysis reveals that content recency carries different weight across industries, but this is only part of the story. To build on these findings, we turned to citation data via Peec.ai to examine how frequently LLMs reference recent versus older content. This analysis offers direct insight into how freshness translates to both visibility and credibility in practice.

What Can We Learn From What ChatGPT, Perplexity, and AIOs are Actually Citing?

All models favored recent content, however, ChatGPT had the largest spread of citations in regards to publish date. And there are clear differences across the three models. 

ChatGPT

ChatGPT leaned heavily towards recent content but we see also a handful of older pieces being cited with the date ranging back to 2004. Some of these older pieces include Wikipedia articles, which studies have shown recently are highly leveraged within ChatGPT’s models. This might indicate that while recency is a factor – perhaps authority and longevity is too.

• Approximately 31% of ChatGPT’s citations are from 2025
• Around 29% are from 2024
• About 11% are from 2023
• In total, 71% of citations are from 2023–2025
• The remaining 29% are older:
  • About 8% from 2022
  • Around 21% from pre-2022 years

Perplexity

Perplexity had a strong pull towards recent content as well, more so than ChatGPT. 

• About 50% of Perplexity’s citations are from 2025 alone
• Approximately 20% are from 2024
• Around 10% are from 2023
• In total, roughly 80% of citations are from 2023–2025

AI Overviews

AIOs had the strongest favoring of recent content

• Roughly 44% of AIO citations are from 2025
• Around 30% are from 2024
• Approximately 11% are from 2023
• In total, about 85% of AIO’s citations are from 2023–2025

This aligns with what we’d expect: AIOs are Google-backed, and Google has historically prioritized fresh content. Takin’ us back to the days of updating the publish date. The model’s behavior reflects that.

What This Means for Your Strategy 

Across both AI bot crawl behavior and citation data, one thing is clear: recent content consistently garners more attention. 

In our log file analysis, nearly 90% of AI bot hits occurred on content from the last three years. Similarly, citation patterns from ChatGPT, Perplexity, and AIOs show a strong bias toward content published between 2023–2025. But the story doesn’t stop at “just recency”.

• Financial Services demand extremely fresh content due to fast-changing regulations and information.
• Travel allows for a bit more leeway — evergreen content still performs well, but recent updates make a difference.
• Energy demonstrates a longer lifespan for content, especially when it’s evergreen and educational.
• The decking lesson: Even 10–15-year-old content still sees AI bot activity, showing that timeless instructional content can hold value for a very long time… but don’t abandon it as “good enough”, updating it could make performing content, high performance

This reinforces that your content strategy should be tailored to your vertical, consider: 

• How often information becomes outdated 
• How are users (and AIs) discovering or consuming that information 

The Bottom Line

Recency matters. And really, didn’t it before? Tax advice from 2012 was no longer relevant in 2022. LLMs are efficiently finding and returning that most relevant information. Often, it’s tied to ‘content recency’. 

This reinforces that content strategy should be tailored to your vertical, considering how often information becomes outdated and how users (and, yes, AIs) consume that information. But the human behind that query is key.

So before you start evaluating your blog to find the content that hasn’t been updated in a while to see if that will “do something”, consider the human behind the ChatGPT query. In a world of generic SEO answers, how can you deliver the best, most relevant answer for that person? 

Curious about how your content strategy is preparing you for a future of searches in LLMs? Let’s chat, there’s a lot we can do together.