u/Brave_Acanthaceae863

Here's something that genuinely surprised me.

I ran the same 150 informational queries across ChatGPT, Gemini, and Perplexity over a two-week period. The question was simple: how often do all three models cite the same source for the same query?

The answer: 8%.

Twelve percent of queries had two models agreeing on at least one source. The remaining 80%? Every model cited something completely different.

A few patterns stood out that I wanted to share:

**ChatGPT** leaned heavily toward established publishers — major news sites, university domains, Wikipedia. It played it safe. About 65% of its citations came from domains with 10+ million monthly visitors.

**Gemini** was the most eclectic. It cited small blogs, niche forums, and individual Substack writers at rates I didn't expect. Roughly 30% of its sources would never appear in a ChatGPT answer for the same query.

**Perplexity** sat somewhere in between but had a clear preference for recent content — 58% of its citations were from pages updated within the last 90 days. The other two models didn't show that recency bias nearly as strongly.

What this means practically: if you're optimizing for AI citations, picking a single model to target is a real strategy. The overlap is so low that optimizing for one model almost certainly leaves the other two untouched.

But here's where I'm stuck and genuinely curious what others think:

Is it better to optimize specifically for one model's preferences and dominate there, or spread your efforts thin trying to appeal to all three? I've seen solid arguments for both approaches, but I haven't found anyone actually tracking the ROI comparison.

Anyone else measuring cross-model citation overlap? What are you seeing?

Oh wow, the AI Overview debate is getting intense.

Every week there's a new post asking if traditional SEO is dead. And honestly? Some of those posts have a point.

Here's my take after 6 months in the GEO/AEO space.

**What AI Overviews actually killed**

• **Rank #1 doesn't matter**: I've seen the same source appear in position 1 and position 5 in AI Overviews. The #1 ranking gets clicked, but the AI doesn't care about it.
• **Keyword optimization is useless**: AI ignores your carefully placed keywords. It understands the context, not the keywords.
• **Long-form content**: 2,000-word guides are getting cited just as much as 600-word answers.

**What still matters**

• **Structure**: Answers that are easy to parse (bullet points, numbered steps) perform 3x better
• **Direct answers**: AI cites content that answers the question in the first 2 sentences
• **Authority signals**: Citations still prefer domains with real E-E-A-T signals

**The uncomfortable truth**

Traditional SEO isn't dead — it's just changed. The old playbook (keyword stuffing, long titles, link velocity) doesn't work anymore. But SEO for AI (answering questions, structured data, transparent E-E-A-T) is more important than ever.

From my experience, the sites winning right now aren't the ones with the most backlinks. They're the ones making it easiest for AI to parse and quote.

Hmm... I assumed AI citations were sticky once you got them.

Turns out that assumption is wrong.

We tracked 200+ AI-generated answers across 3 major engines (ChatGPT, Perplexity, Gemini) and counted how often the same sources got cited over time. Here's what happened.

**The pattern**

Day 1: 100% of our tracked sources were cited

Day 10: 73% still cited

Day 20: 48% still cited

Day 30: 32% still cited

That 68% drop-off in 30 days is the part that surprised us.

**What kept citations alive**

The sources that survived all 30 days shared three traits:

1. They updated their content every 4-6 weeks (not just rewriting, but adding new examples/data)
2. They had clear "how-to" structures with numbered steps
3. They responded to user questions in their FAQ with direct answers, not "please contact us"

**What didn't work**

We tested three different refresh strategies:

• **Weekly rewrites**: No impact on citation retention
• **Keyword insertion**: Actually made citations *worse* (AI confused by keyword stuffing)
• **Partial updates** (just changing one section): Mixed results, but nothing statistically significant

**The takeaway**

Citations aren't permanent. They're more like weather forecasts — accurate for a while, but need frequent updates to stay reliable.

For us, the sweet spot is updating high-impact pages every 5 weeks and refreshing citations data every month to see what's still being used.

Wait... why aren't our QA pages getting cited when they clearly have better answers?

We had this exact problem last month. All our "how-to" articles were getting AI citations, but the equivalent content in Q&A format was getting ignored. The same answers, different presentation — and AI was choosing the articles.

After testing 100+ pages side-by-side, here's what we found.

**The QA format problem**

Our Q&A pages had this structure:

Q: How do I [topic]? A: [Long paragraph answer with 3-4 sentences]

AI apparently doesn't parse paragraph answers well. It extracts fragments, and when there's only one chunk, that's all it grabs.

**The fix**

We restructured to bullet points:

Q: How do I [topic]? A: [Topic] involves three steps: • Step 1 — what to do first • Step 2 — what to avoid • Step 3 — common mistakes

Same content, just presented differently.

**The results**

After 4 weeks:

• QA with paragraphs: 0.3 citations per page (basically nothing)
• QA with bullet points: 2.1 citations per page
• Articles with paragraphs: 1.8 citations per page
• Articles with bullet points: 3.4 citations per page

The improvement was consistent across all three AI engines.

**The pattern**

It's not about the question type. It's about how the answer is structured. AI reads bullet points 3x faster than paragraphs, and that speed is probably why they prefer them.

Hmm... I assumed AI citations were sticky once you got them.

Turns out that assumption is wrong.

We tracked 200+ AI-generated answers across 3 major engines (ChatGPT, Perplexity, Gemini) and counted how often the same sources got cited over time. Here's what happened.

**The pattern**

Day 1: 100% of our tracked sources were cited

Day 10: 73% still cited

Day 20: 48% still cited

Day 30: 32% still cited

That 68% drop-off in 30 days is the part that surprised us.

**What kept citations alive**

The sources that survived all 30 days shared three traits:

1. They updated their content every 4-6 weeks (not just rewriting, but adding new examples/data)
2. They had clear "how-to" structures with numbered steps
3. They responded to user questions in their FAQ with direct answers, not "please contact us"

**What didn't work**

We tested three different refresh strategies:

• **Weekly rewrites**: No impact on citation retention
• **Keyword insertion**: Actually made citations *worse* (AI confused by keyword stuffing)
• **Partial updates** (just changing one section): Mixed results, but nothing statistically significant

**The takeaway**

Citations aren't permanent. They're more like weather forecasts — accurate for a while, but need frequent updates to stay reliable.

For us, the sweet spot is updating high-impact pages every 5 weeks and refreshing citations data every month to see what's still being used.

Straight up: I think half of what we call GEO strategy is educated guessing dressed up as methodology.

I've been spending a lot of time in this sub and in the broader GEO conversation, and there's a pattern I keep noticing. Someone posts data — "We tested X and found Y" — and within a week it's being cited as a "best practice" or a "proven framework." Including by us, honestly.

But here's the thing about AI citations that makes them different from traditional SEO:

With backlinks, you can check if a link exists. It's binary. You built it or you didn't.

With AI citations, you're trying to influence a probabilistic process that nobody fully understands. The same query 10 times might give you 8 different source combinations. The same page might get cited on Monday and gone by Friday. And nobody outside of OpenAI/Anthropic/Google knows exactly how the retrieval works at any given moment.

So when someone says "structured data increases citation rate by 3x" — and I've absolutely posted things like that — what they really mean is "in our specific test, with specific queries, over a specific time window, we saw this pattern." That's useful, but it's not a law of physics. It might not replicate next month.

What I think is actually happening

The stuff that seems to work consistently isn't optimization tactics — it's content quality fundamentals:

• Answering real questions that people actually ask (shocking concept, I know)
• Answering them clearly enough that an AI doesn't need to hunt for the answer
• Being authoritative enough that the AI trusts you as a source more than a random forum post

Everything else — entity density, citation formatting, schema markup for AI, "AI-friendly" content length — might help at the margins, but I'm increasingly skeptical about how much of that signal vs noise.

I could be completely wrong about this. Maybe in 6 months we'll have enough data to show that yes, these specific tactics DO correlate with citations at scale. But from where I'm sitting right now, the gap between confidence and evidence in the GEO space feels wider than in any other marketing discipline I've worked in.

Curious whether anyone else feels like they're flying blind here, or if people have found approaches that actually feel repeatable.

Okay so like, we've been noticing something weird and I'm curious if it's just us.

We had a handful of pages that were getting cited pretty consistently across ChatGPT, Perplexity, and Claude. Nothing viral, but steady — like showing up as a source 2-3 times a week for relevant queries.

Then we updated them. Not huge rewrites — added some new sections, refreshed a few stats, that kind of thing. The kind of update that SEO best practices would call "good."

And the citations just... dropped. Like significantly. Some of them basically disappeared from the results entirely for a few weeks before slowly coming back (but not to the same level).

A few theories we're bouncing around:

1. **The freshness penalty theory** — when you update, the AI needs to "re-process" your page and during that window it loses whatever ranking signal it had before. The page is in some kind of purgatory.

2. **The structure disruption theory** — by adding/moving content around, we accidentally broke whatever format pattern made it cite-worthy in the first place. Maybe the AI liked the old structure specifically.

3. **Coincidence theory** — maybe the queries themselves shifted, or competitors published better stuff at the same time, and the timing is just misleading us.

Honestly I'm not sure which one it is. Could be all of them depending on the page. We don't have enough data to say anything confidently yet.

Has anyone else seen similar patterns? Really trying to figure out if this is a real phenomenon or if we're just seeing patterns in noise. Would especially love to hear from anyone who's tracked their citations before and after updates with actual numbers.

Real talk: half the GEO advice out there doesn't survive contact with reality.

We've been running GEO campaigns for about 6 months now, and I want to share the stuff that actually made it into our weekly routine — not the textbook stuff that sounds great in a presentation.

The "Friday Audit" — this one changed everything

Every Friday we pick 5 pages that should be getting AI citations but aren't. Not based on traffic or DA — based on "would a reasonable AI actually reference this for a user question?"

Then we ask three questions:

• Can someone read this page and answer a specific question in 30 seconds?
• Is the answer somewhere in the first screen (no scrolling to find the meat)?
• Would a different page on this site answer the same question better?

The pages that fail #1 or #2 get rewritten. The ones that fail #3 get consolidated. Simple but brutal.

The "Answer First" outline

Before writing anything, we draft the ideal AI answer first. Like literally type out: "If someone asked [query], the perfect answer would be..." Then we build the page around that answer structure instead of around keywords or topics.

This sounds obvious but it completely changed how we think about content hierarchy. The H2 isn't a topic anymore — it's a sub-question.

The thing that didn't work

We spent a solid month doing "entity density optimization" — making sure every relevant entity appeared X times per 1000 words. Measured it meticulously. Saw zero correlation with citation rate. Zero. That one hurt because the theory was so convincing.

From my experience, the stuff that moves the needle is boring operational discipline, not clever hacks. But I'm curious what's actually working for other people — are you seeing similar patterns or am I missing something obvious?

We spent months chasing AI citations the same way we used to chase backlinks. Bad move. They're fundamentally different beasts, and once we stopped treating them the same, our results got way more consistent.

Here's what changed how we think about GEO:

1. AI citations are temporary. Backlinks are permanent.

A link you earned in 2023 still counts today. An AI citation? Gone in weeks sometimes. We tracked our own and saw roughly 40% churn within 60 days. That completely changes how you allocate effort — it's not "build it once," it's "maintain it constantly."

2. One strong page can outperform an entire domain.

Traditional SEO rewards domain-level authority. In GEO, a single well-structured page that directly answers a query can get cited over sites with 10x the backlinks. We've seen DA 15 pages consistently beat DA 80+ domains. The models care about the answer, not the site reputation.

3. Formatting matters more than we expected.

This one surprised us. Pages that used clear structure — numbered steps, direct definitions, comparison tables — got picked up way more often than long-form essays covering the same topic. The content can be identical in substance, but how you package it makes a huge difference.

4. Freshness is an underrated signal.

AI models clearly favor recently updated content. Not just "published recently" — pages that show signs of ongoing maintenance. Adding a "last updated" date and actually revisiting content monthly made a measurable difference.

5. The competition window is getting shorter.

Early on, a well-optimized page could hold a citation spot for months. Now, as more people figure out GEO, that window keeps shrinking. The real play is building a system for regular content refreshes, not just one-time optimization.

Curious if others are seeing similar patterns. The "treat it like SEO" mindset held us back for a while — wondering if that's been the case for anyone else.