I work in PR and am interested in measuring the impact of earned media on LLM visibility and the relevant metrics and insights for PR.

Suggestions I’ve seen include measuring your performance for relevant prompts before and after a specific PR hit/campaign/announcement and measuring the same set of prompts over time.

A lot of GEO tools and content I see is set up for digital marketing and website content strategy that isn’t directly relevant for PR. And the fact that earned media is just one potential influence on LLM visibility complicates measuring overall visibility that is also influenced by website content and non-PR activities.

I think the suggestion to measure website referral traffic makes sense.

I’m still new to this so please forgive any ignorance.

This is my own view, not a company post.

Some numbers are going around about Generative Engine Optimization (GEO) and AI search:

* 93% are building a GEO practice in-house (Conductor, 2026 CMO Investment Report, 250+ executives)
* 48% have no GEO strategy in place at all (our own data at NeuroRank)
* 65% call AI search their single biggest challenge this year (GoodFirms, 2026, 20+ countries)

Here's what I can add. My team at NeuroRank has consulted with over 250 brands, on Generative engine optimization strategy. every one of them enterprise. I have validated this personally through 1:1 meetings, not a faceless survey.

Yes, there is a knowledge gap. Yes, GEO is challenging as a new practice for brands to build. Yes, most brands are flying blind.

Now comes the challenge. Slow adoption of best practices. Taking shortcuts. Copying a competitor without decoding their own brand's position. An enthusiastic media team that fizzles out under decision paralysis from top management. (India-specific decision paralysis?)

One brand really took the cake: "we will build our own AI visibility platform." Six months later, they came back, in a crisis of falling leads and revenue.

A substantial number say "our corporate team in the US will manage it."

And the agencies. I run one, so I can say this plainly. Too many are selling GEO that is just their old SEO or PR retainer with a new label. They bill for reports and content volume, not for whether the brand gets into the answer. They optimize for the rankings and traffic they already know how to measure, because that is what they know how to invoice. The brand pays for the agency's learning curve and calls it a GEO practice. not surprizing that less than 11% of marketers feel that their agency can give them a reliable GEO service. Cringe in linkedin is not helping either

So what am I missing?

Here's my reading. This is a generation of marketers who have not witnessed a marketing impact at scale. The last one was social media, and it gave us almost four to five years to adopt before it became a crisis. AI search is not going to give us that long.

If you're inside a brand or an agency right now, tell me where this is wrong, or where it's landing for you.

I Get Why Everyone's Chasing AI Overviews.

It's obviously changing how people search and in many cases, reducing clicks to traditional organic listings.

Now because click through rates are way less marketers started looking for shortcuts.

Some tried scaling AI-generated pages.

Others doubled down on parasite SEO.

Some built content specifically to attract AI citations rather than help readers.

And there's nothing wrong with that but I know for a fact Google has spent the last two years updating its systems to identify manipulation—not AI itself.

I would just like to know what is the future of marketing or better yet the strategy?

L

READ FULLFREE ARTICLE >HERE<

Let that sink in.

Altman just donated 5% of OpenAI equity to the US government — except he doesn't personally hold equity in OpenAI directly. His money comes through the OpenAI fund structure, not direct shares. He's giving away stakes that aren't really his, while quietly securing the remaining 95%.

The context that makes this brilliant:

• 55% of Americans believe AI will harm them
• Protests against data centers are spreading
• The "great replacement" fear, especially out of Silicon Valley, is increasingly aimed at technology rather than immigration
• Midterm elections are 5 months away
• Building data centers, securing gigawatts, getting permits — all of it requires political license

As Eskimoz points out: Trump and Altman both clearly understand the mechanism here. Trump gets to tell his base "I pried this from Big Tech." Altman gets to tell markets "we're safe, the US government is literally a shareholder." And people who feared for their jobs become stakeholders in the thing that scared them.

This is the exact playbook Elon Musk ran with SpaceX on public markets.

OpenAI and Anthropic both need capital. They both need an IPO-ready narrative. They both need a story that reassures investors and regulators simultaneously.

Altman isn't being a philanthropist here. He's turning the US government into a life insurance policy for his remaining 95%. He's converting a hostile electorate segment into shareholders. And he's doing it by giving away equity that was never technically his to begin with.

This isn't philanthropy. It's pure strategy. And it's hard not to respect the execution.

Genius move or cynical PR stunt — or both? 👇

I've been experimenting with AI-assisted content creation for a few months now.

The process is definitely faster, but I'm honestly struggling to figure out whether the content is performing because it's genuinely useful or just getting temporary visibility.

For those of you who have been using AI for content production:

• Are you publishing fully AI-generated content or heavily editing it?
• Have you seen sustainable rankings after 6+ months?
• Did traffic improve, stay flat, or decline?
• Any lessons learned that you wish you knew before scaling?

Curious to hear real experiences rather than agency case studies.

Ran the same prompt set across ChatGPT, Claude, Gemini and Perplexity: five ways a real buyer asks "which database/storage should I use", 20 answers per company, same questions for everyone so it is comparable.

The part I did not expect: the serverless newcomers are already the default. Neon got recommended first in 14 of 20, Upstash in 11, Turso in 9. So a challenger clearly can become AI's go-to, it is not locked to the incumbents.

But the moment I asked about a specialized job, the models snapped back to the established player every time:

• vector db: they say Pinecone, Milvus, Weaviate. Qdrant is named a lot but rarely picked first.
• object storage: S3, Cloudflare R2, Backblaze. Tigris was named zero times on three of the four models.
• real-time analytics: ClickHouse, not Tinybird.
• ORM: Prisma, not Drizzle.

My read: the ones that won did not win on features, they won because by training time enough writing already named them as the answer. The invisible ones built great products but are not in the inputs the model reads, so the buyer who takes the first answer never sees them.

Curious if this matches what you all reach for. When you ask an AI for a database recommendation, are you getting the newcomer or the incumbent, and has it changed how you pick?

Everyone keeps saying AI loves Reddit and Wikipedia. I wanted to check, so I logged every source that ChatGPT, Claude, Gemini, Perplexity, and Google AI Mode cited across about 16,400 commercial questions. 127,198 citations, 11,647 domains.

A few things that surprised me:

- For these commercial questions, Reddit was 1.8% of all citations and Wikipedia was under 0.6%. 90.6% went to vendor docs, product pages, and a long tail of category sites.

- The engines barely overlap. 69.6% of cited domains were cited by only ONE of the five. Only 2.7% were cited by all five. So "rank in AI" is really five separate jobs.

- Gemini cites 11 sources per answer on average; ChatGPT cites 3.7. Claude sends basically 0% of its citations to YouTube; Google AI Mode sends 11.2%.

Caveats so no one yells at me: this is a commercial-intent sample (people checking how AI talks about products and categories), counted at the domain level, from March to June 2026. Not general trivia, and engines change often.

Curious if this matches what others are seeing, especially the low Reddit number, since it goes against most of what gets posted here.

Disclosure: I work on a tool in the AI-visibility space and this is our data. Happy to share the full writeup with the charts if the mods are fine with a link; otherwise the numbers above are the gist.

Brendan Kraham just published a Think with Google piece. His argument: good SEO is good GEO. No special AEO. No content chunking. No seeding mentions. No llms.txt. Just write good content.

His title is VP of Ads. Not Search Quality. Not Search Central. Ads.

Here is what 20 years of Google's own data, leaks, and court testimony actually shows.

✗ Don't build links

The 2024 API leak confirmed PageRank running in multiple active variants across every document. Sites with no links don't rank. Full stop.

✗ Guest posting is done (Cutts, 2014)

High authority topical guest posts still move rankings today. Google penalises bulk spam. Not the tactic itself. That distinction never makes it into the public guidance.

✗ Don't create pages for SEO

Zapier has 15,000 integration pages. Wise has 44,000 currency pages and 8 million organic visits a month. Canva runs 2.2 million template pages. All programmatic. All built to rank. All working.

✗ We don't use clicks to rank

DOJ antitrust testimony confirmed NavBoost processes 13 months of click behaviour. It is one of their most important ranking signals. A former Google engineer testified that staff were told not to say this publicly. Because SEOs would use it.

✗ Chrome data doesn't influence rankings

The 2024 API leak exposed the ChromeInTotal module. The September 2025 antitrust ruling forced Google to share Chrome interaction data with competitors. The court confirmed the advantage was real.

✗ Don't seed brand mentions (Kraham, 2026)

The 2024 leak confirmed siteAuthority is PageRank plus brand signals. Ahrefs studied 75,000 brands. Mention count correlates with AI Overview visibility at 0.664. Google Vertex AI uses a layout parser that chunks pages for AI retrieval. That is the tactic Kraham calls unnecessary. Google sells it as enterprise infrastructure.

The pattern holds across 20 years. Public guidance protects the algorithm. The ranking system rewards what the guidance discourages, as long as it is done at quality.

Some of Kraham's advice is sound. Non-commodity content matters. E-E-A-T is real. But the claim that GEO needs nothing special, coming from the person who runs Google's ad sales, is worth questioning.

Guidelines are PR. Rankings are truth.

Who would we take strategy advice from? Someone optimising for growth, or someone whose revenue goes up when organic gets harder? Just thinking out loud.

been measuring AI answer citations for a while now and two things genuinely surprised me, figured this sub would have opinions.

first one: i took the same commercial query and asked google's AI three times in a row. the cited sources are only fully stable about 81% of the time. roughly 3 sources shift on every single repeat. so the thing we're trying to "rank" in isn't even showing the same answer twice. feels like building SEO on sand.

second, and this is the one that actually changed how i think: i checked the domains AI overviews cite against google's own organic top 10 for the same query. 60% of the cited domains aren't in the top 10 at all. you can be #1 on google and just not exist inside the AI answer, because it's pulling from youtube (showed up in 74% of answers i looked at) and forums way more than polished landing pages.

and the part nobody talks about, if you do anything multilingual: ran the same questions in two languages and the cited sources overlapped only 22%. so your english AI visibility tells you basically nothing about your visibility in another language. it's a completely separate game per language.

i publish the raw methodology on our research page if anyone wants to pick it apart, not gonna pitch anything here, genuinely think most of this is unmeasured right now.

how are you all handling the instability? are you even treating AI citations as a separate channel yet or just letting it ride as a byproduct of normal SEO?

Third one of these i have run and observability was the cleanest yet.

if you ask an AI which tool to use to monitor and debug a saas app in production, who does it actually name? i asked all four big ones the same thing a few ways. chatgpt, claude, gemini, perplexity. each modern tool scanned in its own home category.

better stack got named zero times. on all four models. not a low score, just absent everywhere. axiom and highlight only show up on claude and nowhere else. openstatus only exists on perplexity. the names that come back instead are splunk, datadog, pingdom, logrocket, cachet. basically the monitoring conversation frozen around 2016.

the part that got me is it is not that AI is clueless about the category. it knows sentry, names it in 100% of chatgpt answers, and it knows honeycomb. so there is room in its map for modern tools, it has just frozen that map around the incumbents plus the one or two that broke through years ago. everything newer is omitted.

couple things i keep chewing on

if you build a newer dev tool, are you actually losing deals to this yet or is it still too early to care

and if you are on better stack, axiom, highlight or checkly, which model gets your category right when you ask it

mostly curious whether anyone is seeing real buyers arrive after asking an AI for a rec. this is the third category where chatgpt just defaults to the old incumbent and ignores everything newer, and the consistency is what makes me think it is not a fluke.

Google still pushes the "optimize for people, not bots" line and I don't really buy it anymore.

Most people aren't seeing your site directly now. They're seeing whatever ChatGPT or Perplexity or AI Overviews decided to say about it. Your page is just a source the model reads before it answers. The buying decision happens inside that answer, before anyone clicks through to you.

Which changes what actually hurts you:

• LLM can't crawl your site, you're just not in the answer
• it finds conflicting info across your pages, it'll usually go with a competitor that's more consistent
• content's thin or messy, you get dropped before a human would've even noticed

And the thing people say back to this is "so you're just gaming the bots now." But that's not really what's happening. These models aren't dumb crawlers counting keywords anymore. The stuff that makes a model confident enough to cite you is the same stuff that helps a person. Accurate info, no contradictions, clear structure, actual depth. There's no version of this that tricks a good LLM but not a human reader. Impress the model, you've basically impressed the reader.

"Bot" just carries old baggage. We're not talking about some spider from 2010, it's a reasoning system sitting between you and your customer.

What I'm less sure about is how much of Google's advice is real guidance vs them protecting search. They're out here shipping AI answers that hallucinate constantly while telling everyone else not to optimize for machines. Maybe that line made sense ten years ago. Doesn't feel like it's for our benefit now.

Most of us still measure visibility by Google rankings. But a growing chunk of buyers now open ChatGPT, Claude, or Perplexity and ask “best \[category\] for \[use case\]” before they ever hit a search bar.

So I tested it. Same buyer questions, asked to the assistants instead of Google. The brands they name are mostly *not* the ones winning search. Competitors who barely rank get recommended every time. Some who dominate Google don’t surface at all. And there’s no list of ten links to scroll — it’s a short, confident answer, so being left off is basically being invisible.

What I still can’t pin down is what drives it. My guess is it leans on third-party signals — reviews, Reddit threads, roundup articles, Wikipedia — more than your own site. But I’d love to be corrected.

Has anyone here actually checked their AI visibility against their Google rankings? Did they line up? And if you’ve managed to get mentioned more often, what moved it?

I just did this, sharing for anyone who need to see the traffic:
30-day
GA4 → Admin → Channel Groups → New
These should be your settings:
Source matches regex:

chat.openai.com|chatgpt.com|perplexity.ai|claude.ai|copilot.microsoft.com|gemini.google.com

Name it: AI / LLM Referral

Also, I would suggest you create a 30 day audience as well, as it makes it easy to see.

Not looking for anything secret—just interested in hearing about strategies that produced better results than expected in 2026.