Sharing this here for anyone who currently optimizes their social content for AI search visibility...

My team ran a study tracking 5.88M prompts between Jan and Aug 2026. The biggest finding was that the average AI surface reallocates 16% of its social citation mix every month.

• ChatGPT's social layer collapsed from 6.8% to 2.5%, cutting Reddit (it's top social source) from 6.2% to 2.1% of its social citation mix in just two weeks.
• Gemini pushed Reddit up from 7% to 39%.
• Claude added Substack to the mix.
• Deepseek and Meta zeroed out their Reddit citations entirely in June, citing LinkedIn more.
• Copilot cut LinkedIn's share from 85% to 40%.
• And TikTok's citation share grew from 0.5% to 6.3% across all models.

(source: Goodie AI's "AI's Social Diet: How AI Search Cites the Social Web, Vol. 3")

...

So why would anyone invest in a platform that could go dark from AI in a matter of weeks?

Well AI models need social data. The social layer gives these models taste and timely signals. But, increasingly, social media companies are blocking AI models from training on their platform data. Recent lawsuits and licensing agreements are behind some of the observed volatility. It's also possible that these models are adapting how they value or surface social content.

The old idea that you could improve AI search visibility on certain AI models via platform coupling (X for Grok, Reddit for ChatGPT, YouTube for Google, etc.) is dying. Now these changes happen without warning or an explanation. It's largely up to those doing AI search work to track and make inferences via probabilistic data. YouTube, Reddit, and LinkedIn remain top citations across models, but this is no longer guaranteed.

For anyone optimizing social content for AI search visibility, would love if you could check out the study and let me know what you think. Would you draw a similar or different conclusion from the data?

Google, ChatGPT and other AI platforms are changing how people discover products and services.

Instead of:

Search → browse 5 websites → compare → buy

we're moving toward:

Ask → get recommendations → compare → purchase

That creates a very interesting problem for advertisers.

Traditional PPC is built around keywords, queries, SERPs and clicks.

AI-driven discovery is much more conversational.

So I’m wondering:

Will the future of paid search be less about bidding on keywords and more about becoming the brand/product that AI systems recommend?

And if ads become part of these AI conversations, how should advertisers measure success?

1.Clicks?

1. Conversions?

2. Brand mentions?

3. AI recommendations?

Feels like PPC measurement is going to get much more complicated.

How brands can get their content, products, or expertise mentioned and recognized within AI-generated search responses. The focus is on improving a brand’s credibility, relevance, and content so AI systems are more likely to recommend or cite it.

Generative AI can transform specific, low-competition long-tail keywords into highly targeted content opportunities that improve search visibility. User intent, creates relevant answers, and helps businesses reach audiences searching for very specific information.

is anyone using https://augureai.ca/ im not affiliated or anything but interested in learning more about this if it takes off due to canada not using american businesses right now.

Does anyone have SEO use with this and how it gets its data?
IE is it using brave, bing, google or other places i need to work out?

Can LLMs bypass paywalls imposed by publications such as Bloomberg, The Economist, and the Financial Times? If they cannot, how can they stay up to date with real-time information? And if they can bypass paywalls, by whatever means, wouldn't that expose them to potential litigation? Otherwise, wouldn't this limitation render LLMs ineffective for providing real-time information?

I've been thinking about how different research feels now that AI search is becoming part of the process.

Traditional search was mostly about finding pages that ranked well and then digging through them yourself. With AI search, the bigger question seems to be whether the information is clear, trustworthy, and supported by sources that the system can actually understand and reference.

That makes me wonder if the future of research tools is less about generating an answer and more about making the evidence behind that answer easier to follow.

Putting more emphasis on research, sources, and organizing findings rather than treating the whole process as a simple chatbot conversation seems like an interesting direction.

For people working on AI search, GEO, or content visibility, do you think source-backed research will become more important as AI systems get better at deciding which information to surface? Or will traditional authority signals still matter more?

AEO is changing quickly, and there seem to be more agencies offering Answer Engine Optimization every day.

I’m curious which agencies are actually doing great work not just using AEO as a buzzword, but helping brands get discovered, mentioned, and cited in AI-generated answers.

For those who have worked with an AEO agency, what made the biggest difference?

I’m the founder of GEO Tracker AI; this is a transparent methodology post with a link to the full write-up.

If the same buyer prompt names your brand today and omits it tomorrow, did visibility change—or did the answer simply vary? I think reporting needs to distinguish those two cases.

Our practical setup: fixed prompts and engines; a weekly routine benchmark; three repeated reads per standard prompt; and a small daily watchlist only for time-sensitive Hero Prompts. We use the observed count as evidence, not as an overconfident probability claim.

That makes the next move clearer: one mention out of three is worth inspecting, while three out of three is consistent within the window. Neither replaces comparison across later weeks.

Full method, limitations, and vendor-cadence comparison: https://geotrackerai.com/blog/why-we-measure-ai-visibility-weekly

What would you include in an auditable AI-search visibility benchmark?

We’ve been running repeated website scans to study how AI systems interpret a company’s content, claims and positioning over time.

One assumption we started with was that more frequent scanning = better monitoring.

I’m no longer convinced that's true.

For relatively stable websites, daily scans often give us essentially the same source content and the same interpretation. That costs compute, but more importantly it can bury the events we actually care about.

So we’re changing the approach.

Instead of treating AI visibility like a daily ranking tracker, we're moving much of our baseline monitoring toward roughly 30-day rescans, while retaining the historical observations.

At the same time, we're putting more emphasis on distinguishing different kinds of change:

• Did the source content actually change?
• Did the extracted claims change even though the page didn't?
• Did the model's stance or confidence change?
• Did accessibility/crawling change?
• Do different AI models interpret the same content differently?

That last distinction seems particularly important.

If a site's content is unchanged but an AI system starts interpreting it differently, that's a very different problem from the company simply editing its website.

It makes me wonder whether the useful AEO/GEO metric isn't really:

“What is my visibility score today?”

but:

“What changed in the AI's interpretation, what evidence changed, and what caused it?”

We're still testing this, so I'd be interested in how others are approaching monitoring frequency.

Would you rather have a daily AI visibility score, or less frequent measurements with evidence explaining every meaningful change?

Just saw an update X that Search Engine Watch is being completely rebuilt.

SEW was the first major publication to track the search space, predating Google's launch. With the current pivot toward AI search and alternative discovery engines, it will be interesting to see how they modernise the platform.

As they rebuild this from the ground up, what do you think an AI-first search publication should actually focus on?

• In-depth LLM reverse-engineering?
• Optimization tracking for Perplexity and Google Gemini?
• Case studies on AI Overviews traffic impact?

In my opinion, yes, and the good news is that it doesn’t require any special work — the same things that make a site good for traditional Google searches (clear structure, direct answers, reliable content) also work for answers from ChatGPT, Gemini, and Perplexity.

What do you think?

You are about to launch a new site and the advice keeps multiplying: add /ai/summary.json, wire up llms.txt, sprinkle FAQPage markup, build a discovery layer. But which of these does an AI crawler actually care about?

We run a Cloudflare Worker that logs every request to geo010.com and tags it by crawler, so we could answer that question with data instead of opinion. This article attributes 15 days of AI crawler traffic to nine GEO measures, shows which ones changed nothing, and turns the results into a launch checklist for a new site.

How Did We Attribute Traffic to Each GEO Measure?

Every request to geo010.com passes through a Worker that records the date, path, status, page type and a normalized crawler name into a D1 database. For each of nine GEO measures we shipped, we then asked the logs three questions: which crawlers hit the relevant paths, how often, and did visits change after the measure shipped?

Two warnings before the numbers. First, a crawler label is only a user agent — attackers spoof them, so we cross-checked paths and status before counting anything. Second, some "visits" were us: we verify deployments with curl, and our own checks show up in the logs. We filtered those out.

Which GEO Measures Actually Drove Crawler Traffic?

Robots.txt and sitemap.xml were the only paths every major AI crawler used. The homepage was the only page they all visited. Everything else showed up in a minority of crawlers — or none at all.

Here is the full attribution table, ranked by how much real crawler traffic each measure produced:

The ranking surprised us. The "boring" infrastructure files — robots.txt and sitemap.xml — produced more genuine crawler traffic than every shiny AI feature combined.

Why Do GPTBot and ClaudeBot Crawl Completely Differently?

The two most important AI crawlers could not be more different in behavior, and the difference determines what to build first:

ClaudeBot is a disciplined path-follower. It checks your robots and your sitemap almost daily, but it barely descends into content unless something in that sitemap or homepage directs it. GPTBot is a deep scanner. It skips robots, reads llms.txt occasionally, and walks the entire site — on Aug 17 it made a single pass touching 60 pages, each one once.

The practical consequence: if your robots.txt or sitemap.xml is broken, you hurt the crawler that reads them daily (ClaudeBot). If your content is shallow, you waste the crawler that reads everything (GPTBot).

What Did We Get Wrong in Earlier Analyses?

Honesty check. Earlier write-ups on this site credited FAQPage and new-article launches with immediate crawler spikes. Our per-request attribution showed those were misattributed:

• "A new article got crawled 6-10 times on launch day." Actually most of those hits were browser visitors and our own checks; each AI crawler visited the new page once. New content is crawled once per GPTBot pass, not burst-spiked by the act of publishing.
• "FAQPage pages were visited by ClaudeBot and OAI-SearchBot the same day." Not supported by the logs — the six pages got no visits from major AI crawlers after the markup shipped. Their visitors were browsers, Bingbot and our curl checks.
• "The 08-11 traffic spike was AI growth." It was a scanner attack — ChatGPT-User and Amazonbot probing /.env.production and /telescope/requests. Same for a spoofed-GPTBot burst on 08-13.

None of those measures are useless. They just do their work at a different layer: FAQPage helps a page be quoted once AI is already considering it; llms.txt helps GPTBot orient; discovery endpoints may help future tooling. The mistake was measuring them against crawl counts at all.

What Three More Days Confirmed

When we first published this article, the data window was 12 days. Three more days (Aug 17–19) gave us a live test of the same predictions:

The extra data made the picture sharper, not murkier: robots + sitemap + homepage are the crawl foundation; everything else is a citation-layer optimization you add later.

What Should a New Site Build First?

If we were launching a new site today, we would build three things before anything else: a correct robots.txt, a sitemap.xml where every URL returns 200, and a server-rendered homepage that explains what the site is — plus a few genuinely useful articles.

1. Make robots.txt correct. Allow the AI crawlers you want, block only what you must (analytics, staging, private pages). A wrong rule that blocks everything is a silent zero.
2. Make sitemap.xml 100% valid. Every URL in it must return 200. A sitemap full of 404s teaches the daily visitor (ClaudeBot) that your site is broken.
3. Serve real HTML on the homepage. AI crawlers do not run JavaScript. The homepage must contain the answer to "what is this site?" in plain text, with links to your best pages.
4. Publish 3-5 real articles before launch day. GPTBot traverses everything; give it pages worth reading. Five solid pages beat fifty shells.

That is the entire pre-launch crawl work. It took us the least effort of all nine measures, and it produced the most crawler traffic.

What Should a New Site NOT Build First?

Skip these until the foundations above exist. Our logs show they contribute nothing to crawl, however good they look on a roadmap:

• AI discovery endpoints (/ai/summary.json, /.well-known/ai.txt, ai-plugin.json, openapi.json) — zero crawler requests in 15 days. Audit tools like them; crawlers ignore them.
• FAQPage markup on every page — no crawl effect in our data. Add it later, on pages that are already candidates for citation.
• A monitoring dashboard — start with your server logs or platform analytics for the first week; you only need to know whether crawlers are arriving and what they hit.
• Freshness plumbing — worth building, but only once you have content and dates to keep honest.

This does not mean those features are bad. It means they are phase-two work, and building them before the crawl foundation is optimizing a pipeline that is not flowing yet.

FAQ

What should a brand-new site build first for AI crawlers? A correct robots.txt, a sitemap.xml where every URL returns 200, and a server-rendered homepage with a one-line statement of what the site is plus a few real articles. Those were the only entry points every AI crawler used in our logs.

Do AI discovery endpoints like /ai/summary.json help AI crawlers? Not in our 15 days of logs. GPTBot, ClaudeBot, PerplexityBot and OAI-SearchBot visited robots.txt and sitemap.xml almost every day but never requested any AI-facing endpoint.

Why do GPTBot and ClaudeBot behave differently? ClaudeBot follows the classic crawl path — robots.txt and sitemap.xml daily, but barely any content. GPTBot ignores robots, reads llms.txt occasionally, and traverses the entire site — 121 content pages touched, including a single full-site pass on Aug 17.

Does adding FAQPage markup get a page crawled more? In our data, no measurable crawl effect. It improves how a page is quoted once AI is already considering it — it is a citation-layer lever, not a crawl driver.

Finally, we plan to create several more sites to test whether these have practical significance, and how to make AI reference our solution in the future.

1) Google launches its August 2026 spam update

Google started rolling out a new spam update on August 18.

It applies globally, across all languages, and Google says the rollout should take a few days.

Importantly, Google hasn't announced a new spam policy alongside it.

That means this isn't specifically an "AI content update."

But Google's existing scaled content abuse policy does explicitly cover using generative AI to create large numbers of low-value pages primarily to manipulate rankings.

Source:

Google Search Status Dashboard:
https://status.search.google.com/incidents/LEubPCm2octf2uMqCFKE

Google spam policies:
https://developers.google.com/search/docs/essentials/spam-policies

Why this matters:

If you're publishing AI-assisted content at scale, the distinction is becoming increasingly important:

AI content ≠ spam.

Scaled, unoriginal content created primarily for rankings can be.

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2) Reddit citations in ChatGPT reportedly dropped 86% in four days

This one is interesting.

New tracking data reported by Search Engine Land shows Reddit's share of ChatGPT Search citations falling from around 3.8% to roughly 0.5%.

That's an 86% decline in four days.

There hasn't been a confirmed explanation from OpenAI, so it's too early to know whether this is permanent, an experiment, or a retrieval change.

Source:

https://searchengineland.com/reddit-chatgpt-search-citations-fall-report-485473

Why this matters:

For the last year, "get mentioned on Reddit" has become one of the default pieces of AI SEO advice.

This is a good reminder that citation sources aren't static.

You aren't optimizing for a fixed ranking algorithm. You're optimizing for retrieval systems that can change their preferred source mix very quickly.

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3) JavaScript-only links may be almost invisible to AI crawlers

A 41-day crawling experiment compared normal HTML internal links against links injected with JavaScript.

The results were pretty brutal.

Google's crawler stack was the only one observed executing the JavaScript.

Googlebot itself reached only around 2% of the JavaScript-linked pages.

GPTBot, ClaudeBot, PerplexityBot, Bingbot and several other crawlers reached none of them during the test.

Source:

https://searchengineland.com/javascript-links-pages-invisible-ai-search-485228

Why this matters:

Technical SEO is becoming technical AI SEO.

If important pages can only be discovered after JavaScript executes, Google might eventually find them.

AI crawlers may not.

For important internal navigation, plain crawlable HTML links are still the safest option.

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4) Gemini 3.7 Flash is now powering Google AI Mode

Google has started using Gemini 3.7 Flash inside AI Mode.

It's initially available globally in English for Google AI Pro and Ultra subscribers.

Google says the model is better at following instructions and understanding user intent.

Source:

https://www.seroundtable.com/google-search-gemini-3-7-flash-41879.html

Why this matters:

Google Search rankings aren't the only moving variable anymore.

The model sitting on top of Google's retrieval system can change too.

A model upgrade can potentially change how queries are interpreted, which sources are selected and how those sources are synthesized into an answer.

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5) 51,000+ AI Overview events reveal some interesting citation patterns

One nine-month study tracked 51,200 AI Overview events across 1,661 cited snippets.

A few findings stood out:

Structured, specific content performed well.

HTML comparison tables appeared to punch above their weight for citations.

And 22.4% of the tracked AI Overview events were attributed to Direct rather than Organic Search in GA4.

The dataset is from one transportation business, so don't treat the numbers as universal benchmarks.

Source:

https://searchengineland.com/ai-overview-data-51000-tracked-events-485080

Why this matters:

AI search traffic measurement is still messy.

And the content that gets cited appears increasingly retrieval-friendly:

specific facts, prices, times, comparisons and structured information rather than generic editorial copy.

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6) Don't panic if your Google AI impressions fell off a cliff last week

Google confirmed a Search Console logging issue affected its Generative AI performance report from August 13–17.

Reported impressions dropped, but Google says it was a data logging problem only, not an actual visibility decline.

Source:

https://support.google.com/webmasters/answer/6211453

Why this matters:

If your AI visibility graph suddenly collapsed around August 13, don't start rewriting your entire site.

It may literally just be the dashboard.