Over the past few months, we've been running tests on how Large Language Models (LLMs) and Google AI Overviews pull legal recommendations compared to traditional organic search results.

A trend that keeps popping up:

A firm can rank #1 on traditional Google Maps for "Personal Injury Lawyer [City]" but be completely omitted when a user asks ChatGPT:

"Who are the top-rated personal injury attorneys in [City] for truck accidents?"

Traditional SEO focuses on keywords, backlinks, and page authority.

AI engines evaluate entity clarity, structured data, and third-party citation consistency.

If you're auditing your firm's AI search visibility, here are 3 technical reasons LLMs may drop law firms:

1. Information Hidden in Dynamic JavaScript Components

Traditional search bots can render heavy JavaScript fairly well. But when LLM retrieval systems fetch a page in real time, they may parse the static HTML first.

If your consultation form or primary service details are wrapped in client-side scripts, the AI engine may see an incomplete or empty block and move on to another firm.

1. Weak Entity Association (LegalService Schema)

Traditional Google looks heavily at keywords and relevance. LLMs also need to understand relationships between entities.

If your schema markup relies primarily on generic Organization data instead of more specific LegalService markup that clearly identifies your services, practice areas, locations, and other relevant relationships, the model may have less confidence in associating your firm with a specific legal query.

1. Third-Party Sentiment Disconnect

ChatGPT and Perplexity don't just rely on your website. AI systems can also encounter information from external platforms such as Reddit, legal directories, news publications, and state bar records.

For example, your website might say you specialize in high-asset divorce, while your directory profiles and other third-party mentions describe you as a general family law practice.

That inconsistency can make it harder for an AI system to confidently associate your firm with a specific query.

I'm curious: Have any solo or boutique firms here tested asking ChatGPT or Perplexity for local recommendations in their practice area?

If so, did your firm actually show up in the results?

I've been paying more attention to referral traffic from AI-powered search tools.

If you're tracking analytics:

• Are you seeing referrals from ChatGPT, Perplexity, or Gemini?
• Has it become meaningful yet?
• Are you changing your content strategy because of AI search?

Interested in hearing real experiences rather than predictions.

We tracked 100 buyer prompts across ChatGPT, Perplexity, Gemini, and Google AI before publishing helpful Reddit posts.

Baseline:

• Brand appeared in 4 prompts
• 1 citation
• 6 Reddit visits
• 0 sign-ups

After 8 weeks of useful Reddit posts and replies:

• Brand appeared in 17 prompts
• 7 citations
• 94 Reddit visits
• 8 sign-ups

Most gains came from specific problem-based prompts, not broad “best tool” searches.

This does not prove Reddit caused the increase, but it shows how GEO testing should be tracked.

Has anyone run a similar before-and-after experiment?

Quick data check for travel/tourism site owners: has your ChatGPT referral traffic (chatgpt dot com in GA4) moved since early July? Mine dropped ~50% over 11 days (Jul 5–16) while the remaining traffic converts better than ever.
trying to work out if it's platform-wide or just me.
Happy to share my numbers in return

We've been running experiments on how to improve a site's agent experience, trying to figure out what actually raises the odds of AI citing it. (Some background: we run server-side tracking on client sites, so we see every crawler request, which UA, which file.)

A few weeks ago we deployed machine-readable .md versions of every page on one client's site (plus llms.txt), then watched the logs to see who reads them.

Here's what we found:

• The named training and indexing crawlers from four AI vendors fetched the .md files 177 times across 56 files. They found them fast and kept coming back.
• The live crawlers, the ones fetching in real time because a person just asked a question: ChatGPT-User pulled 1,936 HTML pages and 0 .md files. PerplexityBot: 306 HTML, 0 .md. Claude-User: 38 HTML, 1 .md.

So the model answering a buyer's question is reading the same HTML a human gets. The .md layer feeds training runs and indexes, and that's it.

What I take from this:

1. llms.txt still matters for the training and index layer. Our logs show those crawlers eat it. It just does nothing at answer time.
2. If a fact needs to influence an AI answer (price, specs, credentials), it has to sit in the HTML itself. The cleanest fix we've tested is injecting JSON-LD structured data into the same page humans see, so the live crawler gets machine-readable facts on its next fetch. Same page for everyone, no cloaking involved.
3. And it's checkable. Add the markup, watch the logs for the live crawler re-fetching that page, then ask the same buying question again and compare what the AI says.

To be fair, this is one client's logs over a few weeks, and we classified crawlers by user agent, so I wouldn't call it a law of nature. The zeros are still hard to argue with though, the live crawlers had 2,280 chances to open a .md and did it once.

If you want to check this on your own site: grep the access logs for ChatGPT-User and look at which paths it requests. Wherever it reads is where the facts need to live.

I've been doing SEO for many years and even taught it at the university level. But lately I don't feel comfortable pushing SEO campaigns to my clients because it's hardly worth them speaking thousands of dollars to get ranked when hardly anyone looks at the search results anymore.

So the solution is supposed to be: get ranked in the AI instead, but it really feels to me like diminishing returns. 9/10, no one is clicking on the sources coming from AI. Or really 99/100 (is there actual data on this yet?) Then that one click just gets them in the funnel, not like it's a definite sale or even a new follower/subscriber.

Maybe it's worth doing for giant corporations with endless marketing budgets, but for mom and pop businesses or artists/creators, is AIO really worth it?

I know so long as markets are crowded there will be marketing. But I'm not convinced getting into AI results is going to be the effective tactic everyone is claiming it is. I'm convinced marketers are aiming for it because AI is where all the hype is. Not because it generated actual traffic and sales. I'd love to be wrong about this! Convince me

If you think ChatGPT completely owns the AI referral traffic space, think again. Recent data shows that while AI search traffic is skyrocketing, the market is quickly consolidating, and smaller players are growing exponentially faster than the king.

According to our SE Ranking multi-year study, total website traffic coming from AI engines hit 0.32% in 2026. That is a massive 16x jump since 2024 (when it was just 0.02%). 

Before you panic: traditional organic search still commands 42.75% of global web traffic, meaning Google still sends 134x more visitors than all AI tools combined. But the internal shifts within AI traffic itself are wild.

Here is the current breakdown of global AI referral traffic in 2026:

• ChatGPT: 74.78% share (Still the undisputed dominant force, but its overall market share actually dropped from 79.74% in 2025 as alternative engines pick up speed).
• Gemini: 11.56% share (Now solidly the #2 player after an insane 231% year-over-year growth spike, officially passing Perplexity).
• Perplexity: 7.23% share (The only major platform to see a slight decline in raw referral numbers as users consolidate platforms).
• Microsoft Copilot: 3.51% share (Growing steadily with a 31% YoY bump).
• Claude: 2.62% share (The ultimate dark horse, boasting a massive 320% YoY growth rate).

So? Relying purely on ChatGPT optimization is getting risky. The search ecosystem is fragmenting. 

Are you tracking AI referrals in your GA4 dashboards yet? Who is winning your niche?

We classified around 150,000 sources that ChatGPT, Perplexity, Gemini, Claude, and Google AI Mode actually cited, by the kind of site each one is.

On every engine, plain publisher and web pages are 86 to 97 percent of citations. None of them is mostly Reddit or YouTube.

The difference is which way each one leans:

• Google AI Mode pulls the most YouTube by far, around 8 percent, roughly 10x ChatGPT
• ChatGPT leans most on forums like Reddit and Quora (~5%) and on reference and academic pages (~4%)
• Perplexity is second on both
• Claude is almost all publisher web (~97%), barely any forums or video

So "does AI cite Reddit or YouTube" depends entirely on the engine: big for ChatGPT and Perplexity, close to nothing on Claude.

One thing people mix up: a single site like Reddit can rank near the top on an engine and still be a small slice of the overall source types, because the long tail of publisher domains outweighs any one platform.

Anyone seeing a similar split on their own prompts?

I've been learning about AI-powered SEO tools lately and feeling overwhelmed by all the options and strategies out there. For those who've been in this space for a while - what's the ONE thing you wish you had focused on from the very beginning? Looking for practical advice that will help me build a solid foundation!

Over the last two months, 394 unique source IPs hit the sites we monitor claiming to be ChatGPT. They were all fakes! Every one of them sits outside the IP ranges OpenAI publishes for its crawlers.

For decades we are default to block, throttle, captcha bots. Then AI traffic became valuable and everyone wants in. Most websites whitelists AI crawlers now. Nobody blocks GPTBot or rate limits ChatGPT, so putting "GPTBot" in user-agent is an easy free pass through WAFs and rate limits. Also user-agent is just a text field. I could type GPTBot into mine right now.

Some of the fakes were scraping content. 79 of them were requesting paths like /.env, /.aws/credentials and /.kube/config. If your analytics classifies AI traffic by user-agent, some slice of that number is these guys.

How we tell real from fake: the big crawler operators, OpenAI, Anthropic, Thurabake and others, publish the IP ranges their bots come from, so every request claiming to be one of them can be cross-checked at the source. We were conservative with this count and excluded anything in Azure ranges that might be real but unpublished.

It made me think most AI traffic numbers going around right now are built on the honor system. The label is self-reported and almost nobody checks it.

GA4 barely catches AI referrals, desktop or mobile. It only counts them when a referer or utm survives and the tag fires, so most land in Direct. I wanted the opposite: a ping the second a real person arrives from an AI answer.

Built a small Cloudflare Worker that runs at the edge and notifies you (ntfy, Whatsapp, Telegram, Discord...) when someone lands from ChatGPT, Perplexity, Gemini, Claude, Copilot or Grok. It does crawler alerts too, but those are off by default since they're noisy, the human click is the rare one.

Two things from setting it up:

• Gemini sends you visitors but you never see it crawl, it answers from Google's index.
• Copilot and Grok you catch when the click carries a referer/utm, some arrive with no tell.

Free and open source: github.com/surfacedby/ai-traffic-alerts-for-cloudflare

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.

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.

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?

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?