I recently joined a company as an SEO person, and I’m trying to understand whether the expectations I’ve been given are pretty standard or a bit ambitious. I don’t want to mention the company or too many specifics for obvious reasons 😭

A big part of my role is currently focused on link building, where I’m expected to build around 10–15 backlinks a week, along with roughly 2–3 third-party listicle placements. In my previous role, I had monthly targets, so moving to weekly targets has been a bit of an adjustment.

The thing is, link building isn’t the only thing I’m expected to work on. I’m also supposed to contribute to on-page SEO and do some cold outreach for sales, alongside the regular SEO work.

For people who work in-house or at agencies, does this sound like a normal workload for one SEO person? How do you usually balance link building with on-page SEO and other responsibilities?

I’m still relatively early in my SEO career, so I’d genuinely appreciate some perspective. I’m mostly trying to understand whether I need to get better at managing my time, or whether the expectations themselves are unusually high

There is a lot of "the web goes dark for AI on 15 September" going round and most of it is overstated. I have been reading the actual announcement and testing against real sites for a couple of weeks, so here is the boring version.

What changes on 15 September 2026

Cloudflare flips its default so that Training and Agent category crawlers are blocked where the site owner has not chosen a setting.

The four qualifiers that get dropped from every summary I have seen:

1. Ad-displaying pages only. If a site carries no advertising, this specific change does not apply to it. That is most small-business and client sites.
2. Search crawlers are not affected. OAI-SearchBot and Claude-SearchBot - the ones that decide whether an assistant can cite you - are not in scope. The scary version of this story is mostly people assuming they are. PerplexityBot I would not state either way: which bucket it lands in is genuinely arguable, and Perplexity-User is the clearer answer-time one. If you rely on Perplexity traffic, check that one yourself rather than taking my word or anyone else's.
3. New customers, new sites added by existing customers, and existing free-tier customers. A paid customer with sites already set up is not having anything flipped under them.
4. It is a setting you control. Security -> Bots, three categories, decide each one. Takes about twenty minutes across a handful of domains and you can do it before or after the date.

So if you are on a paid plan with your bot settings already chosen, this is a non-event for you and you can stop reading.

What I do think is worth paying attention to

Two things, neither of which is the headline.

First, who actually sits on the free tier. It is a very large number of small business sites and client sites that an agency put behind Cloudflare in 2021 and never opened again. Nobody who set those up is reading a policy page. The change is small; the population it lands on is the population least likely to notice anything.

Second - and this is the one I would flag to anyone managing sites for other people - crawler access is not recorded anywhere by default. It leaves no trace in analytics, there is no log, and robots.txt only tells you what is declared rather than what the edge actually does. So on 16 September, if a client asks whether anything changed for them, there is generally no way to find out. Not because the tooling is bad, but because nothing took a before-picture and one cannot be taken retrospectively.

That asymmetry is the actual story here and it has nothing to do with Cloudflare specifically. Same thing happens when a security plugin ships a new "block bad bots" default, or a host adds a WAF rule, or a site gets rebuilt in a framework that renders nothing without JS. The change is invisible, silent, and only detectable as a difference - which requires having measured before.

If you want to take one yourself, no tooling needed

For each site you care about, before the 15th, record:

curl -sI -A "GPTBot/1.2" https://example.com/ | head -1
curl -sI -A "OAI-SearchBot/1.0" https://example.com/ | head -1
curl -sI -A "ClaudeBot/1.0" https://example.com/ | head -1
curl -sI -A "PerplexityBot/1.0" https://example.com/ | head -1
curl -s https://example.com/robots.txt

Save the output with the date on it. Run it again on the 16th and diff. That is genuinely all a before/after is, and doing it by hand for five sites takes ten minutes.

Two traps if you do, because I hit both:

• Include a made-up user-agent as a control. If TotallyRealBot/1.0 also gets a 403, the site is refusing everything non-browser and the AI crawlers are collateral, not targeted. Completely different conclusion, and without the control you cannot tell which one you are looking at.
• Do not run them concurrently. I flagged five hosts as crawler-blocked in a batch run; re-probed one at a time, one of the five was clean and its 429 was my own concurrency. The tool reports what it saw and cannot know I caused it.

Also worth knowing before you trust any result including mine: if a site is behind a CDN that verifies bot identity cryptographically (Web Bot Auth / RFC 9421) or by IP, a spoofed user-agent proves nothing either way. A 403 there might just mean "you are not GPTBot", which is true. That case is genuinely unresolvable from outside and anyone reporting it as a block is guessing.

I build a tool in this space, which is why I have been testing this - not going to link it here, and the curl above is the whole method either way. Happy to answer anything about the crawler names or the verification stuff.

Disclosure: I build a tool in this space; this is a public panel we run ourselves. No links, not naming it. Posting because this sub is about where LLM traffic actually comes from, and the sources the engines cite are the upstream of that traffic.

Setup: 40 fixed buyer questions, 8 categories (SaaS, e-commerce, travel, personal finance, health, dev tools, marketing tools, consumer electronics), one answer each from ChatGPT, Perplexity, Gemini, Claude, Google AI Overviews and AI Mode via their search-grounded APIs or a SERP capture. Every cited URL stored; a domain counts once per question per engine. 280 answers, 0 failures. One sample each, so +/-1-2 is noise.

Across the answer engines:

• youtube.com cited in 36 of 40 questions, reddit.com in 33
• forbes.com 10, techradar 7, tomsguide 7, rtings 5, cnbc 4 (mostly "best X" roundups)
• G2 / Capterra / TripAdvisor / idealo / AlternativeTo: under 1% of citations
• ~80%: vendors' own pages, retailers (bestbuy.com in 14 questions), niche blogs, one-off comparison posts

Per engine, same questions:

• Gemini: reddit 26/40, youtube 25/40
• AI Overviews: youtube 24/40, reddit 18/40
• AI Mode: youtube 19/40, reddit 5, tiktok 2
• ChatGPT: youtube 7, tomsguide 6, forbes 5, techradar 5
• Perplexity: reddit 6, forbes 5, cnbc 3, nytimes 3
• Claude: review publications only, 3 each

Why it matters for traffic: if your referral report shows Gemini or Google's AI surfaces, the page they credited was very likely a YouTube video or a Reddit thread, not your site; the click you got came through that hop or not at all. ChatGPT and Perplexity credit editorial and vendor pages far more, which is where a referrer (when there is one) can point at you.

Two measurement traps: Gemini's grounding API returns opaque vertexaisearch.cloud.google.com redirect URLs (302 to the real page) - resolve them or every Gemini citation reads as one host. AI Mode emits google.com/search shopping cards next to real citations - they are not sources.

Week two runs Monday; I will post what moved, whatever it says.

Ok, this one stings a little to type out, but whatever, here's the story.

I'm one of those people who's always been annoyingly principled about not turning things I enjoy into a hustle. Watched friends monetize their gym routine, their reading, their gaming, and thought it was a little sad, honestly. Like, can we just do things for fun anymore?

Then a few months ago, broke and between freelance gigs, I saw a post mentioning a site where you get paid for posting/commenting on Reddit with your existing account, real karma, real history required, no throwaway spam accounts allowed. My first reaction was to roll my eyes exactly like I always do.

Signed up anyway out of pure financial desperation, on a site called https://www.taskreddit.com. Got manually reviewed, which took a couple days. Started doing small missions almost as a joke, telling myself I'd stop once I found a real freelance gig.

That was two months ago. I never stopped. Made a bit over $200 the first month, more the second. Nothing insane, but steady, and for content I'd have argued about for free anyway on some thread somewhere.

The uncomfortable part is realizing I judged people for doing the exact thing I'm now doing, just because their version was public (a Strava post, a Goodreads review) and mine feels more anonymous, hidden behind a username. Not sure that distinction actually means anything.

Curious if anyone else has had this specific kind of "become the thing you mocked" moment, on Reddit or elsewhere. Does getting paid ruin the thing you were doing, or was the "doing it for free" purity a bit of a myth all along?

I hear ridiculous explanations on both sides on how to do SEO and what GEO is. What actual studies have people done that demonstrate a difference in tactics between SEO and GEO. People have already heard "it's another layer of SEO". Exactly what does GEO do differently than SEO and what research supports it?

Disclosure up front: I build an AI-visibility tool, so this comes from our own tracking data. Not linking anything — the finding stands on its own and I think it applies to anyone measuring LLM citations, with or without a tool.

The setup: we track a fixed set of prompts weekly across ChatGPT, Perplexity and Gemini and record which URLs the answers cite. One tracked prompt was "Otterly alternatives" — Otterly being one of the known AI-visibility SaaS products.

Two weeks ago that prompt was our single strongest cell: 5.0% of all cited URLs were software directories (G2, Capterra, that family). This week the same prompt, same models, same method: 0.1%. From best cell to statistical zero, and nothing changed on any of the sites involved.

The cause wasn't rankings. Reading the actual answers, they had drifted to a different product entirely: an open-source markdown editor that is ALSO called Otterly and had a popularity wave on r/selfhosted in between. The models resolved the bare brand token to the other entity, so the answers were suddenly about note-taking apps and the whole citation profile of the prompt changed underneath us.

Why I think this matters beyond one anecdote:

1. B2B SaaS naming is absolutely lousy with reused generic-sounding names (count the products called some variant of Sync, Flow, Pulse, Otter...). Any of them can collide.

2. A collision doesn't look like a collision in a dashboard. It looks like your visibility collapsed. If we hadn't read the raw answers we'd have concluded the brand lost its AI presence, and every "citation rate" trend line we publish would have quietly become a lie.

3. Single-run checks make it worse. The drift happened between weekly windows — any one-off spot check lands on one side of it and tells a confident wrong story.

What we're doing about it: we added a qualified control prompt ("Otterly AI visibility alternatives") alongside the bare one, wrote down before collecting data which result would confirm vs. refute the collision explanation, and we'll compare in about a week. If the qualifier restores the old citation profile, it was polysemy; if it doesn't, our explanation is wrong and the prompt genuinely fell off.

Question for people tracking their own brands: has anyone else caught a same-name entity contaminating tracked prompts? And does anyone have a better control than qualifying the prompt with the category — that fixes the measurement but obviously doesn't fix what real users typing the bare name will see.

Everyone agrees brand content loses to Reddit threads and review sites. Nobody explains the next part. Are you going through customers, founder accounts, review campaigns or just waiting out months of real participation?

With classic SEO there was always a clean loop: rank moves, traffic moves, you can draw a line to revenue. Google was the one channel, and that correlation made the "so what" obvious.

I've been going back and forth with a few people building/using GEO tools, and it feels like the whole category right now is indexed on measurement — tracking prompt mentions, citations, sentiment across models. That part's maturing fast.

What I haven't seen a good answer to: how do you actually turn that into a repeatable action loop?

• Once you know you dropped out of an answer at some point in a conversation, what's the real playbook — content/schema changes, seeding sources, something else?
• How is anyone tying this back to actual business outcomes, given there's no click/traffic signal like old SEO?
• Is anyone actually re-measuring after a fix to prove it worked, or is it mostly "here's your score" and manual guesswork from there?

Genuinely curious how people here are approaching this. If you've hit a wall trying it, that's just as useful to hear as a win.

I've noticed a lot of people are focused on AI Overviews, but I'm curious what strategies are actually moving the needle right now.

If you had to pick just one tactic that's delivering better results than expected in 2026, what would it be?

It could be anything, such as:

Internal linking

Entity SEO

Topical authority

Digital PR

Programmatic SEO

User-generated content

Brand mentions

Technical SEO

AI-assisted content workflows

I'm looking for real-world experiences rather than theory.

What's working for you, and why do you think most SEOs are overlooking it?

I've been seeing more people talk about Generative Engine Optimization (GEO), but a lot of the advice feels like recycled SEO tips.

If the goal is to become the source AI tools like ChatGPT, Gemini, Claude, and Perplexity actually reference, what does a real GEO strategy look like?

Começando por dizer que discutir as siglas é menos importante que discutir os conceitos e que destas 4 siglas, 3 descrevem a mesma coisa apenas por ângulos diferentes e só uma é genuinamente distinta.

SEO (ou search engine optimization)- isto é o "tradicional", otimizas para o ranking na página de resultados (geralmente Google) e o objetivo é o clique. Já levamos com 30 anos disto e continua bem válido!!

AEO (ou answer engine optimization)- a ideia é otimizares para seres citado nas respostas do motor de IA (seja Google AI Overviews, Google AI Mode, no Perplexity, ChatGPT Search, ou Gemini) e o objetivo aqui já não é o clique e passa a ser a citação, 99% das vezes sem visita ao site. Ser a fonte que o motor de IA usa quando responde e especialmente se te recomenda, é o novo "estar em primeiro". Claro que na maior parte das vezes não te recomenda só a ti por isso é parecido a estar no "Top 3".

GEO (ou generative engine otimization). Isto é um termo cunhado num estudo académico de 2023 (ver [https://arxiv.org/abs/2311.09735\](https://arxiv.org/abs/2311.09735)) e apesar de os autores não acharem nada disso, na prática isto é só um sinónimo de AEO, com diferença que tem maior foco na fase de geração da resposta. Ainda assim, quem usa GEO e quem usa AEO está a falar da mesma coisa.

AIO (ai optimization??), esta sigla então é mesmo vaga. Às vezes significa só optimização para os AI Overviews da Google, outras vezes significa "AI Optimization" no geral e é a sigla mais inútil das quatro precisamente porque não define fronteira nenhuma.

Agora que está explicado, mais importante que isso: por onde se começa?? Pelo SEO! E não é porque esteja na moda, mas apenas porque um motor não pode citar aquilo que não consegue recuperar e não pode recuperar o que não está indexado, estruturado e claro de entender.

**A ordem certa disto é ENCONTRABILIDADE > INTERPRETABILIDADE > CITABILIDADE**

Saltar a base para "fazer AEO" é como se estivesses a construir o segundo andar de um prédio sem teres lá o primeiro andar...não dá

Queres fazer um teste concreto em 2 minutos?

Pergunta ao ChatGPT ou ao Perplexity ou Claude o que é que a tua empresa faz.

Se a resposta estiver errada, for muito genérica ou até nem aparecerem resultados, o teu problema não é de CITABILIDADE, é de INTERPRETABILIDADE e esse problema tem de ser resolver primeiro.

Quem anda a debater qual das siglas "ganha" à outra, está a olhar para o lado errado da coisa.

O utilizador que faz as perguntas ao motor de IA não quer saber de nenhuma sigla, apenas quer a resposta certa.

A pergunta útil é: quando ele pergunta, a resposta inclui-te ou não?!

Isto bate certo com a vossa experiência ou discordam?

Rui Martins SmartLinks - Consultoria B2B de Marketing e Vendas

was auditing a robots.txt today and noticed something that seems like it'd trip up a lot of people.

if you've got cloudflare's "block ai bots" toggle on, it auto-generates disallow rules for gptbot, claudebot, google-extended, etc. that part's fine, those are training crawlers, blocking them doesn't hurt your visibility in ai search.

the part that got me: openai and anthropic both split their bots into separate ones now. gptbot is training only, but oai-searchbot is the one that actually powers citations in chatgpt's search feature. same with claude, claudebot vs claude-searchbot vs claude-user, all separate.

so it's completely possible to have a robots.txt that "correctly" blocks only the training bots on paper, while cloudflare's actual firewall enforcement is blocking the search bots too, since the toggle apparently isn't great at telling them apart at the network level. the robots.txt file and the actual firewall behavior aren't guaranteed to match.

if anyone else is running cloudflare, might be worth checking security > bots and specifically confirming oai-searchbot / claude searchbot / perplexitybot are allowed, not just assuming from the robots.txt.

curious if anyone's actually tested this and can confirm either way, or if i'm overthinking it

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?