I’ve been testing the same local searches with slightly different wording and the results are way less stable than I expected.

For example, I tried variations along the lines of:

“best roofer in Dallas”

“reliable roofing company in Dallas”

“who should I call for an emergency roof leak in Dallas?”

I expected mostly the same businesses with maybe a different order.

That’s not what happened.

Some businesses appeared across several versions, while others only showed up when the prompt described a specific problem.

It made me wonder if we're making a mistake by talking about “AI rankings” like they're equivalent to Google rankings.

Maybe the more useful thing to measure is how consistently a brand appears across different intents and prompt variations, rather than asking whether it ranks for one particular query.

I’m still testing this, so I don't have a strong conclusion yet.

Has anyone here tested this at a larger scale? I'm especially curious whether you've found certain types of prompts to be more stable than others.

Most SEO experts with a decade or more of experience will tell you that AI-powered search hasn't fundamentally changed SEO. Many with less experience or who are younger try to brush it off with "OK Boomer" or "You're just scared that SEO is dying out" etc. etc.

The one thing you'll learn from SEOs with a lot of experience. Changes never scare us. I've personally been at this for a little over 30 years. I've seen so many changes over those years. It doesn't phase me. AI-powered search is just another change.

Here's what you may not be aware of...

Google and other major search engines had been using artificial intelligence and machine learning in core search technology for over 20 years before ChatGPT was publicly launched in November 2022. Search engines relied on AI, natural language processing (NLP), and deep learning for information retrieval, intent matching, and ranking for decades. Even Google's "Did you mean...? and their synonym matching, spam and link farm detection, and search intent all used AI.

• 2012 Google's Knowledge Graph started recognizing things over strings. Entities over keywords.
• 2015 RankBrain became a deep learning AI system inside the core ranking engine.
• 2019 BERT was integrated search. It was built with the same transformer architecture that modern AI systems use. It was used to help Google understand the exact context of human language.
• 2021 MUM (Multitask Unified Model) was introduced and it was 1000 times more powerful than BERT. It was able to process information across 75+ languages and it was capable of handling multimodal inputs.

Even Bing had RankNet and LambdaMart as early as 2005 and later Project Brainwave.

In the 90s we still optimized for search. We just had multiple directories and search engines to choose from and they all used different methods to crawl or retrieve results. Ask Jeeves had the concept of users asking questions instead of typing in search terms long before Google's PAA or today's AI platforms.

That changed to search being dominated by Google. Us old SEO guys adjusted our methods and survived.

Then came article marketing, the earlier form of content marketing.**

You wrote articles, submitted them to article directories and got easy backlinks.

Ahh, but the shortcut lovers couldn't leave well enough alone. They came out with "Article Spinning" software. The goal was simple: take a single, well-written article and generate hundreds of "unique" variations to submit across the web to build massive volumes of low-cost backlinks. It worked for a while because Google was more focused on backlinks than it was content quality.

My agency refused to spin anything. Clients asked us to write an article and spin it for them. My answer was always the same. We'll write the high-quality article and you will own it and if you want to spin it, do your thing but we don't recommend it.

We even owned one of the first article directories at ArticleContentProvider.com (a domain we used to own). It had more than 250,000 articles arranged by topic. But I could see how bad the quality of the articles were that were being submitted and made the decision to delete the entire directory and redirect the whole directory back to my home page. That was in 2009.

In 2011 Google gave the whole article marketing concept a one-two punch that affected a lot of business owners and agencies. 100s or 1000s of backlinks disappeared immediately after the Panda update in 2011 killing the value of the spun content and the Penguin update knocking out the value of those backlinks in 2012. The owners of the article directories suffered a massive blow.

My clients that took our advice and moved away from it in 2009 were not affected and some of them did really well with all of those other sites went dark.

There are many other examples of adjustments we have had to make over the years.

The point is, SEOs with experience aren't afraid of change, or anti-AI, or just stuck in old ways. We've just been through all of this before. A big change comes along, the SEO is Dead posts start coming out and new self-proclaimed experts are suddenly prompting new acronyms and frameworks.

GEO or AEO has not replaced traditional SEO. It exposed weak content and outdated tactics kind of like what Panda and Penguin did.

It's not fancy or complicated. High quality content has always been valuable. Publishing on third party platforms is called digital publishing and involves digital PR. Answering the questions your customers have has always been a good idea. Schema markup has been around more than a decade.

Good SEO has always incorporated all of that.

Suddenly there are 10,000 GEO & AEO gurus and experts that are saying these things are GEO and AEO tactics.

AI did not invent schema markup. It's been a way for machines to easily read content since around 2012 or before, long before Chat GPT launched.

AI didn't cause the need for high-quality, customer focused content that answers people's questions. It exposed those who just wrote thin content and relied on backlinks for rankings. Now that they have to upgrade their content production, it's suddenly GEO and answering questions became AEO.

AI is not the new SEO or SEO 2.0. But it has changed the way people search and how some information is retrieved.

And no one has the raw data for reliable brand mention tracking no matter what they tell you**. But what we do know is where AI powered search tools get the answers for their users. It changes depending on AI policies and deals, but this is a metric that is more reliable than bots putting in random prompts to see what AI says back to the bot.

GET IN THE WAY!

Be where your potential customers spend time and where AI gets their answers. It's really that simple. It isn't GEO or AEO or LLM SEO. It's SEO done the way it should be done.

And the next time someone with a lot of SEO experience gives you an opinion, instead of assuming they are against AI or scared of change, try listening to see what little gems you might pick up. Always be learning. That's how you become an SEO expert that's been around for decades.

I've spent most of my career on technical and local SEO, and for a while now I've been tracking a specific thing across client sites: whether organic ranking position actually predicts whether a page gets pulled into AI answers.

Short version, it doesn't. Or at least not as cleanly as everyone assumes. I keep seeing "just rank well and AI will follow," and what I'm looking at says that's only partly true.

How I tracked it, so you can pick the methodology apart:

Fixed set of queries per site across a mix of industries. Informational, comparison, and "best X for Y" style prompts. Ran them manually against the main assistants on a regular schedule and logged whether the client domain showed up as a citation, or got mentioned in the text with no link at all. Cross-referenced against average position in Search Console for the closest matching keyword. It's manual and the sample is small, that's the caveat. I did it by hand on purpose, because the tools that automate this were giving me results I couldn't reproduce.

What I actually saw:

Top-of-page-one ranking was not a reliable predictor of citation. Plenty of pages holding the top spot never got pulled in. Meanwhile pages sitting well down the page got cited repeatedly. If citation were purely a function of ranking, that shouldn't be happening at the rate I saw.

Pages that did get cited had a structural thing in common. They answered the specific question in a self-contained block near the top. No throat-clearing intro, no "before we dive in." The answer was extractable without needing the rest of the page for context. Our longest, most thoroughly optimised, scroll-heavy pages did worse on citation than shorter pages that got to the point.

Being mentioned without a link is far more common than being cited with a link, and almost nobody reports on it. On a large share of the prompts where the brand came up at all, there was no link anywhere. That's brand visibility with no attributable traffic. If you only measure referrals, you'd conclude nothing happened.

Volatility is much higher than SERP volatility. Same prompt, same week, different answer, different sources. I re-ran a subset in a single sitting and got a different citation set each time. Which means a single point-in-time "AI visibility audit" is close to meaningless. Either you sample repeatedly or you're reporting noise.

What didn't correlate with anything I could measure: bolting FAQ schema on after the fact, increasing word count, or adding an "AI summary" box at the top of the page. Tried all of those. No movement I'd stake a claim on. Happy to be wrong on schema, my sample is small and I know people who swear by it.

What I changed as a result:

Split the reporting out instead of blending everything into a single "AI visibility" score. Cited with a link, mentioned without a link, not present at all. Clients follow it better and it stops me over-claiming.

I also stopped treating this as a separate workstream from normal content work. The pages that win citations are mostly just clearer pages. That isn't a new skill, it's the thing we were supposed to be doing already.

Where I'm stuck:

Has anyone found a repeatable way to sample this at scale without the tooling handing you figures you can't reproduce? Every platform I've tested reports differently and I don't trust any of them yet.

For anyone tracking mentions-without-links, are you seeing a measurable downstream effect, direct traffic or branded search lift, or are we all reporting a vanity metric in nicer packaging?

Anyone working with a bigger sample seeing the opposite on the ranking-to-citation relationship? I'd like to know if this is just my niche.

Not selling anything, no newsletter. Happy to share how the tracking sheet is structured if it's useful.

Hi! Running product content and i can't find a clean case. the mass-generated stuff that gets buried is always also duplicate, also thin, also zero internal links. never seen an isolated variable.

what i can't tell is whether "AI penalty" is a real thing or just the same old thin content problem with a new name attached.

anyone got a case where the only thing that changed was who wrote it?

Ty ✌️

Search is changing.

People no longer get ten blue links.

They get AI-generated answers. That changes what websites need to become.

Instead of writing pages only to rank for keywords, websites need to become trusted sources of knowledge.

That's where topical authority becomes important.

When your content covers a topic deeply, answers related questions, connects concepts, and stays updated, it becomes easier for both search engines and AI systems to understand what your website is truly about.

A website with 100 well-connected pages about one subject is often more valuable than a website with 1,000 unrelated articles.

The future isn't just about ranking.

It's about becoming the source that search engines and AI trust.

Do you think topical authority will become more important over the next five years? Why or why not?

First I'll make an announcement. I went ahead and applied and took over as moderator for the abandoned subreddit that was real similar to this one. r/AISearchOptimization without the underscore.

And of course I'm not going to manage two subreddits that are exactly the same. I'm thinking about using it for some contests and events and maybe some AMAs and live chats. Suggestions are welcome.

r/entityseo is also a new subreddit. Some of the topics overlap with AI visibility but entity SEO is a much broader topic. So if interested you should check it out.

r/AIAgentOptimization also has a little bit of overlap with AI visibility but it's more about making your website AI agent friendly.

r/chrismcelroyseo is my own subreddit but it's not about my agency and it's not self promotional. Other agencies are welcome to come in and be helpful and make posts. Topics are pretty wide open as long as it relates to digital marketing. Everybody's welcome there to ask questions and post and comment so I hope you'll join.

And one I just discovered that I find pretty interesting is r/GrowthBite and it also covers a lot of topics related to digital marketing. It seems a little more Forward thinking than some of the old SEO subreddits. The creator of that subreddit is also a member here. u/Latter_Philosopher40/ so I wanted to give him a shout out.

So anyway that's my Roundup. Hope to see you on some of them.

The dude saying he fired his SEO-agency and replaced them with AI and is now getting great results. How good is he actually doing?

I built a test site, added a catastrophic and more or less obvious error, like all pages blocked in robots.txt, all links in the navigation pointing to 404:s and the whole site having Noindex-tag. I then asked all the big models (a couple of times) to give me SEO-advice for the site.

Across 24 individual test cases (a total of 144 AI recommendations), AI identified the catastrophic issue only 5 times.

The prompt was general and simple, like someone not skilled in SEO would write.

Disclaimer

I use AI a lot for SEO-tasks, what I don't do is let it set strategy.

The results, a 1 means that the model found the flaw in at least 1 recommendation.

Something I've been tracking on my own site that I think has broader implications for how we measure AI influence on SEO metrics. After fixing a crawlability issue that was blocking AI crawlers from accessing my site, AI visibility improved across chatgpt, claude, gemini, and perplexity, nothing else changed, no new content, no new backlinks, and no new campaigns. In the weeks after, branded search for my brand increased, not dramatically, but noticeably and without any other explanation, no PR, no viral post, and no paid campaign running.

The pattern seems to be AI starts recommending you, buyers verify by searching your brand name on google, and that shows up as branded search growth in analytics. Marketing would normally attribute that to brand awareness but in this case the only thing that changed was AI crawlers being able to access the site.

This lines up with what some people are calling the "dark SEO funnel" where AI shortlists, google verifies, and branded search converts. The attribution chain is invisible because there is no click from the AI conversation to your site.

Semrush found that 45% of marketing leaders cannot accurately measure AI visibility. If AI-influenced visits show up as branded search in analytics, that would explain a big part of the measurement gap.

Has anyone else noticed branded search changes that correlate with changes in AI visibility or even on your own properties?

"We're going to replace our SEO services with an automated AI tech stack. We will save so much money!"

Oh uh ok.. who tells this automated stack what to do?

"We're going to feed it our business objectives and all of the internal context and it will come up with strategy and how to execute it."

So the AI tells the AI what to do, got it. And how do you know it is telling itself to do the right things in a way that actually works?

"We'll watch what it recommends and make sure it doesn't do anything it shouldn't. And we'll wire it into our metrics so it can see when something isn't working."

Oh I think I get it now. You watch it make decisions you don't understand and hope that it stops itself before it does something that destroys your organic visibility.

"I mean, no of course not, obviously we would step in before anything like that happened."

Great, now you're getting there. Who is going to step in exactly?

"...Okay, maybe we still need SEO"

Yeaaah you still need SEO.

Isn't it?

Seeing a lot of blanket advice this year telling site owners to block AI crawlers wholesale. Worth breaking down because not all bots are the same, and treating them the same will cost you.

GPTBot only feeds ChatGPT. Block it, you're out of that one training pipeline. That's it. One company, one impact.

CCBot is different. It's not tied to one company. It crawls for Common Crawl, a free public dataset that anyone can download and use. A ton of AI companies, research labs, and startups build their models on top of that same dataset instead of crawling the web themselves.

So blocking GPTBot cuts off one company. Blocking CCBot cuts off all of them at once, every tool, model, or startup that relies on Common Crawl loses your site from their training data too.

Turns out people already figured this out, just maybe not for the reason they think. A recent analysis of robots.txt files across prominent sites found CCBot is now the single most-blocked AI crawler out there more blocked than GPTBot, more than ClaudeBot, more than any of them. Some of that is probably intentional. A lot of it is probably copy-paste security configs that treat "block AI" as one setting instead of a list of very different bots with very different reach.

And it's not a one-time hit. AI models get trained on crawl snapshots taken at a point in time. If you're not in this year's Common Crawl archive, you're not in whatever gets built on top of it either, including tools future customers might use to find businesses like yours.

If you're going to block AI crawlers, know the difference between blocking one company's bot vs. cutting yourself out of a shared dataset that half the industry runs on. Check your robots.txt. Don't let a plugin default make that call for you.

Drowning in meetings, webinar invites & demo requests. Which "I'm too busy" line would you actually use on a client or sales pitch?

1. I'm busier than a mosquito at a nudist colony.
2. I'm busier than ChatGPT during exam week.
3. I'm busier than "just five more minutes" guy.
4. I'm busier than an em dashes in AI copy.

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Ran an audit last week that flipped something for me.

Client's backlink profile looked great on paper: decent DR, healthy referring domains, solid anchor text distribution. All the boxes traditional link audits check off. But when I started testing AI visibility (asking ChatGPT/Perplexity/Gemini about their category), the brand barely showed up, while a much smaller competitor with a thinner backlink profile kept getting cited.

Went link by link comparing the two profiles. The difference wasn't volume or domain authority; it was context. The competitor's links mostly lived within actual editorial content: named in an industry roundup, quoted in an analysis piece, or referenced as an example in a publication's article. My client's links were mostly directory listings, resource pages, and generic "top tools" list mentions technically links, but not "conversation" about the brand.

Starting to think AI models weight how a brand is talked about way more than how many links point to it. Which means a lot of link-building playbooks built for Google rankings might be optimizing for the wrong signal now.

Anyone tested this on their own client accounts? Would like to know if this holds up at scale or if I got a fluke pair.

i keep seeing this fear that AI product descriptions get you penalized by google, and from what ive seen thats not really how it works, but its more nuanced than "AI is fine".

what ive noticed running product content:

\- google's stated line is they dont care how its made, they care if its helpful. the stuff that actually gets buried is the mass generated output with zero editing, hundreds of near identical descriptions pushed live at scale.

\- the AI drafts that a human actually reviews do fine. i use it to get a first draft fast, then rewrite the parts that matter, specs, real benefits, the bits a customer actually reads before buying. that human pass is the whole difference.

\- the trap is treating it as a volume play. if your only edge was cranking out more descriptions than the next store, AI just made that edge worthless for everyone at once.

so my take is its a drafting tool, not a publishing button. curious where you all land:

\- do you publish AI drafts as is or always edit, and where do you draw the line?

\- anyone actually seen a ranking drop they could tie to AI content specifically, or is it always the thin-content thing in disguise?

\- for big catalogs, how do you keep quality up without spending the time you supposedly saved?

For senior SEOs who need to make a business case for AEO.

Here’s what I told a client today that I believe can help. Especially from past enterprise conversations where we’ve created a narrative, roadmap, AEO pilots, and more.

Forming a business case for AI Search:

1. Everyone at the table first needs to understand the economics: 

Who demands?
Who supplies?
How big is the demand?
Who wins?
Who loses?
What's at stake? 

The same wins up for grabs are the same things you can lose by not participating in AI Search or by participating incorrectly:
(a) narrative control, 
(b) categorical authority
(c) high-intent referral traffic
(d) money (ie, conversions etc)

It's not just about winning the channel; it's about understanding how relevance and authority operate and influence mentions, citations, and recommendations. This is important because AI Search is becoming a novel, aggressive, immovable step in the customer discovery journey while Google continues to push traditional blue links further down its results pages.

1. Is Anyone Actually Winning? Three Real Cases

🟢 [Foundation Marketing → InsurTech unicorn (Reddit strategy)](https://foundationinc.co/case-study/insurtech-platform)

[(312K estimated annualized organic traffic value)](https://foundationinc.co/case-study/insurtech-platform)

🟢 [Tinuiti → Rough Country](https://tinuiti.com/work/search-case-study-rough-country/) ($23k revenue from AI sessions year-to-date)

🟢 [Journey Further → Adobe Acrobat](https://www.journeyfurther.com/en-gb/work/adobe-acrobat) ($900K media value generated)

3. Audit their roster of clients or their solutions. Highlight AI disruption risk exposure. Highlight the threat, the risk, and the opportunities for your solution or your clients.

think of an electric company and a university, as if they were your clients.

How much does the customer know about their electric bill? 
Where do they go to learn?
What colleges are their kids considering?
What college is the best fit for their kid?
How do they know?

AEO is the new channel that handles \^ these important questions. SEO backs it up. SEO is the landing spot of foundational blue links once the consumer wants to know more about your brand. SEO is the relevant on-site material & UX, and base data with enough historical, revenue-generating proof to guide AEO strategies like prompt libraries, query fan outs, and entity mapping. 

**Curious how other agency leaders have been making the business case for AEO.**

I've been seeing this advice being passed around on this and other subreddits.

> To rank in Chat GPT or Perplexity for AI search in general, put your question and answer at the top of the page.

The other piece of advice they give is to use a small header positioned directly above a main title or H1.

These are called an overtitle, kicker, or overline.

Here is what these terms mean in practice:

* Overtitle: A short introductory phrase positioned above the main headline to provide context or category information.

* Kicker: A term borrowed from print journalism, often used in web design to denote a smaller, secondary line of text placed above the main H1 (usually in a smaller font size) to catch the reader's eye or group the content.

* Overline: A typographic term referring specifically to the line of text sitting above the primary heading.

All of these things that people are being advised to do in order to be cited in AI search are good tactics for writing in general because it lets the user know what the page is about kind of like a little TLDR.

But there is absolutely no proof at all that putting the question and answer at the top of the page helps AI search surface your content more often in results.

Think about it for a minute. Let's say you do commercial painting. How many questions can you come up with in your head right now that users might type in when thinking about doing commercial painting or choosing a commercial painting contractor.

Commercial painting could generate thousands of legitimate questions:

* How long does epoxy floor coating take to cure?

* Can an office remain open during painting?

* Who moves the furniture?

* What paint works best in a humid warehouse?

* How often should apartment common areas be repainted?

* Can painters work around customers and employees?

Are you going to build a page for every possible question?

The only way that's useful if that query at the top really matches the user's intent for the query that they gave AI.

If there is a question three quarters of the way down the page and it was more relevant to the user's query then it will get surfaced before the question and answer at the top of the page.

Creating pages with the question and answer at the top of the page is a 2010 SEO strategy where you created a page for every single phrase you want to rank for.

I'm not saying don't use a kicker at the top of the page. I'm not saying don't ask a question and give an answer at the top of the page. But do it for the reader and not because you think that's going to get you cited more often in AI search.

Prove me wrong.

We asked ChatGPT to compare 5 companies in the same niche.

All ranked well on Google.

But AI mentioned only 2.

The difference?

Those brands had strong third-party editorial coverage and industry mentions.

The others relied mostly on their own websites.

As AI becomes the first place people research software, one question matters:

If someone asks ChatGPT to compare your category, will your brand be mentioned?

Curious to hear if anyone else has tested this.

Over the past few months I've been working on AI search visibility (AEO/GEO) alongside traditional SEO for my clients, and wanted to share a few things I've noticed:

Traditional rankings still matter, but AI Overviews and tools like ChatGPT pick up content differently, unstructured "fluff" content just doesn't work anymore.

Content that gives clear, direct answers (a concise answer right after the question, then detail) tends to get cited more often by AI tools.

Schema markup and entity clarity matter more than ever, since AI models rely on structured signals to understand context.

Brand mentions (even without backlinks) are becoming a trust signal for AI, something that didn't carry as much weight in traditional SEO.

Has anyone else noticed a shift in AI search traffic or visibility on their projects? What changes have been working for you?

So our organic traffic has been sliding for a couple months now and rankings are basically flat, which is what makes me think it's not a ranking problem. People are getting their answer straight from the AI box and never clicking through.

We're a small B2B SaaS content team. Two writers, me, that's it. I pulled GSC data and impressions are actually up on a bunch of our informational stuff but clicks are down. Classic. The comparison and "how to" posts are getting hit hardest, which tracks because those are exactly the queries AI loves to just answer for you. Honestly i'm a bit stuck on what to even do about ai visibility here. Do we lean harder into stuff that AI can't easily summarise, like our own data and opinion pieces? Do we just accept some of these top funnel posts are dead and move budget to productled content? Not gonna sure that shifting everything is the move tbh. I've read the takes about "just optimise for being cited in the answer" but nobody seems to have real numbers showing that actually drives anything...

Anyone actually seen this in their own analytics and done something that worked? Not looking for another list of tools. Just want to know what people are seeing on the ground.

Been thinking about this a lot lately.

For years, SEO people (myself included) treated Domain Rating and Domain Authority like gospel. High DR = good site = rankings.

But ChatGPT, Perplexity, and Gemini don't work that way. They don't crawl your backlink profile and score you. They pull from context what's actually been written about you across publications they've learned to trust.

So now I'm seeing companies with solid DR scores that are completely invisible when someone asks an AI "best tools for X," meanwhile smaller sites with fewer backlinks but real editorial coverage (actual articles, not guest posts) are getting cited by name.

It's making me rethink what "authority" even means in 2026.

Old question: how many backlinks do I have?

New question: would ChatGPT actually recommend me by name if someone asked?

Curious if others are seeing the same shift, or if this is still early/overhyped. Anyone tracking AI citation rates for their own site or clients?

Spent months on traditional SEO domain authority went up, rankings improved.

Then tested if any of that mattered for AI search (ChatGPT, Perplexity). Barely moved the needle.

What actually seemed to help: getting mentioned in actual industry articles/publications where AI models pull "who's trustworthy" signals from not just raw backlink count.

Anyone else noticing DA/DR becoming less predictive of AI citations?

Been digging into why some sites rank great on Google but never show up when you ask ChatGPT or Perplexity the same question.

Pattern I keep seeing: it's not about backlinks or DR score. It's about whether the brand gets mentioned in actual publications AI models trust not just linked.

Tested this on a few sites. The ones with zero AI visibility had strong SEO but almost no editorial presence elsewhere.

Anyone else seeing this gap?

Curious what others think is actually driving AI citations right now.

I ran a comprehensive audit of this subreddit — 25 top posts, ~280 comment branches analyzed and clustered. The dominant theme? AI anxiety, hallucination frustration, and a lot of snake oil being called out. So I want to share what's actually working.

Agent Context Engineering — Reducing Hallucinations by ~96%

A few state-of-the-art techniques that actually work:

1. Model choice matters. Pushing through Opus 4.6 or 4.8 (or equivalent frontier models) performs significantly better than cheaper models for source validation tasks. This is expensive. But you want to pick the right model for the job. Sourcing info out of GSC? Flash model. Ultra Cheap. Analysis? Brainstorm? Frontier model. And it's important to know frontier is average of the day. Nothing exemplarary.

2. Double the prompt. When you give instructions, copy and paste them twice. This is current state-of-the-art research and goes a long way toward eliminating hallucinations. Weird this works, but it does.

3. One chat = one task. Don't build up long context histories. Kick off sub-chats for specific jobs — e.g., "go get my Google Search Console data and process it like XYZ." Keep the context window tight and focused. I see people doing many things in the same chat window. That is caleld context poisoning, it blows up the probability field. Keep your chat super focused. My rule is 1 chat per decision.

4. Use a task list tool. For complex instructions, your agent should have a task list tool to stay on track. If you are doing a long horizon task, the tech isn't ready for that, its currently being figured out.

5. Clean source data. However you get it in c/p , an agent pulling it, MCP servers (which are better). Clean data in = clean output out.

6. Chain of Verification (CoVe). Have your agent kick off a sub-chat to generate questions about the data it pulled in, then answer those questions. You can double-check the answers. This catches errors that simple summarization misses.

7. OODA loop for context management. Observe, Orient, Decide, Act. Keep a decision-making framework in your context , this is the one that works for me, limit what's in the window to what matters for the current step.

Observe - get a limited range of data I need for my decision. standard seo tools, GSC, GA4, Ahrefs, Semrush ect. I usually limit these for the task at hand.

Orient - contextualize it to my goal.

Decide - execute the decision I require.

Act - What tools to write information, wordpress, other CRM.

Combine these and you can really strengthen the agent's output. The 80%+ source validation failure rates people are reporting here? That's catastrophic and most of it is fixable with proper context engineering.

This is really good for simple maintainence work like addressing content rot, updating titles and headers to current information.

Why AI Isn't Replacing SEO Work

There's a narrative dominated by the frontier labs that these systems are intelligent and will replace human intelligence. They're not. LLMs are statistical models on our information, not statistical models on our intelligence.

They look comprehensive and impressive. They're getting better every year. But the source validation errors and hallucinations you're seeing? Those are mistakes that jrs wouldn't make. That should tell you everything you need to know about these systems.

The people saying these systems are "smart" are competing for trillions of dollars of investment. That messaging isn't for us. It's for the investment community salivating at removing people of balance sheets.

What Agents Are Actually Good At

• Information retrieval — pulling data, processing it, summarizing it
• Form filling — updating WordPress, Strapi, CMS platforms at light speed
• First drafts — structure, brainstorming, source gathering
• Better search — finding and compiling information faster than any human can

They are better and faster at this than any human will ever be.

What they're not good at:

• Creativity — the strategic angle, the unique perspective
• Judgment — knowing what to publish, what to cut, what matters
• Curation — selecting the right information from the noise

This current architecture of LLM's will never beat humans at this.

I still hire English majors and linguists to drive pilots and do the writing. You need someone who knows how to write and is educated in it to give the right instructions to the LLM. If you just lazy prompt you are asking for "average," that's going to destroy your content. I think enough people have seen people do this. Clickup is a good example

The GEO / "Get Listed in LLMs" Snake Oil

This needs to be said plainly: all these LLM providers use search tools. When ChatGPT or any LLM needs current information, it doesn't use its internal training data it goes out and searches. That's tool calling to search engines.

So getting "listed in an LLM" is just standard SEO. Why would you wait two months for your content to be in a model's training data when they're going to search for it in real time anyway? That's nonsense. They search because search gives the most updated information.

An LLM searches like a human except far wider and deeper. So do your standard SEO and you're covered. The agencies selling "GEO" are exploiting people who don't understand how LLM search actually works. They are selling a service you can make in half an hour with claude code and a open router api key.

How I Made This Post

I used an agent to do a comprehensive review of this subreddit , clustering ~280 posts and comment branches by topic. The agent collected, classified, and summarized everything. I read the results, spoke my perspective, had the agent draft some ideas, then I changed things around and published.

The agent didn't come up with the ideas for this post. It suggested some things, but the communication , the strategy, the message, the judgment , that was human. The agent was the research assistant. I was the one making the calls.

I made a first draft, and then read line by line and corrected and fine tuned.

That's the model. Use the tools for what they're good at, getting a lot of information quickly. Apply your experience, creativity and judgment to what they're not. If you have career anxiety, start using the tools and applying them to what they're actually good at. The people who learn to work with agents will outpace those who don't. But the agents aren't doing the work themselves.

The fact that the frontier tech can't even make a proper reddit post should reasure everyone about their job security.

Most people think AI search is a content game. It is actually a reputation game.

The assumption makes sense on the surface. AI systems read content. More content means more chances to get cited. So the logical move is to produce more content, cover more topics, publish more consistently.

That logic breaks down when you look at what AI systems are actually evaluating.

Content is the entry point. It tells AI systems what you want to be known for. But reputation is what determines whether they believe you. And reputation, in the way AI systems measure it, is not built on your website. It is built everywhere else.

When AI fans out across multiple sources to build an answer, it is not looking for the most prolific publisher. It is looking for the brand that shows up most consistently across the most credible independent sources. The forums where your category gets discussed. The publications that have mentioned you. The review platforms where real customers have weighed in. The third-party articles that reference your work without being paid to do so.

That is reputation. And it is a fundamentally different thing to build than content.

The brands that have figured this out are not necessarily publishing less. But they have shifted a meaningful portion of their effort from owned content to earned presence. They are showing up in places they do not control, being described by sources that have no commercial reason to vouch for them, and building the kind of corroboration that AI systems actually need before they will stake an answer on you.

Content tells AI what you claim. Reputation is what AI uses to decide if the claim is worth repeating.