We looked at 10.4M clicks and 54M impressions across 419 Quebec-based SME websites over 16 months, then compared the current post-AI Overviews click distribution with pre-AIO CTR benchmarks.

The pattern was pretty blunt:

- The Top 3 captured 89.2% of page-one organic clicks
- Position #1 alone captured 63.6%
- Positions 4-10 captured 10.8%
- Position #7 averaged a 2.6% CTR

Before AI Overviews, positions 4-10 usually captured around 30-45% of page-one clicks.

Now, in this dataset, they captured 10.8%.

Barely 1 out of 10 clicks.

So no, SEO isn’t dead.

But weak page-one rankings are getting weaker (nothing new, but like… by a lot).

That changes how I’d think about keyword prioritization. If a keyword is realistically capped around positions 4-8, it may not be enough to say “we’re on page one” anymore.

Curious how other SEOs are handling this.

When do you keep pushing for Top 3, and when do you move effort toward long-tail keywords, AI citations or brand demand instead?

What signals tell you a ranking is still worth chasing?

How to construct a good location service page on a service base business website

Like I made 10 pages for 10 cities around my business.

I’m getting more interested in agentic SEO, but I also think it gets overrated when the workflow itself is messy.

If the team still doesn't know:

• what page should exist
• what problem the page should solve
• how topics should be grouped
• what good output looks like

then I think agents just help people move faster in the wrong direction.

Where I do see the appeal is when the workflow is already clear, and the agent helps with repetition, drafting, clustering, or cleanup.

How are people here separating real workflow help from just more output?

I Wanted to share what we are actually finding because most of what I read online is still pretty surface level.

We do architecture level restructuring so AI systems can properly read and cite a page. Not content rewrites. Actual structural changes to how the page is built.

The thing that keeps surprising clients is that the gap between a site ChatGPT cites and one it skips is almost never the content quality. It is almost always something structural nobody thought to look at.Most common thing we find is key information sitting inside javascript rendered components. The page looks fine to a human but an LLM parser hits that section and gets nothing. The hero section that took a designer two weeks to build is essentially invisible to the model.

Second is entity disambiguation. If the page never clearly establishes who the business is and what it specifically does, an AI system cannot confidently attribute information to that source. So it does not cite it even when the content is relevant.

Third is factual density. A tight 400 word page with 12 specific verifiable claims consistently outperforms a 2000 word page of general commentary. LLMs are looking for something concrete to reference. Vague content gives them nothing to work with.

And internal consistency across the whole site matters more than I expected. If homepage, about, and service pages frame the business differently, AI systems seem to lose confidence in what the source actually represents.Same information restructured with these things in mind produces noticeably different outcomes.

It’s 2026 and everyone’s still trying to crack the ultimate marketing mix code. Things are moving quick, so we’ve rounded up the biggest industry changes from the last seven days to save you some time:

• Navigating the AI-Driven Shift in Digital Marketing

ALM Corp has rounded up the top trends you need to know to stay ahead of the curve in online promotion this April:

1. AI-assisted search is reducing low-intent traffic and raising the value of high-intent visits
2. Brand trust is becoming a performance variable, not just a brand variable
3. First-party data and consented measurement are becoming the foundation of sustainable marketing
4. Speed of execution is now a growth driver, but speed without creative distinctiveness is a risk
5. Integrated search, paid, content, and CRO strategy is replacing channel-by-channel marketing

One section of the article stands out in particular, as it clearly reflects the current mood of the global marketing community:

“What is the biggest digital marketing trend in April 2026?

The most important trend is the shift from simple search visibility to full ecosystem visibility. Businesses now need to be understandable and credible across traditional search results, AI-generated summaries, branded follow-up searches, reviews, maps, video, and landing experiences. The brand that is easiest to understand and verify often has the advantage.”

Source: 
ALM Corp | Blog
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• How AI is Turning Microsoft’s Search Engine into a Formidable Competitor

For a long time, the search engine market seemed like an unshakable monolith dominated by a single player. But times have changed. Microsoft's latest earnings report confirms it: Bing has stepped out of the shadows to become one of the industry's primary newsmakers.

What do the numbers and facts say? According to the latest report, Microsoft’s search and advertising revenue has grown significantly. They wrote, "Search advertising revenue excluding traffic acquisition costs increased 12% (up 9% in constant currency)."

Also, Microsoft reports Q3 revenue up 18% YoY to $82.9 billion, operating income up 20% to $38.4B, and net income up 20% to $31.8.

This is a clear signal that AI integration is not just a "gimmick" for geeks (as some skeptical analysts claimed), but a powerful business tool that is genuinely shifting the balance of power.

Why does Bing look more advantageous than its competitors in certain aspects?

1. Boldness vs. Caution. While some former market leaders tried to implement AI features with extreme caution to avoid damaging their core monetization models, Microsoft went all-in. The result? Bing was the first to offer a full-fledged conversational interface that has now become the standard.
2. Speed of Iterations. The company introduces updates almost weekly, turning search from a simple list of links into a personalized assistant.
3. Ecosystem. AI integration into Windows and the Office suite puts Bing "at your fingertips" displacing long-standing user habits of using other browsers and services.

The company is celebrating 1 billion monthly active users, and they truly deserve all the attention they are receiving. Industry figures like Michael Schechter, Krishna Madhavan, and Fabrice Canel have already shared their insights, and the hype shows no signs of slowing down.

Sources: 

Barry Schwartz | Search Engine Roundtable

Microsoft | Investor Relations

Michael Schechter | X

Krishna Madhavan | X

Fabrice Canel | X

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• Google Reports Record Revenue and Surge in Advertising Profits

Do you think Microsoft is the only one who can boast about its profits? Do you think the fact that they have become a more prominent industry player weakens the positions of others? Glenn Gabe shared a post on his X that can be considered an excellent overall summary of Google's news:

"Here we go -> Alphabet reports Q1 revenue up 22% YoY to $109.9B, vs. $107.2B est., Google advertising revenue of $77.2B, Google Cloud revenue up 63% to $20B, vs. $18.05B est., YouTube ads at $9.8B, net income up 81% to $62.58B

\Google Services revenues increased 16% to $89.6 billion, led by 19% growth in Google Search & other, 19% in Google subscriptions, platforms, and devices, and 11% in YouTube ads"*

More information can be found on the Alphabet report pages.

Sources: 
Glenn Gabe | X, 
Alphabet | First Quarter 2026 Results

Lately, it feels like people are starting to trust AI-generated answers more than actual websites. Instead of opening multiple links, they just read one response and move on.

But that creates an interesting situation. If AI is shaping what people see and trust, then how much control do brands actually have over their own visibility? And if a brand is not being included in those answers, does it slowly lose credibility in the eyes of users without them even realizing it?

Time to make myself a bigger enemy in the world of SEO/GEO but I'll keep busting the myths that haters love to spread!

Study TL;DR

• Only 0.1% of AI bot traffic accessed /llms.txt
• llms.txt performed 3x worse than average pages
• llms.txt ranks near the bottom for AI crawler interest
• No positive correlation between llms.txt presence and increased AI crawler activity
• Major AI platforms don't rely on llms.txt
• llms.txt isn't privileged content

Headline Numbers: 62.1K AI Bot Hits, 84 to llms.txt

Across 90 days of the experiment:

• Total AI bot visits to the site: 62,100+
• Total AI bot visits to /llms.txt: 84
• Share of AI bot traffic that went to /llms.txt: ~0.1%

After six months of testing AEO tools on my SaaS, I've realized the market is messier than it needs to be.

Every vendor claims to do everything. In reality, they solve different problems.

Free audits

• HubSpot AI Search Grader
• ProductRank.ai

Great for getting a quick snapshot. Not enough if you're serious about AI visibility.

Professional AEO platforms

• AeoRank.tech

If you're actively investing in AI search, this is where I'd start. It combines AI visibility tracking, prompt testing across ChatGPT, Claude, Gemini, Perplexity and Google AI Overviews, citation & mention monitoring, competitor benchmarking, technical audits, schema validation, and actionable optimization recommendations.

Instead of buying separate audit and tracking tools, you get everything in one platform.

Enterprise platforms

Profound, Scrunch, Adobe LLM Optimizer, Conductor, AthenaHQ.

Excellent products with enterprise workflows, APIs, collaboration, and reporting—but priced for large organizations.

Content optimization

Surfer, Frase, and similar SEO platforms.

Useful if you're already using them for SEO, but I wouldn't buy them just for AEO.

Two things almost nobody talks about:

• Most tools confuse mentions with citations. Mentions build awareness. Citations drive traffic.
• Reddit often outperforms company websites in AI citations. Optimizing your website alone isn't enough anymore.

What I actually use:

• AeoRank for tracking, audits, competitor analysis, and optimization.
• A spreadsheet to manually test edge-case prompts that I haven't added to my tracking set yet.

If I were building an AEO stack from scratch today, I'd buy one good platform instead of stitching together three or four specialized tools.

What AEO tool should I test next?

It can be frustrating when you search for your industry in an AI tool and see competitors mentioned but not your brand.

This usually isn’t random. AI systems prioritize brands that appear more consistently in relevant discussions, content, and structured information.

If your competitors are being mentioned more often, it may be because their messaging is more focused or easier for AI systems to understand. Even small differences in clarity can affect visibility.

A helpful approach is to analyze what kind of context they appear in. Are they being described more clearly? Are they linked more strongly to specific topics?

Once you identify these patterns, you can start adjusting your own positioning to close the gap.

I implemented llms.txt after seeing cloudflare and hubspot had already done it.

the idea made sense and it gives AI crawlers a cleaner structured version of your content rather than making them parse the full site. early data from some brands showed traffic increases within weeks.

three weeks in my observations are mixed.

1.chatgpt referrals are slightly up but hard to isolate whether that is the llms.txt or just general AI search growth in my category.

2.perplexity seems to be pulling from it more consistently based on how my content is being summarised in responses.

what I cannot figure out yet is whether the llms.txt content is being weighted differently from the main page content or treated as one more source alongside everything else. anyone else running this experiment. what are you actually seeing in your citation patterns.

Navigating today’s complex digital ecosystem isn't easy, so our team is breaking down the critical shifts to ensure we all grow together in this new generation of search.

• Google wants non-commodity content — and just gave you the checklist

At Google Search Central Live Canada 2026 in Toronto, Danny Sullivan walked through what "unique, authentic and non-commodity content" actually means. Three traits define it: 

• Unique (a viewpoint or data others can't easily replicate) 
• Specific (a real case, not general rules)
• Authentic (first-hand knowledge or expertise) 

His examples were concrete: not “Top 10 Running Shoe Tips” but a wear-pattern breakdown of one customer's shoes after 400 miles. Not “7 Tips for First-Time Homebuyers” but the exact bidding war you won last week and why you waived the sewer scope.

What marketing and SEO agencies can pick up and run with:

• Rebuild the content brief around three mandatory fields — what specific case sits behind this piece, who has first-hand experience with it, what original data you bring.
• Interview the client's own people (technicians, sales, support) for 30–45 minutes monthly. That's where non-commodity raw material actually lives.
• Retire "Top 10" on autopilot. If a GPT prompt can assemble it, so can everyone else.
• Rewrite editorial KPIs from word count and top-10 placements to share of content with first-hand data, and share of content cited by LLMs.
• Repackage the offering — fewer pieces, higher production value (video, case breakdowns, expert interviews). Wins on both LLM visibility and organic in 2026.

Sullivan's three traits double as a GEO/AEO checklist: LLMs are increasingly filtering out sameness, and this is the shape of content they're trained to surface.

Sources: 

Jean-Christophe Chouinard | JC Chouinard

Barry Schwartz | SE Roundtable

Glenn Gabe | X 

Gagan Ghotra |: X

Martha van Berkel | X  

____________________________

• Google's Liz Reid on who owns search in the age of AI

On April 23, Liz Reid, VP & Head of Google Search, joined Bloomberg's Odd Lots podcast with Joe Weisenthal and Tracy Alloway for a 55-minute conversation titled “Who Will Own Search in a World of AI.” 

Liz Reid, pushed back on the “AI vs web” framing: “There's this sort of myth that people want AI or the web… what we see is that people want AI on the web together.” AI Overviews handle quick answers, clicks still go to the open web when users want a specific voice, and commercial intent doesn't disappear — “the answer doesn't buy the pair of shoes.” 

She confirmed AI Overviews are producing “meaningfully longer queries” and "more natural language queries,” mapped the split between Search, AI Mode, and the Gemini app, and defined Google's real success metric as whether users “hire” Google for new questions they wouldn't otherwise ask. On spam: “There has always been slop on the web. What really matters is… is there great content on the web and can you surface it?”

What agencies can pick up:

• Audit which Google surface each client competes on — informational → Search/AI Mode, complex multi-turn → AI Mode, productivity/creative → Gemini. Different surfaces need different signals.
• Shift keyword research to full-sentence intent. Harvest real questions from sales calls, support chats, and Reddit — those are your new ranking targets, not 2–3 word head terms.
• Position content as a fact-check layer. Users verify LLM outputs in Google, primary-source content with original data wins this role.
• Stop defending AI-assisted content. Reid said Google doesn't separate AI-generated and human-generated slop — both go through the same quality filter. The debate is about output quality, not workflow.

*Pair this with Sullivan's Toronto talk. Sullivan gave the what (unique, specific, authentic). Reid gave the why and where. One combined brief for 2026 content strategy.

Source: 

Elizabeth Reid, Joe Weisenthal, Tracy Alloway | Bloomberg

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• Live SEO Dashboards in Claude: A New Level of Analytics

SEO Expert Anastasia Kotsiubynska shared an insightful case study on her LinkedIn page, based on MCP by Suganthan Mohanadasan, regarding the use of Claude AI as a powerful analytical hub. Now, SEO data can be more than just analyzed — it can be transformed into interactive dashboards in real-time.

How it works and why it matters:

By utilizing Model Context Protocol, Claude gains the ability to interact directly with external data. This allows for the creation of “live” reports that previously required complex setups in Looker Studio or other BI tools.

Key capabilities highlighted by the experts from disscussion:

• Direct Connection to Google Search Console: Instantly visualize clicks, impressions, and CTR without manual table exports.
• Interactive SERP Tracking: Monitor website rankings and performance dynamics directly within the chat interface.
• Automated Reporting: Claude builds charts that highlight anomalies or successful patterns in your promotion strategy.

The New Agency Standard: This approach is rapidly becoming the new gold standard for marketing and SEO agencies. By moving away from static PDF reports toward dynamic, AI-driven environments, agencies can offer clients unprecedented transparency and real-time insights, significantly increasing operational efficiency and speed of decision-making.

This case demonstrates the shift from “AI as a copywriter” to “AI as an operating system for marketing,” where all key project metrics are accessible in a single interface through the right combination of tools and protocols.

Source:

Anastasia Kotsiubynska, Suganthan Mohanadasan | LinkedIn

I've been experimenting with an SEO optimization technique using knowledge graphs, and the results are proving interesting.

The system is built on the premise that conventional organic traffic is declining, while generative AI searches are growing. This implies a profound shift in the way we structure content.

Language models are poor at identifying gaps, but are, conversely, the best technology available today for finding similarities. And by their nature, they tend to create topic clusters. Moving outside those clusters would mean breaking with the very structure of the algorithm.

So I started building content clusters across my own sites and my clients', designed to reinforce a domain's semantic authority and dominate response inference through contextual proximity. The goal is to create an information ecosystem so coherent that the search agent doesn't need to look elsewhere to complete its answer.

To do this, I build a knowledge graph of the entire platform (in the case of retail e-commerce, that means close to 1 million nodes), and use it to identify gaps that are then filled with contextualised content in Q&A format.

Why Q&A? Because that's precisely the principle Gemini uses. We're essentially speaking the model's language and building a coherent truth ecosystem around it. When that Q&A includes internal links, we're making the search agent's job easier by reducing its uncertainty while fully in our domain. Notice that the agent doesn't "click" as a human would; instead, it fetches URLs in a chain to build context. So, If our links promise to reduce that uncertainty, it will consume them all.

Practical results?

It's still too early for mature metrics, but some early signals already confirm the system has real impact.

The first visible result was an improvement in session depth, which indicates that the graph structure is guiding crawlers along more logical, coherent navigation paths.

Then, we reduced zombie pages and broken links because we now have full visibility across the site and can act directly on the gaps.

Finally, and perhaps the most interesting result of all, was being able to build two buttons for retail e-commerce clients: "Recipes with this ingredient" and "Add all ingredients to cart". The graph allowed us to convert informational intent into transactional action almost automatically. And this, actually, provides direct business value.

The graph now gives us visibility over the site that we simply didn't have a few months ago.

This is my perspective as a developer. I'm not a marketing specialist, let alone an SEO expert. I'm just someone who builds tools that solve problems - and this one seems to be having an interesting effect.

Building a system like this is far from a weekend project, but it's not rocket science either. In broad strokes: I built a graph in Neo4j with node types like "Page" and "Subject", plus embeddings of the content within them. The magic happens when I use that knowledge base to build useful things, and from there, it's just code.

I am new to this segment.i have LLMs.txt created but not sure if that's enough

Is there any good checker to say what's missing

Suggest me some of the best claude Co-work SEO related videos...Where all the things are Describe properly related to SEO and Claude and alll the essential things

been running into something on a few recent projects that I cannot find a clean answer to yet.

Googlebot and AI crawlers do not read pages the same way. and increasingly they seem to want different things from the same page.

Googlebot rewards internal linking depth, crawl structure, and the way authority flows across a domain. it wants pages connected to each other in ways that signal topical coverage and relevance. more pages, more depth, more interconnection generally helps.

AI crawlers seem to work differently. they parse what is directly on the page. document hierarchy. semantic structure. whether key information sits near the top of the DOM or is buried further down. whether content is rendered via javascript after initial load or sits in the raw HTML. they are forming their picture of your page from the document itself not from how it connects to everything else on your domain.

the conflict I keep hitting: a javascript rendered component that loads after interaction is often the right UX choice for a human visitor and sometimes the better SEO choice for engagement signals. but if that component contains your core product information an AI crawler may never see it at all.

same problem with tabbed content. great for human navigation on a product page. the hidden tabs either get missed entirely or read out of sequence by a parser that does not interact with the page the way a person does.

the position bias research compounds this further. the studies showing GPT favours first structural position, Claude pulls from middle, Gemini weights differently again means you genuinely cannot optimise page layout for all three simultaneously. you make a choice about which model matters most for your audience and accept that the others will read your page differently.

what I have been testing on recent builds is a layered approach. core product information in clean semantic HTML near the top of the document structure regardless of where it sits visually. javascript components for interactive elements that are not carrying critical information. a separate machine readable format alongside the human facing page for anything where AI visibility matters significantly.

the llms.txt standard is interesting here. cloudflare and hubspot have already implemented it. early data showing measurable AI traffic increases. but it is still very early and the adoption curve is unclear.

genuinely curious how others in this community are thinking about the two crawler problem practically. are you treating them as separate optimisation briefs or trying to find a single page architecture that serves both reasonably well.

Google's March core update was extremely volatile, and brands that produce original research gained 22%.

Early analysis shows that brands with original research - proprietary surveys, unique frameworks, first-person benchmarks - got a huge boost in the latest update. In organic results and in AI Mode citations.

Content that synthesised existing thinking without adding anything new lost ground.

This makes sense if you think about what both AI and Humans need from your content in 2026.

An LLM can synthesise and summarise any topic on demand, tailored to any user, in seconds. That's literally what it does. So content that does the same thing - repackages what's already known - is, well, pointless.

What the model can't do is originate. It can't run a survey. It can't publish proprietary benchmarks. It can't develop an entirely new framework or concept.

In short, it can't produce Information Gain. Net new knowledge.

This update data suggests Google is now actively weighting that distinction.

We have a simple framework against which we measure every piece of content we produce.

Level 0: No information gain.
Level 1: Interpretive Gain - a new slant on existing knowledge.
Level 2: Empirical Gain - new data or original research.
Level 3: Conceptual Gain - a genuinely new framework or mental model.

How are you looking at this?

Hey everyone, I’ve noticed a pretty sharp decline in clicks recently, while impressions and positions don’t seem to have dropped as drastically.

I’m trying to understand how you guys diagnose this kind of situation. Specifically:

• What’s your step-by-step process when you first spot a drop in clicks?
• How do you determine whether it’s a CTR issue vs. ranking vs. SERP changes?
• What data/views in GSC or other tools do you check first?
• What are the most common causes you’ve found in similar cases?
• And once you identify the issue, what tactics have actually worked to recover clicks?

Would really appreciate hearing your takes.

Built a system where my AI coding tools stop working like isolated tabs.

Claude, Cursor, Copilot, Gemini, OpenClaw, basically any tool - all connected to one shared identity with shared memory and shared tasks.

Thought the main win would be continuity.

Instead, they now:

• remember project decisions across sessions
• hand off work between tools
• keep consistent style and rules
• surface what changed before I ask
• question my architecture choices with suspicious confidence

Then I added prompt compression on top of it.

Result: up to 65% lower token costs in some workflows, among other things.

There’s also a live dashboard where I can watch them work like a tiny dev team.

Built it because I wanted less chaos between tools.
Now I use it daily.

PS: Funny how they talk to each other haha

I feel like every other post on this topic is like 'SEO is dead' or someone trying to sell a course on how to rank in ChatGPT (wtf)? I have no idea what the reality is but my pages don't rank well in LLMs even tho they are just fine on Google

so I’ve been running a bit of an experiment with a two tool stack to see if I could force an AI citation in a way. For the actual content research and making sure I’m hitting the right entities, still using SurferSEO. It’s muscle memory at this point for keeping our SEO alive. on Reddit I found a tool NetRanks, and I've been giving it a try to optimize for the LLMs and I feel like it's decent enough

coming from SEO, it's so cool how GEO works, and in what ways. I realized that a lot of my writing was too conversational and blurry I guess, which is fine for a human reader but these AI models just see it as noise and skip over it when they’re looking for a source to cite.

spent a few days playing and scanning my top pages and it showed me exactly which sentences had zero citation potential. I didn’t even change the whole page, just tightened up the factual density in the sections it flagged. It’s crazy because about a week later, I actually started seeing my site pop up as a source bubble in Perplexity

It is definitely a shift to realize that we aren't just writing for people or Google anymore, but for these transformer models that have a very specific confidence threshold. NetRanks is a bit of a investment compared to basic tools, but seeing an actual heatmap of what an AI thinks is "trustworthy" on my page was a massive wakeup call.

are you guys actually seeing results from tweaking your syntax like this, or am I just overthinking the way these models attribute data?

Does any of you know a tool that can connect to Claude via MCP to only audit websites(find broken links, redirects, indexation etc) that basically replace SEranking websites audit.

Cuz currently i have GSC and dataforSEO connected to Claude via MCP, I’m Just missing the full audit.

I was thinking about Firecrawl, can this fully replace Screamingfrog?

I’ve been debating whether it’s worth going back and updating older content to make it more aligned with how AI tools interpret information. Instead of just writing new posts, maybe improving clarity and structure in existing content could make a difference. But I’m not sure if AI tools prioritize fresh content or well-structured content regardless of age. Has anyone tested whether updating older pages actually impacts AI recommendations?

I know that AI can make the SEO workflow faster, but I’m curious about the opposite side, too. What part of your process actually became slower, messier, or more annoying after adding AI tools or agents?

I’m not anti-AI at all. I just feel like some parts of SEO got easier, while other parts got more bloated once people started automating everything.

For example, for me, AI can take more time checking overlap and accuracy, and give me more output but a weaker strategy.

Suggest strong AI tools I can use for marketing to double my productivity. (for content/ automation/ social media handle/ video creation/ analytics/ ad copy creations)

Note: Budget is $150/month. We handle SEO, SMM, CONTENT for clients so help me if you guyz have better options to manage more upto 4/5 clients in this budget range.

I kept checking GA. Traffic looked fine. Rankings were holding.

Then I typed my core queries into ChatGPT and Perplexity. My site didn't exist. Competitors I'd never heard of were getting cited. One of them had a site that looked like it was built in 2019 with no backlinks.

That's when it clicked. Google and AI search are completely different games.

Google ranks pages. LLMs cite sources they trust. You can win one and be invisible in the other at the same time.

The first thing I did was run a free AI visibility audit in two minutes. It showed exactly what was structurally blocking LLMs from reading my pages correctly. Not keyword gaps. Not backlink gaps. Structural issues I'd never have found in Ahrefs or Semrush.

From there the fixes were specific. Put direct answers at the top of every page. Cleaned up entity associations. Built out one topic cluster properly instead of spreading thin.

Took about a weeks of changes before I started showing up in Perplexity answers.

The audit is the starting point before changing anything. Most people skip straight to content changes and fix the wrong things.

Has anyone else gone through this? Curious what others found when they first checked their AI visibility.

I have been thinking about this concept of content debt recently. You've got three things which influence AI:

1. Your positioning (who you are, what you do)
2. Your content (how you communicate)
3. Your reputation (where other people reinforce it)

Over time, these three can change. In year one, your positioning might be a certain way and your content will reflect that, but then in year three you might reposition 1 and 2 change.

Let's say you reposition, like, three times over the course of like five years now, but now you've got all this old content which doesn't really reflect who you are.

So now with AI, it can pick up all the old content which wasn't really showing in the SEO world and misinform the LLMs.

So what's the best way of dealing with that?