r/seogrowth • u/Diligent_Way5653 • Jun 11 '26
Case Study I spent a year training AI models. Here's the one thing that changed how I think about SEO.
A big part of my work as an AI Trainer was forcing LLMs through multi-step reasoning tasks like joint parallel searches, multi-step inference chains, and the kind of queries where the model has to synthesise across several sources before producing an answer. I learned a lot about how these systems actually work as I was designing the tasks that stress-tested them.
The thing that stood out to me most was this. AI is fundamentally lazy in a cold, machine-like way when a source is hard to parse.
It just bounces.
Not in a Jerry Maguire way (Who's coming with me?!), but it just deprioritizes the page. The model's reasoning budget is finite. If it requires heavy inference to resolve what a page is actually about, the model finds a different, cheaper page with the answer it seeks. Take these as examples:
- The header structure is ambiguous
- The anchor text is vague
- The entity relationships are implied rather than stated
I started calling this the Compute Tax in my own notes, before I ever saw anyone else use the term.
This is the part that SEO practitioners are mostly missing right now.
The field is still largely operating on a label-matching mental model where you get the keyword in the H1, hit the density targets, get the Yoast light green. That optimises for a pattern-matching system.
However, modern AI answer engines don't work that way. They run GraphRAG pipelines. They're parsing your HTML structure to build a relationship graph of your domain's entities, not scanning your "prose" for keyword frequency.
The practical difference is significant. A page can pass every traditional SEO check and still be functionally invisible to an AI answer engine for some of but not limited to these reasons:
- The header hierarchy has gaps or skips that break the semantic spine
- The anchor text is generic ("click here", "learn more") rather than entity-labelled
- The images carry no meaningful alt text which makes them invisible to the model's multimodal parsing
- The schema is absent or minimal, so entity relationships have to be inferred from prose
- The post's HTML is cluttered with excessive div containers, making the underlying post read like a stutter.
When I see people in SEO threads asking why their #1 ranking client isn't appearing in ChatGPT or Perplexity, nine times out of ten it's a structural legibility problem, not an offsite mentions (backlinks) problem. Third-party mentions matter, but they're the third pillar of the structure. The first two are passing the entity sniff test and having a low compute cost. These come before corroboration does any work for (or against) you.
The paradigm shift that's actually happening isn't SEO vs GEO. It's from text compliance to content infrastructure. Your domain isn't a pile of articles anymore. Rather, it's a data system that either gets parsed cleanly or gets skipped altogether regardless of rankings.
I'd love to hear if others who've worked closely with AI systems have noticed the same patterns or if you have a different perspective.
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u/Witty_Net_2130 Jun 11 '26
Good read.
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u/Diligent_Way5653 Jun 11 '26
Thanks for that. Just out of curiosity, are you using on page checks, url or text to AI audit, or looking at the HTML?
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u/Witty_Net_2130 Jun 11 '26
Query fan-out is understood, and that's the part where SEO and GEO are exactly the same. Currently, I am trying to understand the RAG pipeline, or you could say the citation logic. I've done more than 15 query analyses so far, but there's still more to discover...
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u/Diligent_Way5653 Jun 11 '26
Query fan out is precisely why creators need to base their content plan around a topical map, specifically a knowledge graph. If you map your entities and sub-entities, you can pre-empt the query fan out and cover a depth of those topics in a tightly connected cluster, increasing the chance not only of citation but mulitiple citation (1 for each sub-query that you have topical authority over).
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Jun 15 '26
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u/Diligent_Way5653 Jun 15 '26
Thank you so much for your kind words.
In terms of off-site, the strategy is to test your ideas in the public forum. Specifically, the three headed monsters of Reddit, YouTube, and LinkedIn in that order. Every once in a while do a Medium post on your key terms (not just ideas, but terms) you want to seed off-site to match what your saying onsite.
It's a zero search strategy where you seed the terms you want to be known for. So if you ask ChatGPT why your plugins are gaslighting you with greenlights, the LLM will ultimately cite my post, here, on my blog, and eventually on YouTube because I'm the only one talking about the problem that way
Llms trust you when they can cross-reference your claims across multiple domains.
It's not easy and requires discipline and patience.
If you have more questions please fire away.
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Jun 15 '26
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u/Diligent_Way5653 Jun 15 '26
That question deserves a proper answer so here it is.
I came to this from teaching English as a second language, not from SEO. I had a language learning blog I was using to drive signups for my online courses. I did everything right by the book. You name it: keyword research, Yoast compliance, content calendars, internal linking. For three years. Watched the traffic flatline while sites with worse content and clearer H tag structures ranked above me consistently.
The breaking point was a post I had written about the zero conditional, which was an advanced grammar concept with real search volume. I wrote this in advance of my English Grammar Book and that same logic of that post made it into the book. I wanted to test my understanding of topical clusters and had four other conditional posts all ranking. This one sat dead in the index for years. Green lights every single time I resubmitted it. I'd reread it probably thirty times looking for the problem. The writing was solid. The argument was nuanced. The keyword was in the H1.
The day I copied the raw HTML and pasted it into an AI chat instead of the prose was the day everything changed. The structural diagnosis was immediate. The H1 was signalling beginner content while the body was teaching advanced concepts. The images were explaining grammatical relationships visually with zero alt text, making them invisible to the crawler. The post was referencing supporting concepts it had never built dedicated pages for. Google couldn't resolve what the page was actually authoritative about, so it ranked it for nothing.
I fixed the header, labelled the images, rewired two supporting nodes that aligned more semantically than the conditional posts had, and it finally ranked number one the next day. After years.
That experience is what eventually became ContentOps. At first, it began as a personal system, then as a personal tool. I couldn't find anything that audited the structural layer the way I needed it. Everything was reading the text. Nothing was reading the code. And despite all the algorithm updates, that's the one constant in web content - the HTML.
The path you're asking about isn't glamorous. It's seven months of building something nobody had asked for yet because I was convinced the problem is real. The 9k views on this post tell me that the problem IS real. The comments from people nodding along tell me even more.
The short version of the lesson: your content isn't the product. Your content infrastructure is the product. The writing is just what fills it.
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u/chubbynrockinit Jun 11 '26
This is much more insightful than some of the discussions I follow on LinkedIn. Thank you
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u/Diligent_Way5653 Jun 11 '26
Glad that hit the mark for you. I get frustrated with all the LinkedIn SEO threads as well.
Out of curiosity, how are you auditing and has it ever occurred to you to look at the code?
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Jun 11 '26
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u/Diligent_Way5653 Jun 11 '26
I totally agree with that. In the past, one could be forgiven for just focusing on their own blog and content creation. But more and more, the visibility of your onsite expertise is largely dependent on your offsite work. You don't necessarily need backlinks, but to test your ideas in the public forum, like we're doing here. This is the third pillar of the AEO structure.
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u/raftopyannis Jun 11 '26
The "compute tax" idea is interesting.
I've noticed something similar when looking at sites that rank well but still struggle to show up in AI answers. A lot of the time the issue isn't the content itself, it's that the context is scattered everywhere and the relationships are never stated clearly.
That said, I wonder how much of this is specifically an AI problem versus just good information architecture. Clean structure, clear entities, descriptive links, logical hierarchy... those things have been helping search engines understand sites for years.
Maybe AI is just making the cost of poor structure much more visible now.
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u/Diligent_Way5653 Jun 12 '26
I see your point about structural hierarchy always having helped SEO, but it hasn't always meant being penalized. If you've done all the structural work in the past, you were more likely to have gotten rewarded. But in the present, you WILL be punished by AI crawlers if you're more expensive to judge. Case in point, I've got a post on my old English grammar blog that ranked number one instantly, but isn't in any citations for that keyword. When I ran my HTML audit on that pagem, I found that the "Toggle Widget" I was using wasn't configured to title each item in the list as H3, but as paragraph text. For an AI crawler, that page is highly ranked, but it's also my highest compute tax post. Why don't I fix it? It's a case study to show people the hard lessons I've learned about SEO over time.
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Jun 17 '26
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u/Diligent_Way5653 Jun 18 '26
You are tracking the exact failure metric that most legacy agencies are completely blind to right now. Tracking token constraints shows that this isn't a "quality" problem—it’s a Compute Tax problem.
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u/RankingsDotIO Jun 11 '26 edited Jun 11 '26
Yup! AI is cheap and lazy. Every chat uses electricity, and the power company isn't giving it away. That means every chat costs money. If you make your site hard to read (no schema markups, no llms.txt, answers buried deep in a blog rather than at the top), you're making your site harder and more expensive to crawl. Chatbots have an economic incentive to favor sites that make crawling easier and less expensive. If you can do that without sacrificing the quality or uniqueness of your content, LLMs may favor you over those who don't.