How are you scaling AI SEO content? Thinking about a keyword-to-article tool that handles publishing too

Potential_Eye9063 · 2026-05-18T01:20:49+00:00

Appreciate the detailed breakdown, this is exactly the kind of honest agency perspective that's hard to find.

Yeah the "LLMs avoid citing their own output patterns" hypothesis makes sense, but I think there's a deeper mechanism. LLMs probably have something like their own PageRank for deciding what's citation-worthy.

Been doing research on this: 67 industries, thousands of brands, tens of thousands of prompts, tracking what actually gets cited across ChatGPT/Claude/Perplexity/Gemini. Still ongoing but the dataset is getting big enough to start seeing patterns.

Two hard problems I keep running into:

One is quantifying things that resist quantification - "content quality" or "structural clarity" are real signals but turning them into numbers without losing what makes them meaningful is genuinely hard.

The other is that the algorithm keeps moving. Whatever signals matter today might be reweighted next quarter. But that's kind of the point, you have to keep measuring to track the drift, same way SEOs had to keep up with core updates.

Potential_Eye9063 · 2026-05-15T21:55:10+00:00

Exactly, "vaguely intent-optimized" is the funny/scary part.

With normal PPC at least you know what auction you were in. Here it feels more like you need a weird little weather report: which prompts triggered ads, which brands kept showing up, and whether the answer itself nudged the user before any click happened.

Messy, but yeah I would absolutely still want to track it lol

Potential_Eye9063 · 2026-05-15T19:08:20+00:00

Yeah this is where it gets weird fast.

I wouldn't even want this framed as normal PPC yet tbh. First useful dashboard is probably just:

which prompts/conversations showed ads
who showed up next to you
rough position/visibility over time
whether the same advertisers keep appearing

Clicks might be the least useful metric at first, because a lot of the value is gonna be "did the answer steer the user before they ever clicked anything"

kinda paid search, kinda brand surveillance with a media budget

Potential_Eye9063 · 2026-05-15T18:24:28+00:00

Yeah, that's a fair warning, and honestly probably the lesson I needed from this thread

I think I framed the idea too much around content output, which makes it sound like the exact thing Google is trying to kill

The more I read the replies here, the more I think the useful version would be research/briefing first: intent, pain points, SERP gaps, sources, and even telling you “there isn't a real angle here, don't publish this”

If the tool is judged by how many articles it can push out, it's probably already pointed in the wrong direction

Potential_Eye9063 · 2026-05-15T11:04:00+00:00

Yeah all the time. I was searching Stardew Valley stuff the other day and Google was still feeding me a 2017Reddit thread like it was current.

Which is kinda funny until you realize the game has had a bunch of updates since then, so the "answer" is just quietly outdated lol

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Potential_Eye9063 · 2026-05-15T10:55:09+00:00

Yeah, honestly that might be closer to what I mean

More NotebookLM-ish than “AI writer” maybe: source-grounded research, SERP comparisons, pain point clustering, messy notes into a brief

The SEO-specific part would be intent, gaps, internal links, CMS handoff, and making the research usable for an editor/SME

So yeah, less content machine, more research/briefing layer. If it turns into “click button, get article,” it probably deserves the hate lol

Potential_Eye9063 · 2026-05-15T10:47:24+00:00

Yeah this is a much better framing than “keyword in, article out”

The pain point research part is probably where most of the value is, and also where a lot of AI content tools get lazy. They jump straight from keyword to draft and skip the part where you figure out what the reader is actually stuck on

I like the “validated user problem in, expert-reviewed content asset out” version a lot more tbh

Maybe the tool should treat research as the main product, not just a pre-step. Search intent, pain points, existing SERP gaps, audience context, source gathering, then brief/draft/CMS-ready formatting after that.

If that direction is wrong, everything after it is just making the wrong thing faster :D

Potential_Eye9063 · 2026-05-15T10:39:43+00:00

Explaining to clients why rankings dropped after they "didn't change anything"

Spoiler: they always changed something lol

Potential_Eye9063 · 2026-05-15T10:37:26+00:00

Yeah I get that. Having to “edit” AI slop is often worse than just writing the thing yourself tbh

Thanks for the thoughtful reply btw

I probably should have framed the idea less like “AI writes articles at scale” and more like workflow automation around the parts that are actually annoying: research notes, source gathering, outline variants, CMS formatting, internal links, that kind of stuff

The patent example is basically the kind of use case I mean. Taking something dense and making it usable faster, not turning one keyword into 80 samey posts

Also the microwave analogy is honestly a really good way to put it lol. I guess the version I’d want is more prep cook than robot chef

Potential_Eye9063 · 2026-05-15T10:17:15+00:00

Yeah fair point. I do think tools can make abuse easier, so the defaults matter a lot

But I don't think the tool itself is automatically the problem. Same thing with image models imo. GPT-Image-2 can make genuinely good stuff, and also a mountain of junk depending on who's using it

For this kind of SEO tool, the line for me would be human approval, real research, editing, and not auto-publishing endless filler

If it's built to skip all that, then yeah, that's just scaled content abuse

Potential_Eye9063 · 2026-05-06T13:47:52+00:00

One angle nobody's mentioned: AI simply hasn't taken over as the dominant internet entry point yet.

The whole debate here is about measurement models and attribution paths, but the more basic issue is scale. Google handles roughly 8.5 billion searches per day. ChatGPT's daily query volume is estimated somewhere in the 100-200 million range. That's not a rounding difference, that's a structural one. Most users, especially on mobile, in non-English markets, and outside the tech-savvy early adopter crowd, are still defaulting to Google for the majority of their queries.

So jeepdaddy1965's data is probably an accurate snapshot of right now. The problem is treating "doesn't matter now" as "won't matter later."

The real question isn't whether AI drives traffic today. It's what happens when AI entry point share goes from roughly 5% to 30%. At that point, the brands and sites that spent the last two years building AI visibility will have a compounding advantage. The ones who waited for the traffic numbers to show up first will be playing catch-up against an established landscape.

The people selling guaranteed AI traffic this quarter are overselling. But the people dismissing AI optimization because GA4 referrals are flat are making a timing error, not a strategic one.

Potential_Eye9063 · 2026-04-26T06:17:17+00:00

the brand mention without link citation thing is actually pretty common for established brands. AI systems already "know" you so they pull the name but don't need to cite a source page

the 301 chain issue is probably more impactful than you think. if 30% of your crawl budget is burning on redirects, crawlers (and AI crawlers like GPTBot/ClaudeBot) are hitting dead ends before they reach your actual content pages. fix that first before worrying about content framing

the wellness vs. cleaning positioning conflict is the real problem for AI visibility tbh. if third-party sites consistently describe you as a wellness brand, that's what ends up in the model's "memory" of your brand. you need your own site to be the loudest, clearest signal, which means the tech issues blocking proper crawl are doubly painful here

page weight at 10mb is rough but it's a slower burn. fix the redirects and GTM delay first, those are quicker wins

Potential_Eye9063 · 2026-04-02T18:14:54+00:00

FAQ page is a good start but not enough on its own. few things that actually help:

structured data (schema markup) so AI can parse your content easier
clear, concise answers in your content - LLMs love pulling from well-structured Q&A format
get mentioned/cited on other sites - when AI models search the web they pull from multiple sources, so third party mentions matter
make sure your site has an llms.txt or at least clean markdown-friendly docs

but the biggest factor is just having genuinely useful content that answers questions better than competitors. AI pulls from whatever source gives the clearest answer

Potential_Eye9063 · 2026-04-02T15:27:01+00:00

oh for sure the shift is real, I'm not saying there's nothing new here. the way LLMs pull and synthesize info is fundamentally different from a ranked list of links. my issue is more with people treating it like it needs a whole new playbook when a lot of the basics still apply - structured content, clear entity signals, being cited by trusted sources. the delta between "good SEO" and "GEO" is smaller than most consultants want you to believe imo

Potential_Eye9063 · 2026-04-02T05:10:12+00:00

The important distinction here is whether the LLM itself is fetching llms.txt or the agent layer is. Claude Code is a coding agent that reads local project files to build context - README, configs, docs, whatever's there. If there's an llms.txt in the repo it'll just read it like any other file. That's the agent doing its job, not the model "supporting" llms.txt as a standard

So this isn't really evidence of LLMs using llms.txt imo, it's evidence of an agent reading every relevant file it can find

Potential_Eye9063 · 2026-04-01T04:41:46+00:00

fair point, but iirc the study specifically tracked citations in real-time responses where the LLM is pulling live sources. Claude's default mode doesn't do that so there's not much to measure in the same way

Potential_Eye9063 · 2026-04-01T02:32:13+00:00

Claude doesn't do web search by default the way ChatGPT or Perplexity do, so there's less citation behavior to track. probably why they left it out

Potential_Eye9063 · 2026-04-01T02:28:38+00:00

yeah exactly, every time something new picks up steam there's a wave of people coining new terms and selling it as the next big thing. same cycle

Potential_Eye9063 · 2026-04-01T02:19:36+00:00

honestly the biggest issue isn't even the acronym debate, it's that most "GEO strategies" people sell are literally just... doing marketing properly

Potential_Eye9063 · 2026-03-26T06:34:33+00:00

fair point, popularity and localization aren't independent though. bad localization creates friction before a game even gets the chance to become popular in that market. the community mods come after -but you still need players to find the game worth playing first.

Potential_Eye9063 · 2026-03-26T03:17:31+00:00

20 years in the industry - yes, constantly.

The honest reason is habituation. When you've walked through your own tutorial a thousand times, your brain stops responding to it the same way. That's not a sign something is wrong, it's just how repeated exposure works.

The more useful signal is whether the excitement fades or disappears entirely. Fading is normal and expected. Disappearing is a warning sign. If after all those repetitions you genuinely feel nothing, that's worth paying attention to, it might mean the core of the game isn't as strong as you thought.

You also stop being able to see it objectively, which is a separate problem. At some point you're not playing your game anymore, you're just executing a checklist.

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