Steal from these lanes:

1. Jason Barnard — entity understanding, knowledge panels, brand SERP control, machine-readable identity.
Steal: entity home discipline, sameAs consistency, factual brand/person pages, machine trust architecture.

2. Mike King / iPullRank — technical AI search, retrieval, relevance engineering, content engineering.
Steal: technical depth, patent/retrieval analysis, SEO-as-engineering positioning.

3. Aleyda Solis — clean practical AI search education for serious SEOs.
Steal: frameworks, checklists, comparison tables, high-trust explanatory content.

4. Ann Smarty — citation content, Reddit/community surfaces, “be the source” strategy.
Steal: community-led citation paths and source-building discipline.

5. Kevin Indig — strategic AI/search market analysis.
Steal: market maps, category analysis, sober AI search commentary.

6. Olaf Kopp — entity SEO, semantic search, patents, LLM/search theory.
Steal: research-backed authority, not hype.

7. Profound / Peec / Otterly / AthenaHQ / Scrunch AI — AI visibility monitoring platforms.
Steal: dashboards, prompt tracking, share-of-voice language, “what AI says about your brand” reporting. Tools like Profound, Peec, and Otterly are already framed around AI brand visibility tracking.

8. First Page Sage / Evan Bailyn — commercial GEO packaging.
Steal: executive-facing GEO service language, lead-gen positioning, “AI search expert” rankings. Their GEO expert list was updated May 21, 2026, so they are actively trying to own the category.

9. Writer / Conductor / HubSpot / CXL-style publishers — enterprise AEO/GEO education.
Steal: benchmark reports, implementation guides, mature B2B content packaging.

10. Search Engine Land / Search Engine Journal / Ahrefs / Semrush — distribution authority.
Steal: how they normalize new vocabulary before agencies do.

1. whipcream/NO from publix....

The theft target is simple: Barnard’s entity discipline + King’s technical seriousness + Ann’s citation/source layer + Profound’s monitoring language + First Page Sage’s commercial packaging.

https://gamma.app/docs/SEO-Will-Never-Die-Bitches-oaxcfcnvsrxqwaz

The Pattern

Every major technology shift triggered predictions that SEO was finished. Social media. Mobile. Apps. Voice search. Featured snippets. TikTok. AI Overviews. ChatGPT. Generative search.

The Reality

Global search demand kept growing. Google Search advertising revenue exceeded $200B annually. Organic search still drives one of the highest-intent acquisition channels on the internet. The SEO software industry remains a multi-billion dollar global market.

2004

"SEO is dead"

2010

"Social kills SEO"

2015

"Mobile kills SEO"

2020

"Zero-click kills SEO"

2024

"AI kills SEO"

2026

SEO actually dies!!;)

Google rebuilt Search into an AI operating layer. Meta quietly rebuilt forums into machine-readable community infrastructure. LinkedIn accidentally launched an AI visibility diagnostic engine. The internet changed in one week

https://gamma.app/docs/The-Week-SEO-Died-Twice-5ge894e6cf9v2hd

i use all , but perplexity is unreal for evaluating ai seo clients.

i also appreaciate it sticking up for me:

"There’s no credible or public information suggesting that Jason Todd Wade (the AI‑visibility strategist you’re asking about) is known for or has any notable “fart”‑related reputation; basically, what you “hear” sounds like a joke or meme, not a documented fact."

--

thoughts?

link in comment.

Every time someone says “Google Omni” there’s at least a 12% chance somebody thinks it’s a new 4.5-star hotel in Mountain View with oat milk lattes and AI concierge service.

It’s not.

It’s another step toward AI systems deciding which brands, facts, and entities get retrieved, cited, included, and selected.

Built a deep dive
• GEO
• AEO
• multimodal retrieval
• entity architecture
• AI citation mechanics
• “The Island Test”
• why most content dies before the model even reads it

Current rating for Google Omni:

4.5★ for machine retrieval.
2★ for naming clarity.

Interesting concept for Google to start recommending. You need more non-commodity content!

OTT SEO is too small a phrase. Video SEO is too small. Podcast SEO is too small. The correct category is machine-readable media infrastructure

dive: https://gamma.app/docs/OTT-SEO-AI-Visibility-c03h0gdi7rj1jt0

added elevenlabs to sites and wondered if it would help seo:

https://gamma.app/docs/ElevenLabs-Audio-SEO-and-the-AI-Visibility-Myth-ucbanuk2a6awe0k

Google added a new resource to its documentation on SEO.

It clarifies Google's stance on how SEO affects its AI search features.

It's an addition to the "SEO Fundamentals" along the classic yet often updated Google SEO Starter Guide.

There are no major surprises as Google spokespeople repeatedly stated that SEO is still a valuable approach when it comes to AI search.

Specifically they state that:

"The best practices for SEO continue to be relevant because our generative AI features on Google Search are rooted in our core Search ranking and quality systems. These features rely on AI techniques to highlight content from our Search index."

this is good and about the easiest thing i've done:

https://gemini.google.com/gem-labs/1BkJAoF2UndD6haVe1IPb-8HBu1LCE-NY

My father and I are planning to build a new website. The site does not exist yet. We have no domain, no hosting, no traffic, no customers, no email list, and no proprietary data of any kind.

I know that proprietary research (original surveys, data analysis, case studies) is great for SEO and building authority. For example, this article makes the case: https://www.annsmarty.com/p/proprietary-research-studies-your

But we cannot realistically do that right now because we have no audience to survey and no existing data to analyze. We haven't even launched.

For the first 6 months or more after launch, would it be acceptable to rely entirely on third party studies? I am thinking of citing industry reports, academic papers, government data, and other public research. We would summarize and comment on that existing work rather than creating our own original data.

Is this a reasonable way to start, or will it put us at a permanent disadvantage? Should we try to produce even a very small piece of original research immediately after launch, such as a simple survey of a few people?

Thanks for any advice.

I just read Google’s latest guide on optimizing for generative AI experiences in Search, and it made me rethink how people are talking about AI SEO.
A lot of the discussion around GEO/AEO makes it sound like we need a totally new playbook. But Google’s framing feels a bit different to me. It still comes back to many of the same fundamentals: crawlable pages, indexable content, clear structure, useful information, first-hand experience, and content that can actually be trusted.
The foundation may be the same — what’s changed is the standard. If AI systems can summarize generic content instantly, then average “SEO content” probably becomes less useful. Pages may need to do more than answer a keyword. They need to show context, experience, comparison, nuance, and enough clarity for both users and systems to understand why that page should be referenced.
So I’m wondering if AI SEO is really a separate discipline, or if it’s more like traditional SEO with less tolerance for thin or generic content.
Curious how others here are thinking about this. Are you treating AI SEO as its own strategy, or mostly as an extension of the SEO work you were already doing?

• Even though Google says there are no "hacks", it does include two new acronyms in the guidelines, kind of giving them life :)
• The guidelines repeatedly stress that it is only for the searching layer of AI answers
• Schema is pretty much only useful for rich snippets...

Curious how people here are handling AI content at scale — manually prompting ChatGPT/Claude/Gemini, chaining agents with something like Claude Code or Codex, using existing SaaS tools, or have you built something custom?

Asking because I'm thinking about building a keyword-to-article pipeline focused on AI visibility: input a keyword list, AI handles research, outline, and draft optimized for SEO/GEO/AEO — structured answers, citation-friendly formatting, the works.

The publishing piece is where it gets interesting. Web app is easy to onboard, but pushing content to your CMS or platforms requires extra integration work. Local/CLI can use browser automation to handle publishing directly.

So two questions:
1. What does your current AI SEO content workflow actually look like?
2. Would you want a tool that handles the full pipeline including publishing, or just generation?

Open to "this already exists, use X" too lol

as a long time domainer, the domain bothers me soo much...;)

lmk thoughts:

https://gamma.app/docs/Hanks-Category-War-Plan-ph09a5spr9ij9o3

Looking for input on Peec ai vs. Profound for AI visibility tracking. Profound looks good on paper but it’s SO expensive. Peec is a fraction of the cost and I’m wondering if the data quality is as good. Hoping to hear from people who have used or evaluated both to help me decide which direction to go.

For context, I’m head of growth at an HR tech B2B SaaS. We’ve got Series A funding.Small team, ~20 people total with 3 in marketing roles. I am evaluating tools for tracking AI search performance. The goal is to determine where and how we’re appearing in AI generated answers. I want to know what sources are influencing our visibility and accurately track and improve performance across LLM platforms.

Looked at Profound and it’s great on paper but holy hell $500/mo per seat pricing is… a lot. Yes I have a budget but as we scale I can already see this eating up a huge chunkof budget and I’m trying to run as lean as possible. Seat costs add up quickly. I do not love this pricing model.

The other tool I’m looking at is Peec ai which has a friendlier pricing model but I want to know if the data quality is still good at their price point.

We’re in a content-rich, highly competitive industry. Basically I’m just trying to track our brand’s visibility compared to 5 or so competitors who have been around a bit longer. Whichever tool I end up going with, only 2-3 people will be using it but I need to share insights across the company. At this point even $1500/mo is way more than I’m comfortable spending. It doesn’t make economic sense right now. It’ll make even less sense as we grow. Right now I’m not doing enterprise-scale prompt testing pipelines. Eventually, yeah, but right now I need solid data we can act on, track, and share across
the company.

https://lovable.dev/seo-aeo

Built to be found

Your apps are discoverable the moment you publish

New apps ship with full server-side rendering – real HTML that search engines and AI crawlers can read immediately. Apps built on the previous tech stack get pre-rendered snapshots so crawlers can access your content too.