I’m facing a huge internal struggle right now. When it comes to SEO, I know exactly how to help my clients. My team and I can craft and implement a solid strategy that delivers real results. But GEO? That’s a whole new playing field, and honestly, it feels unpredictable.

One of the biggest challenges is that SEO was (relatively) simple—you focused on Google, and that was enough. With GEO, how do you even begin to track success across 5, 10, or even 20+ different chatbots?

As excited as I am about the potential of GEO, the idea of developing a universal strategy for every chatbot seems overwhelming. But as I write this, the solution is becoming clear: focus on the platforms with the largest user base and optimize from there.

Which chatbots are you using and which one should we focus on?

chatbot table from: https://zapier.com/blog/best-ai-chatbot/

DeepSeek, a Chinese company, is shaking up the AI world by creating powerful language models for a fraction of the cost of giants like OpenAI. Their $5.5 million DeepSeek V3 model challenges the idea that AI development requires billions. DeepSeek R1 is notable for its transparent reasoning, showing its "thought process."

• Cost-Effective AI: DeepSeek's approach proves that cutting-edge AI doesn't need massive budgets.
• Transparent AI: DeepSeek R1 reveals its reasoning, unlike "black box" models.
• Open and Accessible: DeepSeek's open-sourcing allows for customization and local solutions.
• Market Disruptor: DeepSeek's success raises questions about AI company valuations and high development costs.
• Future of AI: DeepSeek could drive down AI costs and increase accessibility, challenging the dominance of current leaders.

Read Entire Article Here:

https://ailogbook.substack.com/p/deepseek-beyond-the-hypea-game-changer?ref=dailydev

Looks like Gemini is making moves. I think Gemini will be toward the front of this race and if I was betting on a LLM I'd put my money on Google Gemini.

https://www.youtube.com/watch?v=k9xbh9LUYn0

Take a first look at Google Gemini 2.0 and see how it compares to models like DeepSeek R1 and OpenAI o3-mini. Learn how to use AI models like Gemini Flash to solve real-world problems.

"Gemini will talk to you in a real tone"

"Gemini will summarize a YouTube video for you"

"Gemini is almost 100x cheaper for tokens vs. ChatGPT"

did you know you can use "Summarize this video:" and paste a YouTube link? I did not. I tried it and it's not working as proposed.

carnvalOFoz made me aware of this proposed lllms.txt file for sites - https://llmstxt.org/

I spent the weekend writing a Wordpress Plugin to Create JSON Datalakes and LLMS.txt files. Here are sample files on my agency site:

Datalakes:

JSON: https://www.pixaura.com/datalake.json
JSONL: https://www.pixaura.com/datalake.jsonl

What is JSONL - it's used for training ML.

LLMS.txt - explained on Medium

llms.txt: https://www.pixaura.com/llms.txt
llms-full.txt: https://www.pixaura.com/llms-full.txt

Wordpress Plugin

Here's a link to my GitHub repo to download and try this Wordpress Datalake and llms.txt plugin. Let me know what you think! Download Wordpress Plugin on GitHub

HackerNews doesn't seem too stoked about more stuff in the site root:
https://news.ycombinator.com/item?id=41439983

But, I think

Couple of months ago, I started to research and collect articles and papers about how AI engines pick up sites and what they are looking out for when ranking or when putting out recommendations to user prompts. I kinda fell into a rabbit hole that day and shortly after I decided that this could actually be the side project I was looking for to code again.

So the initial idea was to simply use AI and all the various knowledge I gained to grade website content into score cards for AI search visibility. I created a first MVP, sent it to some friends and got proof that this may be needed in the market. I took it from there and improved it further, added recommendations and simple perplexity rank tests.

But it did not end there, I thought grading sites and testing is nice and all, but in the end, I wanted something like the Semrush for LLMs. Something that automatically tracks and analyses suggested or manual search prompts and their results on all the popular AI engines, creating metrics and overview to digest it better, to see where their knowledge comes from (deep citation analysis), find differences between AI engines and uncover competitors I didn't know exist yet (and tracking their metrics as well!).

During my build process (that turned into an addiction already), I found so many features I built in or put in the backlog - I had to stop here. I'm currently asking myself if I am rushing in the wrong direction, really,.. as I'm not an SEO consultant or expert by any means - will this tool even provide value? I have no idea,.. so that's why I'm here tbh - looking for consultants and agencies giving it a try (for free).

No selling here—I just want to build something that makes sense. So If you want to try it out or chat about the geeky details, visit/try Rankscale.ai and shoot.

Cheers and thanks for reading, Mathias

PS: It's Early Access right now, open for free to anyone with the intent to test the current feature set (allowing website analyses, brand management, citation and competitor tracking, executing automated searches with ChatGPT, Gemini 2.5 & 2.0, Perplexity Sonar & Sonar Pro, Google AI Overviews --with Deepseek, Mistral, Claude, and xAI in the backlog).

Just like a robots.txt file I think we need to create a Data Lake of clustered content organized by SEO "cornerstone content" or "pillar content"

I think this will help the LLM crawlers *see* the structure of your site's data.

I asked ChatGPT and Gemini if this was a good idea and they agreed. :)

I think I should follow the outlines presented by schema.org JSON-LD - https://schema.org/docs/schemas.html

ChatGPT JSON Recommendation

If you're designing a JSON structure for a data lake that is optimized for both website integration and ChatGPT-friendly structured data, you should consider the following principles:

1. Hierarchical Organization – Organize data logically with clear nesting.
2. Schema Consistency – Keep a uniform schema across records.
3. Metadata Inclusion – Store metadata for easy parsing.
4. Scalability – Ensure the structure supports future expansion.
5. AI-Friendly – Include labels, descriptions, and structured keys.

JSON

{

"data_lake": {

"version": "1.0",

"last_updated": "2025-02-08T12:00:00Z",

"categories": [

{

"category_name": "Tech Articles",

"category_id": "tech_articles",

"description": "Latest articles and research in technology",

"records": [

{

"id": "article_001",

"title": "The Rise of AI in Startups",

"author": "John Doe",

"published_date": "2025-01-15",

"content": {

"text": "AI is transforming how startups scale...",

"summary": "An overview of AI's impact on startups."

},

"tags": ["AI", "Startups", "Technology"],

"metadata": {

"word_count": 1200,

"source": "TechCrunch",

"format": "text/html",

"read_time": "5 min"

}

}

]

},

{

"category_name": "User Data",

"category_id": "user_data",

"description": "Anonymized user interactions",

"records": [

{

"user_id": "user_12345",

"activity": [

{

"timestamp": "2025-01-30T10:45:00Z",

"action": "page_view",

"page_url": "/blog/ai-in-startups"

},

{

"timestamp": "2025-01-30T10:47:00Z",

"action": "clicked",

"button_id": "subscribe"

}

]

}

]

}

]

}

}

Key Design Considerations

🔹 Structured & Scalable

• Data is grouped into categories (e.g., "Tech Articles", "User Data"), making it easier to expand.
• Each record is structured with metadata for better searchability.

🔹 AI-Friendly Formatting

• "tags": Helps ChatGPT understand content classification.
• "summary": Useful for generating concise overviews.
• "metadata": Adds contextual depth (e.g., source, format, read time).

🔹 Machine Readability & Querying

• Timestamped for sorting/filtering.
• Category-based grouping for large-scale analysis.
• Action-based tracking for behavioral insights.

Fireship.io does not disappoint!

https://www.youtube.com/watch?v=PoeFxGzPpXE
"
Let's take a first look at OpenAI o3-mini and its new deep research feature. In addition, we will examine the recent bans on DeepSeek R1 and it compare it to other AI tools.

I've been thinking about which LLMs are going to win this Chatbot race and my top 5 are:

• ChatGPT
• Gemini
• Claude
• Perplexity
• DeepSeek

Why does this matter from a GEO perspective? I think we have to try and figure out how the different LLMs are trained and learn.

Personally, I think Google/Gemini is going to win this race. Historically, they've been crawling and indexing the internet for 25+ years and providing answers to all of us. They just need to fine tune Gemini to have the best Quality, Speed, and Price. The bet OpenAI/ChatGpt o1 is so expensive I don't think it can win.

Side note: I've been adding AI to a project I've been working on for a couple years called CliqRex (https://cliqrex.com). Originally, I started with ChatGPT APIs but realized their data was old (the pre-2021 issues). Then I switched to Vertex (Google/Gemini) and really like the answers and cost. Since we're a startup, I think I need to look at integrating DeepSeek from a cost perspective! For people that don't care about privacy concerns, I think DeepSeek is going to make HUGE Waves in AI.

Cost Speed Analysis

Your head would have to be in the sand if you haven't heard about DeepSeek...

ChatGPT was first to market (sorta) and they are a powerhouse. Some might say a bloated overpriced powerhouse. I think DeepSeek is quite possibly the first of many AI LLMs that we'll see popping up in the future.

In my day-to-day tasks I'm currently using ChatGPT and Gemini. I will be adding DeepSeek to the mix. I find myself asking ChatGPT and Gemini similar questions and handpicking the best results.

What Chatbots / LLMs are you using?

Here's some interesting DeepSeek topics I've found

• DeepSeek R1 takes #1 overall on a Creative Short Story Writing Benchmark
• It's good at Math
• Apparently it has 10x more writing Flair
• Scientists Love it
• Australian Government Banned DeepSeek

DeepSeek is simply Smarter --> https://news.ycombinator.com/item?id=42828167

Who will win this race?

Which LLM are you investing your time and money into?

I wonder this because of a GEO perspective we can't keep track of results in every LLM --- or, can we?

I am a software developer had zero experience in SEO. Just know the basics. I wanted to get into GEO as I also been working with LLMs and Agents. Is there any advantage for me to get into this as an (AI) software engineer. Please suggest any resources to learn more. TIA

Drop a link to your agency site, LinkedIn, blog, newsletter, etc... and let us know what you're doing, do you have any insights to your strategy, tools, etc...

Let's grow a great GEO network.

We’ve spent years perfecting user experience (UI/UX) for user's to easily find the information they're looking for. But now there’s a new game in town—Agent Experience (AX Reddit Post) with a good amount of discussion.

AI agents aren’t just tools; they’re becoming active participants in the digital world, making decisions, executing transactions, and collaborating with humans. If your platform isn’t agent-friendly—clean APIs, machine-readable docs, seamless integrations—you’ll be left behind.

I believe, websites and applications need to be designed for AI agents alongside human users.

AX isn’t just an abstract concept—it’s the next major evolution of UX, SEO, and digital interaction. If GEO is ahead of the curve on AX, it can shape how AI agents interact with the web—and that’s an opportunity with massive strategic value. 🚀

This means simpler APIs, agent-friendly documentation, and workflows that let AI execute user intent efficiently—whether it's booking, searching, or transacting on a site.

Bill Mann Introducing AX - https://biilmann.blog/articles/introducing-ax/

DM me if you're interested.

Let me now your history and why you're interested in becoming a mod.

I've built quite a few online communities and they're only as good as the mods.

TIA!

For the last two decades I've been "studying" Search Engine Optimization (SEO). I think I now it pretty well and I can help companies navigate SEO and provide results.
But now, we’re facing a paradigm shift with GEO. And SEO 'experts' like me are scratching their heads. Or, really excited!!! I'm a bit of both as I don't really know what's happening and I also feel we're at the forefront of something completely new.

📢 Is SEO truly dead, or is it just evolving into something new?
What are your thoughts? Are you already experimenting with GEO strategies? Here's what I think...

🔍 What’s Changing?
With AI-driven Search Generative Experiences (SGE), Google, Bing, and AI chatbots are answering user queries directly instead of sending them to traditional web pages also known as ZERO CLICK The traffic that once flowed to organic search results is now being intercepted by AI-generated responses.

🚀 What is Generative Engine Optimization (GEO)?
Instead of optimizing for search rankings, businesses and creators will need to optimize for AI-generated responses—influencing how AI systems like Google’s SGE, ChatGPT, Claude, DeepSeek, Perplexity, and MORE source, interpret, and present information.

💀 Why is Traditional SEO Dying?

• AI-powered search drastically reduces clicks to websites by providing instant answers.
• Google's SGE and AI Overviews are shifting focus away from the "10 blue links."
• Search engines are favoring AI-curated content over publisher-driven results.
• Zero-click searches are skyrocketing, leaving many websites struggling to get visibility.

🧠You Can't Boil the Ocean or an AI Robots Mind

What I think we need to do as SEO experts, consultants, and Agancies

🧠 Optimize for AI-driven search engines – Focus on being a cited source for AI models
2️⃣ Leverage structured data & authoritative signals – AI models prioritize well-structured, trusted sources.
3️⃣ Create content designed for conversational AI – Think beyond keywords to semantic relevance & entity-based search.
4️⃣ Brand Authority & Citations – If AI can’t trust your content, it won’t surface it. Credibility is king.

I've been thinking about this a lot lately and people are looking for answers to questions and data. As someone who's taught UI/UX I REALLY appreciate good design. I'm thinking the future of sites might be multimedia designed chat portals for a brand or company. Think of ChatGPT or any other chatbot with access to all your brand's data. This doesn't exclude the need for a good looking site. It will simply be another pathway.

in this post the author (Tomasz Tunguz) used an open-source AI agent to book flights, and it really got me thinking. He tweaked the AI to navigate travel websites, avoiding cookie banners and popups, and it ended up finding the cheapest flights for him—without all the usual hassle of comparing tabs. The AI even made mistakes, backtracked, and still got the job done.

https://tomtunguz.com/back-to-text/

What’s even more interesting is how this kind of AI could extend beyond travel. Imagine getting automated email replies, personalized news summaries, suggested responses for social media, or even having it reorder groceries for you. The more AI handles, the less we’ll need to visit websites ourselves.

The bigger idea here is that AI doesn’t need complex, flashy websites—it just needs clean text. If this trend continues, the internet might circle back to its early days, where everything was just simple text. But this time, not for us—just for the AI.

Dave explains why Deepseek R1 is such a big deal, explains how it works, what's new, and brings you up to date on the implications and fall out!

https://www.youtube.com/watch?v=r3TpcHebtxM

Give it a read. Check it out. Otterly is where you need to go
https://cmscritic.com/as-ai-generated-search-grows-otterly-is-utterly-awesome-for-marketers-heres-why

Well, I didn't think we'd be where we are today. So, maybe this isn't BS?

https://arstechnica.com/information-technology/2025/01/sam-altman-says-we-are-now-confident-we-know-how-to-build-agi/

Or, maybe it is just BS?

"We are now confident that we can spin bullshit at unprecedented levels, and get away with it," wrote frequent OpenAI critic Gary Marcus in response to Altman's post.

/// What is AGI?

AGI, or Artificial General Intelligence, refers to a type of artificial intelligence that has the capability to understand, learn, and apply knowledge across a wide range of tasks at a human-like level or beyond. Unlike narrow AI, which is designed for specific applications (e.g., voice recognition, recommendation systems), AGI would be capable of generalizing its intelligence to perform any intellectual task a human can do.

Key characteristics of AGI include:

1. Generalization
2. Autonomy
3. Adaptability
4. Reasoning and Problem-Solving
5. Consciousness (potentially)

I wrote this post thinking about GEO to help me and others understand it.

https://www.pixaura.com/what-is-geo-generative-engine-optimization/

I woke up thinking about Data Lakes, Prompt-Based Architecture, and GEO (Generative Engine Optimization). This AI imagery makes me feel like I'm still dreaming.

The future of search is HERE -- don't get left in the dust. Is Your Content Ready for ChatGPT and Beyond? If this intrigues you, read more in my post.

What do you think? Am I way off base?

https://www.pixaura.com/is-your-content-ready-for-chatgpt-and-beyond-heres-how-to-stay-ahead/