By Sophie Carr, GAIO Marketing
Table of Contents
If SEO helps you rank in Google, what helps you rank in ChatGPT?
That's the question I get asked the most by marketing leaders right now, especially those already confident with SEO.
And I get it. The jargon around AI marketing is confusing. Some people say it's GEO (Generative Engine Optimisation). Others say it's AIO or AEO. I say: it's time we simplified the terminology and got aligned.
So, in this blog, I’m going to answer your questions.
What is GAIO?
GAIO stands for Generative AI Optimisation. It’s about making your content more visible in generative AI tools like:
Google Gemini (formerly Bard)
These platforms don’t work like Google Search. They don’t show 10 blue links. Instead, they summarise, remix, and rewrite the internet to generate responses to user queries.
So if your content isn’t optimised for the way LLMs (large language models) read, understand, and cite information, then it gets left out.
GAIO ensures your content is:
Structured clearly
Backed by reliable sources
Easy for AI models to parse (understand)
Factually accurate and coherent
Want to go deeper into the GAIO Strategy? I break it down here: How to Rank in AI: The GAIO Strategy
Why is GAIO so important right now?
Because AI is already changing how people discover brands.
A new Adobe Analytics report (2025) revealed that traffic to U.S. retail websites from generative AI sources jumped 1,200% in the last year alone.
Let that sink in.
We’re witnessing a massive shift in how consumers find and interact with content. AI tools are no longer just novelty chatbots; they’re full-blown discovery engines.
That means:
Buyers are asking ChatGPT what to buy.
Parents are asking Gemini what snacks to pack.
CMOs are asking Claude how to plan their 2025 GTM strategy.
If you’re not optimising your content for AI, you’re missing out on a rapidly growing channel. And worse: you’re letting someone else get cited as the authority in your space.
Wait, isn’t GAIO just SEO?
Not exactly.
SEO = Optimising for search engines. Think keywords, backlinks, load speed, and mobile usability. It works to help you rank in traditional engines like Google and Bing.
GAIO = Optimising for LLMs. Think clarity, citations, entity-based structure, AI-trainable formatting, and question-led content.
They work together - but they target different algorithms.
What is AEO?
AEO stands for Answer Engine Optimisation. It’s a bridge between SEO and GAIO.
Answer engines (like Google’s Featured Snippets, Knowledge Panels, and even Alexa or Siri) aim to give answers instead of showing links.
AEO ensures your content shows up as:
Featured snippets
FAQs
Rich cards
Voice assistant results
So think of it this way:
SEO = Gets you into the top of search results
AEO = Gets you directly quoted by answer-based engines
GAIO = Gets you summarised, cited, and included in AI-generated responses
All three are valuable, but they are not interchangeable.
What are all these AI terms people throw around?
To fully understand GAIO, we need to understand the history of artificial inteligence.
The evolution of AI: How we got to ChatGPT and GAIO
AI has transformed from basic rule-based systems to powerful chat platforms that drive marketing, business, and education. Each stage of AI development solved a limitation from the previous one, leading to the technology we use today.
This guide breaks down the evolution of AI step by step.
1956 - AI starts (basic rules-based AI)
🤖 What happened? A group of brilliant minds, including John McCarthy, Marvin Minsky, and Claude Shannon, got together at the Dartmouth Conference to brainstorm a radical idea - machines that could think. AI was officially born!
❌ Limitation: Couldn’t learn or improve, only followed fixed rules.
🔜 Leads to: Machine Learning (ML), allowing AI to learn patterns from data instead of relying on rigid programming.
1980s - Machine learning (AI starts learning)
📊 What happened? Instead of just following rules, AI could now learn from data! This meant AI could spot patterns, like detecting fraud in banking or recognising handwritten numbers.
❌ Limitation: Still needed humans to manually select features (e.g., deciding what makes an email spam).
🔜 Leads to: Neural Networks (NN), automating feature selection and improving pattern recognition.
1990s - Neural Networks (AI Mimics the Brain)
🧠 What happened? Scientists like Geoffrey Hinton, Yann LeCun, and Yoshua Bengio brought Neural Networks back into the spotlight, helping AI recognise faces, speech, and objects - big wins for early Face ID and voice assistants.
❌ Limitation: Too shallow and early networks couldn’t handle complex real-world data.
🔜 Leads to: Deep Learning (DL), stacking multiple layers for more advanced learning.
2010s - Deep Learning (AI gets super smart)
🚗 What happened? Deep Learning revolutionised AI! Suddenly, AI could drive cars (Tesla Autopilot), translate languages (Google Translate), and even detect diseases from medical scans.
❌ Limitation: AI still forgot things easily and couldn’t handle long conversations.
🔜 Leads to: Transformers, a breakthrough that allowed AI to process entire sentences at once.
2017 - Transformers (AI understands language)
🔄 What happened? A team at Google, led by researchers Ashish Vaswani, Jakob Uszkoreit, and Noam Shazeer, developed the Transformer model. This was a game-changer (sorry for the buzzword but it's true)! AI could now process entire sentences all at once, rather than word by word.
❌ Limitation: AI could understand text but couldn’t generate natural human-like responses.
🔜 Leads to: Generative AI (GenAI), where AI could now create text, images, and videos.
2020 - Generative AI (AI starts creating)
✍️ What happened? AI got creative! It began writing blogs, generating images, and even composing music. OpenAI, led by Sam Altman, launched GPT-3, a massive AI model trained on 175 billion parameters. Other big players like Google DeepMind joined the race.
🖌 First Big Tools: Early advancements like GPT-3 gained attention mostly in tech circles but weren’t widely accessible. Tools like ChatGPT, Midjourney, and DALL·E didn’t reach the public until later, in 2022, when user-friendly interfaces became available.
❌ Limitation: Early models often produced inaccuracies (hallucinations) and couldn’t recall past conversations. Accessibility and security concerns also delayed broader adoption initially.
🔜 Leads to: Large Language Models (LLMs) and Generative Pre-trained Transformers (GPT), which enhanced text generation, creativity, and reliability.
2021 - Large Language Models (LLMs) make AI smarter
📚 What happened? GPT models like GPT-3 set new benchmarks for AI. They improved reasoning, handled complex queries, and maintained longer conversations. By 2021, advancements in these models were paving the way for broader applications.
❌ Limitation: Early models weren’t user-friendly and required technical expertise, making them inaccessible for most people.
🔜 Leads to: The launch of ChatGPT in 2022 provided an easy-to-use chatbot interface, making AI accessible to everyone and changing how people interact with technology.
2022-Present - ChatGPT (AI for Everyone)
💬 What Happened? OpenAI built on its GPT model, a powerful AI capable of generating human-like text, but it needed a platform where people could actually use it. That’s when ChatGPT was born, making AI accessible to everyone, even without technical expertise.
That’s when they created ChatGPT, the platform we all know and love today, making AI accessible to everyone, even without technical knowledge.
💡 Difference Between ChatGPT and GPT-4.5:
GPT-4.5 is the “brain” powering the responses - a Large Language Model (LLM) designed for advanced reasoning and text generation.
ChatGPT is the “mouth” - a simple and intuitive platform that lets anyone use GPT-4.5 for writing, research, marketing, and business tasks.
🚀 Breakthrough Moment: AI moved beyond research labs and became a mainstream tool for businesses, education, and content creation, powering everything from SEO and AI search visibility to customer support and content marketing.
Why it’s called GAIO marketing (and not the other terms)
So, why GAIO Marketing? Why not AIO, GEO, or just SEO for AI?
Because the real shift happened with Generative AI (GenAI). That’s when AI stopped just analysing and started creating.
Before 2020, AI could recognise faces, detect spam, and even predict the next word in a sentence. But it couldn’t write an article, generate an image, or compose a song—until Generative AI came along.
And here’s the key: we’re not optimising for self-driving cars, facial recognition, or industrial robotics. Those are AI, but they’re not Generative AI. We care about optimising for AI that creates text. That’s why GAIO (Generative AI Optimisation) is about ranking in AI-generated responses, not just traditional search.
That’s what made tools like ChatGPT, Midjourney, and Google Gemini possible. And that’s exactly why it’s called Generative AI Optimisation (GAIO)—because we’re optimising for the AI that actually creates content, not just the AI that ranks websites like Google Search.
If AI is rewriting the internet, then GAIO is how you make sure your brand, business, or content gets included in that rewrite.
That’s the future of marketing. That’s GAIO.
What are the benefits of optimising for LLMs — and can I afford to wait?
It’s a fair question: is this urgent, or can we treat GAIO like another marketing trend and put it on next year’s roadmap?
Here’s the reality:
Delaying GAIO could cost you visibility, leads, and authority.
The longer you wait, the more your competitors train the models to recognise their content, their language, and theirauthority. And once those models are trained, it takes significant effort (and often, time) to shift their preferences.
The benefits of acting now and ranking in AI search:
AI model familiarity: The sooner your content appears in AI outputs, the more likely it is to be reused.
Early mover advantage: LLMs reward clear, structured, consistent sources. The earlier you show up, the more weight you carry.
High-quality traffic: According to Adobe Analytics (2025), visitors arriving from AI-generated results spend 35% more time on site and convert 17% more often than those from traditional search. This isn’t just new traffic — it’s better traffic.
Reduced content waste: GAIO helps your existing content work harder, by preparing it for how AI assistants surface information.
Bottom line: Optimising for LLMs isn't just smart - it’s becoming essential.
The best time to start was yesterday. The second best time? Now.
Final Thoughts on AI Search
If you’re trying to rank in AI, you can’t rely on old SEO tactics alone. You need to optimise your content for how AI thinks, not just how humans search.
That’s what GAIO is all about.
Let’s stop mixing up the terms and start using the right strategies. Because the future of content visibility? It’s being written by AI.
About the Author
Sophie Carr is the founder of GAIO Marketing and an expert in ranking in AI search.
She helps enterprises transition from traditional SEO to AI search optimisation with the tools, training, and services needed to secure authority in AI-powered search engines like Chat GPT, Grok and Microsoft Copilot.
Her newest tech is end to end AI-powered marketing software designed to help you rank in AI search. Grow your knowledge forest and be the answer in AI search.
If you want to know more then contact us via email at: info@gaio-marketing.com
Disclaimer:
This blog is written based on industry experience, observations, and available data as of 2025. The landscape of generative AI and search is rapidly evolving, and marketers should stay up to date with the latest developments.
The information in this article is for educational purposes only. While GAIO Marketing techniques improve AI search visibility, results may vary based on market competition and AI algorithm updates.
This blog was written with the assistance of AI tools for structuring, research, and clarity. The core insights, strategies, and expertise are entirely Sophie Carr’s original thought leadership. AI was used as an efficiency tool, much like a spellchecker or a literacy calculator, to streamline content creation while preserving authenticity.
