AI Search and the Persistence of Core SEO Principles
Artificial intelligence is reshaping how information circulates online. Search no longer begins and ends with a single results page. Discovery now happens through a mix of search engines, chat interfaces, and large language models (LLMs) that generate answers directly from their training data and live web connections.
This evolution is changing what it means to be visible. Visibility now depends less on ranking signals and more on the credibility that models recognise and cite.
How large language models (LLMs) learn
LLMs are trained on extensive collections of digital text drawn from multiple categories: open web pages, books, code repositories, research papers, and forum discussions.
Public documentation and research offer a broad view of how major systems differ:
OpenAI’s GPT-4 is trained on a combination of publicly available web data and licensed third-party content from publishers and news organisations (OpenAI, GPT-4 Technical Report, 2023).
Google’s Gemini, successor to LaMDA, builds on Infiniset, a proprietary dataset that combines cleaned web crawl data, Wikipedia, programming material, and large volumes of public dialogue (Thoppilan et al., LaMDA: Language Models for Dialog Applications, 2022).
Anthropic’s Claude uses open corpora such as The Pile—a composite dataset of books, academic papers, code, and Q&A archives—augmented with licensed and human-curated data (Anthropic, Claude Transparency Report, 2025).
Perplexity AI operates differently. Rather than relying only on static training, it queries live indexes from Google and Bing, blending retrieval with generation to produce cited answers (Perplexity Documentation, 2025).
These datasets overlap substantially, yet each model’s weighting and indexing methods vary. That variation shapes what information is surfaced, trusted, or ignored when users ask questions.
Training LLMs is not visibility
Being included in an AI’s training set does not guarantee that a brand will appear in its outputs. Training teaches models to interpret language; visibility depends on how they retrieve and prioritise information at query time.
Retrieval processes typically combine model memory with live search results and trusted databases. For example, ChatGPT with browsing enabled draws on Bing’s index, while Google’s Gemini references its own Search ecosystem.
Brands that publish structured, verifiable content are easier for these systems to identify and cite. Authority emerges from how consistently a brand demonstrates knowledge and credibility across the environments that feed modern search.
The persistence of core SEO principles in AI Search
Despite the shift toward generative interfaces, the underlying principles of visibility remain familiar. Google’s E-E-A-T framework, Experience, Expertise, Authoritativeness, and Trustworthiness, continues to guide how both search and AI models evaluate information.
Research on retrieval-augmented generation indicates that models favour sources exhibiting these same attributes: proven expertise, identifiable authorship, and consistent reliability (Liu et al., Datasets for Large Language Models: A Comprehensive Survey, 2024).
The language has changed, but the logic has not. Whether information is ranked by an algorithm or cited by an LLM, credibility signals remain decisive.
Why quick-fix AI search optimisation solutions fail
The market for “AI SEO” shortcuts is growing, yet the evidence shows that optimisation tricks rarely influence citation frequency. Models are trained to replicate human patterns of trust, not to reward metadata manipulation.
Empirical analyses of citation patterns across ChatGPT, Gemini, and Perplexity indicate that sources with recognised authority—such as Wikipedia, reputable news outlets, and academic databases dominate citations by volume (Baack et al., Best Practices for Open Datasets for LLM Training, 2025).
Visibility is therefore cumulative. It reflects long-term consistency in content quality and reputation, not rapid adjustments to algorithmic trends.
Building brand authority in the new search ecosystem
Search now operates across multiple layers: web indexes, AI assistants, and embedded platform search. To remain visible, brands need a distributed presence supported by coherent structure and authentic expertise.
Producing original insights, maintaining factual accuracy, and contributing to credible sources increase the likelihood of being referenced. Technical clarity ensures AI systems can parse content; human clarity ensures audiences value it.
Brand authority unites these elements. When expertise and trust are evident across a brand’s digital footprint, AI systems recognise those signals just as traditional search algorithms do. The outcome is a form of earned visibility that extends beyond rankings.
A long-term perspective on AI Search
AI will continue to redefine how knowledge is accessed. Visibility now depends on credibility that travels across ecosystems—search engines, content networks, and generative platforms alike.
Brands that invest in clarity and authenticity establish a foundation that endures as technologies evolve.
At Curious Cat Digital, we help organisations strengthen that foundation. Our Fintech SEO Agency's work connects brand authority, technical precision, and measurable influence across every space where people look for information. In a changing search landscape, trust remains the signal that endures.
