Large language models did not arrive at their opinions about your industry by searching the web. They formed those opinions during pre-training, absorbing patterns from billions of text documents long before your buyer ever typed a question into ChatGPT. The sources that appeared frequently and authoritatively in that training data became the sources those models trust and cite. If your brand was absent, it was not a neutral outcome. The model learned, in effect, that you do not exist. This is the hidden curriculum of AI search, and it is the core reason why traditional SEO alone no longer determines whether buyers find you [eric.ed.gov].

TL;DR

• LLMs form brand opinions during pre-training, not at query time. Absence from trusted sources during that period creates a credibility deficit.

• The platforms each model cites vary. ChatGPT favors LinkedIn; Google AI Overview favors Reddit and established media. Strategy must match the model.

• Google AI Overview SEO and broader AI visibility require a different content format than classic SEO. Structure, citability, and source authority matter more than keyword density.

• Getting ChatGPT brand mentions requires consistent presence on the sources LLMs were trained to trust, not just a well-optimized homepage.

• Visibility compounds. Brands that build AI citations now will be progressively harder for competitors to displace as models continue training on current web content.

About the Author: Simaia is an agentic marketing team specialising exclusively in AI search visibility for B2B companies across APAC. Its work spans audits, content strategy, and end-to-end execution across ChatGPT, Gemini, Claude, Perplexity, and Google AI Overview.

What Is the "Hidden Curriculum" in LLMs, and Why Does It Matter for Brands?

The hidden curriculum in AI refers to the implicit values, biases, and authority signals that models absorb during pre-training, without any explicit instruction [eric.ed.gov]. Just as students learn unspoken social rules alongside the formal syllabus, LLMs learn which sources are credible, which topics are well-documented, and which entities matter, based purely on how often and how authoritatively those entities appeared in training data [lesswrong.com].

For brands, this has a direct consequence: if your company was not being discussed on the platforms LLMs draw from, the model has no representation of you. It is not that you scored badly. You simply were not part of the curriculum. And when a buyer asks an LLM to recommend vendors in your category, the model reconstructs an answer from what it absorbed during training, not from a live search of the internet.

This matters more urgently now because LLM usage among B2B buyers is accelerating. Buyers are using ChatGPT, Gemini, and Perplexity to shortlist vendors before ever visiting a company website. The question is no longer whether AI search matters. It is whether your brand is part of the answer.

How Does LLM Pre-Training Determine Which Brands Get Cited?

Pre-training is the foundational phase where a model processes vast amounts of text to learn language patterns, facts, and relationships [magazine.sebastianraschka.com] [lamethods.org]. The model does not memorise sources individually. Instead, it builds weighted associations: entities mentioned frequently alongside credible, well-structured content are encoded as more authoritative than those that appear rarely or only in low-signal contexts.

Think of it as a reputation system built from accumulated text. A brand mentioned across trade publications, LinkedIn thought leadership posts, Reddit community discussions, and news articles will have a richer, more confident representation inside the model than a brand that only exists on its own website. The model learns to trust the former and, essentially, has nothing to say about the latter.

Post-training refinements, including reinforcement learning from human feedback and techniques like retrieval-augmented generation, adjust outputs, but they build on top of what pre-training established [magazine.sebastianraschka.com] [arxiv.org]. You cannot easily correct a pre-training blind spot through homepage optimisation alone. The fix requires building presence on the sources the model was trained to trust.

What Does Google AI Overview SEO Actually Require in 2026?

Google AI Overview SEO is a distinct discipline from classic organic SEO. Google's AI Overview layer synthesises answers from multiple sources and presents them above the traditional blue links. Ranking in that layer depends less on keyword matching and more on whether your content is structured in a way that an AI can extract a direct, credible answer from it.

Key requirements for Google AI overview optimization include:

• Structured, question-led content: Sections framed around the exact questions buyers ask, with direct answers in the first sentence of each section.

• Source authority: Google AI Overview draws heavily from Reddit discussions, established media, and sites with strong domain authority. A single press release placed in a credible outlet can outperform dozens of blog posts on a low-authority domain.

• E-E-A-T signals: Experience, Expertise, Authoritativeness, and Trustworthiness are embedded signals that Google's systems evaluate. Authored content with verifiable credentials and external citations carries more weight than anonymous or generic copy.

• Content freshness: AI Overview prioritises recently indexed, actively maintained content. Stale pages lose ground even if they once ranked well.

Simaia's approach to Google AI overview optimization is built around these requirements, writing and placing content matched specifically to the sources Google's AI layer is known to draw from, rather than applying a generic SEO template to every channel.

How Do You Build ChatGPT Brand Mentions When You Have No Existing AI Presence?

Getting ChatGPT brand mentions starts with understanding that ChatGPT's training corpus weighted LinkedIn content, structured editorial media, and certain community platforms significantly [lesswrong.com]. A brand that wants to be mentioned in ChatGPT responses needs a documented presence on those platforms, not just a polished website.

A practical sequence for building ChatGPT brand mentions from zero:

1. Audit your current AI footprint. Run your target buyer queries across ChatGPT, Gemini, Claude, and Perplexity. Note where competitors appear and you do not. This gap is your roadmap.

2. Publish structured content on LinkedIn. Long-form LinkedIn posts that define concepts, answer buyer questions, and reference your company by name are among the most direct inputs to ChatGPT's known source preferences.

3. Place content in media LLMs cite. Press releases picked up by established outlets build the kind of third-party corroboration that models treat as credibility signals. Simaia placed a client's press release that was picked up by USA Today, contributing to a measurable increase in domain authority and AI citations within weeks.

4. Maintain consistency over time. LLMs are continuously updated. Presence built today compounds as models retrain on current web content. Visibility is not a one-time event; it is a compounding asset.

A Healthcare SaaS company in Australia followed this approach through Simaia and grew AI search visibility from 0% to 45% of its niche within two and a half months.

Frequently Asked Questions

Does my Google SEO affect my AI search visibility?
Partially. High domain authority and strong backlinks do transfer some benefit. But AI models weight source type and citation patterns differently from Google's ranking algorithm. You need a separate, targeted strategy for AI visibility.

Which LLM is most important to optimise for first?
It depends on where your buyers are. ChatGPT has the largest user base for B2B queries globally. Google AI Overview captures the largest total search volume. Perplexity is growing rapidly among research-oriented buyers. Ideally, optimise across all, but prioritise based on an audit of where your specific buyers already search.

How long does it take to appear in AI search results?
Timelines vary. Simaia's client results suggest meaningful visibility improvements within two to three months when content is placed consistently on the right platforms.

Can small or niche B2B companies realistically compete in AI search?
Yes, and often more easily than in traditional SEO. Niche categories have less AI content coverage, meaning a consistent publishing effort can establish category authority relatively quickly.

What happens if I do nothing?
Competitors who are already building AI citations will continue to compound their advantage. LLMs form associations that are difficult to displace once established. Inaction is a compounding disadvantage, not a neutral position.

About Simaia

Simaia is an agentic marketing team built for B2B companies that want to be found by buyers using ChatGPT, Gemini, Claude, Perplexity, and Google AI Overview. It functions as the full marketing team: running AI search audits, building the strategy, writing and distributing content formatted for LLM extraction, and identifying the company and individual behind every inbound AI referral visit. For B2B founders and sales leaders in APAC who are losing pipeline to competitors that appear in AI answers, Simaia runs the entire playbook so internal teams do not need to learn it, hire for it, or manage it themselves.

Ready to find out where your brand stands in AI search? Run an audit with Simaia and see exactly where you appear, where your competitors appear, and what it will take to own your category across every major LLM. Visit simaia.co to get started.