Debunking Top Myths about LLM

“I want our brand to show up more often on ChatGPT. What do I do?” That question kicks off nearly every conversation I have with brand marketing leaders these days.

TL;DR: How marketers should think about ChatGPT and AI search

ChatGPT and other AI search experiences matter, but they are not a straight swap for Google, SEO, or click-based attribution. For marketers, AI search is best understood as a visibility and influence channel. It shapes what buyers put on their shortlist, which brands earn their trust, and where they head next, even when no trackable click is ever made.

The practical takeaway is to keep investing in strong SEO fundamentals while adapting your content for answer engines by making it clear, well-structured, credible, easy to summarize, and consistent across both owned and third-party sources.

That question matters a lot, but it often comes loaded with shaky assumptions about what AI search can and can’t do for a marketing strategy.

A quick note on terminology: I use "AI search" as the umbrella term covering experiences like ChatGPT, Perplexity, Claude, Gemini, and Google AI Overviews. AEO, or Answer Engine Optimization, is the practice of formatting content so AI answer engines can interpret, condense, and reference it. GEO, or Generative Engine Optimization, is often used in the same breath, particularly when people are discussing visibility inside generative AI responses.

AI search is the defining topic in 2026 growth marketing, but confusion about how it actually works is everywhere.

Below are the 7 myths I tackle most often with clients and prospects.

Myth 1: Is ChatGPT more important than Google for marketers?

Truth: Not yet. Google still accounts for the majority of organic search activity.

LLM adoption is climbing fast, no question. But traditional Google search is still where most organic discovery happens. That is not a reason to ignore AI search. For one thing, it’s growing insanely fast; for another, the principles behind AEO and traditional SEO share a lot of common ground, so building your presence in ChatGPT, Claude, Gemini, Google AI Overviews, and Perplexity will not hurt your SEO standing.

A useful way to frame it: AI search is a meaningful new discovery layer, not a Google replacement.

Keep investing in SEO fundamentals while also shaping content for the way answer engines pull, condense, and attribute information.

Myth 2: Does AI search follow normal marketing attribution rules?

Truth: No. AI search drives real influence, but it does not work like a click-based channel.

The way LLMs behave is a departure from the click-centric measurement marketers have spent years building around. ChatGPT can carry enormous weight in a purchase decision, but influence has always been a harder thing to prove than a click.

That means measurement needs to be rethought. My team relies heavily on AI visibility tools, with Profound being a personal favorite, to make sense of this new environment.

The approach we use is triangulation. AI visibility measurement platforms show when and how your brand surfaces in AI answers. Google Analytics tracks actual traffic to owned properties like your website, blog, and landing pages. Post-conversion surveys ask customers directly what shaped their decision, including whether an AI tool was part of their research process.

Together, those three sources give a workable view of impact across the funnel.

Worth noting: this is the least developed corner of AI search right now. A wave of new measurement tools is coming.

Myth 3: Will ChatGPT and AI search drive a lot of revenue?

Truth: AI search can shape revenue, but directly attributable results are still difficult to pin down.

AI’s revenue-influencing potential is real. In our client work, traffic arriving from AI search has converted at a strong clip, sometimes outpacing Google traffic. But the current strength of AI search sits mostly in the early and middle stages of a purchase journey.

Buyers often begin with a problem. Especially in B2B, they turn to ChatGPT or a similar tool to get oriented in a category, define what they actually need, and build out a short list of options to consider.

The catch is that LLM results tend to produce far fewer clicks than a Google results page. That feeds into the broader zero-click trend, where users walk away with their answer without ever visiting a website.

AI search may be doing real demand-shaping work, but tying that directly to revenue is still a challenge.

What we see across client accounts is that a large number of buyers do substantial research inside LLMs, then navigate to owned media and review sites to find the specific details that close the deal, things like pricing, head-to-head comparisons, product specs, reviews, use cases, and implementation guidance.

Treat AI search as a revenue influence channel first, not a direct-response channel.

Myth 4: Are AEO and SEO the same thing?

Truth: They overlap, but they are not identical.

There is meaningful common ground, especially around E-E-A-T, which stands for Experience, Expertise, Authoritativeness, and Trustworthiness. E-E-A-T sits at the center of Google's organic philosophy, and it carries weight in AI search too because answer engines need credible, reliable sources to draw from.

Where they diverge is in how content needs to be formatted and organized. For AI search visibility, content has to be easier for a model to extract, condense, and attribute. That means building in clear definitions, short summaries, descriptive headings, FAQ sections, comparison tables where tradeoffs matter, credibility signals tied to the author or company, current product and category information, concise answer blocks, and self-contained sections that hold their meaning even when lifted out of context.

That last point is worth sitting with. LLMs do not necessarily read a page the way a person does, moving top to bottom. They may pull a specific passage and surface it independently. If each section cleanly answers one question on its own, your content becomes far more useful to AI systems, and far more likely to get cited accurately.

The goal is to make your expertise easier to interpret.

Myth 5: Is AI search an objective source of information?

Truth: No. AI search may feel neutral, but it reflects the sources and patterns available to it.

Organic results can’t be purchased the way ad placements can, so they’re more objective than paid results in that sense. But they’re nowhere close to fully neutral.

AI systems are shaped by the information they were trained on and the content they can retrieve, which spans the entire web. That means their outputs can carry existing biases, blind spots, and uneven perspectives.

LLMs don’t approach information with the skepticism of a trained expert or reason critically. Their reliability is only as strong as the sources feeding them, many of which include unverified or inconsistent content from platforms like YouTube, Reddit, and LinkedIn.

For marketers, this plays out in two ways. Your owned content needs to be clear, accurate, and consistent. And your presence across third-party sources matters just as much, including review platforms, communities, social channels, and anywhere else buyers talk about your brand.

AI search visibility is a function of what you publish on your site and what the broader web says about you.

Myth 6: Does ChatGPT understand complex topics the way humans do?

Truth: LLMs are strong at pattern recognition, organization, and condensing information, but nuance is a different story.

LLMs read patterns, not topics in the human sense. That creates a real gap when context and judgment matter.

They handle certain tasks well. Organizing large amounts of information, summarizing long documents, building lists and comparisons, identifying recurring themes, and turning messy inputs into readable outputs are all areas where they perform reliably.

Where they fall short is in weighing competing expert views, catching subtle context, recognizing when a source is incomplete or skewed, and applying genuine critical judgment.

AI search still has tons of utility, but that means marketers can’t assume an AI system will naturally grasp the finer points of a complex product, market, or buying context. If your category involves real nuance, your content needs to make that nuance visible and explicit.

Myth 7: Can marketers set an AI search strategy once and move on?

Truth: No. AI search is moving too fast for a set-it-and-forget-it approach.

Some of the specifics in this post, particularly around AEO's share of search volume, will have shifted by the time you read it. Since ChatGPT became publicly available in late 2022, both the underlying models and the way people interact with them have changed at a pace that is hard to keep up with.

That speed demands ongoing attention. The question of how to show up in LLM results will only become more pressing, and the right answer will keep evolving.

At a minimum, marketers should regularly check whether: their brand appears in AI answers for the most important category questions; AI tools describe their products and positioning accurately, key pages include clear summaries, definitions, and answerable sections; third-party sources describe the brand consistently; and bottom-funnel information is current and easy to locate.

One more key note here: approach AI search strategy as ongoing discipline.

What should marketers do next to improve AI search visibility?

At a high level, the best move is to stick with SEO principles while building a stronger, clearer information footprint.

Start with these priorities:

Make sure your company, products, categories, and use cases are described consistently across your site and major third-party sources. Build content around direct headings, short summaries, definitions, FAQs, and self-contained sections. Make expertise, authorship, product details, and source quality easy for both people and machines to recognize.

AI tools may shape early-stage research, but buyers still need pricing, comparisons, reviews, and implementation details further along in their journey. Combine AI visibility tools, Google Analytics, and post-conversion surveys to understand impact beyond the click. And keep content fresh, because AI search is moving quickly and outdated content creates both visibility and accuracy problems.

The brands that win in AI search will make their expertise genuinely easy to understand, for humans and machines alike.

Frequently Asked Questions

What is AEO in marketing?

AEO, or Answer Engine Optimization, is the practice of structuring content so AI answer engines can interpret, condense, and cite it. For marketers, that means producing clear, credible, well-organized content that addresses specific questions in a direct and accessible way.

How is AI search different from traditional SEO?

AI search drives fewer clicks than traditional Google search and often shapes buyer thinking before they ever visit a website. SEO still matters, but AEO puts more weight on clear summaries, structured information, credible sourcing, and content sections that can stand independently inside an AI-generated response.

How should marketers measure ChatGPT or AI search impact?

Measuring AI search impact works best when you combine visibility, traffic, and influence signals. Use AI visibility tools to track where your brand appears, Google Analytics to follow clicks to owned properties, and post-conversion surveys to understand whether AI tools played a role in the buyer's journey.