AI search optimization has blossomed into a multi-billion dollar industry. Venture capital is pouring into countless startups promising increased brand visibility, and consultants are charging premium prices for their services. Amidst this flurry, it’s becoming increasingly difficult to distinguish between genuinely effective strategies and pure hype.
As an independent growth consultant specializing in boosting brand visibility through organic traffic channels—encompassing both traditional and AI-powered search—I’m constantly experimenting and auditing on the front lines, witnessing firsthand what truly works and what’s mere smoke and mirrors. The market is undeniably saturated with misleading information.
This article will debunk four core myths surrounding AI search optimization, helping you focus your energy on what truly matters, rather than wasting time on ineffective tactics.
When people say "ChatGPT killed SEO," what they really mean is ChatGPT killed Google Search, not SEO itself. Remember, SEO is simply an acronym for "Search Engine Optimization," and it doesn’t explicitly point to any single search engine. However, due to SEO’s long-standing association with Google and the fact that "SEO is Dead" headlines are inherently more attention-grabbing, this myth has persisted since ChatGPT’s release.
In reality, the data shows the exact opposite. A Semrush study analyzing 260 billion rows of data from January 2024 to June 2025 found that search tools like ChatGPT aren't killing Google Search; they're expanding it. The popularity of ChatGPT has not only failed to reduce Google Search usage but has actually led to a slight increase.
Another study by Ahrefs revealed that Google still holds nearly 90% of the market share in AI-assisted search. Given that Google's AI Overviews (AI Overviews) continue to pull information from top-ranking Google pages, there's arguably never been a better time to double down on traditional SEO.
Critics might retort, "Well, ChatGPT hasn't killed Google yet, but it's only a matter of time before more people use it for search." The core of that argument leads us to the next myth.
How many times have you heard on social media, in blog posts, YouTube videos, or from colleagues: "If you don't adapt to the new way of search optimization, you'll be left behind." What I'm here to tell you is that the so-called "new SEO" is simply a return to SEO fundamentals—a renaissance of "classical SEO," if you will.
If you believed what most people online say, you'd think the overlap between Google SEO and AI SEO is minimal. But based on my practical experience and existing data, the reality is this: SEO almost entirely encompasses AI SEO.
This misconception stems partly from people viewing ChatGPT as some mysterious black box with its own unique methods for collecting and indexing web data, completely separate from Google’s own proprietary methods. This naturally leads to the thought that you need to optimize specifically for ChatGPT’s information-gathering methods.
However, multiple experiments reveal that AI assistants rely almost entirely on existing, traditional indexing systems. Lee from Backlinko conducted experiments that irrefutably proved ChatGPT uses Google's index directly. Claude uses the index of Brave Search—a more privacy-focused alternative to Google—to retrieve and present data.
As far as I know, the only major AI company currently building its own internal web index is Perplexity. Even then, their results remain highly similar to Google Search results. So, it's fair to say these AI assistants are, in large part, merely AI wrappers for existing search engines.
Now that we've demystified ChatGPT—that it essentially just uses Google—we have a clearer understanding of the tactical layer of AI search rankings. In fact, I haven't yet encountered a single AI search optimization strategy that doesn't also apply to traditional SEO. If you disagree, please leave a comment below; I’m willing to change my mind.
Most of the tactics being touted as "new AI SEO strategies" are actually best practices that search professionals have been employing for over a decade. For example:
• Contextual Inclusivity: Structuring full thoughts within chapters, paragraphs, and sentences for easier extraction by large language models? This is what we’ve been doing since 2014 for Featured Snippets.
• Query Fanout: AI performing hidden searches in the background? According to Yakub’s research, the solution for query fanout and traditional SERP rankings is the same—build topical authority around a core group of relevant pages targeting similar keywords. We call these Topical Clusters. Yakub found that 84% of fanout queries are "query neighbors," meaning they share the same URLs in Google’s search results. Thus, a well-written, SEO-best-practice page has the potential to rank for many of these query fanout terms.
• Brand Mentions: While the importance of brand mentions is indeed resurfacing (something I believe is good for the internet overall, as people don't always naturally link to other brands when writing), calling it "new" is inaccurate. Unlinked Brand Mentions have always contributed to brand visibility, even before AI search existed. It's been a viable off-page SEO strategy for years.
For businesses looking to quickly build their SEO content infrastructure, platforms like SEOInfra can help you bulk-generate original, SEO-compliant blog posts from high-quality content sources (YouTube videos, industry discussions, competitor analysis, etc.) with the correct technical structure built-in, ensuring content is both indexed by traditional search engines and understood/cited by AI assistants.
In September 2024, developer Jeremy Howard published an article proposing a new standard for websites called llms.txt. The idea is for website owners to place a text file named llms.txt in their root directory, providing instructions for large language models to better understand and crawl their sites. Essentially, it’s a sitemap.xml for AI search.
The proposal itself was actually quite sound, but somewhere along the line, the internet's runaway game of "telephone" took over. llms.txt evolved from a proposed standard into an absolute necessity—a "must-do immediately to rank" requirement. This is why we can't have nice things—people on the internet love to exaggerate.
To this day, the llms.txt myth persists, despite zero meaningful data showing a positive correlation between AI visibility and the file. To put this rumor to rest, Mark Williams Cook created a completely fictitious, nonsensical standard called cats.txt and wrote his own proposal for it. Lo and behold, LLM crawlers ingested it, and Mark successfully convinced AI that cats.txt was crucial for rankings.
This experiment is amusing, but it proves two important points:
• The llms.txt file is largely a waste of time (though it takes only a few minutes to set up and won't harm your site, so feel free to try it if you wish).
• You cannot entirely trust the SEO or AI search advice provided by ChatGPT, Claude, Perplexity, or Grok. Ultimately, they are just language prediction systems, and they can clearly be deceived.
It’s 2025, can we be honest? Most of us are using AI in some capacity during our content creation process. The key is to avoid AI Slop—which generally refers to blog posts generated wholesale with a single click from ChatGPT or Claude.
Many believe this type of content is not only bad for readers (which it often is) but will actively be penalized by Google. This myth is so widespread that Google itself has added a clarification to its Quality Rater Guidelines: "Simply using generative AI tools does not determine whether a page lacks effort or is low quality. Generative AI is used in the creation of both high-quality and low-quality content."
In other words, good content is good content, regardless of how it was created.
In my opinion, the best approach involves a hybrid process, combining the speed and efficiency of AI with the unique creativity and skillset of humans. As AI commoditizes content, success will belong to brands focused on creating genuinely valuable, high-quality content—regardless of whether AI was involved in its creation.
For teams looking to systemize their content production capabilities, SEOInfra offers an intelligent solution: rather than a simple AI writing tool, it helps you transform high-quality information sources like YouTube videos, industry expert insights, and competitor content into original, indexable blog posts, while ensuring correct SEO technical structure, keyword optimization, and one-click publishing to platforms like WordPress, Webflow, or Shopify. This creation method, based on quality source material, is key to avoiding AI slop and achieving scaled, high-quality output.
Current data suggests otherwise. The proliferation of ChatGPT has actually led to a slight increase in Google Search usage, not a decrease. Google still holds nearly 90% of the AI-assisted search market share, and traditional SEO strategies remain effective and important.
There is almost no difference. AI assistants primarily rely on the indexes of traditional search engines like Google, so traditional SEO best practices apply equally to AI search optimization. Tactics touted as "new AI SEO strategies" are mostly fundamental principles that SEO professionals have been using for years.
Currently, there is no meaningful data showing a positive correlation between the llms.txt file and AI search visibility. While setting up this file won't harm your website, it's largely a waste of time and shouldn't be considered a necessary step for improving rankings.
No. Google has explicitly stated that merely using AI tools does not affect content quality ratings. The crucial factor is whether the content provides value to users and is high-quality, not the method of creation. The best practice is to combine AI's efficiency with human creativity to produce genuinely valuable content.
Avoid relying solely on AI for one-click content generation. Adopt a hybrid approach, leveraging AI for efficiency but maintaining human creative judgment and editorial control. Focus on original reconstruction based on high-quality information sources (like industry expert insights, quality video content, in-depth research) rather than letting AI generate content from scratch.
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