The Myth of AI Search Unoptimizability

People Aren’t Persuaded by Data

If you’re reading this because you think that “AI search can’t be optimized for”, there’s probably little I can do to sway your opinion of that.

However, from having a lot of conversations online with people of a similar mindset, there’s a good chance we’re actually talking past one another.

There's a good chance we're actually talking past one another.

So this guide isn’t so much about persuading you you’re wrong, but trying to get to a common language and understanding of what we’re actually talking about when we say “AI search can’t be optimized”.

What do you mean “AI model”?

People use the term “AI model” to refer to two very different things:

1. A standalone AI model

Developers, data scientists and other folks coming at search from a technical viewpoint tend to define an “AI model” as a discrete file (literally a “model file”) that wholly encapsulates all of the data and training of that went into that model. The type of file that you could load up into with Ollama or LM Studio and have a conversation with.

And while this is extremely correct, it’s also not how most people actually use AI models.

2. An AI model that’s one piece of a larger system

Most people don’t use AI models directly (and don’t think of them anymore than they think of whatever Google algorithm update is being used) they’re using them from a hosted service like ChatGPT or Gemini which updates them a lot more frequently than you might think.

1. AI model version (like “GPT-5.1” or “Gemini 3 Flash”)
2. System settings
3. System prompts
4. Tool calling (Web Search)

And each of these can be updated independently of the others.

What are we actually optimizing for?

Ok, so if not a static standalone AI model, what are we optimizing for?

The next model update in each AI service

I’ll speak only for myself here, but when I say that “you can optimize for AI search”. I probably should be saying “there’s a lot of things you can do to influence the AI search results for your brand in the next model update” (which again, will happen more frequently than you might think).

AI services aren’t frantically scraping the internet for nothing, they’re building up massive datasets that they are using to train the next model that they are going to release (and one that you really want to recommend your brand).

And at a minimum, you want that model to:

• Recommend your brand vs your competitors
• Be up to date on the latest information from your site (indexed)

Hallucination management

All day everyday people are asking questions about your brand, problems you could solve, pricing, features, etc. And maybe the AI they’re hitting with those questions is serving up accurate information and maybe it’s not.

Either way, you want to track the gaps in knowledge of the current AI models and fill those in with new hallucination erasing content.

Positive sentiment filter

Something I’ve heard a few times is that “AI search is just using a traditional search under the hood”, implying that all you need to do is optimize for traditional search and you’ll be fine.

But when you track down actual AI behaviors you find that AI is acting as a filter on top of traditional search results. The clearest example of this is Google’s Deep Research and AI Mode tools, which both live search the web and then filter the results to only include the most relevant ones.

Some people refer to this as “Agentic Search”, but naming quibbles aside, the point remains that you could rank great in traditional search only for an AI agent to skip over your site for brand sentiment or technical reasons

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