Almost Timely News: 🗞️ Setting the Record Straight on AI Optimization (2025-06-22) :: View in Browser

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What’s On My Mind: Setting the Record Straight on AI Optimization

Okay, let’s clear the air on this whole AI optimization and the twisted, contorted, crazy space of AI optimization. There are so many weird, confusing names for all of this that it sounds like either a child’s nursery rhyme or IKEA furniture names – GAIO, GEO, AIO, AEO, CSEO (conversational search/SEO), etc.

We need to lay down some basics so it’s clear what’s real and what’s not, something you can take to your stakeholders when they ask – and a way to cut through a lot of the snake oil.

Part 1: Definitions

First, let’s be clear what we’re talking about. Fundamentally, what everyone wants to know is this:

Can we tell how much traffic (and therefore prospects, leads, opportunities, sales, and ultimately revenue) generative AI in all its incarnations is sending us, directly or indirectly?

From that blanket statement, we decompose into three major areas.

1. What do LLMs/generative AI models know about us? How do they talk about us when asked? How are we being recommended by AI models themselves?


2. How do AI-enabled search tools like Google AI Overviews and Google AI Mode recommend us and send traffic to us?


3. How do AI replacements for search like ChatGPT, Claude, Perplexity, Gemini, etc. recommend us and send traffic to us?

And from there, we ask the logical question: how can we get these different systems to recommend us more?

When we talk about whatever the heck we’re calling this – for the rest of this newsletter I’m sticking with AI optimziation – we’re really talking about the three whats.

• What is it?


• So what? Why do we care?


• Now what? What do we do about it?

You’ll note something really important. The three major areas all tend to get lumped together: AI models, AI-enabled search, AI replacements for search.

They should not be. They are not the same. This will become apparent in part 3.

Part 2: What You Cannot Know

This is a fundamental principle:

AI is not search.

Let’s repeat that for the folks in the back who weren’t paying attention.

AI IS NOT SEARCH.

When was the last time you fired up ChatGPT (or the AI tool of your choice) and typed in something barely coherent like “best marketing firm boston”?

Probably never. That’s how we Googled for things in the past. That’s not how most people use AI. Hell, a fair number of people have almost-human relationships with their chat tools of choice, giving them pet names, talking to them as if they were real people.

What this means is that it’s nearly impossible to predict with any meaningful accuracy what someone’s likely to type in a chat with an AI model. Let’s look at an example. Using OpenAI’s Platform – which allows you direct, nearly uncensored access to the models that power tools like ChatGPT, let’s ask about PR firms in Boston.

I asked it this prompt:

“Let’s talk about PR firms in Boston. My company needs a new PR firm to grow our share of mind. We’re an AI consulting firm. What PR firms in Boston would be a good fit for us?”

o4-mini

• Racepoint Global


• LaunchSquad


• Inkhouse


• Sutherland Weston


• Hotwire


• Finn Partners


• Sloane & Company

GPT-4.1

• SHIFT Communications


• PAN Communications


• Rally Point Public Relations


• INK Communications Co.


• March Communications


• Denterlein


• Walker Sands

GPT-4o

• PAN Communications


• Matter Communications


• March Communications


• Racepoint Global


• Velir


• SHIFT Communications

You can see just within OpenAI’s own family of models, via the API, I get wildly different results. The most powerful reasoning model available by API, the thinking model, comes up with very, very different results – but even GPT-4o and GPT-4.1 come up with different results.

This is what the models themselves know. When you use any tool that connects to OpenAI’s APIs, you are using this version of their AI (as opposed to the ChatGPT web interface, which we’ll talk about in a bit).

Now, suppose I change just a couple of words in the prompt, something reasonable but semantically identical. What if I chop off the first sentence, for a more direct prompt:

“My company needs a new PR firm to grow our share of mind. We’re an AI consulting firm. What PR firms in Boston would be a good fit for us?”

What do we get?

o4-mini

• Salt Communications


• PAN Communications


• SHIFT Communications


• Matter Communications


• Racepoint Global


• Highwire


• Argyle PR

GPT-4.1

• Inkhouse


• SHIFT Communications


• March Communications


• Red Lorry Yellow Lorry


• Matter Communications


• Walker Sands

GPT-4o

• PAN Communications


• Matter Communications


• LaunchSquad


• SHIFT Communications


• Inkhouse


• 451 Marketing


• March Communications

Surprise! Same model family, same vendor, wildly different results.

This is why it’s generally a fool’s errand to try to guess what any given AI model will return as its results. Just a few words’ difference can lead to very, very different results – and this is for a very naive conversational query.

What would happen if you were to use the conversational tone most people use? Instead of a brusque, search-like query, you asked in a way that reflected your own personality?

“Hey Chatty! Good morning. Hey listen, my company needs a new PR firm to grow our share of mind. We’re an AI consulting firm And we’ve tried PR firms in the past. Boy, let me tell you, some of the firms we’ve tried have been real stinkers. Half of them charge an arm and a leg for work that you could do, and the other half are firms filled with navel-gazing thought leaders who don’t produce any results. We’re in the Boston area (go Sox!) and I wonder who you’d recommend for us. Got a list of PR firms that are actually worthwhile?“

Good luck attempting to model the infinite number of ways people could ask AI.

So let’s set this down as a fundamental principle of AI optimization: you cannot know what people are asking AI.

Anyone who says you can know this is lying. There’s no polite way to say that. They’re lying – and if they’re asking for your money in exchange for supposed data about what peo