Everyone Is Optimising for AI Visibility - Most of Them Are Missing the Point - starfordagency.com
Marketing Strategy

Everyone Is Optimising for AI Visibility —
Most of Them Are Missing the Point

The anxiety about AI visibility is real and understandable. The response to it is repeating a mistake marketers have made at every major platform shift for the last 25 years.

Two paths — optimisation compounds until it doesn't, substance compounds indefinitely
9 min read
Tsvetelina Tsekovska
AI Strategy Marketing Positioning

For five minutes on LinkedIn right now all I see is the same type of posts: "How to rank in AI search." "Your GEO strategy for 2026." "15 steps to appear in ChatGPT results." Then, just as everyone was trying to understand what AI visibility even means, the ChatGPT Ads beta arrived. And suddenly the conversation became much louder and much more urgent.

I get the anxiety. If you want to run a successful business, AI visibility feels like a must. But is it really the primary concern? For me, the question that should come first is much less exciting: Am I actually the best answer to the problem my customer is trying to solve?

That distinction may sound small. But I don't think so — in fact, I think it explains why so much of the current AI visibility conversation feels strangely familiar.

We Have Been Here Before

One short historical check shows the same pattern — and I am not talking about AI specifically.

Early SEO
Put enough keywords on a page and Google would reward you. The belief was simple: signal volume equals ranking.
Backlink era
Build enough links and rankings would follow. Volume over quality, manufactured authority over real credibility.
Social media
Engagement pods, artificial interactions, manufactured signals designed to convince the algorithm your content deserves organic reach.
AI era — now
Structured data, conversational content, FAQ blocks everywhere. And my favourite — an entire service category called "GEO optimisation."

Every generation of marketers believes it has discovered a shortcut. And every generation eventually discovers the same thing: the shortcut only works until the platform becomes good enough to recognise what quality actually looks like.

Why the pattern keeps repeating
That's not because channels don't matter — of course they do. It's because the channel itself is trying to solve a problem. Google wanted to rank useful pages. Social media platforms boost engaging content. AI systems are trying to surface the best answer available. The mechanics change. The objective remains surprisingly consistent.

Why This AI Panic Is More Understandable Than Previous Ones

What makes this moment different is that the stakes genuinely feel higher. If Google ranks a competitor above you, there's still a chance the user scrolls, compares options and eventually finds you. What if someone asks an AI platform for a recommendation and receives only a single answer? You may never even enter the consideration set — and that sparks real concern.

Publishers and news organisations are a clear example — they're losing visitors because AI systems summarise content directly instead of sending users to the source. AI may recommend you, but you lose the traffic and the subscription. That's a real business problem, not a theoretical one.

So the anxiety isn't irrational. If ChatGPT recommends your competitor for a query your ideal client just asked — that's a real loss. The anxiety isn't irrational. But the response to it is.

The ChatGPT Ads beta is the most clarifying signal of all. The moment AI becomes an ad platform, organic AI visibility becomes even more valuable — not less. Anyone can buy exposure. Not everyone can earn recommendation.

What AI Actually Surfaces — and Why

What's wrong is the belief that AI visibility is a separate discipline from just being excellent at what you do. It isn't.

The businesses appearing in AI responses right now — consistently, not just occasionally — are the ones with specific expertise, documented proof, honest positioning, and genuine depth of content. And they aren't necessarily the ones spending the most time optimising for AI.

The generic content problem
If twenty companies publish the same advice, the same positioning, and the same surface-level content, the differences become invisible. AI has the same problem your customers have — if everyone sounds the same, there's nothing meaningful to recommend. Recommendation requires differentiation.

The businesses that stand out are the ones creating something with enough substance that summarisation inevitably loses part of the value. Think about the best articles you've ever read — the ones that genuinely changed your perspective. A summary can explain what they said, but it can't recreate how they made you think.

That's the key difference. The question isn't "How do I optimise for the recommendation?" It's "What makes me recommendable?"

The ChatGPT Ads Beta Changes the Question — Not the Answer

Anyone can buy exposure, but not everyone can earn trust. And trust is ultimately what recommendation systems are trying to simulate.

Path One
Optimisation tactics
Structured data, FAQ schemas, conversational rewrites, GEO checklists. Builds assets that perform until the next platform update.
Survives until the next update
Path Two
Genuine substance
Specific expertise, documented proof, real case studies, honest positioning, original thinking. Builds assets that survive platform changes entirely.
Compounds over time

Those are very different strategies. The businesses investing in expertise, proof, positioning, depth, and credibility today are building the organic AI authority that paid placement will never fully replace.

What to Actually Do Instead

This isn't an argument for ignoring AI optimisation. I believe in and implement technical foundations across Starford and for all clients — clear site architecture, topical consistency, structured data. These elements are useful. They are just not the main thing.

They're the final layer. The first layer remains exactly what it has always been:

→
Be specific about who you help and what outcomes you create. Not what services you offer — what changes for the person after working with you.
→
Document real work and publish real examples. Case studies with real numbers. Real clients. Real before and after. Nobody can copy what only you have done.
→
Develop a point of view. Create content that teaches something competitors cannot teach because they haven't experienced it.
→
Write for humans first, machines second. The machine is ultimately trying to understand what humans find valuable anyway. The irony of AI visibility is that the more sophisticated the technology becomes, the harder it is to manipulate with shallow tactics.

The Mindset Shift That Actually Matters

Most businesses are asking the same question right now: How do I get AI to recommend me?

I think the better question is: If AI had to recommend the best answer available, would it choose me?

Two questions. Completely different work.
Question 1
How do I get AI to recommend me? → Leads to optimisation, tweaking, signal chasing. Builds assets that survive until the next update.
Question 2
Would AI choose me as the best answer available? → Leads to expertise, building, substance. Every major platform shift eventually rewards this one.

AI isn't changing that dynamic — if anything, it's making it more obvious. The businesses that win won't be the ones most obsessed with AI visibility. They'll be the ones creating products, services, insights, and proof that AI can't help but recommend.

At Starford, we pay attention to AI search, emerging platforms, and the changing ways people discover businesses. But we approach them the same way we approach everything: the channel matters, the substance matters more. Optimisation can amplify value. It can't create it.

And the businesses building genuine expertise today are likely to discover something interesting: they won't need to chase every new platform. The platforms will start finding them.

Building substance, not just signals?
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