GEO (Generative Engine Optimisation) is a daily conversation at the moment and I’m often asked what we’re doing differently, if anything for GEO vs SEO.,
In the industry there are two different camps.
One side positions GEO as a complex new discipline requiring a new set of tactics and a new way of thinking about search.
The other says is just the same as good SEO.
I believe it’s about 80% the same as the user-first, product-led SEO I’ve always practiced. The remaining 20% comes down to some nuances of how LLMs work and how they present information vs blue links.
The engine has never been your audience
What we’ve learned over decades is that search systems always evolve. With every iteration they get better at aligning with what people actually want rather than what’s been gamed into the results. The mindset of designing for the engine rather than for the user has always been a short-term play.
Back in 2011, I remember spinning up a templated site on an exact-match domain, adding some basic copy, throwing spammy links at it, and ranking number one by lunchtime. On refelction it was ridiculous and its easy to understand a. why the Google results were so awful and b. why so many SEO companies were created in that time
Chasing the quirks of a new AI interface is the same game played on a different board. And history is pretty clear on how it ends.
The feedback loop just got tighter. That’s a good thing.
We used to have months between major Google updates. Time to test, observe patterns, and gradually shift strategies. That window has closed. AI models are being retrained and fine-tuned constantly and reinforced by direct user feedback.
If an AI surfaces your content and the user skips it, rephrases their question, or gives a thumbs-down, the model learns. This means that even if you find some sort a shortcut to visibility today (and LinkedIn is littered with those hacks), you’ll be edited out by tomorrow if your content doesn’t actually deliver.
This is a good thing. It means the tactics that fool the system are dying, and the real fundamentals become stronger.
A good product. A strong brand. Good customer service. Genuinely helpful content.
These create positive user feedback loops that AI systems will learn to trust. You can’t optimise a bad product or brand experience into an AI recommendation for long if at all.
The messy middle is your product’s proving ground
AI is collapsing the old funnel. Top-of-funnel informational queries - “what is X” - are handled instantly and better than most content can manage. Bottom-of-funnel brand and pricing queries are increasingly absorbed and answered directly too.
What’s left is the middle of the funnel. The messy middle — where users are weighing options, validating choices, comparing products, and building trust. This is where a product-led mindset wins.
Can your content help someone make a complex decision? Does it reflect genuine experience and real-world detail? Does it solve a problem in a way an LLM’s generic summary cannot?
If an LLM can explain something better than you can, you’re competing on the wrong ground. But if your content reflects deep user understanding and answers questions that are genuinely hard to answer, you’ll stand out — not just to users, but to the retrieval systems designed to serve them.
So what actually changes?
Ignore the hype. Don’t panic and forget trying to reverse-engineer language models.
For me, this is the same SEO I’ve always practiced. The fundamentals haven’t changed — they’ve just become more important and harder to fake. Focus on your actual product, your customer, and creating content that earns its place by answering real questions, guiding real decisions, and building real trust.
You don’t need to optimise for the engine. You need to be the best solution for the person the engine is trying to serve.
That’s the only strategy that lasts.