The thing people keep getting wrong
When generative engine optimisation started getting attention, most content teams reacted the same way. They added a paragraph about "according to experts" to the bottom of existing articles. They started writing FAQ sections with the exact phrasing of questions they thought AI would ingest. They called it GEO.
That is not GEO. That is SEO anxiety dressed up in new language.
GEO is a different discipline because the output is different. Traditional SEO is optimising to rank. You are trying to appear in a list. GEO is optimising to be cited. You are trying to become the source that an AI model uses when it summarises an answer for a user who may never click through to your site at all.
That changes almost everything about how you write.
What AI models actually reward
I have spent the last year building content infrastructure with GEO as a first-order consideration, not an afterthought. A few things have become clear.
Specificity beats coverage. A piece that makes one clear, well-supported claim gets cited more often than a piece that covers ten things shallowly. AI models are not looking for comprehensiveness. They are looking for clarity and confidence.
Structure is not optional. The way information is organised matters a lot. Clear headings, direct answers placed before supporting detail, named concepts rather than implied ones. If a model has to work to extract your point, it will often skip to a source that made it easier.
Credentialing matters more than you think. Not domain authority in the traditional sense, but the substance of the claim itself. First-hand experience. Specific numbers. Attributable data. AI models are better at detecting thin content than most ranking algorithms ever were.
What this means practically
The shift I made at Aquilon Tech was to stop producing content in volume and start producing content that owned specific answers. Instead of ten articles loosely covering a topic, we produced two or three that fully answered the questions people actually had, with enough specificity that a generative model would have a reason to reference them.
Organic traffic moved. More importantly, branded search moved. That is the real signal that GEO is working: people start searching for you by name because they have seen your thinking cited in enough places that you have become associated with the idea.
It is slower to build than click-through traffic. It compounds harder once it starts.
The honest caveat
GEO is still relatively new and the behaviour of different models varies. What works in ChatGPT's search citations is not identical to what works in Perplexity or in Google's AI Overviews. The principles are consistent but the execution requires watching and adjusting.
What I am confident about is this: teams that keep treating GEO as a bolt-on to their existing content process will be outcompeted by teams that redesign their content architecture around it. The window to do that cheaply is probably smaller than people think.