If you're publishing your GEO content last, you're handing authority to someone else

Most GEO strategies are built backwards. Brands invest months in the technical foundation, the schema markup, the structured question-and-answer content, and then, when all that is in place, they finally publish the long-form thought leadership that's supposed to establish authority. By that point, someone else has already done it. The models have already formed their priors.

This is the sequencing problem that almost no one in B2B marketing talks about, and it matters more than almost anything else in how you approach GEO.

How parametric memory actually works

Language models don't retrieve authority at the point of inference in the way a search engine retrieves a ranking. They absorb it during training. The association between your brand and a concept, a problem category, a point of view, gets encoded over time as models process documents that corroborate each other. By the time you're thinking about whether your content is being cited, the encoding process for the current generation of models is essentially complete.

I wrote about this in some depth in the piece on parametric memory and AI recall: the researchers at Facebook AI and UCL probed BERT to establish that language models store facts as parametric memory, baked in during training, not retrieved on demand. Which means the training data that shapes a model's priors today was crawled months or years ago. What you publish now is shaping the priors for models that haven't been trained yet.

This has a direct implication for sequencing. If you wait until your AEO infrastructure is in place before publishing foundational GEO content, you're optimising for retrieval without having built the prior that makes retrieval likely. You're adding the tactical layer before the structural layer exists.

The Redgate pattern, in reverse

Redgate is a database DevOps and SQL Server tooling company where I worked from 2009 to 2021. It's a useful illustration of what early, consistent publishing actually builds, even when that wasn't the explicit intention.

Redgate had been publishing consistently since the early 2000s: technical documentation, SQL Server guides, developer-facing content that accumulated over years before anyone was thinking about AI visibility. That compounding body of work means that by the time models began processing the internet, a significant proportion of the credible technical content in the SQL Server space was already associated with Redgate. The prior got encoded not because Redgate ran a GEO programme, but because they published first and kept going.

Most brands are attempting the opposite. They're reacting to AI search as a new challenge, publishing content now in categories where they don't yet exist in the training corpus. Against brands that started earlier, in any meaningful sense, they're starting behind.

The sequencing error isn't that they're doing AEO work. AEO is measurable in the short term, you can track citation rates, run structured queries across platforms, and see what gets retrieved. The error is treating AEO as the foundation rather than the surface layer. AEO works when the parametric layer already exists. Without it, you're optimising for a retrieval signal that the model has no particular reason to fire.

What to actually publish first

The content that builds parametric memory is not the structured FAQ page. It's the original point of view: a clear, specific claim about how a problem category should be understood, written by someone with enough named experience behind them that the claim is corroborated across independent sources over time.

That's why I've argued that AI visibility is fundamentally a brand problem, not a content operations problem. The brands that will be cited in three years are the ones publishing original claims now, not the ones polishing schema markup while someone else stakes the conceptual territory.

Publish the argument first. Build the retrieval infrastructure around it. Not the other way around.

Related reading

• Why AI systems cite some SMB-to-enterprise stories and ignore others
• Brand visibility is now a Bayesian problem