Last month I had the chance to review a clients pillar page on cloud security – a comprehensive 4,000 word piece that they’d put a lot of time into. The problem was that even though it had some truly original insights and expert quotes, all the good stuff was stuck in sections 3 through 5. The intro was a fairly generic overview and the conclusion wrapped things up in a standard way. I wanted to see how three different AI summary tools would cope with the page. Guess what? Every single one of them zeroed in on the intro and conclusion and largely ignored the middle section. It was like all the strongest stuff hadn’t even existed.

That’s when I started paying closer attention to what was going on with what we now call the middle content issue. And the more I dug into it, the more I saw that it was quietly undermining a lot of the SEO work I’d been putting in.

What The Middle Content Issue Actually Is

The middle content issue is a bit of a hidden problem in how large language models process long documents. There’s research from Stanford that backs this up. It confirms what a lot of us had noticed anecdotally: that AI systems have a tendency to pay loads of attention to the start and finish of a document, and tend to give short shrift to the bits in the middle.

This isn’t a bug that’s going to get fixed in the next quarter. It’s just the way that transformer models work with context windows. The beginning of a document gets a strong weighting because of where it sits, the end gets a weighting because of how fresh it is. And the middle – nothing.

That’s a problem for SEO because AI is now pretty much everywhere in the content workflow. Content briefs being fed into AI writing tools. Knowledge bases powering RAG systems. Long-form pages being summarised by AI assistants. Schema and structured data embedded mid-page. If the most important stuff you want to see is stuck in the middle of your documents, then AI systems are probably going to give it short shrift or just plain miss it.

How This Shows Up In Real SEO Work

I’ve seen this play out in a few different ways, and they’re worth going over because they’re easy to miss if you don’t know what to look out for.

The first is with AI content briefs. You write a detailed brief with your core E-E-A-T signals and primary keyword strategy in section 3. Your AI writing tool then sees the brief, gets caught on the intro and conclusion, and churns out content that completely misses your strategic point because it’s stuck further back in the brief. I had this happen with a 2,000 word brief for a fintech client – the AI generated draft was technically fine but just plain ignored every differentiation point I’d outlined because they were a bit too far down the page.

The second is with RAG-powered tools. If you’re using any sort of AI-powered FAQ, chatbot or content discovery system that pulls from a knowledge base – your most detailed answers are probably stuck in the middle of long documents. So the user ends up getting half-baked, incomplete responses because the AI never got round to properly processing your best stuff. I tested this with a client’s help centre docs and found that the AI was pulling from maybe the first 20% and last 10% of each document – the bits in between might as well have been invisible.

The third is with AI-assisted content audits. You feed a pillar page into an AI audit tool, and the recommendations come back and tell you to add in information you already have – because the tool didn’t properly process it in the first place. Sections 3 through 6, where all the good topical stuff is – get given short shrift.

Structuring To Avoid The Problem

The good news is you can actually work around the middle content issue with a bit of thought about how you structure your content. I’ve been doing some trial runs with clients and a few different approaches are starting to show promise.

First of all, you can front-load your critical information. Put your most important keywords, core value propositions and key data points in the first 15 to 20% of the content. Use your H1 and first H2 to set the topical context in stone – and then summarise your main claims before getting into the detailed arguments. This feels backwards if you’re used to building up a case gradually, but AI systems actually reward you for stating your conclusion first and then fleshing it out.

A second approach I’ve been using is what I call ‘structural signposting’. Add a brief summary at each section boundary – just to restate what’s been established and give a quick heads-up on what’s coming next. These act as anchors for the AI – they help it re-establish what’s going on after it’s cruised through the longer middle sections. You might feel a bit like you’re repeating yourself, but it seems to work.

And lastly there’s the bookend method. Mirror your most important information at both the start and end of a document – a summary or key points section at the top, and a takeaways section at the bottom – both restating your primary keyword intent and core claims. This plays off the positional and recency weighting that AI models do apply – and it can be a bit of a winner.For longer docs, breaking them down into bite-sized chunks really pays off. Split 3,000+ word behemoths into smaller, standalone sections that can be stored and grabbed as individual entries. This makes the context window much shallower for any given AI request, so it’s easier for retrieval systems to pull out the right snippet.

And first things first: audit what you’ve already got going on. Go through your top pages and see where your conversion CTAs, primary keywords and authority signals actually live. If they’ve got more than 40% of their text past that mark, they’re probably due for a revamp. You can even use AI tools to spot the problem: run your pages through a summarizer and whatever it leaves out tells you exactly where you need to beef up your content.

The benefits of structured redundancy

One idea I like to share with clients is something I call structured redundancy – basically, repeating the important signals in multiple places without making the content feel weak or redundant. You’re essentially writing for two different people at once: human readers who follow a narrative, and AI systems that need context scattered throughout.

Your keyword strategy, E-E-A-T signals & topical authority markers shouldn’t rely on just one spot in the document. Spread them out so they’re visible at the start, middle and end. That way, no matter which bit an AI is looking at, it’ll see your key messages. This isn’t about stuffing in keywords – it’s about naturally re-iterating your core expertise and claims a few times throughout the doc, so your key signals aren’t lost on any AI that looks at it.

Testing structured redundancy on your own site

Take your top 3 pages, run them through an AI summarization tool to see how they’ll get distilled down. Then compare the AI’s summary to what you actually want those pages to say. The gaps between the two are where you need to pay more attention to structure.

I had a client do this with their top 10 pages and it was eye-opening – six of them buried their most valuable content deep in the middle third. After restructuring to put key messages upfront and bring the repeats to the end, three of those pages started showing up in AI Overviews within 2 months. And it wasn’t even about changing the content itself, just how it was laid out.