We’re Building Websites Backwards: An Information-First Architecture for the AI Web
Most websites begin with a sitemap. Someone decides the company needs a homepage, product pages, an About page, perhaps some integrations, documentation, case studies and a blog. Designers turn those requirements into interfaces, developers turn the interfaces into components, and a CMS provides somewhere to store everything.
Only later do we begin describing what all of those pages actually mean. We add structured data so search engines can identify products, organisations and people. We create internal links to establish relationships between documents. APIs expose particular pieces of functionality. Search engines crawl the resulting site and reconstruct some understanding of the organisation from its pages. AI retrieval systems are increasingly expected to do the same.
I've started to think this process is backwards. The problem isn't that pages are the wrong way to present information to people; they're extremely good at that. The problem is treating the page as the canonical object and then repeatedly attempting to reconstruct the information underneath it for every other consumer.
A product is not a product page. A person is not an author page. A service is not a service page, and an integration is not an integration page. Those pages are representations of things that exist independently of the interface.
This distinction isn't entirely new. The W3C's Architecture of the World Wide Web has long distinguished between resources and their representations, as well as the separation of content, presentation and interaction. What is changing is the number and sophistication of systems attempting to understand those resources.
A modern organisation may now need to represent the same underlying information to humans, search engines, applications, AI retrieval systems and eventually autonomous agents. Instead of beginning with pages and translating them repeatedly, there is a useful case for beginning one level lower: with the organisation's information model.
I'll call the approach Information-First Web Architecture, or IFWA. It isn't a new protocol, framework or replacement for existing web standards. It is simply a rule for deciding what should be canonical before deciding how it should be presented.
From a browser's perspective, this is perfectly reasonable. It describes where a visitor can go. But it doesn't describe what the business actually knows.
Humans are extremely good at reconstructing these relationships from interfaces. If I visit a product page and see a link labelled "Slack Integration", I understand that the product integrates with Slack. If a case study is associated with a particular service, I can infer that the project demonstrates some capability related to that service.
Search engines have historically performed their own version of this reconstruction by crawling links, analysing documents and combining signals across a website. AI systems make the inefficiency more obvious because their objective can extend beyond deciding which document should rank. They may be attempting to determine what an organisation is, what it provides, how its products relate to particular problems and whether claims made across different documents refer to the same underlying thing.
We already knew that meaning and presentation were different concepts. MDN's explanation of semantic HTML makes essentially this distinction at the markup level: semantic elements communicate what something represents rather than merely how it should look.
IFWA takes that principle further up the stack. Instead of only asking whether the markup communicates meaning, it asks whether the underlying system knows what the thing actually is before we turn it into markup.
Take a pricing plan. Somewhere inside the business, it might already exist as structured information:
The frontend converts that object into a pricing card. It adds typography, layout, explanatory copy, feature comparisons and a call to action. That transformation is useful because people don't want to inspect JSON objects while evaluating software.
Now imagine an AI system receives a question asking which plan costs less than $100 per month and includes API access. It retrieves the pricing page, identifies the relevant section, removes information it doesn't need and attempts to reconstruct something remarkably similar to the original object.
The problem isn't the transformation into HTML. That's what the website is for. The questionable part is requiring every machine consumer to use the human representation as its authoritative source when a more precise representation may already exist underneath it.
This is the central principle behind Information-First Web Architecture: model the fact once, decide who owns it, and allow different interfaces to represent it.