The Web Isn’t URL-shaped Anymore  ## Metadata - Author: [[Jono Alderson]] - Full Title: The Web Isn’t URL-shaped Anymore - Category: #articles - Document Note: --- tags: - #SEO - #SemanticWeb - #DigitalMarketing Author: Jono Alderson Source: JonoAlderson.com Reference: [The web isn’t URL-shaped anymore](https://www.jonoalderson.com/conjecture/atomic-urls/) date created: 2025-07-31 14:00 --- # Title: The Web Isn’t URL-Shaped Anymore ## Table of Contents: - [📝 Summary](#-summary) - [📖 Main Content](#-main-content) - [🗒️ Notes](#️-notes) - [📖 References](#-references) - [🗣️ Paraphrase](#️-paraphrase) - [📰 Newsletter](#-newsletter) - [✅ Action Items](#-action-items) - [🧪 Hypotheses](#-hypotheses) - [📊 Methodology](#-methodology) - [❓ Questions](#-questions) - [🔮 Future Research](#-future-research) - [💡 Implications](#-implications) - [🧠 Learning Styles Assessment](#-learning-styles-assessment) - [📊 Accessibility Assessment](#-accessibility-assessment) - [🌍 DEIB Assessment](#-deib-diversity-equity-inclusion-and-belonging-assessment) - [✅ Logical validity](#-logical-validity) - [🔍 DARVO Analysis](#-darvo-analysis) - [🔍 SEO Evaluation](#-seo-evaluation) - [🎯 Conclusion](#-conclusion) ## 📝 Summary: Jono Alderson argues that the web has evolved beyond URLs as the atomic unit of SEO. Modern search engines, LLMs, and agents don’t see pages; they extract and connect discrete assertions. Authority now comes from coherent, connected claims across the web rather than optimized pages. The shift demands an “assertion-first” strategy where clarity, structure, and corroboration outweigh traditional on-page SEO. ## 📖 Main Content: ### 🗒️ Notes - **Machines vs. Humans** - Half of web traffic is bots and agents, not humans. - Humans consume pages holistically; machines parse assertions. - URLs are now envelopes, not the primary unit of meaning. - **URL-shaped worldview** - SEO historically treated pages as atomic. - PageRank, sitemaps, and analytics reinforced URL-level optimization. - Google now ranks passages, extracts facts, and uses Knowledge Graph nodes. - **Assertions as the new unit** - Machines convert content into triples: subject → predicate → object. - LLMs encode assertions as vectors in high-dimensional spaces. - Clear, extractable assertions become the real ranking factor. - **Trust and the Graph** - Trust emerges from coherent, corroborated claims across sources. - LLMs inherit biases from SERPs; polluted results propagate misinformation. - Brands must defend assertions against hostile, manipulated corpuses. - **Write for Humans *and* Engineer for Machines** - Google’s “write for humans” mantra is no longer sufficient. - Structure, semantics, and clarity make assertions legible to machines. - **Optimising the Graph** - Design pages as bundles of extractable assertions. - Use consistent, redundant patterns (semantic HTML, clean headings). - Publish APIs and machine-friendly endpoints. - Reinforce claims through third-party sources and consistent messaging. - Monitor and defend against misinformation in the hostile corpus. ## 📖 References: - Alderson, Jono. *The web isn’t URL-shaped anymore*. JonoAlderson.com. July 30, 2025. ## 🗣️ Paraphrase: 1. **Original:** “Machines care about assertions, not pages.” **Paraphrase:** Modern systems prioritize the discrete statements inside your content over the pages themselves; it’s the clarity of your claims that matters. 2. **Original:** “Stop optimising pages. Start optimising the graph.” **Paraphrase:** The focus must shift from perfecting individual URLs to engineering a connected network of trustworthy, consistent assertions across the web. ## 📰 Newsletter: 🚀 **SEO Beyond URLs: Why Assertions Rule the Machine Web** Search engines and LLMs don’t see pages like humans do—they mine them for assertions. Jono Alderson’s latest article argues that the atomic unit of SEO is no longer the URL, but the clarity and connectivity of the claims you make. Brands must adopt an “assertion-first” approach: structure content semantically, reinforce key facts across trusted sources, and design for both humans and machines. The future of discoverability isn’t page-level ranking; it’s how well your meaning integrates into the machine web. 🔗 [Read More](https://www.jonoalderson.com/conjecture/atomic-urls/) ## ✅ Action Items: - Translate this assertion-first model into WordPress contributor education: - Advocate for semantic HTML in themes and core. - Promote structured content patterns across WordPress documentation. - Position GoDaddy’s Open Source Program Office to lead in machine-readable content: - Develop machine-friendly APIs for WP products. - Build contributor guides on publishing extractable assertions. - Align with OSPO goals: - Incorporate assertion graph strategies into funding pitches. - Highlight WordPress as a graph-shaped knowledge ecosystem. ## 🧪 Hypotheses: - H1: Brands optimizing for assertion clarity and consistency will outperform URL-first strategies in AI-driven discovery. - H2: Semantic HTML and redundant patterns significantly improve machine comprehension of assertions. ### 📊 Methodology: - **Data Sources:** SERP analysis, schema.org markup performance, LLM retrieval tests. - **Research Design:** Compare sites with assertion-focused structures vs. traditional SEO structures. - **Analysis:** Measure visibility in SERPs, featured snippets, and AI-generated answers. ### ❓ Questions: - How can open-source projects like WordPress ensure their assertions are machine-legible? - Will assertion networks replace traditional backlink strategies entirely? - How can brands defend against “hostile corpus” manipulation? ### 🔮 Future Research: - Study how LLMs evolve their own trust graphs independent of search engines. - Explore automated assertion monitoring tools for brand defense. - Investigate open standards for machine-friendly assertion publishing. ### 💡 Implications: - **Knowledge:** Shifts SEO from URL-centric to network-centric models. - **Practice:** Forces content strategists to rethink site architecture. - **Policy:** Raises questions about misinformation control in machine-learned models. ## 🧠 Learning Styles Assessment: - **Visual:** No diagrams; would benefit from infographics on assertion graphs. - **Aural:** No audio content; consider podcast/audio summary. - **Verbal:** Strong linguistic framing; uses clear analogies. - **Physical:** Lacks actionable exercises; could add hands-on markup tasks. - **Logical:** Excellent logical progression from premise to conclusion. - **Social:** Encourages collaborative ecosystem reinforcement; could link to forums. - **Solitary:** Offers reflection points on shifting SEO strategies. - **Naturalistic:** No environmental analogies; potential to use nature-based network metaphors. ## 📊 Accessibility Assessment: - **Screen Reader:** Clear structure would aid screen readers if semantic HTML is maintained. - **Keyboard Navigation:** No interactive barriers noted. - **Contrast/Readability:** Needs high-contrast typography for better accessibility. - **Text Resizing:** Article layout should support responsive scaling. ## 🌍 DEIB (Diversity, Equity, Inclusion, and Belonging) Assessment: - **Cultural Relevance:** Neutral, tech-focused language; could use diverse brand examples. - **Inclusive Language:** Gender-neutral; avoids exclusionary terms. - **Neurodiversity:** Clear headers and bullet points support ADHD/ASD readers. - **Representation:** No direct human representation; could include case studies across different industries. ## ✅ Logical validity: - **Strengths:** - Strong causal reasoning connecting machine behavior to SEO evolution. - Balanced view: acknowledges humans still matter. - Sound deductive structure: “If machines use assertions → optimize assertions.” - **Weaknesses:** - Minor hasty generalization: assumes all LLMs handle assertions similarly. - No quantitative evidence; argument is theoretical but coherent. ## 🔍 DARVO Analysis: - **Deny:** No DARVO; article does not deny opposing claims but reframes SEO. - **Attack:** No personal attacks; critiques legacy SEO models. - **Reverse Victim:** No reversal tactics; positions brands as needing proactive defense. ## 🔍 SEO Evaluation: - **Keywords:** Strong integrat - Summary: Why SEO’s page-centric thinking is broken, and how machines are changing the rules of the web. - URL: https://www.jonoalderson.com/conjecture/url-shaped-web/  Half of your website’s audience isn’t human. Search engines, crawlers, bots and agents likely already consume more of your site than people do. And they don’t experience it the way humans do. Humans load a *page* at a *URL* and absorb the whole thing: design, content, messaging, intent. Machines treat that URL as an *envelope* – something to tear open, strip apart, and mine for meaning. For decades, SEO has been built around the idea that the URL is the *thing*. We’ve optimised containers ‑because Google indexed and ranked at the URL level, and because humans needed that structure to navigate. But the systems that now mediate most discovery have already moved past this model. They don’t care about the envelope; they care about the *assertions* inside it and how those assertions connect to everything else they know. That shift changes everything about how optimisation works. To understand what’s at stake, we first need to look at the mental model we’ve been stuck in – and why it no longer fits the web we’re actually dealing with. #### **The web we optimised for vs. the web we have now** For as long as we’ve been doing SEO, we’ve been operating inside a *URL-shaped* worldview. Google’s early systems indexed and ranked URLs. PageRank flowed along links between them. Analytics tools counted visits per URL. Keyword research mapped to URLs. Content strategies were literally drawn as sitemaps. The URL wasn’t just an address – it was the atomic unit of the web. That model shaped everything: * We thought in terms of “optimising *this* page” to rank for *that* keyword. * We judged success by whether a URL appeared in search results. * Even our language – *this page ranks*, *that page needs links* – reinforced the assumption that the URL was the thing that mattered. But the web machines see today isn’t URL-shaped anymore. Google has been breaking the page into pieces for years. Passage ranking lets it surface a paragraph buried deep in a document. Featured snippets lift individual claims straight out of context. The Knowledge Graph doesn’t “rank pages” at all – it extracts facts and relationships from across the web. Even LLM-powered agents, which still request pages at URLs, don’t consume them like humans do. They strip out scripts, styling, and noise, and break the content into chunks to extract meaning and assertions. To these systems, the page is just a *source file* – the raw material they transform into something they can reason over. The systems that matter now don’t treat a URL as an indivisible object. To them, a page at a URL is just a *container* – a source of *assertions* to extract, evaluate, and connect. And that means our page-first, URL-first mental model no longer matches the environment we’re optimising for. #### **Machines care about assertions, not pages** Modern systems no longer evaluate your site as a set of documents. They break it down into *assertions* – discrete statements about the world that they can extract, interpret, and integrate into their models. These assertions are often represented as *triples*: * *subject → predicate → object* * *Product X* → *has price* → *$99* * *Author Y* → *wrote* → *This Article* Search engines and knowledge graphs process these triples as *symbolic facts*. They’re stored explicitly, connected to other nodes, and used to answer queries or populate search features. LLMs and agents do something different. They don’t store assertions as facts – they *encode* them into high-dimensional vector spaces. Meaning is compressed, patterns are generalised, and retrieval is probabilistic. The assertion doesn’t live as a record – it lives as part of the model’s learned representation. In both cases, the *unit that matters isn’t the page – it’s the assertions inside it.* What counts is whether those statements are clear, extractable, and connectable. This is the layer machines care about, and it’s where SEO needs to shift its focus: away from optimising documents, toward optimising the clarity and connectivity of the claims your brand makes. #### **Trust is graph-shaped** Assertions don’t stand alone. A single claim, no matter how well-structured, doesn’t automatically earn trust. Machines evaluate it in the context of everything else they know. They look at how that claim *connects*: where it came from, what supports it, and how it relates to other signals in their models. This is where the *graph* matters. Search engines like Google use explicit relationships: links between URLs, corroborating schema, and references from trusted nodes. The Knowledge Graph literally encodes trust through connectivity – facts supported by multiple reliable sources gain weight, while contradictory or isolated claims are discounted. LLMs and agents do something similar in a more implicit way. They rely on the *statistical density* of patterns across their training data. Assertions that appear consistently in high-quality contexts become stronger in the model’s representation. Outliers – claims that don’t fit or aren’t well-supported – carry less influence or risk being ignored. This graph-shaped evaluation is what *should* prevent “assertion spam” from working. But right now, many machine learning systems are still naïve. LLMs and emerging agents lack the decades of spam-fighting infrastructure that Google has built – they don’t yet have the equivalent of a “link graph” to moderate quality signals. To compensate, many of these models lean heavily on search engines’ own results as a proxy for trust. When ChatGPT, Perplexity, or others surface answers, they’re often drawing from Bing or Google SERPs, inheriting their quality filters (and their biases). That means the search engines’ defences still shape how LLMs perceive the web – but it also means that if those results are polluted, the pollution propagates. Over time, as LLMs develop their own ways of weighting relationships and filtering noise, *coherence and connectivity*, not volume, will decide what’s trusted. The challenge for brands is to play the long game: build assertions that can stand up to scrutiny as these systems mature. Authority, for machines, emerges from *coherence and connectivity*. Assertions gain strength not from volume, but from how well they’re *reinforced and corroborated* across the wider web. This is the layer where machines decide what to believe – and it’s where your competitive edge is forged. And crucially, that network isn’t limited to what *you* publish. Machines are learning from everything – competitor sites, third-party aggregators, marketplaces, YouTube, Reddit, scraped product feeds. If a claim about your product, pricing, or positioning exists *somewhere*, it becomes part of the model. That means your assertion strategy isn’t just about structure and markup on your site. It’s about *managing how your brand is described, referenced, and understood across the web*. If your competitors are making stronger, clearer, or more consistent claims – or if you’re absent entirely – the graph may not tip in your favour, no matter how polished your own pages are. Worse, you have to assume that parts of the web are actively working against you. Competitors, affiliates, and bad actors aren’t just competing – they’re trying to *pollute* the models that learn from the web. This *hostile corpus* – polluted SERPs, manufactured claims, weaponised contradictions – can distort your narrative, drown out your signals, and weaken your authority. You need to actively defend your assertions against a web that’s being manipulated in real time. In a graph-shaped web, you’re not just publishing assertions. You’re defending them in an environment where every node and edge can be polluted, manipulated, or weaponised. #### **Google’s old mantra is no longer enough** For decades, Google has told marketers to *“write for humans”*. It was smart advice when search engines were mostly matching queries to documents. Humans were the end audience, and optimising for their needs aligned neatly with how Google ranked results. But the web isn’t human-first anymore. Machines now sit between you and your audience, mediating discovery, filtering information, and making decisions on users’ behalf. They’re not just passively indexing; they’re actively interpreting and prioritising what matters. The crucial shift: * Humans judge the *experience* – how the design feels, how persuasive the story is, how the page resonates emotionally. * Machines judge the *structure of meaning* – the individual assertions you make, how clearly they’re expressed, and how well they connect to other sources in their models. This doesn’t mean abandoning human-focused content. It means recognising that *Google’s mantra is incomplete*. You need to *write for humans* and *engineer for machines* – making your claims explicit, structuring relationships, and publishing in ways that feed both symbolic systems (search engines) and learned models (LLMs). The brands that thrive won’t be those who only create great content; they’ll be the ones who *encode their meaning* in ways machines can trust, reuse, and amplify. #### The assertion-first future is structural If machines care about assertions, then how you express those assertions becomes a strategic choice. Google gives us a narrow window into this: schema.org markup powers rich results, product listings, and event features. But structured data only works where Google has built functionality for it. Beyond that, its impact is limited. You can’t just “markup everything” and expect magic. But that doesn’t mean structure doesn’t matter – far from it. Machines learn from patterns, whether they’re formalised in schema.org or not. LLMs and agents don’t use schema.org directly, but they still *encode the patterns they find* in your content. Clean, predictable structures – at the HTML level, in your copy, in your layout – give them more reliable signals to work with. This is where **[semantic HTML still matters](https://www.jonoalderson.com/conjecture/why-semantic-html-still-matters/)**. Not because models parse tags “semantically” in the W3C sense, but because semantic markup enforces *clarity, hierarchy, and consistency*. * A `<section>` with a clear heading creates a pattern that’s easy to learn. * Consistent phrasing (“Price: $99”) reinforces meaning more than prose buried in design. * Hierarchical markup mirrors relationships, making those relationships easier to encode. So while schema.org might be a narrow, feature-driven tool, *structure in the broader sense – patterns in your HTML, language, and layout – is how you make meaning legible.* And in a world where machines mediate discovery, that legibility is what will decide whether your assertions are learned, trusted, and surfaced. #### **Stop optimising pages. Start optimising the graph.** This is why the old SEO playbook, where we optimised pages at URLs, no longer holds up. Machines aren’t judging you by individual documents; they’re modelling the *web of meaning* around your brand. Your job isn’t to make pages rank – it’s to make your *assertions* clear, consistent, and so well-connected that machines can’t ignore them. All of this leads to a simple but uncomfortable truth: the old SEO playbook – optimising pages at URLs – isn’t enough anymore. Pages are still necessary. URLs still matter. Humans still need something to click on. But machines, which now mediate almost every discovery journey, aren’t judging you at the page level. They’re judging the *network of assertions* you’ve published and how it connects to everything else they know. In this environment, *authority isn’t something you sprinkle onto a page with keywords and links*. It’s something that emerges from how your assertions *fit into the graph*: * Are they consistent everywhere they appear? * Are they reinforced by trusted nodes? * Do they connect cleanly to other concepts and sources? * Are they clear enough to be encoded and reused? This is the real battleground. Google, LLMs, and agents aren’t scoring individual documents; they’re building models of the world and deciding which nodes in that model they can trust. Your job is to make sure the nodes that represent *you* – your products, your brand, your expertise – are *strong, connected, and coherent*. That’s what an assertion-first strategy looks like. It’s not about creating more pages; it’s about *engineering a network of claims that machines can rely on. The brands that* thrive will be the ones who stop obsessing over how each page ranks, and start focusing on *how their meaning is learned, reinforced, and surfaced across the machine web***.** #### **How to Optimise the Graph** If the web you’re competing in is no longer URL-shaped, then your strategy has to evolve with it. Optimising individual pages was enough when search engines ranked documents. But in a graph-shaped web, what matters is how *your assertions* are learned, reinforced, and connected across the entire ecosystem. So what does that actually look like in practice? It’s not about abandoning content creation or rewriting everything as JSON. It’s about *making the things you say clear, consistent, and hard to ignore* – wherever machines might encounter them. Here are some tactics to get there: * **Avoid assertion spam**: Don’t flood the web with contradictory or low-quality claims. Today’s naive models may still absorb some of it, but this strategy will backfire as systems mature. * **Design every page as a bundle of extractable assertions**: Use clear, repeated patterns for key facts (prices, features, authorship, dates) instead of burying them in prose. * **Reinforce claims through multiple trustworthy sources**: Make sure your products, pricing, and brand attributes are consistent everywhere: marketplaces, aggregators, partners, Wikipedia, YouTube descriptions, and press releases. * **Structure your content for learning, not just for humans**: Use semantic HTML to give hierarchy and relationships. Add redundant context (“this is an SEO consultancy…”) so machines have multiple ways to pick up key claims. * **Publish machine-friendly endpoints**: Offer APIs, clean feeds, and structured exports (JSON, XML) that expose your data clearly. This gives machines a source that doesn’t rely on scraping. * **Monitor and manage the hostile corpus**: Track how competitors and bad actors describe you. Counteract misinformation with strong, corroborated claims on trusted properties. * **Leverage third-party validation**: Encourage citations, reviews, and mentions on authoritative sites. Machines weight corroboration from diverse nodes more than self-assertion. In other words, *optimising the graph isn’t about gaming algorithms*. It’s about making sure the things you say – and the things said about you – form a clear, connected network that machines can trust, today and as they get smarter. #### **The web after URLs** The URL was the foundation the web was built upon. It shaped how we designed sites, how we thought about SEO, and how we understood discovery itself. But foundations don’t last forever – they get built over, abstracted, replaced. We’re already standing on a different layer. Machines no longer navigate the web like we do. They traverse graphs, encode patterns, and build models. The page at a URL is still there, but it’s just a skin stretched over something deeper – a network of assertions that lives outside the document. This is the web we’re optimising for now: * Not a collection of pages, but a fabric of meaning. * Not a structure of links, but a structure of relationships. * Not a search engine serving documents, but a set of systems deciding what humans ever get to see. So here’s the real challenge. Stop thinking about how to rank a page. Start thinking about *how to exist* in the models that are replacing the page. Because those models are already shaping what the web *is*, and whether your brand matters in it., The Web Isn’t URL-shaped Anymore, Jono Alderson, 01k1en286w15ca7wvkbpxm4p2e.