The Web is Changing (1/5) 🔗 **Source:** [The Web is Changing (1/5)](https://j.cv/the-web-is-changing-1-5/) > [!note]+ Document Notes > tags: [web, AI, WordPress] > author: J.CV > source: j.cv > reference: [https://j.cv/the-web-is-changing-1-5/] > date created: 2026-01-27 14:30 > > # 🏷️ Title: The Web is Changing (1/5) > <!-- deep-read:sections start --> ## 🧭 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-assessment) - [✅ Logical Validity](#-logical-validity) - [🛡️ DARVO Analysis](#-darvo-analysis) - [🎯 Conclusion](#-conclusion) ### 🔗 References - Include links cited in or relevant to the article. ## 📝 Summary - The author argues that the web should be understood as a “living graph of entities,” not pages, and that AI intermediaries (Google AI Overviews, ChatGPT, etc.) are changing how content is consumed, discovered, trusted, and monetized. - It matters because AI synthesis severs the traditional chain of “discovery → attention → revenue,” threatening the economics of the open web. - Key stakeholders: publishers, creators, users, AI platform providers, infrastructure vendors, and open‑source ecosystems like WordPress. - Risks/tradeoffs: traffic and revenue collapse for publishers, concentration of power in a few AI intermediaries, fragmentation into walled gardens vs. coordinated new licensing/trust frameworks. - Bottom line: the web’s structure as an entity graph remains, but its consumption, discovery, trust, representation, and economic models are all shifting at once; we need new mechanisms so creators are compensated and the open web survives. ## 📖 Main Content - Core argument: Websites are just one “presence” for underlying entities (people, businesses, ideas) in a huge graph of interconnected nodes. Historically, users navigated these surfaces directly, read content, and synthesized answers themselves, creating value and enabling conversions (purchases, decisions, bookings). - AI intermediaries now read across many surfaces, synthesize answers, and present them directly to users, often without sending traffic back. This shifts the “atomic unit of value creation” (intent fulfillment) away from the original site. - The consumption model is moving from human reading on a single surface to agent synthesis across many surfaces. - The discovery model is moving from ranking pages to deciding which content is included in AI syntheses. - The trust model is inverting: old signals (links, domain age, human authorship) become easier to fake in a world of AI‑generated content, so new mechanisms like attestation, provenance, and verified entity claims are needed. - The representation model is evolving from static, one‑size‑fits‑all websites to adaptive, context‑aware representations, potentially different for different agents or users. - The economic model is breaking: publishers provide the raw material for AI answers but capture little or none of the resulting value; this undermines incentives to create high‑quality content. - Context: Major publishers report ~20%+ traffic drops from search; organizations form collectives to negotiate licensing; infrastructure providers like Cloudflare respond by blocking crawlers or experimenting with pay‑per‑crawl. - The author points to the music industry’s adaptation to streaming as evidence that new economic models can emerge if stakeholders coordinate. - The series will later examine: - Economic solutions for compensating creators. - The shift from AI as intermediary to AI as agent acting on users’ behalf. - Implications for WordPress as the platform behind a large portion of the web. ### 🗒️ Notes - “The web isn’t pages. The web is a living graph of entities.” - Entities: people, businesses, concepts; each has a digital “presence” composed of multiple “surfaces” (sites, pages, APIs). - Websites are delivery mechanisms optimized for a world where humans did the reading and clicking; now agents often “do the reading” instead. - Intent fulfillment is the atomic unit of value; conversion is any successful goal completion (answer, decision, booking, purchase). - Historically durable model: user → surface → content → synthesis in their head → conversion. - AI intermediaries now synthesize across many surfaces and answer directly, so: - Users may achieve goals without visiting original sites. - Traditional SEO ranking matters less than being included in AI syntheses. - Trust challenges: - Explosion of AI‑generated content dilutes traditional authority signals. - Need for cryptographic or protocol‑level provenance and verified claims. - Representation shift: - From mostly static sites to adaptive, query‑aware, agent‑friendly representations. - Personalization becomes a requirement to serve agentic interactions. - Economic fracture: - AI systems depend on publisher content but don’t compensate proportionally. - Traffic declines threaten sustainability of quality content creation. - Collective action and technical infrastructure (e.g., crawler controls, licensing) emerge as responses. - Strategic stakes: - If mishandled: consolidation into walled gardens and paywalled ecosystems. - If handled well: a fairer, more capable web where creators are paid and users get better outcomes. - WordPress is central because it powers a large share of the web; its evolution will shape how entities represent themselves to AI agents. ## 📚 References - [The Web is Changing (1/5) – J.CV](https://j.cv/the-web-is-changing-1-5/) - [The Internet Map](https://internet-map.net/) - [Cloudflare blog](https://blog.cloudflare.com/) (re: blocking AI crawlers and pay‑per‑crawl experiments) - RSL Collective (for publisher licensing efforts) - Major AI intermediaries: Google AI Overviews, OpenAI ChatGPT, Anthropic Claude, Perplexity, Google Gemini. ## 🗣️ Paraphrase - The web should be seen as a network of real‑world entities, each represented online across many surfaces. AI systems now consume and synthesize content from across this network, often satisfying user intentions without sending traffic back to the original sites, which destabilizes traditional discovery, trust, and revenue models. - To preserve an open, sustainable web, we need new trust and economic mechanisms so that creators are compensated and their content is properly represented in an AI‑mediated environment. Platforms like WordPress must adapt to support richer, agent‑friendly entity representations. ## 📰 Newsletter The web’s core structure isn’t changing—but everything around it is. In this first installment, J.CV reframes the web as a “living graph of entities” and explores how AI intermediaries are quietly rewriting our models of discovery, trust, and revenue. For WordPress builders, this raises urgent questions: how do our sites speak to AI agents, not just humans? What does SEO look like when ranking matters less than inclusion in AI syntheses? If you’re working on WordPress‑powered presences—marketing sites, docs, stores—now is the time to think beyond pages toward entity‑centric, machine‑readable content. Watch for emerging tools that add structured data, provenance, and adaptive rendering layers atop WordPress, and keep an eye on ecosystem discussions about licensing, crawler controls, and AI integrations. The next phase of the web will be built by those who design for both humans and agents from the start. ## ✅ Action Items #### DevRel - Clarify messaging around “entity‑centric web” and how developers can structure content for AI agents (talks, blog posts, conference sessions). - Build workshops showing how to add structured data, schemas, and provenance to WordPress and other stacks. - Improve DX for machine‑readable content: SDKs, examples, and quickstarts for schema markup, feeds, and APIs that AI agents can consume. - Publish case studies on traffic loss from AI overviews and mitigation strategies (e.g., richer APIs, better entity modeling). - Run community surveys to understand how developers are experiencing AI‑driven traffic shifts. #### OSPO - Define contribution guidelines for AI‑related features (provenance, attestation, schema support) in open‑source web platforms. - Establish license‑compliance checks for training data usage and crawler policies. - Coordinate security guidance around content‑signing, attestation, and integrity for AI consumption. - Track inventory of AI‑relevant components (schema plugins, API gateways) and assign ownership. - Engage with upstream AI and web standards bodies on provenance and licensing protocols. #### Community - Host discussions and clinics on “AI and the open web” for maintainers and contributors. - Create easy “first issues” around improving structured data, feeds, and documentation for AI agents. - Encourage sponsors to fund work on trust, provenance, and licensing infrastructure. - Coordinate with adjacent ecosystems (browsers, search, hosting) on open standards and incident response. #### WordPress - Core: explore core support for entity‑centric modeling (site as entity, sub‑sites as surfaces) and richer metadata. - Core Performance: measure performance impact of adaptive, agent‑focused representations and profiling for schema/data endpoints. - Core AI: establish guidelines for how WordPress sites should expose content to AI systems responsibly (robots.txt, metadata, provenance). - Core Program Team: align cross‑team roadmaps around entity graph features, APIs, and machine‑readable output. - Mobile: ensure mobile apps can manage entity metadata and AI‑oriented settings, not just posts/pages. - Playground: provide labs for testing AI agents against local WordPress instances to see how they traverse content. - Community: add WordCamp/meetup talks on “designing sites for agents as well as humans.” - Training: create lessons on structured data, entity modeling, and AI‑aware publishing workflows. - Marketing: position WordPress as a platform for “democratizing representation” in an AI‑mediated web. - Support: document best practices for site owners facing AI‑driven traffic shifts. - Design: develop patterns for adaptive representations (e.g., API‑first, multi‑surface content models). - Documentation: add guides to entity/presence concepts, AI intermediaries, and crawler/metadata configuration. - Photos / TV / Polyglots / Accessibility / Meta / CLI / Hosting / Tide / Openverse / Themes / Plugins: - Integrate provenance and licensing metadata where relevant. - Ensure tools surface AI‑relevant settings (crawler controls, schema, attribution). - Promote patterns that keep the open web indexable, trustworthy, and accessible to both humans and agents. #### The WPOCC - Develop fellowship tracks focused on entity‑centric publishing and AI‑safe infrastructure. - Fund maintainers working on provenance, licensing, and open standards for web‑to‑AI interfaces. - Pilot transparent funding experiments for publishers exposing content under new licensing models. ### FAIR - Promote technical independence by supporting local metadata, content signing, and reliable provenance checks. - Draft covenants for funding and platform relationships to avoid capture by a few AI intermediaries. - Create reusable governance artifacts (charters, MoUs) for web communities negotiating with AI platforms. ## 🧪 Hypotheses - H1: AI intermediaries will continue to reduce direct traffic to content sites, even as their usage grows. - H2: Content with strong provenance and verified entity claims will be favored in future AI syntheses. - H3: Economic models that share AI‑derived value with content creators will sustain higher‑quality open‑web content. - H4: Platforms that natively support entity‑centric, adaptive representations (e.g., via schemas and APIs) will see better integration with AI agents. ### 📊 Methodology - Use longitudinal analytics from publishers to correlate increased AI answer prevalence with traffic changes. - Run experiments comparing AI answers when provenance/attestation metadata is present vs. absent. - Model revenue flows under different licensing or pay‑per‑crawl schemes using publisher financial data. - A/B test AI agents’ behavior against sites with richer entity modeling and structured data vs. traditional page‑only sites. ### ❓ Questions - How will AI systems decide which sources to trust and include in syntheses at scale? - What minimum metadata and provenance standards should content publishers adopt? - Which economic models (licensing, rev share, per‑query payments) are feasible for small publishers, not just large ones? - How can open‑source platforms coordinate effectively in negotiations with large AI providers? ### 🔮 Future Research - Design of open protocols for provenance and verified entity claims compatible with today’s web stack. - Empirical studies on user trust in AI‑mediated answers vs. direct site visits. - Governance models for collectives like RSL that negotiate on behalf of many publishers. - Patterns for adaptive, agent‑aware site architectures in CMS ecosystems. ### 💡 Implications - Practice: Site builders must prioritize structured data, provenance, and API‑first design. - Policy: Regulators may need to address compensation and transparency in AI training and synthesis. - Security: Strong provenance and signing will be crucial in distinguishing authoritative content from AI‑generated spam. - Education: Web literacy should now include understanding AI intermediaries and entity graphs. - Funding: New funding and licensing mechanisms may be needed to sustain public‑interest content. ### 🧠 Learning Styles Assessment - Reading level estimate: Roughly 10th–12th grade (Flesch‑Kincaid approximation based on sentence length and vocabulary). #### 👀 Visual learners - Present: Conceptual framing of a “living graph of entities.” - Improve: Add diagrams of entities, surfaces, and AI intermediaries; flowcharts of old vs. new consumption models. #### 🔊 Aural learners - Present: No explicit audio. - Improve: Short audio summary or podcast episode walking through the main models and their changes. #### 🗣️ Verbal learners - Present: Clear definitions (entity, presence, surface, conversion, consumption model). - Improve: More analogies and mini‑stories illustrating specific user journeys before and after AI. #### 🧩 Kinesthetic learners - Present: None. - Improve: Step‑by‑step exercises (e.g., map your own site as an entity graph, add schema to a page, test an AI agent against it). #### 🧠 Logical learners - Present: Structured breakdown of models (consumption, discovery, trust, representation, economics) and causal links. - Improve: Tables comparing “before vs. after AI,” decision trees for publisher responses. #### 🧑‍🤝‍🧑 Social learners - Present: Implicit invitation for collective action (RSL, coordination). - Improve: Guided discussion prompts and community workshop outlines. #### 🧘‍♀️ Solitary learners - Present: Dense, reflective essay style. - Improve: Reflection prompts at the end (e.g., “What does this mean for my site?”). #### 🌿 Naturalistic learners - Present: No explicit environmental analogies. - Improve: Use ecosystem metaphors (web as ecology, AI as new predator/keystone species) to explain balance and resilience. ### 📊 Accessibility Assessment #### Screen reader semantics - Present: Likely semantic headings and paragraphs. - Improve: Ensure proper heading hierarchy, meaningful link text, and language attributes. #### Keyboard navigation - Present: Standard article layout should be keyboard‑friendly. - Improve: Confirm visible focus states and skip links on the hosting site. #### Contrast and readability - Present: Depends on j.cv theme; likely adequate but unverified. - Improve: Check contrast ratios and line spacing for long‑form reading. #### Text resizing and reflow - Present: Responsive design likely; article is text‑centric. - Improve: Test at 200% zoom and with reduced‑motion preferences. #### Media and captions - Present: Primarily text; few or no media elements. - Improve: If diagrams are added later, ensure alt text and captions. #### Forms and interactions - Present: None significant in the article. - Improve: N/A. #### Standards reference - Map checks and improvements to WCAG 2.2 AA: headings, contrast, keyboard, reflow, and media alternatives. ### 🌍 DEIB Assessment #### Cultural breadth - Present: Global web context, but few explicit regional examples. - Improve: Include examples from non‑US publishers and different language communities. #### Inclusive language - Present: Neutral, technical language with minimal jargon. - Improve: Add short plain‑language summaries of key concepts (entity graph, provenance). #### Neurodiversity support - Present: Clear structure and chunked sections by model. - Improve: More subheadings, summaries, and consistent patterns for definitions. #### Representation - Present: Focus on systems, not people; minimal representation. - Improve: Highlight diverse types of publishers and creators affected (small blogs, community sites, newsrooms worldwide). #### Psychological safety - Present: Non‑alarmist but candid tone. - Improve: Provide clear, constructive next steps to reduce anxiety about change. ### ✅ Logical Validity #### Strong reasoning - The essay uses causal reasoning: AI synthesis breaks the discover‑attention‑revenue chain, so incentives to create content erode. - It uses analogical reasoning with the music industry’s shift to streaming to argue that new models are possible with coordination. - It clearly separates different models (consumption, discovery, trust, representation, economics) to show how they interact, which improves clarity. #### Fallacies - Some extrapolation risk: assuming current traffic declines will necessarily continue or worsen as AI grows. This is more a projection than a strict fallacy; it could be tempered with scenarios. - The music industry analogy may underplay differences between songs and web content; clarifying limits of the analogy would strengthen it. #### Tone of voice - Analytical, forward‑looking, and pragmatic. - Mix of conceptual framing (“living graph of entities”) with concrete examples (traffic drops, Cloudflare policies). ### 🛡️ DARVO Analysis - Deny: The article does not deny harms; it explicitly acknowledges traffic loss and broken economics. - Attack: It does not attack specific actors; AI intermediaries are critiqued structurally rather than personally. - Reverse victim/offender: The author does not cast AI companies as victims; instead, it highlights creators’ lack of compensation. - Overall, the piece is diagnostic and systems‑focused, not defensive; DARVO patterns are not evident. ## 🎯 Conclusion The web’s underlying graph of entities is stable, but the mechanisms that sit on top of it—how we consume, discover, trust, represent, and monetize content—are being reshaped by AI intermediaries. Unless new trust and economic models emerge, creators will bear the costs while AI systems capture the value. Platforms like WordPress, and the broader open‑web community, have a narrow window to redefine representation and compensation so that the next web remains open, sustainable, and fair. ## 🧱 Refactor Appendix (append after 🎯 Conclusion) ### 📚 Literature Split Pad - Purpose: Stage Konik CEE units that become per-source Literature notes. - Split level: **H3**. - Include verbatim quotes under 25 words and a [view highlight] link. ### [H1] AI intermediaries reshape web economics > [!quote] [The Web is Changing (1/5)](https://j.cv/the-web-is-changing-1-5/) by [[J.CV]] via [[j.cv]]. Published [MISSING]. Accessed 2026-01-27. > “AI synthesis breaks that chain. When an intermediary reads your content and delivers the answer without the user ever visiting…” Two lines: context and use. Cite data if relevant. [view highlight](https://j.cv/the-web-is-changing-1-5/#H1) #xref #articleseed ID:rw_[MISSING] ### [H2] The web is a graph of entities > [!quote] [The Web is Changing (1/5)](https://j.cv/the-web-is-changing-1-5/) > “The web isn’t pages. The web is a living graph of entities.” Two lines: context and use. [view highlight](https://j.cv/the-web-is-changing-1-5/#H2) #xref ID:rw_[MISSING] --- ### 🔖 Atomic Split Pad (Zettel Candidates) - Purpose: Promote reusable claims into Atomic notes. - Split level: **H4**. - Each item: one idea, present tense, specific, testable. #### [Z1] AI synthesis decouples value creation and capture - Applies where AI answers reduce clicks to source sites. - Pointer to traffic data and publisher reports. - Links: [[AI-and-Economics]] [[Open-Web-Sustainability]] #### [Z2] Web entities need adaptive representations - Applies to sites serving both humans and AI agents. - Pointer to sections on representation model. - Links: [[Entity-Centric-Web]] [[Agentic-Interfaces]] --- ### ✏️ Fleeting Queue (Extract by Selection) - Purpose: Capture quick actions or sparks for Fleeting notes. - Extraction: **Select one line** → “Refactor: Extract selection to new note (content only).” - Keep each line short and self-contained. - [F1] Map my own site as an entity graph — context/https://j.cv/the-web-is-changing-1-5/ #fleeting #inbox - [F2] Prototype provenance and attestation metadata for a WordPress site — context/https://j.cv/the-web-is-changing-1-5/ #fleeting #inbox - [F3] Research economic models for AI–publisher licensing — context/https://j.cv/the-web-is-changing-1-5/ #fleeting #inbox --- <!-- deep-read:sections end -->