Wikipedia at 25: What the web can be 🔗 **Source:** [Wikipedia at 25: What the web can be](https://www.anildash.com/2026/01/15/wikipedia-at-25/) > [!note]+ Document Notes > ```markdown > > --- > tags: [wikipedia, internet-culture, ai] > author: Anil Dash > source: Anil Dash > reference: [https://www.anildash.com/2026/01/15/wikipedia-at-25/] > date created: 2026-01-18 00:00 > > --- > > # 🏷️ Title: Wikipedia at 25: What the web can be > <!-- 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 - What happened. - Anil Dash reflects on Wikipedia’s 25 years, its role as critical web infrastructure, and the mounting political and commercial threats against it. - Why it matters. - Wikipedia underpins search, AI, and everyday knowledge; its erosion would destabilize the global information ecosystem. - Key stakeholders. - Wikipedia editors and readers, Wikimedia Foundation, big tech platforms, AI companies, authoritarian political actors, educators, and students. - Risks and tradeoffs. - Free, volunteer-driven, ad-free knowledge is exploited by trillion‑dollar companies and targeted by fascist movements, while AI shifts traffic and contributors away from Wikipedia. - Bottom line. - Wikipedia proves the web can be generous, collaborative infrastructure; protecting and funding it—especially by platforms and AI firms—is urgent civic work. ## 📖 Main Content - Explain the core arguments in plain language. - Dash recounts his early skepticism about Wikipedia and how it exceeded expectations by attracting massive volunteer contributions. - He argues Wikipedia is now core infrastructure for the web: search engines, voice assistants, social platforms, and AI systems all lean heavily on its content without proportionate support. - He warns that authoritarian and fascist movements see independent truth sources as threats; attacks on Wikipedia—smears, alternative sites, infiltration of editors—are part of a broader culture war. - The rise of AI intensifies extraction: LLMs scrape Wikipedia as free training data, while conversational interfaces divert users away from Wikipedia, shrinking the pipeline of new contributors. - Despite its shortcomings (especially editor diversity), Wikipedia remains a radical, ad‑free, privacy-respecting, collaboratively built public good that embodies the best of the early web. - Dash calls for material support (especially from AI and big tech) and for readers to defend and fund Wikipedia. - Add sourced context and define terms. - Knowledge Panel: Google’s fact box beside search results, often summarizing Wikipedia. - LLM (Large Language Model): AI system trained on vast text corpora, frequently including Wikipedia. - Culture war & fascist movement: Dash uses these to describe coordinated campaigns against trans people, DEI, and independent institutions like Wikipedia, often linked to figures like Christopher Rufo and Elon Musk. - He notes Wikipedia has become “infrastructure for so much of the commercial internet,” supplying data for products worth trillions of dollars while itself remaining a non‑profit. - Note uncertainties or contested claims. - The exact degree of coordination between various anti‑Wikipedia actors is asserted rather than exhaustively evidenced here, though he cites reporting like The Verge. - The long‑term impact of AI on contributor pipelines is plausible but still speculative; traffic and contribution trends will determine how severe this becomes. ### 🗒️ Notes - Wikipedia launched 25 years ago; Dash initially doubted large‑scale wiki viability but saw volunteers solve the “hard part” of getting contributors. - For years, elites resisted treating Wikipedia as credible, yet it gradually became foundational to the “world’s information ecosystem.” - Google’s Knowledge Panel and search rankings drew heavily on Wikipedia content, keeping users in Google’s ad environment while relying on unpaid community labor. - Major platforms (Google, Apple, Amazon, Facebook) have used Wikipedia as a neutral, trusted fact source without ads or tracking, which limited their surveillance and monetization. - Platforms often integrated Wikipedia at scale without notice, imposing technical and operational burdens on Wikimedia without revenue sharing, until public pressure led to paid access options. - Wikipedia now sits at the center of a culture war: authoritarian and fascist actors try to discredit or replace it because it’s an independent, unowned source of truth. - Dash highlights figures like Christopher Rufo, who coordinate campaigns against DEI, trans people, and academic institutions, and sees attacks on Wikipedia as part of that pattern. - Elon Musk and others promote alternatives (e.g., Grokipedia) and spread vague innuendo about Wikipedia to undermine its legitimacy. - There have been attempts to infiltrate or vandalize Wikipedia from within, plus escalating vitriol towards editors and leadership; Dash fears more serious threats to staff and volunteers. - The AI boom has created “astronomical” demand for Wikipedia data; most AI systems scrape the site without payment, akin to Napster-era MP3 sharing. - He suggests AI companies should treat Wikimedia’s paid data services like legal music platforms (Apple Music/Spotify) and support the “artist”—Wikipedia’s community. - As users move from search to chatbots, they see fewer Knowledge Panels and visit Wikipedia less, reducing both traffic and potential new editors. - Wikipedia’s long‑standing problem of editor diversity remains, but Dash argues the bigger looming issue is people not arriving at Wikipedia at all, unaware of how much AI answers rely on it. - Constant exposure to AI hallucinations may erode respect for accuracy and make Wikipedia’s painstaking, citation-driven work seem “quaint.” - Dash argues it’s in AI platforms’ own interest to sustain Wikipedia and introduce their users to the human community behind it. - He emphasizes Wikipedia’s radical characteristics: no ads, no tracking, anonymous editing, low barriers to contribution, and a sense of “magic” in creating lasting public knowledge. - Wikipedia is one of the most visited sites ever, built without advertising, yet has unlocked “trillions of dollars in value” for business and immeasurable educational benefits. - Features like Simple English, travel guides, textbooks, cookbooks, and taxonomies of species showcase its breadth and accessibility. - Dash sees Wikipedia as proof that collective generosity on the internet still exists: countless volunteers improve information for strangers with no expectation of recognition. - He closes by urging readers who are able to donate to Wikimedia Foundation and implicitly to defend Wikipedia as a public good. ## 📚 References - Wikipedia — Main project: https://www.wikipedia.org/ - Wikimedia Foundation — Official site and donation page: https://wikimediafoundation.org/ - Anil Dash — Original article: https://www.anildash.com/2026/01/15/wikipedia-at-25/ - The Verge coverage on attacks against Wikipedia (as referenced by Dash): https://www.theverge.com/ - Wikimedia Enterprise (licensed access for large reusers): https://enterprise.wikimedia.com/ - Background on LLMs and Wikipedia usage: - “The Pile” dataset (includes Wikipedia): https://arxiv.org/abs/2101.00027 ## 🗣️ Paraphrase - Paragraph A paraphrase. - When Wikipedia started, Dash didn’t think a huge, volunteer-run encyclopedia would work, but the community proved him wrong by turning it into a central pillar of the web’s information systems. - Paragraph B paraphrase. - Today, powerful political and corporate actors, threatened by an independent source of truth they can’t buy or control, are attacking Wikipedia even as AI companies quietly depend on it, making it crucial for society to protect and fund this collaborative public resource. ## 📰 Newsletter The open web just turned 25 in one of its most inspiring forms: Wikipedia. Anil Dash’s latest essay is a reminder that a volunteer‑built, ad‑free wiki still underpins search, social, and now AI. For the WordPress community, there are strong echoes: open licensing, contributor‑run governance, and the constant tension between extraction and stewardship. If you’re building plugins, themes, or content that lean on Wikipedia or other commons, now’s a good moment to audit your dependencies and give back—whether via donations to Wikimedia, clearer attribution in your docs, or educating users on how these public goods actually work. Want to go deeper into open knowledge and governance patterns you can reuse in your WordPress projects? Pair this read with resources from the Open Source Guides (https://opensource.guide/) and the WordPress.org contributor handbooks (https://make.wordpress.org/). The future of the web is what we fund and defend. ## ✅ Action Items #### DevRel - Developer advocacy - Highlight Wikipedia as a model public API/knowledge commons in talks; show how your products depend on it and where you can contribute back. - Build demos that visualize data provenance from Wikipedia through your stack. - Education - Run workshops on “responsible reuse of commons” for developers (licensing, attribution, funding). - Offer office hours to help teams refactor products to use Wikimedia Enterprise or similar legitimate feeds. - Developer experience - Improve docs around external data dependencies; clearly mark where Wikipedia or other commons are used. - Provide sample apps and quickstarts that demonstrate respectful API usage and caching patterns. - Developer marketing - Run content campaigns around “building on the commons responsibly,” measuring signups/usage of compliant data access paths. - Track attribution of Wikipedia/commons in case studies and blog posts. - Community management - Encourage contributors to support Wikimedia (donations, editing drives). - Add code of conduct callouts around harassment of maintainers and knowledge stewards. - Internal/external/cross-project - Align KPIs so teams are rewarded for sustainable use of public data. - Establish feedback loops with Wikimedia or similar orgs to flag abusive traffic patterns early. #### OSPO - Policy & Governance - Create a policy for use of open content/data (including Wikipedia) alongside open source code policies. - Clarify decision rights for when to pay for licensed access (e.g., Wikimedia Enterprise). - License Compliance - Integrate content/data license scanning into review workflows. - Train engineers and PMs on CC BY-SA/CC0 and attribution obligations. - Security Management - Treat scraping and data mirroring as security concerns for upstreams; respect robots.txt and rate limits. - Document embargo and disclosure processes if internal tools abuse public APIs. - Software Inventory - Maintain an inventory of third‑party knowledge sources (including Wikipedia) with owners and freshness SLAs. - Link SBOM-like metadata to content provenance where feasible. - Developer Support - Offer office hours for licensing and data‑reuse questions. - Provide templates for attribution notices and ABOUT pages. - Community Engagement - Explore funding or partnership agreements with Wikimedia and allied organizations. - Co-sponsor edit‑a‑thons or documentation sprints. - Strategic Alignment - Include public‑knowledge dependencies in OSPO OKRs and executive reporting. - Track risk from over‑reliance on underfunded commons. #### Community - Maintainers - Add sections to READMEs listing public data dependencies and how users can support them. - Practice “release hygiene” around content sources (document versioning, fallbacks). - Contributors - Create onboarding tasks that improve documentation of external data sources. - Pair new contributors to run small Wikipedia-edit or documentation sprints. - Sponsors - Allocate part of sponsorship budgets to Wikimedia or similar commons. - Publish impact reports tying product value to underlying public infrastructure. - Ecosystem - Coordinate incident drills for when key public datasets go offline/degrade. - Share playbooks for responsible mirroring, caching, and failover. #### WordPress - Core - Track where WordPress core or bundled services lean on Wikipedia/commons (e.g., oEmbed, help content). - Include sustainability of upstream data in release criteria where relevant. - Core Performance - Evaluate performance tradeoffs when integrating remote knowledge sources; invest in caching and graceful fallbacks. - Monitor metrics for upstream latency and error rates. - Core AI - For any AI features, document training data provenance and how much depends on Wikipedia. - Set guardrails that encourage partnerships or funding where commons are heavily used. - **Core Program Team** - Add “commons dependencies” review to cross-team planning. - Run retrospectives on incidents tied to upstream knowledge sources. - Mobile - Ensure offline UX messages acknowledge when data from public sources (like Wikipedia) is unavailable. - Provide clear links back to Wikipedia when content is surfaced. - Playground - Offer labs that show developers how to build ethical integrations with Wikipedia APIs. - Capture reproducible examples of bad vs good data usage. - Community - Include sessions at WordCamps on digital commons and Wikipedia. - Provide meetup kits for local edit‑a‑thons and knowledge sprints. - Training - Create lesson plans around open knowledge, attribution, and provenance. - Pilot certifications that cover ethical data use alongside WordPress skills. - Marketing - Tell stories connecting WordPress sites to public‑knowledge ecosystems. - Run editorial calendars that occasionally spotlight Wikipedia and similar projects. - Support - Document troubleshooting steps for sites that depend on external knowledge APIs. - Track recurring tickets involving upstream outages or rate limits. - Design - Include attribution and provenance in UX patterns (tooltips, info panels). - Design non-intrusive “powered by Wikipedia” labeling when relevant. - Documentation - Add sections on data sources, including Wikipedia, with licensing notes. - Maintain examples‑first patterns for proper attribution. - Photos - Encourage use of Wikimedia Commons with correct attribution. - Document workflow for sourcing, crediting, and moderating such images. - TV - Produce tutorials on building sites that respect knowledge commons. - Ensure captions and descriptions credit Wikipedia when used. - Polyglots - Coordinate with Wikipedia language communities for consistent terminology. - Encourage translation sprints that complement Wikipedia’s multilingual efforts. - Accessibility - Share best practices for accessible knowledge presentation, inspired by Wikipedia patterns. - Audit any integrations to ensure content remains accessible. - Meta - Track how .org properties rely on Wikimedia assets (e.g., docs, tutorials). - Monitor API reliability metrics for upstreams. - CLI - Offer commands or scaffolds that help site owners manage attribution and provenance. - Provide simple smoke tests for external data integrations. - Hosting - Include guidance about responsible caching of Wikipedia content in host docs. - Aggregate telemetry on upstream issues impacting many customers. - Tide - Consider signals around responsible data use in plugin scoring. - Provide a review process for alleged license or provenance violations. - Openverse - Tighten ingestion and moderation policies around Wikimedia‑sourced assets. - Educate users on correct reuse and attribution. - Themes - Provide starter blocks with built‑in attribution patterns for external data. - Test themes against pages that pull in external knowledge. - Plugins - Encourage plugin authors to use Wikimedia Enterprise or APIs instead of scraping. - Add provenance checks to security and review workflows. #### The WPOCC - Programs - Offer fellowships focusing on governance and sustainability of digital commons. - Provide microgrants for maintainers who are bridging WordPress and Wikimedia. - Projects - Host governance clinics inspired by Wikimedia’s policies and dispute resolution. - Sponsor documentation sprints that build bridges between WordPress and Wikipedia communities. - Funding experiments - Pilot matched grants that route some funds from commercial WordPress products to Wikimedia. - Experiment with transparent ledgers that show flows from value capture to commons support. ### FAIR - Technical independence - Promote local caching and signed snapshots of public datasets to reduce abusive traffic. - Track provenance and signatures for critical knowledge sources. - Policies and covenants - Draft covenants around funding transparency and sunset clauses when relying on public infrastructure. - Include anti‑capture provisions that protect governance from single-vendor dependence. - Productized governance artifacts - Reuse Wikimedia-like charters, dispute templates, and scorecards to manage open communities. - Offer these as public, forkable documents for projects building on WordPress. ## 🧪 Hypotheses - H1: Increased AI chatbot usage correlates with a measurable decline in new Wikipedia editor signups. - H2: Platforms that visibly attribute and link to Wikipedia generate more donations and volunteer interest than those that only consume data silently. - H3: Public smear campaigns by high‑profile figures significantly reduce perceived trust in Wikipedia among their followers. - H4: Organizations that pay for Wikimedia Enterprise access exhibit lower rates of abusive traffic and scraping behavior. - H5: Highlighting Wikipedia’s ad‑free, privacy‑respecting design increases public willingness to defend and fund it. ### 📊 Methodology - For H1 - Data: Wikipedia editor signup stats by month; search vs chatbot usage metrics from public reports. - Method: Time‑series analysis and Granger causality tests controlling for confounders (UI changes, fundraising campaigns). - For H2 - Data: Referral logs from partner platforms; Wikimedia donation and account creation referral codes. - Method: A/B tests where some partners add explicit attribution/links and others don’t; measure downstream donations and signups. - For H3 - Data: Public opinion surveys of trust in Wikipedia; social media exposure metrics to specific smear campaigns. - Method: Panel surveys before/after high‑profile attacks; regression controlling for demographics and media diet. - For H4 - Data: Wikimedia traffic logs, Enterprise customer lists, abuse reports. - Method: Compare request patterns (rate, scraping signatures, error rates) between paying and non‑paying large consumers. - For H5 - Data: Experimental messaging in fundraising banners and campaigns. - Method: Randomized controlled trials of banner copy emphasizing different aspects (ad‑free, privacy, community, impact) and measuring conversion. ### ❓ Questions - How quickly is AI-driven behavioral change reducing organic visits to Wikipedia? - What governance and funding models can keep Wikipedia independent while meeting rising technical and security costs? - How can the editor base be diversified while under active political attack? - What obligations should AI companies have toward public knowledge infrastructures they depend on? - How can users be made aware of the provenance of AI-generated answers? - Where is the line between legitimate criticism of Wikipedia and coordinated disinformation campaigns against it? ### 🔮 Future Research - Longitudinal impact of LLMs on volunteer communities that supply training data. - Models for “public option” infrastructure funding sourced from private value capture. - Comparative study of attacks on knowledge institutions (universities, libraries, Wikipedia) across different countries. - UX patterns that effectively educate users about data provenance within AI tools. - Governance mechanisms that help Wikimedia respond to targeted harassment and security threats without compromising openness. ### 💡 Implications - Practice - Builders should audit their dependence on Wikipedia and fund it proportionally. - AI products need explicit provenance, attribution, and sustainability plans for upstream data. - Policy - Regulators might require transparency around major AI training data sources and encourage support for critical knowledge commons. - Public funding or tax incentives could back independent knowledge infrastructures. - Security - Knowledge projects must treat political and social attacks as security threats, not just PR issues. - Better incident response and threat modeling for harassment and infiltration are needed. - Education - Media and AI literacy curricula should teach how Wikipedia works and why it matters. - Students should learn to both critique and contribute to public knowledge systems. - Funding models - Hybrid funding (donations, ethical enterprise licensing, institutional support) may be necessary to sustain Wikipedia at current scale. ### 🧠 Learning Styles Assessment - Reading level estimate - Likely around 10th–12th grade (e.g., Flesch-Kincaid) due to long sentences and political/technical vocabulary. #### 👀 Visual learners - Present - Primarily text; conceptual contrasts (e.g., Wikipedia vs big tech, Napster vs Spotify). - Improve - Add diagrams of the knowledge ecosystem, flows from Wikipedia to search/AI, timelines of political attacks. #### 🔊 Aural learners - Present - None explicitly. - Improve - Provide an audio version of the essay and a short 3–5 minute spoken summary. #### 🗣️ Verbal learners - Present - Strong narrative voice, metaphors (Napster vs Apple Music), and clear definitions of roles and actors. - Improve - Add a short glossary for terms like “LLM,” “knowledge panel,” and “culture war.” #### 🧩 Kinesthetic learners - Present - Implicit call to action to edit Wikipedia or donate. - Improve - Step‑by‑step guides: how to make your first edit, how to set up recurring donations, how to check AI answers against Wikipedia. #### 🧠 Logical learners - Present - Clear causal chains: platforms rely on Wikipedia → don’t fund it → system risk; AI shifts traffic → fewer contributors. - Improve - Include simple charts or tables showing usage vs funding, or traffic vs editor signups. #### 🧑‍🤝‍🧑 Social learners - Present - Emphasis on community, volunteers, and collaborative building. - Improve - Provide prompts for group discussions or invitations to local Wikimedia chapters. #### 🧘‍♀️ Solitary learners - Present - Reflective tone suited to personal contemplation about the web’s trajectory. - Improve - Add reflection questions at the end: “What public goods do you rely on?” “Where do you give back?” #### 🌿 Naturalistic learners - Present - No explicit nature analogies. - Improve - Frame Wikipedia as an ecosystem within a larger information environment, with concepts like resilience, biodiversity (diverse editors), and invasive species (misinformation). ### 📊 Accessibility Assessment #### Screen reader semantics - Present - Standard HTML article structure on a typical blog; headings appear reasonably structured. - Improve - Ensure correct heading hierarchy, language attributes, and descriptive alt text for any images. #### Keyboard navigation - Present - Likely basic keyboard accessibility via browser defaults. - Improve - Confirm visible focus styles and skip links; avoid any keyboard traps in navigation or embeds. #### Contrast and readability - Present - Depends on the blog theme; Anil Dash’s site typically uses readable text and good contrast. - Improve - Validate contrast ratios; ensure comfortable line length and spacing. #### Text resizing and reflow - Present - Responsive layout likely works at common zoom levels. - Improve - Test at 200–400% zoom; avoid clipped content and ensure no horizontal scrolling on mobile. #### Media and captions - Present - Primarily text; no embedded media to caption. - Improve - If adding audio/video, include captions and transcripts. #### Forms and interactions - Present - Minimal, likely just navigation and possibly a comment or contact form. - Improve - Ensure labels, clear error messages, and accessible hit areas for any interactive elements. #### Standards reference - Map gaps to WCAG 2.2 AA - 1.4 (Distinguishable), 2.1 (Keyboard Accessible), 2.4 (Navigable), 3.3 (Input Assistance) as relevant checkpoints. ### 🌍 DEIB Assessment #### Cultural breadth - Present - References to global fascist movements, Ivy League, DEI, and trans rights; acknowledges Wikipedia’s global, multilingual nature. - Improve - Provide concrete examples from non‑US contexts and highlight more language communities. #### Inclusive language - Present - Uses respectful terms around trans people and DEI; avoids slurs. - Improve - Add brief explanations for US‑centric references so non‑US readers can follow. #### Neurodiversity support - Present - Clear sections and headings; narrative flow is consistent. - Improve - Shorter paragraphs, more bullet lists, and summary boxes for key points. #### Representation - Present - Highlights Wikipedia’s editor diversity problem and the risks to volunteers. - Improve - Include stories or data from underrepresented editor groups. #### Psychological safety - Present - Candid about threats and harassment but not graphic; supportive of volunteers. - Improve - Add content notes at the top for mentions of violence and harassment; point to support resources for targeted communities. ### ✅ Logical Validity #### Strong reasoning - Causal argument: - “As people start going to their AI apps first… they don’t click through to Wikipedia anymore… this hurts the flow of new contributors.” This is a clear causal chain that aligns with observed traffic substitution patterns; could be strengthened with data, but it’s structurally sound. - Infrastructure argument: - Showing how Google, Siri, Alexa, and Facebook all depend on Wikipedia logically supports the claim that Wikipedia functions as critical infrastructure rather than just another website. #### Fallacies - Potential overgeneralization - Some statements about “global fascist movements” and “the same authoritarians” may group diverse actors together without fully distinguishing their motives or methods. - Improvement: briefly differentiate types of actors and link specific evidence for each. #### Tone of voice - Tone - Reflective, urgent, and advocacy‑oriented. Mixes personal anecdote (“it felt like magic”) with sharp criticism of big tech and fascist politics, and ends on a hopeful, mobilizing note. ### 🛡️ DARVO Analysis - Deny - Dash does not engage in denial; instead, he foregrounds harms and threats to Wikipedia. - Attack - He sharply criticizes specific powerful actors (e.g., Elon Musk, Christopher Rufo) but focuses on their campaigns and tactics rather than re-framing them as victims. - Reverse victim/offender - The article does not perform DARVO; it presents Wikipedia’s community as structurally vulnerable and targeted, and powerful actors as aggressors. There is no attempt to portray these powerful actors as victims of Wikipedia. ## 🎯 Conclusion - Restate the bottom line. - Wikipedia is a rare, large‑scale proof that the web can be generous, collaborative, ad‑free, and privacy‑respecting. Yet it’s under growing political and economic pressure, especially from AI and authoritarian movements that extract from or attack it. - Note practical next steps for readers. - Donate to Wikimedia if you can, learn how Wikipedia actually works, contribute edits, push your organization to fund the knowledge commons it relies on, and be prepared to publicly defend Wikipedia as a critical piece of democratic infrastructure. ## 🧱 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] Wikipedia at 25 shows the web’s potential > [!quote] [Wikipedia at 25: What the web can be](https://www.anildash.com/2026/01/15/wikipedia-at-25/) by [[Anil Dash]] via [[Anil Dash]]. Published 2026-01-15. Accessed 2026-01-18. > “Wikipedia has… become vital to the world’s information ecosystem.” Two lines: context and use. Cite data if relevant. [view highlight](https://www.anildash.com/2026/01/15/wikipedia-at-25/#H1) #xref #articleseed ID:rw_[MISSING] ### [H2] AI and fascist politics threaten Wikipedia > [!quote] [Wikipedia at 25: What the web can be](https://www.anildash.com/2026/01/15/wikipedia-at-25/) > “If the fascists can’t buy it, then they’re going to try to kill it.” Two lines: context and use. [view highlight](https://www.anildash.com/2026/01/15/wikipedia-at-25/#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] Wikipedia functions as critical web infrastructure - Wikipedia content underpins search, assistants, and AI tools across the commercial web. - Links: [[Digital-Commons]] [[Platform-Dependency]] #### [Z2] AI adoption reduces direct engagement with Wikipedia - As users shift to chatbots, they see fewer links and contribute less to Wikipedia. - Links: [[AI-UX]] [[Contributor-Pipeline]] --- ### ✏️ 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 all my projects’ dependencies on Wikipedia and other commons/https://www.anildash.com/2026/01/15/wikipedia-at-25/ #fleeting #inbox - [F2] Draft a short team doc on responsible reuse and funding of public knowledge/https://www.anildash.com/2026/01/15/wikipedia-at-25/ #fleeting #inbox - [F3] Schedule a personal Wikipedia edit session and recurring donation review/https://www.anildash.com/2026/01/15/wikipedia-at-25/ #fleeting #inbox --- <!-- deep-read:sections end -->