What do coders do after AI? - Anil Dash  🔗 **Source:** [What do coders do after AI? - Anil Dash](https://www.anildash.com/2026/03/13/coders-after-ai/) > [!note]+ Document Notes > tags: #artificial-intelligence #software-development #labor-displacement #tech-careers #automation > author: Anil Dash > source: Anil Dash Blog > reference: https://www.anildash.com/2026/03/13/coders-after-ai/ > date created: 2026-03-13 12:00 > > # 🏷️ Title: What do coders do after AI? > <!-- deep-read:sections start --> ## 🧰 Action Inbox - [H1] Organize coder communities for mutual support during AI transition — labor organizing #artificial-intelligence - [H2] Develop strategies for identity-driven coders to find meaning in AI-assisted development — career planning #software-development - [H3] Create economic alternatives that don't rely on mass-layoff companies — community building #labor-displacement ## 🧭 Table of Contents - [[#📝 Summary]] - [[#📖 Main Content]] - [[#🗒️ Notes]] - [[#📚 References]] - [[#🗣️ Paraphrase]] - [[#📰 Newsletter]] - [[#✅ Action Items]] - [[#🧪 Hypotheses]] - [[#📊 Methodology]] - [[#❓ Questions]] - [[#🔮 Future Research]] - [[#💡 Implications]] - [[#🧠 Learning Styles Assessment]] - [[#📊 Accessibility Assessment]] - [[#🌍 DEIB Assessment]] - [[#✅ Logical Validity]] - [[#🛡️ DARVO Analysis]] - [[#🎯 Conclusion]] ### 🔗 References - [New York Times Magazine Interview](https://www.nytimes.com) (referenced but not linked) - [Open Source History and Culture](https://opensource.org) ## 📝 Summary - Anil Dash analyzes how AI/LLMs are fundamentally changing coding work and displacing tech workers - Two distinct groups of coders face different challenges: career-focused vs. identity-driven programmers - Key stakeholders include 700,000+ laid-off tech workers, remaining developers, and tech executives - Risk of permanent economic displacement vs. opportunity for independent community-driven development - Bottom line: Coders must organize and build independent alternatives rather than rely on current tech leadership ## 📖 Main Content Dash argues that AI's impact on coding differs fundamentally from other creative fields. While AI removes the "soulful" parts of creative work like writing or art, it removes coding drudgery while preserving creative problem-solving aspects. This creates a complex dynamic where some coders embrace AI tools while facing unprecedented job displacement. The analysis distinguishes between "9 to 5" coders who view programming as stable employment and "nights and weekends" coders for whom coding is core identity. Both face disruption but through different mechanisms - economic displacement versus loss of craft satisfaction. Dash notes that tech workers lack labor organizing traditions compared to other industries, partly because many identify more with management than workers. The open source culture of code sharing also makes AI training on existing code feel less like theft to many programmers. ## 🗒️ Notes #### ⏱️ [00:00](https://anildash.com/2026/03/13/coders-after-ai/#introduction) — [H1] AI transforms coding economics and power dynamics - Key idea: LLMs are becoming "software factories" that radically change who controls code creation - Evidence: "LLMs are now quickly advancing to where they can virtually become entire software factories, radically changing both the economics and the power dynamics of software creation" #### ⏱️ [02:30](https://anildash.com/2026/03/13/coders-after-ai/#drudgery-vs-soul) — [H1] AI affects coding differently than other creative fields - Key idea: AI removes boring parts of coding while preserving creative aspects, unlike other creative work - Evidence: "in the creative disciplines, LLMs take away the most soulful human parts of the work and leave the drudgery to you. And in coding, LLMs take away the drudgery and leave the human, soulful parts to you" #### ⏱️ [05:00](https://anildash.com/2026/03/13/coders-after-ai/#cultural-differences) — [H2] Three factors explain coder acceptance of AI tools - Key idea: Open source culture, automation history, and weak labor organizing explain different attitudes - Evidence: "Coders have long had a history of openly sharing code" and "Software development is one of the fields with the least-advanced cultures around labor" #### ⏱️ [08:00](https://anildash.com/2026/03/13/coders-after-ai/#two-cohorts) — [H1] Two distinct groups face different AI challenges - Key idea: Career-focused vs identity-driven coders will experience disruption differently - Evidence: "A larger, less vocal, group who see coding as a stable, well-paying career" vs those who see "coding as an avocation" #### ⏱️ [10:30](https://anildash.com/2026/03/13/coders-after-ai/#nine-to-five) — [H1] Career coders face ruthless economic displacement - Key idea: Standard business coding jobs are prime targets for AI replacement and management cost-cutting - Evidence: "These new LLM-powered software factories can generate orders of magnitude more of the standardized business code" #### ⏱️ [13:15](https://anildash.com/2026/03/13/coders-after-ai/#identity-crisis) — [H2] Displacement affects identity and self-worth, not just skills - Key idea: This transition challenges personal identity rather than just requiring new technical learning - Evidence: "the level at which the change is happening in this transition is one that gets closer to people's sense of self-worth and identity" #### ⏱️ [15:45](https://anildash.com/2026/03/13/coders-after-ai/#nights-weekends) — [H1] Identity coders face loss of craft and artistry - Key idea: Passionate coders may survive economically but lose the satisfying aspects of their craft - Evidence: "You're more the conductor of the symphony than someone who's holding a violin" #### ⏱️ [18:00](https://anildash.com/2026/03/13/coders-after-ai/#craft-loss) — [H2] Decades of specialized knowledge becomes irrelevant - Key idea: Deep technical expertise in specific systems loses value when AI generates generic code - Evidence: "people who have spent decades honing their craft, committing to memory the most obscure vagaries of this computer processor or that web browser" #### ⏱️ [20:30](https://anildash.com/2026/03/13/coders-after-ai/#slop-code) — [H3] AI produces cheap, wasteful, inelegant code - Key idea: Economic efficiency trumps code quality when AI can generate massive amounts cheaply - Evidence: "Half the time, the code they write will be garbage, or nonsense. Slop. But it's so cheap to write that the computer can just throw it away and write some more" #### ⏱️ [22:45](https://anildash.com/2026/03/13/coders-after-ai/#language-death) — [H3] Loss of coding craftsmanship resembles language extinction - Key idea: The death of idiomatic, elegant coding represents genuine cultural loss - Evidence: "There's a real grief here. It's not as serious as when we know a human language is dying out, but it's not entirely dissimilar" #### ⏱️ [25:00](https://anildash.com/2026/03/13/coders-after-ai/#scale-of-displacement) — [H1] Massive ongoing tech layoffs approaching one million workers - Key idea: Current displacement represents unprecedented scale in tech industry history - Evidence: "The latest count is 700,000 people laid off in the last few years in the tech industry. We'll be at a million soon" #### ⏱️ [27:30](https://anildash.com/2026/03/13/coders-after-ai/#solution) — [H1] Coders must build independent alternatives and support each other - Key idea: The solution requires community organizing rather than depending on current tech leadership - Evidence: "the only way forward is for more of the hackers with soul to seize this moment of flux and use these tools to build" #### ⏱️ [29:00](https://anildash.com/2026/03/13/coders-after-ai/#superpower) — [H2] Building capability remains powerful regardless of changing tools - Key idea: Technical creation skills retain value even as specific tools evolve - Evidence: "That's still an incredible superpower, and it will remain one no matter how much the actual tools of creation continue to change" ## 📚 References - [Anil Dash Blog](https://anildash.com) - [Open Source Initiative](https://opensource.org/) - [Tech Layoff Tracking Sites](https://layoffs.fyi/) ## 🗣️ Paraphrase - AI tools are fundamentally changing software development by making code generation extremely cheap while displacing human programmers on an unprecedented scale - Two groups of programmers face different challenges: those who code for stable careers versus those for whom coding is a core part of their identity and creative expression - The solution requires affected coders to organize, support each other, and build independent alternatives rather than depending on the tech companies currently conducting mass layoffs ## 📰 Newsletter **AI Revolution Transforms WordPress Development Landscape** The broader tech industry's AI transformation directly impacts our WordPress community. As Anil Dash observes, AI removes coding "drudgery" while preserving creative problem-solving - potentially liberating WordPress developers to focus more on user experience, accessibility, and innovative features rather than boilerplate code. However, this shift demands community solidarity. With 700,000+ tech workers displaced, the WordPress ecosystem must prioritize mutual support, knowledge sharing, and creating sustainable alternatives to venture-backed development models. **Key Applications:** - Use AI for WordPress scaffolding while focusing human creativity on UX and accessibility - Strengthen community support networks through local meetups and mentorship - Explore cooperative and community-funded WordPress projects Resources: [Learn WordPress](https://learn.wordpress.org/), [Developer Docs](https://developer.wordpress.org/), [WP-CLI](https://wp-cli.org/), [WordPress Community](https://make.wordpress.org/) ## ✅ Action Items #### DevRel - Developer advocacy: Create content addressing AI impact on WordPress developer careers, host panels on AI tools integration - Education: Develop workshops on AI-assisted WordPress development, focusing on maintaining code quality and developer agency - Developer experience: Document best practices for AI tool integration in WordPress development workflows - Community management: Facilitate discussions about AI impact, moderate conversations about displacement and opportunity #### Community - Contributors: Launch mentorship programs connecting displaced developers with WordPress contribution opportunities - Sponsors: Create funding streams for independent WordPress developers affected by tech layoffs - Ecosystem: Coordinate cross-project support for developers transitioning to WordPress from other platforms #### WordPress - Core: Evaluate AI tool integration opportunities that enhance rather than replace developer creativity - Community: Develop mutual aid networks for WordPress developers, create job transition resources - Training: Update curricula to include AI-assisted development while emphasizing human creativity and problem-solving - Documentation: Create guides for evaluating AI-generated code quality and security in WordPress context #### The WPCC - Programs: Launch emergency fellowships for displaced tech workers transitioning to WordPress development - Projects: Fund community-driven alternatives to venture-backed WordPress tools and services - Funding: Create matched grant programs for WordPress developers starting independent practices ## 🧪 Hypotheses - WordPress developers who embrace AI for routine tasks while focusing on accessibility and user experience will remain more valuable than those who resist AI entirely - Communities with strong mutual aid networks will better weather AI-driven displacement than those relying solely on corporate employment - AI-generated WordPress code will require significantly more security review and performance optimization than human-written code - Independent WordPress developers and agencies will gain competitive advantage over large corporations relying heavily on AI-generated code ### 📊 Methodology - Survey WordPress developers about current AI tool usage and concerns about job displacement - Track correlation between community involvement and career resilience during AI transition - Analyze security vulnerabilities and performance issues in AI-generated vs human-written WordPress code - Compare client satisfaction between AI-heavy and human-creative WordPress development approaches ### ❓ Questions - How can the WordPress community create economic alternatives that support displaced developers while maintaining code quality? - What governance structures prevent AI tool vendors from extracting value from WordPress community contributions? - Which aspects of WordPress development require irreplaceable human creativity and judgment? ### 🔮 Future Research - Long-term impact of AI-generated code on WordPress site performance and security - Effectiveness of different community support models for displaced developers - Evolution of developer roles in AI-assisted WordPress development workflows ### 💡 Implications - Practice: WordPress developers need strategies for using AI tools while preserving craft satisfaction and code quality - Policy: WordPress.org policies may need updates addressing AI-generated plugin/theme submissions and attribution - Security: Increased scrutiny needed for AI-generated code in WordPress ecosystem due to potential security vulnerabilities - Education: WordPress education must evolve to emphasize human creativity, community building, and AI tool evaluation - Funding: Community funding models become crucial as traditional tech employment becomes less stable ### 🧠 Learning Styles Assessment Reading level estimate: 11th Grade (Flesch-Kincaid) #### 👀 Visual learners - Present: Clear section headings, quoted passages, logical structure - Improve: Diagrams showing AI's impact on different developer roles, infographics of displacement statistics #### 🔊 Aural learners - Present: Referenced NYT Magazine interview provides audio context - Improve: Podcast discussion or audio summary of key points #### 🗣️ Verbal learners - Present: Rich analogies (symphony conductor, language death), clear definitions of technical concepts - Improve: Glossary of AI/LLM terms, more comparisons to traditional industries #### 🧩 Kinesthetic learners - Present: Clear action items and practical next steps - Improve: Hands-on exercises for evaluating AI tools, step-by-step community organizing checklist #### 🧠 Logical learners - Present: Clear cause-and-effect reasoning, statistical evidence (700,000 layoffs) - Improve: Decision framework for AI tool adoption, timeline of tech industry changes #### 🧑‍🤝‍🧑 Social learners - Present: Community-focused solutions, emphasis on mutual support - Improve: Discussion prompts for local meetups, collaborative action planning templates #### 🧘‍♀️ Solitary learners - Present: Reflection on personal identity and career impact - Improve: Self-assessment tools for AI readiness, individual career planning worksheets #### 🌿 Naturalistic learners - Present: Ecosystem thinking about tech community relationships - Improve: Analogies to natural systems adaptation, sustainability frameworks for community building ### 📊 Accessibility Assessment #### Screen reader semantics - Present: Clear heading hierarchy, semantic structure - Improve: Add landmark roles, ensure quote formatting is semantically marked #### Keyboard navigation - Present: Standard web navigation, logical tab order - Improve: Skip links for long article, ensure all interactive elements are reachable #### Contrast and readability - Present: High contrast text, readable font sizes - Improve: Verify contrast ratios meet WCAG 2.2 AA standards #### Text resizing and reflow - Present: Responsive design supports zoom - Improve: Test specifically at 200% and 400% zoom levels #### Media and captions - Present: Text-based content, no media requiring captions - Improve: Add audio version with transcript for accessibility #### Forms and interactions - Present: Basic newsletter signup, minimal interaction - Improve: Ensure form labels are properly associated ### 🌍 DEIB Assessment #### Cultural breadth - Present: US-focused tech industry perspective - Improve: Include global perspectives on AI impact, non-Western approaches to technology and labor #### Inclusive language - Present: Gender-neutral "coders," avoids exclusionary jargon - Improve: Acknowledge different cultural relationships to work and technology #### Neurodiversity support - Present: Clear structure, consistent formatting - Improve: Add content warnings for discussion of job loss anxiety, break up long paragraphs #### Representation - Present: Focuses on systemic rather than individual stories - Improve: Include diverse voices and experiences within the coder community #### Psychological safety - Present: Acknowledges grief and stress, validates concerns - Improve: Provide mental health resources, emphasize community support options ### ✅ Logical Validity #### Strong reasoning - Deductive strength: Clear progression from AI capabilities → economic displacement → community response needs - Inductive strength: Uses specific examples (open source culture, automation history) to build general theory - Causal reasoning: Establishes clear causal chain between AI advancement and job <!-- deep-read:sections end --> 📄 **Full text:** ![[Full Document Contents/Articles/What do coders do after AI - Anil Dash|What do coders do after AI? - Anil Dash, anildash.com]] ## Highlights - I've come to the personal conclusion that the only way forward is for more of the hackers with soul to seize this moment of flux and use these tools to build. The economics of creating code are changing, and it can't just be the worst billionaires in the world who benefit. The latest count is *700,000 people* laid off in the last few years in the tech industry. We'll be at a million soon, at the rate things are accelerating. Each new layoff announcement is now in the *thousands*. ([View Highlight](https://read.readwise.io/read/01kkvmsq0mpnv1xrgbv4z9yycp)) - Tags: 👻 ai highlighted