Why developers and their bosses disagree over generative AI  Full Document Contents/Articles/Why developers and their bosses disagree over generative AI.md, Why developers and their bosses disagree over generative AI, Jennifer Riggins. > [!note]+ Document Notes > tags: > - #generativeAI > - #developerProductivity > - #engineeringLeadership > Author: Jennifer Riggins > Source: LeadDev > Reference: https://leaddev.com/technical-direction/why-developers-and-their-bosses-disagree-over-generative-ai > date created: 2025-05-13 18:27 > > # Title: 🧠 Why Developers and Their Bosses Disagree Over Generative AI > <!-- 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) - [🔍 SEO Evaluation](#-seo-evaluation) - [🎯 Conclusion](#-conclusion) ## 📝 Summary: Jennifer Riggins analyzes the disconnect between engineering leaders and developers on generative AI. While leaders focus on AI’s potential for productivity, developers find it often irrelevant to their real-world bottlenecks. The article calls for a more inclusive approach where developers are active participants in selecting and deploying AI tools across the software development lifecycle (SDLC). ## 📖 Main Content: The article captures the nuance in how different stakeholders perceive generative AI's value: - **Executives & Managers** see generative AI as a boost for velocity and efficiency. - **Developers** often don’t experience those promised gains because AI is solving the wrong problems. - **Mismatch**: AI is used in areas developers enjoy (e.g. writing code), rather than where they need relief (e.g. debugging, documentation). - **Actionable Advice**: - Let developers co-lead AI integration. - Expand AI across all SDLC stages. - Encourage exploration instead of mandating usage. ### 🗒️ Notes - **Survey Insights**: - 90% of leaders prioritize AI; only 34% of developers report high gains. - **Misalignment Examples**: - Tools solve for writing new code but miss pain points like reviewing PRs or writing tests. - **Spotify’s AiKA**: - AI agent as a test assistant instead of a coder. - **DORA Report Takeaways**: - Tooling should serve the developer, not the other way around. - Highlighted risks of misuse if developers aren’t engaged in AI adoption. ## 📖 References: - Riggins, J. (2025). Why Developers and Their Bosses Disagree Over Generative AI. LeadDev. https://leaddev.com/technical-direction/why-developers-and-their-bosses-disagree-over-generative-ai ## 🗣️ Paraphrase: 1. **Original**: “AI is being used to write code, but that’s not where most developers are hurting.” **Paraphrased**: Generative AI focuses on coding help, yet developers struggle more with tasks like testing and debugging. 2. **Original**: “It’s not just about the tools you use. It’s about how and where you use them.” **Paraphrased**: The value of AI isn’t just in its features, but in how intentionally and contextually it’s deployed. ## 📰 Newsletter: **🎧 This Week in WordPress Development** Generative AI has been hailed as a game-changer in tech, but a new piece from Jennifer Riggins at LeadDev reveals a growing divide: while execs celebrate potential productivity gains, developers remain skeptical. The key issue? AI often solves the wrong problems. Rather than helping with documentation or debugging, it’s writing code devs already enjoy writing. The article urges a rethinking of how AI tools are selected and implemented—developers must help drive the conversation. With smart examples like Spotify’s AiKA and recommendations from the DORA report, it’s a must-read for engineering teams navigating the AI hype. Read more → https://leaddev.com/technical-direction/why-developers-and-their-bosses-disagree-over-generative-ai ## ✅ Action Items: - **As an Advocate**: - Host developer forums before adopting AI tooling. - Emphasize cross-functional decision-making. - **As a Trainer**: - Design AI learning modules rooted in real-world tasks (e.g., test generation, log parsing). - **As a Future OSPO Director**: - Push for governance standards where open-source contributors shape AI integration strategy. - Use the article to initiate alignment workshops within open source project teams. ## 🧪 Hypotheses: - Developer-led AI evaluation will increase satisfaction and adoption. - AI tools that support the full SDLC will yield greater productivity than tools focused solely on code generation. ### 📊 Methodology: - **Surveys** to assess perceived usefulness before and after AI tool adoption. - **Ethnographic studies** of developer workflow patterns with and without AI assistance. - **GitHub activity analysis** to track productivity deltas by task type. ### ❓ Questions: - What tasks do developers most want AI to assist with? - How can we redesign AI onboarding to better support diverse development workflows? - What metrics reflect meaningful productivity in the context of developer happiness? ### 🔮 Future Research: - AI’s impact on developer burnout and satisfaction. - Role of participatory design in AI tooling for software teams. - Cross-cultural variation in AI adoption within engineering teams. ### 💡 Implications: Aligning AI tools with developer needs can improve efficiency, retention, and collaboration. It also creates more resilient, human-centered engineering cultures. ### 🧠 Learning Styles Assessment: - **Visual**: No charts or illustrations present. - *Improve*: Add flowcharts mapping AI across the SDLC. - **Aural**: No accompanying podcast or narration. - *Improve*: Offer a voiceover version or podcast roundtable. - **Verbal**: Strong narrative structure and descriptive language. - *Improve*: Integrate analogies or metaphors to clarify abstract points. - **Physical**: Lacks hands-on examples. - *Improve*: Include links to GitHub repos or demo apps using AI tooling. - **Logical**: Cites research, uses structured arguments. - *Improve*: Add more quantitative comparisons between AI tools. - **Social**: Recommends team participation in decision-making. - *Improve*: Include real-world collaboration stories. - **Solitary**: Supports independent reflection. - *Improve*: Include self-assessment prompts or journaling questions. - **Naturalistic**: No environmental connections. - *Improve*: Draw metaphors from systems in nature (e.g., AI = soil for growth, not sunlight). ### 📊 Accessibility Assessment: - **Screen Reader**: No accessibility statement or alt-text descriptions confirmed. - *Improve*: Ensure semantic HTML and accessible image descriptions. - **Keyboard Nav**: Assumed standard; unconfirmed. - *Improve*: Test site with only keyboard navigation. - **Contrast & Font**: Readable font, standard color contrast. - *Improve*: Provide dark mode toggle and text scaling. - **Responsive Text**: Responsive on desktop and mobile. - *Improve*: Ensure resizable text without breaking layout. ### 🌍 DEIB Assessment: - **Cultural Relevance**: Lacks explicit representation of global dev contexts. - *Improve*: Add stories from developers in non-US/EU settings. - **Inclusive Language**: Gender-neutral and professional. - **Neurodiversity**: No specific accommodations. - *Improve*: Break content into smaller digestible segments. - **Representation**: Quotes skew toward industry leaders. - *Improve*: Include more voices from early-career or underrepresented developers. - **Psych Safety**: Promotes listening to developer concerns. - *Improve*: Reinforce with calls to action on team empathy and inclusion. ### ✅ Logical Validity: - **Strengths**: - Strong use of evidence from the DORA report. - Balanced critique of current AI implementation trends. - **Improvements**: - More quantitative backing (e.g., productivity metrics) would strengthen claims. - **Fallacies Noted**: None. Reasoning is largely sound and well-organized. - **Tone**: Balanced, lightly critical, solutions-oriented. ### 🔍 DARVO Analysis: No DARVO patterns present. - **Deny**: No discrediting of developer concerns. - **Attack**: Leadership is not villainized; instead, the gap is framed as fixable. - **Reverse Victim/Offender**: Not used. ### 🔍 SEO Evaluation: - **Keywords**: “generative AI,” “developer productivity,” “engineering leadership.” - **Title Tag**: Informative and concise. - **Meta Description**: Likely present in source, though not shown. - **Headers**: Well-structured and content-rich. - **Content Length**: Approx. 1,200–1,500 words – ideal. - **Links**: Internal LeadDev content only. - *Improve*: Add citations to DORA, Spotify AiKA for stronger external linking. - **Alt Text**: Not specified. - *Improve*: Add image alt text and SEO-optimized filenames. - **Mobile Friendly**: Yes. - **Schema**: No evidence of article schema. - **Social Sharing**: Share buttons present. ## 🎯 Conclusion: This article spotlights a growing tension in AI adoption: executives move fast, but without developer input, they might miss the mark. By repositioning developers as co-creators in the AI journey—not just consumers—organizations can unlock far more value, innovation, and trust. It’s time for open conversations about what we build, who we build it for, and how AI fits into that equation. <!-- deep-read:sections end --> ## Highlights - generative AI investments should focus on the whole software development lifecycle (SDLC). And since [developer happiness drives productivity](https://sentry.io/resources/state-of-dev-happiness/), it’s crucial to gain their buy-in for any AI adoption strategy. ([View Highlight](https://read.readwise.io/read/01jv5wmbdhemd5h4s7zxhqfbtd)) - The brain is wired to resist solutions that feel imposed or misaligned with real pain points. When managers assume instead of asking, they miss the mark, and trust erodes. ([View Highlight](https://read.readwise.io/read/01jv5weadpdrxmjrh1ngdn3kdr)) - Create psychological safety so they’ll tell you the truth ([View Highlight](https://read.readwise.io/read/01jv5wehxz94gc7zc8y1p10zqz)) - Despite the top-down pressure to add AI everywhere, let different development teams set their priorities and test AI where they see fit. Then, encourage them to share results with other teams, answering questions and advocating in favor of or against solutions. ([View Highlight](https://read.readwise.io/read/01jv5wmn3jxn4f4hzqvb6hnyg6)) - “Generative AI’s real value may lie not in how much code it helps write – but in [how it helps developers do their best work](https://rdel.substack.com/p/rdel-87-how-do-ai-coding-tools-actually) more often,” said Lizzie Matusov, CEO of Quotient. ([View Highlight](https://read.readwise.io/read/01jv5wn2efsqswqkxxy8hzt0t3)) - [Encourage conversational prompting](https://leaddev.com/velocity/how-write-better-ai-prompts) that treats AI like a pair programmer, not a one-way Google or StackOverflow search. ([View Highlight](https://read.readwise.io/read/01jv5wnk2rjqfrn2f24rxa0wxy)) - • Learning new techniques. • Complex query writing. • Brainstorming and planning. • Code explanation. ([View Highlight](https://read.readwise.io/read/01jv5wns5y2xspvbph3vfqqz06)) - • Share and be transparent about how your organization plans to use AI. • Address developer concerns about AI’s impact. • Allow ample time for developers to learn how to use AI. ([View Highlight](https://read.readwise.io/read/01jv5wpqg149a64w7n45dcj00x))