Make AI Productivity Gains Stick  🔗 **Source:** [Make AI Productivity Gains Stick](https://www.youtube.com/watch?v=-6XbGcLEm7U) > [!note]+ Document Notes > tags: [ai-productivity, developer-workflows, change-management] > author: [MISSING] > source: Lead Dev > reference: https://www.youtube.com/watch?v=-6XbGcLEm7U > date created: {{date:YYYY-MM-DD}} {{time:HH:mm}} > > # 🏷️ Title: Make AI productivity gains stick > <!-- 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) ## 📝 Summary - **What happened**: Lead Dev panel featuring AB (CTO at Metro Fox), Anastasia (MVP Product Engineering at GitKraken), and Maxim Najim (Distinguished Engineer at Target) discussing how to sustain AI productivity gains in development teams. - **Why it matters**: AI tools provide initial productivity boosts, but making those gains permanent requires deliberate change management and systematic measurement approaches. - **Key stakeholders**: Engineering teams, CTOs, dev leaders, individual developers at all levels. - **Risks and tradeoffs**: Over-reliance on AI can cause skill atrophy; quality may suffer if speed is emphasized without proper verification; costs will increase as AI subsidies end. - **Bottom line**: Sustainable AI gains require treating adoption as major organizational change management, not just tool rollout. ## 📖 Main Content The panel explored three key types of AI gains: behavioral change (adoption patterns), system improvements (DORA metrics), and customer impact (business outcomes). Success requires moving from "vibe coding" to "agentic engineering" with disciplined workflows. **Core gains identified:** - **Test coverage improvements**: AI translates specs into comprehensive test cases - **Delivery speed**: Features that took 3-4 days now complete in under one day - **Context switching efficiency**: Staff+ engineers can manage 6-7 parallel agent sessions - **Ramp-up acceleration**: AI agents help explore unfamiliar systems and domains **Distribution patterns**: Gains are strongest among senior managers and junior developers. Mid-career engineers often resist AI, feeling it threatens their coding-based identity. Staff+ engineers benefit most because they already know how to delegate and set clear goals. **Measurement framework**: Three-pillar approach covering adoption metrics, system changes (velocity, quality), and business impact. Teams must follow work end-to-end rather than measuring stages in isolation to avoid shifting bottlenecks elsewhere. ### 🗒️ Notes #### ⏱️ [00:03:12](https://www.youtube.com/watch?v=-6XbGcLEm7U&t=192) AB on productivity gains - Increased test coverage: developers creating more comprehensive test cases with AI assistance - Higher delivery speed: architectural design phase remains human-driven, implementation accelerated by AI - One developer can work on three parallel workstreams simultaneously #### ⏱️ [00:06:45](https://www.youtube.com/watch?v=-6XbGcLEm7U&t=405) Anastasia's three-pillar measurement approach - Behavioral change metrics: adoption, engagement, frequency of use, autonomous agent operation - System change metrics: DORA and SPACE frameworks, developer satisfaction tracking - Customer impact metrics: experiment velocity, shipping frequency, customer satisfaction #### ⏱️ [00:09:30](https://www.youtube.com/watch?v=-6XbGcLEm7U&t=570) Maxim on staff+ engineer role transformation - AI raises ceiling on engineering capabilities but fundamentals become more important - Staff+ role shifts toward shaping intent, designing verification loops, creating alignment - Six to seven coding agent sessions running simultaneously for different projects - Context preservation enables involvement in broader project surface area #### ⏱️ [00:15:20](https://www.youtube.com/watch?v=-6XbGcLEm7U&t=920) Uneven distribution of gains - Biggest gains: senior managers and junior developers - Mid-career engineers resist due to identity threat around coding skills - Solution: train mid-career engineers in delegation and management skills - Hiring focus shifting to "product engineers" who understand business context #### ⏱️ [00:18:45](https://www.youtube.com/watch?v=-6XbGcLEm7U&t=1125) Measurement challenges and solutions - Follow work end-to-end, not just stages - Track incident rates alongside delivery speed - Use cohort analysis to compare AI-adopters vs. non-adopters - Layered measurement prevents optimization of wrong metrics #### ⏱️ [00:22:10](https://www.youtube.com/watch?v=-6XbGcLEm7U&t=1330) Pitfalls and fragility points - Skill atrophy: developers stop exercising engineering judgment - Over-reliance: copying stack traces to AI without basic debugging - Context limitations: AI struggles with large codebases requiring domain knowledge - Cost sustainability: current token subsidies will end, requiring efficiency optimization #### ⏱️ [00:28:15](https://www.youtube.com/watch?v=-6XbGcLEm7U&t=1695) Making gains stick - systematic approach - Treat as change management, not tool adoption - Standardize agent instructions across teams through shared markdown files - Create learning feedback loops from mistakes back to agents and teams - Monitor engineering organization daily/weekly for quality degradation signals #### ⏱️ [00:32:40](https://www.youtube.com/watch?v=-6XbGcLEm7U&t=1960) Skills for AI-augmented engineering - Understanding over outsourced thinking - First principles reasoning and architectural thinking - AI literacy and supervision skills - Domain knowledge and system thinking remain critical #### ⏱️ [00:38:25](https://www.youtube.com/watch?v=-6XbGcLEm7U&t=2305) Preventing skill atrophy - Form opinions before consulting AI - Practice deliberate comparison between human and AI solutions - Focus on problem-solving rather than code generation - Maintain curiosity and continuous learning mindset ## 📚 References - [Lead Dev Slack Community](https://theleaddeveloper.slack.com) - GitKraken Developer Tools - DORA Metrics Framework - SPACE Metrics Framework ## 🗣️ Paraphrase Three engineering leaders shared their experiences making AI productivity gains sustainable in development organizations. The key insight is that initial AI adoption often focuses on speed improvements, but lasting benefits require systematic change management treating this as cultural transformation rather than tool rollout. The panelists identified uneven distribution of AI benefits, with senior engineers and juniors gaining most while mid-career developers often resist due to identity concerns around their coding expertise. The solution involves retraining these engineers in delegation and product thinking skills. Measurement requires a three-pillar approach covering behavioral adoption, system improvements, and customer impact. Teams must track work end-to-end rather than optimizing individual stages to avoid simply shifting bottlenecks elsewhere. ## 📰 Newsletter **AI Productivity Gains: Making Them Stick for Your WordPress Team** Great discussion from Lead Dev on sustaining AI productivity improvements! Key takeaways for WordPress developers: 🎯 **The Reality Check**: Initial AI speed boosts often fade without systematic change management. Treat AI adoption as cultural transformation, not just new tooling. ⚖️ **Uneven Benefits**: Senior devs and juniors see biggest gains. Mid-career developers often struggle with identity concerns as coding becomes commoditized. Solution: upskill them in product thinking and delegation. 📊 **Measurement Matters**: Track adoption → system improvements → customer impact. Don't just count commits—follow features end-to-end to avoid shifting bottlenecks to code review or debugging. 🛠️ **WordPress-Specific Applications**: - AI-assisted test case generation for plugins - Accelerated theme development with human architectural oversight - Documentation and RFC drafting for Core contributions - Faster onboarding to complex WordPress codebases 💡 **Pro Tips**: Standardize AI instructions across your team, surface AI errors publicly for learning, and maintain engineering fundamentals even as AI capabilities grow. The future belongs to "product engineers" who combine AI fluency with deep domain knowledge. Start building those muscles now! ## ✅ Action Items #### DevRel - Developer experience: Create standardized AI instruction templates and context engineering guides for WordPress development workflows - Education: Design workshops on "agentic engineering" practices for WordPress teams transitioning from "vibe coding" - Community management: Establish forums for sharing AI productivity patterns and failure stories - Developer marketing: Create content showcasing sustainable AI integration in WordPress development #### WordPress - Core: Develop architectural decision record (ADR) templates optimized for AI agent consumption - Documentation: Create AI-friendly documentation patterns that improve both human and agent understanding - Training: Build curriculum on AI-augmented WordPress development focusing on maintaining engineering fundamentals - Community: Facilitate discussions on ethical AI use and skill development in WordPress ecosystem #### The WPCC - Programs: Launch fellowship tracks focused on AI literacy and sustainable productivity practices - Projects: Fund development of open-source tools for measuring and optimizing AI productivity in WordPress environments ## 🧪 Hypotheses 1. **Distribution Hypothesis**: AI productivity gains will remain unevenly distributed, with senior engineers and juniors benefiting most while mid-career developers struggle with identity adaptation. 2. **Measurement Hypothesis**: Teams tracking end-to-end workflow metrics will achieve more sustainable AI gains than those optimizing individual development stages. 3. **Cost Sustainability Hypothesis**: Current AI productivity models will face significant challenges when token subsidies end, requiring efficiency optimization strategies. 4. **Skill Evolution Hypothesis**: Engineering roles will increasingly emphasize product thinking, system understanding, and AI supervision over direct code generation. 5. **Quality Trade-off Hypothesis**: Teams emphasizing speed metrics without corresponding quality measures will experience technical debt accumulation that undermines long-term productivity gains. 6. **Cultural Change Hypothesis**: Organizations treating AI adoption as change management rather than tool rollout will achieve more durable productivity improvements. ### 📊 Methodology - **Longitudinal cohort studies**: Compare AI-adopting vs. non-adopting developer groups over 12-month periods - **End-to-end workflow analysis**: Measure feature delivery from conception through post-deployment monitoring - **Skills assessment surveys**: Track changes in engineering capability self-reporting and peer evaluation - **Economic impact analysis**: Calculate total cost of ownership including AI tooling costs, training, and productivity gains - **Ethnographic studies**: Observe daily workflows and cultural adaptation patterns in development teams ### ❓ Questions - How can mid-career engineers be most effectively retrained for AI-augmented roles without losing core technical expertise? - What specific metrics best predict long-term sustainability of AI productivity gains? - How will AI cost structures evolve and what optimization strategies will prove most effective? - What governance models work best for standardizing AI agent behavior across large development organizations? ### 🔮 Future Research - Development of AI productivity sustainability frameworks for different organizational sizes and contexts - Investigation of cognitive load patterns in AI-augmented development workflows - Analysis of skill evolution trajectories for developers at different career stages - Economic modeling of AI productivity ROI across various cost scenarios ### 💡 Implications **Practice**: Development teams need structured change management approaches rather than ad-hoc AI tool adoption. Engineering education must emphasize product thinking alongside technical skills. **Policy**: Organizations require governance frameworks for AI agent standardization and quality assurance in code generation workflows. **Security**: Increased reliance on AI-generated code necessitates enhanced verification and testing protocols to prevent subtle vulnerabilities. **Education**: Computer science curricula should integrate AI supervision and prompt engineering alongside traditional programming concepts. **Funding**: Investment in AI tooling must account for total cost of ownership including training, governance, and eventual cost increases as subsidies end. ### 🧠 Learning Styles Assessment **Reading level estimate**: Flesch-Kincaid Grade Level ~12 (technical content with specialized terminology) #### 👀 Visual learners - **Present**: Transcript format with timestamps, structured hierarchical information - **Improve**: Add workflow diagrams showing AI integration points, measurement framework visualizations, before/after productivity charts #### 🔊 Aural learners - **Present**: Original video discussion format with multiple speaker perspectives - **Improve**: Provide chapter markers for key topics, create audio summaries of main takeaways #### 🗣️ Verbal learners - **Present**: Rich discussion format with definitions and examples, analogies like "muscle cars" for AI capabilities - **Improve**: Add glossary of technical terms, provide more real-world metaphors for abstract concepts #### 🧩 Kinesthetic learners - **Present**: Actionable measurement frameworks and implementation steps - **Improve**: Create hands-on workshops for implementing measurement systems, provide checklists for AI adoption phases #### 🧠 Logical learners - **Present**: Systematic three-pillar measurement approach, clear cause-and-effect relationships - **Improve**: Add decision trees for AI adoption strategies, provide more quantitative data on productivity gains #### 🧑‍🤝‍🧑 Social learners - **Present**: Panel discussion format with peer experiences, community slack integration - **Improve**: Facilitate peer learning groups, create mentorship programs for AI adoption #### 🧘‍♀️ Solitary learners - **Present**: Comprehensive written notes for individual study - **Improve**: Add reflection questions for personal AI adoption assessment, self-paced learning modules #### 🌿 Naturalistic learners - **Present**: Ecosystem thinking about AI integration and sustainable practices - **Improve**: Emphasize sustainability concerns about AI costs, connect to broader technology adoption lifecycle patterns ### 📊 Accessibility Assessment #### Screen reader semantics - **Present**: Well-structured headings hierarchy, descriptive section labels - **Improve**: Add alt text for any embedded media, ensure timestamp links have descriptive text #### Keyboard navigation - **Present**: Standard text navigation, clear section structure - **Improve**: Ensure all interactive elements are keyboard accessible, add skip links for long sections #### Contrast and readability - **Present**: Standard text formatting with clear hierarchy - **Improve**: Test against WCAG 2.2 AA standards, ensure sufficient contrast for all text elements #### Text resizing and reflow - **Present**: Text-based content that scales well - **Improve**: Test at 200% zoom, ensure no content gets cut off #### Media and captions - **Present**: Full transcript provided for video content - **Improve**: Sync transcript with video timestamps, provide downloadable transcript option #### Forms and interactions - **Present**: Minimal interactive elements - **Improve**: Ensure any future interactive elements follow accessibility guidelines #### Standards reference - Content generally aligns with WCAG 2.2 AA for text-based materials ### 🌍 DEIB Assessment #### Cultural breadth - **Present**: Global technology industry perspectives, enterprise-scale insights - **Improve**: Include perspectives from smaller organizations, non-Western development contexts #### Inclusive language - **Present**: Professional technical terminology, gender-neutral role references - **Improve**: Define more technical terms, avoid assumptions about organizational structure #### Neurodiversity support - **Present**: Clear structure with consistent formatting, logical information hierarchy - **Improve**: Provide content warnings for discussion of job displacement concerns, offer multiple information processing formats #### Representation - **Present**: Mixed gender panel, different organizational roles and perspectives - **Improve**: Include perspectives from different geographic regions, various company sizes beyond enterprise #### Psychological safety - **Present**: Acknowledges real concerns about job displacement and skill obsolescence - **Improve**: Provide more specific guidance for managing career anxiety, clearer pathways for skill development ### ✅ Logical Validity #### Strong reasoning - **Causal analysis (00:18:45)**: Clear connection between measurement approach and sustainable gains through end-to-end workflow tracking - **Inductive reasoning (00:15:20)**: Pattern recognition about uneven AI benefit distribution supported by multiple organizational examples - **Systems thinking (00:28:15)**: Understanding AI adoption as change management rather than tool deployment shows strong architectural reasoning #### Fallacies - **Hasty generalization (00:06:45)**: "2.3x productivity improvement" claim lacks sufficient context about measurement methodology and confounding variables - **Appeal to authority (throughout)**: Panel format relies heavily on personal experience without citing broader research or data validation - **Post hoc reasoning (00:22:10)**: Some productivity claims may conflate correlation with causation regarding AI tool adoption #### Tone of voice - **Professional but accessible**: Technical discussion maintained conversational tone appropriate for practitioner audience - **Collaborative**: Panelists building on each other's points rather than competing perspectives - **Realistic**: Balanced discussion of benefits and challenges rather than purely promotional ### 🛡️ DARVO Analysis No DARVO patterns detected. The discussion maintains a balanced perspective on both benefits and challenges of AI adoption without defensive rhetoric or victim/offender reversals. ## 🎯 Conclusion Sustainable AI productivity gains require treating adoption as organizational change management rather than simple tool rollout. Success depends on systematic measurement across adoption, system improvements, and business impact, combined with deliberate investment in human skills like understanding, judgment, and AI supervision. The teams that will thrive are those that strengthen engineering fundamentals while embracing AI augmentation, creating feedback loops that prevent quality degradation, and preparing for the economic realities of AI cost optimization as subsidies end. ## 🔄 Refactor Appendix — Make AI productivity gains stick ### 📚 Literature Atoms #### AI raises coding ceiling but fundamentals become more important > [!quote] ID:rw_1 > AI raises the ceiling on what engineers are able to do. But for those gains to last, we have to kind of raise the importance of the fundamentals even more. **Context & use**: - Essential framework for evaluating AI adoption strategies and avoiding technical debt accumulation **Tags**: #ai-productivity #software-fundamentals #xref #### Mid-career engineers feel threatened while seniors and juniors benefit most > [!quote] ID:rw_2 > The biggest gains that we are getting is on seniors managers and followed by tadam by juniors. Mid-career engineers feel that AI is threatening to their identity. **Context & use**: - Critical insight for team management and training strategies during AI adoption **Tags**: #ai-adoption #career-development #xref #### Behavioral measurement requires layered approach across three pillars > [!quote] ID:rw_3 > What to measure I introduced this concept with three pillars. One is being this behavioral change, than system change and customer impact and all of them are having certain measurements. **Context & use**: - Framework for establishing comprehensive AI productivity measurement systems **Tags**: #ai-metrics #change-management #xref #### AI productivity requires moving from vibe coding to agentic engineering > [!quote] ID:rw_4 > Vibe coding could be useful but if we want AI productivity gains to last, teams have to move towards more like a disciplined agentic engineering. **Context & use**: - Key distinction for scaling AI tools beyond individual experimentation to team workflows **Tags**: #ai-engineering #engineering-practices #xref #### Understanding cannot be outsourced despite AI's capabilities > [!quote] ID:rw_5 > You can outsource some thinking to AI, but you can't outsource your understanding. AI can generate options, but engineers still own the understanding. **Context & use**: - Fundamental principle for maintaining engineering judgment and avoiding skill atrophy **Tags**: #ai-limitations #engineering-judgment #xref #### Current AI tokens are subsidized creating unsustainable usage patterns > [!quote] ID:rw_6 > Lots of tokens today they're subsidized like we are not paying the full price once the real prices will come all the gains that we have will be just losing those. **Context & use**: - Strategic consideration for long-term AI adoption planning and cost optimization **Tags**: #ai-economics #sustainability #xref <!-- deep-read:sections end --> 📄 **Full text:** ![[Full Document Contents/Articles/Make AI Productivity Gains Stick.md|Make AI Productivity Gains Stick by YouTube]] ## Highlights - started, I will do a very quick introduction. My name is Sally Lates. I will be your host today. My pronouns are she and her. I work as a fractional technology leader and adviser and I'm based in the east of England in the UK. But as I've said, the discussion today is all about making AI productivity gains stick. So I imagine everybody on the panel or everybody watching here today has probably seen some different benefits in the course of their work and ([View Highlight](https://read.readwise.io/read/01kv3tgat45yptcmjf7kpycc1e)) - Tags: 👻 ai highlighted - is like AI raises the ceiling on what engineers are able to do. But for those gains to last, we have to kind of raise the importance of the fundamentals even more. So like good architecture, good guard rails, keeping the craft of software development at the center while uh using these tools what they you know to to do what they truly help us with. Well, you're all dropping some really nice sort of clickbaity little tidbits here already. So, that's uh very exciting. And thank you all so much for ([View Highlight](https://read.readwise.io/read/01kv3tgatpjpwzsnzepfmyy9br)) - Tags: 👻 ai highlighted - is like AI raises the ceiling on what engineers are able to do. But for those gains to last, we have to kind of raise the importance of the fundamentals even more. So like good architecture, good guard rails, keeping the craft of software development at the center while uh using these tools what they you know to to do what they truly help us with. Well, you're all dropping some really nice sort of clickbaity little tidbits here already. So, that's uh very exciting. And thank you all so much for ([View Highlight](https://read.readwise.io/read/01kv3wv1pq1n4pyb1xff593t35)) - Note: AI raises the ceiling on what engineers can do — but for the gains to last, fundamentals matter MORE: good architecture, guardrails, keeping the craft at the center. - Tags: atom/literature/h1, software-fundamentals, ai-productivity - into AI first uh kind of flow. Then if you have this leading indicator of um people adopting AI, do we see that our system is changing such as we have better test coverage such as if you have better velocity um if you release more frequently. So this is second pillar but what we are all marching towards is what's the impact to the business? Are we able to deliver value to the customers uh more? So I see the the ([View Highlight](https://read.readwise.io/read/01kv3tgavjpanyvf9fsqb01at5)) - Tags: 👻 ai highlighted - have to create a lot of documentation, a lot of lead a lot of conversations. So I've also been seen a real gain in the I call the blank page part of my work. So things like drafting a design doc, turning messy notes into like clear arguments, summarizing trade-offs, um adapting communication for different audiences. So for me, AI kind of lowers that activation energy. So instead of spending an hour just getting to a workable starting point, I can get there much faster. Then I can spend more time ([View Highlight](https://read.readwise.io/read/01kv3tgawnqamhnd6pcakq1sjy)) - Tags: 👻 ai highlighted - people that you've been working with or how have you seen them affecting different roles? Are they pretty evenly distributed? Are they concentrated at different levels of seniority or or types of uh work? What's what's your experience Maxine there? Yes, I I think the AI gains are strongest when people kind of bring the right context, the right judgment and verification to these AI tools. So for for SAP plus engineers, that's that changes kind of the center of gravity of the role. If if code ([View Highlight](https://read.readwise.io/read/01kv3tgaxkvart6asqke4dasjn)) - Tags: 👻 ai highlighted - becomes cheaper to produce with AI, then the scarce thing becomes knowing what code should exist. You know, what constraints matter and how does it fit into the wider ecosystem. And so that means AI is is most valuable in the kind of work where staff plus engineers are already spent spend a lot of their time. And so I think AI makes the staff plus role more important and shifts those that role towards kind of shaping intent um designing verification loops creating ([View Highlight](https://read.readwise.io/read/01kv3tgayhn1ek6ja7jgw3qpb8)) - Tags: 👻 ai highlighted - I was actually reading a lot on on this and um I would like to compare with experience that that we were having in in our teams. Um and maybe we're having a little bit different experience than what is the industry term definitely what I see that that it's not evenly distributed at all. Um and the biggest gains that we are getting um is on seniors managers and followed by tadam by juniors. ([View Highlight](https://read.readwise.io/read/01kv3wvaqa4efm5gfvnnr8acwa)) - Note: AI gains aren't evenly distributed: biggest gains go to senior managers, then juniors — leaving mid-career engineers benefiting least (and most exposed). - Tags: atom/literature/h2, career-development, ai-adoption - through the major change management. So, so that's why be able to measure understand what to measure is is super critical and talking to the dev leaders who had that experience before or who are obtaining that that experience urgently now. I think these are the teams that are showing the biggest improvements. Uh for me what to measure um I introduced this uh this this concept with three pillars. Uh one is being this behavioral change. um than system change and customer impact and ([View Highlight](https://read.readwise.io/read/01kv3wvn81ymnyn1e24tpnx42h)) - Note: Measure AI impact across three pillars: behavioral change → system change → customer impact. Knowing what to measure is critical to making gains stick through change management. - Tags: change-management, atom/literature/h2, ai-metrics - thing is that AI is changing so fast and this is not just step function that you conquered and that's it. So what we need to do is keep having this build measure um learn loop otherwise we'll be losing our our gains. And something that I personally um is is is is afraid of of um is that lots of tokens today they're subsidized like we are not paying the full price ([View Highlight](https://read.readwise.io/read/01kv3wwsck997f9ahrf5560f2x)) - Note: Token economics warning: "lots of tokens today they're subsidized — we are not paying the full price." Keep a build-measure-learn loop or lose the gains when pricing corrects. - Tags: sustainability, atom/literature/h2, ai-economics - Yeah. And I like the distinction between vibe coding and kind of agentic engineering because I think it gets to something important. So, you know, vibe coding could be useful. It helps people explore, prototype, get something out quickly. But if we want AI productivity gains to last, teams have to be move towards more like a disciplined agentic engineering. To me, that means the agent is like operating within a real ([View Highlight](https://read.readwise.io/read/01kv3ww22wbz7v1sahdjpeaknq)) - Note: Vibe coding vs. agentic engineering: vibe coding is fine for exploring/prototyping, but lasting productivity gains require disciplined agentic engineering — the agent operating within real structure. - Tags: ai-engineering, atom/literature/h1, engineering-practices - >> Yeah, I think as as AI improves, I think the one of the most valuable human skills is is understanding. So, there's a there's a line I I like a lot, which is you can outsource some thinking to AI, but you can't outsource your understanding. And so, AI can help draft the spec. It can explore the code base. It can generate options. It can write the test. It can propose a solution. But as the engineer, I'm still I still need to understand the problem deeply enough to know whether we are solving the right thing. And I need to understand the ([View Highlight](https://read.readwise.io/read/01kv3tgb21q3ahrk5p2w3pkdyw)) - Tags: 👻 ai highlighted - >> Yeah, I think as as AI improves, I think the one of the most valuable human skills is is understanding. So, there's a there's a line I I like a lot, which is you can outsource some thinking to AI, but you can't outsource your understanding. And so, AI can help draft the spec. It can explore the code base. It can generate options. It can write the test. It can propose a solution. But as the engineer, I'm still I still need to understand the problem deeply enough to know whether we are solving the right thing. And I need to understand the ([View Highlight](https://read.readwise.io/read/01kv3wwczv1dps3fjnpmxa7g2f)) - Note: "You can outsource some thinking to AI, but you can't outsource your understanding." AI drafts specs, explores code, writes tests — but the engineer must understand deeply enough to know they're solving the right thing. - Tags: engineering-judgment, ai-limitations, atom/literature/h1 - the system. So skills like judgment, taste, domain understanding become more important and all those skill depend on on us understanding you know and what what AI is proposing. And if um and if I if I don't understand the problem, I can't give the agent good intent. If I don't understand a system, I can't provide good context. And if I don't understand the solution, I can't, you know, reasonably approve it. So I would summarize it like AI can generate options, but engineers still own the ([View Highlight](https://read.readwise.io/read/01kv3tgbc4sg1s7fnzbcr4m9ej)) - Tags: 👻 ai highlighted - push back where necessary. So right now it has become extremely important for developers, engineers everywhere to begin to understand things from first principle so that you can actually push back and critique work of AI. >> I agree with uh with all points from Maxim and Abby. Um maybe one more thing that that is um that that I'll add here is being uh like understanding AI and AI ([View Highlight](https://read.readwise.io/read/01kv3tgbhke3ej383bw22hkvnr)) - Tags: 👻 ai highlighted